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Cryptococcosis due to Cryptococcus gattii is endemic in various parts of the world , affecting mostly immunocompetent patients . A national surveillance study of cryptococcosis , including demographical , clinical and microbiological data , has been ongoing since 1997 in Colombia , to provide insights into the epidemiology of this mycosis . From 1 , 209 surveys analyzed between 1997–2011 , 45 cases caused by C . gattii were reported ( prevalence 3 . 7%; annual incidence 0 . 07 cases/million inhabitants/year ) . Norte de Santander had the highest incidence ( 0 . 81 cases/million/year ) , representing 33 . 3% of all cases . The male: female ratio was 3 . 3∶1 . Mean age at diagnosis was 41±16 years . No specific risk factors were identified in 91 . 1% of patients . HIV infection was reported in 6 . 7% of patients , autoimmune disease and steroids use in 2 . 2% . Clinical features included headache ( 80 . 5% ) , nausea/vomiting ( 56 . 1% ) and neurological derangements ( 48 . 8% ) . Chest radiographs were taken in 21 ( 46 . 7% ) cases , with abnormal findings in 7 ( 33 . 3% ) . Cranial CT scans were obtained in 15 ( 33 . 3% ) cases , with abnormalities detected in 10 ( 66 . 7% ) . Treatment was well documented in 30 cases , with most receiving amphotericin B . Direct sample examination was positive in 97 . 7% cases . Antigen detection was positive for all CSF specimens and for 75% of serum samples . C . gattii was recovered from CSF ( 93 . 3% ) and respiratory specimens ( 6 . 6% ) . Serotype was determined in 42 isolates; 36 isolates were serotype B ( 85 . 7% ) , while 6 were C ( 14 . 3% ) . The breakdowns of molecular types were VGII ( 55 . 6% ) , VGIII ( 31 . 1% ) and VGI ( 13 . 3% ) . Among 44 strains , 16 MLST sequence types ( ST ) were identified , 11 of them newly reported . The results of this passive surveillance study demonstrate that cryptococcosis caused by C . gattii has a low prevalence in Colombia , with the exception of Norte de Santander . The predominance of molecular type VGII is of concern considering its association with high virulence and the potential to evolve into outbreaks .
Cryptococcosis is a fungal disease that affects humans and animals and is caused by two species , Cryptococcus neoformans and Cryptococcus gattii [1] . C . gattii has been recognized as a distinct species from C . neoformans due to differences in the morphology of the basidia , environmental niches , multiple gene genealogies , unique patterns generated by different molecular typing techniques , inefficient crossing of species with the production of sterile progeny and a lack of genetic recombination [2] . C . gattii can be easily and reliably differentiated from C . neoformans through a simple phenotypic procedure , growth on CGB ( canavanine , glycine and bromothymol blue ) culture medium [3] . C . gattii assimilates glycine , is resistant to canavanine and changes the color of the media due to an alteration in pH when creatinine is degraded into ammonia . C . neoformans is not able to assimilate glycine; therefore , it does not grow on this media [3] . The two species cause different clinical manifestations and have different biological characteristics [1] . C . neoformans is responsible for most cases of cryptococcosis worldwide [4] . Until recently C . gattii was considered rare , however cryptococcosis by C . gattii has gained importance because of its emergence in 1999 , which resulted in an outbreak on Vancouver Island and other closely related regions in British Columbia , Canada [5] and the increasing number of cases since 2004 in the Pacific Northwest of the United States [6] . Recent studies based on Multilocus Microsatellite Type ( MLMT ) and Multilocus Sequence Type ( MLST ) analyses , as well as whole genome analysis , point towards South America as a potential origin for the outbreak strains [7] , [8] . Previously , it was thought that C . gattii was restricted to tropical and subtropical regions [9] , but the emergence of the outbreak events due to virulent strains in temperate areas of North America suggest a more global distribution of this yeast [5] , [6] . In addition , independent cases have been reported from Mediterranean Europe [10] . The knowledge about the epidemiology of C . gattii is recent , although the first publications of meningeal cryptococcosis was in a child from the Congo in 1970 [11] , while another proven infection occurred in a patient with a lumbar tumor as described by the French physician Ferdinand Curtis in 1896 [12] . C . gattii is a fungal pathogen globally set , with a potential primary ecological niche being associated in some way with decaying wood from a large range of tree species [13] . C . gattii has been associated with at least 54 species of trees native to tropical , subtropical and temperate regions [13] , [14] . Currently , four major molecular types of C . gattii ( VGI = AFLP4 , VGII = AFLP6 , VGIII = AFLP5 and VGIV = AFLP7 ) are accepted according to the characterization by PCR fingerprinting , Random Amplification of Polymorphic DNA ( RAPD ) , Amplified Fragment Length Polymorphism ( AFLP ) , MLMT and MLST analyses , which are different to the genotypes of C . neoformans ( VNI to VNIV ) [15]–[17] . In addition a number of hybrids , including inter-species ( C . neoformans var . neoformans x C . gattii DB , C . neoformans var . grubii x C . gattii AB ) and intra-species ( C . neoformans var . grubii x C . neoformans var . neoformans AD ) hybrids , have been identified [18] , [19] . In Colombia , a national survey on cryptococcosis is ongoing since 1997 , led by the Instituto Nacional de Salud in Bogotá ( INS ) and the Corporación para Investigaciones Biológicas in Medellin ( CIB ) [20] , [21] . The objective of the present work was to analyze demographic , clinical and microbiological data concerning cryptococcosis caused by C . gattii received through the survey during the period of 1997–2011 . Our overarching aim was to provide a broad brushstroke picture of this mycosis in Colombia .
This is a descriptive study of the clinical , epidemiologic and microbiological characteristics of cases of cryptococcosis due to C . gattii in Colombia , identified through an ongoing national survey . The information was obtained by a survey designed according to the guidelines of the European Confederation of Medical Mycology , and processed by health professionals in different public and private health care institutions in Colombia , which also sent the corresponding strains from each case to the INS for centralized genotyping . The survey contained the following information: year of diagnosis of cryptococcosis , patient's demographic data , such as gender and age , department ( Colombia political divisions ) and place of residence , risk factors for cryptococcosis ( HIV infection , use of corticosteroids , autoimmune disease , organ transplants , presence of malignant solid tumors and hematologic malignancies , diabetes mellitus , liver cirrhosis , chronic kidney disease and sarcoidosis ) . In cases associated with HIV infection , we asked if cryptococcosis defined AIDS; the date of diagnosis of HIV , the clinical manifestations of cryptococcosis and the type of initial treatment . Also , diagnostic tests performed viz . direct examinations , cultures and the determination of the capsular antigen in serum and cerebrospinal fluid ( CSF ) and the findings of diagnostic imaging ( radiographs of the chest and cross-sectional neuroimaging ) . In addition to the aforementioned information , the evolution of the disease was determined for patients from Norte de Santander . The study was approved by the Ethics Committee of the CIB . Additionally , it had technical and ethical approval of the INS . With the information received , a database was created using Biolomics ver . 7 . 5 . 44 ( BioAware SA . , Belgium ) , while the data were analyzed statistically using Epiinfo ver . 6 . 1 ( CDC , USA ) . A case was considered probable when there were clinical findings consistent with cryptococcosis . Cases were confirmed after isolation of a Cryptococcus spp from a normally sterile site , from sputum , bronchoalveolar lavage or biopsy . The mean annual incidence rate was determined using as denominator the Colombian population census done in 2003 , an intermediate year of the surveillance , determined by DANE [22] . For Norte de Santander , the population for the year 2003 was also used [22] . Strain identification was done using conventional mycology techniques . Determination of species was performed by culturing the strains on CGB media [3] . In the first years of surveillance , serotype ( B versus C ) was determined using specific antisera available commercially ( Iatron , Japan ) . The susceptibility profiles to amphotericin B ( AMB ) using the E-test ( BioMerieux , France ) and to fluconazole ( FCZ ) and voriconazole ( VCZ ) using disc diffusion method M44-A described by the Clinical Laboratory Standards Institute ( CLSI ) were determined at the CIB laboratory . Quality control was done by including the Candida albicans strain ATCC 90028 , which shows an inhibition range between 32–43 mm . Molecular type was determined in all strains using PCR fingerprinting with the primer ( GTG ) 5 [15] . Mating type a or α ( alpha ) was determined using specific primers described previously [23] . For 44 strains , seven unlinked genetic loci , including CAP59 , GPD1 , LAC1 , PLB1 , SOD1 , URA5 and the IGS1 region , were amplified following the ISHAM consensus MLST typing scheme for C . neoformans and C . gattii [16] . Amplification of loci was carried out in the Molecular Mycology Research Laboratory , University of Sydney at Westmead Hospital , Westmead , Australia , and the sequences were obtained commercially ( Macrogen Inc . , Korea ) . The generated sequences were manually edited using the Sequencher ver . 5 . 2 ( Gene Codes Corporation , USA ) software . With the concatenated sequences , a dendrogram showing the genetic relationships between the strains was constructed with the program Mega version 5 . 05 [24] , based on maximum likelihood analysis . Allele types and sequence types ( ST ) were identified using the ISHAM consensus MLST database at mlst . mycologylab . org . The reference strains for C . gattii WM 179 ( VGI = AFLP4; B/alpha ) , WM 178 ( VGII = AFLP6; B/alpha ) , WM 175 ( VGIII = AFLP5; B/alpha ) and WM 779 ( VGIV = AFLP7; C/alpha ) , were included in the genetic analyses for verification of the four major molecular types of C . gattii [15] . Genetic diversity was assessed by calculating the Simpson's diversity index ( D ) [25] . A search was performed using the Medline database about cryptococcosis by C . gattii , reported from January 1970 to 31 July 2014 , its prevalence and the molecular types of clinical strains from the world using the following search terms in English: Cryptococcus gattii , epidemiology , meningitis , HIV , AIDS , children . Articles in Spanish and Portuguese were searched in the databases SciELO and Lilacs . Only available articles were included . In addition , some references cited in articles obtained in the primary search were included also .
Over the period from January 1997 to December 2011 , 1 , 209 surveys were completed , with corresponding cultured isolate , and were submitted from 76 centers in 25 departments of Colombia and Bogotá D . C . Of these , 45 ( 3 . 7% ) corresponded to cases of cryptococcosis by C . gattii . The number of cases received per year is shown in Figure 1 and Table S1 and the origin and number of cases per department is shown in Figure 2 and Table S1 . It should be noted that almost a third of the patients ( 33 . 3% ) resided in Norte de Santander . The average annual incidence of C . gattii in Colombia was 0 . 07 cases per million inhabitants per year , but in Norte de Santander , it was 0 . 81 cases per million inhabitants per year . A preponderance of males 34 ( 75 . 5% ) was found , with the male: female ratio being 3 . 3∶1 . The average age of the patients was 40±16 years with a range of4 to 68 years . The age and gender distribution is shown in Figure 3 . There were 2 ( 4 . 5% ) cases in children under 16 years-of-age . No predisposing risk factors were reported in 41 ( 91 . 1% ) of patients . From the cases in which the risk factors were known , the most frequent were: HIV infection ( 3; 6 . 7% ) and autoimmune disease with the use of steroids ( 1; 2 . 2% ) . Analysis of the Norte de Santander patients revealed that 93% had no apparent risk factor . Clinical manifestations were available for 41 patients ( Table 1 ) . The most frequent abnormal physical findings were: headache ( 33; 80 . 5% ) , nausea and vomiting ( 23; 56 . 1% ) , mental changes ( 20; 48 . 8% ) and visual alterations ( 18; 43 . 9% ) . The most common clinical presentation was neurocryptococcosis ( 39 cases; 86 . 7% ) , followed by pulmonary cryptococcosis ( 3; 6 . 7% ) and disseminated disease ( 2; 4 . 4% ) . Clinical presentation was not determined in 1 case ( 2 . 2% ) . In 21 ( 46 . 7% ) cases results of chest X-ray were available , with 7 ( 33 . 3% ) . showing abnormalities . Computed tomography ( CT ) of the head was conducted in 15 ( 33 . 3% ) cases with abnormalities reported in 10 ( 66 . 7% ) . Initial antifungal therapy was reported in 30 ( 66 . 7% ) surveys . AMB was used most often , in 29 ( 96 . 7% ) patients . Various diagnostic methods were carried out for cryptococcal identification , including direct microscopy , determination of the capsular antigen in serum and CSF and culture of CSF and respiratory samples . The results of these tests are described in Table 2 . The serotype was determined in 42 ( 93 . 3% ) strains: 36 ( 85 . 7% ) were serotype B , while 6 ( 14 . 3% ) were serotype C . Susceptibility to antifungals was determined for 42/45 C . gattii strains . All strains were susceptible to AMB ( MIC <2 µg/ml ) . Thirteen ( 31% ) were susceptible ( MIC ≤8 µg/ml ) , 14 ( 33 . 3% ) susceptible dose-dependent ( SDD ) ( MIC 16–32 µg/ml ) and 15 ( 35 . 7% ) resistant ( MIC ≥64 µg/ml ) to FCZ . With regard to VCZ , 39 ( 92 . 9% ) were susceptible ( MIC ≤1 µg/ml ) , 1 ( 2 . 4% ) SDD ( MIC 2 µg/ml ) and 2 ( 4 . 8% ) resistant ( MIC ≥4 µg/ml ) . The molecular types of the 45 VG isolates in order of frequency were: VGII 25 ( 55 . 6% ) , VGIII 14 ( 31 . 1% ) , and VGI 6 ( 13 . 3% ) ( Table 2 ) . The distribution of isolates per molecular type per department is shown in Figure 2 . In regard to mating type of the strains , 24 ( 53 . 3% ) were mating type α and 21 ( 46 . 7% ) were mating type a ( Table 2 ) . For 11 patients treated in Norte de Santander , follow-up was undertaken . Nine ( 81 . 8% ) were discharged alive , while 2 ( 18 . 2% ) died during hospitalization . Table S3 reflects the prevalence of cryptococcosis by C . gattii around the world , as well as their molecular types , when reported [26]–[112] .
The incidence of cryptococcosis caused by C . gattii globally is generally low overall [113] . The mean annual incidence found in this study for Colombia , of 0 . 07 cases per million inhabitants per year reflects this rarity . Similar findings ( 0 . 09 cases per million per year ) have been described from New Zealand [111] . However , some regions and population groups of the world have very high incidence , such as Papua New Guinea ( 43 cases per million per year ) [114] , the Australian aborigines domiciled in the Northern Territory ( 6 . 3 cases per million per year ) [115] and British Columbia , Canada ( 5 . 8 cases per million per year ) [62] . It is striking that the incidence of this disease in the department Norte de Santander is eleven times higher than the national ( 0 . 81 cases per million per year ) , which puts it at the level described for Australia as a continent ( 0 . 61 cases per million per year ) [115] . Regarding the prevalence of C . gattii infections , the value of 3 . 7% found in this survey reflects Colombia is a country of overall low prevalence , despite being in the torrid zone of the planet . However , Norte de Santander has a high prevalence of 33 . 3% ( 60% in patients without AIDS ) [59] , placing this region of Colombia on level with other countries of high prevalence such as Papua New Guinea [107] , [108] , the Northern Territory of Australia [110] , and Brazil [27] ( Table S3 ) . In Brazil , there is a clear difference between the North and South . In the area that covers the North and Northeast regions , the prevalence is very high , with values above 20% [28]–[33] , while in the regions Center West , Southeast and South , the prevalence is low [34]–[48] . Interestingly , a high proportion of cases of cryptococcosis due to C . gattii in the northern region of Brazil have been present in HIV-negative children [28] . A high prevalence of disease has also been reported from Venezuela [15] , [52]–[54] , French Guiana [55] Vietnam [100] and Hong Kong ( China ) [87] . Similar findings have been reported in some African countries , especially Botswana and Malawi , where a prevalence of 13% of C . gattii cryptococcosis was found in patients with AIDS , a high value for this type of population [76] . Another study reported a prevalence of 30% of C . gattii in hospitalized AIDS patients in Botswana [77] ( Table S3 ) . On the other hand , the overall prevalence in Europe is low , and many of the cases are from people immigrating coming from other regions in the world where cryptococcosis is more common[10] , [68] , [69] . Against this trend , an apparently endemic strain from an environmental source in the Netherlands was recently reported [116] . A low prevalence of cryptococcosis by C . gattii was also reported from South Africa , where AIDS-related cryptococcosis due to C . neoformans is epidemic [82] . Similar findings have been reported from other countries of the African continent [79]–[81] , [84] , in Mexico [50] , [51] , Argentina [15] , [56] , [57] and Asia including China ( without Hong Kong ) [85] , [86] , India [93]–[95] and in Southeast Asia [96]–[99] but excluding Vietnam and Thailand ( Table S3 ) . As in cryptococcosis patients infected with C . neoformans , disease referable to C . gattii is more frequent in men [117] , typically young adults [115] , although in this study we found children also and older patients . The presence of cryptococcosis by C . gattii in children domiciled in tropical areas in Brazil [28] , [34] , French Guiana [55] , and parts of Australia ( Meyer , unpublished ) in a remarkable finding that invites further enquiry and research . The majority ( 91 . 1% ) of the Colombian patients described in the current study did not report any apparent risk factor . There were , however , some cases in patients with AIDS or immunosuppressed by another means . The behavior of Colombian patients is in accord with the descriptions of previous studies [111] , [118] , [119] . The epidemiology of cryptococcosis by C . gattii has changed in the last decade , with the recognition that disease affects immunocompetent and immunosuppressed patients , including patients with AIDS , in regions of the world outside of the tropical and subtropical areas [14] , [120] , [121] . In Australia , where this trend is also observed , the number of immunosuppressed patients without AIDS increased from 9% to 28% [115] . In British Columbia , Canada the risk factors for infection with C . gattii were the use of oral steroids , the presence of pneumonia and other lung diseases [120] . Also , cryptococcosis was more frequent in patients older than 50 years , active smokers , HIV-positive individuals and a history of invasive cancer [120] . In the Pacific Northwest of the United States , patients infected with the outbreak strains of C . gattii , when compared with patients infected with different strains , were significantly more likely to have predisposing conditions and respiratory symptoms and a lower probability of having CNS involvement [121] . In Colombian patients , the most common clinical presentation was the CNS involvement with predominance of cryptococcal meningitis and intracranial hypertension . These clinical manifestations have been described in Latin America [39] , Asia [96] , Africa [82] , [83] , Australia [115] and Papua New Guinea [107] , [108] . This contrasts with the patients described in Vancouver , the Pacific Northwest of the United States and the Northern Territory of Australia where primary lung disease with granuloma formation predominates [120] , [121] . It is believed that C . gattii is clinically more virulent than C . neoformans , as well as being more likely to be a primary pathogen , with a propensity to cause multiple cryptococcal granulomas in the lungs and the brain of affected patients [111] , [118] . In a mouse model , clinical isolates of C . gattii from the outbreak in British Columbia induced an inflammatory response less protective because the organisms somehow inhibit the migration of neutrophils towards the sites of infection . Additionally , in contrast to C . neoformans , they fail to induce the production of protective cytokines [122] . In vitro studies of the same isolates showed that dendritic cells are able to destroy cryptococci , but C . gattii evades adaptive immunity by preventing maturation of those cells and causing an inadequate activation and proliferation of T cells [123] . Recently , it has been shown in mice that infection by C . gattii decreases the effective response of Th1/Th17 mediated by dendritic cells and regulates the low expression of lung cytokines , resulting in an inability to produce a protective immunity in immunocompetent hosts [124] . Compared to patient with C neoformans infections , meningoencephalitis caused by C . gattii responds more slowly to antifungal therapy , and patients require a longer duration of treatment [111] , [119] . Diagnostic imaging of the lung and brain are typically abnormal in patients infected with C . gattii . In the current study a third of the patients presented with gross lung abnormalities . In immunocompetent patients affected by C . gattii , pulmonary nodules or masses with diameters ranging from 5 to 52 mm in diameter and focal areas of consolidation have been described [125] . Some clinical considerations in the differentiation of the infections caused by C . gattii and C . neoformans were recently revised [126] . The diagnosis of the vast majority of the Colombian patients reported in the study was obtained by analysis of CSF samples . Direct microscopic examination showed high positivity ( 97 . 7% ) , compared with much lower rates reported for this test in patients without AIDS [87] , [127] . Also , capsular antigen in the CSF was reactive in all cases as measured by latex agglutination . Definitive diagnosis was established in all patients using culture , and the use of CBG agar established which cases were caused by C . gattii . The most frequently identified serotype in Colombian clinical strains was serotype B ( 85 . 7% ) , which is the most prevalent serotype in clinical and environmental samples . C . gattii serotype C was less common although has been associated with AIDS and with immunocompetent patients [13] . In Colombian patients , three of the four molecular types of C . gattii were found with a clear predominance of VGII , followed by VGIII and to a lesser extent VGI . The preponderance of the molecular type VGII in Colombia is similar to that reported in Western Australia [109] , [112] , Brazil ( especially in the Northern region ) [27] and Venezuela [15] . Equally , the molecular type VGII is the main biotype responsible for outbreaks in British Columbia , Canada [63] and the Pacific Northwest of the United States [64] . Hence the importance of determining the taxonomy of C . gattii strains because of the epidemic potential associated with the VGII molecular type . The emergence of specific genotypes of endemic and epidemic disease has been reflected in the large global effort being made to increase knowledge about the population genetics of C . neoformans and C . gattii [15] , [112] . MLST data has demonstrated that Colombian C . gattii strains are genetically diverse . In spite of the small number of isolates studied , several genotypes were identified belonging to the full range of molecular types , in contrast to the less diverse and rather clonal C . gattii populations reported in other countries such as Canada , USA , eastern Australia and Thailand , where few genotypes have been identified amongst a much larger number of strains [5] , [6] , [23] , [63] , [99] . The identification of the same STs in different departments ( Figure 2 ) , for example the prevalent VGII ST25 found in 7 different departments , the VGI ST51 and ST58 , and the VGIII ST64 , ST79 and ST146 , suggests the circulation of genotypes in the country . The finding of STs previously reported in other parts of the world , shows a wider geographic dispersion of some C . gattii genotypes , especially amongst the VGI isolates , for which the herein identified STs have been reported from several countries; ST51 was previously found in Australia , China , India , Mexico , Papua New Guinea and USA [10] , [16] , [128] , while ST58 had already been described from the Netherlands , Germany and USA [68] . Among the VGII isolates , the commonest ST identified in this study , ST25 , was already reported in one isolate from Aruba [129] , while the STs among the VGIII isolates , ST79 and ST146 , were reported in two and one isolates from Mexico and the USA , respectively [129] . In Colombia , the majority of the strains of C . gattii are mating type a [130] , in contradistinction to what was found in British Columbia , Canada [5] and of the Pacific coast of the United States [6] , and southwestern Western Australia where the main mating type is α , which could suggest the possibility of genetic exchange that could have had an impact on the origin of the outbreaks . C . gattii has been recovered frequently from the environment in Colombia and in the city of Cúcuta ( Norte de Santander ) , with both serotypes B and C having been cultured [131] , [132] . The initial treatment of cryptococcosis in almost all patients from Colombian was done with AMB , with or without FCZ , which is in agreement with the results of susceptibility testing of all strains of C . gattii to these antifungals and according with the international guidelines for countries with limited resources [133] . The resistance of approx . half of the Colombian strains to fluconazole is a phenomenon that is being studied by the group of the CIB ( C de Bedout , personal communication ) . | Cryptococcosis is caused by Cryptococcus neoformans and C . gattii , with the most serious manifestation of disease being infection of the central nervous system ( CNS ) . C . neoformans tends to cause disease in immunosuppressed patients , especially those infected with HIV , while C . gattii affects immunocompetent patients preferentially . C . gattii is usually endemic in tropical and subtropical areas . However , highly virulent strains have recently emerged in temperate areas , such as British Columbia in Canada and the Pacific Northwest of the United States . The Colombian national cryptococcal survey characterized the demographic , clinical manifestations and microbiological aspects of C . gattii cryptococcosis . An annual average incidence of infection of 0 . 07 cases/million inhabitants/year was determined . In contrast , in Norte de Santander the incidence reached 0 . 81 cases/million inhabitants/year . The national prevalence was 3 . 7% among all forms of cryptococcosis . Involvement of the CNS ( 88% ) was the commonest clinical manifestation of cryptococcosis . Molecular type VGII , which is the same molecular type as described in the recent outbreaks of this mycosis , was the most prevalent . Overall , clinical C . gattii strains from Colombia showed great genetic diversity . This work contributes to knowledge of the global epidemiology of cryptococcosis and its clinical behavior in Colombian patients . | [
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] | 2014 | Retrospective Study of the Epidemiology and Clinical Manifestations of Cryptococcus gattii Infections in Colombia from 1997–2011 |
Protrusion and retraction of lamellipodia are common features of eukaryotic cell motility . As a cell migrates through its extracellular matrix ( ECM ) , lamellipod growth increases cell-ECM contact area and enhances engagement of integrin receptors , locally amplifying ECM input to internal signaling cascades . In contrast , contraction of lamellipodia results in reduced integrin engagement that dampens the level of ECM-induced signaling . These changes in cell shape are both influenced by , and feed back onto ECM signaling . Motivated by experimental observations on melanoma cells lines ( 1205Lu and SBcl2 ) migrating on fibronectin ( FN ) coated topographic substrates ( anisotropic post-density arrays ) , we probe this interplay between intracellular and ECM signaling . Experimentally , cells exhibited one of three lamellipodial dynamics: persistently polarized , random , or oscillatory , with competing lamellipodia oscillating out of phase ( Park et al . , 2017 ) . Pharmacological treatments , changes in FN density , and substrate topography all affected the fraction of cells exhibiting these behaviours . We use these observations as constraints to test a sequence of hypotheses for how intracellular ( GTPase ) and ECM signaling jointly regulate lamellipodial dynamics . The models encoding these hypotheses are predicated on mutually antagonistic Rac-Rho signaling , Rac-mediated protrusion ( via activation of Arp2/3 actin nucleation ) and Rho-mediated contraction ( via ROCK phosphorylation of myosin light chain ) , which are coupled to ECM signaling that is modulated by protrusion/contraction . By testing each model against experimental observations , we identify how the signaling layers interact to generate the diverse range of cell behaviors , and how various molecular perturbations and changes in ECM signaling modulate the fraction of cells exhibiting each . We identify several factors that play distinct but critical roles in generating the observed dynamic: ( 1 ) competition between lamellipodia for shared pools of Rac and Rho , ( 2 ) activation of RhoA by ECM signaling , and ( 3 ) feedback from lamellipodial growth or contraction to cell-ECM contact area and therefore to the ECM signaling level .
Rho GTPases are central regulators that control cell polarization and migration [15 , 16] , embedded in complex signaling networks of interacting components [17] . Two members of this family of proteins , Rac1 and RhoA , have been identified as key players , forming a central hub that orchestrates the polarity and motility response of cells to their environment [18 , 19] . Rac1 ( henceforth “Rac” ) works in synergy with PI3K to promote lamellipodial protrusion in a cell [16] , whereas RhoA ( henceforth “Rho” ) activates Rho Kinase ( ROCK ) , which activates myosin contraction [20] . Mutual antagonism between Rac and Rho has been observed in many cell types [19 , 21 , 22] , and accounts for the ability of cells to undergo overall spreading , contraction , or polarization ( with Rac and Rho segregated to front versus rear of a cell ) . The extracellular matrix ( ECM ) is a jungle of fibrous and adhesive material that provides a scaffold in which cells migrate , mediating adhesion and traction forces . ECM also interacts with cell-surface integrin receptors , to trigger intracellular signaling cascades . Important branches of these pathways are transduced into activating or inhibiting signals to Rho GTPases . On one hand , ECM imparts signals to regulate cell shape and cell motility . On the other hand , the deformation of a cell affects its contact area with ECM , and hence the signals it receives . The concerted effect of this chemical symphony leads to complex cell behavior that can be difficult to untangle using intuition or verbal arguments alone . This motivates our study , in which mathematical modeling of GTPases and ECM signaling , combined with experimental observations is used to gain a better understanding of cell behavior , in the context of experimental data on melanoma cells . There remains the question of how to understand the interplay between genes ( cell type ) , environment ( ECM ) and signaling ( Rac , Rho , and effectors ) . We and others [19 , 21–27] have previously argued that some aspects of cell behavior ( e . g . , spreading , contraction , and polarization or amoeboid versus mesenchymal phenotype ) can be understood from the standpoint of Rac-Rho mutual antagonism , with fine-tuning by other signaling layers [28] . Here we extend this idea to couple Rac-Rho to ECM signaling , in deciphering the behavior of melanoma cells in vitro . There are several overarching questions that this study aims to address . In experiments of Park et al . [11] melanoma cells were cultured on micro-fabricated surfaces comprised of post density arrays coated with fibronectin ( FN ) , representing an artificial extracellular matrix . The anisotropic rows of posts provide inhomogeneous topographic cues along which cells orient . In [11] , cell behavior was classified using the well-established fact that PI3K activity is locally amplified at the lamellipodial protrusions of migrating cells [36] . PI3K “hot spots” were seen to follow three distinct patterns about the cell perimeters: random ( RD ) , oscillatory ( OS ) , and persistent ( PS ) . These classifications were then associated with three distinct cell phenotypes: persistently polarized ( along the post-density axis ) , oscillatory with two lamellipodia at opposite cell ends oscillating out of phase ( protrusion in one lamellipod coincides with retraction of the other , again oriented along the post-density axis ) , and random dynamics , whereby cells continually extend and retract protrusions in random directions . The fraction of cells in each category was found to depend on experimental conditions . Here , we focus on investigating how experimental manipulations influence the fraction of cells in different phenotypes . For simplicity , we focus on the polarized and oscillatory phenotypes which can be most clearly characterized mathematically . The following experimental observations are used to test and compare our distinct models of cell signaling dynamics . For a graphical summary of cell phenotypes and experimental observations , see Fig 1 .
We discuss three model variants , each composed of ( A ) a subsystem endowed with bistability , and ( B ) a subsystem responsible for negative feedback . In short , Model 1 assumes ECM competition for ( A ) and feedbacks mediated by GTPases for ( B ) . In contrast , in Model 2 we assume GTPase dynamics for ( A ) and ECM mediated feedbacks for ( B ) . Model 3 resembles Model 2 , but further assumes limited total pool of each GTPase ( conservation ) , which turns out to be a critical feature . See Tables 1 and 2 for details . We analyze each model variant as follows: first , we determine ( bi/mono ) stable regimes of subsystem ( A ) in isolation , using standard bifurcation methods . Next , we parameterize subsystem ( B ) so that its slow negative feedback generates oscillations when ( A ) and ( B ) are coupled in the model as a whole . For this to work , ( B ) has to force ( A ) to transition from one monostable steady state to the other ( across the bistable regime ) as shown in the relaxation loop of Fig 2d . This requirement informs the magnitude of feedback components . Although these considerations do not fully constrain parameter choices , we found it relatively easy to then parameterize the models ( particularly Models 1b and 3 ) . This implies model robustness , and suggests that broad regions of parameter space lead to behavior that is consistent with experimental observations . Parameters associated with rates of activation and/or feedback strengths are summarized in the S1 Text . The parameters γi represent the strengths of feedbacks 1 or 2 in Fig 2 ( b ) and 2 ( c ) . γR controls the positive feedback ( 2 ) of Rac ( via lamellipod spreading ) on ECM signaling , and γρ represents the magnitude of negative feedback ( 1 ) from Rho to ECM signaling ( due to lamellipod contraction ) . γE controls the strength of ECM activation of Rho in both feedbacks ( 1 ) and ( 2 ) . When these feedbacks depend on cell state variables , we typically use Hill functions with magnitude γi , or , occasionally , linear expressions with slopes γ ¯ i . ( These choices are distinguished by usage of overbar to avoid confusing distinct units of the γ’s in such cases . ) Experimental manipulations in [11] ( described in Section “Experimental observations constraining the models” ) can be linked to the following parameter variations . In view of this correspondence between model parameters and experimental manipulations , our subsequent analysis and bifurcation plots will highlight the role of feedback parameters γR , ρ , E in the predictions of each model . Rather than exhaustively mapping all parameters , our goal is to use 1 and 2-parameter bifurcation plots with respect to these parameters to check for ( dis ) agreement between model predictions and experimental observations ( O1–O3 ) . This allows us to ( in ) validate several hypotheses and identify the eventual model ( the Hybrid , Model 3 ) and set of hypotheses that best account for observations . We first investigated the possibility that lamellipod competition is responsible for bistability and that GTPases interactions create negative feedback that drives the oscillations observed in some cells . To explore this idea , we represented the interplay between lamellipodia ( e . g . , competition for growth due to membrane tension or volume constraints ) , using an elementary Lotka-Volterra ( LV ) competition model . For simplicity , we assume that AE , LE depend linearly on Rac and Rho concentration , and set BE = 0 . ( This simplifies subsequent analysis without significantly affecting qualitative conclusions . ) With these assumptions , the ECM Eq ( 3c ) reduce to the well-known LV species-competition model . First consider Eq ( 3c ) as a function of parameters ( AE , LE ) , in isolation from GTPase input . As in the classical LV system [45] , competition gives rise to coexistence , bistability , or competitive exclusion , the latter two associated with a polarized cell . These regimes are indicated on the parameter plane of Fig 3a with the ratios of contractile ( LE ) and protrusive ( AE ) strengths in each lamellipod as parameters . ( In the full model , these quantities depend on Rac and Rho activities; the ratios LE ( ρk ) /AE ( Rk ) for lamellipod k = 1 , 2 lead to aggregate parameters that simplify this figure . ) We can interpret the four parameter regimes in Fig 3a as follows: I ) a bistable regime: depending on initial conditions , either lamellipod “wins” the competition . II ) Lamellipod 1 always wins . III ) Lamellipod 2 always wins . IV ) Lamellipods 1 and 2 coexist at finite sizes . Regimes I-III represent strongly polarized cells , whereas IV corresponds to an unpolarized ( or weakly polarized ) cell . We next asked whether , and under what conditions , GTPase-mediated feedback could generate relaxation oscillations . Such dynamics could occur provided that slow negative feedback drives the ECM subsystem from an E1-dominated state to an E2-dominated state and back . In Fig 3a , this correspond to motion along a path similar to one labeled ( d ) in Panel ( a ) , with the ECM subsystem circulating between Regimes II and III . This can be accomplished by GTPase feedback , since both Rho and Rac modulate LE ( contractile strength ) and AE ( protrusion strength ) . We show this idea more explicitly in Fig 3 ( c ) –3 ( e ) by plotting E1 vs LE1 while keeping LE1 + LE2 constant . ( Insets similarly show E2 vs LE1 . ) Each of Panels ( c-e ) corresponds to a 1-parameter bifurcation plot along the corresponding path labeled ( c-e ) in Panel ( a ) . We find the following possible transitions: In Fig 3c , we find two distinct polarity states: either E1 or E2 dominate while the other is zero regardless of the value of LE1; a transition between such states does not occur . In Fig 3d , there is a range of values of LE1 with coexisting stable low and high E1 values ( bistable regime ) flanked by regimes where either the lower or higher state loses stability ( monostable regimes ) . As indicated by the superimposed loop , a cycle of protrusion ( green ) and contraction ( blue ) could then generate a relaxation oscillation as the system traverses its bistable regime . In Fig 3e , a third possibility is that the system transits between E1-dominated , coexisting , and E2-dominated states . In brief , for oscillatory behavior , GTPase feedback should drive the ECM-subsystem between regimes I , II , and III without entering regime IV . Informed by this analysis , we next link the bistable ECM submodel to a Rac-Rho system . To ensure that the primary source of bistability is ECM dynamics , a monostable version of the Rac-Rho sub-system is adopted by setting n = 1 in the GTPase activation terms AR , Aρ in Eqs ( 3a ) and ( 3b ) . We consider three possible model variants ( 1a-1c ) for the full ECM / GTPase model . In view of the conclusions thus far , we now explore the possibility that bistability stems from mutual antagonism between Rac and Rho , rather than lamellipod competition . To do so , we chose Hill coefficients n = 3 in the rates of GTPase activation , AR , Aρ . We then assume that ECM signaling both couples the lamellipods and provides the requisite slow negative feedback . Here we consider the case that GTPases are abundant , so that the levels of inactive Rac and Rho ( RI , ρI ) are constant . We first characterize the GTPase dynamics with bR , ρ as parameters . Subsequently , we include ECM signaling dynamics and determine how the feedback drives the dynamics in the ( bR , bρ ) parameter plane . Isolated from the ECM influence , each lamellipod is independent so we only consider the properties of GTPase signaling in one . This mutually antagonistic GTPase submodel is the well-known “toggle switch” [50] that has a bistable regime , as shown in the ( bR , bρ ) plane of Fig 4a . ECM signaling affects the Rac / Rho system only as an input to bρ . A linear dependence of bρ on Ek failed to produce an oscillatory parameter regime , so we used a nonlinear Hill type dependence with basal and saturating components . Furthermore , for GTPase influence on ECM signaling we use Hill functions for the influence of Rho ( in LE ) and Rac ( in BE ) on protrusion and contraction . We set AE = 0 in this model for simplicity . ( Nonzero AE can lead to compounded ECM bistability that we here do not consider . ) Given the structure of the bρ − bR parameter plane and the fact that ECM signaling variables only influence bρ , we can view oscillations as periodic cycles of contraction and protrusion forming a trajectory along one of horizontal dashed lines in Fig 4a . This idea guides our parametrization of the model . We select a value of bR that admits a bistable range of bρ in Fig 4a . Next we choose maximal and minimal values of the function bρ ( EK ) that extend beyond the borders of the bistable range . This choice means that the system transitions from the high Rac / low Rho state to the low Rac / high Rho state over each of the cycles of its oscillation . With this parametrization , we find oscillatory dynamics , as shown in Fig 4b . We now consider the two-lamellipod system with the above GTPase module in each lamellipod; we challenge the full model with experimental observations . Since each lamellipod has a unique copy of the Rac-Rho module , ECM signaling provides the only coupling between the two lamellipods . First , we observed that inhibition of ROCK ( reduction of γρ in Fig 4b ) suppress oscillations . However the resulting stationary state is non-polar , in contrast to experimentally observed increase in the fraction of polarized cells ( O1 ) . We adjusted the coupling strength ( lc ) to ensure that this disagreement was not merely due to insufficient coupling between the two lamellipods . While an oscillatory regime persists , the discrepancy with ( O1 ) is not resolved: the system oscillates , but inhibiting ROCK gives rise to a non-polarized stationary state , contrary to experimental observations . Yet another problematic feature of the model is its undue sensitivity to the strength of Rac activation ( bR ) . This is evident from a comparison of the dashed lines in Fig 4a . A small change in bR ( vertical shift ) dramatically increases the range of bistability ( horizontal span ) , and hence the range of values of bρ to be traversed in driving oscillations . This degree of sensitivity seems inconsistent with biological behavior . It is possible that an alternate formulation of the model ( different kinetic terms or different parametrization ) might fix the discrepancies noted above , so we avoid ruling out this scenario altogether . In our hands , this model variant failed . However a simple augmentation , described below , addresses all deficiencies , and leads to the final result . In our third and final step , we add a small but significant feature to the bistable GTPase model to arrive at a working variant that accounts for all observations . Keeping all equations of Model 2 , we merely drop the assumption of unlimited Rac and Rho . We now require that the total amount of each GTPase be conserved in the cell . This new feature has two consequences . First , lamellipods now compete not only for growth , but also for limited pools of Rac and Rho . This , along with rapid diffusion of inactive GTPases across the cell [30 , 31 , 51] provides an additional global coupling of the two lamellipods . This seemingly minor revision produces novel behavior . We proceed as before , first analyzing the GTPase signaling system on its own . With conservation , the bR − bρ plane has changed from its previous version ( Fig 4a for Model 2 ) to Fig 5a . For appropriate values of bR , there is a significant bistable regime in bρ . Indeed , we find three regimes of behavior as the contractile strength in lamellipod k , bρ ( Ek ) , varies: a bistable regime where polarity in either direction is possible , a regime where lamellipod j “wins” ( Ej > Ek , left of the bistable regime ) , and a regime where lamellipod k “wins” ( right of the bistable regime ) . Only polarity in a single direction is possible on either side of the bistable regime . As in Model 2 , we view oscillations in the full model as cycles of lamellipodial protrusion and contraction that modify bρ ( Ek ) over time , and result in transitions between the three polarity states . To parameterize the model , we repeat the process previously described ( choose a value of bR consistent with bistability , then choose the dependence of bρ on ECM signaling so as to traverse that entire bistable regime . ) We couple the GTPase system with ECM equations as before . We then check for agreement with observations ( O1–O3 ) . As shown in Fig 5 ( e ) and 5 ( f ) , the model produces both polarized and oscillatory solutions . To check consistency with experiments , we mapped the dynamics of this model with respect to both ROCK mediated contraction and PI3K mediated protrusion ( Fig 5c ) . Inhibiting ROCK ( Fig 5b , decreasing γρ ) results in a transition from oscillations to polarized states , consistent with ( O1 ) . PI3K upregulation promotes oscillations ( increasing γR , Fig 5c ) , characteristic of the more invasive cell line 1205Lu ( consistent with O2 ) . Finally , increased fibronectin density ( increased γE , Fig 5d ) also promotes oscillations , consistent with ( O3 ) . We conclude that this Hybrid Model can account for polarity and oscillations , and that it is consistent with the three primary experimental observations ( O1–3 ) . Finally , Model 3 can recapitulate such observations with more reasonable timescales for GTPase and ECM dynamics than were required for Model variant 1b . It is apparent that Model 3 contains two forms of lamellipodial coupling: direct ( mechanical ) competition and competition for the limited pools of inactive Rac and Rho . While the former is certain to be an important coupling in some contexts or conditions [52] , we find that it is dispensable in this model ( e . g , see lc = 0 in Fig 5c ) . We comment about the effect of such coupling in the Discussion . In the context of this final model , we also tested the effect of ECM activation of Rac ( in addition to the already assumed effect on Rho activation ) . As shown in Fig 5d ( dashed curves ) , the essential bifurcation structure is preserved when this modification is incorporated ( details in the S1 Text , and implications in the Discussion ) . To summarize , Model 1b was capable of accounting for all observations , but required conservation of GTPase to do so . This model was however rejected due to unreasonable time scales needed to give rise to oscillations . Model 2 could account for oscillations with appropriate timescales , but it appears to be highly sensitive to parameters and , in our hands , inconsistent with experimental observations . Model 3 , which combines the central features of Models 1b and 2 , has the right mix of timescales , and agrees with experimental observations . In that final Hybrid Model , ECM based coupling ( lc ) due to mechanical tension or competition for other resources is not essential , but its inclusion makes oscillations more prevalent ( Fig 5b and 5e ) . Furthermore , in this Hybrid Model , we identify two possible negative feedback motifs , shown in Fig 2b . These appear to work cooperatively in promoting oscillations . As we have argued , feedbacks are tuned so that ECM signaling spans a range large enough that bρ ( Ek ) traverses the entire bistable regime ( Fig 5a ) . This is a requirement for the relaxation oscillations schematically depicted in Fig 2c . Within an appropriate set of model parameters , either feedback could , in principle , accomplish this . Hence , if Feedback 1 is sufficiently strong , Feedback 2 is superfluous and vice versa . Alternatively , if neither suffices on its own , the combination of both could be sufficient to give rise to oscillations . Heterogeneity among these parameters could thus be responsible for the fact that in ROCK inhibition experiments ( where Feedback 1 is essentially removed ) , most but not all cells transition to the persistent polarity phenotype . The Hybrid Model ( Model 3 ) is consistent with observations O1–O3 . We can now challenge it with several further experimental tests . In particular , we make two predictions .
Migrating cells can exhibit a variety of behaviors . These behaviors can be modulated by the cell’s internal state , its interactions with the environment , or mutations such as those leading to cancer progression . In most cases , the details of mechanisms underlying a specific behavior , or leading to transitions from one phenotype to another are unknown or poorly understood . Moreover , even in cases where one or more defective proteins or genes are known , the complexity of signaling networks make it difficult to untangle the consequences . Hence , using indirect observations of cell migration phenotypes to elucidate the properties of underlying signaling modules and feedbacks are , as argued here , a useful exercise . Using a sequence of models and experimental observations ( O1–O3 ) we tested several plausible hypotheses for melanoma cell migration phenotypes observed in [11] . By so doing , we found that GTPase dynamics are fundamental to providing 1 ) bistability associated with polarity and 2 ) coupling between competing lamellipods to select a single “front” and “rear” . ( This coupling is responsible for the antiphase lamellipodial oscillations ) . Further , slow feedback between GTPase and ECM signaling resulting from contraction and protrusion generate oscillations similar those observed experimentally . The single successful model , Hybrid Model ( Model 3 ) , is essentially a relaxation oscillator . Mutual inhibition between the limited pools of Rac and Rho , sets up a primary competition between lamellipods that produces a bistable system with polarized states pointing in opposite directions . Interactions between GTPase dynamics and ECM signaling provide the negative feedback required to flip this system between the two polarity states , generating oscillations for appropriate parameters . Results of Model 3 are consistent with observations ( O1–O3 ) , and lead to predictions ( P1–P2 ) , that are also confirmed by experimental observations [11] . In [11] , it is further shown that the fraction of cells exhibiting each of these behaviors can be quantitatively linked to heterogeneity in the ranges of parameters representing the cell populations in the model’s parameter space . In our models , we assumed that the dominant effect of ECM signaling input is to activate Rho , rather than Rac . In reality , both GTPases are likely activated to some extent in a cell and environment-dependent manner [41 , 42] . We can incorporate ECM activation of Rac by amending the term AR so that its magnitude is dependent on ECM signaling ( Ek ) . Doing so results in a shift in the borders of regimes we have indicated in Fig 5d ( dashed versus solid borders , see S1 Text for more details ) . So long as Rho activation is the dominant effect , this hardly changes the qualitative results . As the strength of feedback onto Rac strengthens , however , the size of the oscillatory regime is reduced . Thus if feedback onto Rac dominates , loss of oscillations would be predicted . This is to be expected based on the structure of these interactions . Where ECM → Rho mediates a negative feedback , ECM → Rac mediates a positive feedback , which would be expected to suppress oscillatory behavior . Thus while the ECM likely mediates multiple signaling feedbacks , this modeling suggest feedback onto Rho is most consistent with observations . We have argued that conservation laws ( fixed total amount of Rac and fixed total amount of Rho ) in the cell plays an important role in the competition between lamellipods . Such conservation laws are also found to be important in a number of other settings . Fully spatial ( PDE ) modeling of GTPase function has shown that conservation significantly alters signaling dynamics [27 , 31 , 54] . In [55] , it was shown that stochastically initiated hot spots of PI3K appeared to be globally coupled , potentially through a shared and conserved cytoplasmic pool of a signaling regulator . Conservation of MIN proteins , which set up a standing wave oscillation during bacterial cell division , has been shown to give rise to a new type of Turing instability [56] . Finally , interactions between conserved GTPase and negative regulation from F-actin in a PDE model was shown to give rise to a new type of conservative excitable dynamics [46 , 47] , which have been linked to the propagation of actin waves [57] . These results provide interesting insights into the biology of invasive cancer cells ( in melanoma in particular ) , and shed light onto the mechanisms underlying the extracellular matrix-induced polarization and migration of normal cells . First , they illustrate that diverse polarity and migration patterns can be captured within the same modeling framework , laying the foundation for a better understanding of seemingly unrelated and diverse behaviors previously reported . Second , our results present a mathematical and computational platform that distills the critical aspects and molecular regulators in a complex signaling cascade; this platform could be used to identify promising single molecule and molecular network targets for possible clinical intervention . | Cells crawling through tissues migrate inside a complex fibrous environment called the extracellular matrix ( ECM ) , which provides signals regulating motility . Here we investigate one such well-known pathway , involving mutually antagonistic signalling molecules ( small GTPases Rac and Rho ) that control the protrusion and contraction of the cell edges ( lamellipodia ) . Invasive melanoma cells were observed migrating on surfaces with topography ( array of posts ) , coated with adhesive molecules ( fibronectin , FN ) by Park et al . , 2017 . Several distinct qualitative behaviors they observed included persistent polarity , oscillation between the cell front and back , and random dynamics . To gain insight into the link between intracellular and ECM signaling , we compared experimental observations to a sequence of mathematical models encoding distinct hypotheses . The successful model required several critical factors . ( 1 ) Competition of lamellipodia for limited pools of GTPases . ( 2 ) Protrusion / contraction of lamellipodia influence ECM signaling . ( 3 ) ECM-mediated activation of Rho . A model combining these elements explains all three cellular behaviors and correctly predicts the results of experimental perturbations . This study yields new insight into how the dynamic interactions between intracellular signaling and the cell’s environment influence cell behavior . | [
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"cell... | 2017 | A mathematical model coupling polarity signaling to cell adhesion explains diverse cell migration patterns |
Numerous experimental and epidemiological studies have demonstrated a link between Clonorchis sinensis ( C . sinensis ) infestation and cholangiocarcinoma ( CCA ) as well as hepatocellular carcinoma ( HCC ) . The underlying molecular mechanism involved in the malignancy of CCA and HCC has not yet been addressed . Csseverin , a component of the excretory/secretory products of C . sinensis ( CsESPs ) , was confirmed to cause obvious apoptotic inhibition in the human HCC cell line PLC . However , the antiapoptotic mechanism is unclear . In the present study , we investigated the cellular features of the antiapoptotic mechanism upon transfection of the Csseverin gene . In the present study , we evaluated the effects of Csseverin gene overexpression on the apoptosis of PLC cells using an Annexin PE/7-AAD assay . Western blotting was applied to quantify the activation of caspase-3 and caspase-9 , the mitochondrial translocation of Bax and the release of Cyt c upon Csseverin overexpression in PLC cells . Laser scanning confocal microscopy was used to analyze the changes of intracellular calcium . Fluorescence assay and immunofluorescence assays were performed to observe the changes of the mitochondrial permeability transition pore ( MPTP ) . The overexpression of Csseverin in PLC cells showed apoptosis resistance after the induction of apoptosis . Additionally , the activation of caspase-3 and caspase-9 was specifically weakened in Csseverin overexpression PLC cells . The overexpression of Csseverin reduced the increase in intracellular free Ca2+ , thereby inhibiting MPTP opening in PLC cells . Moreover , Bax mitochondrial translocation and the subsequent release of Cyt c were downregulated in apoptotic Csseverin overexpression PLC cells . The present findings suggest that Csseverin , a component of CsESPs , confers protection from human HCC cell apoptosis via the inactivation of membranous Ca2+ channels . Csseverin might be involved in the process of HCC through C . sinensis infestation in affected patients .
Clonorchis sinensis ( C . sinensis ) causes clonorchiasis , which is widely distributed in East Asia with heavily endemic zones in China , Taiwan , Vietnam , Russia , and Korea[1] . C . sinensis was reclassified as a group-I biocarcinogen for cholangiocarcinoma ( CCA ) by the International Agency for Research on Cancer ( IARC ) in 2009[2] . In endemic areas of China , 16 . 44% of hepatocellular carcinoma ( HCC ) patients were infected with C . sinensis , while 2 . 40% of non-tumor patients were infected [3] . This biocarcinogen has been included in control programs of neglected tropical diseases by the WHO[4] . The illumination of the precise mechanism linking C . sinensis with the development of HCC and CCA will help to prevent or postpone disease progression . Excretory-secretory proteins from C . sinensis ( CsESPs ) play important roles in the interactions between the worm and host , including the pathogenesis of inflammation , immune responses and carcinogenesis induced by the infection . Escaping from apoptosis is an important aspect of cancer pathogenesis and has been widely recognized as a trait of most types of cancer [5] . In a previous study , we observed that Csseverin ( a component of CsESPs ) , a homologous protein of the gelsolin family , caused obvious apoptotic inhibition in the human HCC cell line PLC . By promoting apoptosis suppression , Csseverin might accelerate the progress of HCC patients combined with C . sinensis infection [6] . It is worth studying the exact molecular mechanisms involved in the anti-apoptotic effects induced by Csseverin . The gelsolin family had been implicated in the regulation of cell motility , apoptosis and phagocytosis [7] . The expression of gelsolin family proteins is reduced in many cancers , associated with poor prognosis and therapy resistance [8–9] . There is now increasing evidence that gelsolin family proteins are multifunctional regulators of cell apoptosis and cell metabolism , which involves multiple mechanisms[10–14] . Apoptosis can be executed in two distinct signaling cascades: the extrinsic pathway and the intrinsic pathway[15–16] . In the extrinsic pathway , apoptosis is triggered by death receptors , such as FAS-associated death domain protein ( FADD ) , activating caspases 8 and 10 ( the initiator caspases ) , which in turn activate executioner caspases 3 , 6 and 7[17] . In the intrinsic pathway , the mitochondrial permeability transition pore ( MPTP ) plays a pivotal role in regulating the release of pro-apoptotic proteins , such as cytochrome c ( Cytc ) . The released Cytc from mitochondria initiated the assembly of apoptosomes , activating factor 1 ( Apaf-1 ) and caspase 9 , an initiator caspase that cleaves and activates caspase 3 and 7[18] . MPTP is regulated by Bcl-2 proteins that induce the oligomerization of BAX ( Bcl-2-associated protein ) or BAK ( Bcl-2 antagonist ) [19] . The results of a previous study indicated that Csseverin binds to calcium ions in solution and actin filaments inside cells . We also demonstrated that the co-incubation of PLC cells with Csseverin in vitro led to apoptosis suppression based on the detection of the apoptosis-associated changes of mitochondrial membrane potential[6] . To further understand the anti-apoptotic role of Csseverin , we constructed stable Csseverin-overexpressing PLC cells ( pEZ-LV203-Csseverin PLC ) to avoid interference from endotoxin through the use of recombinant Csseverin . We detected a suppression effect of Csseverin on the early wave apoptosis of PLC cells . Furthermore , to investigate the mechanisms involved in Csseverin induced apoptosis suppression , we explored the effects of Csseverin on the activation of the caspase cascade , leading to the suppression of the permeability transition pore ( MPTP ) , the mobilization of calcium , and the translocation of Cyt c and Bax .
The Ethics Committee of Sun Yat-Sen University reviewed and approved the protocols and experiments used in this study . The methods were carried out in accordance with the approved protocols . The data were collected and analyzed anonymously . The human HCC cell line PLC were a gift from Dr . Wang Shutong ( the First Affiliated Hospital of Sun Yat-Sen University ) and routinely cultured in high glucose DMEM medium ( Gibco , USA ) supplemented with 10% fetal bovine serum ( Gibco , USA ) and penicillin-streptomycin ( 100 units/ml ) in 5% CO2 at 37°C . The human 293T cells were kindly provided by GeneCopoeia ( Rockville , MD , USA ) and maintained in high glucose DMEM supplemented with 10% fetal bovine serum in 5% CO2 at 37°C . Cox-IV , Caspase 3 , Caspase 9 , Bax and Cytochrome c were purchased from Cell Signaling Technology ( Danvers , MA , USA ) . β-actin was obtained from Proteintech ( USA ) . Anti-Csseverin serum was prepared as previously described [6] . The pEZ-LV203 lentiviral vector harboring the eGFP reporter gene was purchased from GeneCopoeia ( Rockville , MD , USA ) . The pEZ-LV203 vector and Csseverin gene fragments were digested with EcoRI and Apa I , respectively , and subsequently ligated using T4 DNA ligase . The recombinant plasmid pEZ-LV203-Csseverin was identified by enzyme digestion and sequencing . To generate the lentivirus , the pEZ-LV203-Csseverin plasmid or PEZ-LV203 control plasmid was cotransfected into 293T cells using the Lenti-Pac HIV Expression Packaging Kit ( GeneCopoeia , USA ) according to the manufacturer’s instructions . Supernatant containing the recombinant lentiviral particles was collected at 48 h post-transfection , filtered by a Millipore filter and subjected to ultracentrifugation . The lentiviral particles were re-suspended in cold phosphate-buffered saline ( PBS ) and used to infect PLC cells . The PLC cells were divided into three groups , pEZ-LV203-Csseverin PLC ( transfected with pEZ-LV203-Csseverin plasmid ) , pEZ-LV203 PLC ( PEZ-LV203 control plasmid ) and PLC ( no transfection ) . After 48 h , the cells were incubated in selection medium containing puromycin ( 3 mg/ml ) for 7 days to select stably Csseverin-overexpressing PLC cells ( pEZ-LV203-Csseverin PLC ) and control PLC cells ( pEZ-LV203 PLC ) . The transfection efficiency of pEZ-LV203-Csseverin PLC was evaluated by the expression of eGFP , and the Csseverin protein expression levels of pEZ-LV203-Csseverin PLC were measured by Western blot analysis . http://dx . doi . org/10 . 17504/protocols . io . kcdcss6[PROTOCOL DOI] Apoptotic cells were assessed by Annexin PE/7-AAD detection as previously described [6] . Briefly , Control groups ( pEZ-LV203 PLC and PLC ) or pEZ-LV203-Csseverin PLC cells were plated at a density of 105 cells per well in 6-well plates , and apoptosis was spontaneously induced after serum starvation for 48 h . The cells were collected by centrifugation , washed with cold PBS , and subsequently resuspended in 500 μl of 1× Binding Buffer prior to incubation with 5 μl of Annexin PE and 5 μl of 7-AAD ( Keygentec , Nanjing , China ) . The cell samples were incubated at room temperature for 20 min and subsequently detected by a flow cytometer ( Beckman Coulter Gallios , USA ) to determine the apoptotic cell fractions . http://dx . doi . org/10 . 17504/protocols . io . kcecste[PROTOCOL DOI] The pEZ-LV203-Csseverin PLC cells were pretreated by serum starvation for 48 h , and pEZ-LV203 PLC and PLC cells were used as controls . A total of 5 × 106 cells were collected and treated with 300 μl of RIPA buffer ( 150 mM NaCl , 50 mM Tris , pH 7 . 4 , 1% NP40 , 0 . 1% SDS , and 0 . 5% sodium deoxycholate ) supplemented with protease and phosphatase inhibitors ( Keygentec , Nanjing , China ) . To monitor the shift in Cytc from the mitochondria and Bax from the cytosol , we fractionated the cytosolic and mitochondrial fractions using a Cell Mitochondria Isolation Kit according to the manufacturer's instructions ( Beyotime Institute of Biotechnology , China ) . The pEZ-LV203-Csseverin PLC cells were pretreated by serum starvation for 48 h , and pEZ-LV203 PLC and PLC cells were used as controls . A total of 5 × 106 cells were collected after brief trypsinization , followed by two more washes with PBS , and the cell pellet was resuspended in 200 μl of mitochondria extraction buffer containing 0 . 02 mM phenylmethanesulfonyl fluoride ( PMSF ) and proteinase inhibitors ( Keygentec , Nanjing , China ) . After incubating on ice for 20 min , the cells were homogenized using a glass Dounce and pestle . The homogenates were centrifuged at 600 g for 15 min at 4°C , and the resulting supernatant was collected and centrifuged at 11 , 000 g for 15 min at 4°C to separate the mitochondria ( pellet ) and cytoplasmic proteins ( supernatant ) . The mitochondria pellet was lysed in mitochondria extraction buffer ( KeyGen Biotech , Nanjing , China ) . http://dx . doi . org/10 . 17504/protocols . io . kcecstedx . doi . org/10 . 17504/protocols . io . kcfcstn [PROTOCOL DOI] Western blotting analysis to determine the levels of apoptosis-related proteins was performed using standard techniques . The concentration of protein was determined by the BCA protein assay kit ( Beyotime Institute of Biotechnology , China ) . Equal amounts of protein were subjected to Western blotting analysis . The proteins ( 40 μg ) were separated according to molecular weight on a 12% SDS-PAGE gel and transferred onto a polyvinylidene difluoride ( PVDF ) membrane . The membranes were blocked with 1% bovine serum albumin in Tris-Buffered Saline Tween-20 ( TBST , pH 7 . 4 ) at room temperature for 2 h , and probed overnight at 4°C with specific primary antibodies at the following dilutions: β-actin and Cox-IV , 1:2000; anti-Csseverin sera , 1:100; caspase 3 and caspase 9 , 1:1000; and Bax and Cyt c , 1:500 . After washing with TBST , the membranes were incubated with goat-anti-mouse or goat-anti-rabbit horseradish peroxidase-conjugated secondary antibody ( 1:5000 ) for 1 h at room temperature . Immunoreactive bands were visualized by the enhanced chemiluminescence detection kit ( KeyGen Biotech , Nanjing , China ) and quantified using the Gel-pro 4 . 5 Analyzer ( Media Cybernetics , USA ) . The intracellular Ca2+ concentration was estimated by co-incubating the cells with a cell-permeant Ca2 + fluorophore , Rhod-2 AM ( 2 μM ) . PEZ-LV203-Csseverin PLC cells were seeded at a density of 102 cells onto a confocal culture dish and treated by serum starvation for 48 h , and pEZ-LV203 PLC and PLC cells were used as controls . The cells were washed with cold PBS and incubated in a 5% CO2 humidified incubator at 37°C for 20 min after adding 20 μl of Rhod-2 AM working solution ( AAT Bioquest , USA ) . Next , the cells were washed twice with PBS and the changes of intracellular calcium were evaluated by a laser scanning confocal microscope ( Zeiss LSM 710 , Germany ) . The Rhod-2 AM fluorescence was observed at 525 nm excitation ( Ex ) /590 nm emission ( Em ) . http://dx . doi . org/10 . 17504/protocols . io . kcecstedx . doi . org/10 . 17504/protocols . io . kcgcstw[PROTOCOL DOI] The mitochondrial permeability transition pore ( MPTP ) was detected by tetramethyl rhodamine methyl ester ( TMRM ) in the Cell MPTP assay kit ( Genmed Scientific Inc . , Arlington , TX , USA ) . TMRM is a membrane-permeable fluorophore . In live cells , the hydrolysis of TMRM by intracellular esterases produces strongly red fluorescent tetramethyl rhodamine , a lipophilic compound well retained in cell mitochondria . The cytoplasm was stained with the methyl ester derivative of TMRM quenching of mitochondria rhodamine fluorescence . This nature of TMRM enables the assessment of MPTP opening[20] . Briefly , the control groups ( pEZ-LV203 PLC and PLC ) or pEZ-LV203-Csseverin PLC cells were seeded at a density of 103 cells per well onto 6-well plates , and spontaneous apoptosis was induced through serum starvation for 48 h . The cells were rinsed with GENMED cleaning solution and incubated with 1 ml GENMED staining solution for 20 min at 37°C in the dark . The supernatant was subsequently discarded , and the cells were washed twice with GENMED cleaning solution . Subsequently , the changes of MPTP were monitored using an inverted fluorescence microscope ( Leica DMI4000B , Germany ) . Quantitative changes of MPTP during cell apoptosis were measured by flow cytometry with the TMRM probe . After induced spontaneous apoptosis by serum starvation for 48 h , 105 cells were harvested and resuspended with GENMED cleaning solution . Subsequently , the cell suspensions were incubated with 0 . 5 ml of TMRM working solution for 20 min at 37°C in the dark . The staining solution was removed by centrifugation . The cells were washed twice with GENMED cleaning solution , subsequently resuspended in 200 μl of buffer solution and detected using a flow cytometer ( Beckman Coulter Gallios , USA ) . http://dx . doi . org/10 . 17504/protocols . io . kcecstedx . doi . org/10 . 17504/protocols . io . kchcst6[PROTOCOL DOI] The data were analyzed for statistical significance using SPSS 13 . 0 software ( SPSS , Chicago , IL , USA ) . The results are expressed as the means±SD from at least 3 independent experiments performed in duplicate . Statistical comparisons of the results were performed using one-way analysis of variance ( ANOVA ) . A P value < 0 . 05 was considered statistically significant .
As shown in Fig 1A , green fluorescence was observed in pEZ-LV203-Csseverin PLC cells containing the pEZ-LV203-Csseverin vector fused with the eGFP reporter gene ( Fig 1A ) . Compared to control PLC cells ( pEZ-LV203 PLC ) , Csseverin expression was significantly increased in pEZ-LV203-Csseverin PLC cells , as shown by Western blotting ( Fig 1B ) . We conducted an Annexin PE/7-AAD binding assay using flow cytometry and detected the total ratio of Annexin PE+/7-AAD- and Annexin PE+/7-AAD+ cells . The apoptotic ratio of pEZ-LV203-Csseverin PLC cells was 9 . 85% , obviously lower than that of the control cells ( pEZ-LV203 PLC and PLC ) , which showed 32 . 1% and 34 . 51% , respectively ( Fig 2 ) . To determine the apoptotic pathways involved in the Csseverin-suppressed early wave of apoptosis , we further explored changes in the activities of initiator caspase ( caspase 9 ) and effector caspase ( caspase 3 ) by Western blot analysis . The results showed the accumulation of cleaved caspase 9 and cleaved caspase 3 in the control groups ( pEZ-LV203 PLC and PLC ) , while expression levels of cleaved caspase 9 and cleaved caspase 3 were decreased in pEZ-LV203-Csseverin PLC cells ( Fig 3 , P < 0 . 05 ) . The opening of the MPTP marks the irreversible point of cell apoptosis [21]; therefore , we examined whether the MPTP participates in the anti-apoptotic mechanism induced by Csseverin . TMRM revealed significantly enhanced red fluorescence intensity in Csseverin pEZ-LV203-Csseverin PLC cells compared with the control group ( pEZ-LV203 PLC and PLC ) ( Fig 4A ) . The geometric mean , indicating the average red fluorescent intensity of pEZ-LV203-Csseverin PLC、pEZ-LV203 PLC or PLC cells emitting red fluorescence , was 4 . 82、3 . 58 and 3 . 42 ( Fig 4B ) , respectively , suggesting decrease in MPTP opening . In a previous study , we showed that Csseverin binds to Ca2+ in vitro . Since Ca2+ has been demonstrated as a key substrate associated with apoptosis in different cell types [22] , and MPTP has been recognized as a major target of Ca2+[21] , we further confirmed whether Csseverin-inhibited apoptosis was associated with Ca2+ imbalance in PLC cells . The cells were stained with the fluorescent probe dihydrorhod-2 AM ( Rhod-2 AM ) for the analysis of intracellular free calcium . The concentration of intracellular free Ca2+ obviously increased in the control groups , pEZ-LV203 PLC and PLC , while intracellular free Ca2+ was predominantly reduced in pEZ-LV203-Csseverin PLC cells ( Fig 5 ) . The loss of mitochondrial membrane potential induces mitochondrial permeability by opening the MPTP , which primarily initiates the translocation of the apoptogenic protein Cyt c from mitochondria into the cytoplasm[23] . Subcellular fractionation was performed to examine Cyt c levels in both cytosolic and mitochondrial compartments . Compared to those of the control groups , the significant downregulation of cytoplasmic Cyt c expression and the upregulation of mitochondrial Cyt c expression in pEZ-LV203-Csseverin PLC cells were observed , indicating an inhibitory effect on the release of Cyt c from mitochondria into the cytoplasm ( Fig 6A , P < 0 . 05 ) . The mitochondrial translocation of Bax is a key step that prompts the release of Cyt c from the mitochondria[24] . Western blot analysis showed that compared with control cells ( pEZ-LV203 PLC and PLC ) , Csseverin overexpression PLC cells ( pEZ-LV203-Csseverin PLC ) showed a drastic reduction in the translocation of Bax to mitochondria ( Fig 6B , P < 0 . 05 ) .
Previous studies have shown that Csseverin could induce apoptotic inhibition in spontaneously apoptotic human HCC PLC cells . In the present study , we confirmed the anti-apoptotic role of Csseverin and explored the involved mechanisms . We generated stably Csseverin-overexpressing PLC cells ( PEZ-LV203-Cssevein PLC ) and control cells ( PEZ-LV203 PLC ) in the present study . The results demonstrated significant suppression during the early period of apoptosis in pEZ-LV203-Csseverin PLC cells compared with pEZ-LV203-PLC and PLC cells . Apoptosis occurs via two different pathways: the extrinsic pathway ( death receptors ) and the intrinsic pathway ( mitochondria and endoplasmic reticulum ) [15–16] . In a previous study , we observed that Csseverin led to the recovery of mitochondrial membrane potential ( MMP ) in PLC cells and speculated that the mitochondrial signal pathway may be involved in Csseverin-mediated protection from apoptosis . Mitochondria are sensitive to the external environment , responding with MMP alterations that lead to the release of apoptosis-related factors and cell apoptosis[25] . There are several specific proteins in the mitochondrial-mediated pathway . Caspase 3 and caspase 9 are the key factors associated with the mitochondrial-mediated pathway . Caspase 9 activity is primarily dependent on the intrinsic pathway ( mitochondrial-mediated ) regulated by members of the Bcl-2 family[26] . In the present study , compared with control cells ( PLC and pEZ-LV203 PLC ) , we observed a decrease in caspase 9 activity in spontaneously apoptotic pEZ-LV203-Csseverin PLC cells . The reduced activation of caspase 9 subsequently suppressed downstream caspase 3 , which was activated through the mitochondrial-mediated pathway . Therefore , these results suggested that via intrinsic ( mitochondrial-mediated ) , extrinsic ( death receptors ) or other intrinsic ( endoplasmic reticulum ) pathways , Csseverin might confer protection from the early wave of apoptosis in PLC cells . Bax , a proapoptotic member of the Bcl-2 family proteins , is an initiator in the mitochondrial-mediated pathway[27] . In healthy living cells , Bax is predominantly located in the cytosol and migrates to the mitochondrial membrane during early apoptosis[28] . This translocation induced Cyt c release from mitochondria to the cytoplasm [29] . Cyt C can combine with procaspase 9 and Apaf-1 to form an apoptosome to activate caspase-9 and other caspases that induce the downstream caspase cascade . We detected the mitochondrial translocation of Bax and the release of Cyt c . The present study showed that the overexpression of Csseverin significantly suppressed the mitochondrial translocation of Bax , followed by the decreased release of Cyt c from mitochondria . The gelsolin family ( include Csseverin ) plays a leading role in controlling actin filament reorganization/remodeling[12] . In several models of cell apoptosis , gelsolin has demonstrated an anti-apoptotic property associated with its effects on the dynamic actin cytoskeleton by preventing the loss of mitochondrial membrane potential and activation of caspase 3[30–31] . The organization/remodeling of actin filaments can also release Ca2+ from the F-actin store and open the influx pathway for the external release of Ca2+ into the cell[32] . Intracellular Ca2+ is used as a second messenger to regulate most crucial biological processes , such as cell survival , proliferation and gene transcription[33] . In some experimental systems , the elevation of intracellular Ca2+ levels is regarded as a pivotal element of apoptosis[34–35] . Thus , the Rhod-2 AM Ca2+ fluorophore , which emits red fluorescence , was used to evaluate changes of intracellular Ca2+ . Previous studies have shown that Csseverin binds to Ca2+ and cytoskeletal actin filaments [6] . The results of the present study showed a significant decrease of intracellular calcium in Csseverin overexpression PLC cells , associated with the effect of Csseverin on apoptosis suppression . The intracellular Ca2+ level is affected by mitochondrial Ca2+ sequestration , which might eventually stimulate the prolonged opening of the MPTP . MPTP is a multi-protein complex formed between mitochondrial membranes , and persistent MPTP opening results in the osmotic dysregulation of the mitochondrial membrane[36] . Once the MPTP is opened , various apoptosis-related proteins , such as Bax , could enter mitochondria and lead to a decrease of the mitochondrial membrane potential , the release of Cyt c , and the induction of early apoptosis[37] . We also measured the changes in MPTP using a TMRM probe . Compared with PLC and pEZ-LV203 PLC cells ( negative control ) , enhanced fluorescence intensity was observed in pEZ-LV203-Csseverin PLC cells after induced spontaneous apoptosis by serum-starvation for 48 h , indicating the inhibition of MPTP opening . Collectively , these data indicated that Csseverin can reduce calcium-mediated MPTP opening , which may be mediated through binding to actin and Ca2+ . The inhibition of MPTP opening subsequently suppressed the translocation of Bax to mitochondria and the release of Cyt c from mitochondria , which in turn downregulates caspase 9 activities and caspase 3 protein expression , inducing obvious apoptotic suppression ( Fig 7 ) . Taken together , these findings will be helpful to further illuminate the mechanism involved in tumorigenesis induced by C . sinensis infestation . Whether interventions according to this pathway are effective for the control of the disease progression is worthy of further exploration . | Multiple studies have contributed to the association between Clonorchis sinensis ( C . sinensis ) infestation and cholangiocarcinoma ( CCA ) as well as hepatocellular carcinoma ( HCC ) in past years . However , studies on the underlying pathogenic mechanisms of C . sinensis lag behind those of other parasitic diseases . The excretory/secretory products of C . sinensis ( CsESPs ) are pathogenic , as these products promote cell proliferation , suppress cell apoptosis and stimulate inflammation . Csseverin , a component of CsESPs , inhibited the apoptosis of the human HCC cell line PLC in our previous study . The present study illustrated that Csseverin conferred human HCC cells protection from apoptosis via an intrinsic pathway ( mitochondrial-mediated ) triggered by the inactivation of membranous Ca2+ channels . | [
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"... | 2017 | Csseverin inhibits apoptosis through mitochondria-mediated pathways triggered by Ca2 + dyshomeostasis in hepatocarcinoma PLC cells |
The biology of the metastatic colonization process remains a poorly understood phenomenon . To improve our knowledge of its dynamics , we conducted a modelling study based on multi-modal data from an orthotopic murine experimental system of metastatic renal cell carcinoma . The standard theory of metastatic colonization usually assumes that secondary tumours , once established at a distant site , grow independently from each other and from the primary tumour . Using a mathematical model that translates this assumption into equations , we challenged this theory against our data that included: 1 ) dynamics of primary tumour cells in the kidney and metastatic cells in the lungs , retrieved by green fluorescent protein tracking , and 2 ) magnetic resonance images ( MRI ) informing on the number and size of macroscopic lesions . Critically , when calibrated on the growth of the primary tumour and total metastatic burden , the predicted theoretical size distributions were not in agreement with the MRI observations . Moreover , tumour expansion only based on proliferation was not able to explain the volume increase of the metastatic lesions . These findings strongly suggested rejection of the standard theory , demonstrating that the time development of the size distribution of metastases could not be explained by independent growth of metastatic foci . This led us to investigate the effect of spatial interactions between merging metastatic tumours on the dynamics of the global metastatic burden . We derived a mathematical model of spatial tumour growth , confronted it with experimental data of single metastatic tumour growth , and used it to provide insights on the dynamics of multiple tumours growing in close vicinity . Together , our results have implications for theories of the metastatic process and suggest that global dynamics of metastasis development is dependent on spatial interactions between metastatic lesions .
Metastasis , the spread of cancer cells from a primary tumour to secondary location ( s ) in the body , is the ultimate cause of death for the majority of cancer patients [1 , 2] . Although studied for more than 180 years [3] , increasing efforts in recent years contributed to a better understanding of this aspect of tumour development [2 , 4] , with exciting new discoveries [5–8] that potentially have important clinical implications . The metastatic process can be coarsely divided into two major phases: 1 ) dissemination of detaching cells from the primary tumour to a secondary site and 2 ) colonization of this distant organ [1 , 9] . While the former has been relatively well elucidated , in particular due to recent advances about the epithelial-to-mesenchymal transition [10] and advances on our understanding of molecular and genetic determinants [11 , 12] , the latter remains not fully understood , especially during the colonization phase [1 , 12] . This is due , in part , to experimental limitations that hinder our ability to observe colonization of organs by tumour cells and the development of tumour lesions . In this context , mathematical models provide powerful tools to potentiate data analysis , infer hidden information , test biological hypotheses against the empirical data and simulate a range of conditions that may be confronted to the biological reality . In recent years , several models for tumour growth have been developed ( see [13 , 14] for historical reviews ) , based on multiple modelling techniques from non-spatial ordinary differential equations models ( see [15] for a benchmark of these against experimental in vivo data ) to discrete agent-based models [16–18] and continuous partial differential equations based on tissue mechanics laws [19 , 20] . However , despite a large body of literature for modelling tumour growth , relatively little effort has been devoted to the development and validation of mathematical models describing the biology of the metastatic process ( see [21 , 22] for an early and notable exception , [23 , 24] for more recent studies and [25] for a recent review ) . In 2000 , Iwata and colleagues proposed a simple mathematical model for the growth of a population of metastatic colonies [26] , which was recently shown able to fit experimental data describing the increase in total metastatic burden [27 , 28] . In this mathematical description , each metastasis grows independently from the others and from the primary tumour . We report herein a theoretical study to test this hypothesis using in vivo data derived from a metastatic renal carcinoma model in mice . We show that the standard theory of metastatic initiation in which distinct foci grow independently from each other ( as assumed in [21] ) predicted an unrealistically large number of metastases , while the tumours sizes were too small . In a space-limited organ ( such as the lungs ) , where two neighbouring metastatic foci are growing in close vicinity , they might enter in contact and interactions occur , ultimately leading to the merging of the metastatic foci . This phenomenon is not taken into account in a classical description of metastasis development , although it can lead to important differences in the number and sizes of the colonies . Moreover , mechanical interactions could occur during metastases merging , possibly impacting the global dynamics . Therefore , we next conducted a simulation study to quantify the effect of mechanical interactions between two neighbouring tumours . Based on mechanical laws for tissue growth , we derived a minimally parameterized model ( 2 parameters ) . This second , spatial model , based on a pressure-mediated growth law , once fitted to magnetic resonance imaging data of individual metastatic tumour growths , offered an adapted framework to perform simulations of spatially interacting tumours . These revealed significant impact of the interactions on the global growth and allowed to test if merging by passive motion could explain the data that are not in accordance with the classical model . To our knowledge , this is the first time that data on size distribution of metastasis at this resolution ( with such a small visibility threshold , of the order of 0 . 05 mm3 ) is reported and analysed in lights of a theoretical model .
As an initial step , we studied the growth rates of individual metastatic tumours . Then , we calibrated a more elaborated mathematical model of tumour growth and metastatic dissemination using quantitative data derived from green fluorescent protein ( GFP ) -tracking of primary and metastatic tumours ( see Materials and methods , n = 31 mice ) . Finally , we used the model to investigate predictions of the standard theory with regard to number and sizes of metastatic lesions and compared them to Magnetic Resonance Imaging ( MRI ) data ( see Materials and methods , n = 6 mice ) .
Using a combined approach between experimental data and mathematical models , we demonstrated that the standard theory of metastasis formation and growth , where metastases grow independently from the rest of the system , was biologically unlikely . To explain our findings , we proposed several hypotheses , including the possibility of metastatic foci merging by passive motion . To investigate whether this hypothesis would have quantitatively non-negligible impact on the kinetics of the total metastatic burden ( thus requiring more intricate modelling for the model describing the size distribution at the scale of the organ ) , we introduced a parsimonious spatial model of tumour growth . After calibrating the model to the growth of single metastases , we found ( in simulations ) that spatial interactions resulted in a significant reduction of tumour growth . Our results indicate that spatial interactions should be considered in future efforts for the development of a general quantitative theory of metastatic colonization . Based on the rationale that lung capillaries have a diameter of the order of one tumour cell ( 20 μm ) and that metastatic cells have lost expressions of cell-cell adhesion proteins such as cadherins [2] , we assumed in our simulations , that metastases originated from one cell . This might be arguable and metastasis could start from tumour cell clumps [31 , 32] . To resolve this further and assess the robustness of our results , we performed the entire data analysis ( fit of the total metastatic burden and resulting prediction of the metastatic size distribution ) for values of the initial number of cells of 1 , 10 , 100 and 500 ( S3 Fig ) . Initial numbers of 10 , 100 and 500 cells could be in agreement with the data at day 19 . However , with V0 = 10 cells , the predicted number of macro-metastases at day 26 was 3-fold higher than in the data . For V0 = 100 cells and V0 = 500 cells , the predicted macro-burden was 2-fold smaller than the observed one . Moreover the largest metastasis at day 26 was still predicted much smaller in the model than in the data ( 3 . 11 mm3 for V0 = 10 cells , 3 . 58 mm3 for V0 = 100 cells , 3 . 8 mm3 for V0 = 500 cells , against 13 . 6 mm3 in the data ) . Furthermore , in animal experiments the vast majority of detaching tumour cell clumps has been shown to comprise less than 10 cells [31] with a range of 2–50 cancer cells [32] , which makes the theories V0 = 100 cells and V0 = 500 cells unlikely . This suggests that , if the metastases started from a substantial amount of cells , the grouping of these cells probably occurred at the distant site , after extravasation from the blood circulation . Similarly , we did not consider any cell loss at the moment of initial sub-capsular injection . We could make theoretical assumptions of cell loss ( of 10% , 20% , etc… ) , which would simply consist in multiplying V0 by the relevant factor . For instance , considering a 90% loss ( i . e . that only 10% of the cells remain viable ) would be equivalent to multiplying V0 by 10 . As demonstrated in S3 Fig , it is necessary to assume an initial size of at least 100 V0 to recover plausible values for the number of metastases at time T = 26 days . Combining the two ( cell loss of 10% and initial metastatic size of 10 cells ) thus gives a hypothesis that we are not able to infirm given the data we dispose . The spatial model for tumour growth that we introduced is based on a pressure-induced decrease of the growth rate . Contact inhibition between cells is a mechanism for maintaining tissue homeostasis [4] . The ability of cancer cells to ignore these inhibition signals is a hallmark of cancer . In a recent study , Stylianopoulos et al showed that the uncontrolled proliferation of tumour cells results in mechanical stresses in the surrounding micro-environment of transplanted and human tumours [48] . Furthermore , they also showed that such an exerted pressure impairs in vivo proliferation via two mechanisms: reduced cancer cell proliferation in direct response to increased pressure , as well as a pressure-induced collapse of blood vessels within the tumour , leading to nutrient deficiency for tumour cells [49] . Based on these considerations , it seems relevant to consider that tumour expansion depends on the pressure . In our spatial growth model , the tissues motion is mediated by pressure gradients . It means that cells within a tumour tissue proliferate and that the exerted pressure pushes the neighbouring tissues . This pressure is not solely due to mechanical constraints ( solid stresses , interstitial fluid pressure , … ) exerted by the neighbouring cells on each other , but represents a more phenomenological pressure , that reflects the basic assumption of our modelling strategy for the tumour tissue being constituted by a fluid mixture in a porous medium . The effect of the pressure on proliferation has also been studied using numerical simulations elsewhere . In [47] , Montel et al discussed the fact that cells proliferate faster on the surface than in the bulk of a tumour spheroid . A classical reason is that nutrients do not penetrate deeply in the spheroid . However , Montel et al . suggested a mechanical effect due to the necessity for a cell to deform its environment in order to proliferate . In an in silico study on two-dimensional monolayers and three-dimensional spheroids , based on experimentally determined biophysical parameters , Drasdo and Höhme suggested that pressure conditions have a higher impact on doubling time than lack of nutrients [16] . Moreover , in [47] , Montel et al . performed experiments where tumour cells were submitted to different pressure constraints and observed a decrease in proliferation when pressure was applied . In their study , simulation results that were compared to experimental ones showed an exponential decreasing of proliferation with pressure , consistently with the modelling adopted here . However , the bulk and surface division rate were not affected equally by stresses . In our model , we used a similar pressure-mediated proliferation law translating direct effects of mechanical stresses on proliferation as well as indirect effects of proliferation on the micro environment ( collapsing of blood vessels leading to lack of nutrients ) . Our proposed hypotheses should be further experimentally reinforced , by , for example , implanting orthotopically and injecting intravenously two groups of cells into mice , each group being tagged with a different colour , and by quantifying single or mixed-coloured tumour foci . Similar experimental protocols have been already performed in [7 , 32] . Furthermore , in vivo investigations by observing two ( or more ) growing tumours in close vicinity that would enter mechanical interactions and then assess with a Ki-67 staining if the proliferation is impaired in the contact area , would further reinforce our contentions . The inability of the merging theory to explain all of the observed volumes may indicate that besides merging by passive motion due to proliferation , other mechanisms such as chemokine-mediated cells attraction occur [6 , 50] . Circulating tumour cells may be attracted by some established niches and explain the abnormally fast volume expansions that we observed . Indeed , such chemokine-mediated attractions are presumed to play an important role for the pre-metastatic and metastatic niches establishment , in mediating myeloid and tumour cells attraction [6 , 50 , 51] . Moreover , chemo-attractants may play a role in tissue tropism of metastatic cells [52] . Chemotactic gradients can attract metastatic cells that express the chemokine receptor to specific locations . In the future , additional phenomena such as aggregation and recruitment of cells during the metastatic process from the circulation should be integrated in the standard mathematical model . Another phenomenon that could possibly explain the observed volumes would be the presence of circulating tumour cell clusters that would give rise to metastases [32] . Indeed , Aceto et al . recently showed in a breast cancer animal model that metastases do not originate from single cells only but also from tumour cells clusters that have a higher metastatic potential than single cells . However , they did not show evidence of this phenomenon for kidney cancer and in their experiments , clusters were formed by at most 50 cells . As indicated above , this order of magnitude of the initial cell numbers that colonizes the lung is not able to describe the dynamics of metastasis formation in our model and experimental data . Taken together , our results indicate that spatial interactions are an essential component for the dynamics of metastasis development in the lung and probably also in other organs . However , it is unlikely that they alone control metastasis expansion . Indeed , when trying to assess whether this concept alone explains the fast growth of various metastases from the beginning of organ colonisation ( from the first cell at days 12–14 to 0 . 022-0 . 67 mm3 at day 19 ) , unrealistic numbers were found for two of the tumours . Thus , other mechanisms are probably also involved such as recruitment of additional cells from the blood stream and micro-environmental cues such as nutrient depletion or responses to environmental stress . Our methodology and results illustrate , furthermore , how a combined approach using multimodal biological data on one hand , and multimodal modelling analysis on the other , provides powerful insights into tumour biology and , in particular , into the metastatic process .
Ethical approval for all animal studies was obtained from the Institutional Animal Care and Use Committee of the INSERM Institute in accordance with the National Advisory Committee for Laboratory Animal Research Guidelines licensed by the French Authority . Animal facility: Animalerie mutualisée de Bordeaux 1 , authorisation number: B33-522 , Date: February 8th , 2012 . Investigator: Andreas Bikfalvi ( authorisation number: R-45GRETA-F1-10 ) . | We used mathematical modelling to formalize the standard theory of metastatic initiation , under which secondary tumours , after establishment in a distant organ , grow independently from each other and from the primary tumour . When calibrated on the experimental data of primary tumour and total metastatic burden in the lungs in an animal model of renal cell carcinoma , the initial model predicted a size distribution of metastatic foci that did not fit with observations obtained experimentally using magnetic resonance imaging ( which provided size and number of macro-metastases ) . The model predicted an increase in the number of lesions , but of smaller size when compared to the data . This led us to revise the standard theory and to propose two hypotheses in order to explain the observations: 1 ) small metastatic foci merge into larger ones and/or 2 ) circulating tumour cells may join already established tumours . We then derived a spatial model of tumour growth in order to explore the quantitative implications of tumours merging on global tumour growth and estimated the numbers of required metastatic foci to obtain the observed metastatic volumes . | [
"Abstract",
"Introduction",
"Results",
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] | [] | 2015 | Computational Modelling of Metastasis Development in Renal Cell Carcinoma |
TORC1 is a master regulator of metabolism in eukaryotes that responds to multiple upstream signaling pathways . The GATOR complex is a newly defined upstream regulator of TORC1 that contains two sub-complexes , GATOR1 , which inhibits TORC1 activity in response to amino acid starvation and GATOR2 , which opposes the activity of GATOR1 . While the GATOR1 complex has been implicated in a wide array of human pathologies including cancer and hereditary forms of epilepsy , the in vivo relevance of the GATOR2 complex remains poorly understood in metazoans . Here we define the in vivo role of the GATOR2 component Wdr24 in Drosophila . Using a combination of genetic , biochemical , and cell biological techniques we demonstrate that Wdr24 has both TORC1 dependent and independent functions in the regulation of cellular metabolism . Through the characterization of a null allele , we show that Wdr24 is a critical effector of the GATOR2 complex that promotes the robust activation of TORC1 and cellular growth in a broad array of Drosophila tissues . Additionally , epistasis analysis between wdr24 and genes that encode components of the GATOR1 complex revealed that Wdr24 has a second critical function , the TORC1 independent regulation of lysosome dynamics and autophagic flux . Notably , we find that two additional members of the GATOR2 complex , Mio and Seh1 , also have a TORC1 independent role in the regulation of lysosome function . These findings represent a surprising and previously unrecognized function of GATOR2 complex components in the regulation of lysosomes . Consistent with our findings in Drosophila , through the characterization of a wdr24-/- knockout HeLa cell line we determined that Wdr24 promotes lysosome acidification and autophagic flux in mammalian cells . Taken together our data support the model that Wdr24 is a key effector of the GATOR2 complex , required for both TORC1 activation and the TORC1 independent regulation of lysosomes .
In metazoans multiple conserved signaling pathways control the integration of metabolic and developmental processes . TORC1 is an evolutionarily conserved multi-protein complex that regulates metabolism and cell growth in response to an array of upstream inputs including nutrient availability , growth factors and intracellular energy levels [1] . The catalytic component of TORC1 is the serine/threonine kinase Target of Rapamycin ( TOR ) . When nutrients are abundant , TORC1 activity promotes translation , ribosome biogenesis as well as other pathways associated with anabolic metabolism and cell growth . However , when nutrients or other upstream activators are limiting , TORC1 activity is inhibited triggering catabolic metabolism and autophagy [2] . The Seh1 associated/GTPase-activating protein toward Rags ( SEA/GATOR ) complex is a newly identified upstream regulator of TORC1 that can be divided into two putative sub-complexes GATOR1 and GATOR2 [3–5] . The GATOR1 complex , known as the Iml1 complex or the Seh1 Associated Complex Inhibits TORC1 ( SEACIT ) in yeast , inhibits TORC1 activity in response to amino acid limitation [3 , 5 , 6] . SEACIT/GATOR1 contains three proteins Npr2/Nprl2 , Npr3/Nprl3 and Iml1/DEPDC5 . Recent evidence , from yeast and mammals , indicates that the components of the SEACIT/GATOR1 complex function through the Rag GTPases to inhibit TORC1 activity [3 , 5] . Notably , Nprl2 and DEPDC5 are tumor suppressor genes while mutations in DEPDC5 are a leading cause of hereditary focal epilepsies [7–16] . The GATOR2 complex , which is referred to as Seh1 Associated Complex Activates TORC1 ( SEACAT ) in yeast , activates TORC1 by opposing the activity of GATOR1 [3 , 5 , 17 , 18] . The SEACAT/GATOR2 complex is comprised of five proteins , Seh1 , Sec13 , Sea4/Mio , Sea2/WDR24 , and Sea3/WDR59 . Computational analysis indicates that multiple components of the GATOR2 complex have structural features characteristic of coatomer proteins and membrane tethering complexes [4 , 19] . In line with the structural similarity to proteins that influence membrane dynamics , in Drosophila the GATOR2 subunits Mio and Seh1 localize to multiple endomembrane compartments including lysosomes , the site of TORC1 regulation , and autolysosomes [18] . In metazoans , members of the Sestrin and Castor family of proteins bind to and inhibit the GATOR2 complex in response to leucine and arginine starvation respectively [20–25] . This interaction is proposed to inhibit TORC1 activity through the derepression of the GATOR1 complex [22 , 23 , 26] . However , how GATOR2 opposes GATOR1 activity , thus allowing for the robust activation of TORC1 , remains unknown . Additionally , the role of the GATOR2 complex in the regulation of both the development and physiology of multicellular animals remains poorly defined . Recent evidence from Drosophila indicates that the requirement for the GATOR2 complex may be context specific in multicellular animals [18] . In Drosophila , null alleles of the GATOR2 components mio and seh1 are viable but female sterile [27 , 28] . Surprisingly , somatic tissues from mio and seh1 mutants exhibit little if any reductions in cell size and have nearly normal levels of TORC1 activity [18] . In contrast , TORC1 activity is dramatically decreased in ovaries from mio and seh1 mutant females [18] . This decrease in TORC1 activity is accompanied by the activation of catabolic metabolism in the female germ line , a dramatic reduction in egg chamber growth and difficulties maintaining the meiotic cycle [27 , 28] . Thus , there is a surprising tissue specific requirement for the GATOR2 components Mio and Seh1 during oogenesis . However , the in vivo role of the other members of the GATOR2 complex in the regulation of cellular metabolism remains undefined . Here we define the in vivo requirement for the GATOR2 component Wdr24 in Drosophila . We find that Wdr24 has two distinct functions . First , Wdr24 is a critical effector of the GATOR2 complex that promotes TORC1 activity and cellular growth in a broad array of tissues . Second , Wdr24 is required for the TORC1 independent regulation of lysosome function and autophagic flux . Notably , two additional members of the GATOR2 complex , Mio and Seh1 , also have a TORC1 independent role in the regulation of lysosome function . Taken together our data support the model that multiple components of the GATOR2 complex have both TORC1 dependent and independent roles in the regulation of cellular metabolism .
Sea2/Wdr24 is a conserved component of the SEA/GATOR complex in yeast and mammals and has been implicated in the regulation of TORC1 activity and autophagy [3–5] . The genome of Drosophila melanogaster contains a single Sea2/Wdr24 homolog encoded by the gene CG7609 that shares 25% identity and 44% similarity to yeast Sea2 and 37% identity and 54% similarity to the human homolog WDR24 . In the work presented here Drosophila CG7609 is referred to as Wdr24 . To confirm the association of Wdr24 ( CG7609 ) with other components of the SEA/GATOR complex in Drosophila , we co-expressed GFP-Wdr24 with HA-Mio , HA-Seh1 , and V5-Nprl3 in S2 cells and found that GFP-Wdr24 co-immunoprecipitated with all three SEA/GATOR complex components ( Fig 1A–1C ) . Moreover , we found that the immunoprecipitation of a FLAG-Mio-HA tagged protein expressed in the female germ line co-immunoprecipitated all 7 additional members of the GATOR complex strongly suggesting that the association of these proteins is conserved in Drosophila ( S1 Table ) . Thus , as is observed in mammals and yeast , Wdr24 is a conserved component of the SEA/GATOR complex in Drosophila . We recognize , however , that these data do not rule out the possibility Wdr24 is present in additional complexes . Next , we wanted to determine the intracellular localization of Wdr24 . Notably , the GATOR2 components Mio and Seh1 localize to lysosomes , the site of TORC1 activation , and autolysosomes [3 , 18] . Consistent with the localization of Mio and Seh1 we found Wdr24-mCherry expressed in the female germ line co-localized with GFP-Lamp1 , a marker for both lysosomes and autolysosomes , under fed and starved conditions ( Fig 1D–1E” ) . Additionally , we examined if Wdr24 co-localized with LysoTracker , a dye that marks acidic compartments including late endosomes , lysosomes and autolysosome [29] . Similar to GFP-Lamp1 , GFP-Wdr24 co-localized with LysoTracker ( Fig 1F–1G” ) . We noted in both sets of experiments that there was a substantial increase in the Wdr24-mCherry / GFP-Lamp1 and Wdr24/LysoTracker positive puncta in ovaries from starved versus fed females . We reasoned that the increased number and size of the puncta under starvation conditions was likely the result of the onset of autophagy and the production of autolysosomes . Consistent with this idea , GFP-Wdr24 co-localized with mCherry-Atg8a , a component of autophagosomes and autolysosomes , under starvation conditions . However , this co-localization was not observed when ovaries were taken from females cultured in nutrient rich conditions ( Fig 1H–1I” ) . Finally , Wdr24-mCherry colocalizes to puncta with the GATOR1 component GFP-Nprl2 when co-expresses in the female germline under conditions of amino acid starvation . Minimal co-localization was observed under nutrient replete conditions ( Fig 1J–1K” ) . Taken together these data demonstrate that as is observed with the GATOR2 components Mio and Seh1 , the Wdr24 protein localizes to lysosomes , the site of TORC1 regulation , and to autolysosomes . To examine the in vivo function of Wdr24 , we obtained the wdr24 mutant CG76091 from the Bloomington Stock Center , which contains a 1 . 3 kb deletion removing 1163 bp of coding region including the start codon ( S1A Fig ) . In this study , the CG76091 mutant is referred to as wdr241 . We find that wdr24 is not required for viability in Drosophila . RT-PCR demonstrated that the wdr24 mRNA is present in WT ( wild type ) but not in wdr241 homozygous or wdr241/Df adults ( S1B Fig ) . These data confirm that wdr241 is a null allele . In mio and seh1 mutants , the constitutive inhibition of TORC1 in the female germ line results in female sterility and a dramatic reduction in egg chamber growth [18] . Similarly , wdr241 females have small ovaries and exhibit a 90% reduction in eggs laid per day relative to heterozygous controls ( Fig 2A and 2B , S1C and S1D Fig ) . Homozygous germline clones of wdr241 result in reduced egg chamber growth , indicating that wdr24 acts cell autonomously in the germ line to promote growth ( Fig 2C ) . Surprisingly , however , we did not observe an oocyte loss phenotype in wdr241 mutant egg chambers , as has been reported in a high percentage of the egg chambers from both mio and seh1 mutant females ( S2 Fig ) [27 , 28] . Finally , the small ovary and egg laying deficits of wdr241 females were rescued by expressing GFP-Wdr24 in the wdr241 mutant background using the germline specific driver Nanos-Gal4 ( S1C and S1D Fig ) . These data confirm that Wdr24 is required for ovary growth and female fertility . Thus , while the GATOR2 components Mio , Seh1 and Wdr24 are required for female fertility and egg chamber growth , our data suggest that individual GATOR2 subunits have unique functions , and/or make differential contributions to the regulation of oocyte development . Next we examined multiple somatic tissues to determine if there is a requirement for Wdr24 outside of the female germ line . Under standard culture conditions , null mutations of mio and seh1 do not result in dramatic changes in cell size or TORC1 activity in somatic tissues [18] . In contrast , we noted that wdr241 mutant adults appeared smaller than their heterozygous siblings ( Fig 2D ) . Consistent with this observation , well-fed wdr241 and wdr241/Df mutant males weigh approximately 30% less than sibling controls ( Fig 2E ) . The decreased body weight phenotype was rescued by expressing GFP-tagged Wdr24 in the wdr241 mutant background using the ubiquitous driver , Ubi-Gal4 ( S3 Fig ) . Thus , wdr24 mutant adults have an overall decrease in body size and weight . In order to determine if the effects of wdr24 on cell growth were cell autonomous , we generated homozygous mutant clones of the wdr241 null allele . We found that wdr241 homozygous mutant clones generated in both the adult fat body and the somatically derived follicle cells have a decreased nuclear size relative to adjacent wild-type heterozygous cells ( Fig 2F–2I ) . From these results we infer that there is a cell autonomous requirement for wdr24 to promote cellular growth in multiple somatic tissues . In summary , we find that there is a critical requirement for the GATOR2 component Wdr24 in promoting cell growth in both germline and somatic tissues of Drosophila . In order to determine if the decreased growth observed in wdr241 mutants is accompanied by decreased TORC1 activity , we examined the phosphorylation status of S6 kinase , a downstream TORC1 target [30] in wild-type and wdr24 mutant males and females . We found that wdr241 mutant males had an approximately three-fold decrease in TORC1 activity relative to control males while wdr241 mutant females had a four-fold decrease in TORC1 activity relative to control females ( Fig 2J and 2K ) . Taken together our data demonstrate that unlike the GATOR2 components Mio and Seh1 , Wdr24 plays a central role in promoting TORC1 activity and growth in both germline and somatic tissues of Drosophila under standard culture conditions . TORC1 activity inhibits catabolic metabolism and autophagy [1] . Thus , we next examined if the low TORC1 activity observed in wdr241 mutants resulted in the induction of autophagy in the absence of starvation . To assess the metabolic state of wdr241 egg chambers , we followed GFP-Lamp1 and the autophagy marker Atg8a in ovaries from well-fed wild type and wdr241 mutant females . Consistent with the activation of autophagy , egg chambers from well-fed wdr241 mutant females are filled with Atg8a positive puncta that are also positive for the lysosomal marker GFP-Lamp1 ( Fig 3A–3B” ) . This co-staining strongly suggests that these cytoplasmic puncta are autolysosomes . Thus , wdr241 mutant egg chambers activate autophagy and accumulate autolysosomes in the female germ line independent of nutritional status . Next we examined the regulation of autophagy in somatic tissues . Under rich culture conditions , the larval fat bodies from wild-type and seh1 null mutants contain a relatively small number of LysoTracker positive puncta ( S4 Fig ) [18] . In contrast , as is observed in wdr241 mutant ovaries , fat bodies from well-fed wdr241 larvae are filled with GFP-Lamp1 and Atg8a positive structures ( Fig 3C–3D” ) . Importantly , this phenotype was rescued when GFP-tagged Wdr24 protein was expressed in the fat bodies of wdr241 mutant larvae ( S5 Fig ) . Taken together our data indicate that the Wdr24 component of the GATOR2 complex prevents the inappropriate accumulation of autolysosomes in both germline and somatic tissues of Drosophila . In Drosophila , a tagged version of the autophagic marker Atg8a ( GFP-mCherry-Atg8a ) can be used to examine autophagic flux [29] . The double-tagged Atg8a protein is yellow ( green merged with red ) in autophagosomes , which are nonacidic structures but is red in autolysosomes due to the quenching of GFP fluorescence in acidic conditions [31] . To monitor autophagic flux we expressed GFP-mCherry-Atg8a protein in the fat body of wild-type and wdr241 mutant larvae . In wild-type larvae starvation activates autophagy resulting in the accumulation of Atg8a positive puncta that are predominantly red , reflecting the accumulation of acidic autolysosomes ( Fig 4A–4B” ) . In contrast , under both fed and starved conditions , the puncta in wdr241 mutant fat bodies were yellow ( Fig 4C–4D” ) . Similar yellow GFP-mCherry-Atg8a autolysosomes are observed in the fat body after starvation in knockdowns of subunits of the V-ATPase that is responsible for the acidification of lysosomes [32] . Thus , our data indicate that Wdr24 is required for autolysosome acidification and autophagic flux . The GATOR2 components Mio and Seh1 promote TORC1 activation by opposing the activity of the GATOR1 complex [3 , 5 , 18] . Therefore , in order to determine whether the phenotypes observed in wdr24 mutants are due to the unopposed TORC1 inhibitory activity of GATOR1 , we examined the epistatic relationship between wdr24 and the GATOR1 components nprl2 and nprl3 . First , we depleted nprl2 and nprl3 in the female germline of wdr241 mutant females using RNAi . Notably , depleting nprl2 or nprl3 substantially rescued the wdr241 ovarian phenotype , resulting in a dramatic increase in ovary size and a nearly six-fold increase in the number of eggs laid per female per day ( Fig 5A–5E ) . In order to determine whether the reduced body size in wdr241 mutant adults also reflects the unopposed TORC1 inhibitory activity of the GATOR1 complex , we used the ubiquitous GAL4 driver Hsp70-GAL4 to globally deplete the nprl3 and nprl2 transcript in the wdr241 mutant background . Similar to our results from the germline , depleting nprl3 and nprl2 in somatic tissues significantly rescued the reduced body weight phenotype of wdr241 mutants ( S6 Fig ) . Thus , in Drosophila Wdr24 is required to oppose the activity of the GATOR1 complex in both germline and somatic tissues . As described above , wdr241 mutants accumulate autolysosomes in the germline-derived nurse cells and oocytes of developing egg chambers as well as in the somatically derived cells of the larval fat body ( Fig 3 ) . One model to explain this phenotype , is that in wdr24 mutants , the deregulation of the GATOR1 complex results in low TORC1 activity leading to the constitutive activation of catabolic metabolism and autophagy [18] . In this model , the accumulation of autolysosomes in wdr241 mutant egg chambers is downstream of GATOR1 deregulation and low TORC1 activity . Surprisingly , we find that while depleting nprl2 or nprl3 in the germline of wdr241 mutant females rescues the egg chamber growth defect , these egg chambers continue to accumulate a large number of LysoTracker positive puncta ( Fig 5F–5I ) . The accumulation of these acidic puncta occur even though the levels of TORC1 activity in nprl2RNAi; wdr241 , and nprl3RNAi , wdr241ovaries are similar or higher to those observed in wild-type ovaries ( Fig 5J and 5K ) . Consistent with increased TORC1 activity , the acidic puncta in the nprl2RNAi; wdr241 , and nprl3RNAi , wdr241 ovaries are not positive for the autophagic marker Atg8a indicating the autophagy pathway has not been activated ( Fig 5F’–5I’ ) . These data strongly suggest that the accumulation of autolysosomes in wdr241 mutant ovaries is not solely the result of low TORC1 activity , but may reflect a second role for wdr24 in the regulation of lysosome function and autophagic flux . To confirm that Wdr24 affects lysosome dynamics independent of TORC1 activity , we knocked down tuberous sclerosis 1 ( tsc1 ) in the germline of wdr241 mutant females . The Tsc1/2 complex is a potent inhibitor TORC1 activity that functions independently of the GATOR1 complex [33 , 34] . In Drosophila , mutations in tsc1 and tsc2 ( gigas ) increase the baseline levels of TORC1 activity independent of nutrient status [35–38] . We found that similar to what was observed for depleting nprl2 and nprl3 , depleting tsc1 in the wdr241 mutant background rescues the ovary growth deficit and results in TORC1 activity that is markedly higher than that observed in wild-type ovaries ( S7A and S7B Fig ) . However , tsc1RNAi; wdr241 egg chambers continue to accumulate large numbers of LysoTracker positive puncta ( S7C–S7E Fig ) . These data confirm that low TORC1 activity is not the cause of autolysosome accumulation in wdr24 mutant ovaries . Finally , we used epistasis analysis to formally test the model that wdr24 regulates lysosome dynamics independent of the GATOR1 component nprl3 . To accomplish this goal we generated a deletion allele of the GATOR1 component nprl3 , that we named nprl31 , which removes 90% of the nprl3 ORF ( S8 Fig ) . A high percentage of nrpl31 single mutant females die as pupae or pharate adults . Thus , we focused our analysis on phenotypes observed in the larval fat body . First we assayed TORC1 activity by determining the phosphorylation status of the downstream TORC1 target S6 kinase . TORC1 activity was dramatically increased in larval fat bodies from nprl31 null mutants but was slightly decreased in larval fat bodies from wdr241 null mutants . In wdr241 , nprl31 double mutants , TORC1 activity was dramatically increased in the larval fat body relative to fat bodies from wild-type and wdr241 mutant larvae ( Fig 6A and 6B ) . These data confirm that nprl3 is epistatic to wdr24 with respect to the regulation of TORC1 activity . However , although TORC1 activity is high , the fat bodies from wdr241 , nprl31 double-mutants accumulate large numbers of GFP-Lamp1 positive puncta . Importantly , these GFP-Lamp1 positive puncta are not observed in fat bodies from nprl31 single mutants ( Fig 6C–6F ) . Thus , wdr24 is epistatic to nprl3 with regards to the accumulation of GFP-Lamp1 positive puncta . Taken together our data strongly suggest that the Wdr24 component of the GATOR2 complex has TORC1 dependent and independent functions . Finally , GFP-Lamp1 positive puncta in wdr241 single mutants , that activate autophagy , are greatly enlarged relative to GFP-Lamp1 positive puncta that accumulate in wdr241 , nprl31 double mutants , which fail to activate autophagy ( compare Fig 6D to 6E ) . These observations are consistent with the model that wdr24 mutants fail to properly digest the contents of autolysosomes resulting in the accumulation of partial digested cellular components . This possibility is examined in greater detail below . TORC1 inhibition activates the autophagy pathway initiating the formation of autophagosomes , which fuse with lysosomes to produce autolysosomes [2] . Thus , the accumulation of autolysosomes could be the result of a disruption in the regulation of autophagy or a disruption in the regulation of lysosome biogenesis and/or function . The high TORC1 activity observed in the fat bodies of wdr241 , nprl31 double-mutants should inhibit the activation of the autophagy pathway . Consistent with this idea , although wdr241 , nprl31 double-mutants have a large number of GFP-lamp1 positive puncta they have very few puncta that are positive for the autophagy marker Atg8a ( Fig 6C’–6F’ ) . Thus , the majority of the abnormal GFP-Lamp1 positive puncta found in wdr241 , nprl31 mutants represent late endosomes or lysosomes not autolysosomes . Similar observations were also made in nprl2RNAi , wdr241 , and nprl3RNAi , wdr241ovaries ( Fig 5F’–5I’ ) . From these data we infer that wdr24 regulates lysosome dynamics independent of the down-regulation of TORC1 activity and the activation of the autophagy . In order to formally test the hypothesis that wdr24 mutants accumulate abnormal lysosomes independent of the activation of the autophagy we generated wdr241 and atg7d14/d77 double mutants . atg7d14 and atg7d77 are deletion alleles of atg7 that function as null alleles [39 , 40] . In Drosophila , atg7 is required for the activation of autophagy in response to starvation [41] . Therefore , if the autolysosome accumulation we observed in wdr241 mutant egg chambers requires the activation of autophagy , the LysoTracker positive puncta should be dramatically reduced or absent in atg7d14/d77 , wdr241 double mutant egg chambers . Notably , however , we continue to observe a large number of LysoTracker puncta in atg7d14/d77 , wdr241 double-mutant egg chambers ( S9 Fig ) . Again , these puncta are not Atg8a positive , strongly suggesting they are late endosomes/lysosomes not autolysosomes . Taken together , our data demonstrate that wdr24 regulates lysosome dynamics independent of the activation of autophagy . The WDR24 protein is conserved from yeast to mammals [17] . Thus , to investigate the cellular mechanism of lysosome/autolysosome dysfunction observed in the wdr24 mutants in Drosophila , we knocked out the WDR24 gene in human HeLa cells using CRISPR/CAS9 [42] . Specifically , we deleted the WDR24 genomic region from 587–882 bp , which includes the start codon ( S10A Fig ) . Western blot analysis showed that the wdr24-/- cell line did not express the WDR24 protein ( S10B Fig ) . To confirm the role of WDR24 in TORC1 regulation , we analyzed the phosphorylation status of S6 kinase and 4E-BP1 in wdr24-/- cells . As expected , the wdr24-/- cell line had significantly reduced TORC1 activity ( Fig 7A , S10C and S10D Fig ) . Moreover , the levels of S6 kinase phosphorylation were rescued when an HA tagged WDR24 protein was expressed in the wdr24-/- cells ( S10E Fig ) . Thus , WDR24 functions to promote TORC1 activity in HeLa cells . In addition to having decreased TORC1 kinase activity , wdr24-/- HeLa cells accumulate large numbers of autolysosomes , suggesting a conserved function for WDR24 in the regulation of autophagic flux in Drosophila and mammals ( Fig 7B–7E ) . To better define the role of WDR24 we measured the levels of the autophagy protein LC3-II . Upon autophagy activation , the LC3-I protein is lipidated to generate LC3-II , which is hydrophilic and can integrate into autophagosomal membranes [43 , 44] . We found that LC3-II levels were dramatically increased in wdr24-/- cells relative to controls ( Fig 7A ) . Increased LC3-II levels can be associated with either enhanced autophagosome synthesis or reduced autophagosome turnover due to diminished lysosomal degradation [44] To differentiate between these two mechanisms we measured p62 expression in wdr24-/- cells . p62 is a ubiquitin-binding protein that is degraded by autophagy; when autophagic flux is blocked , the degradation of p62 is reduced and the protein accumulates [45] . Conversely , if autophagic flux is increased , the degradation of the p62 protein is accelerated and thus the level of the protein decreases [46 , 47] . As predicted from our work in Drosophila , p62 levels are increased in wdr24-/- cells ( Fig 7A and S11 Fig ) , indicating that autophagic flux requires the GATOR2 component WDR24 . An additional assay to measure autophagic flux utilizes the lysosomal inhibitor chloroquine . In wild-type cells LC3 II levels are dramatically increased upon treatment with chloroquine ( Fig 7F and 7G ) . This increase in LC3 II results from a block to autophagic flux due to decreased lysosomal activity . However , wdr24-/- mutant cells treated with chloroquine do not have increased LC3 II levels indicating that these mutants have diminished autophagic flux independent of chloroquine treatment ( Fig 7F and 7G ) . Taken together these data strongly suggest that wdr24-/- cells exhibit reduced autophagic flux . To further explore the proteolytic capabilities of lysosomes in wdr24-/- mutant cells , we used DQ BSA Bovine Serum Albumin ( BSA ) Red dequeching assay , a derivative of BSA labeled with BODIPY dyes that are strongly self-quenched . The proteolysis of the BSA molecule in lysosomes releases dequenched protein fragments , thus lysosomal proteolytic activity can be monitored by fluorescent intensity [48] . Notably , while wdr24-/- cells have a large increase in the number of autolysosomes there are fewer dequenched DQ-BSA-containing vesicles in wdr24-/- cells relative to wild-type cells ( Fig 7H–7I’ ) . These data indicate that wdr24-/- lysosomes have decreased proteolytic activity . As a further measure of lysosomal proteolytic activity we measured cleaved Cathepsin D levels by Western blot . Cathepsin D is a lysosomal protease that is cleaved in lysosomes into a mature enzyme [49] . We find that the levels of processed Cathepsin D are significantly reduced in wdr24-/- cells , again strongly suggesting that the lysosomes have diminished proteolytic activity ( Fig 7J and 7K ) . However , it is possible that Wdr24 influences the trafficking of lysosomal enzymes , such as Cathepsin D , to lysosomes [50] . In order to test if Cathepsin D is properly localized to lysosomes in the wdr24-/- mutant cells , we stained mutant and wild type HeLa cells with antibodies against LAMP1 and Cathepsin D . Notably , these proteins strongly co-localize to lysosomes in both wild-type and wdr24-/- cells ( S12 Fig ) . These data strongly suggest that trafficking of lysosomal enzymes to lysosomes is not dramatically diminished in the wdr24-/- HeLa cells . Chloroquine blocks autophagic flux by increasing lysosomal pH [51] . Thus we reasoned that loss of Wdr24 protein might increase lysosomal pH . In order to test this possibility we used two different reagents , LysoSensor Green DND-189 and LysoSensor Yellow/Blue DND-160 , to measure lysosome acidification . LysoSensor Green DND-189 fluorescent intensity increases in more acidic environments [51–53] . As shown in Fig 7L , wdr24-/- HeLa cells had lower fluorescent intensity , and thus higher lysosomal pH , relative to wild-type HeLa cells . Additionally , using LysoSensor Yellow/Blue DND-160 , a ratiometric dye that can be used to measure the pH of acidic organelles , we found that the pH of lysosomes in wdr24-/- cells was increased to 5 . 25 relative to the pH of lysosomes in controls cells 4 . 92 ( Fig 7M ) . Taken together these data support the model that the inhibition of autophagic flux observed in wdr24-/- knock out HeLa cells is due in part to the impairment of lysosomal acidification . The transcription factor EB ( TFEB ) regulates lysosome biogenesis and function by promoting lysosomal gene expression [54–57] . Under non-starvation conditions , TFEB is phosphorylated by mTORC1 and is retained in the cytoplasm . When cells are starved , TFEB becomes dephosphorylated and is subsequently translocated into the nucleus to drive lysosomal and autophagic gene expression [54–56 , 58] . We find that the majority of TFEB is located in the nucleus of wdr24-/- cells indicating that TFEB has been activated ( Fig 7N–7O’ ) . This observation is consistent with the reduced TORC1 activity of wdr24-/- cells ( Fig 7A ) . These data suggest that Wdr24 does not affect lysosome function by preventing the activation of TFEB . Finally , consistent with the immunofluorescence and biochemical studies , Transmission Electron Microscopy ( TEM ) of wdr24-/- cells reveals a dramatic accumulation of autolysosome like structures that contain partially digested material ( S13 Fig ) . Taken together our results provide strong evidence that in addition to regulating TORC1 activity the WDR24 protein promotes lysosome function and autophagic flux in mammalian cells . Finally , an important question remained . Does Wdr24 regulate lysosome function in the context of the GATOR2 complex or does Wdr24 function in an alternative complex to regulate lysosome dynamics ? To address this question we examined if other GATOR2 complex components regulate lysosomal dynamics independent of their role in the regulation of TORC1 activity . For these experiments we returned to Drosophila . Specifically , we depleted the GATOR1 components nprl2 and nprl3 in the mio2and seh1Δ15 mutant backgrounds . As was previously reported , we found that nprl2 and nprl3 depletions rescue the small ovary and fertility deficits of mio2and seh1Δ15 mutants [18] . Importantly , however , although we found that the nprl2 and nprl3 depletions rescued the low TORC1 activity of mio2 and seh1Δ15 ovaries , the depletions failed to rescue the accumulation of lysotracker positive puncta ( Fig 8 ) . From these data we infer that multiple components of the GATOR2 complex function in the TORC1 independent regulation of lysosomal function and autophagic flux in Drosophila . However , we note that we have not shown that all components of GATOR2 complex function in the regulation of lysosomes .
Whole animal studies often reveal tissue-specific and/or metabolic requirements for genes that are not readily observed in cell culture . In mammalian and Drosophila tissue culture cells , RNAi based depletions of the GATOR2 components Mio , Seh1 , Wdr59 , and Wdr24 result in decreased TORC1 activity in return to growth assays [3 , 18] . These data have resulted in the model that all components of the GATOR2 complex are generally required for TORC1 activation ( 3 ) . However , the characterization of mio and seh1 null mutants in Drosophila , demonstrated that Mio and Seh1 are critical for the activation of TORC1 and inhibition of autophagy in the female germ line , but play a relatively small role in the regulation of TORC1 activity and autophagy in somatic tissues under standard culture conditions [18 , 27 , 28] . Thus , the requirement for at least a subset of GATOR2 complex components is tissue and/or context specific . Here we report that the GATOR2 component Wdr24 is required for the full activation of TORC1 in both germline and somatic cells of Drosophila . Consistent with the global down-regulation of TORC1 activity in the absence of Wdr24 , we find that wdr24 mutant adults are notably smaller than controls and are female sterile . Depleting the GATOR1 components nprl2 and nprl3 in the wdr24 mutant background rescued the low TORC1 activity , growth defects , and female sterility of wdr24 mutants . Thus , the GATOR2 component Wdr24 is required to oppose GATOR1 activity in both germline and somatic cells of Drosophila . From these results we propose that Wdr24 is a key effector of the GATOR2 complex required for the full activation of TORC1 in most cell types . There are several potential models to explain the differential requirement for individual GATOR2 proteins in Drosophila . First , there may be tissue specific requirements for individual GATOR2 subunits . In this model the different phenotypes observed in the seh1 and mio versus wdr24 mutants reflects a qualitative difference in the requirement for these proteins in different tissues . However , we favor an alternative model in which Wdr24 is the core effector of GATOR2 activity , with Mio and Seh1 functioning primarily as positive regulators of GATOR2 activity . In this second model , the differential phenotypes observed in the seh1 and mio versus wdr24 mutants reflects a quantitative difference in the requirement for GATOR2 activity in different tissues . The distinction between these two models awaits the identification of the molecular mechanism of Wdr24 and GATOR2 action . We have identified a novel TORC1 independent role for Wdr24 in the regulation of lysosome dynamics and function . In wdr24 mutants , the down-regulation of TORC1 activity and the accumulation of autolysosomes occur independent of nutrient status . Our initial hypothesis was that in the absence of the GATOR2 component Wdr24 , the deregulation of the GATOR1 complex results in low TORC1 activity , triggering the constitutive activation of autophagy and the accumulation of autolysosomes . Surprisingly , however , our epistasis analysis determined that the accumulation of lysosomes could be decoupled from both the chronic inhibition of TORC1 activity and the activation of autophagy . Raising TORC1 activity in the wdr24 mutant background , by depleting either components of the GATOR1 or TSC complex , failed to rescue the accumulation of abnormal lysosomal structures . Notably , we determined that two additional members of the GATOR2 complex , Mio and Seh1 , also regulate lysosomal behavior independent of both GATOR1 and the down-regulation of TORC1 activity . From these data we infer that multiple components of the GATOR2 complex have a TORC1 independent role in the regulation of lysosomes . An increased number of autolysosomes is often associated with reduced autophagic flux due to diminished lysosomal degradation [44] . Consistent with reduced autophagic flux , in Drosophila wdr24-/- mutants accumulated enlarged autolysosomes filled with undegraded material . Moreover , lysosomes in the wdr24-/- mutants failed to quench the GFP fluorescence of a GFP-mCherry-Atg8a protein . These phenotypes are consistent with decreased lysosomal pH and degradative capacity [44] . In order to examine in detail the role of Wdr24 in the regulation of lysosome function we generated a wdr24-/- knockout HeLa cell line that recapitulated the phenotypes observed in Drosophila wdr24-/- mutants . Specifically , wdr24-/- HeLa cells had have decreased TORC1 activity and accumulate a large number of autolysosomes . Using multiple assays we determined that wdr24-/- lysosomes had reduced degradative capacity and autophagic flux and thus accumulate proteins that are normally degraded by lysosomal enzymes such as p62 , LC3II and Cathepsin D . Additionally , we determined that wdr24-/- lysosomes have increased pH relative to wild-type cells , again consistent with reduced lysosomal function . Taken together these data confirm that Wdr24 plays a key role in the regulation of lysosomal activity . Here we show that components of the GATOR2 complex function in the regulation of TORC1 activity and in the TORC1 independent regulation of lysosomal dynamics and autophagic flux . These two functions suggest that the GATOR2 complex may regulate cellular homeostasis by coordinating TORC1 activity with the dynamic regulation of lysosomes during periods of nutrient stress . Intriguingly , several recent reports describe a very similar dual function for the RagA/B GTPases in both mice and zebrafish [59 , 60] . RagA/B play a critical role in the activation of TORC1 in the presence of amino acids [61 , 62] . Surprisingly , however , TORC1 activity was not found to be significantly decreased in cardiomyocytes of RagA/B knockout mice ( 56 ) . Nevertheless , the RagA/B mutant cardiomyocytes have decreased autophagic flux and reduced lysosome acidification . From their data , the authors conclude that the RagA/B GTPases regulate lysosomal function independent of their role in the regulation of TORC1 activation in some cell types [59] . Similarly , RagA is required for proper lysosome function and phagocytic flux in microglia [60] . Notably , Mio , a component of the GATOR2 complex is found associated with RagA [3] . Thus , in the future it will be important to determine if components of the GATOR2 complex function in a common pathway with the Rag GTPases to regulate lysosomal function . In Saccharomyces cerevisiae single mutants of wdr24/sea2 and wdr59/sea3 do not exhibit defects in TORC1 regulation but do have defects in vacuolar structure [4 , 5 , 63] . Moreover , several recently identified genes that regulate the GATOR2-GATOR1-TORC1 pathway in response to amino acid limitation are restricted to metazoans [20–25] . These data make it tempting to speculate that the ancestral function of the GATOR2 complex maybe the regulation of lysosome/vacuole function and autophagic flux . Indeed , the finding that GATOR2 components regulate lysosome dynamics is particularly intriguing in light of the observation that GATOR2 complex is comprised of proteins with characteristics of coatomer proteins and membrane tethering complexes [17] . Notably , the GATOR2 complex components Mio , Seh1 and Wdr24 localize to lysosomes and autolysosomes [18] . Similarly , these proteins associate with the vacuolar membrane in budding yeast [17] . Thus , going forward it will be important to examine if the GATOR2 complex acts directly on lysosomal membranes to regulate their structure and/or function . More broadly , future studies on the diverse roles of the SEACAT/GATOR2 complex will further our understanding of the complex relationship between cellular metabolism and the regulation of endomembrane dynamics in both development and disease .
MTD-GAL4 , UASp-mCherry-Atg8a ( y1 w1118; P{UASp-mCherry-Atg8a}2; Dr1/TM3 , Ser1 ) , UASp-GFP-mCherry- Atg8a ( y1 w1118; P{UASp-GFP-mCherry-Atg8a}2 , wdr241 ( CG76091 ) , UAS-Tsc1 RNAi ( y1 sc* v1; P{TRiP . GL00012}attP2 ) , HS-FLP; Ubi-GFP FRT82B/TM3 and Df ( 3R ) BSC547 lines were obtained from the Bloomington Stock Center . The mio1 , mio2 , seh1Δ15 , nprl RNAi and nprl3 RNAi lines were described previously [18 , 64] . GFP-Lamp1 ( w1118; P{W+ , Tub>GFP-Lamp1}1/CyO; TM6b , Hu boss1/Sb boss1 ) was kindly provided by Helmut Kramer ( UT Southwestern ) . Nanos-Gal4 ( P{NANOS GAL4 VP-16} , yw; D/TM3 , Ser , Sb ) was kindly provided by Sharon Bickel ( Dartmouth College ) . atg7d77/Cyo-GFP and atg7d14/CyO-GFP were kindly provided by Thomas P . Neufeld ( University of Minnesota ) [41] . All fly stocks were maintained on JAZZ-mix Drosophila food ( Fisher Scientific ) at 25℃ . The wdr24 coding region was cloned into a pENTR-1A vector ( Invitrogen ) . The mCherry coding region was inserted into pENTR-wdr24 plasmid . The pENTR-wdr24 and pENTR-wdr24-mCherry plasmids were recombined with pPGW vector ( DGRC ) and pPFHW vector ( DGRC ) separately and to generate UASp-GFP-wdr24 and UASp-wdr24-mCherry using Gateway LR Clonase II Enzyme ( Invitrogen ) . UASp-GFP-wdr24 and UASP-wdr24-mCherry plasmids were used to generate transgenic lines ( Best gene Inc ) . All primers used for PCR amplification are listed in S2 Table . Ovaries from the transgenic flies that stably express Mio proteins tagged with FLAG and HA were homogenized in 50 mM Tris-HCl ( pH 7 . 4 ) , 300 mM NaCl , 5 mM EDTA , and 1% Triton X-100 supplemented with proteinase inhibitor cocktail ( Roche ) [27] . Cell lysates were cleared by centrifugation at 15 , 000 × g for 15 min and Proteins were purified using FLAG-HA tandem affinity purification kit ( Sigma ) Proteins were precipitated with 10% trichloroacetic acid ( TCA ) , washed with acetone , air-dried , and analyzed by liquid chromatography ( LC ) /MS at the Taplin MS facility ( Harvard Medical School ) . S2 cell transfection and immunoprecipitation were performed as previously described [18] . 2 μg GFP antibody ( Roche ) , 20 μl protein G agarose ( Millipore ) and 10 μl protein A agarose ( Roche ) were used for each experiment . Whole flies or HeLa cells were lysed in RIPA buffer containing complete protease inhibitors and phosphatase inhibitors ( Roche ) . Western blots were performed as described previously [18] . Antibodies were used at the following concentrations: rabbit anti-P-S6K T398 at 1:1000 ( Cell Signaling ) , guinea pig anti-S6K at 1:10 , 000 ( 24 ) , mouse anti-actin at 1:10 , 000 ( Abcam ) , rabbit anti-LC3A/B at 1:1000 ( Cell Signaling ) , rabbit anti-P-S6K T389 at 1:1000 ( Cell Signaling ) , rabbit anti-S6K at 1:1000 ( Cell Signaling ) , rabbit anti-GAPDH at 1:3000 ( Cell Signaling ) , rabbit anti-P-4E-BP1at 1:1000 ( Cell Signaling ) , rabbit anti-4E-BP1 at 1:1000 ( Cell Signaling ) , rabbit anti-GFP at 1:500 ( Cell Signaling ) , rabbit anti-SQSTM1/p62 at 1:500 ( Cell Signaling ) , Mouse anti- SQSTM1/p62 at 1:1000 ( Novus Biologicals ) , rabbit anti-WDR24 at 1:1000 ( Novus Biologicals ) and goat anti-Cathepsin D at 1:500 ( Santa Cruz ) . The band intensity was quantified using Image J analysis tool ( NIH ) . Immunofluorescence was performed as described [65] , using the following antibodies: goat anti-GFP FITC conjugated ( 1:400 , Abcam ) , rabbit anti-LC3A/B ( 1:1000 , Cell Signaling ) , mouse anti- SQSTM1/p62 ( 1:100 , Novus Biological ) , rabbit anti-TFEB ( 1:100 , Cell Signaling ) , rabbit anti-CathD ( 1:100 , Calbiochem ) and rabbit anti-Atg8a ( 1:200 , Abcam ) . Anti-rabbit and anti-mouse Alexa Fluor secondary antibodies ( Invitrogen ) were used at 1:1000 . Nuclei were visualized by staining the DNA with DAPI or Hoechst 33324 ( Invitrogen ) . Images were acquired using a Leica TCS SP5 confocal microscope . Live cell images were obtained as previously described [18] . To generate wdr241 homozygous clones , HS-FLP; Ubi-GFP FRT82B/wdr241 FRT82B third instar larvae were heat shocked for 1hr in a 37°C water bath two times per day . The adult females were collected and aged for 5 to 7 days . Fat bodies or ovaries from adult flies were dissected and stained with GFP antibody ( Abcam ) and Hoechst 33324 ( Invitrogen ) . Homozygous wdr241 clones were marked by the absence of GFP expression . To generate knock out nprl3 flies , guide RNAs ( gRNA ) that target nprl3 were designed using the online CRIPR design tool ( http://crispr . mit . edu/ ) . To make the deletion mutants , two different gRNAs were cloned into pBFv-U6 . 2B as previously described [66] . pBFv-U6 . 2B-nprl3 plasmids were injected into y[1] , vas-Cas9 , w[1118] embryos . All the oligonucleotides used for cloning and screening are listed in S2 Table . To generate knock out WDR24 HeLa cells , two pairs of oligonucleotide encoding the guide RNAs were cloned into px330 vector [42] . On day one , 50 , 000 cells were seeded into 24-well plate . Each well was transfected with a total of 1 μg of a pair of px330 guide constructs . The third day , cells were pooled into 10 cm dishes with 200 cells each dish . After 10 days , all the clones were collected and seeded into 24 well plates . Cells were grown and expanded , and the positive colonies were identified by PCR and sequencing . All primers used for PCR amplification are listed in S1 Table . HeLa cells and knock out cells were cultured at 37°C , 5% CO2 in DMEM medium ( Life Technology ) supplemented with 10% fetal bovine serum ( FBS , Life Technology ) , 50 U/ml penicillin and 50 μg/ml streptomycin . Plasmid transfection was performed using Lipofectamine 2000 ( Life Technologies ) . HAWDR24 pRK5 ( Plasmid #46335 ) was obtained from Addgene . For the DQ BSA Red ( Life Technology ) assay , Cells were incubated for 4 h with DQ BSA ( 10 μg/ml ) and then washed twice with HBSS . Subsequently the cells were incubated in the starvation medium ( HBSS , starvation media; Life Technology ) to induce autophagy . After 1 hour the DQ BSA fluorescence was detected using a Leica TCS SP5 confocal microscope . Cells grown on coverslips were fixed in 2% ( wt/vol ) formaldehyde and 2% ( wt/vol ) glutaraldehyde in 0 . 1 M sodium cacodylate ( pH 7 . 4 ) , postfixed in 1% aqueous OsO4 , and stained en bloc with 2% ( wt/vol ) uranyl acetate . Upon dehydration and embedding in EMBed-812 ( EM Science , Horsham , PA ) , the coverslips were removed by hydrofluoric acid , cells were thin-sectioned parallel to the glass , and sections were stained with uranyl acetate . The samples were examined on an FEI Tecnai 20 transmission electron microscope operated at 80 kV , and images were recorded on a Gatan Ultrascan CCD camera . Lysosomal pH measurements were performed as describe [67] . In brief , cells were stained with 1μM LysoSensor Green DND-189 in DMED regular medium for 20 min at 37°C , 5% CO2 . Subsequently , cells were washed with PBS twice and analyzed by a microplate reader ( 485/530 nm ) in triplicate . Lysosomal pH quantification was performed using LysoSensor Yellow/Blue DND-160 . Cells were labeled with 1μM LysoSensor Yellow/Blue DND-160 for 30 min at 37°C , 5% CO2 in DMEM medium and then washed twice with PBS . To generate a calibration curve cells were treated for 10 min with 10μM monensin and 10μM nigericin in 25mM MES calibration buffer , pH 3 . 5–6 . 0 , containing 5 mM NaCl , 115 mM KCl , and 1 . 2 mM MgSO . The fluorescence was measured with a microplate reader ( 340/440 nm and 380/530 nm ) at 37°C . These experiments were performed in triplicate . | TORC1 is a conserved multi-protein complex that regulates metabolism and cell growth in response to many upstream inputs including nutrient availability . When amino acids are limiting , the GATOR1 complex inhibits TORC1 activation . The inhibition of TORC1 slows cellular metabolism and promotes cell survival during times of protein scarcity . A second critical response to amino acid limitation is the activation of autophagy . During autophagy cells degrade intracellular components in specialized membrane-bound organelles called autolysosomes that are formed when lysosomes fuse with autophagosomes . In times of nutrient stress , the process of autophagy allows proteins and other building blocks of the cell to be broken down and repurposed for vital cellular functions . Here we demonstrate that Wdr24 , a component of the multi-protein GATOR2 complex , has a dual role in the regulation of cellular metabolism in Drosophila . First , Wdr24 is required to oppose the activity of the GATOR1 complex , thus activating TORC1 in a broad array of Drosophila tissues . Second , Wdr24 promotes the acidification of lysosomes and thus facilitates autophagic flux . Our data support the model that Wdr24 uses both TORC1 dependent and independent pathways to regulate cellular metabolism . | [
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... | 2016 | The GATOR2 Component Wdr24 Regulates TORC1 Activity and Lysosome Function |
Peribunyaviridae is a large family of RNA viruses with several members that cause mild to severe diseases in humans and livestock . Despite their importance in public heath very little is known about the host cell factors hijacked by these viruses to support assembly and cell egress . Here we show that assembly of Oropouche virus , a member of the genus Orthobunyavirus that causes a frequent arboviral infection in South America countries , involves budding of virus particles toward the lumen of Golgi cisternae . As viral replication progresses , these Golgi subcompartments become enlarged and physically separated from Golgi stacks , forming Oropouche viral factory ( Vfs ) units . At the ultrastructural level , these virally modified Golgi cisternae acquire an MVB appearance , and while they lack typical early and late endosome markers , they become enriched in endosomal complex required for transport ( ESCRT ) proteins that are involved in MVB biogenesis . Further microscopy and viral replication analysis showed that functional ESCRT machinery is required for efficient Vf morphogenesis and production of infectious OROV particles . Taken together , our results indicate that OROV attracts ESCRT machinery components to Golgi cisternae to mediate membrane remodeling events required for viral assembly and budding at these compartments . This represents an unprecedented mechanism of how viruses hijack host cell components for coordinated morphogenesis .
The Bunyavirales is one of the largest orders of RNA viruses , containing nine virus families , including the Peribunyaviridae with five genera , one of which is the Orthobunyavirus with 48 species [1] . The genus Orthobunyavirus comprises zoonotic arboviruses that cause different diseases in humans , varying from febrile illness and encephalitis caused by Oropouche virus ( OROV ) and La Crosse virus , to hemorrhagic fever caused by Ngari virus [2] . OROV is the etiologic agent of Oropouche fever , a frequent arboviral infection in South America countries , especially in the Amazon region of Brazil , Peru and Venezuela and with documented cases also in Panama , Suriname and Trinidad [3–5] . Similar to other Bunyavirales , OROV is an enveloped , single-stranded RNA virus composed of a tripartite genome . The large ( L ) segment encodes an RNA-dependent RNA polymerase ( RdRp ) that catalyzes both transcription and replication . The medium ( M ) segment encodes a polyprotein that is co-translationally cleaved in the endoplasmic reticulum ( ER ) to form the envelope glycoproteins Gc and Gn , and a nonstructural protein NSm . Gc ( ~110 kDa ) and Gn ( ~32 kDa ) are type I integral membrane proteins that form an heterodimer in the ER which is transported to the Golgi complex [6] . The small segment ( S ) encodes the nucleocapsid N ( 25–30 kDa ) protein , an abundantly produced viral protein that oligomerizes and encapsidates the virus genome , and the nonstructural protein NSs . The N protein also interacts with RdRp , Gn , and Gc , playing a major role in virus assembly [2] . During infection , the Orthobunyavirus Bunyamwera induces the formation of unique viral compartments , also known as viral factories ( Vfs ) around the Golgi complex , the site where viral glycoproteins interact with N protein for virus particle assembly [2 , 7 , 8] . Indeed , Bunyamwera virus infection causes major remodeling of Golgi membranes , leading to alterations in the organization of the Golgi stacks [7 , 9] . However , the host cell machinery involved in these membrane-remodeling events is unknown . For most enveloped viruses , assembly and budding are closely linked processes that require the invagination of membranes away from the cytosol , followed by membrane severing . Such events may occur at the plasma membrane ( PM ) , or in an intracellular compartment that will later fuse with the PM for virion release [10] . Similar “reverse topology” membrane-remodeling activities take place during the maturation of early endosomes into multivesicular bodies ( MVBs ) and require the Endosomal Sorting Complex Required for Transport ( ESCRT ) machinery [11] . In endosomes , the ESCRT machinery is composed of four main components ( ESCRT-0 , I , II and III ) that commonly act in a sequential order . ESCRT-0 promotes cargo recruitment , ESCRT-I/II also bind cargo and initiates membrane invagination , while ESCRT-III facilitates membrane fission [11–13] . Finally , Vps4 , an ATPase member of the AAA family , disassembles and recycles ESCRT-III components and is essential for sustained ESCRT machinery functioning in various processes [14] . Another important player is Alix/AIP1 , a multifunctional protein that may act in parallel to ESCRT-I/II exerting activities such as cargo selection [13] , membrane deformation [15 , 16] , and binding/recruitment of ESCRT-III subunits [17–19] . Many enveloped viruses have evolved to usurp elements of the ESCRT machinery for assembly and budding [10] . In the present study , we demonstrate that ESCRT machinery elements are recruited to the Golgi complex in OROV infected cells and are required in the late steps of the viral replication cycle . We show that OROV infection induces the formation of prominent Vfs by modifying the Golgi cisternae , which acquires an MVB appearance . Although these viral-induced compartments lack typical early and late endosome markers , they are enriched in Vps4 and Alix . ESCRT activity is required for OROV morphogenesis since impairment of the ESCRT-III recycling by overexpression of an ATPase-defective mutant of Vps4 ( Vps4E/Q ) produces enlarged Vfs , where Vps4E/Q accumulates . Additionally , depletion of Tsg101 or Alix significantly compromises Vf biogenesis and infectious OROV particle production . To our knowledge , this is the first report showing the recruitment of ESCRT components to the Golgi complex and their involvement in the assembly/egress of an Orthobunyavirus .
Initially , we monitored the kinetics of OROV replication cycle , in mammalian cells . To this end , HeLa cells were infected with OROV ( MOI = 1 ) and the TCID50/mL from both cell lysate and supernatant samples were determined at different time points post-infection ( p . i . ) . During the first 6 h p . i , the intracellular viral titers were continuously reduced and were barely detected , indicating virus eclipse ( Fig 1A ) . This pattern was followed by a rapid increase in viral titers in cell lysates and culture supernatants , reaching peak levels in cell lysates at approximately 24 h p . i . Viral cycle progression was also analyzed by determining the levels of OROV protein accumulation in cells and culture media by immunoblot . Using an anti-OROV polyclonal antibody the viral nucleocapsid ( N ) protein ( MW ~25 kDa ) was the most abundantly detected protein in cell extract and supernatant samples ( Fig 1B ) . However , the glycoprotein Gc ( MW ~124 kDa ) was also detected at late times p . i . ( Fig 1B ) . The immunoblot data are in agreement with the virus titration data by TCID50 assay , and help to define the times p . i . when viral proteins start to be produced and released . Next , we assessed the subcellular distribution of OROV proteins in host cells during the replication cycle . At approximately 1–3 h p . i . , viral proteins presented a puncta distribution dispersed throughout the cytoplasm ( Fig 1C ) , likely representing endocytosis of viral particles as previously suggested [20] . Between 4–6 h p . i . , virus staining could not be detected due to virus eclipse ( Fig 1D ) . In contrast , after 7 h pi . , viral proteins displayed a reticular pattern ( Fig 1E ) , followed by accumulation in vesicle-like structures between 13–18 h p . i . ( Fig 1F ) . These structures may represent the virus-induced cellular compartments that function as a scaffold for virus assembly , termed as viral factories ( Vfs ) [2 , 21] . At later time points ( 19–24 h p . i . ) , these Vfs were still present , but an additional staining was also detected at the cell periphery ( Fig 1G ) . Importantly , at 24 h p . i . almost all cells were positive for OROV without presenting a noticeable cytopathic effect . Taken together , these results indicate that the interval between 18 h and 24 h p . i . is a suitable period for studies on virus assembly site and budding . Considering the reticular pattern displayed by OROV proteins at early times p . i . ( 7–12 h p . i . , Fig 1E ) , we investigated the participation of ER membranes during the formation of Vfs . In non-infected cells , staining of calnexin-2 , an ER-resident integral membrane protein responsible for folding glycosylated proteins [22 , 23] , showed a reticular pattern of distribution throughout the cytoplasm ( S1A–S1D Fig ) . Although calnexin-2 labeling displayed poor overlap with OROV proteins ( S1 Table ) , OROV infection changed the distribution of calnexin-2 , as this ER-chaperone appeared to concentrate specifically at the vicinity of Vfs at 13–18 h p . i . ( S1E–S1H Fig ) . This alteration in calnexin-2 distribution could be the result of changes in the trafficking of this transmembrane protein , or due to the direct recruitment of ER-membrane to Oropouche Vfs . To gain insights into this process , we investigate the behavior of a soluble ER-resident protein comprising the yellow fluorescent protein ( YFP ) fused to the ER-retention sequence , KDEL ( SS-YFP-KDEL ) . Similar to calnexin-2 , a clear enrichment of this lumenal ER-protein was seen in the vicinity of Oropouche Vfs ( S1I–S1P Fig ) , strongly suggesting the active recruitment of ER-membrane to the OROV assembly sites . It has been proposed that most orthobunyaviruses use the Golgi complex and/or the trans-Golgi network ( TGN ) as assembly sites in mammalian cells ( Elliott 2014 ) , which led us to analyze the role of these compartments during OROV assembly . To this end , we immunostained the cis-Golgi and the TGN using anti-giantin and anti-TGN46 antibodies , respectively , in OROV infected cells . Giantin is a coiled-coil protein that regulates Golgi architecture and function , by facilitating vesicle tethering and fusion processes in the cisternae [24] . TGN46 is thought to cycle between the TGN and the plasma membrane and at steady state is mostly detected on tubules and vesicles associated with the TGN [25] . In cells analyzed at 0 h p . i . , TGN46 and giantin presented a juxtanuclear localization ( Fig 2A–2E ) . In contrast , during OROV infection ( 24 h p . i . ) these proteins were partially relocated to Vfs scattered throughout the cytosol , displaying colocalization with OROV staining at these structures ( Fig 2F–2J and S1 Table ) . Taken together , these results indicate that , in addition to ER membranes , Golgi and TGN membranes contribute to the formation of Oropouche Vfs and that OROV infection induces the modification and scattering of Golgi elements . To obtain further insights into the morphological features of Oropouche Vfs , we analyzed HeLa cells infected with OROV at 18 h p . i . by transmission electron microscopy ( TEM ) and super-resolution microscopy . At this time of infection , prominent Vfs are starting to be detected ( Fig 1E ) . In the non-infected cells , the Golgi complex was organized in thin parallel cisternae with numerous Golgi-associated vesicles at the proximity of the distal ends of the cisternae ( Fig 3A ) . In infected cells , the Golgi cisternae appeared dilated and viral particles were observed within the lumen of these structures ( Fig 3B and 3C ) . Moreover , the three-dimensional organization of Golgi and TGN cisternae in infected and non-infected cells was resolved by structured illumination microscopy ( 3D-SIM ) and show that Vfs were commonly associated with Golgi and TGN membranes ( Fig 3D–3F and S1 Movie ) . These EM and 3D-SIM results further support that the Golgi complex serves as platforms for the formation of viral compartments where OROV budding occurs . Oropouche Vfs were also analyzed by immuno-TEM using a polyclonal anti-OROV antibody at 24 h p . i . ( Fig 3G , g’ and g” ) . This analysis revealed that Vfs constituted membrane-enclosed compartments with an endosomal-like morphology that resembles MVBs . OROV proteins were detected on the limiting membrane and inside of these compartments ( Fig 3G , g’ and g” ) . Moreover , dilated ER cisternae were often detected in proximity to Vfs ( Fig 3G ) . The MVB appearance and the scattered distribution pattern of Vfs prompted us to investigate the presence of endosomal proteins in these structures . OROV staining presented a small colocalization with either CD63-GFP ( ~35%; S2A–S2H Fig and S1 Table ) or Lamp1 ( ~10%; S2I–S2P Fig and S1 Table ) , proteins that are mostly localized to late endosomes/MVBs and lysosomes , respectively , and are widely used as markers for these compartments [26] . Similarly , OROV-staining rarely ( ~18% ) overlapped with that of internalized transferrin ( ~18% ) , often used as a marker for recycling endosomes ( S3A–S3H Fig and S1 Table ) or of sorting nexin-2 ( SNX2 ) ( S3I–S3P Fig and S1 Table ) , a component of the retromer complex found in early endosomes [27] . Strikingly , we observed a higher degree ( ~64% ) of colocalization between the OROV staining and that of HRS ( S3Q–S3X Fig and S1 Table ) , a peripheral early endosomal protein that is a subunit of ESCRT-0 [28] . In fact , OROV Vfs were clearly enriched with HRS . Together , these results suggest that Vfs are not derived from early or late endosomes; rather , they indicate that the ESCRT-0 protein HRS is recruited to Golgi-derived Vfs during OROV biogenesis . The enrichment of HRS in Oropouche Vfs prompted us to investigate the role of ESCRT machinery in OROV assembly and externalization . To avoid affecting virus internalization , HeLa cells were first infected with OROV ( MOI = 3 ) and at 6 h p . i . cells were transfected with plasmids encoding GFP-tagged Vps4Awt or a dominant-negative Vps4A mutant ( Vps4E/Q ) , in which the ATPase activity was compromised . In non-infected control cells , Vps4wt-GFP displayed a cytosolic distribution ( S4A–S4D Fig ) as previously observed [29] . However , this pattern was altered by OROV infection , where the Vps4wt-GFP was redistributed mainly to Vfs ( S4E–S4G Fig ) . By conventional confocal microscopy analyses , we noticed that Vps4wt-GFP appeared to be recruited to TGN46-positive structures that also contained OROV proteins ( Fig 4A–4E ) . To analyze this phenotype with more detail , we used 3D-SIM and confirmed that Vps4wt accumulates in Oropouche Vfs associated with TGN46 ( Fig 4F–4J and S2 Movie ) . Consistently , the ATPase inactive Vps4E/Q-GFP mutant strongly accumulated in TGN46/OROV-positive structures ( Fig 4K–4O ) and caused the enlargement of these structures ( Fig 4P ) . Moreover , Vps4E/Q-GFP co-localization with TGN46 was clearly dependent of OROV infection ( S4H–S4K Fig and S1 Table ) . Next , we investigated the presence of specific viral molecules in the OROV-induced compartments containing Vps4 . To this end , we expressed an OROV N-protein fused to mCherry ( mCherry-N ) in control and OROV infected HeLa cells . In control cells , mCherry-N showed a cytoplasmic distribution , localizing to discreet foci that may represent its association to membranes ( S5A Fig ) . The pattern is different for Vps4-GFP , which is homogenously distributed in the cytoplasm and leaks out to the nucleoplasm , as previously shown ( S5B Fig ) . In OROV infected cells , mCherry-N accumulates in larger structures to which Vps4-GFP is relocated and that are often found in the vicinity of TGN46-positive membranes ( S5F–S5J Fig ) . It was previously shown that double stranded RNA ( dsRNA ) , an intermediate of orthobunyavirus RNA replication , localizes to Golgi-derived viral tubes in Bunyawera infected cells [7] . S5K–S5O Fig shows that in non-infected cells , immunostaining for dsRNA is faint and is not spatially related to that of Vsp4-GFP . In contrast , a robust signal for dsRNA is detected in OROV infected cells , which partially overlaps with either Vps4-GFP alone ( S5P , S5Q and S5S Fig arrows ) or Vps4-GFP and TGN46 ( S5P–S5T Fig ) . Taken together , these results indicate that Vps4A is recruited to the TGN during OROV replication and its enzymatic activity is required for proper Oropouche Vfs biogenesis . To further establish the involvement of the ESCRT machinery during OROV assembly , we used RNAi to reduce the expression of Tsg101 ( a subunit of the ESCRT-I complex ) or Alix ( an ESCRT-accessory protein ) in HeLa cells and monitored the production of infectious viral particles . Two distinct siRNA sequences targeting either Tsg101 ( siTsg101#1 and siTsg101#2 ) or Alix ( siTsg101#1 and siTsg101#2 ) were used in these experiments . A reduction of 94% ( ± 6% ) and 95% ( ± 6 . 5% ) in the expression of Tsg101 was achieved using siTsg101#1 and siTsg101#2 , respectively . For Alix expression , a respective reduction of 80% ( ± 6% ) and 81% ( ± 12% ) was achieved using either siAlix#1 and siAlix#2 . Depletion of Tsg101 , decreased the amount of infectious viral particles released in approximately 53% in cells treated with siTsg101#1 , and ~37% in cells treated with siTsg101#2 ( Fig 5A ) . Alix KD also decreased the total amount of infectious viral particles released by HeLa cells , a reduction of approximately 80% for siAlix#1 and 77% for siAlix#2 ( Fig 5B ) . Although knocking down Tsg101 or Alix did not block OROV entry process ( S6 Fig ) , the depletion of either protein resulted in a significant reduction ( ~42% in both cases ) in the area of the OROV vesicular structures observed at 24h p . i . ( Fig 6A–6G ) . Finally , immuno-TEM analysis of Vfs labeled with anti-OROV antibody , revealed that depletion of either Tsg101 or Alix leads to smaller viral compartments that contain a significantly lower number of intraluminal viral-like particles , compared to cells transfected with control siRNA ( Fig 6H–6L ) . Together these results indicate that the activity of Tsg101 and Alix are required for efficient OROV Vf morphogenesis and proper viral particle assembly . The experiments involving Alix depletion suggest that this protein may be recruited to Vfs for OROV replication . Alix dimerization was shown to be crucial for its association with endosomal membranes [30] , and for mediating HIV budding [16] . To obtain further evidence for a role of Alix during OROV assembly , we used the bimolecular fluorescence complementation ( BiFC ) technique [31] , in which the N- and C-terminal halves of a fluorescent protein are fused to Alix [30] . In non-infected cells , Alix-dimer/BiFC signal is mostly separated from TGN46 signal ( Fig 7A–7E and S1 Table ) . In contrast , analysis of Alix-dimer formation in OROV infected cells by confocal microscopy revealed that Alix-dimers are strongly recruited to TGN46-positive structures at 24 h p . i . ( Fig 7F–7J and S1 Table ) . Indeed , by 3D-SIM imaging , we confirmed that Alix-dimer signals clearly colocalize with TGN46 signals in OROV-positive structures ( Fig 7K–7O and S3 Movie ) , and Alix-dimer/TGN46 colocalization is specifically induced by OROV infection ( S1 Table ) . These results suggest that the ESCRT machinery components are recruited to Golgi/TGN membranes during OROV infection and provide additional evidence for a role of Alix in the biogenesis Oropouche Vfs . Taken together , our results show a novel critical requirement of the ESCRT machinery in post-entry events involved in the assembly and/or release of orthobunyaviruses .
The Peribunyaviridae is a large order of RNA viruses , which are widely distributed and may cause severe diseases in humans and cattle . However , prior to this work , there was no characterization of the cellular factors acting in assembly and budding of Bunyavirales in general . In this study , we analyzed the assembly pathway of the OROV , a neurotropic orthobunyavirus [32–34] that causes a frequent arthropod-transmitted viral disease in Latin American countries , with more than 500 , 000 cases confirmed in Brazil only [3–5] . We show that OROV induces the enlargement of Golgi cisternae , to where ESCRT machinery elements are recruited and support virion budding toward the lumen of this modified membrane-enclosed compartment . For most Bunyavirales , viral particle formation is believed to start at the Golgi membranes , where the viral envelope glycoproteins ( Gc and Gn ) , reaching this organelle from the ER , are retained [35] . At the Golgi membranes , the Gn cytosolic tail is thought to associate with viral ribonucleoproteins [36] comprised of the viral RNA segments , the RNA polymerase , and the nucleocapsid N-protein . In fact , studies with Bunyamwera virus , an orthobunyavirus considered a prototype of the genus Orthobunyavirus , indicated that the Golgi complex is the central organelle where viral particle assembly starts [9] . Further TEM and 3D reconstruction analyses of Bunyamwera virus infected baby hamster kidney ( BHK-21 ) cells revealed the formation of large and complex Vfs at the juxtanuclear region [7] . It was determined , that these Vfs are composed of repetitive units constituted by one or more Golgi stacks , mitochondria , RER cisternae and virus-induced tubular membrane structures linking these organelles [7] . Here we provide evidence that OROV induces the formation of similar Vf structures in HeLa cells . However , OROV proteins do not appear to accumulate at the perinuclear region during the replication cycle . Rather , as infection progresses , OROV proteins concentrate in vesicular structures scattered throughout the cytoplasm ( Fig 1 ) . These structures are derived from enlarged Golgi cisternae ( Fig 3 ) and are enriched in Golgi and TGN membrane marker proteins ( Figs 2 and 3 ) . Moreover , ER membrane domains appear to be actively recruited to the vicinity of these vesicular structures ( S1 Fig ) . These results suggest that modified Golgi cisternae , in particular , the trans sub-compartments , are released from the Golgi stack and nucleate as physically separated Oropouche Vf units . The results of this work also demonstrate a novel role for ESCRT machinery components in OROV Vf morphogenesis and virus production . The activity of ESCRTs in virus replication was initially identified , and is best characterized , for retroviruses [37–40] . Specifically , HIV-1 Gag can interact with both Alix and Tsg101 , and use these proteins to recruit later ESCRT components to the plasma membrane for budding [10] . In fact , Tsg101/Alix co-depletion or impairment of Vps4 activity block HIV-1 budding [41 , 42] . Currently , the requirement of ESCRTs for the efficient assembly and cell egress has been demonstrated for many other viruses [11 , 40 , 43–48] . However , a role of ESCRTs in Bunyavirales assembly and replication remained unknown . In this study , we provide evidence that ESCRT machinery components are recruited to OROV replication sites in HeLa cells . First , we observed that endogenous HRS ( an ESCRT-0 subunit ) , normally localized to early endosomes , appears to concentrate at viral replication sites ( S2 Fig ) . Moreover , overexpressed Vps4wt , which is typically dispersed in the cytosol ( S3 Fig ) , labels puncta structures that are also co-stained for OROV proteins and TGN46 in OROV infected cells ( Fig 4 and S4 Fig ) . These virally induced Vps4wt-positive compartments were the sites of OROV N-protein accumulation and are often found in the vicinity of dsRNA , a component of the viral replication complex ( S5 Fig ) . Although possible , the colocalization observed between either HRS or Vps4 , and OROV proteins is unlike to represent mere leakage of OROV to endosomal compartments , because OROV staining display poor overlap with staining for markers for lysosomes ( Lamp1 ) , late endosomes/MVBs ( CD63 ) , recycling endosomes ( transferrin ) and for another early endosome protein ( SNX2 ) ( S2 and S3 Figs ) . Further evidence for ESCRT recruitment to Vfs was provided using the ATPase-defective Vps4E/Q mutant . This mutant is still able to bind ESCRT-III and to associate with ESCRT-III positive membranes , but is unable to dissociate the complex [14] . Consistently with a role for the ESCRT machinery in OROV assembly , Vps4E/Q localization is strongly shifted to OROV replication sites where it colocalizes with TGN46 ( Fig 4 ) . Finally , using BiFC assays , we show that in OROV infected cells Alix-dimers assemble at TGN46-positive structures that are enriched in OROV proteins ( Fig 7 ) . Alix-dimer formation is crucial for Alix function as an ESCRT-accessory protein and generally takes place at endosomal membranes [30] , but not at Golgi compartments ( Fig 7 ) . Besides implicating the ESCRT machinery in Bunyavirales assembly processes , these data are also relevant because they provide new evidence that ESCRT proteins may be relocated to Golgi compartments during viral replication . Interestingly , the ESCRT machinery has been recently reported to be recruited to the ER during the replication cycle of flavivirus [49] , showing that the PM and endosomes are not the exclusive sites of ESCRT-activity during viral replication . The function of ESCRT machinery elements is most likely required for OROV assembly and egress . Disturbing the ESCRT-III recycling via overexpression of an ATPase-defective Vps4 mutant leads to an enlargement of Vfs ( Fig 4 ) , indicating the importance of proper Vps4 enzymatic activity during OROV assembly . Moreover , depletion of either Alix or Tsg101 compromised Oropouche Vf formation , leading to smaller viral vesicles containing a lower number of intraluminal viral-like particles ( Fig 6 ) . Additionally , plaque forming units assays showed that the amount of infectious OROV particle production was reduced in cells depleted of either Tsg101 or Alix . Importantly , these detrimental effects observed for either Tsg101 or Alix depletion were not due a blockage in virus entry ( S6 Fig ) . Therefore , our data strongly indicate that OROV requires ESCRT components for Vf biogenesis and the formation of infectious viral particles . To conclude , this study reveals an additional function for the ESCRT machinery and contributes to clarify and amplify the current understanding of Bunyavirales replication cycle . Moreover , we identify a novel strategy of how viruses usurp and dysregulate the host cell machinery for efficient morphogenesis and egress .
Vero C1008 cells ( American Type Culture Collection , Manassas , VA ) and a previously described HeLa cell line ( HeLa-I ) that expresses ICAM-I on the cell surface [50] , were maintained in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS , Thermo Scientific , Rockford , IL ) , L-glutamine and penicillin-streptomycin solution ( Thermo Scientific ) . Cells were incubated at standard conditions ( 37°C with 5% CO2 ) . OROV strain ( BeAn19991 ) was propagated in Vero cells grown in DMEM supplemented with 2% FBS , L-glutamine and penicillin-streptomycin solution . Virus titers were measured by TCID50/mL assay as previously described [51] . Subconfluent monolayers of HeLa-I cells were incubated with OROV stocks ( MOI 1–3 ) for virus adsorption for 2 h on a rocker at 4°C . Monolayers were washed with ice-cold PBS and incubated with 0 . 5 ml of DMEM with 2% FBS in 5% CO2 at 37°C . At different times post infection ( 0 , 1 , 3 , 2 , 4 , 6 , 8 , 12 , 18 , 24 and 48 hours ) harvested cells and supernatants from triplicate monolayers were collected and used for quantification of viable virus by TCID50/mL and protein expression by immunoblotting . The pEGFP-C2 Vps4A-wt and pEGFP-C2 Vps4A-E/Q ( E223Q ) plasmids were gifts from Philip Woodman ( University of Manchester , United Kingdom ) . The pSS-YFP-KDEL ( encoding the signal sequence of prolactin ) plasmid was a gift from Jennifer Lippincott-Schwartz ( NICHD , NIH , EUA ) . The pCD63-EGFP plasmid was kindly donated by Juan Bonifacino ( NICHD , NIH , EUA ) . To generate the pmCherry-N plasmid , the OROV N-protein ORF was firstly amplified from pTVTOROVS ( Acrani et al . , 2015 ) , a generous gift from Gustavo Acrani ( Universidade Federal da Fronteira Sul , Brazil ) , and cloned into pmCherry-C2 ( Takara Bio USA , Mountain View , CA ) . To prevent translation of NSs , which is encoded from a downstream AUG initiation codon on the same mRNA transcript as the N protein , we performed site-directed mutagenesis to introduce a T24C silent mutation that removes this alternative AUG in the pmCherry-N plasmid . For BiFC experiments , we used the pcDNA3 . 1/Zeo-VCt-Alix [52] and the pcDNA3 . 1/Zeo-VNt-Alix plasmid , generated as follows: the Alix open reading frame ( ORF ) was removed from pcDNA3 . 1/Zeo-VCt-Alix and used to replace the leucine zipper ( LZ ) sequence in the pcDNA3 . 1/Zeo-VNt-LZ [53] . This resulted in the VNt sequence ( residues 1 to 158 of Venus protein ) fused to a spacer sequence ( GGGGSGGGGSSG ) , followed by the Alix sequence . HeLa cells transfections were performed using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) , as follows . For the pSS-YFP-KDEL and pCD63-EGFP plasmids , cells were first transfected and incubated for 12h for the proteins to reach a stead state and this was followed by infection with OROV ( MOI = 3 ) for the times indicated in the legends . For the plasmids pEGFP-C2 Vps4A-wt and pEGFP-C2 Vps4A-E/Q cells were initially infected with OROV ( MOI = 3 ) during 6 h to allow virus entry and viral protein expression , and then transfected and analyzed after 24 h p . i . The transfection of Alix-VNt and VCt-Alix plasmids were performed after 16 h p . i . ( MOI = 3 ) and cells were harvested and analyzed after 24 h p . i . siRNA were purchased from Sigma-Aldrich as nucleotide duplexes with 3′dTdT overhangs , designed to target human Tsg101 sequence #1 ( 5’-CCUCCAGUCUUCUCUCGUC-3’ ) , Tsg101 sequence #2 ( 5’- CUCAAUGCCUUGAAACGAA-3’ ) , Alix sequence #1 ( 5’-GCAGUAAUAUGUCUGCUCA-3’ ) , and Alix sequence #2 ( 5’-GAACAAAUGCAGUGAUAUA-3’ ) . The MISSION siRNA Universal Negative Control ( SIC001 , Sigma-Aldrich ) was used in control experiments . HeLa cells were subjected to one round of siRNA ( 20 nM ) transfection using Oligofectamine reagent ( Invitrogen , Carlsbad , CA ) , according to the manufacturer’s instructions . After 24 h of siRNAs transfection , cells were infected with OROV ( MOI = 1 ) and then collected for the analyses after 24 h p . i . The mouse anti-OROV antiserum ( kindly donated by Dr . Luis Tadeu Moraes Figueiredo ) was used for immunofluorescence , western blot , and immunoelectron microscopy assays . Rabbit polyclonal antibodies to Calnexin-2 ( H-70; Santa Cruz , CA ) , Giantin ( Covance , NJ ) and Lamp-1 ( Cell Signaling Technology , MA ) , sheep polyclonal antibody to TGN46 ( AbD Serotec , Oxford ) , and J2 mouse monoclonal antibody ( Mab ) anti-double-stranded RNA ( dsRNA; English & Scientific Consulting Kft , Hungary ) , were used for immunofluorescence assays . Polyclonal goat antibodies to Alix ( N-20; Santa Cruz Biotechnology , CA ) , and mouse monoclonal antibodies to Tsg101 ( BD Bioscience , CA ) , GAPDH ( Sigma Aldrich , SG ) and Actin ( Ab-5; BD Bioscience , CA ) were used for immunoblot experiments . Secondary antibodies conjugated to Alexa-fluorophores as indicated in the figure legends were purchased from Thermo Scientific ( Rockford , IL ) . Horseradish peroxidase-conjugated donkey anti-mouse immunoglobulin G ( IgG ) , donkey anti-rabbit IgG , and donkey anti-goat IgG were obtained from GE Healthcare . Supernatant samples from knockdown assays were collected after 18 h p . i . and centrifuged by 3 . 000 g 10 min°C . Then , those samples were equalized according to the total amount of cellular proteins , measured by Bradford assay . Titration was performed on Vero E6 cells seeded at an 80–90% confluence in a 24-well plaque . Cells monolayer were infected with serial dilutions of virus samples for 1 hour at 37°C under slight agitation and then overlaid using 0 . 3% agarose in DMEM 2% FBS . 72 h post-infection , cells were fixed with 10% formaldehyde and stained using crystal violet to visualize plaques . Plaques were counted , and virus yield was calculated and expressed as PFU/ml . Cells were seeded on 13-mm-diameter coverslips and fixed at the indicated times p . i . and processed for Immunofluorescence assay as previously described [54] . For dsRNA detection , using J2 antibody , samples were processed according to [55] . To detect recycling endosomes , we incubated serum starved OROV-infected HeLa cells with transferrin-Alexa 488 ( life technologies ) at 20 μg/mL in Opti-MEM for 30 min at 4°C . Then , cells were washed with ice-cold PBS to remove non-attached transferrin and incubated for an additional 1 h at 37°C . Cells were analyzed on a Zeiss confocal laser scanning microscope ( LSM ) 780 ( Zeiss , Jena , Germany ) or a Leica TCS SP5 laser scanning confocal microscope ( Leica Microsystems , Wetzlar , Germany ) . Post-acquisition image processing and colocalization analysis were accomplished as previously described [52] . To determine the area of Vfs we used the analyze particles tools of the Fiji software [56] , setting the lower and upper threshold levels of each image to 30 , 000 and 63 , 000 , respectively . Alternatively , fixed cells images were acquired by a DeltaVision OMX SR system ( GE Healthcare Life Sciences , Issaquah , WA , USA ) by structured illumination microscopy ( SIM ) . After deconvolution , projection of z-stacks ( 0 , 125 μm interval each ) , 3D sections or 3D reconstruction images were analyzed by Fiji software [56] . Total cell lysates were prepared , equalized for total protein levels , and used for SDS-PAGE and Western blot , as described previously [52 , 57] . To obtain virus lysates , cell culture supernatants were firstly clarified by centrifugation ( 2 , 000 x g at 4°C for 10 min ) and then subjected to ultracentrifugation at 100 , 000 x g for 2 hours at 4°C through a 20% sucrose cushion . The pellets containing viruses were mixed with sample buffer [57] , and proteins were resolved by SDS-PAGE followed by Western blot . Blots were probed with primary antibodies and HRP-conjugated secondary antibodies . Proteins in the blots were visualized using enhanced chemiluminescence solutions ( GE Healthcare ) and the ChemiDoc Imaging System equipped with the ImageLab software ( Bio-Rad Laboratories , CA ) . HeLa cells grown in 6-well plates were infected with OROV ( MOI = 1 ) and collected at 18 h and 24 h p . i . Cells were then fixed in 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer ( pH 7 . 4 ) for 1 h and routinely processed for Electron Microscopy analysis as described further below . For pre-embedding immunoelectron microscopy , cells were processed as previously described [58] . Briefly , cells were fixed by microwave irradiation in 0 . 05% glutaraldehyde plus 4% formaldehyde in 0 . 1 M cacodylate buffer ( pH 7 . 4 ) and subsequently immunolabeled with anti-OROV antibody and with goat anti-mouse IgG conjugated to nanogold ( Nanoprobes ) . Cells were then fixed with 2 . 5% glutaraldehyde in cacodylate buffer for 1h before the nanogold was enhanced using GoldEnhance Electron Microscopy Plus ( Nanoprobes ) according to the manufacturer’s directions . In all TEM experiments cells were post fixed in 1% reduced OsO4 ( Electron Microscopy Sciences ) in 0 . 1 M cacodylate buffer ( pH 7 . 4 ) , rinsed in Milli-Q water , and dehydrated in a graded ethanol series . Cells were removed from the tissue culture plates with propylene oxide and embedded in EMBED 812 ( Electron Microscopy Sciences ) . Thin sections were cut with a diamond knife , mounted on copper grids , and stained in Reynolds’s lead citrate and 0 . 5% aqueous uranyl acetate . Cells were imaged using a JEOL JEM-100CX II transmission electron microscope ( JEOL USA , MA , EUA ) . To determine the area of Vfs we used the analyze particles tools of the Fiji software [56] . First we selected freehand tools to delimitate the viral vesicle , and then used measure tool to measure the vesicle area . All statistical data are demonstrated as mean ± SEM from at least three independent experiments ( as indicated in each analyses ) . Statistical significance was calculated using unpaired t-test or One-way ANOVA followed by Bonferroni’s post-test , as indicated . P values are as displayed as follows: *P<0 . 05; **P<0 . 005; ***P<0 . 0005; ns , not significant . By convention , differences were considered statistically significant at P<0 . 05 . | The Peribunyaviridae is a large order of RNA viruses that are globally distributed and may cause human diseases . In this study , we analyzed the assembly pathway of Oropouche virus ( OROV ) , a peribunyavirus of the orthobunyavirus genus that is the etiologic agent of a frequent arthropod-transmitted viral disease in Latin American countries . OROV is neurotropic and causes a debilitating febrile illness , which can progress to meningitis and/or encephalitis in some patients . We show that assembly of OROV involves budding of virus particles toward the lumen of Golgi cisternae . During this process , endosomal sorting complex required for transport ( ESCRT ) components are hijacked to Golgi membranes to participate in the morphogenesis of viral factories and are required for efficient release of infectious OROV particles . Although many enveloped viruses have evolved to usurp elements of the ESCRT machinery for assembly and budding , the data provides unprecedented evidence that ESCRT proteins may be relocated to Golgi compartments during viral replication , and that this machinery is required for assembly/egress of an Orthobunyavirus . | [
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"e... | 2018 | ESCRT machinery components are required for Orthobunyavirus particle production in Golgi compartments |
Anatomical substructures of the human brain have characteristic cell-types , connectivity and local circuitry , which are reflected in area-specific transcriptome signatures , but the principles governing area-specific transcription and their relation to brain development are still being studied . In adult rodents , areal transcriptome patterns agree with the embryonic origin of brain regions , but the processes and genes that preserve an embryonic signature in regional expression profiles were not quantified . Furthermore , it is not clear how embryonic-origin signatures of adult-brain expression interplay with changes in expression patterns during development . Here we first quantify which genes have regional expression-patterns related to the developmental origin of brain regions , using genome-wide mRNA expression from post-mortem adult human brains . We find that almost all human genes ( 92% ) exhibit an expression pattern that agrees with developmental brain-region ontology , but that this agreement changes at multiple phases during development . Agreement is particularly strong in neuron-specific genes , but also in genes that are not spatially correlated with neuron-specific or glia-specific markers . Surprisingly , agreement is also stronger in early-evolved genes . We further find that pairs of similar genes having high agreement to developmental region ontology tend to be more strongly correlated or anti-correlated , and that the strength of spatial correlation changes more strongly in gene pairs with stronger embryonic signatures . These results suggest that transcription regulation of most genes in the adult human brain is spatially tuned in a way that changes through life , but in agreement with development-determined brain regions .
The human brain is organized in a hierarchy of multiple substructures , whose cell composition and circuitry are believed to allow each substructure to carry out its distinct function . While physiological and histological differences and similarities between structures have been intensively studied [1–4] , the molecular profiles giving rise to those differences are far from being understood . Specifically , it is not known which principles govern the expression patterns of genes across the adult brain and what determines their spatial organization . Recent high-resolution genome-wide transcriptome profiling studies allow addressing these questions [5 , 6] . The current paper explores the role of development in determining adult expression patterns . In early development of the vertebrate nervous system , the posterior part of the neural tube develops into the spinal cord , and its anterior part divides into three primary vesicles: the prosencephalon , the mesencephalon and the rombencephalon . The prosencephalon further develops into two secondary vesicles: the telencephalon and the diencephalon . The most posterior vesicle , the rombencephalon , forms two secondary vesicles as well , the metencephalon , and the myelencephalon . These five vesicles are aligned along the rostral-caudal axis of the developing brain and establish the primary organization of the central nervous system ( Fig 1A ) [7] . During early development , several gene families exhibit distinct spatial expression patterns [8] , including , for example , genes involved in axon guidance and in segmentation and compartmentalization of brain regions [9 , 10] . Genes including netrins , semaphorins , and ephrins function as molecular signatures , guiding axons to form long-range connections [11–13] . Patterning genes [14] , like the Hox gene family , play a crucial role in forming brain regions [15] . Some genes , including Hox genes , were shown to retain unique expression patterns related to tissue specificity across the adult body [16–18] . Many other genes , change their expression patterns during development , both before or after birth [19–21] . However , for most genes that are not directly involved in brain development , it is usually not known which processes determine their spatial expression pattern across the adult brain . Zapala and colleagues [22] studied areal expression across the adult mouse brain by clustering brain regions based on the expression levels of ~2000 genes . They found that the pattern of gene expression in a brain region was correlated with the position of that region along the anterior-posterior axis of the neural tube . Regions with similar expression profiles often shared a common embryonic origin . One possible explanation of this finding would be that brain regions sharing an embryonic origin contain similar cell-types , which in turn express shared gene markers . For example , pyramidal neurons are the main excitatory neurons both in the neocortex and in the hippocampus . The agreement between embryonic origin and expression similarity may therefore be explained by a small number of cell-type specific gene markers . Indeed , French et al . have shown that regions with similar expression profiles tend to have similar neuronal connectivity patterns [23] . Ko et al . have shown that neuron-specific and astrocyte-specific gene markers show distinct patterns of expression across brain regions [24] . Grange et al . have estimated the spatial densities of 64 cell types in the mouse brain and identified genes with a localized pattern of expression [25] . The question remains however , how genome-wide is the agreement of expression-pattern with embryonic origin and how does this agreement develop during the lifetime of an organism . It is also not clear to what extent expression differences are pronounced between sub regions of the major brain structures . Specifically , the cortex is sometimes viewed as largely homogeneous in various properties , including neuronal density , connectivity patterns and distribution of cell-types , properties which were shown to be consistent across cortical sub-regions [4 , 26] . Also , cortical microcircuits can be induced to perform processing tasks that are naturally performed in other regions [27–30] . These views are in agreement with the results of Ko et al . [24] , where clustering brain voxels based on their expression profile showed that the cortex is significantly different from other structures , but is largely homogeneous by itself . However , the level of homogeneity of cortex expression patterns has not been quantified directly so far . To quantify the relation between expression patterns across the adult human brain and the embryonic origin of the corresponding brain regions on a gene-by-gene basis , we analyze two genome-wide mRNA expression datasets . For each gene separately , we computed an index that measures how its expression pattern in the adult agrees with a brain-region ontology based on embryonic brain development . Surprisingly , we found that almost all genes exhibit a spatial expression pattern that significantly agrees with the brain-region ontology . This effect is particularly strong in neuron-specific genes as expected , but also in many genes that serve more generic functions . This suggests that the brain tunes the areal expression pattern of genes in a way that strongly depends on embryonic development , and this holds even for genes that participate in brain-wide functions . Furthermore , pairs of genes sharing related functions tend to be spatially more anti-correlated if their expression pattern agrees with the brain-region ontology .
We evaluated how expression of each individual gene across the brain agrees with an ontology of brain regions provided by the Allen Institute ( http://human . brain-map . org ) [31] . This ontology is coarsely based on brain development , covering both developing and adult human brain structures . The fine structure of the ontology , including cortical parcellation , is based on classical cytoarchitecture . Instead of analyzing expression variability across regions using a ‘flat’ representation of regions , the ontology allows to take into account the ‘structured’ similarities among regions stemming from the shared embryonic origin of regions . We used the full tree ontology which contained 1534 brain regions . Brain samples from the ABA6-2013 dataset were associated with 414 ontology regions , and those from the Kang-2011 dataset were associated with 16 regions of the ontology ( see Methods S2 and S3 Tables ) . Fig 1A depicts the region-ontology tree at a coarse resolution for visualization purposes . Nodes in the tree are colored blue-to-red , roughly corresponding to position of regions on the anterior-posterior axis . Fig 1B depicts the same regions on a 3D model of a human brain using the corresponding colors . In the mouse brain , regions that share similar expression patterns group in a way that matches the region ontology [22 , 32] . To test if these result are reproduced in human , we clustered brain regions based on the full-genome expression profile ( see Methods ) . The resulting clustering agrees with the findings in the mouse . Fig 1C shows the hierarchical clustering of 16 brain regions from ABA6-2013 , where brain regions with a common developmental origin , share similar expression patterns . However , this clustering depends on the joint expression patterns of all genes , and the question remains: which genes and processes contribute to this effect ? To quantify how the expression pattern of each individual gene agrees with the region ontology , we defined an index , which we call Brain-Region Ontology agreement score ( BRO-agreement score ) , calculated as follows . For a given gene , we consider all pairs of tissue-samples , and for each pair we computed two measures of distances . The first , expression distance , is the absolute difference of expression values in the two samples . The second , ontology distance , is the distance between the corresponding regions in the ontology tree . The BRO score for each gene is defined as the spearman correlation between the two distances computed for all tissue pairs . It provides a measure of the agreement between expression difference and ontology distance ( for more details see Methods ) . We also tested a second index based on triplet ranking which yielded similar results ( see Methods ) . To illustrate the use of the BRO score , consider the expression pattern of the gene NEUROD1 , a transcription factor involved in regulation of brain development . Fig 1D depicts the joint distribution of the ontology distance and the expression distance computed for NEUROD1 expression measured at all tissue pairs . The two distance measures are strongly correlated , with a BRO score of 0 . 65 , suggesting that the variability in the expression of NEUROD1 across the adult brain is largely explained by the position of the region in the development region ontology . We computed the BRO index for every gene in the ABA6-2013 dataset based on a fine-resolution ontology of 414 brain regions ( see Methods ) . To assess significance , the BRO score of each gene was compared to a randomized BRO score obtained by permuting the expression profile of the gene across regions . We find that 92% of genes significantly agree with the brain-region ontology more than random ( FDR–corrected , q-value < 0 . 01 ) . Using the triplets score ( see Methods ) , 95% of the genes in this dataset were significant . This surprisingly-high fraction suggests that most genes have distinct areal expression patterns across the adult brain , and that these patterns are largely determined by the embryonic origin of the brain regions they are expressed in . To test reproducibility and robustness of these results , we compare the BRO index obtained for every gene in the two microarray datasets ( see Methods ) , covering a total of 26 postmortem brains . Fig 2A shows the joint distribution of BRO-agreement scores ( light dots ) computed in ABA6-2013 ( abscissa , 414 regions ) and Kang-2011 ( ordinate , 16 regions ) . It also shows the distribution of the baseline random distribution that was generated using permutation test ( Fig 2A , dark dots , see Methods ) . We further tested that the effect was robust across subjects ( see Methods and supplemental S7 Fig ) and compared the results obtained with the BRO index to those obtained with ANOVA ( see Methods ) . In the Kang-2011 dataset , Fig 2A and 2D , 66% of the genes have significant BRO scores ( same as with the triplets method ) . The BRO scores for the two datasets are significantly correlated across the two datasets ( Spearman ρ = 0 . 53 , p-value < 10-16 , n = 16947 , Fig 2A ) . The differences between the two datasets in the fraction of genes that reach the significance threshold is due to the smaller number of tissue samples in Kang-2011 and due to the limited coverage of non-cortical regions , since only 144 samples out of 491 ( 29% ) were from non-cortical regions in Kang-2011 , compared to 2187 out of 3702 ( 59% ) in ABA6-2013 . See full details in supplementary S1 Table . We provide BRO scores for all genes as a supplementary data file ( S1 Data ) . We also quantified BRO scores in mouse , but due to multiple differences between the datasets , direct comparison of the results is hard ( S1 Text ) . Variability across regions and samples may be due to tissue-wide effects such as fluctuations in sampling , biases in cell density or variability in cell-type proportions , and these may be correlated with the developmental ontology . To test for a possible effect of cell density fluctuations across samples , we repeated the experiment after scaling each sample by its mean expression across genes . Fluctuations in the mean expression across samples were small ( coefficient of variation , CV = std/mean = ~2 . 5% ) and did not have a significant effect on BRO scores ( Spearman ρ = 0 . 997 comparing with and without scaling ) . Similar results were obtained when normalizing to a set of highly-stable genes ( see Methods ) ( CV = ~3% ) . This type of normalization is effective when expression of the gene is highly stable and the relation between cell density and number of transcript is largely linear . As a second control , we aimed to test if BRO scores simply reflect the proportions of glia-neuron mixture , which varies across the brain . Since estimating cell-type proportions is challenging [25] , and not yet available in human , we tested the relation between BRO-agreement scores and correlation with known cell-type specific markers , by quantifying how well the spatial variability of a gene can be explained by known neuronal and glial markers ( see Methods ) . For neuronal markers , the median explained variance was 0 . 14 ( median absolute deviation of 0 . 12 , maximum explained variance = 0 . 26 ) , and similar results were obtained with glial markers . One interpretation that is consistent with these finding , is that the mixture proportions of cell types in a sample , based on the markers available to us today , has a limited explaining power of spatial variability . It should be noted however , that cell type specific markers have both a limited sensitivity and limited specificity , and do not reflect perfectly cell type proportions . These results suggest that BRO-agreement is not a mere reflection of neuron-to-glia mixture proportions . The transcriptome of the cerebellum is known to differ substantially from that of the rest of the brain [5 , 21 , 33] . This is apparent in Fig 1C , where the cerebellum is well separated from other regions . To test how strongly the cerebellum contributes to the high BRO agreement scores , we recomputed the scores while excluding cerebellar samples . Even with cerebellar samples excluded , 90% of genes are BRO-significant . An intuition for the robustness of the BRO can be gained from a principle component analysis ( PCA ) in a subsequent section . The non-cerebellar structures are aligned along an anterior-posterior dimension when projected onto the first two principal components . The order along this axis agrees with their place of origin in the neural tube and is captured by the BRO-score . The BRO-agreement score reflects how strongly adult expression pattern of a gene may be driven by the ontology , which was coarsely based on brain development . It is natural to test how BRO-agreement scores change during life . We therefore computed the per-gene BRO-agreement scores in subjects of multiple ages based on the data of [6] . Samples were grouped as in [6] , and covered postnatal and embryonic age groups starting at 13 post-conception week , all having a common set of brain regions . When considering ages from late embryonic development to adulthood , the mean BRO score follows an “hourglass” pattern , with BRO scores being lowest around birth ( Fig 2E ) . This developmental pattern is in agreement with previous studies which analyzed aerial variability in the mouse [21] and human [19] brain . Based on these previous studies , the elevated BRO scores in embryonic development are due to higher areal variability in neural and brain development functions , while the postnatal rise in BRO scores is due to elevated variability in signaling and plasticity functions . The life-long BRO-agreement profile has two major differences from previous studies , namely , the BRO scores of both very early and very late stages are significantly lower , deviating significantly from the hourglass pattern . These differences are captured here because the scores are computed separately for very early embryonic and very late-life stages . What genes and functions achieve the highest BRO scores ? To answer this question , we first tested functional enrichment of gene ontology ( GO ) categories [34] . We used a threshold-independent approach based on ranking genes by their BRO ( mHG [35] ) . The top enriched biological processes ( S4 Table ) are all brain related , and mainly belong to two families of functions: cell-to-cell signaling like synaptic transmission , and development-related categories like neuron differentiation and neurogenesis . The first family , genes in cell-signaling categories , included both genes from generic signal transduction pathways and receptors of more specific neuromodulator systems . For instance , the two genes with highest BRO score in synaptic transmission were RASGRF2 –which coordinates activation of MAPK signaling , and CAMK2A—which is involved in calcium signaling ) . The 3rd and 4th genes in that category were genes coding for serotonin receptors HTR2A and HTR4 . Finally , some of the top ranked genes are related to brain-related disorders: G-protein signaling regulator RGS4 , serotonin receptor HTR2A and the postsynaptic protein encoder–NRGN are related to schizophrenia . HTR2A , CHH9 are related to autistic disorders . To understand better which genes achieve the highest BRO scores , we further studied gene families and functions that are of particular interest: genes expressed in specific cell types , and genes involved in regulation of brain development . First , it has been suggested that genes that are expressed distinctively in specific cell types , contribute significantly to expression differences among brain regions [24] . Specifically , the combined expression of neuronal gene markers was shown to correspond to the major subdivisions of the brain [24] . Cahoy et al [36] identified genes that are enriched in specific cell types in the mouse brain and can be used as cell-type specific markers . Operating under the assumption that the human orthologs of those markers have preserved spatial expression patterns , their BRO-scores provide a way to test quantitatively how strongly neuron-specific genes agree with the brain region ontology . Fig 3A depicts three subsets of cell-specific gene markers , all of which are in particularly strong agreement with the tree structure: markers for neurons , astrocytes and oligodendrocytes . Cell-type specific genes have higher BRO scores than other genes on average ( Fig 3A bottom ) . Interestingly , neuron-specific gene markers agree more strongly with the region-ontology than the average gene ( p-value < 10−70 , Wilcoxon comparing the median of the distributions of BRO scores of neuron-specific genes , median = 0 . 33 and the general population of genes , median = 0 . 11 ) while oligodendrocytes-specific and astrocyte-specific markers are far less so ( oligodendrocytes median = 0 . 19 , Wilcoxon , p-value = 10−5 , astrocytes median = 0 . 16 , Wilcoxon , p-value = 10−3 ) . These results are in agreement with Ko et al . [24 , 37 , 38] , which showed that the combined expression pattern of genes that are cell specific agrees with the region-ontology . The analysis above extends their results by showing that the region-ontology agreement occurs at the level of individual genes and is prevalent across cell-specific markers ( neurons: 270/271 which are 99% of the neuronal markers are BRO significant; astrocytes: 151/160 which are 94% of the astrocytes markers; oligodendrocytes: 103/106 which are 97% of the oligodendrocytes markers ) . Using the same set of markers , Tan et al . showed that genetic markers for neurons and oligodendrocyte are on the opposite ends of the first principal component [37] . This means that while neurons and oligodendrocyte are similar in that they both agree with the developmental ontology , they also show a very distinct pattern of spatial expression . Fig 3B depicts the distribution of BRO scores for three gene families involved in regional specificity during brain development: axon guidance genes , Hox genes , and Pax genes . Genes involved in early brain developmental have been shown to have regional expression patterns in the adult [39 , 40][23] . Here , we find that genes involved in axon guidance have very high BRO scores ( Higher than the average gene , Wilcoxon , median = 0 . 21 p-value = 10−7 ) ( Fig 3B bottom ) . This suggests that beyond their embryonic role , genes involved in axon guidance may assume other functional roles in the adult brain . Second , Hox genes play a major role in anterior-posterior patterning across the body and across the brain during development and largely retain these patterns in the adult body [16] . Their role in the adult brain is less clear . Here we find that many Hox genes have BRO scores above the random set , but on average , their scores are lower than the average gene ( Wilcoxon , median = 0 . 03 p-value = 10−9 ) . This suggests that unlike other gene groups discussed above , Hox genes are less involved in regional patterns in the adult brain . These view is also supported by Takahashi et al . which observed that while Hox genes show an expression gradient through the entire adult body , only one third of Hox genes are differentially expressed in brain-specific tissues [16] . Finally , we examined Pax genes . These genes are involved in early regionalization of the embryo brain and were suggested to play a role in differentiation and maintenance of specific subsets of cells in the adult brain [41 , 42] . It has been shown before that genes important for brain developmental have regional expression patterns in the adult [39 , 40] , including genes involved in brain connectivity [23] . Here we find that three Pax genes , PAX2 , PAX3 and PAX6 , obtain significantly large BRO scores ( Fig 3B ) . Interestingly , PAX6 is a major determinant of regionalization in the mammalian brain [9 , 10 , 42] . It was shown to be essential to cortex development , to mark cortex regionalization and to regulate radial migration of neuronal precursors [39 , 43 , 44] . The differential areal expression pattern of PAX6 in the adult raises the hypothesis that PAX6 continues to play a region-specific role in the adult brain . The above results suggest that spatial regionalization of human brain expression is present both in brain-specific functions and also in more generic processes that can be found in simpler organisms . Importantly , spatial regionalization of the nervous system is not unique to mammals or vertebrae , and some of the mechanisms controlling spatial patterning are shared across evolutionary-remote species [45] . For instance , Hox genes , whose expression exhibit anterior-posterior gradients in mammals , also hold spatial information in species that diverged from the human lineage early in evolution [46–48] . The natural question therefore arises: how is brain regionalization of a gene related to the evolutionary age of that gene ? For instance , one may hypothesize that genes with high BRO agreement would be genes those that evolved recently , in organisms having a nervous system similar to the mammalian brain . To test this hypothesis , we compare the BRO index with an index quantifying the evolutionary age genes [49] . Surprisingly , we found that evolutionary-older genes have on average higher BRO scores than evolutionary-recent genes ( Fig 4 ) ( Cellular organisms: median = 0 . 129; Primates: median = 0 . 068 , Wilcoxon p-value = 10−6 ) . These older genes are also active in signaling pathways and other basic functions in the cell ( S4 Table ) . The top BRO-scoring genes have orthologs across a wide variety of species , and participate in functions that are not specific to neural processes . Presumably , these genes were conserved as the result of a pressure to preserve these basic functions . For example , the gene ENC1 encodes an actin-binding protein involved in regulation of neuronal process formation and in differentiation of neural crest cells . As another example , CAMK2A is involved in calcium signaling as part of the NMDAR signaling complex . At the same time , CAMK2A has an early evolutionary origin and has orthologs even in rice . On the other range of the evolutionary timeline , genes associated with speciation of primates obtain lower BRO scores on average . These results suggest that genes with strong BRO scores and spatial patterns are not necessarily specific to neural processes , but rather that the brain spatially tunes the expression of genes involved in fundamental molecular functions . With that said , it is also possible that these newer genes exhibit more refined differences across brain regions , but that these changes are not captured by the current coarse-scale analysis ( also compare with [50] ) . Expression variability has many contributing factors , including subject-to-subject variability , regional variability and experimental noise . The above results suggest that the variability between brain regions is significant for most genes . But , how large is regional variability compared to other sources of expression variability ? To answer this question , we used principal component analysis ( PCA ) to extract the main axes of variability in the data ( see Methods ) . Interestingly , the PCA of the human expression was also analyzed previously by Tan et al . [31] . Tan et al . used PCA to embed genes in a low dimension space that preserved much of the gene-to-gene variability . In that space , they found that neurons and oligodendrocytes are on the opposite end of the first principal component . Here we address the complementary analysis , looking for the dimensions that preserve the sample-to-sample variability . Fig 5A shows the projection of brain samples onto the 1st and 2nd principal components , which together account for 34% of the variance ( S3 Fig ) . Samples are colored by the brain region from which they were taken . Brain regions are well separated in this projection , in a way that matches the anterior-posterior axis and the BRO . The isolated cluster of samples on the left belongs to the cerebellum , which is well known to exhibit a unique molecular and cellular organization [21 , 51 , 52] . This analysis shows that the BRO is a major determinant of variability in human brain transcriptome . As a comparison , Fig 5B shows the same projection on the two top PCs , but this time the samples are colored by the subject from which each sample was taken . Expression differences between people are pronounced mostly in frontal regions ( top right samples ) , but are dramatically weaker than the differences between brain regions . Subject-to-subject differences are more pronounced when projecting on the 3rd and 4th principal components ( S2 Fig ) . To quantify the relative contribution of subject identity and region of origins to expression variability , we computed the fraction of variance explained by these two features . For every gene , we examined it expression across samples separately and computed the fraction of explained-variance ( See Methods ) ( S8A Fig ) . The subject-identity explains 0 . 13 ( +/- 0 . 12 ) of a gene’s expression-variance , while the region explains 0 . 28 ( +/- 0 . 21 ) of a gene’s expression-variance . Together , both sources explain nearly half of the sample variability ( median at 0 . 43 +/- 0 . 18 ) . Region and subject identity explain “different” component of the variance: the fraction of variance explained by region is inversely correlated with the variance explained by subject id . ( Spearman ρ = -0 . 51 S8B Fig ) . Using the same dataset , Hawrylycz et al . recently took the complementary track and searched for stable expression-patterns across subjects [53] . They showed that genes with conserved patterning across subjects display strong relationships to anatomical structure , functional connectivity and other features of the human brain . With many genes exhibiting spatial expression that matches the developmental origin of brain regions , the question remains if and how expression variability is used by the brain to tune the functional properties of cells and circuits . One particularly interesting aspect of such tuning is how the brain controls the expression of similar genes , including paralog and other functionally-related gene pairs . In many cases , the brain is known to switch from expressing one paralog variant to another variant . Such switches have been studied mostly in the context of development and synaptic pathways , including the widely studied switch in NMDA receptors from subunit NR2A to NR2B [54–57] . These developmental switches can be traced to occur within a brain region , and in some cases well after birth [54 , 56] . Here we study spatial switching in pairs of genes , where genes coding for different protein variants are expressed in different brain regions . We set to study the relation between spatial expression switching and developmental origin of regions , using the per-gene BRO-agreement score . To study fine spatial tuning , we aimed to focus on pairs of genes that share similar functions . To collect such gene pairs we used two approaches . First , we used a set of paralog genes defined by Ensembl ( denoted ensemble-based paralogs ) . Second , to further focus on genes with putative similar function , we collected pairs of genes that share the same functional role in cellular pathways , as captured by KEGG . We also required that these gene pairs have a significant sequence similarity and denote this set Kegg-based pairs ( see Methods ) . Both sets were restricted to brain-related synaptic pathways . We first compared the distribution of spatial correlation strengths of similar gene pairs , quantified by log ( p-values ) on ABA6-2013 data . We found that both the KEGG-based gene pairs and the Ensembl-based paralogs are significantly more spatially anticorrelated than random gene pairs ( Fig 6A ) . Furthermore , KEGG-based gene pairs are more anticorrelated than Ensembl paralogs in these pathways ( Fig 6A ) . The spatial correlations of KEGG-based pairs are fairly consistent when compared to the correlation using the same gene pairs in adult brains from the Kang-2011 dataset , considering they were measured by different labs and in different brain regions ( Fig 6B ) . Next , we compared the strength of spatial correlation of each pair of genes with their combined BRO-agreement scores , and found a strongly significant correlation between the two ( KEGG-based set , Spearman with log ( p-value ) , ρ = 0 . 36 p-value < 10-42 , n = 1496 ) . More surprisingly , when considering anti-correlated gene pairs , pairs with high BRO-scores tend to be more strongly anti-correlated ( Spearman rho = -0 . 27 p-value<10−9 n = 1496 ) . This effect is not simply due to some pairs having more variable expression across the brain , since the dependency on BRO is significantly stronger ( p-value <10−42 ) than the dependency on spatial variability ( p-value < 0 . 01 , quantified using the standard deviation across samples ) . One interpretation of these findings is that the brain tunes the expression of pairs of functionally-related pairs of genes , such that they are expressed differently in brain regions , and that this tuning is in strong agreement with the developmental origin . Together with the BRO results , these findings suggest that correlated spatial expression may be formed early in development . To test this hypothesis we computed the spatial correlations for each subject in the Kang-2011 data , which allows tracing how spatial correlations develop with age . We then searched for KEGG-based pairs whose spatial correlations follow a trends , and found that 17% of the pairs exhibit a significant trend ( 257/1496 , FDR corrected p-value<0 . 01 , F-test from fitting a linear regression model , as compared with the constant model ) . Far fewer pairs of Ensembl paralogs exhibit a significant trend ( 6 . 8% of the pairs 240/3503 ) . Fig 6C shows the top pair in the KEGG-based pairs set ( p-value of trend < 10−4 ) . It corresponds to a pair of Serotonin receptor genes , HTR2A and HTR1F from two different receptors ( 5-HT1 , 5-HT2 ) . Interestingly , their spatial correlation is negative prenatally and is around zero around birth . However , it continuously grows throughout life , reaching high positive correlation at adulthood . This pattern is interesting for several reasons . First , the gradual increase in correlations throughout life is not likely to be caused by changes in cell proportions , since there is a significant change in correlation between childhood and adulthood . Second , other genes in the Serotonin system exhibit different patterns . Fig 6D shows a pair of genes coding for Serotonin receptors HTR5A and HTR2C . Here the early embryonic positive correlation is replaced by a negative correlation around birth , which remains quite stable during life . Fig 6E and 6F show similar patterns in two pairs of Ensembl paralogs , CACNA1A vs CACNA1D two genes coding for Calcium channels , and HTR7 vs . HTR5A , two serotonin receptors . HTR7 is known to be involved in both early and post-natal development [58] . One possible interpretation of the prevalence of high BRO-agreement scores is that the expression patterns of many are determined early in development , and are preserved through life and in the adult brain . Alternatively , it is also possible that gene-expression changes in a dynamic way through life , but keep following patterns that agree with the embryonic origin of regions . To test these two hypotheses , we quantified the relation between the strength of expression changes of pairs through life , and the BRO scores of the gene pair . We find that the two are positively correlated ( ρ = 0 . 14 , p-value<10–7 , S9A Fig , for KEGG-based pairs , and , ρ = 0 . 17 , p-value<10–21 , S9B Fig for Ensembl-based paralogs ) , namely , pairs of genes with higher BRO scores actually tend to exhibit more changes in their spatial correlations , consistent with the second hypothesis . These results are consistent with the view that spatial expression patterns in the adult are not a mere reflection of the brain structure as determined in early development , but are tuned to use genes coding for different protein variants in a differential way across the brain .
To characterize the areal patterns of gene expression in the human brain , we analyzed two datasets of mRNA expression from post-mortem adult donors . For each gene , we computed an index that measures how its expression pattern agrees with a hierarchical ontology of brain-regions , based on their developmental origin . We find that 92% of human genes exhibit an expression pattern that significantly agrees with the known brain-region ontology . The fact that such a large fraction of the human genome is differentially expressed across brain regions suggests that control of expression in the brain is largely region-specific . When focusing on genes that are expressed specifically in neurons , glia and oligodendrocytes , we find that cell-type specific genes tend to strongly agree with the tree-structured ontology . This suggests that not only do these markers differ between regions , as suggested by Ko et al . [24] , but that they also follow a specific pattern of expression which corresponds to the embryonic origin of the region and to a larger extent than the average gene . Interestingly , significant BRO scores are not limited to neurons , which are often known to differ across brain regions , but are also observed in glia-specific genes , which are often viewed as performing brain-wide and generic functions . Having adult expression patterns that strongly agree with the developmental brain region ontology could have various interpretations . First , adult spatial expression patterns could be determined by the embryonic origin of each region , for example because brain regions differ by their cell-type profiles , or due to the expression of region-specific markers . Alternatively , adult expression may reflect delicate tuning of expression where different brain regions utilize different protein variants , optimized for the function of each brain region . We find evidence that support the second alternative . First , gene with high BRO-scores tend to change their expression more during development . Second , pairs of functionally-related genes ( participating in a similar role in synaptic pathways ) have stronger spatial anti-correlation than paralogs in those pathways . Finally , in those pairs of functionally related genes , pairs with higher BRO scores tend to have stronger spatial anti-correlation . The approach we presented has various limitations . Transcriptome data measured from brain tissues involves a mixture of various cell types whose proportions and conditions are not known . Developing demixing approach to infer proportions from the mixture [25] is an important challenge , can be based on single-cell transcriptomics ( as in Darmanis et al 2015 ) , and is likely to significantly change our understanding of brain transcriptome . Genes involved in patterning and axon guidance clearly exhibit regional patterns during early development [59] . The above results show that their expression continues to be governed by the region ontology in the adult brain , long after their developmental role has been completed . As one specific example , consider a gene from the top BRO-scorers in the ABA6-2013 dataset , FEZF2 ( forebrain embryonic zinc finger protein 2 ) , a transcription repressor involved in specification of subcerebral projection neurons [60 , 61] . FEZF2 is believed to play a role in layer and neuronal patterning of subcortical projections and axonal fasciculation and was shown to regulate axon targeting of layer 5 subcortical projection neurons , where axons of FEZF2 deficient mice failed to reach their targets [62] . In the adult human brain , our results show that FEZF2 retains strong areal differences in adulthood , and is strongly expressed in the cortex , less so in the midbrain and the least in the hindbrain ( S6 Fig ) . Indeed , the mouse variant of FEZF2 is known to be expressed in adult projection neurons [62] . Importantly , these results suggest that FEZF2 assumes another functional role in the adult cortex . Indeed , it has been shown that projecting neurons in the mouse motor cortex expressing Fezf2 have distinct physiological characteristics [63] . The abundance of genes that retain their areal differential expression in adulthood suggests that this may be the general case , and many genes that play a role in brain development later assume new roles that affect the function of the adult brain . The fraction of genes having distinct areal expression pattern has been previously estimated using a different method ( ANOVA ) . In the Kang-2011 data , it was found to be on the order of hundreds of genes in the adult human brain ( Pletikos et al . [19] , Fig 2B ) . In the ABA6-2013 data 84% of the genes were found to have differential expression across brain regions [5] . ANOVA estimates are sensitive to differences in the mean expression of regions , regardless of the region ontology , and could capture genes whose expression pattern in some brain regions is different from others . As such , they are more sensitive to genes that are uniquely expressed in one or few region . The BRO-score can therefore be viewed as a complementing measure , which is sensitive to global areal-differential expression that is consistent with the brain region ontology . The fact that the expression of most genes in the adult brain is governed by earlier development stages suggest that many studies which deal with regional differential expression should be carefully interpreted . For example , combining samples taken from ontology-distant regions would lead to large expression variance , reflecting the developmental origin of the structures tested . Furthermore , areal differential expression should be measured compared to a baseline expression profile that takes in to account the region ontology . The results in this paper suggest that spatial expression patterns in the adult human brain are controlled in a way that follows the embryonic origin of regions , but at the same time that spatial patterns of related genes may change during development in a correlated way . It remains to be discovered which transcription control mechanisms maintain these distinct areal expression patterns .
We analyzed gene expression data from two sources . First , a set of 3702 microarrays provided by the Allen Human Brain Dataset ( ABA6-2013 ) from human . brain-map . org [5] . We mapped 58692 microarray probes to genes based on mapping provided by the Allen Institute . When multiple probes were available for a gene , we selected the probe that was most consistent across the 6 human subjects; suggested by human . brain-map . org . Specifically , when we analyzed genes with multiple probes , we first computed the expression correlation across regions of each probe , then averaged the correlation scores across all pairs of subjects and chose the probe that was most correlative . Overall , we analyzed 20773 transcripts . The number of samples per donor ranged from 363 to 946 , for a total of 3702 tissue samples . The second dataset was a set of 1340 microarray samples collected by Kang and colleagues from 57 postmortem brains containing expression values for 17565 genes [6] . We refer to this dataset as Kang-2011 . We limited the analysis to donors that are older than 12 , yielding a total of 20 donors and 491 tissue samples . The Kang dataset was also used for the analysis of BRO-agreement over development ( Fig 2E ) . For this analysis we also used the pre-natal subjects and early-childhood subjects . The subject ages range from 13 post conception weeks to 82 years . We grouped the subjects into 12 age-groups following the original Kang paper , and computed BRO scores per age group . We used the brain region ontology hierarchy provided by the Allen institute human . brain-map . org containing 1534 regions organized in a hierarchical manner . From the full set of regions we used two ontologies: A fine region-ontology with 414 regions which had measurements that were associated with them , and a coarse region-ontology with 16 brain regions . The list of 16 gross regions is given in supplemental S2 Table . The coarse part of the ontology ( upper part in Fig 1A ) was largely based on brain development while the fine parcellation of regions was based more on cytoarchitecture . The results we report are based on the coarse 16 region-ontology . We report below results for both coarse and fine grained ontologies . Measurements from the Kang-2011 dataset were obtained from 16 regions . We mapped those regions to 16 regions of the Allen ontology , and the mapping is given in supplemental S3 Table . The BRO-agreement score was computed separately for each gene as follows . For each pair of samples ( a , b ) , we define their tree similarity as the distance ( number of edges ) between the regions in the ontology hierarchy tree dtree ( a , b ) . We define their expression similarity as the absolute difference between the expression values of the two samples for the current gene ( i ) —dexpression ( ai , bi ) . We computed the two distances over all pairs of tissue samples , and computed the Spearman correlation between the two as the BRO score . To generate random scores , we calculate Bro-agreement scores of permutated vectors . Genes with a BRO-score above the top 1% of the permuted scores were considered significant . We also tested a second ontology-agreement score based on triplet ranking . We randomly selected 106 sample triplets ( a , b , c ) and computed the fraction of times that a triplet is ranked with the same ordering in both the tree and the expression distance measures: BRO_triplet ( gene i ) ≔# ( dtree ( a , b ) <dtree ( a , c ) ) & ( dexpression ( a , b ) <dexpression ( a , c ) ) ) # ( dtree ( a , b ) <dtree ( a , c ) ) This score gave similar results which are not shown here . To handle biases that could arise from different scales in the samples we also checked a normalized version of the Bro-agreement . In this normalized version we first normalized each sample to zero mean and unit variance and then computed the BRO-agreement . The results were robust to this change and we choose to present the un-normalized BRO-agreement scores . To handle biases that could arise from the number of regions in the ontology , we used two different granularities of the ontology tree . The first uses 16 gross regions and the second uses the entire tree ( 414 regions ) . We tested two ways to combine expression measures from multiple subjects into a single BRO score . First , we simply aggregated all samples of all subjects from a given region , and computed the BRO agreement score . Second , in the ABA6-2013 dataset , the number of samples per subject is large enough , such that a BRO score can be computed separately for each subject . We then computed a global BRO score of a gene as the average over the 6 individual-subject BRO scores . 94% of the genes were significant compared to random , according to the first score , and 92% according to the second method . For consistency with the Kang-2011 data , the figures use the first method . We report the more conservative estimate of significant genes as 92% . We computed BRO-scores which uses the fine region-ontology . The upper branches in this ontology are more developmental oriented and the lower branches are more cytoarchitecture-driven . Using this ontology 89% of the genes are BRO significant . The BRO scores of the fine region-ontology and of the coarse 16 region-ontology are very similar with a spearman correlation of 0 . 98 . To test if these results are sensitive to the number of region available in ontology tree , we repeated the analysis of the ABA6-2013 dataset at a coarser resolution of 16 regions . Using this coarse resolution , 95% of the genes were BRO significant . To test if the number of BRO significant genes is sensitive to the number of available samples , we randomly subsampled subsets of size 500 samples , which decreased the fraction of BRO significant genes to 51% ± 2% ( supplementary S1 Table ) . The percent of BRO-significant was tested for robustness using the ABA6-2013 dataset , where hundreds of samples are available for each subject . For each gene , we calculated its BRO-score but this time for each subject separately . The fraction of genes with a significant BRO score ( p-value < 0 . 01 ) is stable across the individual subjects , yielding 89% , 90% , 76% , 91% , 83% and 86% ( supplementary S7 Fig ) . The fraction of significant genes at the group level is slightly higher; suggesting that the groups score manages to remove some of the inter-subject noise . Expression variability of a single gene across regions is sometimes captured by comparing the mean expression level in each region using ANOVA [5 , 6] . This approach would find a gene as significant even if it is differentially expressed in a single region , or if it is expressed in a set of regions regardless of their position in the ontology tree . Hence in principle , the BRO agreement index poses a stronger requirement of agreement with the ontology . When computing ANOVA across 414 regions of the ABA6-2013 dataset , and using the average scores for each of the 6 subjects , 86% of the genes ( 17895 out of 20773 ) were significantly differentially expressed across regions ( FDR-corrected q-value < 0 . 01 ) , 83% of these genes ( 17266 out of 20773 ) were also BRO-significant . 8% of the genes ( 1719 out of 20773 ) had BRO-significant scores but not ANOVA-significant , most likely because the BRO index combined weak affects across multiple nodes of the tree . For each gene , we computed one-way ANOVA on samples expression levels . The p-values reported are under the null hypothesis that samples are drawn from regions which have the same mean expression . We computed ANOVA separately for each human subject and report the average ANOVA score across subjects for each gene . Similar to the BRO scores we performed ANOVA on the fine grained ontology and on the coarse ontology . We then corrected the p-values for multiple comparisons using FDR . For the genome wide analysis of BRO scores We used the human orthologs of the set of genes characterized by Cahoy et al . [36] , who used microarrays to profile expression patterns in purified populations of neurons , astrocytes and oligodendrocytes . For testing how spatial variability could be explained by cell-type specific markers , we used a set of known markers collected from various sources including [36] . For neuronal markers we used EMX1 , MAP2 , GRIA2 , DLG4 , DLG3 , NRGN , STMN2 , SYT1 , CELF4 , CELF5 , CELF6 . For glia markers we used GFAP , MBP , SLC1A2 , SLC1A3 , DLG4 , DLG3 , MAP2 . To compute the dependence of spatial variability on those markers we fitted a quadratic function for each of the genes separately using a least square loss , and computed the explained variance R2 , compared to a constant model . We studied two sets of gene pairs: Ensembl paralogs and KEGG-based gene pairs . For the first set , we used the paralogs available from Ensembl ( ensembl . org , May 2016 ) , and limited to gene pairs that had an Entrez id and were included in synaptic and brain related pathways as described by KEGG . Specifically , these included 17 pathways with KEGG accession numbers 04020 , 04724 , 04725 , 04726 , 04727 , 04728 , 04730 , 05010 , 05012 , 05014 , 05016 , 05030 , 05031 , 05032 , 05033 , 05034 , 04080 . Second , for KEGG-based gene pairs , we created a set of gene pairs designed to capture functionally-related genes , by collecting gene pairs that reside within the same functional element in KEGG pathway repository . These KEGG elements group together proteins with common functionally and interaction partners . We found these KEGG elements to be usually more functionally-coherent than protein families , and at the same time less specific than protein sub-families . We further required that pairs have sequence similarity above 30% ( See also [64] ) . To find pairs whose spatial correlation has a trend , we fitted a linear regression model with least square loss with age as the predicting variable and spatial correlation as the predicted variable . Significance of the trend was measured based on the F statistic of the explained variance and was FDR corrected for multiple hypotheses . We used the set of genes that belong to the human axon guidance pathway . The set was manually curated by KEGG ( www . genome . jp/kegg ) . The Hox genes used are: HOXA1 , HOXA10 , HOXA11 , HOXA2 , HOXA3 , HOXA4 , HOXA5 , HOXA6 , HOXA7 , HOXA9 , HOXB1 , HOXB13 , HOXB3 , HOXB4 , HOXB5 , HOXB6 , HOXB9 , HOXC10 , HOXC12 , HOXC13 , HOXC4 , HOXC5 , HOXC8 , HOXC9 , HOXD1 , HOXD12 , HOXD13 , HOXD3 , HOXD4 , HOXD8 , HOXD8 , and HOXD9 . The Pax genes used are: PAX1 , PAX2 , PAX3 , PAX4 , PAX5 , PAX6 , PAX7 and PAX8 . A set of 11 genes collected by Eisenberg et al . [65] and available at ( http://www . tau . ac . il/~elieis/HKG/ ) . We used agglomerative hierarchical with average linkage and Euclidean distance over 3702 samples from ABA6-2013 obtained from six subjects . Samples from the same brain regions were first averaged to create a single profile for each region . We used all 3702 samples from ABA6-2013 to compute the covariance matrix of gene expression levels , and then computed the top principal component of the expression covariance matrix . For each gene , we evaluated how much of its expression variance ( over samples ) can be explained using two sources of information: The region the sample was taken from and the identity of the subject the sample was extracted from . The explained variance was computed for each gene by fitting linear model using each of these sources of information , and using both of them together . For robustness , we combined two BRO-score of each gene by multiplying the BRO-score computed with the Kang-2011 data with that computed with the ABA-2013 data . We performed the ranked based enrichment analysis using Gorilla ( http://cbl-gorilla . cs . technion . ac . il/ ) . We used the gene age-index published by Domazet-Lošo and Tautz [66] . | Genome-wide measurements of gene expression across the human brain can reveal new principles of brain organization and function . To achieve this , we aim to discover which genes are differentially expressed and in what brain regions . We found that almost all genes in the adult human brain bear a developmental ‘footprint’ which determines their areal expression-pattern based on the developmental ontology of brain regions , while at the same time their spatial expression pattern changes during life . Furthermore , pairs of paralog genes and similar genes with stronger embryonic footprint , tend to be more strongly correlated ( or anti correlated ) suggesting that their expression is more strongly spatially tuned , and this tuning changes in development . | [
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"expression"... | 2016 | On Expression Patterns and Developmental Origin of Human Brain Regions |
Chlamydia trachomatis is an obligate intracellular human pathogen that exhibits stage-specific gene transcription throughout a biphasic developmental cycle . The mechanisms that control modulation in transcription and associated phenotypic changes are poorly understood . This study provides evidence that a switch-protein kinase regulatory network controls availability of σ66 , the main sigma subunit for transcription in Chlamydia . In vitro analysis revealed that a putative switch-protein kinase regulator , RsbW , is capable of interacting directly with σ66 , as well as phosphorylating its own antagonist , RsbV1 , rendering it inactive . Conversely , the putative PP2C-like phosphatase domain of chlamydial RsbU was capable of reverting RsbV1 into its active state . Recent advances in genetic manipulation of Chlamydia were employed to inactivate rsbV1 , as well as to increase the expression levels of rsbW or rsbV1 , in vivo . Representative σ66-dependent gene transcription was repressed in the absence of rsbV1 or upon increased expression of RsbW , and increased upon elevated expression of RsbV1 . These effects on housekeeping transcription were also correlated to several measures of growth and development . A model is proposed where the relative levels of active antagonist ( RsbV1 ) and switch-protein anti-sigma factor ( RsbW ) control the availability of σ66 and subsequently act as a molecular 'throttle' for Chlamydia growth and development .
Chlamydia trachomatis is the leading cause of bacterial sexually transmitted infection worldwide [1] , as well as the leading cause of infection-associated blindness [2] . Members of the Chlamydiaceae are obligate intracellular parasites that must complete a unique intracellular development cycle in order to propagate . This cycle is characterized by phenotypic variation between an Elementary Body ( EB ) that is infectious but metabolically quiescent , and a Reticulate Body ( RB ) that is replicative but not infectious . Cellular infection is initiated by the EB , which attaches and induces its own intake via translocation of effector cargo . Once inside the cell , the EB differentiates into an RB , which replicates via binary fission . During the late stages of infection , RBs asynchronously re-differentiate into the EB form ( reviewed in [3] ) . Alternatively , certain stress conditions mediate the onset of a separate growth mode ( termed persistence ) , which was defined as a ‘viable but non-cultivable’ state , in which chlamydiae fail to complete the development cycle and instead differentiate into aberrant , enlarged particles; this phenotype is reversible upon abatement of the mediating stress [4] . While modulations in transcript levels are associated with the stages of acute development [5–7] and the onset of persistence [8 , 9] , the regulatory mechanisms that govern these phenotypic shifts are not understood . Sigma factors are responsible for the recruitment of the core RNA polymerase ( RNAP ) to cognate promoter elements , and , thus , their function dictates the subset of genes transcribed within a cell . Chlamydia encodes three sigma factors , σ66 , σ54 , and σ28 , whose individual expression patterns [10–12] fail explain the stage-specific transcription profiles observed in acute chlamydial development [12] . Assuming these sigma factors participate in distinct functions , then post-expression mechanisms of regulation must theoretically be employed . Switch-protein kinase modules are common effectors of energy and stress responses in bacteria . One of the best studied is the ‘Regulator of SigmaB’ or Rsb system in Bacillus subtilis . Within this type of module , a component called the switch-protein can either bind to and affect the function of a target protein , or function as a kinase in the phosphorylation of a network antagonist . Phosphorylation of the antagonist prevents further interaction with the switch-protein . The function of a competing PP2C-like phosphatase controls the level of active antagonist . In the absence of active ( i . e . non phosphorylated ) antagonist , the switch protein is driven towards its regulatory function . Commonly , the target of the switch-protein is a sigma factor , as is the case for the RsbW protein in B . subtilis ( reviewed in [13] ) . Analogues of a switch-protein kinase regulatory module are conserved in the C . trachomatis genome [14] and were named after the B . subtilis module . Putative components of the Rsb switch-protein regulatory system , outlined in Table 1 , include one analogue of the switch-protein kinase ( RsbW ) , two analogues of the module antagonist ( RsbV1 and RsbV2 ) , and three proteins that contain recognized PP2C-like phosphatase domains ( RsbU , CT589 , and CT259 ) [14] . Henceforth , chlamydial analogues with common family-member protein names will be designated with a ‘Ct’ subscript , e . g . RsbWCt . Previous studies have revealed that all potential module members are expressed [12 , 15] and that RsbWCt is a kinase specific for RsbV1 and RsbV2- although a ‘switch’ function regulatory target has not been identified [16 , 17] . The current study expands on these works , and further outlines the functions of Rsb module members in Chlamydia . In vitro and in vivo evidence indicate that the regulatory target of RsbWCt is the primary σ-factor for Chlamydia , σ66 , and that opposing functions of RsbWCt and the antagonist , RsbV1 , contribute to the amount of σ66 that is available for association with the core RNAP complex .
The regulated target of the putative switch-protein kinase analogue in Chlamydia ( RsbWCt ) had not been identified at the onset of this study . Therefore , a bacterial adenylate cyclase two-hybrid ( BACTH ) system [18] was employed to screen for interactions between RsbWCt and the three chlamydial sigma factors: σ28 , σ54 , and σ66 ( Fig 1A ) . In order to gauge the relevance of this method for the screening of RsbW-type interactions , homologous proteins of known function from B . subtilis [19 , 20] were used as positive ( σB + RsbWBs ) and negative ( σB + RsbTBs ) control combinations . As expected , interaction of σB with its cognate anti-sigma factor , RsbW-Bs , complemented adenylate cyclase ( AC ) activity , and expression of the cAMP-dependent lacZ reporter cassette was detected via the Miller Assay . In contrast , co-expression of σB with RsbTBs ( a paralogue of RsbWBs that does not bind to σB ) did not exhibit complementation . When RsbWCt was expressed with the chlamydial alternative sigma factors , σ28 and σ54 , similar activities to the empty vector controls were observed ( t = 0 . 124 and t = 0 . 023 , respectively; One-way ANOVA , Bonferroni's multiple comparison post test ) . In contrast , an interaction between RsbWCt and σ66 was clear ( t = 3 . 633 ) , as LacZ levels approached those of the positive control ( GCN4 leucine zipper domains; ‘zip’ ) . Interaction between σ66 and RsbWCt was observed in both cloning permutations of the BACTH assay , and expression of σ66 did not artificially activate the cAMP dependent reporter LacZ cassette in the absence of an RsbWCt fusion protein ( S1 Fig ) . To validate the BACTH screen , molecular interactions were monitored in real time by surface plasmon resonance ( SPR ) . Recombinant , purified σ-factors ( σ66 , σ28 , and σ54 ) were immobilized to consecutive flowcells of an individual CM5 sensorchip , before the chip was charged with RsbWCt analyte at various concentrations . The change in response units ( RU ) over time , relative to a reference flowcell ( immobilized Glutathione-S-transferase; GST ) , served as a function of binding between the ligand ( sigma factor ) and analyte ( RsbWCt ) . Curves of best fit were applied to RU sensorgrams and the rate of equilibrium ( Req ) for each curve was determined ( for example , a σ66-binding plot overlay is shown in Fig 1B ) . In order to compare the relative binding of RsbWCt to the three σ-factors , Req values were normalized by the theoretical maximum RU ( Rmax ) for each ligand , which was based on the original immobilization levels for each sigma factor . At every concentration tested , RsbWCt bound a greater percentage of immobilized σ66 than either of the alternative sigma factors ( Fig 1C ) . This trend was consistent in multiple experiments ( n = 3 ) , although the overall percentage of theoretical Rmax bound appeared to decrease with the age of the immobilized ligand on each chip ( from 1 to 3 days ) , indicating that the activity or conformation of the immobilized ligands decreased over time . When a GST-antibody capture system was used , in which GST-tagged sigma factors were captured by immobilized anti-GST immunoglobulin immediately prior to addition of RsbWCt , up to 40% of the σ66 Rmax was bound at a concentration of 5 μM ( S2 Fig ) . Thus , these data indicate that RsbWCt associates with the sigma factor , σ66 . RsbWCt is a kinase specific for RsbV1 and RsbV2 , and the phosphorylated residues of the antagonists have been mapped to Serine-56 and Serine-55 , respectively [17] . In order to gain insight into the kinetics of the kinase activity for RsbWCt against RsbV1 and RsbV2 , aliquots from phosphorylation reactions were removed iteratively and resolved on a polyacrylamide gel supplemented with Phos-tag , an agent which causes an electromobility shift of phosphorylated proteins . When incubated in the presence of ATP , RsbWCt induced a mobility shift of both RsbV1 and RsbV2 , although there was a clear distinction in the rates at which the two antagonists were phosphorylated; after only 10 minutes , 100% of RsbV1 migration was shifted , whereas only a fraction of RsbV2 was shifted after 2 hours of incubation with RsbWCt ( Fig 2A and 2B ) . To determine the direct effects of phosphorylation on the interaction between RsbWCt and antagonist , SPR was again utilized; RsbV1 and RsbV2 antagonists were immobilized onto individual flow-cells of a sensorchip and RsbWCt was then supplied in the absence or presence of ATP . Without ATP , RsbWCt did not bind to RsbV1 ( Fig 2C ) or to RsbV2 ( Fig 2D ) . When ATP was supplied along with RsbWCt , both ligands produced abnormal binding curves , in which an initial increase in response gradually returned to the baseline level . Subsequent applications of RsbWCt plus ATP yielded no response with either ligand . We reasoned that upon addition of ATP , RsbWCt was able to associate initially with , then phosphorylate RsbV1 and RsbV2 , but that phosphorylation prevented their further interaction . To explore this hypothesis , similar SPR experiments were carried out on derivatives of RsbV1 and RsbV2 , in which the phospho-accepting residues were mutated to mimic either an immutable non-phosphorylated state ( serine to alanine ) , or a permanent phosphorylation state ( serine to aspartic acid ) . The addition of RsbWCt without ATP exhibited no binding response to any of the four antagonist derivatives ( Fig 2E and 2F ) . Supplementation of ATP with RsbWCt yielded no binding response to the RsbV1 S56D or RsbV2 S55D derivatives , suggesting that a negative charge ( from the phosphate group or , in this case , the aspartic acid residue ) at position 56/55 , respectively , of the antagonist abrogated association with RsbWCt . Supplementation of ATP with RsbWCt produced a typical saturation-binding curve to the RsbV1 S56A and , to a lesser extent , RsbV2 S55A antagonist derivatives . The S56A or S55A derivative antagonists gave a similar binding response on subsequent RsbWCt plus ATP injections , presumably because of their inability to accept phosphorylation and , rendering them inactive . Together these data indicate that the binding of RsbWCt to its RsbV1 and RsbV2 antagonists depends on both the phosphorylation state of RsbV1 or RsbV2 and ATP . Three proteins contain recognized PP2C-like phosphatase ( IPR001932 ) domains in C . trachomatis: CT259 , RsbUCt and CT589 ( Table 1 ) . RsbUCt and CT589 are paralogues , both predicted to transverse the membrane with a hypothetical domain localized in the periplasm and a PP2C-like domain localized in the cytoplasm [14 , 17] . In contrast , CT259 appears to consist of a single cytosolic domain ( Fig 3A ) . When aligned with the PP2C-like domains of RsbUBs and SpoIIE from B . subtilis , the residues involved in two Mn2+ coordination sites , which are essential for phosphatase activity , are conserved in RsbUCt , but not in CT589 [17] . CT259 appears to maintain both divalent metal coordination sites , although the first site exhibits conservative aspartic acid to glutamic acid mutations ( S3 Fig ) . To test if the carboxy-terminal PP2C-like domain of RsbUCt was an active phosphatase , the sequence corresponding to the final 258 amino acids was cloned into an expression vector for recombinant purification . This C-terminal domain of RsbUCt ( referred to as C-RsbUCt ) was incubated with RsbV1 or RsbV2 that had been previously phosphorylated by RsbWCt . The phosphorylation states of the antagonists were distinguished by resolution on a Phos-tag supplemented acrylamide gel . Introduction of C-RsbUCt caused a rapid shift from the phospho- to non-phosphorylated form of RsbV1 , however phospho-RsbV2 remained phosphorylated throughout the entire time-course ( Fig 3B ) . CT259 was also examined for phosphatase activity against the two phospho-antagonists , though no activity was observed ( Fig 3C ) . Thus , while the C-terminal domain of RsbUCt appears to maintain phosphatase activity for phospho-RsbV1 , no phosphatase capable of recognizing RsbV2 was identified . A chlamydial shuttle vector for the controlled expression of a target cassette in Chlamydia was engineered in order to further investigate the role of Rsb components in vivo . Briefly , the shuttle vector , pGFP::SW2 [21] , was modified such that the constitutive promoter driving the gfp-cat cassette was replaced with the tetracycline inducible promoter system from pRPF185 [22] , producing pCT308-GFP ( S4A Fig ) . The gfp cassette was then replaced with sequences corresponding to rsbW or rsbV1 from C . trachomatis serovar D/UW/CX genomic DNA to make pCT1310-RsbW and pCT1310-RsbV1 , respectively . These three shuttle vectors transformed C . trachomatis strain L2/25667R ( originally lacking any plasmid; henceforth referred to as L2R ) and were stable through several passages in penicillin-supplemented medium , prior to plaque purification , expansion , and density gradient harvest . Strains harboring these shuttle vectors were as resistant to penicillin as the original parent shuttle vector , pGFP::SW2 [21] with concentrations of 0 . 01–0 . 05 U/ml failing to have any effect on generation of infectious progeny , despite the same concentrations inhibiting the C . trachomatis L2R strain lacking any plasmid ( S4B Fig ) . Endogenous fluorescence was not detected in Chlamydia harboring pCT308-GFP in the absence of ATc , but was detected upon supplementation of the growth medium ( S4C Fig ) . Additionally , a strain in which rsbV1 was inactivated using insertional mutagenesis [23] was engineered . Briefly , a GII intron carrying the aadA-marker ( to confer resistance to spectinomycin ) was targeted for insertion into the 5’ region of rsbV1 , creating DFCT15 ( rsbV1::GII[aadA] , S5 and S6 Figs ) . The mutant strain was resistant to spectinomycin and sequencing of the disrupted rsbV1 locus confirmed the GII intron insertion , resulting in alteration of the wild type RsbV1 sequence after 10 amino acids . Serial passage in the absence of spectinomycin followed by PCR analysis of the insertion-site using rsbV1-specific primers confirmed marker stability in the absence of selection , as previously observed for the GII intron carrying the bla-marker [23] . Consequently , experiments were performed without spectinomycin when matched with non-spectinomycin resistant strains . Based on the results of in vitro binding assays and the respective functions of homologous proteins in other bacteria , we hypothesized that RsbWCt was a negative regulator of σ66 and that non-phosphorylated RsbV1 would act as a positive regulator of σ66 by antagonizing RsbWCt . To test this hypothesis in vivo , we monitored the expression of bona fide σ66-dependent genes in our L2R transformant and rsbV1 knockout strains . Normalization of transcript expression in Chlamydia is not trivial ( e . g . [8 , 24] ) , especially considering our hypothesis of differential ‘housekeeping’ transcription . We tested a number of different normalization techniques that did not assume that any chlamydial gene would correlate unequivocally to the number of chlamydiae present within the sample . While use of C . trachomatis-specific gDNA from parallel samples as an exogenous control has been employed frequently ( e . g . [8 , 25] ) , this type of normalization fails to account for variation in RNA loading / efficiency of reverse transcription as a source of error . We found that use of the geometric mean of Chlamydia-specific gDNA from parallel samples and endogenous host cell gapdh as a normalization factor for each sample produced a data set with the lowest cumulative intrasample error ( S7 Fig ) . Primary Cq values , along with normalization calculations , are available in S1 Dataset . As expected , rsbW transcript expression was elevated in the L2R pCT1310-RsbW strain ( Fig 4A ) , whereas rsbV1 transcript expression was elevated in the L2R pCT1310-RsbV1 strain ( Fig 4B ) . Transcript of rsbV1 was not detected by RT-qPCR in DFCT15 ( rsbV1::GII ) , confirming genetic analysis ( S6 Fig ) . Expression of the gfp-cassette in L2R pCT308-GFP strain reached similar levels of the other expression cassettes ( data available in S1 Dataset ) . To test whether ‘housekeeping’ transcription was affected in the transformant / knockout mutant strains , we assessed the transcription of two genes with bona fide σ66-dependent promoter systems , 16S rRNA [26 , 27] and ompA [28 , 29] , limiting our analysis to time-points prior to the typical RB to EB re-differentiation in order to avoid re-differentiation as a confounding variable . As predicted , σ66-dependent transcription was elevated in the L2R pCT1310-RsbV1 strain , with the pooled relative expression of ompA and 16S rRNA reaching a geometric mean of 2 . 32-fold over the control ( 95% CI = 1 . 72- to 3 . 11-fold ) . Conversely , the relative expression of σ66-dependent transcription was reduced to 0 . 362 of control ( 95% CI = 0 . 224 to 0 . 583 ) in the rsbV1::GII strain . Notably , σ66-dependent transcription in the L2R pCT1310-RsbW strain was reduced from the control expression ( 95% CI = 0 . 585 to 0 . 988 ) , however did not reach similar repression levels as the rsbV1::GII strain . This negative effect has been observed in each of two previous analysis of expression experiments ( summary figures shown S8 and S9 Figs ) . The observation that the rsbV1::GII strain exhibits lower amounts of σ66-mediated transcription compared to the RsbWCt expression strain may indicate that the levels of rsbW achieved in these experiments were insufficient to completely overcome antagonism from endogenous RsbV1 levels , resulting in a subtle but consistent phenotype . As a control , we also assessed whether σ28-dependent transcription of hctB [30] , was affected in the transformant / knock-out mutant strains , with the caveat that all known σ28-dependent genes are transcribed during the late stages of the developmental cycle due to repression by an early-stage transcriptional repressor ( EUO ) [31 , 32] . Indeed , we were not able to detect hctB transcription in all samples at 12 hpi , and levels at 18 hpi neared the limit of detection . Thus , we analyzed expression of hctB during the late stages of the developmental cycle . At these time-points , no differential expression was observed . Taken together with the in vitro binding data , our results suggest RsbWCt is an anti-sigma factor of σ66 , and that RsbV1 is a bona fide antagonist of the RsbWCt to σ66 association . We hypothesized that modulating the activity of the ‘housekeeping’ sigma factor would have detectable effects on Chlamydia , yet no obvious effect on growth or development was observed during the passage and selection of pCT1310-series transformants . Immunofluorescent analysis ( IFA ) revealed that all transformant strains exhibited a morphology consistent with acute development , and that no aberrant , enlarged chlamydial particles consistent with the persistent phenotype were observed . However , we did observe that inclusions from the L2R pCT1310-RsbV1 strain appeared much larger than the GFP or RsbWCt expression strains ( p<0 . 0001; One-way ANOVA , Tukey’s multiple comparison post-test versus both other samples; S10 Fig ) . We reasoned that increased inclusion size observed in the L2R pCT1310-RsbV1 strain might be attributed to increased metabolic capacity upon elevated ‘housekeeping’ transcription , and if so , this may correlate to modulations in growth and development . To test this hypothesis , we analyzed the transformant and rsbV1 mutant strains for genomic replication ( 1-step growth ) , recoverable infectious progeny ( 2-step growth ) , and plaque expansion . For both 1-step and 2-step growth analysis , data was normalized by the empirical IFU input for each experiment , such that the data presented accounts for the actual number of infection events for each sample . Differential genomic replication was not overtly evident between the strains , though analysis of the area underneath each growth curve did reveal a trend that mirrored levels of σ66-dependent transcription , with L2R pCT1310-RsbV1 exhibiting the highest chlamydial load , followed by L2R pCT308-GFP , and then L2R pCT1310-RsbW and the rsbV1 mutant ( Fig 5A ) . As a second indicator of development , recoverable infectious progeny ( 2-step growth ) was also monitored ( Fig 5B ) . At 24 hpi , the L2R pCT1310-RsbV1 strain exhibited a 2 . 3-fold increase in infectious progeny compared to control ( p = 0 . 0026; One-way ANOVA with Tukey’s multiple comparisons post-test ) , whereas L2R pCT1310-RsbW exhibited a 3 . 1-fold reduction ( p = 0 . 181 ) . The rsbV1 mutant also displayed a reduction in infectious progeny that exceeded that of L2 pCT1310-RsbW ( p = 0 . 108 versus control ) . Thus recoverable infectious progeny and , to a lesser extent , genomic replication exhibited a pattern that correlated with observed σ66-dependent transcription . However , because neither one-step or two-step growth analysis alone is a perfect measure of Chlamydia fitness , we chose to analyze transformant and mutant strains in a plaque expansion assay . Because plaque expansion depends on all aspects of chlamydial development ( e . g . replication , re-differentiation , cell exit , and secondary host cell entry ) , the relative size of plaques can be used as an indicator of overall chlamydial fitness . Specific locations within HeLa monolayers were inoculated with the modified Chlamydia strains and plaques were measured after 8–9 days , using a semi-automated procedure in FIJI [33] . An example of this process is shown in Fig 5C , in which plaques from day 9 post-inoculation are shown . In concordance with the 1-step and 2-step growth profiles , the L2R pCT1310-RsbV1 strain exhibited the highest rate of plaque expansion ( Fig 5D ) . Moreover , the L2R pCT1310-RsbW and DFCT15 ( rsbV1::GII ) strains yielded smaller plaques than the GFP expression control . Thus , in concert with in vitro and transcript expression data , these results support a model in which the availability of σ66 is affected by relative levels of RsbW and RsbV1 and that their experimental manipulation was capable of influencing the rate at which C . trachomatis infection progresses in a cell culture model .
The evidence presented in this report indicates that the primary target of the Rsb phosphoregulatory network in Chlamydia trachomatis is σ66 , the main ‘housekeeping’ sigma factor of the pathogen . Interaction of RsbWCt with σ66 was shown indirectly by bacterial two-hybrid assay and directly by SPR analysis . Moreover , previous results that RsbWCt is a kinase for both RsbV1 and RsbV2 in vitro [17] were confirmed , although there is a difference in efficiency between these phosphorylation events , with RsbV1 being the preferred substrate . RsbWCt does not associate with RsbV1 or RsbV2 in their phosphorylated forms , or , interestingly , in the absence of ATP . We further observed in vitro phosphatase activity from the RsbUCt PP2C-like domain that is specific for phospho-RsbV1 , but not for phospho-RsbV2 . Thus , a complete signaling module ( consisting of a system phosphatase , antagonist , switch-protein , and target ) has been characterized in this report . To verify the model generated from in vitro assays , mutant and transformant strains of Chlamydia were engineered for in vivo analysis . Elevated expression of RsbV1 correlated with the enhanced expression of bona fide σ66-dependent transcripts and a more rapid growth profile in multiple assays . In contrast , elevated expression of RsbWCt and the inactivation of its antagonist both resulted in reduced transcription of representative σ66-dependent genes and a depressed growth profile . Taken together , these results provide a mechanism by which the Rsb network could control σ66 availability , and perhaps growth rate , in response to various stimuli . We postulate the following working model ( Fig 6 ) . Under steady-state conditions , the equilibrium of the Rsb network provides a molecular ‘speed-limit’ on σ66 activity and subsequently on metabolic activity ( Fig 6A ) . Upon experiencing increased levels of active ( i . e . non phosphorylated ) RsbV1 , the equilibrium of RsbWCt function would be driven away from sequestration of σ66 , resulting in amplified levels of ‘housekeeping’ transcription . Possible inputs for this pathway would be the increased activity/expression of RsbUCt , or increased expression of RsbV1 ( Fig 6B ) . Alternatively , upon decreased expression or activity of RsbV1 , the equilibrium of RsbWCt would be driven towards sequestration of σ66 , limiting ‘housekeeping’ transcription and possibly restricting metabolism . Potential inputs for this shift would include decreased expression or activity of RsbUCt , decreased expression of RsbV1 ( Fig 6C ) , or , intriguingly , decreased levels of ATP . A mechanism that links energy stress to decreased general levels of transcription seems plausible , however a procedure for the manipulation or even measurement of Chlamydia ATP levels ( i . e . to distinguish host versus pathogen ATP pools ) has not been elucidated , making exploration of this hypothesis difficult . We were initially surprised to observe that the association between RsbWCt and RsbV1 is dependent on ATP , which represents a mechanism distinct from Rsb components in the in B . subtilis module [34] . However , this distinction may reflect the differences in regulated targets with σB as an alternative sigma factor responsible for activating transcriptional response to stress , and σ66 as the primary sigma factor for Chlamydia . Under low ATP conditions in B . subtilis , RsbWBs is not able to inactivate RsbVBs such that they associate stably , liberating σB for the initiation of the stress response [34] . However , under low ATP conditions in Chlamydia , the theoretical association between anti-sigma factor and antagonist would not occur , driving the function of RsbWCt towards sequestration of σ66 and the reduction in ‘housekeeping’ transcription . We postulate that the ATP-dependence of anti-sigma to antagonist interaction theoretically must switch the Rsb system from one of stochastic activation in B . subtilis [35] to one of stochastic inactivation in Chlamydia . This study extends the work of Hua et al [17] , which characterized several interactions within this module , including kinase activity of RsbWCt and detailed in silico analysis of the module members . Yet , they failed to observe any interaction between RsbWCt and any of the chlamydial σ-factors in a yeast-two hybrid system . Furthermore , in vitro transcription assays showed no interaction between RsbWCt and σ28 , and a separate study failed to observe any effect of RsbWCt in a heterologous σ28-mediated transcription assay in Salmonella enterica [16 , 17] . While we concur that σ28 does not interact with RsbWCt , we provide evidence that RsbWCt indeed interacts with σ66 . One possible explanation for this discrepancy could be that the σ66-RsbW complex was not targeted properly to the yeast nucleus due to its size , stoichiometric ratio , etc . By using a bacterial two hybrid reliant on generation of a cytosolic metabolite , neither complex size nor nuclear import was an issue . Interestingly , other proteins have been described as modulators of σ66 function in Chlamydia . For instance , CT663 has been likened to the Rsd protein in E . coli [36] , which inhibits σ70-mediated transcription during stationary phase growth [37] . Another protein , GrgA , reported as a non-specific DNA binding protein , also associates with the non-conserved region of σ66 . By interacting with both simultaneously , GrgA enhances the transcription of σ66-dependent promoter systems in vitro [38] . Thus , there is a clear precedent for the alteration of σ66 activity as an evolved strategy in Chlamydia . We add RsbWCt to this list of σ66 modulators characterized in vitro , and additionally provide in vivo evidence to support its function in this role . We would like to note that we also attempted to transform C . trachomatis with shuttle vectors capable of expressing RsbV1_S56A , RsbV2 , RsbV2_S55A , RsbU , CT589 , and CT259 , of which none transformed Chlamydia in at least 2 attempts ( in which a positive control for transformation was employed and successful ) . We also attempted insertional inactivation of rsbV2 , rsbU , and ct589 , but were unsuccessful . As transformation methodologies are relatively new for Chlamydia , we are unsure whether these observations can be attributed to effects on chlamydial fitness ( e . g . such manipulations are lethal or at least highly detrimental ) or are simply technical failures . The use of a positive control vector for each transformation , as well as the use of multiple plasmid backbones ( pCT1310- and p2TK-SW2 [39] ) for attempted transformations suggest the former may be more likely . Interestingly , of the genes targeted for manipulation , those that were successfully altered exhibited mild , but reproducible , phenotypic changes . Thus , it is plausible that any robust alteration in the function of Rsb module ( e . g . the overexpression of constitutively active RsbV1 S56A ) could be highly detrimental to chlamydial growth under selective cell culture conditions . Further insights into the process of chlamydial transformation should shed light onto the nature of these negative results . What , then , is the evolutionary function of the Rsb system in Chlamydia ? To our knowledge , there is no phenotype ( occurring during acute or persistent modes of growth ) that demonstrates a global modulation in σ66-mediated transcription . Perhaps this is not surprising considering that beyond basic expression and availability of sigma factors , other global transcriptional repressor proteins are likely to be influential at all stages of Chlamydia development . For instance , the protein EUO controls transcription of both σ66- and σ28-dependent late genes via interaction with operating elements found in both promoter types during the early stages of infection [31] . Thus , even if the Rsb network dictated an increased availability of σ66 , transcription of σ66-dependent late genes would be occluded by the presence of the EUO DNA-binding protein . While it is possible the Rsb module works in concert with other global regulators in order to actualize RB to EB re-differentiation , we favor a model where its function provides a molecular 'speed-limit' for housekeeping transcription ( and subsequently metabolism ) as a way to avoid overuse of potentially limited nutrients . Manipulation of expression levels of RsbWCt and RsbV1 via ectopic expression or insertional inactivation resulted in differential growth and development in a nutrient-replete cell culture model . The elucidation of the activities of the system components in response to inimical growth conditions could provide more resolution into the question of the evolutionary role of this system in Chlamydia trachomatis . In conclusion , this report provides multiple lines of evidence that indicate the Rsb module is a bona fide molecular circuit capable of influencing the availability of the main sigma factor in Chlamydia . The potential of this network to accelerate and restrict growth rate and development substantiates it as an important pathway for further study , and may even constitute a novel target for generation of attenuated , or even accelerated , growth mutants .
Chlamydia trachomatis serovar L2/25667R , pGFP::SW2 shuttle vector , and the BACTH vectors and DHM1 E . coli were provided by Dr . Scot Ouellette ( University of South Dakota , Vermillion ) . The pRFP185 plasmid was received from Dr . Robert Fagan ( University of Sheffield , Sheffield ) . Bacillus subtilis subspecies subtilis strain 168 was a received from Professor Neil Fairweather ( Imperial College , London ) . Chlamydia trachomatis serovar D/UW/Cx was provided by Dr . Rey Carabeo ( University of Aberdeen , Scotland ) . All PCR reactions intended for cloning purposes were performed with Phusion High-Fidelity Polymerase ( NEB ) . Either C . trachomatis genomic DNA ( Serovar D/UW/CX ) or B . subtilis sbsp . subtilis 168 genomic DNA was used for template in reactions . Primer sequences are listed in S1 Table . Insert and vector ligations were either performed using traditional restriction endonuclease digest ( NEB ) and ligation ( T4 Ligase; NEB ) , or in a one-step Gibson Assembly cloning reaction ( NEB ) . Plasmids intended for recombinant protein expression are listed in S2 Table , and plasmid shuttle vectors for the transformation of C . trachomatis are listed in S3 Table . Site directed mutagenesis of pGEX expression vectors for the antagonist proteins was also carried out with PCR based Gibson Assembly . All plasmid insert sequences were verified by Sanger sequencing ( GATC; Germany or Macrogen , USA ) . The bacterial adenylate cyclase two-hybrid method for assessment of protein-protein interaction was completed as per the instructions of the manufacturer ( EuroMedex ) . Detailed methods are described in S1 Text . The Miller Assay determined expression of the reporter LacZ [40] . Sample groups in all graphs represent an equal number of biological replicates , indicated in figure legends . The Biacore 3000 instrument ( GE Healthcare ) revealed biomolecular interactions via surface plasmon resonance ( SPR ) . Specifics for each run are described in supplemental information . CM5 sensorchips were used for all experiments . The optimal pH for pre-concentration of ligands was determined using the pH scouting wizard ( Biacore 3000 software ) , and ligands were using the amine coupling kit . All experiments were carried out in a buffer of 10mM HEPES , 150mM NaCl , and 1mM MgCl2 at 30°C . All consumables were purchased from GE Healthcare . Proteins were expressed from pGEX-derived vectors in PC2 E . coli [41] . Soluble proteins were purified by standard techniques . Insoluble proteins were liberated from inclusion bodies using sarkosyl as described in the S1 Text . All proteins were assessed for purity via SDS-PAGE and Coomassie-Blue stain . Detailed protocols are available in S1 Text . All DNA and protein sequence diagrams were generated using Geneious version 7 . 0 . 2 , created by Biomatters . Secondary structures were predicted by the Phyre2 algorithm [42] . Transmembrane regions were predicted using the Hidden Markov Model in Geneious . InterPro domains were identified using the European Bioinformatics Database ( EBI ) . Accession numbers listed are from the UniProtKB database . In vitro kinase/phosphatase assays were performed using recombinant , purified preparations of the system components and are described in detail in the S1 Text . Phos-tag reagent ( Alpha Laboratories ) was utilized to shift the migration of phosphorylated RsbV1 or RsbV2 during SDS-PAGE . Transformation of C . trachomatis L2/25667R ( plasmid-deficient ) with ectopic expression vectors was carried out , as described [21 , 43] with slight modifications highlighted in the S1 Text . Every transformation attempt utilized pGFP::SW2 as a positive control ( 100% success rate ) . DFCT15 was generated from the transformation of C . trachomatis strain L2/434/Bu with plasmid pDFTT6aadA as described in [23] with the exception that spectinomycin was used for selection at 500 ug/ml instead of ampicillin for mutant selection and plaque isolation of clones . The intron was targeted to insert between base pairs 28 and 29 of rsbV1 ( TCCCTTGTAAATGAAGGATGCCTGTTTGGC—intron–CTTGTTCTTCTTTCT ) in an antisense orientation . The predicted insertion efficiency values were an E-value of 0 . 75 and a score of 8 . 51 . Intron re-targeting was performed as directed by the TargeTron manual ( Sigma-Aldrich ) . Both transformant and knock-out strains were considered plasmid-competent . HeLa cells ( ATCC ) at 80–95% confluence in a 6- or 12-well cluster plate were inoculated with C . trachomatis diluted in Hank’s Balanced Salt Solution prior to centrifugation ( 500xg for 15 minutes at 20°C ) and incubation at 37°C for 30 minutes . The inoculum was then aspirated before addition DMEM supplemented with 10% fetal bovine serum ( FBS ) and other supplements as noted . For cassette induction , anhydrous tetracycline ( Sigma ) was supplemented to a final concentration of 5 ng/ml at 6 hours post infection . C . trachomatis strains were grown on the same cluster plate for 6–30 hpi . At designated time points , infected monolayers were washed , trypsin-treated into suspension , pelleted and stored in PBS at -80°C . Total genomic DNA was extracted and diluted to a final concentration of 1 ng/ml . Chlamydia-specific gDNA was assayed twice using qPCR ( see Quantitative PCR below ) with primers amplifying a region of the hypothetical gene , ct652 . 1 , or the 16S ribosomal subunit gene . Starting quantities were normalized against the empirical IFU input for each strain in order to account for any differences the actual versus intended MOI within each sample . Samples intended for quantification of recoverable infectious progeny were dislodged into 1ml SPG buffer ( 220mM Sucrose , 10mM Na2HPO4 , 4mM KH2PO4 , 5mM Glutamic Acid ) using sterile glass beads , and stored at -80°C . Sample infectious titers were quantified as described previously [25] . A similar process was used to determine the empirical infectivity of primary sample infections . Titers were normalized by this empirical infection rate for each strain within the individual experiment to exclude any effects of variation in stock aliquots . Fluorescent imaging for empirical infectivity and for recoverable infectious progeny titer was carried out on a Nikon Eclipse TE2000 epifluoresence microscope . For morphology and inclusion size analysis , images were captured using a Leica SP5 Resonant inverted confocal microscope , using identical settings for each sample . Montage images and scale bars were generated in FIJI . Inclusion sizes were determined in FIJI , using elipses to circumscribe each inclusion within a field of view from which the measurement tool provided the inclusion area ( pixels ) . Genomic DNA extractions were prepared using the DNeasy Blood and Tissue Kit ( QIAGEN ) . For RNA extraction , RNALater fixative was removed from infected monolayers and RNA was extracted by Trizol reagent ( Life Technologies ) . RNA samples were treated with DNase ( Turbo DNA-free kit , Applied Biosystems ) for 1 hour , before DNase inactivation via the instructions of the manufacturer . cDNA was then generated from 2 . 5 μg of RNA sample using the Superscript III Reverse Transcriptase kit ( Invitrogen ) , and diluted 1:8 in nuclease-free water before storage at -20°C . No Reverse Transcriptase controls were generated for all samples . Quantitative PCR was conducted in triplicate on the BioRad CFX96 Real-time system . Each reaction consisted of 1X Power SYBR green Mastermix , 0 . 45μM of each primer , and 2μl of sample in a volume of 25μl . Each run for a Chlamydia gene contained a standard curve of L2/25667R gDNA to assess amplification efficiency . NRT controls were performed for all cDNA samples . Equal amounts of total nucleic acid were loaded for each assay type ( 2 ng for gDNA , and the cDNA generated from 37 . 5 ng of RNA ) . To control for both the number of C . trachomatis particles and for differences in reverse transcription efficiency , target gene expression was normalized by the geometric mean of an exogenous ( C . trachomatis specific gDNA ) and endogenous ( host gapdh transcript ) controls . Normalized expression was calculated as in [44] , using the formula: ETarget ( -Cq[Target] ) / Geometric mean ( 2 ( -Cq[GAPDH] ) , EgDNA ( -Cq[gDNA] ) ) , where E represents the amplification efficiency ( e . g . 100% = 2 ) , Cq represents the mean cycle threshold of three technical replicates for the given run ( thus , error presented is biological , not technical ) . All cDNA samples were assayed for contaminating gDNA with No Reverse Transcriptase ( NRT ) controls , of which none exhibited gDNA contamination to mathematically relevant levels . Graphs were generated using GraphPad Prism v5 . 0f . Statistical analysis of BACTH and SPR data were also completed with GraphPad Prism software . The data sets generated for gene expression , plaque expansion size , 1-step growth , and 2-step growth were analyzed and statistical analysis performed using R . R analysis scripts are deposited available at doi: 10 . 6084/m9 . figshare . 1466906 ( 1-step growth , 2-step growth , gene expression analysis ) and doi: 10 . 6084/m9 . figshare . 1466907 ( Plaque size analysis ) . Sigma66 ( rpoD / ct615; P18333 ) ; Sigma28 ( fliA / ct061; O84064 ) ; Sigma54 ( rpoN / ct609; O84615 ) ; RsbWCt ( rsbW / ct549; O84553 ) ; RsbV1 ( rsbV_1 / ct424; O84431 ) ; RsbV2 ( rsbV_2 / ct765; O84770 ) ; RsbUCt ( rsbU / ct588; O84592 ) ; CT589 ( ct589; O84593 ) ; CT259 ( ct259; O84261 ) ; 16S rRNA ( ctr01 / 16S rRNA_1; 884531 ) ; HctB ( hctB / ct046; Q06280 ) ; OmpA ( ompA / ct681; O84605 ) ; EUO ( ct446; O84452 ) ; GrgA ( ct504: O84512 ) ; RsbWBs ( bsu04720; P17905 ) ; RsbVBs ( bsu04710; P17903 ) ; RsbTBs ( bsu04690; P42411 ) ; SigmaB ( bsu04730; P06574 ) . | Chlamydia trachomatis is the leading cause of both bacterial sexually transmitted infection and infection-derived blindness world-wide . No vaccine has proven protective to date in humans . C . trachomatis only replicates from inside a host cell , and has evolved to acquire a variety of nutrients directly from its host . However , a typical human immune response will normally limit the availability of a variety of essential nutrients . Thus , it is thought that the success of C . trachomatis as a human pathogen may lie in its ability to survive these immunological stress situations by slowing growth and development until conditions in the cell have improved . This mode of growth is known as persistence and how C . trachomatis senses stress and responds in this manner is an important area of research . Our report characterizes a complete signaling module , the Rsb network , that is capable of controlling the growth rate or infectivity of Chlamydia . By manipulating the levels of different pathway components , we were able to accelerate and restrict the growth and development of this pathogen . Our results suggest a mechanism by which Chlamydia can tailor its growth rate to the conditions within the host cell . The disruption of this pathway could generate a strain incapable of surviving a typical human immune response and would represent an attractive candidate as an attenuated growth vaccine . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Rsb Phosphoregulatory Network Controls Availability of the Primary Sigma Factor in Chlamydia trachomatis and Influences the Kinetics of Growth and Development |
Enteropathogenic Yersinia circulate in the pig reservoir and are the third bacterial cause of human gastrointestinal infections in Europe . In West Africa , reports of human yersiniosis are rare . This study was conducted to determine whether pathogenic Yersinia are circulating in pig farms and are responsible for human infections in the Abidjan District . From June 2012 to December 2013 , pig feces were collected monthly in 41 swine farms of the Abidjan district . Of the 781 samples collected , 19 Yersinia strains were isolated in 3 farms: 7 non-pathogenic Yersinia intermedia and 12 pathogenic Yersinia enterocolitica bioserotype 4/O:3 . Farm animals other than pigs and wild animals were not found infected . Furthermore , 2 Y . enterocolitica 4/O:3 strains were isolated from 426 fecal samples of patients with digestive disorders . All 14 Y . enterocolitica strains shared the same PFGE and MLVA profile , indicating their close genetic relationship . However , while 6 of them displayed the usual phage type VIII , the other 8 had the highly infrequent phage type XI . Whole genome sequencing and SNP analysis of individual colonies revealed that phage type XI strains had unusually high rates of mutations . These strains displayed a hypermutator phenotype that was attributable to a large deletion in the mutS gene involved in DNA mismatch repair . This study demonstrates that pathogenic Y . enterocolitica circulate in the pig reservoir in Côte d'Ivoire and cause human infections with a prevalence comparable to that of many developed countries . The paucity of reports of yersiniosis in West Africa is most likely attributable to a lack of active detection rather than to an absence of the microorganism . The identification of hypermutator strains in pigs and humans is of concern as these strains can rapidly acquire selective advantages that may increase their fitness , pathogenicity or resistance to commonly used treatments .
Yersinia enterocolitica is an enteropathogenic bacterium responsible for human gastroenteritis . This species belongs to the genus Yersinia , and to the family Enterobacteriaceae . Clinical presentation of yersiniosis includes diarrhea , abdominal pain , fever , and sometimes vomiting [1] . Although the infection is often mild and self-limiting , more severe clinical presentations such as pseudo-appendicular syndromes mimicking an appendicitis [2] or septicemia in elderly and immuno-compromized patients [3] can occur . Reactive arthritis and erythema nodosum are the most frequent secondary complications [4] . Enteric yersiniosis is a foodborne disease [5 , 6] , which is transmitted through the fecal-oral route . The species Y . enterocolitica is subdivided into 6 biotypes [7] . Biotype 1A is non-pathogenic while the 5 other biotypes ( 1B , 2–5 ) cause human and/or animal infections . The biotype the most frequently responsible for human infections worldwide is biotype 4 [8] , which is almost systematically associated with serotype O:3 ( 4/O:3 ) , followed by bioserotype 2/O:9 . Pigs are the main reservoir of bioserotype 4/O:3 strains [8] . These animals are asymptomatic carriers of the bacteria in their tonsils and intestinal tract , and they shed the enteropathogen in the environment with their stools . Contamination of pork meat often occurs during pig evisceration at slaughter [9] . Although Y . enterocolitica represents the third cause of bacterial diarrhea in Europe , after campylobacteriosis and salmonellosis [10] reports of human yersiniosis are scarce in West Africa . A systematic survey of children with diarrhea in Ouagadougou ( Burkina Faso ) allowed the isolation of Yersinia strains from 1 . 7% of the stools tested [11] . However , no indication of the species or biotype of the strains was provided , making difficult to estimate whether these isolates corresponded to saprophytic or pathogenic species . Y . enterocolitica strains were also isolated from the stools of children with gastroenteritis in Gabon , but their bioserotype was not determined [12] . In Ghana , a few Y . enterocolitica strains were isolated from blood products at transfusion centers [13] or were detected by PCR in the blood of some patients with sepsis [14] , indicating that severe cases of yersiniosis occur in this country . The most active West African country for the survey of Yersinia infections is Nigeria . Several studies reported the isolation of pathogenic Y . enterocolitica from the stools of patients presenting with enteric infections [15–19] . An active surveillance of the animal reservoir also revealed the presence of pathogenic bioserotypes of Y . enterocolitica pigs , bovine and sheep in Nigeria [20–23] . In contrast , a survey of the pig reservoir in Senegal [24] and Burkina Faso [25] did not identify any Yersinia strains . The search for these bacteria in water sources in Nigeria also remained negative [26] . These data suggest that Y . enterocolitica is present in the animal reservoir and causes human infections in West Africa . Insufficient public health surveillance and inappropriate isolation procedures may account for the paucity of reports of this infection [27] . The fast-growing demand for milk and meat in urban centers in resource-limited countries is leading to the intensification of livestock production systems , especially in peri-urban areas . However , because efficient zoonosis surveillance and food safety are lacking , the risk for zoonosis transmission is increasing , particularly in rapidly growing urban centers of resource limited countries [28] . In Côte d'Ivoire , there is some indication that Y . enterocolitica circulates and may be a cause of human infections . In 1983 , a search for the presence of fecal coliforms in drinking well water and in human stools allowed the isolation of a few Yersinia strains [29] . As their species were not determined , whether they were environmental non-pathogenic strains , or enteropathogens could not be established . More recently , three Y . enterocolitica strains of bioserotype 4/O:3 were isolated from pig carcasses at slaughter [30] . These results thus showed the presence of pathogenic Yersinia in the pig reservoir , suggesting that Y . enterocolitica may be a cause of human gastroenteritis in Côte d'Ivoire . This study aimed at further investigating the carriage of Y . enterocolitica in the animal reservoir and at estimating the prevalence of enteropathogenic Yersinia in human diarrheal diseases in the Abidjan area of Côte d'Ivoire .
The human and animal components of this study were approved by the National Ethical Research Committee of Côte d’Ivoire ( Ref 095/MSLS/CNER-kp ) . Written informed consents were obtained from the patients or their parents , by medical doctors and laboratory teams in charge of surveillance activities , and from farmers and animal owners . This study was carried out from June 2012 to December 2013 . Forty-one swine farms distributed in 4 sub-prefectures of the Abidjan district ( Abidjan , Anyama , Bingerville and Songon ) were selected based on their high pig production capacity and were visited monthly . Two to 3 stool samples were collected by rectal swab from apparently healthy pigs randomly sampled in each farm and pooled . Fecal samples were also taken from cattle in the infected farms and pooled . Wild animals ( rats and gigantic snails ) living around the farms were also captured and their intestinal contents were individually analyzed for the presence of Yersinia . Fecal samples were collected from humans with digestive disorders in 8 hospitals from the 4 sub-prefectures . Animal feces and intestinal contents , as well as human stools were collected freshly and stored cold during transportation in an ice box to the laboratory for immediate processing . Yersinia strains were isolated using two stages enrichment procedures as described by the Department of Food and Environmental Hygiene in Finland [31] . This procedure included prior-enrichment at 25°C during 24h of 1 ml of sample in 9 ml of Brain Heart Infusion broth containing 2 . 5 mg/l novobiocin . This was followed by an enrichment step for 7 and 14 days at 4°C in a modified phosphate buffer saline supplemented with 1% mannitol , 0 . 15% bile salts , and 0 . 5% soy peptone . An alkali treatment for 20s of 0 . 5 ml of the enriched sample in 4 . 5 ml of 0 . 25% potassium hydroxide was then performed to reduce the background-contaminating flora as described [32] . A 10 μl volume of the enriched sample was then streaked on Cefsulodin-Irgasan-Novobiocin agar ( CIN ) . The plates were incubated under aerobic conditions at 30°C for 18h–48h . Putative Yersinia colonies were identified with oxidase , Kligler iron and Christensen’s urea . Oxidase-negative , glucose-positive , H2S-negative and urease-positive colonies were finally identified with API 20E strips . Colonies displaying typical patterns were further characterized at the French Yersinia Reference Laboratory ( Institut Pasteur , Paris ) for species determination , biotyping and serotyping [3] . Phage typing was performed and interpreted as described in [33] , except that phage type VIII was characterized by a susceptibility to all phages except phage l ( observation from the Yersinia Reference Laboratory ) . From an 18-24h bacterial culture onto trypticase soy agar , a bacterial suspension in saline was prepared at McFarland 0 . 5 ( equivalent to ≈108 cfu/ml ) . Antibiotic susceptibilities were determined by the disc diffusion method on Mueller-Hinton agar ( Oxoid ) according to the procedure described in [34] . The results were interpreted according to the guidelines of the European Committee on Antimicrobial Susceptibility Testing ( EUCAST: http://www . eucast . org ) . The antimicrobial drugs tested and their concentrations on the discs ( BioRad ) were the following: amoxicillin ( 25 μg ) , amoxicillin-clavulanic acid ( 20 μg/10 μg ) , cefalotin ( 30 μg ) , cefoxitin ( 30 μg ) , ceftriaxone ( 30 μg ) , ciprofloxacin ( 5 μg ) , nalidixic acid ( 30 μg ) , trimethoprim ( 5 μg ) , sulphonamide ( 200 μg ) , tetracycline ( 30 UI ) and ticarcillin ( 75 μg ) . Bacterial suspensions from 8-24h bacterial cultures were diluted to 104 cfu/ml in sterile water ( Eurobio ) and centrifuged for 10 min at 13 , 300 x g at 4°C . Genomic DNA was extracted using the phenol/chloroform and alkali lysis methods [35] . Primers specific for the chromosomal genes ail ( attachment invasion locus ) and ystA ( Yersinia heat-stable enterotoxin ) , and for the pYV plasmid-borne gene yadA ( Yersinia adhesin ) of Y . enterocolitica were used ( S1 Table ) . PCR reactions were carried out in a volume of 50 μl containing 2 . 5 μl of 5X Green Flexi buffer ( Promega ) , 2 . 5 μl of 5X Colorless Flexi buffer 3 μl of 25 mM MgCl2; 1 μl of 10 μM nucleotides dATP , dTTP , dGTP and dCTP; 1 μl of a 20 μM solution of each primer ( S1 Table ) ; 0 . 2 μl of 5 U/μl Taq DNA polymerase and 5 μl of DNA . The amplifications were performed in a thermal cycler with the following conditions: denaturation at 94°C for 5 min , followed by 40 cycles of denaturation at 94°C for 30s , annealing at 60°C for 30s , and extension at 72°C for 1 min , with a final extension at 72°C for 5 min . PCR products ( 10 μl ) were subjected to electrophoresis in a 2% agarose gel and stained with ethidium bromide . Pulsed Field Gel Electrophoresis ( PFGE ) of Y . enterocolitica 4/O:3 isolates was carried out as previously described [36] . The genomic DNA was digested with PmeI and SpeI and subjected to electrophoresis for 24h using pulse times ranging from 1 to 13 s at 14°C for PmeI , and from 1 to 15 s at 17°C for SpeI , with an angle of 120° in a CHEF-DR III apparatus A middle-range PFGE size marker ) was used . MLVA ( Multiple-locus variable-number tandem-repeat analysis ) was based on six loci previously defined [37] and was performed by sequence analysis of the PCR products . The PCR mixtures ( 40 μl ) contained 100 ng of DNA template , 0 . 2 μM of each primer , 1 . 25 unit of Taq DNA polymerase ( Thermo Scientific ) , 200 μM of dNTPs , 1 . 25 mM MgCl2 , and a final concentration of 1X Taq buffer . The amplification was carried out in a DNA thermocycler with a pre-denaturation step at 94°C for 10 min , followed by 35 cycles of denaturation at 94°C for 30 s , annealing at 58°C for 30 s , and elongation at 72°C for 30s . A final 3 min elongation step at 72°C was done after the last cycle to ensure complete amplicon extension . PCR products were sequenced , and the number of repetitions for each locus was determined by the analysis of each sequence . Two ml of overnight bacterial cultures at 28°C were centrifuged at 5 , 000 x g for 5 min , and the genomic DNA was extracted from the cell pellet using the Genomic DNA mini kit . The DNA was suspended in 100 μl of elution buffer and quantified with the LUX ( Thermo Fisher Scientific ) . Genomic libraries were prepared with 0 . 1 ng of DNA as template , using the Nextera XT protocol on the SureCycler 8800 thermal cycler ( Agilent ) . The libraries were purified with the AMPure beads and quality control was performed with the High Sensitivity D1000 kit on Tape station 2200 Inserts were sized ( 400–900 bp ) using the Pippin Prep kit CDF 1510 and enriched with 35 cycles of qPCR with the KAPA kit on the Lightcycler 96 before library quantification and validation . Hybridization of the library to the flow cell and bridge amplification was performed to generate clusters . Paired-end reads of 150 cycles were collected on a NextSeq500 sequencer using the HighOutput kit . Demultiplexing and generation of FASTQ files from the raw sequence data were performed using the bcl2fastq software . Trimming , clipping , and filtering off exogenous and/or non-confident bases ( options:–q 13 , -l 30 , -p 80 ) within FASTQ files were performed with AlienTrimmer [38] . Redundant or over-represented reads were reduced using the khmer software package ( option:–c 70 ) [39] . Finally , sequencing errors were corrected using Musket [40] and overlapping paired reads were merged with FLASH [41] . A de novo assembly was performed for each strain with the quality-filtered reads using SPAdes v3 . 6 ( options:-k 21 , 33 , 55 , 77—only-assembler—careful ) [42] . The alignment of the quality-filtered reads against a reference genome YE1203 ( accession number: HF933425 ) was performed using BWA v0 . 7 . 7 , and variant calling ( SNPs and indels ) using SAM tools v1 . 2 and VarScan v2 . 3 . 6 ( options:–min-coverage 30 and–min-var-freq 0 . 8 ) [43] . Genome sequences are available in European Nucleotide Archive under BioProject PRJEB13626 ( sample ID starting with ERS and accession numbers starting with FK ) for strains IP134 ( ERS1122523 , FKKS01000001-FKKS01000141 ) , IP35459 ( ERS1122524 , FKKM01000001-FKKM01000146 ) , IP35462 ( ERS1122525 , FKKN01000001-FKKN01000154 ) , IP35463 ( ERS1122526 , FKKL01000001-FKKL01000153 ) , IP35464 ( ERS1122527 , FKKW01000001-FKKW01000150 ) , IP35465 ( ERS1122528 , FKKU01000001-FKKU01000159 ) , IP35466 ( ERS1122529 , FKKJ01000001-FKKJ01000148 ) , IP35467 ( ERS1122530 , FKKT01000001-FKKT01000151 ) , IP35470 ( ERS1122531 , FKKV01000001-FKKV01000149 ) , IP35471 ( ERS1122532 , FKKP01000001-FKKP01000150 ) , IP35472 ( ERS1122533 , FKKQ01000001-FKKQ01000151 ) , IP35474 ( ERS1122534 , FKKO01000001-FKKO01000149 ) , IP35475 ( ERS1122535 , FKKK01000001-FKKK01000157 ) , IP35477 ( ERS1122536 , FKLN01000001-FKLN01000190 ) , IP35478 ( ERS1122537 , FKKR01000001-FKKR01000152 ) . Strains were streaked on TSA agar plates . Ten colonies were picked and were grown in 10 ml Luria Bertani ( LB ) broth under agitation at 28°C for 24h . OD600 values were recorded and 200 μl of each culture were plated onto LB agar containing 50 μg/ml nalidixic acid or 100 μg/ml rifampicin . All plates were incubated at 28°C for 3 days . Colony enumeration was performed with a Scan500 automatic colony counter . The results were expressed as the number of colonies on antibiotic plates per 109 cfu of the original inoculum .
A total of 781 samples of pooled pig feces collected over the 19 months study period in 41 farms of four sub-prefectures of the Abidjan district were analyzed for the presence of Yersinia . The two steps enrichment procedure used in this study allowed the isolation of 19 strains ( Table 1 ) . Seven of them belonged to the non-pathogenic species Yersinia intermedia . All 7 Y . intermedia strains were of biotype 4 , but they had different serotypes: 2 were of serotype O:7 , 8-8-8 , 19 , while the remaining 5 were of serotype O:7 , 8-8-13-8 , 19 ( S2 Table ) ; indicative of the circulation of different Y . intermedia strains among pigs in this area . These strains were isolated from 3 farms in 2 sub-prefectures ( Bingerville and Abidjan , Fig 1 ) over a ≈1-year period ( S2 Table ) . All other 12 strains belonged to the species Y . enterocolitica and were of bioserotype 4/O:3 ( Table 2 ) . These pathogenic Y . enterocolitica were recovered from pig feces during a period extending from March to August without isolation during the other 6 months of the year , suggesting a periodicity in the carriage of pathogenic Y . enterocolitica by swine in the Abidjan district . In contrast , some Y . intermedia strains were isolated in February ( S2 Table ) . The Y . enterocolitica strains were isolated from the same 3 farms from which the Y . intermedia strains were isolated ( Table 2 ) . The isolation rate of Yersinia strains ( pathogenic and non-pathogenic ) was similar ( 8 to 9% ) in the 3 farms ( S3 Table ) . Farms B and C were close to each other ( 1 . 5 km ) in the Bingerville sub-prefecture ( Fig 1 ) , arguing for a local circulation of Y . enterocolitica in this area . Farm A was located in the Abidjan sub-prefecture and was distant by more than 30 km from the other two , indicating the existence of at least two different geographical foci of yersiniosis in the Abidjan district . To determine whether pathogenic Y . enterocolitica strains were circulating among other animal species living in the farms , fecal samples were taken from sheep and bovine and pooled after each visit . None of these samples ( 178 from cattle and 109 from bovine ) were found infected with a Yersinia strain ( Table 1 ) . Furthermore , 202 rodents ( Rattus , Rattus norvegicus and Thryonomys swinderianus ( greater cane rat ) ) and 95 gigantic snails ( Achatina fulica ) living around the farms were captured . Their intestinal content was taken during autopsy and analyzed for the presence of Yersinia . No Yersinia strains were recovered from these wild animals . These results confirm the preferential association of Y . enterocolitica 4/O:3 with pigs [44 , 45] Between June 2012 and December 2013 , 426 fecal samples were collected from humans in 8 hospitals from the 4 sub-prefectures ( Fig 1 and Table 1 ) . They were collected from 287 patients with diarrhea during a childhood diarrhea surveillance program ( 205 samples ) , or as part of routine stool cultures in medical microbiology laboratories ( 82 samples ) . The 139 other patients presented with digestive disorders ( abdominal pain , nausea , vomiting ) without diarrhea . Overall the patients aged between 7 months and 55 years with an average of 4 years . Two Yersinia strains were recovered from the 426 human fecal samples analyzed , indicating a prevalence of 0 . 47% for all patients with intestinal disorders , and of 0 . 69% for patients with diarrhea . The two patients were infant females aged 1 year 7 months , and 3 years 2 months who presented with diarrhea and fever . They were seen at the university hospital and at the annex of the hospital ( 2 km away ) in the sub-prefecture of Abidjan ( Fig 1 ) . Both clinical strains were pathogenic Y . enterocolitica 4/O:3 ( Table 2 ) . They were isolated in April and September from the 2 patients ( Table 2 ) , i . e . at a time of the year where pigs were also found to be carriers of Y . enterocolitica 4/O:3 strains . Phage typing is a simple means , routinely used by the French Yersinia Reference Laboratory to subgroup 4/O:3 strains . This subtyping method was applied to the Ivorian Y . enterocolitica strains to determine whether the porcine and human isolates share the same phage type ( ΦT ) . Six strains of pig origin were of ΦT VIII ( Table 2 ) , which is by far the most common ΦT among bioserotype 4/O:3 strains worldwide [3 , 46] . The other 8 strains exhibited ΦT XI ( Table 2 ) , which is highly unusual in 4/O:3 strains . These ΦT XI strains were isolated from all 3 farms and from the 2 human patients . ΦT XI means susceptibility profiles to the 12 lysogenic phages different from the well established ones ( VIII , IXa and IXb ) for 4/O:3 strains [33] . Actually , a susceptibility or resistance phenotype to each phage was sometimes hard to assign because the growth of ΦT XI strains was heterogeneous within the lysis zone . We often noted a bacterial growth that was less dense in contact to than at distance from the phage drop , or isolated colonies growing within the lysis zone . These suggested mixed bacterial populations composed of colonies susceptible and resistant to each phage . To test this hypothesis , 3 ΦT XI strains ( IP35471 , IP35477 and IP35478 ) were selected and 6 individual colonies from each of them were picked and individually phage typed . Two to 3 susceptibility profiles were observed for each strain ( S4 Table ) , confirming heterogeneity of the bacterial population . Some colonies of strains IP35471 and IP35477 displayed the usual ΦT VIII , arguing for the emergence of variants from this parental phenotype . The 2 other susceptibility profiles were designated ΦT XIa and XIb . ΦT XIa was identified in colonies from the 3 strains , while ΦT XIb colonies were detected in 2 strains ( S4 Table ) . Since a single colony from each biological sample was originally picked and stored , these data show that mutations leading to variable susceptibility or resistance to the set of lysogenic phages occurred in these strains . Altogether these results argue for an unusual propensity of some Ivorian strains to display heterogeneous profiles of resistance to the set of Y . enterocolitica lysogenic phages . As commonly observed [47 , 48] , the Y . enterocolitica 4/O:3 strains isolated in our study were resistant to amoxicillin , amoxicillin/clavulanic acid , cefalotin , and ticarcillin . However , they were susceptible to cefoxitin , ceftriaxone , ciprofloxacin , nalidixic acid , trimethoprim , sulphonamide and tetracycline , indicating that they had not acquired any unusual antibiotic resistances . As expected , all 14 Y . enterocolitica 4/O:3 strains carried the ail and ystA chromosomal virulence genes ( Table 2 ) , further indicating their pathogenic potential . Only 9 of them ( 64 . 3% ) harbored the pYV-borne yadA gene , in line with the observation that the pYV plasmid is easily lost upon in vitro subculture of pathogenic Yersinia [9] . PFGE analysis after digestion with PmeI and SpeI of the 14 Y . enterocolitica 4/O:3 strains isolated from pigs and humans showed a single profile with each enzyme for all strains analyzed ( illustrated in Fig 2 ) . This unique profile differed only slightly from that of an epidemiologically unrelated Y . enterocolitica 4/O:3 control strain ( IP134 , isolated in Sweden ) . As the discriminatory power of PFGE has recently been shown to be low for pathogenic Y . enterocolitica [49] , the strains were then subjected to MLVA , which has a higher discriminatory power [36 , 37] . This MLVA analysis based on six loci also gave a unique pattern ( 7-8-7-8-8-2 ) for the 14 Y . enterocolitica strains analyzed . This pattern differed drastically from that of the IP134 control strain ( 11-2-9-16-6-X ) , indicating either that the Y . enterocolitica strains isolated in the Abidjan district are genetically closely related , or that the same strain circulated in the 3 pig farms and in humans . To further examine the genetic relatedness of the Y . enterocolitica 4/O:3 strains isolated from pigs and humans in the Abidjan district over the study period , the genomes of the 14 Y . enterocolitica strains collected were sequenced . A pair-wise analysis of Single Nucleotide Polymorphism ( SNP ) showed that the Ivorian strains displayed between 590 and 675 SNPs with the epidemiologically unrelated Y . enterocolitica 4/O:3 strain IP134 ( Table 3 ) . In contrast , the number of SNPs between each pair of Ivorian strains was always <96 , indicating that they were genetically much closer to each other than to the epidemiologically unrelated strain . Two isolates ( IP35462 and IP35464 ) had no SNPs between each other . They were both of ΦT VIII and were isolated at one-month interval from the same farm , suggesting that one strain was persisting and isolated twice from farm C . The other strains displayed some degrees of genetic polymorphism ( Table 3 ) . These results thus demonstrate that most of the strains that circulate in the pig and human populations in the Abidjan area are distinct from each other . A minimal spanning tree ( MST ) based on whole genome SNP analysis was constructed to determine the genetic relatedness between the Ivorian Y . enterocolitica isolates . This tree further showed that the Ivorian strains were genetically much closer to each other than to the epidemiologically unrelated Y . enterocolitica 4/O:3/VIII strain IP134 ( Fig 3 ) . The isolates grouped neither according to their geographical location , nor to their date of isolation , arguing for a circulation of strains between the 3 farms , even though farm A was distant from farms B and C . Interestingly , with the exception of strain IP35465 , the Ivorian isolates separated into two clusters based on their ΦT ( Fig 3 ) . This and the fact that strain IP134 branched with Ivorian strains of ΦT VIII , further strengthen the hypothesis that strains exhibiting the unusual ΦT XI are variants that emerged and gradually diverged from an ancestral ΦT VIII strain . The lipopolysaccharide O-antigen has been shown to act as a receptor for various Y . enterocolitica phages [50] . Although this has not been demonstrated for the set of phages used for Y . enterocolitica phage typing at the Reference Laboratory , we hypothesized that mutations in the O-antigen gene cluster might have occurred in the ΦT XI branch of the Ivorian isolates and could be responsible for the unusual phage susceptibility patterns observed . However , no polymorphism in the 6 . 8 kb nucleotide sequence of the O-antigen gene cluster was observed in Ivorian strains , indicating that their unusual susceptibilities to the set of phages are due to other unidentified mutations . To further understand the genetic bases for the heterogeneity of the phage susceptibility profiles , the genomes of 2 colonies of ΦT XIa ( #5 and #6 ) and of 1 colony of ΦT VIII ( #4 ) from strain IP35471 were sequenced . The aim was to identify genetic modifications that would be common to the 2 ΦT XIa colonies and absent from the ΦT VIII colony , and could thus account for the change in the phage susceptibility profiles . No gene deletions , insertions or mutations ( SNPs ) having these characteristics were identified among the 3 genomes . However , this analysis revealed an unexpectedly high number of SNPs ( 9 to 12 ) between individual colonies of the same strain . To further explore this phenomenon , the number of within-strain SNPs was evaluated on 9 individual colonies from 3 strains of ΦT XI and compared to that of individual colonies from a typical ΦT VIII Y . enterocolitica 4/O:3 strain ( IP33927 ) . While the number of SNPs between colonies of strain IP33927 was always ≤2 ( average of 1 SNP ) , the 3 Ivorian strains of ΦT XI exhibited between 9 and 16 within-colonies SNPs ( average of 12 SNPs ) ( S5 Table ) , indicating a >10-fold higher mutation rate in these strains . The higher mutation rate observed in the Ivorian strains of ΦT XI was suggestive of a hypermutator phenotype [51] . To determine whether ΦT XI Y . enterocolitica Ivorian isolates do have a higher rate of mutations than typical ΦT VIII strains , colonies from 3 ΦT XI strains: IP35471#5 ( XIa ) , IP35477#3 ( XIa ) and IP35478#4 ( XIb ) were selected along with strain IP35463 that exhibits the usual ΦT VIII . The capacity to grow on nalidixic and rifampicin agar plates was evaluated for 10 colonies from each of these 4 strains . While very few colonies ( ≤3 ) of IP35463 ΦT VIII grew on rifampicin and nalidixic acid agar plates , an average of ≈1000 to 2000 spontaneous RifR and NalR mutants were observed in the 3 ΦT XI strains ( Table 4 ) . Our results thus demonstrate that some Ivorian Y . enterocolitica strains have a hypermutator phenotype . A hypermutator phenotype has been linked to mutations in several genes involved in the fidelity of DNA replication , and more particularly in mutS [51] . When we looked at this gene in the genome of all 14 Ivorian strains , we observed that the 6 ΦT VIII strains had an intact and identical sequence , while all 8 ΦT XI strains , except IP35465 , exhibited a 960 bp deletion at the 3' end of mutS ( S1A Fig ) , corresponding to position 935 , 820 to 936 , 779 in the reference genome YE1203 . This deletion would lead to the synthesis of a protein truncated of more than one third of its normal size ( S1B Fig ) . Therefore the hypermutator phenotype most likely results from a large deletion of the mutS gene in some Ivorian isolates .
Diarrheal diseases are a major public health problem in developing countries , with a high infant mortality rate in Africa [52] . Adapted therapeutic measures and control strategies are essential , but cannot be implemented without a proper identification of the etiological agents . Yersiniosis is the third most frequent bacterial disease causing human enteric infections in Europe [10] , but reports of this disease are extremely infrequent in developing countries . In West Africa , only few countries reported the isolation of Yersinia from clinical cases [11–20] , most likely because an active search for these bacteria is not performed . This is supported by the observation that in Nigeria , where studies were carried out to specifically look for this pathogen in human and animal samples , Yersinia strains were isolated [15–19] . A major reason for the lack or poor detection of these bacteria is the difficulty to recover them from poly-contaminated samples such as stools , which contain an abundant bacterial flora [53] . Indeed , Yersinia strains differ from other enterobacteria by a slower growth rate ( 48h instead of 24h ) and an optimal growth temperature of 28°C instead of 37°C . Therefore , cultures performed under conditions suitable for most enteropathogens are not effective for the recovery of Yersinia colonies from polycontaminated biological samples . Specific procedures are required to enhance the isolation rate [53–55] , but these procedures are time consuming and costly , and therefore are not performed on a routine basis in most laboratories in West Africa . There were some indications that pathogenic Yersinia are circulating in the swine reservoir in the Abidjan area of Côte d'Ivoire , as a few pathogenic strains of Y . enterocolitica were isolated from pig carcasses at slaughter houses [30] . In this work we wanted to get an estimate of the number and distribution of Yersinia-infected pig farms , and most importantly to determine whether enteric yersiniosis is a cause of human diarrhea in the Abidjan district of Côte d'Ivoire . Using a procedure for the specific isolation of Yersinia strains that included several enrichment steps ( growth at 25°C , addition of novobiocin , enrichment at 4°C , and growth on CIN agar ) , we were able to isolate 19 Yersinia strains from 781 samples of pig stools collected in 41 farms over 19 months . Seven of these strains belonged to the non-pathogenic species Y . intermedia . This species was also previously recovered form pigs at slaughter in the Abidjan region , but the strains had biotypes or serotypes different from those of this study , and they were isolated from other areas [30] . Y . intermedia strains were also isolated from rectal or tongue swabs of healthy pigs in Nigeria [21 , 22] , suggesting that the environmental conditions in West African countries are favorable for the maintenance of this non-pathogenic species . The other 12 strains isolated from pig feces were pathogenic Y . enterocolitica . Pigs are regarded as the major reservoir of enteropathogenic Y . enterocolitica in most countries worldwide [56] . Although these animals were also found to be carriers of Yersinia in the Abidjan district , none of the other cattle sampled within these farms and of the rodents captured in the vicinity of the farms were found infected with Y . enterocolitica . Snails were also sampled because giant African snails may be abundant around the farms , they are widely consumed as a source of protein , and it was previously shown that they may carry enteropathogenic Y . enterocolitica for long periods of time [57] . However , none of the 95 snails analyzed were found infected . Therefore our findings support the hypothesis that , as in many other countries , pigs are the main reservoir of enteropathogenic Y . enterocolitica in the Abidjan district . According to the 2012 annual report of the Department of Animal Production , over 60% of the national pig production is concentrated in the farms of the Abidjan District . At pig slaughterhouses , meat inspection is limited to a search for macroscopic lesions on the carcasses , without any microbiological investigations . The prevalence of infected pigs at slaughterhouses is usually higher than in farms because samplings are performed on tonsils , which are the most reliable tissue to evaluate the carriage of enteropathogenic Yersinia [58] , while this cannot be done in live pigs owing to animal welfare . Since excretion of Y . enterocolitica in the feces is transient , the prevalence of infected pig farms ( 3/41 ) in the Abidjan district is thus most likely an underestimation of the risk of human exposure to yersiniosis upon consumption of pork meat . Although some Yersinia strains were previously isolated from human stools in Côte d'Ivoire [29] , their species and bioserotype were not determined , so it was not possible to establish whether they were enteropathogenic . Our active search for Yersinia in patients presenting with digestive disorders in the Abidjan district identified two human cases of Y . enterocolitica infections . This is the first demonstration that yersiniosis is a cause of human diarrhea in this country . These patients were two female infants from the same area ( Yopougon ) . However , they were infected at 4 months interval , indicating that their infection was not caused by the consumption of the same contaminated product . The 2 human strains had the same bioserotype 4/O:3 as the pig strains . Three strains of this bioserotype were also previously recovered from raw pig samples at slaughterhouse in the Abidjan district [30] . This bioserotype is the most frequently isolated from pigs and human cases in most countries worldwide [8] , although 2/O:9 was the predominant bioserotype in Y . enterocolitica strains isolated from pigs and human cases in the recent years in Nigeria [18 , 20] . However , since the selective CIN agar we used for the enrichment procedure is inhibitory for some Y . enterocolitica strains of serotypes O:8 and O:9 , and for Y . pseudotuberculosis [59 , 60] , the possibility that other pathotypes of Yersinia circulate in the Abidjan region cannot be excluded . The frequency of pathogenic Y . enterocolitica isolated from human stools greatly varies depending on the study , the country , the patients recruited , and the isolation procedures . For instance it was reported to be 0 . 19% ( 82/41 , 848 patients ) in Finland [61] , 4% ( 24/600 patients ) in Palestine [62] , 0 . 74% ( 36/4 , 841 patients ) in the USA [63] , 0 . 16% ( 6/3 , 784 patients ) in UK [64] , 0 . 6% ( 46/7 , 090 patients ) in Crete [65] , 2 . 46% ( 267/10 , 838 patients ) in Belgium [66] , 0 . 13% ( 3/2250 patients ) in Canada [67] , 0 . 42% ( 4/956 patients ) in China [68] , or 0% in Ireland ( 0/1 , 189 patients ) [69] and the Netherlands ( 0/857 patients ) [70] . In the Abidjan district , the isolation rate was 0 . 46% ( 2/427 patients ) , and therefore equivalent to or higher than those reported in several European countries ( Finland , UK , Ireland and the Netherlands ) , Canada and China . This demonstrates that , although neglected , Y . enterocolitica may be a cause of human diarrhea as frequent in Côte d'Ivoire as in European countries . As usually observed , the Y . enterocolitica 4/O:3 isolates from the Abidjan district were susceptible to most antibiotics commonly used to treat Gram-negative enteropathogens , and were resistant to penicillin and first and second-generation cephalosporin , due to the presence of the chromosomal blaA and blaB genes [71 , 72] . If the diagnosis of yersiniosis is not made , these classes of antibiotics may be used to treat patients , thus leading to treatment failure . Although pigs and human patients were sampled all year round , pathogenic Y . enterocolitica strains were only isolated during a period extending from March to September . This period overlaps both the 2 dry seasons ( March to May , and August to September ) and the rainy season ( May to August ) , with no major temperature variations along the year in the Abidjan district . The periodicity of the Y . enterocolitica carriage by pigs and consequent human infections may thus have causes independent of the climatic conditions . The Ivorian Y . enterocolitica 4/O:3 isolates shared an identical PFGE and MLVA profile , and at the whole genome level they were genetically much closer to each other than to a non-Ivorian isolate . The relative clonality of the strains isolated in the Abidjan district is suggestive of a single import followed by the spread and diversification of the introduced strain . However , we noted that the Y . enterocolitica isolates could be phenotypically subdivided into ΦT VIII and ΦT XI , the latter being highly unusual . Indeed , in the collection of the French Yersinia Reference Laboratory , which includes numerous strains of worldwide origins , 7 , 853 out of the 8 , 295 Y . enterocolitica 4/O:3 that were phage typed were of ΦT VIII ( 94 . 7% ) , while only 25 of them ( 0 . 3% ) were of ΦT XI . This unusual ΦT and the existence of ΦT subgroups within colonies of the same strain prompted us to perform a whole genome analysis . This analysis revealed that ΦT XI isolates had actually a hypermutator phenotype most likely caused by a large deletion at the 3' end of the mutS gene . In Escherichia coli , large deletions removing the 3' end of mutS have also been shown to cause an excessive rate of point mutations [73] . mutS is involved in DNA mismatch repair that ensures the fidelity of replication of the bacterial chromosome [74] . The variety of ΦT observed in colonies harboring the mutS deletion thus probably results from random mutations occurring at higher frequencies in various genes , some of them used by phages to enter or kill their bacterial hosts . Of note , only one ΦT XI strain ( IP35465 ) carried an intact mutS gene , and this is the only ΦT XI isolate that grouped with ΦT VIII strains in the minimal spanning tree . It is therefore likely that this strain harbored a mutation in a gene conferring resistance to some phages , but because its genome was not prone to multiple mutations , it was genetically closer to the non-mutator strains of ΦT VIII . Since the deletion of mutS was identical in all Ivorian isolates , this event probably occurred once in an ancestral strain from which the other mutS strains derived . The ability to rapidly expand mutant cell types is a clear advantage for pathogenic organisms to evade host defenses and drugs , and to adapt to stresses and changing environments [73] . The longer branches between Ivorian strains in the hypermutator cluster , as compared to the non-hypermutator cluster in the minimal spanning tree , are indicative of a faster genetic diversification of the mutator strains . This may thus lead to the expansion of pathogenic Y . enterocolitica strains with new phenotypes that may increase their capacity to multiply in their animal host or to cause severe infections in humans . Mutations leading to resistances to several classes of antibiotics may also arise at higher frequencies . Since the hypermutator phenotype is most likely caused by a large deletion of mutS , the reversion to a non-mutator phenotype is now hardly possible in these strains . In conclusion , this study demonstrated that pathogenic Y . enterocolitica are circulating in the pig reservoir in Côte d'Ivoire and are causing human infections with a prevalence comparable to that of some European countries . The paucity of reports of this infection in African countries is most likely attributable to a lack of active detection rather than to an absence of the microorganism . The identification of hypermutator strains circulating in the pig reservoir and in humans may be of concern as these strains may acquire at a faster rate selective advantages that may increase their fitness , pathogenesis or resistance to commonly used treatments . | Diarrhea is a major public health problem in developing countries , especially in Africa , but the causative agents are often unknown , impeding the implementation of appropriate therapeutic measures . Although pathogenic Yersinia enterocolitica are a frequent cause of gastroenteritis in developed countries , reports of human yersiniosis are scarce in West Africa . We performed this study to determine whether this pathogen was present in pigs ( the natural reservoir of this bacterium ) in various swine farms of the Abidjan district , and whether it was causing human gastro-intestinal infections . We show here that this bacterium is indeed circulating among Ivorian pigs and is causing human digestive disorders with a frequency similar to that reported in developed countries . The paucity of reports of this infection in African countries may be explained by the difficulty of isolating Y . enterocolitica from stools and the need for specific and time-consuming procedures . During this study we also made the unexpected observation that some Ivorian strains have acquired the capacity to mutate at a much higher frequency than normal strains . This property may render them better fitted to new environments , more virulent to their host , or capable of resisting some commonly used treatments , which could be of great public health concern . | [
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"... | 2017 | Yersinia enterocolitica, a Neglected Cause of Human Enteric Infections in Côte d’Ivoire |
Tunisia has one of the highest burdens of extrapulmonary tuberculosis ( EPTB ) among tuberculosis ( TB ) cases but the contribution of MTBC-mediated human EPTB is unknown . EPTB diagnosis is challenging due to the paucibacillary nature of clinical samples . Therefore , a need of a simplified molecular method for sensitive and specific TB detection and differentiation of MTBC members caused EPTB remains a priority to an early diagnosis , optimize successful anti-TB treatment and minimize transmission . We evaluated the performance of a single tube tetraplex Taq Man real time PCR for EPTB detection and differentiation between MTBC members directly on extrapulmonary samples . Extrapulmonary samples obtained from clinically suspected EPTB patients from 2013 to April 2015 were tested by Ziehl Neelsen Staining , mycobacterial culture and qPCR assay for RD1 , RD9 , RD12 and ext-RD9 targets ( MTBC-RD qPCR ) . The performance of qPCR was compared to a reference standard based on MTBC culture and/or at least two criteria of a composite reference standard ( CRS ) including clinical , radiological , histopathological and therapeutic findings . EPTB was identified in 157/170 ( 92 . 4% ) of included patients of whom 99 ( 63% ) were confirmed by culture and 58 ( 36 . 9% ) by CRS criteria . The sensitivity and specificity of qPCR , in comparison to the reference standard were 100% ( 157/157 ) and 92 . 3% ( 12/13 ) , respectively . The sensitivity of qPCR was statistically significant as compared to culture and smear microscopy ( P< 0 . 001 ) . QPCR results showed M . bovis identification in 77 . 1% of extrapulmonary samples in occurrence to lymphadenitis infection . M . tuberculosis and M . bovis BCG were detected in 21 . 6% and 1 . 3% of cases , respectively . MTBC–RD qPCR proved to be a rapid and sensitive assay for simultaneously TB detection and MTBC members identification on extrapulmonary samples within 1 . 5 days after sample receipt . Its high sensitivity could make this method a useful tool in diagnosing TB in addition to routine conventional methods and TB clinical parameters .
Tuberculosis ( TB ) remains a leading cause of morbidity and mortality worldwide [1] . According to annual surveys conducted by the World Health Organization ( WHO ) , 10 . 4 million new active TB cases and 1 . 8 million deaths occurred in 2015 [1] . Although pulmonary TB is the most common presentation of this disease , it can involve any organ in the body [2] . Extrapulmonary Tuberculosis ( EPTB ) is defined as the isolated occurrence of TB in any part of the body other than lungs [2] . The prevalence of EPTB is highly variable which is essentially attributable to the geographic origin of the patient . A high incidence is observed among the immunocompromised HIV co-infected patients [3] . In Tunisia , EPTB makes up to 57% of TB cases despite the low prevalence of HIV which is higher compared to other countries [4] . EPTB can be caused by Mycobacterium tuberculosis complex ( MTBC ) , such as Mycobacterium tuberculosis , Mycobacterium bovis , Mycobacterium bovis BCG and Mycobacterium africanum [5] . An early and rapid TB diagnosis as well as distinction between the different MTBC members are essential to determine the EPTB etiology and to optimize efficient anti-TB treatment since Mycobacterium bovis and Mycobacterium bovis BCG are intrinsically resistant to pyrazinamid ( PZA ) , an important first-line anti-TB drug [6 , 7] . Indeed , the natural mode of infection and surveillance measures for EPTB differ between complex members . For example , early diagnosis of M . bovis might prompt questions to ascertain the risk factors of zoonotic exposure or a contamination of derived food/dairy products from diseased cattle , the primary routes of EPTB infection [2 , 5] . In fact , the diagnosis of EPTB poses difficulties due to the diverse manifestations which may mimic other pathologies [8 , 9] , the difficulty of sampling , the paucibacillary nature of specimens as demonstrated by the low sensitivity of acid-fast bacilli smear as well as culture methods and a longer incubation delay for the growth of mycobacteria [5 , 8 , 9] . Indeed , the most commercially molecular tested assays are of limited use in differentiation between MTBC species and which were not evaluated directly on clinical specimens . Therefore , there is a great need of molecular simplified amplification methods for rapid , sensitive and specific detection and differentiation of MTBC members directly on clinical specimens . The new generation of real-time PCR ( qPCR ) has been particularly developed for these purposes [6 , 10] . Recently , comparative genomics of the MTBC identified several regions ( regions of difference; RD ) , ranging in size from 2 to 12 . 7 Kb , that were present in Mycobacterium tuberculosis H37Rv and absent in others members of MTBC . These results suggested that deletion of genomic regions have contributed to generating genetic diversity within this complex [11] . Based on these findings , Halse et al , have published a single-tube , multiplex protocol of Taq Man qPCR assay ( MTBC-RD qPCR ) developed for rapid detection and differentiation of MTBC members from TB clinical specimens . This MTBC-RD qPCR was based on comparative genomic deletion analysis , using RD motifs that are either common to MTBC members or specific to each one [6] . To the best of our knowledge , no studies concerning EPTB-related infectious mycobacteria in south Tunisia have previously been conducted . Accordingly , in this study , we evaluated the performance of a single tube tetraplex MTBC-RD qPCR for EPTB detection and differentiation between MTBC species from extrapulmonary samples .
In our study , non redundant specimens were obtained from patients with suspected EPTB infection in any extrapulmonary site from January 2013 to April 2015 . All analyses were performed prospectively in the regional hygiene care department of Microbiology , Mycobacteriology Laboratory of Hedi-Chaker University Hospital in Sfax ( South of Tunisia ) . The laboratory provides routine mycobacterial diagnostic tests for TB specimens obtained during clinical routine diagnosis for consulting or hospitalized suspected TB patients in Hedi-Chaker University Hospital . Patients ( adults and children , i . e . individuals< 18 years of age ) with suspected EPTB based on clinical signs and symptoms , cytological /histological and/or radiological signs suggesting TB were eligible for our study . Patients were excluded in case of they had a contaminated culture; when they were treated with an anti-TB treatment ( ATT ) within the past 1 month from the first presentation to the clinician and if they died . Specimens were aliquoted upon arrival in the laboratory . One aliquot was processed in the mycobacteriology laboratory for Ziehl Neelsen staining ( ZNS ) technique , culture and identification of mycobacteria . Another aliquot was used freshly or after storage at -80°C for DNA extraction and MTBC-RD qPCR performed in the research laboratory separately by another technician anonymously to clinical results . Approval for usage of the remaining clinical specimens for our study was obtained by the Ethics Committee of Hedi Chaker Hospital . For DNA extraction , 50 mg of the decontaminated and grinded specimen was heat treated at 80°C for 1 h before using the High Pure PCR Template Preparation kits ( Roche , Rotkreuz , Switzerland ) as described by the manufacturer . An additional lysis treatment step using lysozyme was included in the protocol following the proteinase k digestion . Briefly , the suspension was resuspended in 200 μl of lysis buffer and 40 μl of proteinase k . The mixture was incubated immediately at 56°C overnight . 200 μl of supernatant obtained after centrifugation at 5000 g for 5 min , were mixed with 5 μl lysozyme solution followed by incubation in thermo mixer ( Eppendorf ) at 37°C and 550 rpm for 15 min . Then , 200μl of binding buffer was added . The sample was incubated immediately at 70°C for 10 min . The spined solution was added to the DNA binding columns provided by High Pure PCR Template Preparation kit and processed as described by the manufacturer . Finally , the DNA template was eluted in 100 μl of the elution buffer and used as template in PCR-protocol . DNA extraction was performed in a biological hood using filter tips ( Tip One; Starlab , Bagneux , France ) and gloves were changed between each sample . A multiplex protocol of Taq Man real-time PCR for the detection and differentiation between the MTBC members was performed to test the DNA samples extracted from EPTB human specimens , as well as positive and negative controls , following a protocol previously described by Halse et al [6] . We have used the same primers and probes of the original protocol for RD1 , RD9 , RD12 and ext-RD9 targets without the use of RD4 one since they are sufficient to distinguish between MTBC members as demonstrated in Table 1 [6] . Briefly , the qPCR was performed in a 25 μl final volume with Ex Taq Premix Tli RNaseH Plus ( Takara , Japan ) as described previously [6] . QPCR was performed on a CFX96TM real-time PCR cycler ( Biorad , USA ) . Pure DNA was amplified in duplicate without and with a 1:5 dilution . Thorough preventive measures were taken to avoid DNA contamination during extraction and qPCR manipulation as mentioned previously [15] . The reference standard for EPTB diagnosis was defined as a positive culture for MTBC and/or a positivity of at least two criteria of a composite reference standard ( CRS ) including: ( a ) TB clinical symptoms , ( b ) histology/cytology findings , ( c ) radiological tests ( site specific computerized tomography scan/ magnetic resonance imaging ) , ( d ) therapeutic response to ATT . The histology/cytology findings of the specimen were defined positive by the presence of caseation necrosis and/or epithelioid granuloma . Radiological positive tests were noted when infiltrates or cavities , hilar lymph nodes , pleural effusions , or tuberculomas , leptomeningeal and basal cistern enhancement were found . A positive response to ATT therapy was considered when patient had clinically improvement 3 months after the date of enrollment . Diagnosis and clinical management of patients were done according to the reference standard and to clinician report . Analyses were done using Epi Info ( Info 7 ) and SPSS 13 . 0 ( SPSS; Chicago IL ) softwares . Sensitivity , specificity , positive and negative predictive values with 95% confidence intervals were calculated for MTBCRD-qPCR accuracy , against MTBC culture and against the reference standard based on culture and/or CRS criteria according to the Standards for Reporting of Diagnostic Accuracy Studies ( STARD ) recommendations [16] .
Of the 187 eligible patients , 17 were excluded . Thus , 170 patients were included in the study analyses ( Fig 1 for flow chart study details ) . Available specimens were 144 lymph nodes biopsy samples ( fine needle aspirates , n = 114 , and tissues , n = 30 ) , as well as 26 additional samples including pus and abscess ( n = 10 ) , 9 body fluids including cerebrospinal fluid ( CSF ) ( n = 8 ) and pleural fluid ( n = 1 ) , and finally 5 tissue and 2 bone scarping samples . The age range of our patients was 4 months to 80 years with a mean age of 31 . 3 years ( SD ± 16 . 93 years ) . The majority of cases 78 . 8% ( 134/170 ) were adults and 21 . 2% ( 36/170 ) were children ( aged <18 years ) . The sex-ratio was 0 . 54 . The majority of patients 71 . 2% ( 121/170 ) originated from southern governorates of Tunisia and 28 . 8% ( 49/170 ) from Sfax . Contact with livestock farming or cattle was identified in 23 cases and ingestion of unpasteurised dairy products and milk in 53 cases . In addition , 3 patients had a possible risk of exposure related to their family contact and 91 patients had no information records . None of the patients was tested positive for HIV antibodies . Out of the 170 patients , 99 ( 58 . 2% ) had positive cultures for MTBC on MGIT 960 liquid and/or solid media ( Table 2 ) ; 58 ( 34 . 1% ) culture-negative patients had EPTB diagnosis based on CRS which were clinically , radiologically , and/or histologically/cytologically positive ( Table 2 ) . All patients were positive for ATT response after follow up ( Table 2 ) . Smear microscopy was positive for AFBs in 29/170 specimens ( 17 . 1% ) from patients of whom 17 had positive MTBC culture and 12 being CRS positive for EPTB . Thirteen out of 170 patients ( 7 . 6% ) who had the reference standard negative for EPTB ( Table 2 ) , had other non-TB diagnoses including: viral meningitis ( n = 2 ) , sarcoidosis ( n = 1 ) , brucellosis ( n = 3 ) , aspergillosis ( n = 2 ) ; Non Hodgkin lymphoma ( n = 3 ) , thyroid abscess ( n = 1 ) and mycobacteriosis ( n = 1 ) . Thus , in total , EPTB was identified in 157 /170 ( 92 . 4% ) of the included patients ( Fig 1 ) . EPTB samples were culture positive for M . bovis ( 76/99 , 76 . 8% ) , M . tuberculosis ( 21/99 , 21 . 2% ) and for M . bovis BCG ( 2/99 , 2% ) . One NTM strain was isolated . Thus , 99 specimens were considered as true positive for MTBC growth . The sensitivity of smear microscopy and culture was 18 . 5% ( 29/157 ) and 63% ( 99/157 ) , respectively among patients with a positive reference standard ( Table 3 ) . MTBC RD qPCR was positive in 157/157 ( 100% ) of patients with EPTB diagnosis considered as true positive based on the reference standard and in one non TB patient 1/13 ( 7 . 7% ) demonstrating one false positive case . Upon comparison with the reference standard , the sensitivity of the MTBC-RD qPCR was 100% ( 157/157 ) which is statistically significant as compared to culture ( P< 0 . 001 ) and smear microscopy findings ( P< 0 . 001 ) . The specificity of AFB smear , culture and MTBC-RD qPCR tests was 100% , 100% , 92 . 3% , respectively . The overall sensitivity of the MTBC-RD qPCR test compared to culture was 100% ( 99/99 ) with a lower specificity of 16 . 9% ( 12/71 ) . Table 3 presents the sensitivity , specificity positive and negative predictive values with 95% confidence intervals of MTBC RD qPCR assay , smear microscopy and culture . M . bovis , M . tuberculosis , and M . bovis BCG were identified in 77 . 1% ( 121/157 ) , 21 . 6% ( 34/157 ) and 1 . 3% ( 2/157 ) of extrapulmonary samples , respectively by MTBC-RD qPCR ( Table 4 ) . M . tuberculosis was identified in one out the 13 specimens from non TB patients . M . bovis and M . tuberculosis infection were identified in 45 ( 28 . 7% ) and 13 ( 8 . 3% ) , respectively of culture negative specimens from patients being CRS positive for EPTB . Regardless of the main site of EPTB infection , M . bovis was present exclusively in 80 . 4% of cases from the lymphatic population ( 111/138 ) compared to 19 . 6% ( 27/138 ) for M . tuberculosis . However , M . bovis and M . tuberculosis were detected with different proportion in cases with non lymphadenitis infection ( 52 . 6% vs 36 . 8% ) , respectively . M . bovis BCG was detected in pus of 2 children post BCG vaccination ( Table 4 ) . Among all samples tested by MTBC-RD qPCR , 99 had paired MGIT liquid and/or solid media culture with biochemical and Genotype MTBC tests species identification results for comparison . In total , 97 specimens ( 97 . 9% ) were concordant by qPCR and the latter methods and 2 were discordant ( 1 M . bovis and 1 M . tuberculosis ) . The identification showed the occurence of MTBC as follows: M . tuberculosis ( 20 ) , M . bovis ( 75 ) , M . bovis BCG ( 2 ) ( Table 5 ) . The concordance of M . tuberculosis , M . bovis and M . bovis BCG was 95 . 2% ( 20/21 ) and 98 . 7% ( 75/76 ) and 100% ( 2/2 ) respectively ( Table 5 ) .
In this study , a single tube tetraplex MTBC-RD qPCR assay for the simultaneous detection and identification of MTBC species directly on extrapulmonary specimens was evaluated and was compared to the conventional methods . Here , we have chosen a qPCR test based on the amplification of specific mycobacterial RD motifs . Their presence or absence indicates a specific molecular profile that could differentiate between different MTBC species [6 , 10 , 17] . Though the identification of MTBC members based on the detection of RD patterns by PCR has been suggested previously [10] , the majority of the published data used syber green detection and melting curve or conventional PCR or focused solely on positive culture materials [10 , 18 , 19 , 20 , 21] . There is only one study which evaluated the MTBC-RD qPCR directly on clinical specimens [6] . However , this latter work relied essentially on TB specimens initially positive for MTBC by IS6110 qPCR of which 5 . 7% were extrapulmonary samples compared to 94 . 3% from pulmonary TB which can be diagnosed more easily than EPTB [6] . Thus , our current study is the first to evaluate the MTBC-RD qPCR for the presence of MTBC DNA directly on 170 clinical non-respiratory specimens from patients with suspected EPTB . Thus , we could demonstrate that MTBC RD qPCR detected MTBC DNA in 100% ( 99/99 ) of samples from patients that were microbiologically confirmed EPTB using MTBC culture . MTBC RD qPCR was positive also in 58/58 ( 100% ) of specimens from patients with EPTB diagnosis based on the CRS criteria . Upon comparison with the reference standard , the sensitivity of the MTBC-RD qPCR was 100% ( 157/157 ) which is statistically significant compared to culture ( 99/157 ) ( p = <0 . 001 ) and smear microscopy ( 29/157 ) ( p = <0 . 001 ) . Twelve out of 13 specimens from patients who had a reference standard negative for EPTB ( true negative patients ) were negative by MTBC-RD qPCR indicating a specificity of 92 . 3% of this molecular assay . When comparing qPCR accuracy to culture known as the basic gold standard , the specificity was much lower ( 16 . 9% , 12/71 ) due to the lower sensitivity of the culture . Thus , our findings emphasize that the MTBC- culture most likely underestimates the mycobacterial detection i . e the diagnosis of EPTB . This could be essentially due to the paucibacillary nature of the extrapulmonary specimens and especially those from childhood TB or to the presence of other microorganisms in the same culture that have overgrown MTBC [22 , 23 , 24 , 25 , 26] . As reported previously , EPTB diagnosis requires an elaborated diagnostic algorithm based on the use of molecular methods such qPCR ( e . g . GeneXpert ) which is critically dependent on the CRS based on clinical diagnosis TB parameters [25 , 26] . Accordingly , our data also showed and extend previous studies that the use of qPCR on non respiratory materials could be the method of choice for a rapid , specific and sensitive EPTB detection . Indeed , the current study raises the issue of the reference standard based on culture and/or CRS criteria to be used in the comparative evaluation of our tetra-plex qPCR test rather than considering only culture positivity or conventional PCR as a basic standard for EPTB detection . Therefore , its high sensitivity , reliability and ease of use could make this method a useful tool in diagnosing TB in addition to routine conventional diagnostic tests and TB clinical parameters . The high sample size of extra-pulmonary lymphatic specimens used in our study could be an additional advantage to evaluate the post decision of the clinical utility of this assay in EPTB clinical settings . The MTBC-RD qPCR seems to be useful regardless of the specimen type especially in lymph node biopsies and aspirates which constituted 84 . 7% of all samples . However , the low sample size of analyzed specimens from cases without lymphadenitis infection is the weak point of this work . In addition , the strength of our approach , i . e . the high qPCR sensitivity most likely results from the combination of four different Mycobacterial targets in one reaction step , the stringent precautionary measures taken at each step to prevent a possible contamination as well as the use of an appropriate DNA preparation method . It has also been chosen to maximize extraction efficiency by minimizing the potentially inhibiting effect of extra pulmonary samples inhibitors on the taq polymerase . A maximum sensitivity is essential when the main objective is the amplification of a potentially low bacterial copy number such as EPTB related Mycobacterium thus allowing a reliable diagnosis and a rapid initiation of an appropriate drug treatment [6 , 27] . In the present study , multiplex MTBC-RD qPCR targeting four different mycobacterial genes enabled the specific identification of M . bovis , M . tuberculosis and M . Bovis BCG DNA in 77 . 1% , 21 . 6% and 1 . 3% of clinical extrapulmonary specimens from all EPTB patients . We found a good concordance ( 97 . 9% ) between qPCR and conventional methods which relied on a well validated procedure based on the combination of culture , biochemical and Genotype MTBC tests used only on isolated strains to differentiate the MTBC species . In fact , it would be worth using the combination of these three assays for mycobacterial identification . However , the limitations of culturing are still unavoidable ( time delay of 4–12 weeks for mycobacterial isolation ) ; in addition , biochemical assays are slow , cumbersome , imprecise and non-reproducible [17 , 18 , 28] . Indeed , the Genotype MTBC assay involves several separate reactions requiring post amplification steps thus increasing the contamination risk and/or the delay of mycobacterial identification . Consequently , the MTBC RD qPCR is significantly sensitive and able to provide a rapid identification yielding a diagnosis within 1 . 5 days after sample receipt . M . bovis and M . tuberculosis DNA were detected in 59 culture-negative samples which were not included in the comparative study . Indeed , there were 2 culture-positive samples showing discordant MTBC species identification ( 1 M . bovis and 1 M . tuberculosis ) that were misidentified by MTBC-RD qPCR . They were further tested in a singleplex test using only the RD9 target and one presented a positive amplification result and the other one provided a negative RD9 signal . Our findings were concordant with those of Halse et al who reported a highly specific and sensitive multiplex qPCR for detection of 376 M . tuberculosis , 15 M . bovis , 12 M . africanum and 6 M . bovis BCG but misidentifications were found in 2 culture-positive M . tuberculosis samples misidentified as M . africanum showing a negative RD9 signal amplification [6] . The authors explained this misidentification by the fact that the RD9 set of primers and probe is less efficient than the other used RD target [6] . Therefore , it is possible that the complexity of the single tube tetra-plex qPCR associated with the composition of one particular sample could lead to false-negative results for RD9 target . In Tunisia , so far there is no molecular study available on EPTB related infectious myco-bacterial species even though this country is known to have a relatively a high prevalence of TB cases [4] . Interestingly , Ghariani et al have shown a high prevalence of M . bovis ( 76% ) as a causative agent of lymphadenitis TB in north Tunisia evaluating only culture positive lymph node specimens by classical conventional methods [29] . In south Tunisia , the contribution of M . bovis-mediated human EPTB is unknown . Multiplex MTBC-RD qPCR results found in the current study extend those reported by Ghariani et al and could also demonstrate M . bovis identification in 77 . 1% of extrapulmonary samples in occurrence to lymphadenitis infection . Indeed , M . tuberculosis was detected in 21 . 6% of cases . However , M . bovis , the agent of bovine TB , may still be considered a potential cause of human cases , especially in developing countries where control measures for bovine TB in cattle and/or milk and dairy products are not always satisfying [3] . However , our findings emphasize that EPTB M . bovis disease is very likely underestimated in Tunisia since the control measures for herd , livestock and unpasteurized dairy products as well as milk ingestion are consistently declining . In fact , small cattle herds were dominant in the private sector representing 70% of cattle livestock posing many challenges for control and prevention to veterinary medicine [30] . The consumption of unpasteurized milk and dairy products is still traditionally widespread among many people especially in rural areas . Of note , most of our patients 71 . 2% ( 121/170 ) originated from southern governorates of Tunisia , a rural area with many animal breeding centers . From a clinical perspective , the genetic characterization of the M . bovis population implicated in human EPTB using spoligotyping and MIRU-VNTR is of considerable interest in order to confirm not only the species identification but also yields further insights in the diversity and dynamics of M . bovis strains circulating in this particular setting . In conclusion , we present data of a single tube tetra-plex qPCR assay for the detection and differentiation of MTBC species . As a result of this thorough evaluation , MTBC–RD qPCR proved to be a rapid and sensitive assay for simultaneously detecting TB and differentiating MTBC members on extrapulmonary samples . This diagnostic approach contributes valuable and reliable information allowing an optimal therapeutic regimen and helping to avoid further TB transmission . | Mycobacterial related EPTB diagnosis remains a challenge . In fact the paucibacillary nature of human specimens realized from inaccessible sites might be one of the causes giving a low sensitivity of routine used diagnostic tests . Therefore the use of Real time PCR ( qPCR ) contributes to a specific , sensitive and rapid EPTB diagnosis which helps to a successful anti TB treatment . However almost all the previous studies using qPCR to improve the molecular diagnosis of EPTB have focused only on positive culture materials or have identified only the Mycobacterium genus . Indeed , despite the relatively high occurrence of TB cases in Tunisia , there is no study available on EPTB related mycobacteria in southern Tunisia . Thus , our study is the first to evaluate a single tube tetraplex MTBC-RD qPCR in order to ( i ) detect and differentiate between the different MTBC members directly on EPTB specimens ( ii ) correlate qPCR results with a reference standard based on culture and/or at least two criteria of a composite reference standard ( CRS ) including clinical , radiological , histopathological and therapeutic findings . In our study , MTBC-RD qPCR was shown to give a high sensitivity and specificity compared to the reference standard . M . bovis is the major cause of EPTB in occurrence to lymphadenitis infection . Finally , M . bovis and M . tuberculosis were identified by qPCR among patients with negative culture being CRS positive for EPTB . | [
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"artificial",... | 2017 | First-time detection and identification of the Mycobacterium tuberculosis Complex members in extrapulmonary tuberculosis clinical samples in south Tunisia by a single tube tetraplex real-time PCR assay |
For many infectious disease processes such as emerging zoonoses and vaccine-preventable diseases , and infections occur as self-limited stuttering transmission chains . A mechanistic understanding of transmission is essential for characterizing the risk of emerging diseases and monitoring spatio-temporal dynamics . Thus methods for inferring and the degree of heterogeneity in transmission from stuttering chain data have important applications in disease surveillance and management . Previous researchers have used chain size distributions to infer , but estimation of the degree of individual-level variation in infectiousness ( as quantified by the dispersion parameter , ) has typically required contact tracing data . Utilizing branching process theory along with a negative binomial offspring distribution , we demonstrate how maximum likelihood estimation can be applied to chain size data to infer both and the dispersion parameter that characterizes heterogeneity . While the maximum likelihood value for is a simple function of the average chain size , the associated confidence intervals are dependent on the inferred degree of transmission heterogeneity . As demonstrated for monkeypox data from the Democratic Republic of Congo , this impacts when a statistically significant change in is detectable . In addition , by allowing for superspreading events , inference of shifts the threshold above which a transmission chain should be considered anomalously large for a given value of ( thus reducing the probability of false alarms about pathogen adaptation ) . Our analysis of monkeypox also clarifies the various ways that imperfect observation can impact inference of transmission parameters , and highlights the need to quantitatively evaluate whether observation is likely to significantly bias results .
There are many circumstances in infectious disease epidemiology where transmission among hosts occurs , but is too weak to support endemic or epidemic spread . In these instances , disease is introduced from an external source and subsequent secondary transmission is characterized by ‘stuttering chains’ of transmission which inevitably go extinct . This regime can be defined formally in terms of the basic reproductive number , , which describes the expected number of secondary cases caused by a typical infected individual . Stuttering chains occur when in the focal population is non-zero but less than the threshold value of one that enables sustained spread ( i . e . ) . Transmission is therefore subcritical , and epidemics cannot occur . However there are many settings where such transmission dynamics are important . A major set of examples comes from stage III zoonoses , such as monkeypox virus , Nipah virus , and H5N1 avian influenza and H7N7 influenza [1]–[6] . Because most human diseases originate as zoonoses , there is significant public health motivation to monitor stage III zoonoses [7]–[10] . Subcritical transmission is also associated with the emergence of drug-resistant bacterial infections in some healthcare settings , such as hospital-acquired MRSA [11] . In addition , stuttering chains characterize the dynamics of infectious diseases that are on the brink of eradication , such as smallpox in the 1960s and 1970s [12] and polio now [13] , [14] . Furthermore , stuttering chains are seen with measles and other vaccine preventable diseases when they are re-introduced to a region after local elimination [15]–[17] . A top priority in all of these settings is to quantify transmission , in order to determine the risk that the pathogen could emerge and become established in the human population of concern . This could occur due to demographic or biological changes that increase transmission , such as declining vaccine coverage [15] or pathogen adaptation [18]–[20] . Yet a recent review of transmission models for zoonotic infection identified a marked shortage of models that address the dynamics of zoonoses exhibiting stuttering chain transmission [4] . One major cause of this gap is that high-resolution data describing individual-level disease transmission are rare . The introduction events that trigger the stuttering chains are sporadic , and the transient nature of stuttering chains makes them difficult to track closely . Furthermore , contact tracing is logistically challenging because it requires rapid response surveillance teams and techniques for differentiating specific routes of disease transmission . In contrast , the total size of a transmission chain ( i . e . the total number of cases infected ) is much easier to obtain , since it does not require detailed contact tracing and can be assessed retrospectively based on case histories or serology . Accordingly , the most common data sets for stuttering pathogens are chain size distributions , which describe the number of cases arising from each of many separate introductions . Such data can be used to make estimates of ( or the ‘effective reproductive number’ in the presence of vaccination; for simplicity we will use the term for all settings ) [2] , [15] , [21]–[23] . This strategy has been applied successfully , particularly in the context of vaccine-preventable diseases , but one important simplification is that these analyses typically have not allowed for an unknown degree of heterogeneity in disease transmission among individual cases . This is an important omission , because individual variation in infectiousness is substantial for many infections [24] and can cause significant skews in the chain size distribution [25] . Thus it may be expected to affect conclusions about chain size distributions . For example , failure to account for superspreading events caused by highly infectious individuals can trigger false alarms in systems designed to detect anomalously large chains [2] , [19] . We use simulations and epidemiological data to explore the influence of transmission heterogeneity on inference from chain size data , and to show that the degree of heterogeneity can actually be inferred from such data . Building upon prior studies we assume that the offspring distribution , which describes the number of secondary infections caused by each infected individual , can be represented by a negative binomial distribution . This has been shown to be an effective model for the transmission dynamics of emerging pathogens [24] , and it encompasses earlier models ( based on geometric or Poisson offspring distributions ) as special cases . The negative binomial model has two parameters: the mean number of secondary infections , , and the dispersion parameter , , which varies inversely with the heterogeneity in infectiousness . Knowledge of and has important applications for stuttering chains , including quantifying the risk of endemic spread , predicting the frequency of larger chains , identifying risk factors for acquiring disease , and designing effective control measures . Such information helps to predict how changes in environmental or demographic factors might affect the risk of emergence . Meanwhile , the dispersion parameter alone is a useful measure of transmission heterogeneity , and serves as a stepping stone towards understanding whether heterogeneity arises from variance in social contacts , different intensities of pathogen shedding , variability in the duration of infectious period or some other mechanism . Until now , estimation of individual variation in infectiousness ( summarized by ) has depended on relatively complete contact tracing data , or on independent estimates of combined with the proportion of chains that consist of isolated cases [24] . While this approach has been successful , its application has been limited severely by data availability . Also it has sometimes led to internal inconsistencies within previous analyses , as for example when an estimate predicated on the assumption that was used to obtain estimates of [24] . We show that maximum likelihood ( ML ) approaches can be used to estimate and determine reliable confidence intervals from stuttering chain data , while allowing for an unknown amount of heterogeneity in transmission . The relationship between , and the chain size distribution has been derived for varying degrees of heterogeneity [22] , [23] , but none of these studies has treated as a free parameter and this introduces a wildcard into the inference process . By providing a unified framework for inference of and , we prevent such difficulties . We demonstrate the epidemiological significance of our ML approach by analyzing chain size data obtained during monkeypox surveillance in the Democratic Republic of Congo from 1980–1984 [26] , [27] . Monkeypox is an important case study for these methods , because recent reports indicate that its incidence has increased 20-fold since the eradication of smallpox in the late 1970s [28] , raising the urgent question of whether the virus has become more transmissible among humans . Meanwhile , challenging logistics make the collection of follow-up data difficult and resource-intensive . Fortunately , surveillance data from the 1980s data set is unique in its detail and it allows us to demonstrate how chain size data yields results that are consistent with harder-to-obtain contact tracing data . This suggests that future monitoring of can be achieved by monitoring chain size data by itself . We demonstrate that accurate knowledge of the dispersion parameter is important for reliably determining when an apparent change in transmissibility is statistically significant . In addition , our focus on chain size distributions permits us to determine quantitative thresholds for chain sizes that can be used during surveillance to decide if a particular transmission chain is unusually large and likely to indicate an abrupt increase in . Such indications can facilitate targeted , cost-effective implementation of control measures . Lastly , we consider the real-world difficulties that can arise in obtaining transmission chain data , including the possibility that cases remain unobserved and the complications of overlapping transmission chains . We present a summary of when such observation errors can interfere significantly with reliable inference of transmission parameters .
To characterize the transmission of subcritical diseases , epidemiologists might record data describing the total disease incidence , the number of cases in each transmission chain , the number of transmission generations in each transmission chain , or complete contact tracing data . Because the collection of high-resolution epidemiological data is resource and labor intensive , there is great benefit to understanding the type and quantity of data needed for a specific type of assessment . For instance , total incidence data on its own is not sufficient to infer human-to-human transmissibility for subcritical infections , because the contribution of spillover cases is unspecified . However , chain size and contact tracing data can be used to infer . In fact , for our negative binomial model of disease transmission , the ML estimate of is identical when the likelihood is based on either chain size data only , chain size data coupled with knowledge of the transmission generation when the chain went extinct , or complete contact tracing data ( see methods ) . This shows that for the purpose of estimating , chain size data can be equivalent to contact tracing data . However these theoretical observations must be placed in proper context as contact tracing is often valuable for many other reasons , such as helping to ensure data quality and minimizing unobserved cases . The detailed and accurate data describing human transmission of monkeypox virus in the 1980s [26] , [27] provide an opportunity to compare the result obtained by inferring and from chain size data to those obtained from contact tracing data . Inference results show that the confidence region obtained from contact tracing data is nested within that obtained from chain size analysis ( figure 1A and table 1 ) . In fact , the ML value for and the associated univariate confidence intervals are identical for the two methods . Meanwhile , the ML value for is similar for the two methods , but the confidence interval is broader for chain size analysis than for contact tracing analysis . When compared to previous inference results [24] our chain size and contact tracing estimates for tend to lower values ( though confidence intervals overlap ) . Since the previous results were based entirely on the first generation of transmission , this indicates that transmission of secondary cases may be more variable than transmission by primary cases . The chain size distribution predicted by models fitted under various assumptions about transmission heterogeneity exhibit subtle , but important differences ( figure 1B ) . Overall , allowing a flexible amount of transmission heterogeneity produces a model that has a higher proportion of isolated cases and larger chains , but a lower proportion of intermediate-sized chains . Meanwhile , all of the models are compatible with a higher proportion of longer chains ( cases ) than were actually observed . This suggests that household structure or some other factor may act to reduce transmission after chains reach a moderate size ( possibly because the local pool of susceptibles is depleted ) , but the data do not support a definitive conclusion . When incidence of an emerging disease increases , a frequent goal of surveillance is to assess whether this is attributable to a rise in transmissibility in the focal population , as manifested by an increased . For instance , the observed 20-fold rise in incidence of human monkeypox [28] might be explained by an increased in the human population or by an increase in animal-to-human transmission . Since a relatively low incidence limits the data available for monkeypox ( and many other subcritical diseases ) , it is helpful to determine how the type and quantity of data impacts the ability to detect a specific change in . Utilizing the results of and inference for monkeypox in the 1980s , we can ascertain how the power to detect a statistically significant change in varies with the size of the data set and the magnitude of the change in ( figure 2A ) . As expected , the more data that are available , the more statistical power there is to detect a change in . The sensitivity of chain size analysis for detecting a change in is almost identical to that of contact tracing analysis ( when allowing to be a free parameter in both analyses ) . This suggests that when faced with a trade-off , monitoring of is enhanced more by obtaining additional data on chain sizes ( provided the sizes are accurately assessed ) than by obtaining detailed contact tracing on a subset of available data . Equally as important as detecting a change in is knowing when there may be an inaccurate report of a change . In the case of monkeypox , we find that assuming an incorrect level of transmission heterogeneity in a chain size analysis can lead to over-confident detection of a change in relative to the 1980s data . This is because under-estimating the degree of transmission heterogeneity leads to inappropriately narrow confidence intervals for the estimated . Over-confident detection of a change in is most worrisome when two data sets simulated using identical parameters give rise to distinct estimates of more often than expected ( table 2 ) . This over-confidence arising from incorrect assumptions about can also lead to a lack of specificity for detecting a change in in simulated data sets , when inference based on letting be a free parameter is used as the gold standard ( figure 2B ) . While it could initially appear preferable that incorrect values can lead to greater probabilities of detecting changes in , this trades off against the higher rate of false positive detections and a general loss of statistical integrity ( e . g . the coverage of confidence intervals will not match the nominal levels ) . For many surveillance systems , large chains are more likely to be detected than isolated cases . This could give rise to biases in the chain size distribution data , which we address in a later section . In these situations , an alternative approach to detecting a change in is to determine the size of the largest chain that would be expected by chance ( for some arbitrary threshold in the cumulative probability distribution ) [2] . The size cutoff for what is then considered an anomalously large chain depends on the values of both and ( figure 3 ) . As the assumed value of decreases , the chain size that is considered anomalously large will rise because superspreading events become more frequent . If chain size probabilities are calculated using traditional assumptions of or , then too many false alarms may be raised concerning the number of chains that are perceived to be anomalously large , particularly for pathogens that exhibit significant transmission heterogeneity . The determination of a chain size cutoff also depends on whether the detection of large chains is based on individual reports versus the investigation of the largest chains in a collection of surveillance data ( compare figures 3A and 3B ) . In some situations , a rapid response protocol might be instituted to quickly investigate worrisomely large chains . In this case , an anomalous size cutoff can be chosen based on there being real-time reports of the size of single chains ( as distinct from considering the largest chain obtained from an entire surveillance data set ) . However , assuming an incorrect value of could trigger many false alarms for chains that are actually consistent with known transmission patterns ( table 3 ) . For instance if we assume that monkeypox transmission follows the parameters estimated with our ML model ( blue line of figure 1B ) , then for a 99 . 9% cumulative distribution threshold setting will result in five-fold more chain investigations than if is set at the ML value of . In other situations , chain sizes may be evaluated collectively after a predefined period of surveillance . For the ML values of and estimated for monkeypox in the 1980s , the cumulative distribution of chain sizes shows that there is a 95% chance that the largest of 100 observed chains will be less than 17 cases and a 99 . 9% chance that the chains will all be less than 31 cases . These results suggest cutoffs for chain sizes that deserve increased investigation ( 17 cases ) and provides a chain size cutoff for determining when has almost certainly increased ( 31 cases ) . This contrasts with the 95% and 99 . 9% chain size cutoffs of 10 and 16 obtained when is assumed . By demonstrating the concordance of results based on chain size and contract tracing data when inferring and , our analysis of monkeypox data provides motivation to further characterize the performance of inference based on chain size data . To evaluate the accuracy and precision of ML inference of and from chain size data , we ran simulations for various combinations of , , and number of observed transmission chains , . For each simulated dataset , we determined the ML and values ( equations 9 , 11 and 12 ) and evaluated whether the realized coverage probability of the 90% confidence intervals conformed to expectations ( equations 22 ) . Due to the challenges of illustrating the dependence of inference error on three variables , this section considers two special cases of parameter values . First we fix and consider how the inference error depends on and ( figure 4 - left column ) . This provides an assessment of error bounds when a realistic amount of data is available and when there is no prior information on or . Next we fix and consider how the inference error depends on and ( figure 4 - right column ) . This scenario highlights the relationship between inference accuracy and data set size when a significant amount of transmission heterogeneity is present . Qualitatively similar results are obtained when fixing different values for or ( data not shown ) . We limit our simulation results to because when is close to zero there are too few secondary infections for inference to be meaningful . We also limit ourselves to because large stuttering chain sizes become increasingly likely when approaches one , and so our modeling assumption that transmission is independent of stuttering chain size becomes increasingly dubious . Consistent with the range of inferred from prior analysis of a variety of infectious diseases , we restrict our analysis to [24] . Meanwhile , we focus on since is similar to the Poisson distribution limit of [29] . Lastly , we choose a range of 10 to 1000 for since this reflects the size of most empirical data sets . The preceding analyses have shown the potential for accurate inference of transmission parameters from chain size data , but we have not yet considered how imperfect case detection impacts inference results . We have also ignored complications arising when multiple chains are mixed into a single cluster . This latter scenario allows the possibility that some primary infections are falsely classified as secondary cases . Here we consider whether and how these types of data limitations impact inference results . Several of our modeling assumptions deserve further exploration . In particular , the assumption that transmission can be described by independent and identical draws from a negative binomial offspring distribution is a simplification of some forms of transmission heterogeneity . For example , if heterogeneity is driven largely by population structure , such that susceptibility and infectiousness are correlated , then the relation between and heterogeneity can differ from what is represented in our model [35] . Specific scenarios that can give rise to such correlations include the existence of clustered pockets of susceptible individuals , impacts of coinfection or immunosuppressive conditions , or transmission heterogeneity that arises chiefly from variation in contact rates rather than variation in the amount of virus shed [36]–[38] . This issue is especially relevant for preventable diseases such as measles , because large outbreaks in developed countries are often associated with particular communities in which vaccine refusal is common [16] , [39] . Local depletion of susceptible individuals , which can even occur within a household , can also impact the estimation of and . By diminishing the possibility of large outbreaks , the depletion of a susceptible population is likely to decrease estimates of and increase estimates of . We hope that our use of a likelihood function that combines and transmission heterogeneity will facilitate future work that addresses these modeling challenges in a self-consistent manner . Data acquisition is often the limiting factor for assessing the transmission of subcritical diseases that pose a threat of emergence . Our findings can assist future surveillance planning by drawing attention to the utility of chain size data when contact tracing data are too difficult to obtain . We have shown that both and the degree of transmission heterogeneity can be inferred from chain size data , and have demonstrated that chain size data can give equivalent power to contact tracing data when deciding if has changed over time . In fact , even knowledge of the largest chain size alone can be helpful for monitoring change in , provided that the degree of transmission heterogeneity has been reliably measured . Conversely , we have demonstrated that inaccurate assumptions about transmission heterogeneity can lead to errors in estimates and possible false alarms about increased transmission . We have also found that inference can be accomplished when transmission chains are entangled into infection clusters , provided that the number of primary infections in each cluster is known . For the particular case of human monkeypox , our findings support previous analyses that have identified substantial transmission heterogeneity , but conclude that endemic spread would only be possible if there is significant demographic change or viral adaptation to enable greater human-to-human transmissibility . Since a mechanistic understanding of transmission dynamics is important for quantifying the risk of emerging diseases and predicting the impact of control interventions , we hope our findings will assist in providing robust epidemiological assessments for relevant public health decision-making .
We analyzed previously reported data describing monkeypox cases identified between 1980–1984 in the Democratic Republic of Congo ( formerly Zaire ) [1] . These data were collected in order to assess the potential of monkeypox to emerge as an endemic human pathogen in the wake of smallpox eradication . Contact tracing and subsequent analysis by epidemiological teams classified each identified cases as a primary case , arising from animal-to-human transmission , or a secondary case , arising from human-to-human transmission . The data set consists of 125 infection clusters [26] , [27] . Most clusters contained just one primary case and thus constituted a single transmission chain . However nineteen of the clusters had overlapping transmission chains , because contact tracing revealed they contained more than one primary case . The raw cluster data for monkeypox was obtained from table 1 of [26] . Our baseline inference of transmission parameters is based on considering all the possible ways this cluster data can be separated into individual transmission chains . To explore the specific impact of entangled transmission chains on the inference of transmission parameters , we also investigated the impact of three approaches of using the cluster size data to assign an explicit chain size distribution ( table 4 ) . In the ‘simple cluster analysis’ approach , we treat all clusters as though they were a complete stuttering chain and ignore the complications of multiple primary infections . The other two approaches use different algorithms to divide the clusters that have multiple primary infections into constituent chains . In our ‘homogeneous assignment’ distribution , clusters were divided as evenly as possible . For example , a cluster of total size four with two co-primary cases is tabulated as two chains of size two . Meanwhile , our ‘heterogeneous assignment’ distribution maximized the number of isolated case counts when disentangling clusters . For this distribution , a cluster of size four with two co-primaries is tabulated as a chain of size one and a chain of size three . We analyze the transmission dynamics of stuttering chains using the theory of branching process [22] , [40] , [41] . The key component of this theory is the probability generating function , of the offspring distribution . This function describes the probability distribution for the number of new infections that will be caused by each infected case . The probability that an infected individual directly causes infections is , and hence the probability that an individual is a dead-end for transmission is . Subject to the standard assumption that transmission events are independent and identically distributed , contains all the information needed to determine the size distribution of stuttering chains . The choice of offspring distribution is important because it defines the relationship between the intensity and heterogeneity of transmission . We adopt a flexible framework by assuming secondary transmission can be characterized by a negative binomial distribution with mean and dispersion parameter . The corresponding generating function , valid for all positive real values of and , is [24] ( 1 ) A key advantage of using a two-parameter distribution over a one-parameter distribution ( such as the geometric or Poisson distribution ) is that modulating permits the variance to mean ratio , , to range from one to without any change in . Further , the geometric and Poisson distributions are conveniently nested cases of the negative binomial distribution when and respectively . All simulated chains start with a single primary infection . Then the number of first generation cases is decided by choosing a random number of secondary cases according to a negative binomial distribution with mean and dispersion parameter . For each case in the first generation ( if any exist ) , a new random number is chosen to determine how many consequent second generation cases there are . This is repeated until the stuttering chain goes extinct . Since our focus is on , all simulated chains eventually go extinct . Simulated contact tracing data consisted of the individual transmission events that produce simulated chain size data . To simulate imperfect observation , we first simulated a set of true transmission chains , then simulated whether each case would be observed according to the passive observation probability . Finally , for chains where at least one case was detected passively , we simulated which additional cases were observed according to the active observation probability . All calculations and simulations are performed with Matlab 7 . 9 . 0 . Code is available in Text S2 . The next two subsections derive the average size and variance of the distribution . As a by-product , we obtain a first order moment estimator for and a second order moment estimator for . We will see that the first order moment estimator of exactly matches the ML value of . This finding provides a simple relationship between observed data and inference . Beyond determining the relationship between , , and , our assumptions about the transmission process allow us to use branching process theory to characterize the complete size distribution of stuttering chains [23] , [40]–[42] . Let be the probability of a transmission chain having overall size . If one defines , then [44] , ( 8 ) where is the th derivative of . See the supporting text ( Text S1 ) for a derivation of this formula that develops intuition for the specific application to disease transmission . In particular , the supporting text explains the validity of equation 8 for both and , which extends recent findings of Nishiura et al . [23] . Based on equation 1 the formulae for and are , where the latter formula was derived by induction . Substitution into equation 8 gives , Noting that the Gamma function satisfies and that for integer , we can rewrite the last formula as ( 9 ) This equation matches the relation derived by Nishiura et al . for the specific case of [23] . This relationship was verified by using a stochastic simulation model to simulate many stuttering chains as described above ( data not shown ) . Equation 9 forms the basis of interpreting chain size distribution data because it provides the probability that a randomly chosen stuttering chain has a size . However , from the perspective of considering how chain size observations reflect overall disease burden , it is also helpful to consider the probability , , that a randomly chosen case is in a stuttering chain of size . This ‘weighted’ probability density is obtained by scaling each by and then renormalizing . Accordingly , ( 10 ) For a given value of , decreasing leads to both a higher number of isolated cases and a higher number of large stuttering chains ( figure 9 ) . Meanwhile , the homogeneous Poisson offspring distribution maintains the highest probabilities for intermediate sized stuttering chains ( seen most clearly in figure 9D ) , Thus , branching process theory provides an analytical foundation for prior computational results showing that greater transmission heterogeneity results in a higher frequency of relatively large stuttering chains [24] , [25] , [29] , [43] , [45] . Of particular interest , the fraction of stuttering chains that consist of a single isolated case is substantial for all parameter sets considered . Meanwhile , the weighted probability density shows that the probability of a case occurring as an isolated case can be significantly less than the probability of a randomly chosen stuttering chain having size one . We employ maximum likelihood estimation for and inference because it is asymptotically unbiased and maximally efficient ( i . e . there is minimum sampling variance ) . To implement ML estimation for and using stuttering chain size distribution data , we let denote the total number of stuttering chains in a given dataset , and represent the number of chains with size . Then the likelihood , , of the data set is , ( 11 ) The ML estimate of and is found by maximizing the log-likelihood function with respect to both parameters . The maximum occurs when . Focusing on finding the ML estimate for , one finds , Then since the total number of chains is and the observed average chain size is , Solving for gives , ( 12 ) which is identical to the first moment estimator given by equation 2 . The ML calculation for the dispersion parameter , , is not analytically tractable and depends on . Thus , is obtained by computational optimization of the log likelihood . Since the limits and lead to convergence difficulties , we set lower and upper limits of 0 . 00001 and 1000 for . This lower bound for is well below the range needed to infer biologically relevant values of and the upper bound for is essentially equivalent to a Poisson distribution . We cannot attempt inference when a simulated data set has no secondary transmission ( implying ) . Therefore these data sets , which occasionally occur when both and are low , were discarded from our simulation-based characterization of inference . To study the precision and accuracy of our ML approach , we simulated many data sets for a range of values of , and . We inferred the ML values of and from the simulated data , and compared these values to the true values used in the simulation . To determine whether two data sets on chain size distribution correspond to statistically distinct values of , we performed a likelihood ratio test . First , we combined all data together and calculated the likelihood , , for a single pair of and values . Then we computed a second likelihood , where and are the likelihoods for each set of data and each of these likelihood functions has its own parameter . We kept constant for both sets of data in order to focus on whether there is a statistically significant change in . Because is nested within ( equality occurring when ) , we apply the likelihood ratio test with a 95% confidence interval cutoff to determine whether a second parameter is justified [31] . For figure 2A , was equal to the combinatorial likelihood calculation for the 1980s contact tracing monkeypox data . Meanwhile , was calculated from simulation data in which was fixed at the ML value for the 1980s data . We conducted one thousand simulations for each value of , and computed the proportion of simulations for which a second parameter was supported by the likelihood ratio test . For figure 2B , a similar set of calculations was performed , except that the ML values were obtained by fixing at either or in the calculation of and . The results presented in table 2 concerning the probability that a change in is erroneously detected were determined by simulating two sets of chain size data using the ML values for and from contact tracing data for human monkeypox the 1980s ( , ) . Likelihood scores were calculated for the stated values of , and the likelihood ratio test was used to assess whether had changed significantly between the two data sets . Because a 95% confidence level was used for this test , a statistical difference is expected just 5% of the time . Higher frequencies of falsely detecting a change in correspond to artifacts of the inaccurately narrow confidence intervals obtained when transmission heterogeneity is under-estimated . The probability that a chain has a size less than is the sum of the individual chain size probabilities , . The probability , , that chains all have a size less than is the product of the individual probabilities for each chain to have a size less than :Figure 3 plots the first value of for which exceeds the indicated probability threshold . | This paper focuses on infectious diseases such as monkeypox , Nipah virus and avian influenza that transmit weakly from human to human . These pathogens cannot cause self-sustaining epidemics in the human population , but instead cause limited transmission chains that stutter to extinction . Such pathogens would go extinct if they were confined to humans , but they persist because of continual introduction from an external reservoir ( such as animals , for the zoonotic diseases mentioned above ) . They are important to study because they pose a risk of emerging if they become more transmissible , or conversely to monitor the success of efforts to locally eliminate a pathogen by vaccination . A crucial challenge for these ‘stuttering’ pathogens is to quantify their transmissibility , in terms of the intensity and heterogeneity of disease transmission by infected individuals . In this paper , we use monkeypox as an example to show how these transmission properties can be estimated from commonly available data describing the size of observed stuttering chains . These results make it easier to monitor diseases that pose a risk of emerging ( or re-emerging ) as self-sustaining human pathogens , or to decide whether a seemingly large chain may signal a worrisome change in transmissibility . | [
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"modeling... | 2013 | Inference of R0 and Transmission Heterogeneity from the Size Distribution of Stuttering Chains |
The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge . As much as the levels of natural pathogen circulation vary over time and geographical location , the development of invariant efficacy measures is of major importance , even if only indirectly inferrable . Here a method is introduced to assess host susceptibility to pathogens , and applied to a detailed dataset generated by challenging groups of insect hosts ( Drosophila melanogaster ) with a range of pathogen ( Drosophila C Virus ) doses and recording survival over time . The experiment was replicated for flies carrying the Wolbachia symbiont , which is known to reduce host susceptibility to viral infections . The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection . More generally , our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions .
Hosts exposed to disease-causing agents respond in accordance to the challenge dose . Therefore dose-response curves contain information about disease processes that can be extracted by suitable analytic frameworks . Early examples concerning microbial risk assessment include counting lesions caused by tobacco mosaic virus on plant leaves [1] , as well as human responders to experimental challenge with polio viruses [2] , Vibrio cholerae [3] and Streptococcus pneumoniae [4] , for escalating challenge doses . Dose-response models have been in use for analyses and extrapolation of experimental datasets [5] . Models that account for the sigmoidal shape in log-linear scale of the typical dose-response curve have been derived mechanistically , based on the assumption that each individual pathogen has a probability of infection independent of others , the so-called independent action hypothesis [6] . This results in a one-parameter exponential-function model [7] . The frequent observation of shallower-than-exponential , or overdispersed , relationships has then prompted the implementation of heterogeneity in the probability of infection of individual hosts [8]–[10] . In the 1960s , Furumoto and Mickey [9] developed a dose-response model that could accommodate both shallow and steep increases in the response by considering the probability of infection of individual hosts described by a Beta-distribution . Although a mechanistic justification for this specific distribution has not been given , the model has been widely applied in microbial risk assessment due to its ability to outperform the simple exponential model [5] . Susceptibility distributions other than Beta have also been considered and are more commonly used in frailty models adopted in survival analysis [11] , where the data consist of survivor counts over time in host groups that are constantly subject to a hazard [12] , [13] . These frailty models appeared in the 1980s and have since been adapted to infection hazards , where surviving signifies remaining uninfected [14]–[16] . While most informative when the exposure is continued or repeated over time , these formalisms would be inadequate for estimating distributions of susceptibility to infection from instantaneous challenge protocols . The importance of accounting for time between challenge and observable toxicity responses to pathogens or other agents has been recognized . Recent models in ecotoxicology [17] , [18] , consider explicit kinetics within exposed organisms . Also in microbial risk analysis , previous studies [19] , [20] have included time postinoculation as an additional parameter in classic dose-response models , although using an approach that conceptually allows for a different susceptibility distribution at each time point . Here we present a schema to infer a distribution of host susceptibilities to infection that holds consistently across dose and time . We introduce an experimental design and inference framework that enables such inferences by analyzing simultaneously a collection of survival curves , each representing a different challenge dose . The resulting Beta distributions are compared against those obtained by classic dose-response models based on single day measurements . Recent evidence for symbiotic interactions that reduce host susceptibility to pathogens has stimulated the development of quantitative frameworks to assess the levels of individual and population protection attributable to specific symbionts . The intracellular bacterium Wolbachia , found among many arthropod species including Drosophila melanogaster , is one such symbiont [21] , [22] . To analyze the protection conferred by Wolbachia to D . melanogaster , we apply our inference framework simultaneously to two sets of time-dependent dose-response data: in one set the flies carry the symbiont bacterium Wolbachia ( Wolb+ ) ; while in the other they do not ( Wolb− ) . In this instance we extract the Beta distribution that best describes individual protection attributable to Wolbachia , as well as population statistics valid across entire dose ranges .
We used virus free D . melanogaster lines with DrosDel w1118 background , with or without the endogenous Wolbachia strain wMelCS [21] , [23] , [24] . Flies were reared in standard food at 25°C . To assure that potential for heterogeneities are minimized by the experimental procedure , we used fifty 3–6 days old adult males per group , 10 per replicate and 5 replicates . To study the response to viral infection , we anesthetized with CO2 and pricked flies with different doses of Drosophila C virus ( DCV ) . We used tenfold serial dilutions – from 1010 TCID50/ml to 104 TCID50/ml – in Tris-HCl buffer , pH 7 . 5 . Controls were pricked with buffer solution only . We used the pricking protocol described in [24] , produced and titrated virus as in [21] . After pricking , we kept flies at 18°C and checked daily survival until day 80 and twice a week until the end of the experiment . Food was changed every 5 days . We summarized the data in 16 dose-response curves ( 8 per group , including control ) from day 0 after treatment until day 139 ( Dataset S1 ) . Starting from established models , we refine the occurrence of mortality from infection , i . e . the response , as a function of the concentration of infectious units given to hosts , i . e . the dose . We present a step-by-step derivation of descriptions that integrate dimensions that are usually treated separately as well as the motivations for doing so . Assuming independent action of infectious units , each unit has probability p of causing an infection , while for d infectious units infection occurs with a probability described by . Given further considerations about the distribution of infectious units in a homogeneous solution ( see [9] for a complete derivation of the expression ) , the number of units causing infection can be described by a Poisson distribution , resulting in the exponential dose-response model [7] , that describes the probability of infection in a host challenged with pathogen dose d: ( 1 ) This most basic formulation is hereafter referred to as the homogeneous dose-response model . Furumoto and Mickey [9] expanded this formulation by allowing the probability of infection to be described by a parametric distribution , specifically the Beta distribution . To facilitate normalization across datasets , here we maintain the probability p fixed across individual hosts ( as in [25] ) , and introduce a multiplicative parameter , the susceptibility factor , to describe any natural or induced effect that decreases susceptibility . We assume that susceptibility to infection is Beta-distributed so as to describe the variation of susceptibility in the host population . Thus , we obtain the probability that a host contracts infection as ( 2 ) where and B is the Beta function . We refer to this formulation as the heterogeneous dose-response model . At last we introduce a small parameter ε to account for a small probability of ineffective challenge , such that is the random variable representing the number of infected hosts , in a group of n hosts challenged with a given dose . Assuming that an ineffectively challenged host behaves like a control host with regard to death rates , the probability that m hosts are dead a number of days after challenge is then ( 3 ) where is either ( 1 ) or ( 2 ) depending on which dose-response model is adopted . The parameters to be estimated for this dose-response model are the maximum probability of infection per infectious unit ( p ) , the shape parameters for the Beta distribution that describes the susceptibility factor ( a , b ) , and the probability of ineffective challenge ( ) . These models require a choice of how many days post-challenge cumulative mortality should be measured , which is difficult to establish for host-pathogen systems where times to death from infection or other causes overlap significantly . To overcome this difficulty , we develop a model that integrates an explicit representation of time to death with the dose-response process for infection just described . It should , however , be noted that time is introduced with the main purpose of enabling the use of survival curves to obtain robust estimates for probabilities of infection given different challenge intensities and consistently infer susceptibility to infection . From this perspective , parameters defined from now on should be regarded as auxiliary and will be implemented as simply as possible . We first consider a survival model for a control group of flies pricked with buffer solution only ( no DCV ) , subject to two hazards: , an age-dependent death hazard rate; and , a background age-independent death hazard rate . The overall death hazard rate for uninfected hosts is therefore ( 4 ) Denoting the random variable representing time to death of control hosts , we have ( 5 ) where and are the times to death from and , respectively . Their corresponding distributions are assumed to be and , where is the background mortality rate , is the mean time to death , and is the shape parameter for the Gamma distribution of day of death from aging . Hosts challenged with pathogen can become infected or remain uninfected and this infection status is hidden . If uninfected , they are subject to the age-dependent hazard rate that affects control hosts , ; if infected , they are subject to an infection hazard rate , , and the age-independent background mortality . Thus the overall hazard rate of infected hosts is ( 6 ) Now let be the random variable representing the number of hosts infected by challenge with a given pathogen dose . Then the probability that i hosts are infected after n hosts were challenged is ( 7 ) where is either ( 1 ) or ( 2 ) depending on which dose-response model is adopted . Let T be the random variable representing the time to death of hosts challenged by a given pathogen dose . The probability density of observing a death event at time t given that i hosts are infected is ( 8 ) where denotes the distribution of time to death of infected hosts , given by ( 9 ) and is the distribution of times to death from the infection hazard rate . This distribution is assumed to follow , where is the mean time to death of infected hosts , and is the shape parameter for the Gamma distribution of day of death from infection . In setting the priors for parameter estimation we note that background mortality is small and therefore is kept small by setting to be much greater than the last day of the experiment . To enforce that deaths due to infection occur earlier than deaths due to aging , we constrain the mean time to infection death to be lower than old-age death , i . e . , and the probability of dying before the end of the study to be greater for infected hosts , i . e . , where is the last day of the experiment . To construct the likelihood to be maximized by the parameter estimation procedure , we let be the random variable denoting the day fly died and the random number of survivors up to . Then the likelihood of observing the actual number of survivors and the times of death , for a given dose is ( 10 ) Since the observations for each dose are independent , taking the product of the likelihoods over the different doses yields the global expression for the likelihood of the entire dataset . In this time-dependent dose-response model , the parameters to be estimated are the maximum probability of infection per infectious unit ( p ) used for normalization purposes , the Beta distribution shape parameters to describe variation in susceptibility factor ( a , b ) , the parameters that control death due to aging ( , ) , infection ( , ) , and background mortality ( ) , as well as probability of ineffective challenge ( ) . Parameters and are typically small and were introduced to improve performance of the likelihood . Model parameters were estimated using Markov chain Monte Carlo sampling implemented with the PyMC package [26] ( code available from [27] ) . The prior distributions considered are listed in Table 1 . Initial values were chosen so as to start with a non-zero likelihood . Using Metropolis-Hastings algorithm , we ran two separate chains for 252 , 000 iterations . The first 27 , 000 iterations were discarded . The recording interval was set to 250 so that the autocorrelation between samples was negligible . Convergence was assessed by inspection of the trace plots . All analyses were performed on the pooled samples from the two replicate chains .
To emphasize the importance of day selection to infer distributions of susceptibility to infection by classic dose-response models [5] we have applied these procedures to mortality data observed by two specific days ( 30 and 50 ) . Parameter estimates from these models are listed in Table 2 . The model fits to the mortality data at the selected days are shown in Figure 2 , as well as the associated distribution of Wolb+ susceptibilities and the posterior samples for the Beta distribution shape parameters . For simplicity we have adopted the homogeneous model for Wolb− and focus on comparing susceptibility distributions of Wolb+ inferred at different days . Mean protection conferred by Wolbachia in this illustration is estimated as 79% and 56% , based on mortality measurements at day 30 and 50 , respectively . Moreover , the distributions have fundamentally different shapes , with the appearance of a high susceptibility group as time progresses . This sensitivity to the day by which mortality data are collected is a concern that raises the need to disentangle infection status from the associated time-dependent mortality . In the following sections , infection and mortality are estimated explicitly using the integrated time-dependent model described in Methods . The procedure is illustrated in Figure 3 . Control curves from Wolb− and Wolb+ flies pricked with buffer solution ( no DCV ) were compared with the Kaplan-Meier method using the log-rank test and no significant difference was found ( with a p-value of 0 . 47 ) . By fitting the uninfected time-dependent model ( 4–6 ) to the control survival curves ( Figure 1 ) we estimated the parameters describing aging ( ) and background ( ) mortality ( Table 3 ) . For each group of flies ( Wolb− and Wolb+ ) , the time-dependent dose-response model constructed in Methods was fitted simultaneously to the entire dataset of survival curves ( one for each DCV challenge dose ) , fixing across doses the distribution of times to death from infection ( , ) and aging ( ) , while estimating the susceptibility parameters ( p , a , b ) that govern the dependence of response on challenge dose according to the adopted dose-response model . The estimated parameter values are listed in Table 4 . The deviance information criterion ( DIC ) [28] favored the homogeneous model for the Wolb− group and the heterogeneous model for Wolb+ ( Text S1 ) . Mean time to death from infection is 9 and 14 days in the Wolb− and Wolb+ groups , respectively . The variance in time to death from infection is lower for Wolb− , with a standard deviation of 2 days , compared to 6 days in the Wolb+ . Figure 4 compares fitted with observed survival curves . The fitted dose-response curves that result from this analysis are shown in Figure 5A , while the inferred distribution of Wolb+ susceptibilities normalized by the Wolb− measure is displayed in Figure 5B and the corresponding posterior distribution of the Beta shape parameters is in Figure 5C . Given the homogeneity in the Wolb− group , the distribution of susceptibility in Wolb+ provides a direct indication of how antiviral protection conferred by Wolbachia is distributed among its carriers . Typically defined as , where RR is the risk reduction attributed to the susceptibility modifier ( Wolbachia in this case ) , we determine the mean protection conferred by the symbiont to its host as 85% ( with a 95% HPD of 60–93% ) . To assess the best possible performance of classic methods [5] in the inference of susceptibility distributions ( for Wolb+ in the case ) we must have previously reduced the set of survival curves to a set of effectively infected proportions - one entry per challenge dose . To search for a range of days in which absolute mortality might provide an approximate indication of infection , we compare the estimated proportions effectively infected by each challenge dose with the mortality proportion measured at each day . Using a normalized Euclidean distance between these two measures , a day-selection score is provided by the red curve in Figure 6 . We identify day 30 as optimal and 17–46 as the interval of days in which the score is at least 95% of the optimal . Reassuringly , the optimal day appears to coincide with the saturation of infection-induced mortality ( see position of vertical dash-dotted gray line in relation to the Gamma distributions ) . We now recall Figure 2 and Table 2 for the inferences based on day 30 mortality data to confirm that classic dose-response models can in principle infer susceptibility distributions that are consistent with those obtained under our extended model ( Figure 5 ) . A major issue , however , is that results are sensitive to a day-selection criterion that relies on having previously carried out the entire procedure . The appearance of a high susceptibility group in distributions inferred at later days are an artifact due to the accumulation of background mortality that should be factored out . These results highlight the importance of adequately representing the time dimension in the analysis .
Dose-response models have become standard quantitative frameworks in microbial risk assessment . Less recognized is their ability to estimate host trait distributions . Here we illustrate the concept by extracting host susceptibility distributions from mortality measured as a function of pathogen challenge dose , but similar procedures can be developed for measures of infection or infectiousness ( instead of mortality ) , and can be made a function of other environmental variables such as temperature or humidity ( instead of dose ) . Understanding how to detach host trait distributions from environmental variables is crucial for the formulation of measures that can be transported between laboratory and natural conditions [29] , [30] . We address this problem with an experimental design and inference framework that enables the estimation of distributions of host susceptibility to infection by analyzing simultaneously a collection of survival curves , each representing a different challenge dose ( Figure 3 ) . The procedure is illustrated on a specifically collected dataset where two distinct groups of hosts ( D . melanogaster ) were experimentally challenged by viruses ( DCV ) : one group consists of isogenic flies where no significant variability in susceptibility to infection is found; and another with the same genetic background but now carrying the symbiont bacterium Wolbachia known to reduce susceptibility to DCV [21] , [22] . Our inferences indicate that Wolbachia confers on average 85% DCV protection to D . melanogaster under the specified laboratory conditions , and suggest significant variability in this effect . This variance in susceptibility is induced by the symbiont , since model selection criteria did not support heterogeneity in the susceptibility of flies not carrying Wolbachia . Since the Drosophila and Wolbachia populations used in this study are isogenic , the heterogeneity in susceptibility of Wolbachia-carrying flies uncovered here indicates variation in the host-microorganism interaction that lacks a genetic basis . A simple hypothesis is that variance in Wolbachia levels at the individual host level leads to variance in resistance to viruses . Although several lines of evidence support this hypothesis [31]–[34] , further experiments are required to discriminate whether heterogeneity in resistance is directly linked to variance in Wolbachia levels or , alternatively , a result of another environmental/physiological variance that is only expressed in the presence of Wolbachia . Previous estimates of protection were based on survival analysis or viral titres in a dose-specific manner [21] , [22] , [24] . To our knowledge , the experimental design and analysis presented here provides the first estimation of protection in way that is detached from challenge dose . Future developments might consider: estimation of alternative distributions to compare with the shapes suggested by the Beta family; extension of the adopted experimental design to measure responses other that mortality; and move towards host populations and environmental conditions that are closer to natural systems . The parameters estimated here should not be seen as isolated from the relevant ecological context . On the contrary , they are intended as a first step to inform the construction of ecological and epidemiological models where Wolbachia , other symbionts , or interventions that modify host susceptibility to infection , are introduced to induce desired transitions in populations . The introduction of Wolbachia into Aedes aegypti and other arthropod vectors is being considered as a promising strategy to control dengue and other infectious diseases of humans ( see [35] and references therein ) . The inference frameworks presented can be readily adapted to provide accurate quantification of Wolbachia-induced protection and integrated in population models of public health importance . The challenge of considering the time dependence of processes leading to observable ecotoxicity responses has also been addressed in toxicology where the so-called General Unified Model of Survival ( GUTS ) has been proposed [18] . These models simulate the time-course of external and internal processes leading to toxic effects on organisms to generate an output that can be fitted to mortality over time . While those studies tend prioritize the mechanistic descriptions of the toxicokinetic and toxicodynamic processes that damage the organisms , we have chosen to adopt a phenomenological approach and focus on the inference and interpretation of how susceptibility to infection is distributed in a population . In epidemiological systems , the baseline transmission intensity is often not directly measurable but indirectly inferred in a model-based manner . Dose-response models , on the other hand , can account for experimentally controlled patterns of exposure [36] , [37] . Variation in host susceptibility to pathogens is one component of both classes of systems that mostly influences estimates of intervention impacts [29] . Therefore , building on the methods developed here furthers our potential to accurately evaluate the burden of infectious diseases and design effective interventions . | While control options for plant , animal , and human pathogens are emerging rapidly , reliable assessment of the effect of interventions in biological systems presents many challenges . A major question is how to connect laboratory experiments and measurements with the relevant process in natural settings , where hosts are subject to pathogen exposures that vary in time and geographical location . With this aim , measures of protection that are invariant under varying exposure intensity need to be developed and integrated with mathematical models . In this article , we introduce a method to assess host susceptibility to pathogens , and apply it to survival of Drosophila melanogaster challenged with different doses of Drosophila C virus . By replicating the procedure in groups of flies that carry the symbiont Wolbachia , we are able to estimate how the viral protection induced by this intracellular bacterium is distributed in the host population . Our results disentangle host infection status from observed mortality , accounting naturally for time since exposure . The multiple-dose design proposed challenges traditional study designs to assess interventions . | [
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] | 2014 | Unveiling Time in Dose-Response Models to Infer Host Susceptibility to Pathogens |
While we lack a complete understanding of the molecular mechanisms by which parasites establish and achieve protection from host immune responses , it is accepted that many of these processes are mediated by products , primarily proteins , released from the parasite . Parasitic nematodes occur in different life stages and anatomical compartments within the host . Little is known about the composition and variability of products released at different developmental stages and their contribution to parasite survival and progression of the infection . To gain a deeper understanding on these aspects , we collected and analyzed through 1D-SDS PAGE and LC-MS/MS the Excretory-Secretory Products ( ESP ) of adult female , adult male and microfilariae of the filarial nematode Brugia malayi , one of the etiological agents of human lymphatic filariasis . This proteomic analysis led to the identification of 228 proteins . The list includes 76 proteins with unknown function as well as also proteins with potential immunoregulatory properties , such as protease inhibitors , cytokine homologues and carbohydrate-binding proteins . Larval and adult ESP differed in composition . Only 32 proteins were shared between all three stages/genders . Consistent with this observation , different gene ontology profiles were associated with the different ESP . A comparative analysis of the proteins released in vitro by different forms of a parasitic nematode dwelling in the same host is presented . The catalog of secreted proteins reflects different stage- and gender-specific related processes and different strategies of immune evasion , providing valuable insights on the contribution of each form of the parasite for establishing the host–parasite interaction .
Lymphatic filariasis ( LF ) is a disabling and disfiguring parasitic disease caused by the adult and developing forms of filarial nematode parasites residing in the lymphatic system of a mammalian host . The infection in humans is caused by Wuchereria bancrofti , Brugia malayi or B . timori [1] and puts at risk an estimated 1307 million people in 83 endemic countries in subtropical and tropical regions of the world [2] . Lymphatic filarial parasites have a two-host life cycle . Infection is initiated with the release by the mosquito of third stage larvae ( L3 ) during feeding on the host . The L3 enter the host at the puncture site , penetrate the dermis and enter the lymphatic system . L3 parasites initiate a developmental program that culminates in a molt to fourth stage larvae ( L4 ) 9–14 days post-infection . L4 undergo dramatic growth during the next 6 to 12 months as they develop into mature adults . Adult worms tend to localize in the varices of lymphatic vessels of the lower extremities . After insemination , zygotes develop in utero to microfilariae ( Mf ) over a three-week period . Adult female ( F ) parasites can remain reproductively active for >5 years . Females release hundreds to thousands of fully-formed , sheathed microfilariae per day into the lymphatic circulation of the host . From the lymph , they transit into the peripheral circulation . Dramatic clinical manifestations , including hydrocoele , recurrent adenolymphangitis , lymphedema and elephantiasis are associated with chronic infection . Nevertheless , the majority of infected individuals have no clinically apparent sequelae , despite the presence of circulating Mf ( and parasite antigens ) in the peripheral blood [3] . Associated with the asymptomatic state is a suppression of both Th1 and Th2 responses , which may lead to high parasite loads and reduced immune-related damage to the host . This down-regulation of the host immune response is characterized by impaired proliferation of T cells , increased production of the regulatory cytokine IL-10 , and higher levels of IgG4 [4] . The complexity of immune responses in LF is due , among other factors , to the presence of different life cycle stages of the parasite and the different levels of anatomical compartmentalization in which they reside [5] , [6] . In addition , the presence in filarial nematodes of a Wolbachia endosymbiont , a matrilineally inherited obligate intracellular bacteria , contributes to this complexity , as Wolbachia antigens have been related to the development of inflammatory-mediated filarial disease [7]–[9] . While we lack a complete understanding of the molecular mechanisms by which pathogens achieve protection from host immune responses , it is generally accepted that parasitic nematodes release a variety of products , primarily proteins ( many having posttranscriptional modifications ) , that enable infection by facilitating penetration of tissue barriers , migration through host tissues and evasion of immune responses . The characteristics and functions of these products are diverse and must reflect , among other factors , the lifestyle of each parasite . Even though their importance for establishing and maintaining the host-parasite interaction is accepted , relatively little is known about the mechanism ( s ) by which proteins secreted from nematodes regulate the immune system . Proteins released from these parasites during culture in vitro are conventionally named excretory/secretory products ( ESP ) . Several have been identified and characterized , particularly from Brugia malayi , an organism that can be maintained in the laboratory as a model of filarial nematodes and was chosen as a representative species to be analyzed for the Filarial Genome Project [10]–[12] . A draft of this genome was recently released [13] , allowing the identification through proteomic analysis of the ESP from adult males ( M ) and females ( F ) of this parasite maintained together in vitro [14] . To gain a deeper understanding of how these parasites survive in their particular host milieu and the contribution of each form to the progression of the infection , we present a comparative analysis of the ESP independently released by Mf , F and M B . malayi .
Mf and adult B . malayi were recovered >120 days post-infection from the peritoneal cavities of jirds ( Meriones unguiculatus ) infected subcutaneously with 200–300 L3 . Infected jirds were obtained from the Filariasis Research Reagent Repository Center ( Athens , Georgia USA ) . Adult worms were washed several times in RPMI 1640 medium [with L-glutamine , 20 mM HEPES , 100 µg/ml penicillin , 100 units/ml streptomycin ( Gibco ) , pH 7 . 2] ( henceforth , RPMI 1640 ) and separated by gender . Mf were obtained through several washes of the peritoneal cavity with 37°C RPMI 1640 . The combined washes were centrifuged 5 min at 1000×g to pellet the Mf , which were subsequently purified from host cells by passage through PD-10 columns equilibrated with pre-warmed RPMI 1640 as described [15] . Animal procedures were reviewed and approved by the Facility Animal Care Committee of McGill University – Macdonald campus and were conducted in accordance with the guidelines of the Canadian Council on Animal Care . Parasites were cultured in RPMI 1640 at 37°C for 4 days with changes of media each 24 hr [16] . F , M and Mf were maintained at densities of 6 , 15 and 2 . 5×105 parasites/ml , respectively . A cocktail of protease inhibitors [4- ( 2-aminoethyl ) benzenesulfonyl fluoride hydrochloride; bestatin hydrochloride; N- ( trans-epoxysuccinyl ) -L-leucine 4-guanidinobutylamide; pepstatin A; phosphoramidon disodium salt] ( Sigma No P8849 , St . Louis , MO ) was added to media containing ESP following sterilization by passage through a 0 . 22 µm filter . Media were stored at −30°C until analysis . The combined volume ( 30–165 ml ) of 3 different incubations was concentrated to 1–1 . 5 ml in an Amicon Ultra 3000 MWCO ( Millipore ) . Proteins were then precipitated with Trichloroacetic acid ( TCA , 20% final conc . ) . The pellet was washed 3 times with cold acetone ( −30°C ) and air-dried . Proteins were resuspended in Tris-HCl [20 mM , pH 8 . 0] and quantified ( Quant-iT™ Protein Assay on a Qubit fluorimeter; Invitrogen ) . Concentrated ESP from Mf , F and M were centrifuged at 20000×g for 3 min and resuspended in loading buffer containing 2-mercaptoethanol . Final amounts of 37 µg , 65 µg and 15 µg of protein , respectively , were separated by SDS-PAGE on a 2 . 4 cm gradient gel ( 7–15% acrylamide ) . The gel was stained with Coomassie Brilliant blue G . The entire lanes were subjected to automated band excision , to generate 15 bands per lane ( see Figure 1 ) . The Protein Picking Workstation ProXCISION ( Perkin Elmer ) was set to excise 5 to 7 pieces per band , depending on the width of the lane . Proteins from gel bands ( 5 to 7 gel pieces per band/well ) were subjected to reduction , cysteine-alkylation and in-gel tryptic digestion in a MassPrep Workstation ( Micromass , Manchester , UK ) as previously described [17] . Briefly , gel pieces were pre-washed twice in 100 µl HPLC grade water for 10 min . Gel pieces were destained in 2–10 min incubations in 50 µl 100 mM ammonium bicarbonate followed by 5 min in 50 µl 100% acetonitrile . Destained and dehydrated gel pieces were reduced and alkylated by incubation in 50 µl 10 mM dithiothreitol for 30 min , followed by addition of 50 µl 55 mM iodoacetamide for 20 min and finally 100 µl 100% acetonitrile for 5 min . Gel pieces were washed by incubation for 10 min in 50 µl 100 mM ammonium bicarbonate , followed by a 5 min incubation in 50 µl 100% acetonitrile and were then dried for 30 min at 37° . Proteins were digested in-gel by incubation in 25 µl trypsin solution ( 6 ng/µl in 50 mM ammonium bicarbonate , Promega ) for 30 min at room temperature , followed by 4 . 5 hr at 37°C . Peptides were initially extracted with 30 µl of a mix containing 1% formic acid and 2% acetonitrile at room temperature and then by two successive extractions with 12 µl of a mix of 1% formic acid and 2% acetonitrile and 12 µl of 100% acetonitrile . Using the Micro Well-plate sampler and the IsoPump modules of an an Agilent 1100 Series Nanoflow HPLC , 20 µl of the tryptic digest solution was injected on a Zorbax 300SB-C18 pre-column ( 5×0 . 3 mm , 5 µm ) linked to an Agilent 1100 Series HPLC-system previously conditioned with water containing acetonitrile ( 3% ) and formic acid ( 0 . 1% ) . The sample was washed for 5 min at 15 µl/min and subsequently the valve holding the pre-column was actuated to connect it between the NanoPump module and the flushed through a 75 µm ID PicoFrit column ( New Objective , Woburn , MA ) filled with 10 cm of BioBasic C18 ( 5 µm , 300 Å ) in order to allow elution of the peptides towards the mass spectrometer at a flowrate of 200 nL/min . The acetonitrile concentration was first raised linearly from 10% to 40% in 40 min . It was increased linearly to 70% in 8 min , then to 95% in 5 min . The acetonitrile was held at 95% for 2 min then brought back to 10% in 2 min . The system was left at 10% acetronitrile for 3 min before starting the next injection . The total cycle time was 65 min . Eluted peptides were analyzed in a QTof micro ( Waters Micromass , Manchester , UK ) ) equipped with a ZSpray Nanoflow stage modified with a ADPT-MZS nanospray adapter ( New Objective , Woburn , MA ) . MS survey scan was set to 1 s ( 0 . 1 s interscan ) and recorded from 350 to 1600 m/z . MS/MS scans were acquired from 50 to 1990 m/z , scan time was 1 . 35 s and the interscan interval was 0 . 15 s . The doubly and triply charged selected ions were selected for fragmentation with collision energies calculated using a linear curve from reference collision energies . MS/MS raw data were transferred from the QTof micro computer to a 50 terabytes server and automatically manipulated for generation of peaklists by employing Distiller version 2 . 1 . 0 ( http://www . matrixscience . com/distiller . htmls ) software with peak picking parameters set at 30 as for Signal Noise Ratio ( SNR ) and at 0 . 6 for Correlation Threshold ( CT ) . The peaklisted data were searched against a copy of the Universal Protein Resource ( UniProt ) data base ( March 03 , 2008 ) by employing Mascot ( http://www . matrixscience . com ) version 2 . 1 . 04 , and restricting the search to up to 1 missed ( trypsin ) cleavage , fixed carbamidomethyl alkylation of cysteines , variable oxidation of methionine , 0 . 5 mass unit tolerance on parent and fragment ions , and monoisotopic . The search was limited to the Brugia and Wolbachia taxonomies ( Taxonomy ID: 6278 and 953 , respectively ) ( 17537 sequences; 5684760 residues ) . Mascot results from bands 1 to 15 ( based on spectra assigned to tryptic peptide sequences at the 95% confidence level ) generated peptide identifications that were then linked to the proteins and sorted by protein to produce an initial list of protein identifications . The list was quite redundant since about 5% of the spectra matched more than one peptide and 40% of the peptides identified occur in more than one protein . Consequently , the sequences were processed by a grouping algorithm [18] to generate a list of proteins defined by distinct sets of proteins . That is , the minimum number of protein sequences needed to explain the peptides observed . This minimal list of proteins was summarized on a SubGroup Count Report . Sequences from the minimal list of proteins were retrieved from the UniProt database and scanned for prediction of signal peptides and subcellular localization with SignalP 3 . 0 [19] , TargetP 1 . 1 [20] and SecretomeP 2 . 0 [21] . GO annotations were performed using Blast2GO [22] . The initial Blastp search was performed against the NCBI nonredundant database with a minimum expectation value of 1×10−3 and a high scoring segment pair cut-off of 33 . Annotations were made with default parameters; the pre-eValue-Hit-Filter was 1×10−6 , Annotation cut-off was 55 , and GO Weight was 5 . Annotation was augmented by using the Annotation Expander ( ANNEX ) [23] and by addition of the GO terms associated with functional domains derived from scanning the InterPro database [24] , [25] . The statistical framework GOSSIP [26] was used to identify statistically enriched GO terms associated with gender- or stage-specific secreted proteins compared to the GO terms associated with the complete set of identified proteins . Contingency tables for each GO term in the test group were generated and P values calculated in a Fisher's exact test . The P values were adjusted for multiple testing by calculation of the false discovery rate and the family wise error rate .
ESP were obtained from F , M and Mf under previously described conditions [16] , [27] , [28] . Average protein recoveries from several incubations were 71 and 41 ng protein per worm per day for F and M , respectively , and 165 ng protein per 1×106 Mf per day , values that are in the same order of magnitude as reported by others [16] , [27] . No remarkable changes in motility or physical integrity of the worms were observed during the 4 day incubation , suggesting that the collected media contained the products of normal physiological activity of the parasites and not the products of death or leaking caused by the procedures . The analysis of ESP from parasitic nematodes is limited by the very low amount of protein usually recovered from in vitro incubations . We addressed this issue by comparing several techniques for concentration and desalting at small scale . Although trichloroacetic acid ( TCA ) precipitation gave higher yields of protein than Amicon ( MWCO 3000 ) ultrafiltration , scaling the TCA precipitation up to 20 ml caused the retention of a significant amount of salt in the preparation . This generated wide lane profiles on SDS-PAGE , resulting in dilution of the proteins in the gel and diminishing the chances for identification . A preliminary analysis under these conditions led to the identification of 15 proteins in F , M , and Mf preparations , which ranged in amount from 6 . 5 to 13 µg protein ( not shown ) . As an alternative , we concentrated and desalted ESP by using Amicon devices to attain a final volume of 1–1 . 5 ml , and then precipitated the proteins with TCA . Preparations obtained in this way provided narrower running profiles with no apparent signs of proteolytic degradation ( Figure 1 ) . For this analysis , we pooled and concentrated the total media recovered from 3 sets of independent incubations . One set of incubations corresponds to the total number of worms and larvae recovered from one infected animal . This procedure led to the recovery of 37 . 3 µg , 65 . 3 µg and 15 . 4 µg of protein for Mf , F and M , respectively , subsequently subjected to SDS-PAGE ( Figure 1 ) . For protein identification , each lane of a gradient 2 . 4 cm SDS-PAGE gel was excised , digested with trypsin and analyzed by Liquid Chromatography – Tandem Mass Spectrometry ( LC-MS/MS ) . Initial peptide matches led to the preliminary identification of 286 proteins in the total ESP set . Refinement of the assignment of peptides to authentic proteins as described in the Methods section . This approach allowed us to identify 228 distinct proteins as part of the ESP of Mf , F and M ( Table 1 and S1 ) . All except one , annotated as Mmc-1 ( UniProt ID: Q9NDV4 ) , were matched to proteins annotated in the B . malayi genome database . Peptides identified in the MS/MS analysis were assigned with high confidence to B . pahangi Mmc-1 , which was also previously identified in Mf-ESP from this parasite [29] . A homolog nucleic acid sequence for the B . pahangi Mmc-1gene is also present in the B . malayi EST data set ( Gene Index: TC11321 ) . Its absence at the moment from the B . malayi protein models likely reflects the incomplete assembly available in the current version of the database and not a false-positive assignment . Many proteins have been identified as being secreted by B . malayi and related filarial nematodes by more traditional biochemical techniques ( Table S2 ) . All but two of the proteins reported to be secreted by B . malayi were found in the current survey . Neither of these two ( acetylcholinesterase and Transforming growth factor - 2 ) were reported in the initial proteomic analysis [14] . Proteins reported to be secreted by other filariae all had homologues in the B . malayi ESP set . This set included 59 of the 80 proteins ( 73 . 8% ) reported in [14] from incubations of M and F worms in joint culture . Proteins not identified in the current study included 8 assigned in [14] through screening of the B . malayi EST database ( Gene Index: TC7940 , AI783143 , TC9625 , TC7985 , TC8258 , TC8116 , TC7986 and AA592049 ) . None of these is found in the current assembly of the B . malayi genome database , suggesting that further analysis may alter the assignment of the peptides to a genomic locus . In addition , nuclear function associated proteins ( Pub locus: Bm1_03115 , Bm1_46120 , Bm1_25620 ) , several with undetermined function ( Bm1_19065 , Bm1_57465 , Bm1_46475 , Bm1_11505 , Bm1_01245 , Bm1_09845 and Bm1_33310 ) , 6-phosphofructokinase ( Bm1_01930 ) , a tropomyosin family protein ( Bm1_02060 ) and phosphatidylethanolamine-binding protein 2 ( Bm1_31500 ) were identified as relatively low abundance ESP proteins in [14] but were not detected in the current study . As reported in [14] , none of the proteins detected in B . malayi ESP in the current study could be assigned to Wolbachia proteins . According to the Prorated Query Count percentage values ( NQPCT ) , which provide a measure of relative abundance of a protein in a sample [18] , we found triosephosphate isomerase ( TPI ) to be one of the most abundant proteins in the ESP of all gender/stages , indeed the most abundant in F-ESP , consistent with other observations [14] . Although results from our separate incubations of M ad F worms generally agreed with those from the combined culture [14] , we found some differences . A fasciclin domain containing protein ( UniProt ID: A8P605 ) and an endochitinase ( P29030 ) were exclusively associated with Mf-ESP and were not present in ESP from adults . These two proteins have relatively high NQPCT values in the Mf-ESP sample and , therefore , their identification as low abundance ESP proteins in [14] may be related to release of Mf in adult worm cultures . 62 ( 27 . 2% ) of the identified proteins were predicted by SignalP [19] to have an amino-terminal secretion signal peptide and therefore may be secreted through the classical pathway . This value represents an enrichment of 11 . 8% in comparison to the proportion of total gene models in the B . malayi database having a predicted signal peptide . In addition , 81 ( 35 . 5% ) of these proteins may be secreted through non-classical secretory pathways , as they were identified by SecretomeP [21] to share features with mammalian proteins secreted in this manner . The proportion of proteins bearing a secretion motif is similar in the current study compared to the proteins identified in [14] , suggesting that the presence in ESP of proteins lacking known secretion motifs is not an artifact . 76 proteins were identified in Mf-ESP . 160 and 119 were identified in F and M-ESP sets , respectively . Only 32 proteins ( 14 . 0% ) were shared by all three stages/genders ( Figure 2 ) . Approximately half of these proteins had no annotated function or were poorly annotated ( assigned a named match but with no associated functional domain or Gene Ontology term ) . F and M shared 54 proteins ( 23 . 7% ) , whereas Mf showed a much lower degree of similarity with the adult stages , with only 7 ( 3 . 1% ) and 2 ( 0 . 9% ) proteins shared with F and M-ESP , respectively . Differences expressed as presence/absence of a protein can be extended to protein abundance . Table 1 presents a list of the 15 most abundant proteins as determined by NQPCT in each ESP . Most of the Mf-ESP proteins presented in this table were only identified at this stage or were not found as highly abundant proteins in ESP from adults . Differences in ESP composition between M and F were also observed; notable among these are the presence of Major Sperm Protein ( MSP ) family proteins as highly abundant in M-ESP , the appearance of a homolog of the human macrophage migration inhibitory factor ( MIF-1 , A8PJU3 ) as an abundant protein in F-ESP but not in M-ESP and numerous differences in the relative abundances of other proteins between the sexes . We used the Blast2Go analysis tool to mine the GO based data to illuminate the different functions and processes in which the proteins identified in the ESP are putatively involved , The initial annotation was augmented by using the Annotation Expander ( ANNEX ) [23] and by addition of the GO terms associated with functional domains resulting from scanning the InterPro database [24] . This analysis provided ≥1 GO terms for 171 sequences ( 75% ) from the total set . Of these , 157 ( 68 . 8% of the total ) sequences could be assigned to terms associated with molecular functions and 136 ( 59 . 6% of the total ) with biological functions . In the total set of ESP and the individual subsets ( Mf , F and M ) , catalytic activity ( GO:0003824 ) and binding ( GO:0005488 ) were the two major molecular function categories ( Figure 3A ) . Other molecular function categories include enzyme regulator activity ( GO:0030234 ) and antioxidant activity ( GO:0016209 ) . Structural molecule activity ( GO:0005198 ) was not found in Mf-ESP but was common in M-ESP ( Figure S1 ) . At a higher level of ontology ( Figure 4A ) , most of the assigned binding activity could be assigned to metal ion binding and cation binding ( GO:0046872 and GO:0043169 ) , purine nucleotide binding ( GO:0017076 ) , ribonucleotide binding ( GO:0032553 ) and , to a lesser extent , sugar binding ( GO:0005529 ) and several terms related to protein binding ( GO:0051082 , GO:0008092 and GO:0042802 ) . The catalytic activity function is populated by diverse types of reactions , with a major contribution from peptidase activity ( GO:0008233 ) . Interestingly , the protease inhibitor activity term ( GO:0030414 ) was the most common in the enzyme regulator activity category . The most common biological function categories ( Figure 3B ) were cellular process ( GO: 0009987 ) , metabolic process , multicellular organism process ( GO:0032501 ) , developmental process ( GO:0065007 ) and , less commonly , biological regulation ( GO:0065007 ) , growth ( GO:0040007 ) and reproduction ( GO:0000003 ) . As with the molecular function classes , proteins in these categories were found in the total set and individual subsets of ESP ( Figure S2 ) . In addition and as expected , proteins associated with reproduction ( GO:0000003 ) were predominantly found in adult ESP . A higher level of ontology ( Figure 4B ) shows that most of the cellular and metabolic processes are related to synthesis and degradation of macromolecules , particularly proteins and carbohydrates ( GO:0044260 , GO:0019538 , GO:0043283 , GO:0005975 , GO:0009057 ) , whereas the largest contribution to the multicellular organism and developmental processes came from terms such as embryonic development ending in birth or egg hatching ( GO:0009792 ) and larval development ( GO:0002164 ) that can be associated with the release and development of Mf . The GOSSIP statistical framework [26] was used to determine the enrichment of particular functions or processes in the ESP from Mf , M and F . We compared the terms associated with the proteins identified in each of the 3 ESP sets against those from the total GO term annotated proteins . Several processes and functions had significant P values ( P<0 . 05 ) in the single test ( Figure 5 ) . Nevertheless , to correct for multiple testing , a more stringent comparison using both a false discovery rate and a family-wise error rate was performed , and only the terms in the Mf-ESP were found to be enriched . These terms are all children of the parent ion binding term ( GO:0043167 ) , with zinc ion binding ( GO:0008270 ) the term with the highest level of ontology .
About 14% of secreted proteins were found in all stages/genders . One can speculate that this group is likely to include proteins that are essential for survival in the host , for instance , by deflecting the immune response . However , a significant portion of the common ESP proteins have no annotated function or GO term that can help in inferring their roles; therefore , the challenge is to elucidate their possible functions as determinants of parasitism . This includes proteins putatively assigned to the transthyrethin-like family , recently reported in [14] , a set of hypothetical proteins and previously identified but functionally uncharacterized antigens . An intriguing aspect of the common ESP set is the prominent presence of the glycolytic enzymes enolase ( A8PFE3 ) and TPI ( A8PKM4 ) . Although this finding could be due to death or compromised integrity of the parasites in culture , we believe this is not the case . First , other glycolytic enzymes , at least as abundant in cytoplasm as these two , do not appear in ESP . Secondly , these proteins have been previously identified in ESP from B . malayi and other parasitic nematodes [14] , [35] , [37] , [38] . Finally , evidence has appeared about their multiple roles and interaction with surface components in eukaryotic cells; an extracellular role thus cannot be excluded [39]–[41] . Other proteins identified in all 3 stages/genders include potentially immunomodulatory proteins , including a homolog of MIF-1 ( A8PJU3 ) [42] , a galectin ( GAL-1 , A8PGM6 ) , a cystatin-type cysteine proteinase inhibitor ( CPI-2 , O16159 ) [43] and leucyl aminopeptidase ( LAP , A8QH34 ) , a homolog of an ESP product that in Acanthocheilonema viteae ( ES-62 ) is modified with N-Type glycans containing phosphorylcholine ( PC ) [44] . In contrast , the protein identified in [14] as harboring the PC moiety in B . malayi , a core-2/I-branching enzyme family protein ( A8NPW6 ) , was only found in adult stages together with another isoform of human MIF ( MIF-2 ) ( Q9NAS2 ) [45] and GAL-2 [14] . Taking into account the number of proteins identified , their abundance and the possible functions and processes in which they can be predicted to be involved , it is clear that the composition of ESP from Mf is quite distinct from adult ESP . This result is perhaps unsurprising , since adults and larvae may exhibit different physiological repertoires reflecting their different developmental stage and anatomical location ( blood vs . lymph ) . Differences in ESP composition also suggest that there may be differences in the mechanisms of immune evasion between the stages . Several proteins with no assigned function were identified in Mf-ESP , including the protein identified as antigen R1 ( A8NW22 ) , which was the most abundant . This protein seems to be preferentially expressed in Mf , although it was also identified in ESP from M and F . Although no function is annotated for this protein , recombinant R1 has been used in diagnostic IgG4 ELISAs with excellent success for the detection of B . malayi infection using serum from Mf ( + ) patients , with however significant positivity in Mf ( − ) patients [46] . Many Mf-ESP proteins with predicted function are associated with developmental processes and regulation of enzyme activity . Some may play a role in the immunology of the host-parasite relationship , including an endochitinase ( P29030 ) and a serpin ( A8PJW0 ) , which were the next most abundant proteins in Mf-ESP; both were only found at this developmental stage . Chitinases are essential for chitin degradation during molting of larval filariae [35] . P29030 was originally reported as an antigen recognized by MF1 , a monoclonal antibody that mediates the transient clearance of Mf in jirds infected with B . malayi [47] . Immunization of jirds with recombinant endochitinase induced partial protection against Mf , but did not reduce adult worm burdens , suggesting that this protein is crucial for Mf development but not for adult viability [48] . GO analysis revealed that protease inhibition was the most common functional class in the ‘regulation of enzyme activity’ category . Mf secreted a completely different set of protease inhibitors than adults . In particular , homologues of serine protease inhibitors ( SPI ) were abundant in Mf-ESP . This set included serpins , a class of proteins having a wide spectrum of functions at extracellular and intracellular levels in eukaryotic cells [49] . In mammals , serpins are involved in the regulation of fundamental processes , including coagulation , complement activation and inflammation [50] , [51] , but their potential roles as modulators of host responses in lymphatic filariasis are uncertain [52] . The serpin identified as A8PJW0 is the predicted gene model for B . malayi serine protease inhibitor 2 ( Bm-SPN-2 , UniProt Accession No . O18656 ) . Bm-SPN-2 induces a Th1 response as characterized by the in vitro production of IFN-γ but not IL-4 or IL-5 in murine T cells [53] . At least 14 serpins are predicted in the B . malayi genome , two of which ( A8PHV4 and A8PHV1 ) were present in lower abundance compared to Bm-SPN2 , and both of them only in Mf-ESP . In addition to serpins , another abundant serine protease inhibitor in Mf-ESP is a putative trypsin inhibitor ( A8P664 ) . A8P664 contains a trypsin inhibitor-like cysteine rich domain ( TIL ) that can potentially inhibit peptidases belonging to families S1 , S8 , and M4 . A8P664 shares 50% identity with a trypsin inhibitor from the gastrointestinal nematode Ascaris suum , but is less related to serine protease inhibitors from the filarial nematodes Dirofilaria immitis ( Di-SPI-1 ) and Onchocerca volvulus ( Ov-SPI-1 , Ov-SPI-2 ) [54] . Bm-SPI-1 , another inhibitor from B . malayi [54] , was not found in any of the ESP and has no significant homology with A8P664 . In A . suum and other gastrointestinal nematodes , secretion of trypsin inhibitors has been proposed to interfere with the action of host digestive enzymes and with immunological effector mechanisms [55]–[58] . The Ov-SPI proteins seem to play a crucial role in nematode molting and in processes such as embryogenesis and spermatogenesis [54] . The roles of A8P664 may be different , as it is only found in Mf-ESP . Another potential serine protease inhibitor present in relatively high abundance in adult and Mf-ESP is a homolog of the O . volvulus antigen Ov16 , identified as A8Q2C4 [59] . A8Q2C4 was also identified in [14] in adult ESP as a homolog of proteins in the phosphatidylethanolamine-binding protein ( PEBP ) family , including a secreted 26-kDa antigen from the ascarid Toxocara canis [60] and a mouse PEBP with inhibitory activity against several serine proteases [61] . An intriguing finding in Mf-ESP is the presence of several zinc finger ( ZnF ) C2H2- type family proteins ( A8QFZ3 , A8PEN7 , A8PLP4 , A8QHJ5 , A8QHP5 , A8ND91 ) . These proteins were classified in the GO analysis with the Zinc Ion binding term , a molecular function that was enriched in Mf-ESP compared to the total protein set of ESP . The most common role assigned to ZnF proteins is the control of gene transcription through binding to specific DNA segments [62] . In addition , ZnF motifs mediate RNA , protein and lipid binding [63] , [64] . Although no apparent function has been assigned to these nematode proteins as mediators of extracellular processes , the fact that all of them were found in a particular stage with relatively high levels of secretion in comparison to other known secreted proteins deserves further consideration . Filarial nematodes deploy several mechanisms to detoxify reactive oxygen and nitric oxide derivatives produced by the host [65] . Based on our results , the different stages may use different mechanisms to overcome this type of stress . For example , gp29 ( P67877 ) , a glutathione peroxidase believed to act as a lipid hydroperoxidase , protecting parasite membranes from peroxidation caused by oxygen free radicals [66]–[68] , was identified in all 3 ESP sets . However , the markedly higher abundance of gp29 in adult compared to Mf ESP suggests that this enzyme may play a more important role in adult survival compared to Mf . In contrast , γ-glutamyl transpeptidase ( γ-GT , O97392 ) [69] , glutathione S-transferase ( GST , A8Q729 ) [70] and Zn-Cu superoxide dismutase ( SOD , Q6T8C4 ) [71]–[73] appeared with similar abundance in ESP from all three gender/stages of the parasite . Thus , mammalian-stage parasites may use common strategies to defuse free radicals in the host microenvironment by modulating the levels of glutathione , or by using the thioredoxin ( TRX-1 , A8Q921 ) system as a source of reducing equivalents [74] , [75] . In addition to its role in transferring the γ-glutamyl moiety of glutathione to an acceptor [76] , B . malayi γ-GT can trigger autoimmunity against human γ-GT in lung epithelial cells and may play a role in the development of the local pulmonary pathology syndrome known as Tropical Pulmonary Eosinophilia [77] . Several sex-specific secreted proteins were identified in this study , characterized by relatively low abundance . This list may be underpopulated due to the fact that some of the proteins identified in both stages could be misclassified as the result of transferring of proteins from male to females worms during insemination previous to the recovery of worms from the jird host , as is likely the case for major sperm protein 2 ( MSP-2 ) , expressed only in M [78] but found in both M and F-ESP . Other members of the MSP family were found only in M-ESP . In male B . malayi and other nematodes , MSP are expressed in the developing sperm and reproductive system and are essential for nematode sperm motility [79] , [80] . It is possible that proteins exclusively identified in F-ESP are released in uterine fluid , as some of them including the embryonic fatty acid binding protein ( Bm-FAB-1 , Q9GU91 ) and the papain family cystein protease containing protein ( A8NND7 ) have been reported to be expressed in the uterus as well as in developing gametes and embryos [79] . This proteomic analysis led to the identification of multiple components of the ESP of Mf , F and M B . malayi . In addition to the report of new identified ESP , the opportunity to compare the composition of ESP in all three stages/genders allowed us to propose different stage and gender specific related processes and to identify candidate proteins that may underlie stage specific strategies of immune evasion . | To succeed in infection , parasites must have ways to reach the host , penetrate its tissues and escape its defense systems . As they are not necessarily fatal , most helminth parasites remain viable within their host for many years , exerting a strong influence over the host immune function . Many of these functions are performed by products that are released from the parasite . We exploited the remarkable sensitivity of modern proteomics tools together with the availability of a sequenced genome to identify and compare the proteins released in vitro by adult males , adult females and the microfilariae of the filarial nematode Brugia malayi . This parasite is one of the etiological agents of lymphatic filariasis , a disease that poses continuing and significant threats to human health . The different forms of the parasite inhabit different compartments in the mammalian host . We found that the set of proteins released by each form is unique; they must reflect particular developmental processes and different strategies for evasion of host responses . The identification of these proteins will allow us to illuminate the biology of secretory processes in this organism and to establish a path for developing an understanding of how these parasite proteins function in immune evasion events . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/helminth",
"infections",
"chemical",
"biology/protein",
"chemistry",
"and",
"proteomics"
] | 2008 | Stage- and Gender-Specific Proteomic Analysis of Brugia malayi Excretory-Secretory Products |
There is evidence to suggest that the yaws bacterium ( Treponema pallidum ssp . pertenue ) may exist in non-human primate populations residing in regions where yaws is endemic in humans . Especially in light of the fact that the World Health Organizaiton ( WHO ) recently launched its second yaws eradication campaign , there is a considerable need for reliable tools to identify treponemal infection in our closest relatives , African monkeys and great apes . It was hypothesized that commercially available serological tests detect simian anti-T . pallidum antibody in serum samples of baboons , with comparable sensitivity and specificity to their results on human sera . Test performances of five different treponemal tests ( TTs ) and two non-treponemal tests ( NTTs ) were evaluated using serum samples of 57 naturally T . pallidum-infected olive baboons ( Papio anubis ) from Lake Manyara National Park in Tanzania . The T . pallidum particle agglutination assay ( TP-PA ) was used as a gold standard for comparison . In addition , the overall infection status of the animals was used to further validate test performances . For most accurate results , only samples that originated from baboons of known infection status , as verified in a previous study by clinical inspection , PCR and immunohistochemistry , were included . All tests , TTs and NTTs , used in this study were able to reliably detect antibodies against T . pallidum in serum samples of infected baboons . The sensitivity of TTs ranged from 97 . 7-100% , while specificity was between 88 . 0-100 . 0% . The two NTTs detected anti-lipoidal antibodies in serum samples of infected baboons with a sensitivity of 83 . 3% whereas specificity was 100% . For screening purposes , the TT Espline TP provided the highest sensitivity and specificity and at the same time provided the most suitable format for use in the field . The enzyme immune assay Mastblot TP ( IgG ) , however , could be considered as a confirmatory test .
Treponema pallidum is the bacterium that causes venereal syphilis ( ssp . pallidum ) and the non-venereal diseases yaws ( ssp . pertenue ) and endemic syphilis ( ssp . endemicum ) in humans [1] . The spirochete is able to cause a life-long chronic infection in untreated individuals [2] and elicits a strong adaptive immune response against a wide array of antigens [3–4] with strong serum IgM and IgG response [5–7] towards a number of lipoproteins ( e . g . , Tp15 , 17 , and 47 ) , endoflagellar proteins ( e . g . , FlaA , FlaB1 , 2 , and 3 ) , and the Tpr family proteins ( e . g . , TprA-TprL ) [6] . Furthermore , infection-related cellular damage is known to induce the production of non-treponemal antibodies mainly directed against cardiolipids [8 , 9] . Recently , we have reported that T . pallidum can infect large numbers of African monkeys and great apes [10] . To date , all simian isolates seem to be closely related to T . pallidum ssp . pertenue , the pathogen causing human yaws [11 , 12] and at least the Fribourg-Blanc simian strain , which was isolated from a baboon in Guinea [13] , has the potential to cause sustainable infection in humans [14] . Thus , there is evidence to suggest that yaws exists in non-human primate populations residing in regions where humans are also infected [15] . The clinical manifestations in non-human primates ( NHPs ) however , show regional differences . While West African simian strains of T . pallidum mostly cause no clinical signs [16] , gorillas in the Republic of the Congo show yaws-like lesions [17] and baboons in East Africa are known to develop severe genital ulceration [11 , 18] . However , independent of the clinical manifestations simian strains induce a pronounced serological response in the respective host [10] , which may be used to screen and identify host populations for their potential as a natural reservoir . In the context of the possible zoonotic potential of simian strains [14] , the identification and knowledge of a nonhuman reservoir for T . pallidum is crucial to disease elimination or eradication efforts and could help to identify hot spots for potential simian-to-human disease transmission . There is therefore considerable need to validate treponemal tests ( TTs ) and non-treponemal ( NTTs ) for their use in NHPs . Due to the close relationship of simian and human treponemes [12] , we hypothesized that A ) commercially available serological tests are able to detect simian anti-T . pallidum IgM and IgG in serum samples of baboons , a NHP species with high infection rates and B ) that the serological tests will be equally reliable in terms of sensitivity and specificity in baboon sera compared to the human sera .
Baboon serum samples were taken in accordance with the Tanzania Wildlife Research Institute’s Guidelines for Conducting Wildlife Research ( 2001 ) and with permission of Tanzania National Parks ( TNP/HQ/E . 20/08B ) as well as Commission for Science and Technology in Tanzania ( 2007-56-NA-2006-176 ) . The committee of Tanzania National Parks and Tanzania Wildlife Research Institute approved sample collection . Baboon serum samples from the German Primate Center were granted from the institute’s bio bank and originated from healthy animals that were sampled during post-mortem examination . The Animal Welfare and Ethics Committee of the German Primate Center approved the use of samples for this study . In a previous study , we were able to detect T . pallidum infection in wild olive baboons ( Papio anubis ) at Lake Manyara National Park in Tanzania [18] . Although the isolated strain is most closely related to T . pallidum ssp . pertenue [11] , the pathogen causes severe genital ulceration . Diagnosis was based on gross pathology , histology , and molecular biological tests . The latter included quantitative [19] and qualitative PCR [20] , targeting the polA gene of T . pallidum . DNA was extracted from skin tissue samples [18] . Data and corresponding serum samples that were constantly stored at -80°C of 57 untreated baboons from this study were available for analysis in 2013 . An additional set of 11 serum samples of healthy captive olive baboons from the German Primate Center were included as negative control . The extent of genital ulceration was used to classify and group animals as clinically healthy , initially- , moderately- , or severely-infected ( Fig . 1 ) . It is not known whether simian infection develops in stages similar to human infection . Generally , the definition of a test result was based on the individual’s overall infection status ( Table 2 ) . Details of infection status including genital ulceration status and each test’s interpretation can be found in the Supporting Information ( S1 Table ) . Statistical analyses were performed using Prism 6 . 0 ( GraphPad Software ) . Results of the TTs were first compared to the result of the Serodia TP-PA and second to the consensus of infection status , as it is defined in Table 2 , and which takes into account the appearance of clinical symptoms ( genital health status ) , IHC and skin tissue PCR results of the same animals as published elsewhere [18] . While the TP-PA was used as the gold standard for TTs , we compared results to the baboon’s infection status for further verification of test results and accuracy . With regard to NTTs it was assumed that a significant proportion of tests might become nonreactive in chronically infected baboons , as it is described for untreated human syphilis infection [25–27] . Test performances of the NTTs were therefore evaluated exclusively on the basis of the consensus of infection . A non-parametric test for nominal scale data , the two-tailed Fisher’s Exact Test , was used to compare the proportions among the serological tests , skin tissue PCR results and clinical signs of infection as well as for the analysis of sensitivity and specificity of the serological tests .
Tables 3 and 4 summarize sample size , proportions , and performance characteristics of the serological tests that were used in the 57 baboon serum samples from Lake Manyara National Park and an additional set of 11 serum samples from olive baboons of the German Primate Center in Germany . The latter were included for the purpose of additional negative control . When comparing TT performances with the TP-PA , we observed test sensitivity in the range of 91 . 3–100% , and specificity ranging from 94 . 7–96 . 0% ( Table 3 ) . When test results were compared to the consensus of all test results including PCR , however , the observed sensitivity of the TTs was in the range of 97 . 7–100%; whereas the specificity reduced slightly to the range of 88 . 0–100% ( Table 4 ) . This reduction was almost exclusively caused by the test performance of Syphilitop Optima ( Table 4 ) . NTT performances were not compared to TP-PA results since positive TT results in untreated , chronically infected patients do not necessarily predict reactivity of the corresponding NTT [25–27] . However , both NTTs used in this study reliably detected anti-lipoidal antibodies in serum ( Table 4 ) or plasma samples ( S2 Table ) of baboons . When infection status was considered in the context of all test results including PCR analysis , NTT sensitivity in serum samples was lower ( 83 . 3% ) than the average of the TTs ( 99 . 54% ) . The specificity of VDRL and RPR in serum samples was 100% and therefore higher than the performance of the standard TP-PA ( 92 . 0% ) and Syphilitop-Optima rapid test ( 88 . 0% ) . The performance data of NTTs for plasma samples are listed in S2 Table . Anti-T . pallidum antibodies were found in 97 . 3% of baboons with genital ulceration and in 6 of 20 animals that were clinically healthy ( 30 . 0% , Table 5 , Fig . 1 ) . For comparison , a remarkable proportion of genital-ulcerated baboons ( 13 . 5%; n = 5/37 , Table 5 ) had a negative PCR outcome of their respective skin tissue biopsy . Vice versa , we found 30% ( n = 6/20 ) genital non-genital-ulcerated baboons with positive T . pallidum PCR of their corresponding skin biopsy . Yet , genital-ulcerated baboons were significantly more often reactive for T . pallidum in serology ( TTs , p < 0 . 0001 ) and skin tissue PCR ( p < 0 . 0001 ) compared to clinically healthy and thus non-ulcerated baboons . Both treponemal rapid tests , Espline TP and Syphilitop Optima , were more sensitive than the Serodia TP-PA ( Two-tailed Fisher’s exact test , p < 0 . 0001 , Table 4 ) , although specificity in the Syphilitop Optima was much lower than Serodia TP-PA . The same applied , when Mastafluor FTA-ABS IgG and Mastablot TP IgG were compared to the Serodia TP-PA particle agglutination assay . In both tests the proportion of positive results matched the results of the Serodia TP-PA ( Two-tailed Fisher’s exact test , p < 0 . 0001 , Table 4 ) . No correlation was found when anti-T . pallidum IgG positive serum samples were tested in the immunoblot assay Mastablot TP for the presence of IgM antibodies against T . pallidum . Only a limited number of animals , 6 out of 59 ( 10 . 2% ) , tested positive for both anti-T . pallidum IgG and IgM antibodies . We did not find any samples that were positive for anti-T . pallidum IgM only . Even after log10-transformation of antibody titers , the clinically healthy and the initial stage genital-ulcerated group ( Fig . 1; it is not known whether NHPs develop three stages similar to humans; initial stage refers to the severity of genital ulceration as describe elsewhere [18] ) were not normally distributed . The Kruskal-Wallis test using Dunn’s correction for multiple comparisons showed that antibody titers in the severe genital-ulcerated group of baboons were significantly higher when compared to clinically healthy animals ( mean rank diff . = -30 . 04 , p ≤ 0 . 0001 ) and baboons with an initial stage of genital ulceration ( mean rank diff . = -17 . 56 , p ≤ 0 . 05 ) . The same was found for moderate genital-ulcerated baboons , which had significantly higher antibody titers against T . pallidum than animals without genital ulceration ( mean rank diff . = -19 . 95 , p ≤ 0 . 05 ) . Fig . 1 provides an overview .
The use of a NTT for the initial screening in the traditional algorithm in human infection is to avoid the detection of previously treated and non-active cases [40] . NTTs are known to produce a higher percentage of false positives [41] and test performance data of the NTTs as they are reported in Table 4 need to be interpreted with caution since it is neither known when an individual was infected nor how long anti-cardiolipid antibodies can be found in the due course of infection in wild baboons . The decision to use and recommend a TT as a screening test for T . pallidum infection in NHPs was based on the following three reasons and is in accordance with the current European Guidelines on the Management of Syphilis [23] . First , wild baboons are rarely treated and once infected , treponemal clearance may be an exception rather than the norm . Second , there is a paucity of data on cross-reactivity of proteins derived from human T . pallidum strains with antibodies against the simian strain in baboons . Lastly since the majority of baboons were chronically infected , we had reason to belief that a number of these chronically infected baboons were non-reactive in NTTs , as it was described in untreated human syphilis infection [25–27] . However , while a lifelong anti-T . pallidum antibody titer in baboons provides a most useful readout for the identification of a disease hot spot that offers the possibility for simian-human infection , therapeutic interventions in wild NHPs , as it is already conducted in baboons at Gombe Stream National Park in Tanzania ( Collins et al . pers . communication ) may benefit from the use of the traditional algorithm since NTTs may allow the differentiation of active and inactive infection . It is generally believed that yaws has no animal reservoir . Until identical T . pallidum strains are found circulating in nonhuman primates and humans in their natural environment this understanding cannot change . Yet , to this end , more research is needed before nonhuman primates can definitely be ruled out to serve as a natural reservoir for human infection . We have only recently begun to explore the range of nonhuman primate infection in Africa . Because the human-livestock-wildlife interface is constantly growing , the potential for inter-species transmission increase significantly . It is also possible that simian strains do naturally infect humans but do not cause clinical manifestations , as it is the case in Guinea baboons ( Papio papio ) in Senegal; or it may be that at least the East African simian strains cause genital ulceration in humans and may therefore not be diagnosed as yaws based on their genetics . Clearly , further research is needed before any answers can be given and serological surveys are an important tool to support these investigations and to complete our picture of T . pallidum infection in humans and nonhuman primates . Based on the outcome of this study we propose an algorithm for the screening of wild non-treated NHP populations ( Fig . 2 ) . The algorithm aims to identify T . pallidum infection in wild baboons and other NHPs and may complement the current yaws eradication campaign [42] . All tests used in this study provided reliable results to detect anti-T . pallidum antibodies in serum of baboons . We therefore favor hypothesis A , which suggests that commercially available serological tests are able to detect simian anti-T . pallidum IgM and IgG in serum samples of baboons , with the exception of IgM class anti-T . pallidum antibodies . It would be necessary to examine more animals in the initial stage of infection in order to test this part of the hypothesis , something that is difficult to achieve since the time of infection in wild baboons in general is not known . While NTTs may help to plan treatment and control of infections in baboons , TTs are most useful to screening non-treated baboon population for the presence of T . pallidum . Hypothesis B was partly rejected because some serological tests were not equally reliable in their sensitivity and specificity in baboon samples compared to human serum samples . For screening purposes , the immunochromatography based Espline TP test provided the highest sensitivity and specificity values and in addition had the most suitable format for use in the field . For confirmation , the treponemal test Mastablot TP IgG had the best performance characteristics and is therefore recommended as a gold standard . Serodia TP-PA was able to quantify antibodies against T . pallidum in baboons and results were consistent with the chronicity of infection . Based on this study a testing algorithm for the screening of NHP populations for T . pallidum infection is proposed , which may help future yaws eradication campaigns or wildlife management to identify baboons as a potential reservoir for human yaws infection . | The success of any disease eradication campaign depends on considering possible non-human reservoirs of the disease . Although the first report of T . pallidum infection in baboons was published in the 1970’s and the zoonotic potential was demonstrated by inoculation of a West African simian strain into humans , nonhuman primates have not yet been considered as a possible reservoir for re-emerging yaws in Africa . Simian strains are genetically most closely related to the strains that cause yaws in humans . The identification of baboons as a reservoir for human infection in Africa would be revolutionary and aid important aspects to yaws eradication programs . Reliable serological tests and a useful standardized test algorithm for the screening of wild baboon populations are essential for studying potential transmission events between monkeys and humans . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Validation of Serological Tests for the Detection of Antibodies Against Treponema pallidum in Nonhuman Primates |
Stimulus-specific adaptation ( SSA ) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus , but recovers when a different stimulus is presented . It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales ( e . g . , mismatch negativity in the event related potential ) , and participate in the control of attention and the formation of auditory streams . This article presents a spiking-neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs . The model is anatomically plausible , comprising just a few homogeneously connected populations , and does not require organised feature maps . The model is calibrated to match the SSA measured in the cortex of the awake rat , as reported in one study . The effect of frequency separation , deviant probability , repetition rate and duration upon SSA are investigated . With the same parameter set , the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations , such as block , sequential and random stimuli . A new stimulus paradigm is introduced , which generalises the oddball concept to Markov chains , allowing the experimenter to vary the tone probabilities and the rate of switching independently . The model predicts greater SSA for higher rates of switching . Finally , the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards . The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date , including its purported novelty component , and that non-trivial networks of depressing synapses can intensify this novelty response .
Natural acoustic environments play host to a wide variety of sounds that are either repetitive or follow a regular pattern . If an organism that inhabits one of these environments hears a repeating sound and does not react to the first few salient presentations , then it is unlikely that further repetitions will be behaviourally relevant . On the other hand , if the organism is to respond to changes in its environment , then it cannot adapt to stimuli indiscriminately; rather , it must remain sensitive to even small deviations from an established pattern . It is within such an evolutionary context that the brain has acquired stimulus-specific adaptation ( SSA ) mechanisms that operate across several time scales and sensory resolutions [1] . SSA in response to tone sequences has been measured in the spiking of single neurons at various stages of the auditory pathway , including the inferior colliculus ( IC ) in the rat [2] , [3] , medial geniculate body ( MGB ) of the thalamus in the mouse [4] and rat [5] , thalamic reticular nucleus in the rat [6] , and primary auditory cortex in the cat [7] , [8] and rat [9] . It has been suggested [7] , [8] , [10] that SSA in single neurons lies on the path leading to the generation of mismatch negativity ( MMN ) –a frontocentrally negative-going deflection in the event-related potential [11] , [12] , evoked in response to violations of an established temporal sound pattern , including changes in frequency , intensity , duration and even the omission of an expected stimulus ( for a recent review , see [13] ) . It is thought that MMN , in turn , may be implicated in the redirection of attention [14] , maintain the representation of the auditory context [12] , and contribute to auditory scene analysis [12] , [15] . In this article we describe a neurocomputational model of SSA based on a small network of spiking neurons connected by dynamic synapses . The model components are all drawn from the literature [16]–[19] and are implemented without significant modification in order to keep free parameters to a minimum . In terms of its overall architecture , the model rests upon few anatomical assumptions , as it consists solely of a small number of homogeneous populations joined together in uniform patterns of connectivity , which could exist in the brain ( e . g . , all-to-all , sparse/random ) . The model requires feature-tuned inputs but does not require that these inputs be mapped topographically . As frequency selectivity in neurons is best understood , and most SSA studies to date have only manipulated frequency , the inputs of our model are tuned to frequencies . This study offers three distinct contributions to the ongoing discussion concerning stimulus-specific adaptation in single neurons: a new model of SSA that accounts for an array of experimental results; a description of a novel stimulus paradigm , accompanied by predictions from the model that can be tested experimentally; and an exploration of the effect of linking adapting processes in series on SSA and novelty detection in general . It is sometimes remarked that the time scale of recovery from adaptation to tones measured in cortex is consistent with the time it takes cortical synapses to recover from synaptic depression [7] , [8] , [20] . Despite the strikingly suggestive similarity in the dynamics , and the availability of a light-weight model of a depressing cortical synapse [18] , we are not aware of any modelling study to date that has explicitly attempted to bridge this explanatory gap: assembling these model synapses into networks with a view to replicating the results of SSA experiments . Here we undertake just such a study , taking as our primary data the results obtained by von der Behrens et al . [9] in the auditory cortex of the awake rat , which are presented in such a format as to be particularly conducive to the calibration of a model . A more general , theoretical treatment of the properties of networks constructed using this dynamic synapse model is given in [21] . Some mathematical results pertaining to SSA when viewed as an abstract computational process are discussed in [22] . Having configured the model to respond to oddball sequences in a manner consistent with the published physiological data , we then probe it with patterns of standards and deviants generated by first-order Markov chains [23] , wherein the probability that a given tone is standard or deviant depends on its immediate predecessor . Oddball sequences actually constitute a specific subset of two-state Markov chains . Progressing to general Markov chains enables one to vary not only the probability of a deviant ( ) , but also the probability of switching between deviants and standards ( ) ; or , from another perspective , to control the degree to which deviants and standards “clump together” in the sequence , whilst maintaining their overall proportions . The model furnishes explicit predictions regarding the response of SSA neurons to tone sequences generated by Markov chains . Finally , we examine serial arrangements of depressing synapses as a possible basis for certain types of novelty detection . This architecture is motivated by the fact that some neurons respond more vigorously to deviant tones if they are embedded in a background of a single standard frequency than if they appear as one of many , equiprobable random tones [7] , [10] . At the very least , the difference in the responses is not so great as one would expect from a model based on adaptation within channels [24] . A similar sensitivity to novelty is also apparent in the mismatch negativity [25] , [26] . The idea of a two-layer model rests on the plausible suggestion that the pre-synaptic inputs to some depressing synapses themselves undergo adaptation due to synaptic depression elsewhere . In the current study , we found that cross-channel adaptation within a single layer of depressing synapses was sufficient to account for the excess response to deviants embedded in a single standard provided that was large enough . However , introducing two layers of synaptic depression in series enhanced the effect , in that this excess response was larger , and the required to elicit the effect was smaller . In summary , on the one hand , our results support the case for an explanation of SSA based solely on adaptation , at least as far as frequency is concerned . On the other hand , commentators that adopt an adaptation-based interpretation of SSA tend to speak exclusively in terms of the depression or fatigue associated with afferents , whereas we demonstrate that linking depressing synapses in series ( and , in principle , recurrently ) can dramatically modify these effects .
Population A comprises sub-populations of Poisson neurons , each of which fires at a rate that depends on the frequency of the input tone . The best frequencies of the sub-populations are spaced uniformly on an octave scale . The number of sub-populations and the range of octaves spanned is task-dependent: two-tone tasks utilise 96 inputs spanning a range of 2 octaves; multi-tone tasks utilise 144 inputs spanning a range of 3 octaves . The firing rate ( Hz ) of sub-population with best frequency in response to tone frequency has the form of a raised Gaussian profile , where is the spontaneous firing rate in the absence of a signal; is the maximum firing rate , elicited when the tone and best frequencies coincide; and controls the width of the tuning curve . As a measure of bandwidth , we take the separation , in octaves , between the frequencies that evoke firing rates half-way between the maximum and spontaneous rates , and denote this quantity . Unlike stimulus parameters , which can be chosen to match the original SSA experiments exactly , the tuning of the putative input channels can , at best , only be inferred from the results of the SSA experiments themselves , or estimated in line with other experimental data . We typically set in this study , which we consider to be conservative , given the tuning width of certain neurons in the inferior colliculus [29] and fibres at the auditory periphery [30] . Alternative values for are also investigated , however . Figure 2A depicts the overlap between two tuning curves with best frequencies separated by half an octave . The AB model is the simplest instance of an adaptation-based model that exhibits SSA . It consists of two populations labelled A and B ( see Figure 1A ) . The computations relevant to SSA are effectively performed by a single , feed-forward layer of depressing synapses ( ) . Population B consists of 48 AdEx neurons , each of which receives a connection from a distinct Poisson neuron in every sub-population of A via a depressing , excitatory synapse . Thus population A contains or Poisson neurons , depending on whether the experiment is two-tone or multi-tone , respectively . It is the depression of the synapses which guarantees the basic behaviour required of the model , namely , that the responses in B reduce if the same tone is presented repeatedly , but recover if another tone is presented . This scenario is presented diagrammatically in Figure 2B . The degree of overlap in the tuning of the Poisson inputs determines how SSA varies with the frequency separation between the tones . When is small , the synaptic resources associated with the standard and deviant frequencies coincide to a greater extent , and the SSA measured is smaller . The ABC model extends the AB model by adding an inhibitory population , C , consisting of 48 AdEx neurons , and two additional synaptic pathways , and ( Figure 1B ) . The connectivity of the pathway is identical to that of , described above , with the exception that the synapses involved do not depress . Each unit in population B receives input from sixteen randomly-chosen units in population C via fast , inhibitory synapses , which collectively form the pathway . As in the AB model , SSA is sought in population B . In this model , the indirect pathway does not participate in the generation of SSA . Rather , the tonic inhibition of population B ensures that spontaneous activity is minimised , so that spiking activity reflects the input signal , not the background noise . Peri-stimulus time histograms ( PSTH ) from a study of SSA in the awake rat [9] , show a transient response at the tone onset , followed by a period of spiking below the spontaneous rate , suggestive of inhibition , which lasts for the duration of the tone ( see Figures 1A and 3A in [9]; see also Results ) . In summary , the SSA responses in the ABC model are essentially generated in the same way as those in the AB model , namely , through the depression and recovery of the synapses . There is , however , a difference in the resultant firing patterns . In the AB model , activity in population B persists throughout the tone , until the synapses are depressed to the extent that the units can no longer reach threshold . In the ABC model , in contrast , the neurons receive a strong , delayed , shunting inhibitory input , which suppresses both spontaneous and stimulus-driven spiking . Thus , if a neuron in population B is to fire at all , the excitatory component from population A must cause it to reach threshold in the short time window before it is inhibited . An appropriate balance of excitation and delayed inhibition leads to binary spiking , i . e . , the tendency to respond to a stimulus with either no spikes or one spike , which is observed in auditory cortical neurons in general [31] , and also in SSA studies in cortex [9] and MGB [5] . Synaptic depression weakens the excitatory contribution to the post-synaptic potential and effectively turns this binary response from ‘on’ to ‘off’ . The ABD model extends the AB model by adding population D , which consists of 48 AdEx neurons , and an excitatory synaptic pathway , . There are no inhibitory populations in this model . The units in population D receive input from population B only , via depressing synapses , connected in an all-to-all pattern ( Figure 1C ) . Our primary interest is SSA in population D , although SSA is also present in population B . Whilst several authors have suggested adaptation on the inputs to a neuron as the mechanism whereby SSA is generated [7]–[9] , none have considered the properties of a network consisting of a cascade of depressing synapses . The ABD model is used to investigate the simplest instance of such a network , in which there are just two depressing pathways ( ; ) . The pathway has a recovery time constant of [7] , [32] . The synaptic weights are . The original motivation for the ABD model was the suggestion that the responses obtained for deviants embedded in a single standard exceeded those obtained for the same deviants embedded in a “many standards” control condition [10] . We elaborate on the descriptions of these protocols below . Here it will suffice to sketch the intuitive difference in the stimuli and the behaviour required of the model . If deviant tones are presented against a background of a single , repeating standard frequency , then they are conspicuous , and the model should respond . However , if the same deviant frequency appears as one of many equiprobable random tones , then it is no longer conspicuous , it is simply one tone amongst many , and the model should not respond . In summary , the model must respond to the novelty of the tone , not simply its rarity–which is the same in both conditions . Figure 2C–D illustrates how the two-layer architecture can make this distinction . Figure 2C shows how the model responds to a deviant embedded in a single standard . A repetitive standard ( left ) causes the synapses associated with that frequency to depress , and the neurons in population B stop firing . Because the activity in population B is low , the synapses do not depress . When a deviant tone is presented ( right ) , there is a recovered synaptic pathway leading from population A to D , via B , and the neurons in population D respond . Now we consider the many standards configuration . Figure 2D ( left ) depicts the presentation of many standards . Because the frequencies of the standards vary , there is usually time for the synapses to recover between presentations . As a consequence , the average response in population B is high , and synapses are depressed . Now , when the nominal deviant tone is presented ( right ) , there is no longer a complete pathway of recovered synapses leading from A to D , and the neurons in population D are silent . The units in population D of the ABD model react to deviants in an appropriate context-dependent manner , whereas the units in population B do not . In closing , we emphasise that the binary distinctions firing/not firing and depressed/recovered are drawn for the benefit of the illustration . In the model , we seek only differential effects consistent with this general behaviour .
This section reports the response of the AB model to oddball sequences only . The responses of the AB model to other types of sequence are discussed in the ABD Model section .
We have proposed a model of stimulus-specific adaptation in single neurons based on the convergence of depressing synapses . The inputs to the model are Poisson processes , whose mean firing rates depend on stimulus features . In this work , the stimulus feature considered is frequency , represented on an octave scale . The firing-rate profiles are Gaussian-shaped , with bandwidths similar to auditory filters . Although we have concentrated exclusively on frequency as a stimulus feature , SSA in response to other features , such as intensity , duration and modulation , can in principle be modelled , provided that a population encoding of these features is available as input to the model . The objective was to model the spike counts of individual neurons in response to tones embedded in various types of sequence . The model was initially calibrated to match the SSA recorded for oddball sequences in one study [9] . Stimulus configurations used in other studies were submitted to the model , including oddball sequences at other repetition rates [3] , [5] , [7] and sequences in which the tones were presented in blocks , in ascending sequences , and at random [2] . The trends in the results generated by the model ( e . g . , with respect to , and ISI ) were consistent with those measured in the respective physiological experiments , even though the latter covered a variety of brain areas , species and anaesthetic protocols . A second contribution of the work concerns the proposed Markov stimulus paradigm . A two-state Markov chain provides a particularly useful generalisation of the oddball sequence , in that it allows the experimenter to decouple the effects of probability ( i . e . , the ratio of standards to deviants ) from the effects of switching . In a conventional oddball sequence , the rate of switching is implicitly dictated by the deviant probability . As Markov chains have not yet been used in SSA experiments , the model also provides a direct prediction concerning the outcome of such experiments , if the explanation of SSA based on the convergence of depressing synapses is correct . Specifically , the SI for a fixed deviant probability should increase as the probability of switching increases . The current formulation of the model allows that SSA be generated de novo wherever depressing synapses receive stimulus-specific inputs . A one-layer model does not account for the entire range of SSA effects observed to date , for example , instances where SSA increases as increases because the response to the standard declines [3] , the tonic SSA responses observed in cortex [7] , the slow adaptation component apparent over the course of an entire oddball sequence [8] , the fact that SSA does not always decay with increasing SOA [4] , [5] and that SSA is sometimes still strong even at very long SOAs [5] . However , a large-scale model , formed by assembling ‘modules’ of this kind , could plausibly account for the spread of SSA throughout the auditory pathway . In fact , there is evidence that neurons in IC and MGB might integrate distinct sources of SSA from multiple locations and at various latencies [3] , [5] . As a first step towards this proposal , we created a two-layer model , in which one adapting process receives input from another . ( This organisation is possibly reminiscent of a feed-forward process in which cortical neurons are driven by depressing thalamo-cortical synapses , which in turn are driven by adapting IC neurons , though we did not have this anatomical organisation in mind exclusively . ) The neurons in the second population exhibited stronger SSA , and the “novelty component” of the response–as measured using the deviant amongst many standards control [25] , [26]–was also stronger after the signal had traversed multiple depressing layers . However , some care is required when interpreting these results . An increase in SI can in part be explained by the thresholding effect of the neurons , which would be present , whether or not the intermediate synapses were depressing . It was also shown that a one-layer model can explain the fact that responses to deviants are larger when embedded in a sequence consisting of a single standard , provided the frequency separation between the deviant and standard is large enough . Arranging depressing synapses in series leads to the elicitation of novelty responses for smaller frequency separations . Stimulus-specific adaptation in single neurons is likely to remain the subject of intense investigation in the foreseeable future , as it demonstrates a primitive form of auditory memory , upon which other novelty-related neural responses , such as auditory mismatch negativity , could build [10] . This article brings a new stimulus paradigm ( Markov chains ) and network architecture ( two layers of adaptation , linked in series ) to the attention of the research community . We suggest that adaptation should not be prematurely dismissed as the principal cause of SSA , as it is possible to capture a richer range of phenomena if adapting channels are allowed to form more complex circuits . | For processing real-life auditory scenes , it is not enough that auditory neurons code only for basic stimulus properties , such as frequency and intensity; at some point , these isolated properties must be woven into a pattern . Stimulus-specific adaptation ( SSA ) , whereby neurons adapt to common stimuli but otherwise remain sensitive to other , rare stimuli , has been proposed as a low-level substrate for such abstract pattern processing . SSA has been previously investigated using ‘oddball sequences’ of tones , in which one frequency is common , the other rare . In this article , we present the first neurocomputational model of SSA and show that it can reproduce a wide range of published data . We also propose a natural generalisation of the oddball paradigm , based on Markov chains , which allows the experimenter to manipulate other characteristics of the sequence such the rate of switching . Finally , we show that a small network of neurons can distinguish novelty from mere rarity; e . g . , a B stands out in the sequence ABAAA in a way that it does not in CBADE , even though it is equally probable in both . We demonstrate that cascades of depressing synapses can adequately encode this difference , whereas the simple adaptation-based models proposed to date cannot . | [
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] | 2011 | A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences |
Plant zygote divides asymmetrically into an apical cell that develops into the embryo proper and a basal cell that generates the suspensor , a vital organ functioning as a conduit of nutrients and growth factors to the embryo proper . After the suspensor has fulfilled its function , it is removed by programmed cell death ( PCD ) at the late stages of embryogenesis . The molecular trigger of this PCD is unknown . Here we use tobacco ( Nicotiana tabacum ) embryogenesis as a model system to demonstrate that the mechanism triggering suspensor PCD is based on the antagonistic action of two proteins: a protease inhibitor , cystatin NtCYS , and its target , cathepsin H-like protease NtCP14 . NtCYS is expressed in the basal cell of the proembryo , where encoded cystatin binds to and inhibits NtCP14 , thereby preventing precocious onset of PCD . The anti-cell death effect of NtCYS is transcriptionally regulated and is repressed at the 32-celled embryo stage , leading to increased NtCP14 activity and initiation of PCD . Silencing of NtCYS or overexpression of NtCP14 induces precocious cell death in the basal cell lineage causing embryonic arrest and seed abortion . Conversely , overexpression of NtCYS or silencing of NtCP14 leads to profound delay of suspensor PCD . Our results demonstrate that NtCYS-mediated inhibition of NtCP14 protease acts as a bipartite molecular module to control initiation of PCD in the basal cell lineage of plant embryos .
Plant development begins with asymmetric division of the zygote , giving rise to two daughter cells with distinct developmental fates . A small apical cell is the founder of cell lineage generating the embryo proper , whereas a larger , basal cell establishes cell lineage leading to the embryo-suspensor . The function of the suspensor is to connect embryo to the surrounding seed tissues and to transport nutrients and growth factors to the embryo proper [1]–[5] . The suspensor is an ephemeral organ , which is not required at the advanced stages of embryogenesis and therefore eliminated , providing the earliest manifestation of programmed cell death ( PCD ) in plant ontogenesis [4] , [5] . Dismantling of the suspensor cells is a slow process characterized by the gradual digestion of all cellular content by the growing lytic vacuoles [4] , and is therefore ascribed to the class of vacuolar cell death [6] . Apart from the requirement of type II metacaspase mcII-Pa [7] , [8] and short polypeptide kiss of death [9] for the execution of suspensor PCD in Norway spruce and Arabidopsis embryos , respectively , molecular regulation of suspensor PCD remain elusive . In particular , molecular trigger responsible for the timely initiation of cell death in the suspensor is unknown . Tobacco ( N . tabacum ) embryos provide a tractable model system to address the molecular mechanisms of suspensor PCD , because of the formation of a single file of four or five suspensor cells through a highly stereotyped and precisely timed sequence of cell divisions , beginning from the first zygotic division ( Figure 1A ) [10]–[12] . As the first step towards the molecular understanding of the early stages of tobacco embryo development , we recently identified two groups of genes differentially expressed in the apical and basal cells of two-celled proembryos [13] . Here we demonstrate the function of one of the basal cell-specific genes , NtCYS , which encodes a cysteine protease inhibitor cystatin . We show that NtCYS protects the basal cell lineage from precocious initiation of cell death and is indispensable for suspensor formation and completion of embryogenesis . NtCYS exerts its anti-cell death effect by directly inhibiting cathepsin H-like protease NtCP14 . Our findings establish NtCYS-dependent inhibition of NtCP14 as a bipartite molecular module regulating the fate of the basal cell lineage at the early stages of plant embryogenesis .
From a single cell transcript profile analysis designed to identify cell lineage-specific genes in two-celled proembryos of tobacco [13] , we selected EST 858 ( National Center for Biotechnology Information [NCBI] [http://www . ncbi . nlm . nih . gov]: HS084274 , HS084275 ) because of its strictly basal cell-specific expression pattern . Rapid amplification of cDNA ends ( RACE ) followed by three rounds of genome walking produced a 683-bp cDNA and a 4 , 247-bp upstream region containing an ATG initiation codon . Analysis of the ORF indicated that the gene contains a complete ORF of 420 nucleotides encoding a protein of 140 amino acids . BLASTP search returned several matches to proteins containing a cystatin-like domain and a cystatin-specific QxVxG motif , indicating that the gene is a new member of the cystatin family in N . tabacum ( Figure S1A ) . The gene was named NtCYS . The encoded protein clusters most closely with proteins from Populus trichocarpa , Glycine max , and Medicago truncatula , but is distant from homologs present in Arabidopsis thaliana and Brassica oleracea ( Figure S1B ) . Most cystatins can inhibit activity of cysteine proteases from the papain C1A family [14] . To investigate the biochemical properties of NtCYS , inhibition assays with recombinant NtCYS were carried out against papain ( from papaya latex ) and human liver cathepsins L and B using substrates Z-FR-AMC , Z-FR-AMC , and Z-RR-AMC , respectively . While NtCYS efficiently inhibited the activities of papain ( Ki = 64 . 5±5 . 9 nM ) and human cathepsin L ( Ki = 0 . 39±0 . 04 nM ) , no measurable inhibition of human cathepsin B was detected . These data establish NtCYS as a member of the cystatin family with inhibitory activity against a subset of cysteine proteases . To investigate the expression pattern of NtCYS , RNA was extracted from sperm cells , egg cells , zygotes , two-celled proembryos , and eight-celled , 32-celled , and heart embryos , and NtCYS expression was determined using semi-quantitative reverse transcription PCR ( RT-PCR ) . NtCYS transcripts were detected in all samples , except for sperm cells and heart embryos ( Figure 1B ) . We also assessed the levels of NtCYS transcripts at nine successive stages of embryogenesis , as well as in different plant organs by quantitative real-time RT-PCR ( RT-qPCR ) . NtCYS was highly expressed in two- and four-celled proembryos ( stages 1 and 2 , respectively ) and in the eight-celled embryos ( stage 3 ) , whereas the expression was barely detectable at more advanced stages of embryogenesis and was absent in non-embryonic organs ( Figure 1C ) . To gain insight into the spatio-temporal pattern of NtCYS expression , we generated transgenic plants carrying the proNtCYS::NtCYS-GFP construct . In the two-celled proembryos , NtCYS-GFP was detected only in the basal cell , but never in the apical cell ( Figure 1D ) . When the apical cell divided , giving rise to a two-celled embryo proper , NtCYS-GFP signal continued to stay exclusively in the basal cell ( Figure 1E ) . Surprisingly , two rounds of cell divisions in the basal cell lineage did not change the localization of NtCYS-GFP , which remained restricted to the basal cell of the two- and four-celled suspensors ( Figure 1F and 1G ) . NtCYS-GFP expression markedly declined in the basal cell of the eight-celled embryos ( stage 3 ) and was barely detectable in embryos at stage 4 and 5 ( Figure 1H and 1I ) . These results confirm basal cell-specific expression of NtCYS in the two-celled proembryos and demonstrate that NtCYS-GFP has strict basipetally polarized distribution , resulting in its persistent localization in the basal cell throughout the early stages of embryogenesis . We investigated the dynamics of suspensor PCD during a time-course of tobacco embryo development using an array of cell death markers . Following zygotic cell division , the basal cell undergoes two successive divisions , giving rise to a four-celled suspensor at the eight-celled embryo stage ( stage 3; Figure 1A ) . Deoxynucleotidyl transferase ( TdT ) -mediated dUTP nick-end labeling ( TUNEL ) and double staining with fluorescein diacetate ( FDA ) and propidium iodide ( PI ) showed that all cells in both apical and basal cell lineages at stages 1 to 3 maintained nuclear DNA and plasma membrane integrity ( unpublished data ) . The first TUNEL signal was detected in the enlarged basal cell of the 32-celled embryo ( stage 4 ) ( Figure 2A ) . During embryo development through stages 4 to 8 , TUNEL staining gradually progressed from the basal cell to the uppermost cell of the suspensor ( Figure 2B–2E , and 2L ) , Unlike TUNEL , in the most embryos analyzed PI staining was absent in the suspensor cells until stage 8 ( Figure 2F–2K , and 2M ) . Consequently , DNA fragmentation precedes plasma membrane permeabilization in this PCD system . We further investigated cytological features of the suspensor PCD using transmission electron microscopy ( TEM ) . The nuclear envelope appeared intact until globular-to-heart transition ( stage 7 ) , when the nucleus became lobed and crenulated ( Figure 3A and 3B ) . These early nuclear envelope events were followed by its progressive disassembly until complete disintegration at stage 9 ( Figure 3C and 3D ) . Consistent with other examples of vacuolar cell death in plants [6] , gradual clearance of cell contents was correlated with increased vacuolization of the cytoplasm without discernible loss of cellular turgor ( Figure 3A–3D ) . Recent evidence suggests the involvement of proteases with caspase 1 , 3 , and 6-like activities in various types of PCD in plants [15]–[20] . To establish whether caspase-like activities are involved in tobacco suspensor PCD , we studied the localization of cell-permeable fluorescent inhibitors of caspases 1 , 3 , and 6 in the embryos at stages 3 to 7 ( Figure 3E–3H ) . None of these activities were detected at stage 3 ( Figure 3H ) . The first fluorescent signal was detected at stage 4 for caspase 3-like activity , which persisted until stage 6 or 7 ( Figure 3F and 3H ) . Caspase 1- and 6-like activities were first detected at stage 5 and remained in most of the embryos until stage 7 ( Figure 3E , 3G , and 3H ) . Notably , the spatio-temporal pattern of all three proteolytic activities followed that of TUNEL , first appearing in the basal cell and then moving acropetally towards the uppermost suspensor cell ( Figures 2A–2E and 3E–3G ) . No caspase-like activity was detected in the embryo proper . Together , our data demonstrate that PCD in the tobacco embryo-suspensor is a highly ordered and stereotyped process , which starts in the basal cell of 32-celled embryo ( stage 4 ) and then progresses acropetally towards the uppermost suspensor cell adjacent to the embryo proper . This PCD exhibits cytological hallmarks of vacuolar cell death , including cytoplasm vacuolization , nuclear envelope disassembly , DNA fragmentation , and the absence of protoplast shrinkage [6] , and is accompanied by the activation of proteases with caspase-like specificity . Since the onset of the suspensor PCD occurred concurrently with a steady decrease in NtCYS expression in the embryos ( Figures 1 and 2 ) , we speculated that NtCYS could control this PCD . To address this possibility we silenced NtCYS using three independent RNA interference ( RNAi ) constructs expressed under the native promoter and selected six homozygous RNAi lines for detailed analysis ( Figure 4A ) . We observed that in contrast to wild type ( WT ) , a substantial fraction of two-celled proembryos in these lines ( 18 . 4%–45 . 3% for different lines ) had PI-positive and FDA-negative basal cells , indicating that they were already dead ( Figure 4B and 4C ) . However , the apical cells in these proembryos were still alive ( Figure 4C ) . To further investigate premature death of the basal cells triggered by NtCYS deficiency , we evaluated nuclear integrity in the two-celled proembryos in WT and three NtCYS-silenced lines using TUNEL and TEM . The frequency of TUNEL in the basal cells in all three lines increased dramatically compared to the WT ( Figure 4D and 4E ) . Furthermore , TEM revealed nuclear lobing and condensation in the basal cells but not in the apical cells of the proembryos from NtCYS-silenced lines ( Figure 4F–4K ) . As a substantial part of two-celled proembryos from RNAi lines developed further to four-celled proembryos ( stage 2 ) and then to eight-celled embryos ( stage 3 ) , we assessed whether these more advanced stages were also associated with the appearance of TUNEL in the suspensor . In contrast to the WT , where most proembryos initiated DNA fragmentation in the suspensor when the embryo proper had 32 cells ( stage 4 ) ( Figure 2 ) , NtCYS-silenced lines revealed frequent occurrence of TUNEL in the suspensor at stages 2 and 3 ( TUNEL frequencies 28 . 9% and 15 . 6% , respectively , n = 90 ) . Precocious activation of PCD in the basal domain caused abortion of 20 . 7% to 40% of seeds in different NtCYS-deficient lines ( Figure 4L and 4N ) . Among the aborted seeds analyzed in the NtCYS-deficient line L2-6 , 44% of embryos were arrested at the transition from stage 1 to stage 2 and 51 . 7% at the transition from stage 2 to stage 3 ( 1 , 283 seeds analyzed ) . Cessation of embryogenesis was correlated with aberrant patterns of cell division in the apical cell lineage ( Figure 4M ) . Collectively , these data demonstrate that NtCYS acts to suppress cell death in the basal cell lineage throughout the early stages embryogenesis until the eight-celled embryo ( stage 3 ) . Furthermore , this anti-cell death role of NtCYS is crucial for the formation of a functional suspensor and completion of embryogenesis . Since NtCYS encodes a cystatin , which inhibits papain and human cathepsin L in vitro , we investigated whether cathepsin-like activity correlates with NtCYS expression in tobacco proembryos . For this , we compared proteolytic activities of the total protein extracts prepared from two-celled proembryos in WT and NtCYS RNAi plants using the fluorescent peptides Z-FR-AMC ( for cathepsin L-like proteases ) , Z-RR-AMC ( for cathepsin B-like proteases ) , and Bz-FVR-AMC ( for cathepsin H-like proteases ) . Silencing of NtCYS led to 1 . 4- and 2 . 6-fold increase in proteolytic activity towards Z-RR-AMC and Bz-FVR-AMC , respectively , but did not affect activity towards Z-FR-AMC ( Figure 4O ) . This suggests that the anti-cell death effect of NtCYS in the basal cell lineage may be reliant on inhibition of cathepsin B- and/or cathepsin H-like proteases . Normal progression of PCD in the tobacco embryo-suspensor is associated with increased caspase-like activity ( Figure 3 ) . To determine whether NtCYS deficiency-induced death of the basal cells in the two-celled proembryos implicates activation of proteases with caspase-like specificity , we measured proteolytic activity in the extracts of two-celled proembryos from WT and NtCYS RNAi plants using caspase substrates . Silencing of NtCYS induced 1 . 6- , 2 . 0- , and 7 . 4-fold increase in the activities of proteases with caspase 1- , caspase 3- , and caspase 6-like substrate specificity , respectively ( Figure 4P ) . The specificity of the individual activities was confirmed by competition assays using aldehyde ( CHO ) caspase inhibitors specific for corresponding activities ( Figure 4P ) . Our data corroborate that the NtCYS-inhibitable cell death pathway in the basal cell lineage involves activation of proteases with caspase-like substrate specificity . We speculated that NtCYS could directly inhibit cysteine proteases in the basal cell lineage until the 32-celled embryo stage , thereby preventing precocious activation of cell death . To retrieve cysteine protease genes from the tobacco genome , about 320 , 000 EST sequences were analyzed . Twenty genes encoding cysteine proteases were found to be expressed in two-celled proembryos ( unpublished data ) . Next , we used bimolecular fluorescence complementation ( BiFC ) analysis in the transiently co-transfected tobacco epidermal cells to study interaction between NtCYS and the cysteine proteases , which revealed interaction with six proteases , named NtCP3 , NtCP6 , NtCP8 , NtCP14 , NtCP15 ( Figure 5A ) , and NtCP23 ( unpublished data ) . Alignment of the predicted amino acid sequences showed that all six proteases contained a conserved non-contiguous ERFNIN motif ( EX3RX3FX2NX3I/VX3N ) ( Figure S2 ) , which is typical for cathepsin L- and H-like proteases , but not for cathepsin B-like proteases [21] . Next , we assessed the substrate specificities of these proteases using recombinant proteins and a panel of peptidic substrates specific for cathepsins ( with Arg at the P1 position ) , legumains ( with Asn at P1 ) , and caspases ( with Asp at P1 ) . None of the six recombinant proteases could cleave caspase and legumain substrates , but all readily cleaved cathepsin substrates , confirming their classification as cathepsins ( Figures 5B and S3 ) . Plant cysteine cathepsins are generally divided into four subtypes: B- , F- , H- , and L-like , which can be distinguished by their amino acid sequences and different specificities towards residues at the P2 position in their substrates [22] , [23] . Considering the increased cathepsin H-like but not L-like activity found in the two-celled proembryos from the NtCYS RNAi plants ( Figure 4O ) , we investigated which of the six NtCYS-interacting proteases belong to cathepsin H type by comparing their preference towards four cathepsin substrates with different P2 residues . While five out of six proteases preferred the cathepsin L substrate Z-FR-AMC ( Figure S3B ) , a single protease , NtCP14 , displayed a preference for Bz-FVR-AMC , a substrate of cathepsin H-like proteases ( Figure 5B ) [23] . To corroborate our BiFC results showing NtCYS interaction with NtCP14 in vivo , we confirmed this interaction by co-immunoprecipitation ( Figure 5C ) . As expected , recombinant NtCYS could efficiently inhibit cathepsin H-like activity of recombinant NtCP14 ( Ki ( app ) = 0 . 74±0 . 18 µM ) ( Figure 5D ) . To gain insight into intracellular localization of NtCYS and NtCP14 , we transiently expressed fluorescent proteins in onion ( Allium cepa ) epidermal cells , and found that both proteins are cytoplasmic ( Figure 5E ) and have strong co-localization with endoplasmic reticulum ( ER ) marker [24] , but not with Golgi marker ( Figure S4 ) [25] . On the basis of the above results , we chose to work further with the NtCYS-inhibitable cathepsin NtCP14 , which possessed an in vitro P2 substrate specificity similar to that found in protein extracts from the NtCYS-deficient two-celled proembryos ( Figures 4O and 5B ) . The level of NtCP14 expression was high at the early stages of embryogenesis , indicating significant overlap between NtCP14 and NtCYS expression profiles ( Figures 1B , 1C , S5A , and S5B ) . However , comparison of spatio-temporal expression patterns of two genes revealed one key difference . While NtCYS was expressed only in the basal cell , NtCP14 promoter was active in both apical and basal cell lineages ( Figures 1D–1I and S5C ) . To genetically address the role of NtCYS-inhibitable cathepsin H-like protease NtCP14 in embryogenesis , we generated tobacco lines overexpressing NtCP14 , as well as the selected cathepsin L-like genes NtCP6 , NtCP8 , and NtCP15 , under the control of the constitutive promoter proZC1 ( Figures 6A and S6 ) . Overexpression of NtCP14 led to a marked increase in cathepsin H-like proteolytic activity in the two-celled proembryos ( Figure 6B ) . While embryogenesis progressed normally in the cathepsin L-like protease-overexpressing lines ( unpublished data ) , overexpression of NtCP14 induced massive embryonic death , resulting in the abortion of 23% to 54% of seeds in various lines ( Figure 6C ) . Embryonic death occurred mostly at the two-celled proembryo stage , when both apical and basal cells displayed PI-positive nuclei and a lack of FDA staining ( Figure 6D and 6E ) . Furthermore , up to 59% of the two-celled proembryos in the overexpressing lines contained TUNEL-positive nuclei in both apical and basal cells ( Figure 6F and 6G ) . Similar to NtCYS RNAi plants , precocious cell death in the two-celled proembryos from NtCP14-overexpressing plants was accompanied by increased caspase-like activities ( Figure 6H ) . Together , these data indicate that NtCP14 is a pro-death protease , which is able to induce cell death in both basal and apical cell lineages when overexpressed . So far we have shown that NtCP14 is expressed in both apical and basal cell lineages ( Figure S5C ) and that the increase in cathepsin H-like activity achieved through constitutive overexpression of NtCP14 in both cell lineages induces cell death at the two-celled proembryo stage ( Figure 6 ) . A noteworthy point is that in contrast to the constitutive overexpression of NtCP14 , its basal cell-specific overexpression using the NtCYS promoter did not reveal simultaneous loss of viability in apical and basal cells . Instead , we observed precocious cell death in the basal cell only , with subsequent developmental arrest and a high incidence of seed abortion ( Figure S7 ) ; i . e . , a phenotype similar to that of NtCYS RNAi lines . These data suggest that during the normal course of embryogenesis , the proteolytic activity of NtCP14 must be tightly regulated to sustain apical-basal patterning and embryo survival . To further corroborate this point , we measured cathepsin H-like proteolytic activity in the microsurgically separated WT embryo propers and suspensors at developmental stages 3 and 4 using the substrate Bz-FVR-AMC . The proteolytic activity in the suspensor was at least 6-fold of that in the embryo proper ( Figure 6I ) . Next , we evaluated the temporal profile of cathepsin H-like activity in the basal cell lineage at successive stages of embryogenesis , beginning from the basal cell of the two-celled proembryo ( stage 1 ) until the fully differentiated suspensor of the heart-stage embryo ( stage 8 ) . The activity in the basal cell lineage remained low in the proembryos and then increased dramatically once the embryos progressed through stages 3 ( eight-celled embryo ) and 4 ( 32-celled embryo ) , i . e . , the stages when suspensor PCD began ( Figure 6J ) . Importantly , the peak of the NtCP14-dependent proteolytic activity coincided with the steady decline in NtCYS expression level ( Figure 1C ) , providing additional evidence for the functional role of NtCYS-mediated inhibition of NtCP14 in the regulation of the timing of suspensor PCD . Collectively , these results indicate that activation of NtCP14 in the basal cell lineage of eight- to 32-celled embryos is required for the timely onset of suspensor PCD . Because silencing of NtCYS or overexpression of NtCP14 induced precocious cell death in the basal cell lineage , we argued that NtCYS-NtCP14 could act as a bipartite molecular module to control initiation of suspensor PCD . To explicitly address this hypothesis , we investigated the fate of the embryo-suspensor in NtCYS-overexpressing ( Figure 7A ) and NtCP14 RNAi lines ( Figure 7B ) . We found that overexpression of NtCYS or silencing of NtCP14 led to a marked decrease in the level of cathepsin H-like activity in the embryos ( by 35% in NtCYS-overexpressing line L-5 and by 30% in NtCP14 RNAi line L2-8 compared to the WT , n = 150–210 ) . In contrast to WT , a large proportion ( 77% to 81% for different lines ) of 32-celled ( stage 4 ) embryos in the NtCYS-overexpressing lines had TUNEL-negative suspensors ( Figures 2L , 7C , 7H , and S8A ) , indicating profound delay in the onset of DNA fragmentation . Furthermore , only 42% to 45% of heart embryos ( stage 8 ) from the NtCYS-overexpressing lines contained four TUNEL-positive nuclei in the suspensor ( Figures 7H and S8A ) , as compared to 66% in the WT embryos ( Figure 2L ) . In addition , approximately half of heart- and torpedo-stage embryos from the NtCYS-overexpressing lines revealed FDA-positive and PI-negative suspensors ( Figure 7I–7K ) , which were mostly dead at the corresponding stages in the WT embryos ( Figure 2J , 2K , 2M ) . Similar phenotype with delayed suspensor PCD was observed in both NtCP14 RNAi lines ( Figures 7L and S8B–S8G ) and lines expressing NtCYS-GFP ( Figure S9 ) . Taken together , our reverse genetic experiments demonstrate that artificial intervention in the balance between antagonistic actions of NtCYS and NtCP14 in tobacco embryos can upset ( accelerate or delay ) initiation of cell death in the basal cell lineage . This establishes NtCYS-NtCP14 as the bipartite molecular module that controls PCD in the embryo-suspensor .
Once the full-length suspensor is formed in a tobacco seed through a few rounds of transverse cell divisions , suspensor cells start dying , with the basal cell committed to death in the first place . Execution of suspensor PCD is a slow process that takes 4 to 5 d from the first signs of DNA fragmentation in the 32-celled embryo until the loss of plasma membrane integrity in the heart-stage embryo . The progression of suspensor PCD occurs in a gradient-like fashion , starting from the basal cell of the 32-celled embryo and then moving acropetally to the uppermost suspensor cell while at the same time the embryo proper undergoes rapid development ( Figures 2 and 3 ) . Notably , the formation of the PCD gradient in the embryo-suspensor seems to be an evolutionarily conserved phenomenon in plants , occurring regardless of phylogenetic position and embryo morphology , as a similar gradient of suspensor PCD has been described in a gymnosperm , Norway spruce [4] , [26] . In the present work we show that premature initiation of cell death in the basal cell lineage leads to embryo lethality . This implies that plant embryos must be equipped with a mechanism ensuring timely onset of suspensor PCD and preventing its precocious initiation . In tobacco embryos this mechanism is based on cystatin NtCYS-mediated inhibition of cathepsin H-like protease NtCP14 ( Figure 8 ) . NtCYS is a basal cell-specific gene asymmetrically expressed upon zygotic division ( Figure 1 ) . The expression level of NtCYS is high during transition from the eight-celled to the 32-celled embryo and then decreases dramatically , with simultaneous initiation of DNA fragmentation in the basal cell . Identification of NtCP14 as an interacting protein of NtCYS explains the anti-cell death mechanism of NtCYS , which is essential for protecting the basal domain of tobacco embryos from precocious cell death . While NtCP14 is expressed in all embryonic cells throughout the entire period of embryogenesis ( Figure S5 ) , high catalytic activity of the encoded protease is restricted to the suspensor cells of eight- and 32-celled embryos , thus correlating with the decreased NtCYS expression and the onset of PCD ( Figures 6J and 8 ) . We envisage NtCYS and its target protease NtCP14 as a bipartite molecular module that evolved to control PCD in the basal cell lineage ( Figure 8 ) . The balance between anti-death ( NtCYS ) and pro-death ( NtCP14 ) components of the module ensures timely initiation of suspensor PCD and successful embryo development . An intriguing question is what is the primary signal causing rapid downregulation of NtCYS expression and triggering suspensor PCD and where this signal comes from . Since PCD starts from the basal end of the suspensor , the signal might be derived from cells at micropyle . An alternative would be that the source of signal is the embryo proper , a scenario supported by the observations that suspensor PCD always starts when embryo proper has attained 32-celled stage . In either case , detailed investigations should be carried out to understand the nature of the signal . Cathepsins is a collective term for a large number of structurally unrelated cysteine , serine and aspartic proteases , most of which can be found in the lysosomes or acidic vacuoles . There is a growing body of experimental evidence for the role of cathepsins B , D , and L in the execution of cell death and in the mediation of cross-talk between different types of cell death in animals [27] , [28] . In contrast to animals , little is known about the role of cathepsins in plant PCD , except that cathepsin B proteases have been shown to act as pro-death components during hypersensitive response [29] , [30] . Apart from regulating neurotransmitters [31] and tumor progression [32] in animals , the physiological role of cathepsin H-type proteases remained poorly understood . In the present work , we demonstrate that NtCP14 is a cathepsin H-type protease with a preference for substrate Bz-FVR-AMC and is a positive regulator of suspensor PCD . In contrast to a single cell-specific expression pattern of NtCYS , NtCP14 is expressed in all embryonic cells , suggesting that the encoded protease may have other roles in embryogenesis besides regulating suspensor PCD . While there are no close homologues of animal caspases in plants , and metacaspases ( ancestral proteases found in protozoa , fungi , and plants ) do not have aspartate cleavage specificity [33] , at least three types of proteases with caspase-like activity exist in plants and mediate initiation and execution of PCD . These include vacuolar processing enzymes with a caspase 1-like substrate preference [16] , the proteasome with caspase 3-like activity [18] , [20] , and a small subset of subtilisin-like serine proteases possessing caspase 6-like activity [34] . Apart from the observation that all three types of caspase-like activities correlate with the progression of suspensor PCD in WT tobacco embryos ( Figure 3 ) , we found that increase of NtCP14 activity in the two-celled proembryos achieved through genetic intervention in the NtCYS-NtCP14 module is accompanied by a corresponding increase in caspase-like activities ( Figures 4 and 6 ) . Therefore , the NtCYS-NtCP14 molecular module seems to occupy an apical position in the PCD signaling pathway , upstream to the proteases with caspase-like substrate specificity . Further studies are required to characterize and link together downstream components of the NtCYS-NtCP14 dependent PCD , in particular proteases with caspase-like specificity , metacaspases [7] , [8] , [33] and kiss of death peptide [9] . Unraveling of the NtCP14 degradome will also advance our understanding of the PCD pathways operating in the plant embryo-suspensor . The very narrow , basal cell-specific expression pattern of NtCYS in tobacco embryos poses an intriguing question regarding how the proteolytic activity of NtCP14 is controlled in the other suspensor cells , as well as in the cells of the apical cell lineage , which are destined to survive but devoid of NtCYS ( Figure 8 ) . As suggested recently by Cambra and co-authors [35] , the activities of cathepsin proteases in vivo may be controlled by several mechanisms , including local zymogene concentration and the presence of specific repertoire of cystatin inhibitors . Regardless of the actual mechanism that prevents activation of NtCP14 in the living embryonic cells , we demonstrate here that it is its local interaction with NtCYS in the basal cell that determines the life-or-death fate of both basal and apical cell lineages in the plant embryo .
N . tabacum L . cv . Petite Havana SR1 plants were grown under 16 h/8 h light/dark cycles , at 25°C in greenhouse . N . benthamiana plants were grown under 14 h/10 h light/dark cycles , at 21°C in greenhouse . Embryo isolation was according to our previous protocol with minor modification [11] . Detailed procedure is described in Text S1 . A full-length cDNA for NtCYS and cathepsin-like genes obtained using the rapid amplification of cDNA ends ( RACE ) . Total RNA was isolated from seeds at 5 d after pollination and used as a template to synthesize first-strand cDNA with the SMART RACE cDNA Amplification Kit ( Clontech ) . The NtCYS and NtCP14 promoters were isolated using the Genome Walker DNA walking method with the Genome Walker Universal Kit ( Clontech ) . All reactions were performed according to the manufacturer's instructions . A multiple sequence alignment of the known cystatin family genes in dicot plants was conducted using CLUSTALX v . 1 . 81 with the default multiple alignment parameters . The phylogenetic tree was constructed with PHYLIP v . 3 . 68 . mRNA isolation from sperm cells , egg cells , zygotes , and embryos , and cDNA synthesis were performed as described previously [13] . Total RNA was extracted from leaves , roots , stems , anthers , pollen , and sepals using TRI Reagent Solution ( Ambion ) . All total RNAs were treated with RNase-free DNaseI ( Promega ) , and cDNA was synthesized using Transcriptor Reverse Transcriptase ( Roche ) according to the manufacturer's instructions . RT-qPCR analysis was conducted as described [13] and all the data represent the mean ± standard error ( SE ) from three independent experiments . The coding region of NtCYS lacking signal peptide sequence was inserted into an NdeI/XhoI -digested pMXB-10 vector ( NEB ) . The resulting plasmid was transformed into Escherichia coli BL21 ( DE3 ) . The recombinant NtCYS protein was expressed and purified according to the manufacturer's instructions . The purified NtCYS was re-purified by anion exchange chromatography with a Bio-Scale Mini UNOsphere Q Cartridge ( Bio-Rad ) using a linear gradient of NaCl ( 0–300 mM ) in Tris-HCl ( 20 mM [pH 8 . 0] ) on BioLogic DuoFlow system ( Bio-Rad ) . Expression and purification of cysteine proteases were performed according to [36] with minor modifications . Sequences of cysteine protease genes lacking signal peptides were inserted into the expression vector pET28a ( Novagen ) to yield constructs encoding proteases with both N- and C-terminal six-histidine tags . Protein expression , purification , and refolding were performed as described [36] . The final protein concentrations were quantified using a Coomassie Plus kit ( Thermo ) with bovine serum albumin as a standard . For determination of the Ki values for the interaction of NtCYS with cysteine proteases , papain ( Sigma-Aldrich ) , human liver cathepsin L ( Sigma-Aldrich ) , and human liver cathepsin B ( Sigma-Aldrich ) substrate hydrolysis progress curves were monitored . Papain activity was measured in 50 mM phosphate buffer ( pH 6 . 0 ) , using the synthetic fluorogenic substrate Z-FR-AMC ( Sigma-Aldrich ) . Human cathepsin L activity was measured in 400 mM sodium acetate buffer ( pH 5 . 5 ) , using the synthetic fluorogenic substrate Z-FR-AMC . Cathepsin B activity was measured in 50 mM phosphate buffer ( pH 6 . 0 ) , using the substrate Z-RR-AMC ( Sigma-Aldrich ) . Hydrolysis proceeded at 25°C ( cathepsin L ) or 37°C ( papain and cathepsin B ) with 40 µM substrate after or without the addition of NtCYS under reducing conditions . The activity levels were monitored using a Spectra Max M2 ( Molecular Device Co . ) with excitation and emission filters of 360 and 455 nm , respectively . To determine the optimal substrates for the recombinant cysteine proteases , the proteases were activated in a buffer containing 100 mM sodium acetate ( pH 4 . 0 ) , and their activities were measured using substrates Z-FR-AMC ( Sigma-Aldrich ) , Z-RR-AMC ( Sigma-Aldrich ) , Bz-FVR-AMC ( Bachem ) , L-R-AMC ( Sigma-Aldrich ) , Z-AAN-AMC ( Bachem ) , Ac-YVAD-AMC ( Sigma-Aldrich ) , Ac-DEVD-AMC ( Sigma-Aldrich ) , and Ac-VEID-AMC ( Bachem ) . Hydrolysis proceeded at 30°C with 40 µM substrate in each reaction ( pH 6 . 0 ) under reducing conditions . The Ki ( app ) of NtCYS for each recombinant cysteine protease was assessed according to the following equation: Ki ( app ) = [I0]/ ( V0/Vi−1 ) . To perform endogenous protease activity assays , the embryos were frozen in 15 mM sodium phosphate or sodium acetate buffer with 0 . 1% Brij-35 at −80°C until use in protease assays . Proteolytic activity was measured in 10-µl reaction mixtures containing the proteins released from unfrozen embryos , and 125 µM of individual substrates , 1 mM EDTA , 10 mM L-Cys , and 0 . 01% Brij-35 in 15 mM sodium phosphate or sodium acetate at 30°C . The amount of AMC released was determined by capillary electrophoresis . Detailed procedure for capillary electrophoresis is described in Text S1 . Standard methods for analyzing cell viability and DNA fragmentation are also described in supplemental information Text S1 . In situ detection of caspase-like activity in the embryos was performed using Carboxy fluorescein FLICA Apoptosis Detection kits ( Immunochemistry Technologies , LLC ) , which include cell-permeable fluorochrome-conjugated inhibitors of caspases . Detailed procedure is described in Text S1 . BiFC constructs were prepared using the vectors pSPYNE-35S and pSPYCE-35S [37] . Expression cassettes digested from pSPYNE-35S and pSPYCE-35S using HindIII and EcoRI were ligated into the plant transformation vector pCAMBIA1300 , resulting in pCAMBIA-SPYNE and pCAMBIA-SPYCE . All full-length ORFs ( without stop codons ) of cysteine protease genes were inserted in-frame into the vector pCAMBIA-SPYCE . Similarly , the NtCYS gene was inserted into the vector pCAMBIA-SPYNE . All BiFC constructs were transferred into Agrobacterium tumefaciens strain GV3101 , which was used to infiltrate N . benthamiana leaves . The transient expression was assayed according to Sparkes [38] . Co-expression of NtCYS-SPYNE and empty vector SPYCE was used as negative control . NtCYS and NtCP14-GFP were co-expressed in N . benthamiana by Agrobacterium-mediated transient expression . GFP was used as the negative control . Infiltrated leaves of N . benthamiana were collected and ground into powder . Extracts for co-IP were prepared at 4°C in a buffer containing 15 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , and protease inhibitor cocktail ( 1∶50; Roche ) . Next , a Chromotek GFP-trap ( Allele Biotech ) was used to capture the GFP-tagged proteins according to the manufacturer's instructions . The immunoprecipitates were then subjected to SDS-PAGE and the target protein was transferred to nitrocellulose membranes and probed with anti-NtCYS ( 1∶1 , 000 ) and anti-GFP ( 1∶2 , 000; Abmart ) . NtCYS polyclonal rabbit antiserum was produced against recombinant NtCYS ( GenScript ) . Co-IP experiment was repeated independently three times . For co-localization analysis , NtCYS-GFP and NtCP14-RFP were co-expressed in onion ( A . cepa ) epidermal cells through particle-mediated transient transformation using PDS-1000/He instrument ( Bio-Rad ) . For subcellular localization , NtCYS-GFP and NtCP14-GFP were co-expressed with ER marker containing an N-terminal signal peptide derived from an Arabidopsis vacuolar basic chitinase and the C-terminal amino acid sequence HDEL ( RFP-ER ) [24] and Golgi marker ST-RFP ( a fragment of a rat a-2 , 6-sialyltransferase fused to RFP; a gift from Chris Hawes , UK ) [25] . Coating of gold particles and bombardment were performed according to the manufacturer's instructions ( Bio-Rad Laboratories ) . Stained embryos , transfected tobacco leaves , and transformed onion epidermis were observed under confocal microscope ( Olympus FluoView FV1000 ) . Images were processed with Image J or Adobe Photoshop . For ultrastructural examination , tobacco seeds were fixed with glutaraldehyde ( 2 . 5% ) in PBS buffer ( 100 mM [pH 7 . 4] ) for 24 h , dehydrated in a graded ethanol series , post-fixed with OsO4 ( 0 . 25% ) in 30% ethanol overnight and embedded in Spurr's resin . Ultrathin sections were post-stained with uranyl acetate/lead citrate and examined with a transmission electron microscope ( FEI Tecnai G2 20 TWIN ) . Sequence data used in this work can be found in the GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) under following accession numbers: NtCYS ( KF113570 ) , NtCP3 ( Z99173 ) , NtCP6 ( KF113571 ) , NtCP8 ( KF113572 ) , NtCP14 ( KF113573 ) , NtCP15 ( KF113574 ) , and NtCP23 ( AB032168 ) . | Similar to animals , plants eliminate tissues and organs that have transient function via programmed cell death ( PCD ) . This is especially apparent during plant embryogenesis , when a plant zygote divides into an apical cell , which develops into a mature embryo , and a basal cell , which generates a single organ called the suspensor . During early seed development , the suspensor connects the embryo to the surrounding seed tissues and transports the nutrients and hormones required for its early development . Once its functions are fulfilled , the suspensor is subsequently eliminated by PCD . In this study , we answer a long-standing question in the field by elucidating the mechanism that is responsible for initiating suspensor PCD at a specific time during embryogenesis . Our findings show that in tobacco plant embryos , suspensor PCD is controlled by two antagonistically acting proteins—the pro-death cathepsin protease NtCP14 and its inhibitor cystatin NtCYS , which co-localize to the basal-most cell of the suspensor . High expression levels of NtCYS during early embryogenesis confer suspensor growth and viability by suppressing NtCP14 activity . When the suspensor ceases to grow , NtCYS expression is downregulated , leading to increased NtCP14 activity and to the initiation of PCD . The genetic modulation of this NtCYS/NtCP14 expression ratio either delays or hastens suspensor PCD , demonstrating its indispensable role in plant embryo development . | [
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] | 2013 | A Bipartite Molecular Module Controls Cell Death Activation in the Basal Cell Lineage of Plant Embryos |
Many protein engineering problems involve finding mutations that produce proteins with a particular function . Computational active learning is an attractive approach to discover desired biological activities . Traditional active learning techniques have been optimized to iteratively improve classifier accuracy , not to quickly discover biologically significant results . We report here a novel active learning technique , Most Informative Positive ( MIP ) , which is tailored to biological problems because it seeks novel and informative positive results . MIP active learning differs from traditional active learning methods in two ways: ( 1 ) it preferentially seeks Positive ( functionally active ) examples; and ( 2 ) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis . We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers . This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers , and restoring active p53 in tumors leads to tumor regression . MIP found Positive ( cancer rescue ) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning , with only a minor decrease in classifier accuracy . Applying MIP to in vivo experimentation yielded immediate Positive results . Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations , from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants . In vivo assays showed that the predicted Positive Region: ( 1 ) had significantly more ( p<0 . 01 ) new strong cancer rescue mutants than control regions ( Negative , and non-MIP active learning ) ; ( 2 ) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and ( 3 ) rescued for the first time the previously unrescuable p53 cancer mutant P152L .
The p53 gene encodes a tumor suppressor protein that is a key cellular defense against cancer . p53 mutations occur in about 50% of human cancers . The vast majority of these mutations are single point missense mutations in the p53 core domain [9]–[12] . Thus , many human cancers express full-length p53 cancer mutants that lack tumor suppressor function . As demonstrated in vivo , p53 cancer mutants can be reactivated through intragenic second-site suppressor ( “cancer rescue” ) mutations [13]–[15] . Reactivated p53 holds great therapeutic promise because animal models have shown that reintroduction of active p53 , even in advanced tumors , leads to tumor regression [16]–[18] . Consequently , there have been many efforts to find small molecule drugs that mimic the cancer rescue effect of reactivating p53 and suppressing tumor growth [19]–[24] . Despite some promising discoveries in p53 in specific , and small molecule docking in general , these efforts are hampered by a limited understanding of the p53 mutation-structure-function relationship [11] , [25]–[28] . A larger and more diverse collection of cancer rescue mutations that reactivate p53 cancer mutants is therefore desired . Such a collection could lead to insight into general structural changes that can rescue p53 cancer mutants , and thereby facilitate rational drug design approaches by exploiting similar effects . Several p53 cancer rescue mutants were identified previously by random mutagenesis in a region spanning amino acid residues 225–241 . A portion of this region ( 235 , 239 , and 240 ) thus was empirically identified as a “Global Suppressor Motif” , the first p53 cancer rescue region [13] . The biological goal of this paper is to use computational techniques to discover novel p53 cancer rescue mutants and regions . The active learning paradigm was developed in the machine learning community to reduce the number of expensive examples that need to be acquired to build an accurate classifier [29] . Active learning typically starts with a small initial amount of labeled data . The initial data is used to determine a small informative set of unlabeled examples to label . Once labeled , these new examples are added to the pool of labeled data and a new unlabeled set is chosen . The process repeatedly labels new data until the classifier reaches some pre-determined criteria . Active learning methods increase the efficiency and cost effectiveness of the process by reducing the number of examples that need to be labeled . The active learning paradigm is readily applicable to biological experimentation , as it reduces the number of tedious and expensive experiments to be performed . In a biological active learning paradigm , a computational classifier is trained with an initial set of examples labeled by direct experimentation . In the case of p53 cancer rescue mutants [4] , this initial set consists of empirically labeled p53 mutants . The computational classifier then predicts which mutants should next be labeled to most improve the classifier accuracy . These mutants are then made , labeled by biological assays , and added to the classifier . The cycle repeats , iteratively improving classifier accuracy and adding to the set of p53 mutants with known function . In this way , an optimum active learning classifier would adequately explore a mutant sequence space while using a minimum amount of expensive biological experimentation [4] . It is important to note that in the context of biological experimentation , the slowest part of active learning is generally the biological experiments required to label the unknown examples . Therefore , any reference to speed in this paper refers to the number of expensive biological experiments ( i . e . iterations of the active learning cycle ) and not to computational speed . The computational goal of this paper is to provide and test computational methods that can discover gene regions wherein mutations produce proteins with a desired function , while requiring as few experiments as possible . Here we present a formal description of the active learning problem . Notation is summarized in Text S1 . Let be the Total set of all examples under consideration . Each example mutant , , has a labeling function , , such that = Positive , Negative , or Unknown . During each active learning iteration , , is partitioned into two groups: ( 1 ) , examples with Known labels ( i . e . , = Positive or Negative ) ; and ( 2 ) , examples with Unknown labels ( i . e . , = Unknown ) . A third set , , Chosen from , contains examples to be tested and labeled in this step . During each iteration the classifier provides a decision function , , trained on the examples with a known label , . Each unlabeled example is predicted by the decision function to be Positive or Negative . A score function , , ranks each example in . As a control , Random active learning assigns each mutant a random score . The highest ranked examples become and are then tested and labeled . is merged with to create and deleted from to create . In the case of the p53 cancer rescue mutants here , each example is a p53 mutant . = Positive if and only if mutant exhibits wild-type like p53 transcription activator activity . The Methods section presents a description of active learning , the MIP paradigm , computational evaluation methods , and the biological experimental design . The Results section shows in silico results indicating the computational techniques best suited to the p53 cancer rescue mutant problem and in vivo results showing how well those techniques performed in experiments . The Discussion section recites medical significance , sketches possible computational extensions of the MIP method , and concludes that a computational classifier and modeled structure-based features can guide function-based experimental discovery .
The techniques presented in this paper build on previous research using machine learning techniques to find p53 cancer rescue mutants [3] , [4] . This section contains a brief overview of the foundational structure-based features and active learning techniques . Structure-based features [3] , [4] for each mutant considered were extracted from atomic-level homology models . Modeled mutant proteins were produced in silico using the B chain of the wildtype p53 core domain crystal structure ( PDB ID: 1TSR ) [33] . Amino acids were substituted and model energies were minimized using the Amber™ molecular modeling software [34] . Features [3] were extracted from the minimized mutant model using 1D sequence and amino acid substitution information , 2D surface cartographical and electrostatic models , 3D steric analysis , and “4D” thermal stability predictions . Those features on the surface of the p53 core domain outside known binding sites [35] were compressed , resulting in 5 , 867 features per mutant . Conditional Mutual Information Maximization [36] selected various subsets of these features . It was found that 550 selected features resulted in the highest classifier accuracy [4] . Seven previously studied [4] active learning algorithms were used here . Two of these methods are standard active learning techniques , taken from the literature , that work by separating the data into two classes with an n-dimensional hyper-plane . Minimum Marginal Hyperplane [37] selects examples based on the margin , i . e . , the “distance” from the hyper-plane . Maximum Entropy [38] selects examples based on a class probability calculated from the margin and is related to the information theory concept of entropy . Two methods , Maximum Marginal Hyperplane and Minimum Entropy , are negative controls expected to perform badly . They were created by choosing the least informative example in the previous two methods . The other three methods were created specifically for this p53 cancer rescue research project [4] and are based on the anticipated change in classifier accuracy or correlation coefficient if a given example is chosen and labeled . These include Additive/Maximum Curiosity [4] , which uses a cross-validated correlation coefficient to estimate classifier accuracy , and Additive Bayesian Surprise , which is based on the Kullback-Leibler ( KL ) divergence [39] . MIP optimizes the mutants chosen so that they are most likely to both improve the classifier and rapidly uncover Positive examples . To understand why this is important , suppose that Positive examples are sparse , as here , and one has only sufficient resources to assay 100 new examples . MIP active learning seeks to maximize the number of novel Positive examples discovered during those 100 assays , and at the same time quickly improve classifier accuracy . Traditional active learning also seeks to find an accurate classifier quickly , but may discover only very few novel Positives while so doing . MIP active learning chooses by first considering only those unlabeled examples predicted to be Positive ( i . e . , = Positive ) . Those predicted to be Positive and having the highest score , , are chosen for . Only if too few examples in were predicted to be Positive would a Negative informative example be chosen for . Active learning algorithms may become MIP algorithms by preferentially labeling those informative examples that are also predicted to be Positive . There are many ways to apply MIP to a specific active learning algorithm . Here we give a simple example , which shows a general approach and applies to nearly all active learning algorithms . Recall that ranks unlabeled examples , and high-ranking examples are chosen to be labeled at the next iteration . To convert a traditional active learning algorithm to a MIP active learning algorithm , it is sufficient to weight the scoring function so that examples predicted to be Positive have a higher score than those predicted to be Negative: ( 1 ) where is a constant with if = Positive , and if = Negative . For this paper and much biological research , the goal of iterative exploration is to uncover as many informative Positive examples as quickly as possible , i . e . , with the fewest biological experiments . We require metrics to measure success at this task . The Halfway Point metric measures the fraction of iterations necessary before half of all Positive examples in an unlabeled data set are uncovered . Several additional metrics were explored to measure how quickly Positive examples were found , including enrichment factor and positive area , but only Halfway Point is presented here for illustrative clarity because it is simple to explain and it provides similar results to the other metrics . Formally , Halfway Point = , where is the smallest number of iterations such that contains half of all Positive mutants in and is the number of mutants labeled at each iteration . Since MIP optimizes a classifier to preferentially choose Positive mutants for , it is reasonable to wonder if there may be a corresponding loss of classifier accuracy . One way to estimate classifier accuracy for an active learning algorithm is to use the average 10-fold cross-validated accuracy and correlation coefficient of the training set across all iterations of one or more of the Data Partitions described below . Accuracy is the fraction of correct predictions . The correlation coefficient is a standard of the machine learning community [40] , and a better measure than accuracy when the data set contains unbalanced numbers of Positive and Negative examples . This is the usual case for biological data sets such as the p53 cancer rescue mutant data set , where the ratio of Negative to Positive mutants is about 4∶1 . Several other metrics for accuracy were explored , including forward prediction accuracy , 3-point accuracy , and a more complicated cross-validation strategy , OECV [4] . Average 10-fold cross-validated accuracy and correlation coefficient were chosen for illustrative clarity here because they are simple to explain and give similar results to the other metrics . To evaluate the MIP methodology in silico , MIP and non-MIP versions of seven active learning methods plus a random control were compared using the cross-validated metrics described above . Three previously studied partitions of the data set [4] were used to compare to previous research . These partitions test three common starting conditions for active learning: The data set had about 20% Positive and 80% Negative mutants . Active learning and MIP as discussed so far apply to individual mutants . Limitations of this approach include loss of classifier accuracy when applied to new mutants from unfamiliar regions , leading to many experiments that yielded few Positive examples [4] . We generalized MIP active learning to apply to single amino acid changes in contiguous gene regions . This generalization supported several desirable outcomes . It allowed MIP active learning to exploit high throughput saturation mutagenesis techniques . The resulting training set enrichment should allow more accurate prediction of new Positive mutants , especially those requiring multiple amino acid changes . Regions enriched for rescue mutants may indicate promising candidate drug target sites . Formally , let be the set of all mutants containing a cancer mutation plus a single putative rescue at amino acid location , excluding mutants that exist in the initial training set . Let ( 2 ) where is the subset of for which = Positive . Positive regions were ranked by summing across each region . The Positive Region used below was chosen to be the 10 sequential amino acid long window with the highest average across that window . Similarly , let ( 3 ) where is the subset of for which = Negative . The Negative Region was chosen to be the 10 sequential amino acid long window with the highest average across that window . A similar non-MIP control region was constructed to be informative to the classifier regardless of whether mutants were predicted to be Positive or Negative . Let ( 4 ) The non-MIP Region was chosen to be the 10 sequential amino acid long window with the highest average across that window . To detect p53 cancer rescue regions , the task is to identify areas of the p53 core domain that are likely to have many Positive cancer rescue mutants . We considered ten p53 cancer mutants that are commonly found in human cancer [12] and can be constructed so that they differ by two or more nucleic acid changes from the wild-type . consisted of these 10 common p53 cancer mutants paired with all possible single amino acid changes at each location in the core domain . All predictions and curiosity calculations were made with a training set , , of 463 mutants ( 91 Positive and 372 Negative ) . These 463 mutants contained the 261 mutants used for the Data Partitions plus 202 created during other experiments using variants of the yeast assay described below [3] , [4] , [13] , [14] . The MIP Additive Curiosity [4] algorithm was used to choose the regions because it performed best in in silico trials ( see Results ) . It was adapted to select three 10-amino acid long regions in the p53 core domain: a Positive region , a Negative region , and a non-MIP control region . A Weka Support Vector Machine , SMO , [41] , was used to predict the activity , , for each mutant . The score for each mutant was calculated using MIP Additive Curiosity . These values were averaged over every possible 10-amino acid window . The classifier considered the resulting 34 , 776 putative cancer rescue mutants and selected ∼3 , 980 mutants in three regions . These regions were selected for the following criteria as described above: The Expert Region , spanning residues 114–123 , was considered a potential cancer rescue region because several Positive mutations with multiple amino acid changes occurred there spontaneously in previous cancer rescue mutant screens . Therefore , this region was considered likely to have cancer rescue mutants with single amino acid changes ( [13]; Brachmann , R . K . , personal communication ) . No single amino acid change cancer rescue mutations had been found previously in any of the Positive , Negative , non-MIP , or Expert regions . All mutants produced in this study were initially created with a novel regional saturation mutagenesis method based on the Quick Change site-directed mutagenesis kit ( Stratagene , La Jolla , CA , USA ) , ( manuscript in preparation ) . Briefly , a set of overlapping degenerate oligonucleotides was designed such that each primer contained exactly one random codon . A standard site-directed mutagenesis reaction was performed with a mixture of oligonucleotides that collectively represented each possible codon change in the target region ( 30 base pairs ) . The overlapping primer design prevented multiple mutations in the same mutagenesis product . The generated mutants were analyzed for p53 activity using a yeast-based p53 activity assay [13] . Briefly , yeast cells were engineered to depend on active p53 for expression of the URA3 gene . The URA3 gene product is required for the synthesis of uracil . When cells are grown in medium lacking uracil , cell growth is proportional to p53 activity ( URA3 expression ) . The products of the saturation mutagenesis for all ten p53 cancer mutants in all tested regions were first selected for their ability to grow in the absence of uracil , indicating re-activated p53 . All putative positive mutants were analyzed by DNA sequencing to determine the nature of the rescue mutation . Mutations were then recreated by site-directed mutagenesis , confirmed by resequencing , and retested . As shown in Figure 1 , mutants were designated as strong Positive mutants if the yeast cell growth was very robust . Mutants contained in yeasts that showed minimal growth were designated as weak Positive mutants . Strong and weak Positive mutants were collectively designated Positive . Those that did not grow were designated Negative . p53 mutants are described as <Cancer Mutation>_<putative rescue mutation> . For example , P152L_q100i identifies a cancer mutation with leucine replacing proline at amino acid 152 and a putative rescue mutation with isoleucine replacing glutamine at amino acid 100 .
For the purposes of this study , the best active learning method was the method with the lowest Halfway Point , i . e . , the method that discovered half of the Positive mutants in the test set using the smallest fraction of possible iterations . From Table 1 , the best MIP method reached the Halfway Point in 33% fewer iterations , and the average MIP algorithm needed 28% fewer iterations , than their non-MIP counterparts ( p<0 . 006 ) . Even the MIP versions of the negative control methods , Maximum Marginal Hyperplane and Minimum Entropy , performed better than any of the non-MIP methods . A graph showing the Halfway Point for select active learning types with Data Partition 1 , = 25 and = 236 , is presented in Figure 2 . Applying the MIP methodology improves how quickly a given active learning algorithm uncovers the Positive mutants , but what effect does it have on overall classifier accuracy ? The 10-fold cross-validated results , presented in Table 2 and Table 3 , show that MIP reduced the cross-validated accuracy by on average 1 . 1% ( statistically significant , p-Value = 0 . 012 ) and the correlation coefficient by on average 0 . 001 ( not significant , p-Value = 0 . 755 ) . The MIP Additive Curiosity algorithm performed best in Tables 1 , 2 , and 3 , and so was used to select three 10 amino acid long regions as the Positive , Negative , and non-MIP Regions . The Positive Region from residues 96–105 had the highest average score ( . 938 ) and contained 351 mutants predicted to be Positive out of 1900 total . The Negative Region from residues 223–232 had the highest average score ( . 937 ) and contained 33 mutants predicted to be Positive . The non-MIP Region from residues 222–231 had the highest score ( . 938 ) and contained 53 mutants predicted to be Positive . For comparison , the Expert Region from residues 114–123 had a score of ( . 462 ) and contained 34 mutants predicted to be Positive . See Figure 3 for the scores across possible Positive and Negative Regions and Figure 4 for a graph illustrating those regions within the p53 core domain . Regional Saturation Mutagenesis produced all possible single amino acid mutations in these regions combined with the 10 common cancer mutants tested . A biological selection was performed to identify all rescue mutants based on re-activation of p53 cancer mutants in vivo . The summary of these results is recorded in Table 4 . The Positive Region contained 8 strong and 3 weak mutants , the Expert Regions contained 6 strong and 7 weak mutants , while the Negative and non-MIP regions each contained only 2 weak mutants . Table 4 also shows the p-values associated with the null hypothesis “Positive mutants are equally likely to be drawn from the Positive Region as the Negative , non-MIP , or Expert Region . ” From this analysis we are at least 99 . 5% confident ( one-tail ) that the Positive Region contains more strong cancer rescue mutants than the Negative or non-MIP Region . Similarly , we infer that there is no significant difference between the number of cancer rescue mutants in the Positive and Expert regions . The novel p53 cancer rescue mutants found in the Positive , Negative , and non-MIP regions are presented in Table 5 and summarized in Table 6 . Three different cancer mutants were rescued by these regions: P152L , R158L and G245S . R158L was rescued strongly by the Positive Region , and weakly by the Negative and non-MIP regions . G245S was rescued weakly by the Negative and non-MIP regions . P152L , a previously unrescued cancer mutant , was rescued only by the Positive Region , and rescued strongly . In addition to Additive Curiosity , six other ( non-Random ) active learning methods were considered . Table 7 shows the Positive , Negative , and non-MIP regions selected by those other methods . The non-MIP region was similar to the Negative region due to the preponderance of predicted Negative mutants in the test set . Minimum Entropy and Maximum Marginal Hyperplane are versions of Maximum Entropy and Minimum Marginal Hyperplane ( repectively ) designed to do poorly , as negative controls . Indeed , the Negative Region chosen by Minimum Entropy overlaps nine of ten residues with the Positive Region chosen by Minimum Marginal Hyperplane . Similarly the Negative Region chosen by Maximum Marginal Hyperplane overlaps eight of ten residues with the Positive Region chosen by Maximum Entropy . One might wonder if the classifier would have found the Expert region as a Positive Region in future experiments . Figure 5 indicates the next Positive regions that would be selected , after the mutants found in the current Positive , Negative , and non-MIP regions , but not the Expert region , were added to the training set . There , the most informative positive mutants were found in the region from 130–156 , but the region 103–119 also scored well , overlapping the Expert Region ( 114–123 ) . This is somewhat surprising as the classifier does not consider the Expert criteria , i . e . , whether or not this residue appeared in a rescue mutant previously . To better understand the regions selected and their relationship to the p53 protein , it is helpful to consider molecular visualizations of p53 . Here , p53 is visualized with UCSF Chimera [33] , [42] . Figure 6 presents a visualization of the Positive , Negative , non-MIP , and Expert regions on the p53 core domain . It is noteworthy that all of the regions selected in this study appear near the surface of the p53 molecule even though that was not explicitly a criterion in their selection . Figure 7 shows the surface residues selected by the mutual information algorithm [36] to be significant in determining the activity of p53 mutants [12] . Figure 8 shows all known single amino acid rescue mutations . Figure 9 shows the 10 cancer mutants presented in Table 6 , Figure 10 including the newly rescued P152L . Figure 10 shows a different visualization of Figure 7 .
The ten different cancer mutants studied here account for about one million diagnosed cancers per year . The rescue of cancer mutant P152L by a mutation in the Positive Region is the first report that this common cancer mutant can be rescued at all . The in silico identification and biological verification of a new cancer rescue region is a small but hopefully useful step towards selection of p53 surface regions that potentially result in p53 cancer rescue when appropriately modified . Such regions eventually might be targeted by small molecule drugs . For example , Figure 10 shows an area on the surface of the p53 core domain that is: ( 1 ) away from the DNA binding region; ( 2 ) overlapping or adjacent to the Positive Region; ( 3 ) implicated by mutual information as influential in determining p53 activity; and ( 4 ) located where structural changes restore functional activity to some cancerous p53 mutants . Better knowledge of p53 mutant structure-function relationships eventually might lead to successful pharmaceutical manipulation of p53 mutant function . It has been hypothesized that different p53 cancer rescue mutants have different rescue mechanisms corresponding to different types of cancer mutations [22] , [25] . For example , the Expert Region rescued the more frequent p53 cancer mutant G245S while the Positive Region did not . Conversely , the Positive Region is unique in its ability to rescue the P152L mutant . Different rescue regions may implement different rescue mechanisms , and so contribute different facets to knowledge of cancer rescue . From Figure 4 , the Expert Region had both low average curiosity ( . 462 ) and relatively few ( 34 ) mutants predicted Positive . Thus , this region was not selected by the classifier , yet a significant number of rescue mutants were identified in this region . This is not surprising , as the classifier was not directly exposed to the criteria used for selecting the Expert Region . Conversely , it is not surprising that an expert cancer biologist could pick a fruitful region for reasons unknown to the classifier . Adding expert-level knowledge to a performance system is a long-time success story of artificial intelligence [43] . Integrating diverse expert sources and methods using bioinformatics leads to biomedical discovery acceleration [44] . Adding new features that encode expert or literature knowledge directly into the feature vector that encodes each example is one simple way to make expert knowledge visible to any feature-based learning system . Similarly , the classifier does not now weigh the medical impact of different p53 cancer mutants . Cancer mutation occurrence frequencies were not given to the classifier , so it is not surprising that it rescued a less frequent cancer mutant than did the expert . Weighting by cancer mutation frequency , or by any other desired utility function , is one simple way to implement a selection preference for some informative Positives over others . MIP active learning using modeled structural features was introduced and shown to be a useful framework for function-based biological research . It provided an analysis tool yielding results that otherwise would have been unexpected or unavailable . From the perspective of a biologist , the computer-selected Positive Region would not have been chosen as a potential region for cancer rescue: It did not contain any known cancer rescue mutants , and none of the random biology-based approaches had ever identified rescue activity in this region . This result provides a proof-of-concept that a computational classifier and modeled structure-based features can provide insight to help guide function-based experimental discovery . All code and data used in this paper is freely available online at https://sourceforge . net/projects/p53cancerrescue/files/ . The data is also available in Dataset S1 . All mutant DNA vectors are available under standard material transfer agreements through the UCI Office of Technology Alliances ( http://www . ota . uci . edu/ ) . | Engineering proteins to acquire or enhance a particular useful function is at the core of many biomedical problems . This paper presents Most Informative Positive ( MIP ) active learning , a novel integrated computational/biological approach designed to help guide biological discovery of novel and informative positive mutants . A classifier , together with modeled structure-based features , helps guide biological experiments and so accelerates protein engineering studies . MIP reduces the number of expensive biological experiments needed to achieve novel and informative positive results . We used the MIP method to discover novel p53 cancer rescue mutants . p53 is a tumor suppressor protein , and destructive p53 mutations have been implicated in half of all human cancers . Second-site cancer rescue mutations restore p53 activity and eventually may facilitate rational design of better cancer drugs . This paper shows that , even in the first round of in vivo experiments , MIP significantly increased the discovery rate of novel and informative positive mutants . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biochemistry/molecular",
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"oncology",
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] | 2009 | Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning |
Given the extraordinary ability of humans and animals to recognize communication signals over a background of noise , describing noise invariant neural responses is critical not only to pinpoint the brain regions that are mediating our robust perceptions but also to understand the neural computations that are performing these tasks and the underlying circuitry . Although invariant neural responses , such as rotation-invariant face cells , are well described in the visual system , high-level auditory neurons that can represent the same behaviorally relevant signal in a range of listening conditions have yet to be discovered . Here we found neurons in a secondary area of the avian auditory cortex that exhibit noise-invariant responses in the sense that they responded with similar spike patterns to song stimuli presented in silence and over a background of naturalistic noise . By characterizing the neurons' tuning in terms of their responses to modulations in the temporal and spectral envelope of the sound , we then show that noise invariance is partly achieved by selectively responding to long sounds with sharp spectral structure . Finally , to demonstrate that such computations could explain noise invariance , we designed a biologically inspired noise-filtering algorithm that can be used to separate song or speech from noise . This novel noise-filtering method performs as well as other state-of-the-art de-noising algorithms and could be used in clinical or consumer oriented applications . Our biologically inspired model also shows how high-level noise-invariant responses could be created from neural responses typically found in primary auditory cortex .
Invariant neural representations of behaviorally relevant objects are a hallmark of high-level sensory regions and are interpreted as the outcome of a series of computations that would allow us to recognize and categorize objects in real life situations . For example , view-invariant face neurons have been found in the inferior temporal cortex [1] and are thought to reflect our abilities to recognize the same face from different orientations and scales . The representation of auditory objects by the auditory system is less well understood although neurons in high-level auditory areas can be very selective for complex sounds and , in particular , communication signals[2] . It has also been shown that auditory neurons can be sound level invariant [3] , [4] or pitch sensitive [5] . As is the case for all neurons labeled as invariant , pitch sensitive neurons respond similarly to many different stimuli as long as these sounds yield the same pitch percept . Both sound level invariant and pitch sensitive neurons could therefore be building blocks in the computations required to produce invariant responses to particular auditory signals subject to distortions due to propagations or corruption by other auditory signals . The existence of such distortion invariant auditory neurons , however , remains unknown . Similarly , the neuronal computations required to recognize communication signals embedded in noise are not well understood although it is known that humans [6] and other animals [7] excel at this task . In this study , we examined how neurons in the secondary avian auditory cortical area NCM ( CaudoMedial Nidopalium ) responded to song signals embedded in background noise to test whether this region presents noise-invariant characteristics that could be involved in robust song recognition . We chose the avian model system because birds excel at recognizing individuals based on their communication calls [8] , often in very difficult situations [9] . Moreover , the avian auditory system is well characterized and it is known that neurons in higher-level auditory regions can respond selectively to particular conspecific songs [10] . We focused our study on NCM because a series of neurophysiological [11] , [12] and immediate early gene studies [13] , [14] have implicated this secondary auditory area in the recognition of familiar songs . In addition , although neuronal responses in the primary avian auditory cortex regions are systematically degraded by noise [15] , studies using immediate early gene activation suggested that responses to conspecific song in NCM were relatively constant for a range of behaviorally relevant noise levels [16] .
As illustrated on the left panels in Fig . 1 , responses of some neurons to song signal were almost completely masked by the addition of noise . In these situations , the post-stimulus time histogram ( PSTH ) obtained for song only ( third row ) is very different than the one obtained for song + ml-noise ( fifth row ) . However , some neurons also showed strong robustness to noise degradation as illustrated on the right panels of Fig . 1 . Those neurons had similar PSTHs for both conditions . To quantify the degree of noise robustness , we calculated two measures of noise-invariance: a de-biased correlation coefficient between the PSTHs obtained for the song alone and song + ml-noise stimuli ( called ICC ) and the ratio of the SNR estimated for the song + noise response and the song + ml-noise response ( ISNR invariance ) . The ICC metric is a normalized measure that ranges in values between −1 and 1 . It is 1 when the response pattern observed to song+ml-noise is identical to the one observed to song , irrespective of the relative magnitude of the two responses . For ISNR , we defined the response SNR as follows . For the response to song alone , the signal power was defined as the variance in the PSTH across time and the noise was defined as the mean firing rate . For the response to song plus noise , the signal was taken to be the time-varying response that could be predicted linearly from the response to song alone and the noise was the mean of this predicted response ( see methods ) . This second value of invariance is bounded between 0 and 1 and captures not only the similarities in response patterns but also magnitudes of time-varying responses that carry information about the song . As shown in the supplemental material , the two measures were highly correlated and subsequent analyses resulted in very similar results and identical conclusions . For brevity , we show the analysis using the ICC metric in the main paper . Some of the results with the ISNR metric are included in the supplemental material . We found neurons with different degrees of noise invariance throughout NCM but the neurons in the ventral region tended to have highest Icc ( Fig . 2B ) . NCM also exhibits some degree of frequency tonotopy along this dimension with higher frequency tuning found in more ventral regions [18] , [19] . Indeed , in our data set , we also found a strong correlation between dorsal/ventral position and the best frequency ( BF ) of the neuron ( Fig . 2C ) . We estimated a neuron's best frequency from the peak of the frequency marginal of its spectral-temporal receptive field ( STRF ) . We found a range of BF from 1300 Hz to 3300 Hz with a dorsal-ventral gradient ( adjusted R2 = 0 . 34 , p<10−3 ) . Although the frequency range of our song stimulus and ml-noise stimulus was identical , the frequency power spectrum of song has a peak around 4 KHz [20] that could have lead to stronger and thus potentially more noise invariant responses to song for neurons with higher best frequency . A linear regression analysis between invariance and the neuron's best frequency could not confirm that hypothesis ( Adjusted R2 = 0 . 06 , p = 0 . 1 ) . Thus , if this relationship exists , it can only have a very small effect size . To further attempt to understand how noise invariance was achieved in this system , we examined how the neurons' responses for particular joint spectral-temporal patterns that are unique to song could have contributed to robust coding of song in noisy conditions . To do so we estimated the STRF of each neuron and examined the predicted response to song and to song plus noise . The STRF describes how acoustical patterns in time and frequency correlate with the neuron's response [21] , [22] . The STRF can also be used as a model of the neuron to estimate predicted responses for arbitrary sound stimuli . The STRF model is often described as “linear” but can include both input and output non-linearities . In this study , the stimulus was represented as a log spectrogram and the output of the linear filter was half-wave rectified ( see methods ) . Although we have shown that better response predictions could be obtained using additional non-linear elements such as gain control [23] , in this study we have used the simpler STRF model to more explicitly describe the spectral-temporal tuning of each neuron ( examples of STRF predictions are found in Fig S2 ) . To determine whether a neuron's tuning for particular spectral-temporal features characteristic of song and less common in noise could explain the observed invariance , we use the STRF to obtain estimated responses to song and noise . We then regressed the ICC values that we measured directly from the neuron's response against the ICC values obtained from the predictions of STRF model ( Fig . 3A ) . Two results come out of this analysis . First , the measured invariance and the model invariance are positively but weakly correlated showing that the neurons' STRFs can in part explain the observed noise-invariance ( Adjusted R2 = 0 . 12 , p = 0 . 034 ) . Second , we found that , for most neurons , the degree of invariance predicted by the STRF model was greater than the one found in actual neurons . In other words , non-linearities not captured in the STRF model made these neurons less invariant . Although this result might seem surprising for an auditory region believed to be important for song recognition , it has a simple explanation . Many high-level neurons show adapting responses to sound intensity levels [24] and this common non-linear response property is not captured in this STRF model . Intensity adapting neurons would exhibit a decrease in response to the song in noise relative to the song alone due to the adaptive changes in gain . This decrease in response gain without a corresponding decrease in background rate would result in a decrease of the response's SNR . Therefore , for the task of extracting the song from noise , the most effective non-linearities appear to be the simple thresholding non-linearity ( i . e . for neurons with STRFs closest to the x = y line in Fig . 3A ) or a yet to be described additional non-linearity boosts invariance ( n = 3/32 ) . Although the specific non-linearities that could be beneficial for preserving signal in noise still need to be described , previous research have characterized higher-order non-linearities response that could play an important role: neurons in NCM exhibit stimulus specific adaptation [11] and neurons in another avian secondary auditory area , CM ( Caudal Mesopallium ) , respond preferentially to surprising stimuli [25] . These non-linearities could facilitate noise invariant responses since they tend to de-emphasize the current or expected stimulus ( in this case noise like sounds ) without decreasing the gain of the neuron to sound at the same frequency . Since the STRF could partially explain the observed noise-invariance , we asked what feature of the neurons spectral-temporal tuning was important for this computation . By estimating the modulation gain from the neurons' STRFs , we found that tuning for high spectral modulations and low temporal modulations correlate with neural invariance ( Fig . 3B–C ) . Neurons sensitive to higher spectral modulations are more invariant ( Adjusted R2 = 0 . 192 , p = 0 . 007 ) and neurons sensitive to lower temporal modulations are more invariant ( Adjusted R2 = 0 . 15 , p = 0 . 015 ) . To assess the effect size of these two relationships taken together , we used multiple linear-regression with spectral and temporal modulation tuning as regressors used to explain the neurons invariance and found an adjusted R2 of 0 . 23 ( p = 0 . 009 ) . Thus the contributions of spectral and temporal tuning to invariance are not completely additive . The ensemble modulation transfer functions further illustrate how the spectral and temporal modulation tuning co-vary along the noise-invariance dimension ( Fig . 3D ) . Noise invariant neurons exhibit the combination of longer integration times and sharp spectral tuning . In addition , the sharp excitatory spectral tuning was often combined with sharp inhibitory spectral tuning as well . These properties make noise-invariant neurons particularly sensitive to the longer harmonic stacks present in song ( and other communication signals ) even when these are embedded in noise as illustrated in the example neuron in Fig . 1 ( right panel ) . The generation of the observed modulation tuning properties of the more noise invariant neurons described in this study is not a trivial task: most neurons in lower auditory areas have much shorter integration times and lack the sharp excitation and inhibition along the spectral dimension that we observed here . From comprehensive surveys of tuning properties in the avian primary auditory cortex ( Field L ) [22] , [26] , we know that a small number of neurons with similar characteristics exist in these pre-synaptic areas [22] . Similarly , in the mammalian system , neurons in A1 have been shown to have a range of spectral-temporal tuning similar to that seen in birds but few with the sharp spectral tuning seen here [27] , [28] . Thus it is reasonable to postulate that noise-invariance in NCM ( and putatively in mammalian secondary auditory cortical regions ) is the result of a series of computations that are occurring along the auditory processing stream . However , it is also known that NCM possesses a complex network of inhibitory neurons and that these play an important role in shaping spectral and temporal response properties [29] . We also found a higher concentration of noise invariant neurons in the more ventral regions of NCM but failed to find a correlation between invariance and best frequency . On the other , we found that both temporal modulation tuning ( adjusted R2 = 0 . 12 p = 0 . 02 ) and spectral modulation tuning ( adjusted R2 = 0 . 15 p = 0 . 01 ) were also correlated with depth: lower temporal and higher spectral modulation tuning is found in ventral regions of NCM . This organization of tuning properties is reminiscent of the organization of the primary auditory areas , field L , where the output layers have a higher concentration of neurons with longer integration times [30] . Thus both upstream and local circuitry are almost certainly involved in the creation of noise-invariant neural representations . Since the tuning of noise invariant neurons described by their STRF and the threshold non-linearity only describes a fraction of the invariance , we were interested in assessing whether noise invariant neurons were selective for longer sound segments such as those that might be useful to distinguish one song from another . To begin to investigate this idea , we examined the invariance of all the neurons for each song and calculated the standard deviation and the coefficient of variation ( CV ) of the invariance metric for each neuron . These results are shown as a two-dimensional heat map on Fig . 4 . Although , the degree of invariance varied somewhat across songs ( and the most invariant neurons could have invariances above 0 . 9 for certain sounds ) , the variability was remarkably low: highly invariant neurons tended to show noise-invariance to most song stimuli . The CVs for the 10 most invariant neurons were similar and all below 0 . 5 . We therefore conclude that neurons that show a high degree of invariance could be useful to extract signal from noise not only for a specific song but also for an entire stimulus class . For example , noise invariant neurons could detect short acoustical features that are characteristic of many zebra finch songs . The STRF analysis shows that sensitivity to features up to 100 ms in duration is more than sufficient to generate in model neurons noise invariance of similar magnitude to that observed in the actual data . However , since the STRF only explains a fraction of both the observed invariance and the response , selective response properties that involve longer integration times could also be involved in the generation of noise invariant responses . Inspired by our discovery of noise invariant neurons in NCM , we engineered a noise filtering algorithm based on a decomposition of the sound by an ensemble of “artificial” neurons described by realistic STRFs . We developed this algorithm both for biological and engineering purposes . Our biological goal was to demonstrate that an ensemble of noise-invariant responses such as the one observed here could indeed be used to recover a signal from noise . We also wanted to show whether an optimization process designed to extract signals from noise would rely on responses of particular artificial neurons with properties that are similar to those found in the biology . Finally , we also wanted to explore to what extent is the invariance of signal in noise dependent on the exact statistics of the signal and noise stimuli . Our engineering goal was to develop a real-time algorithm inspired by the biology that could potentially be used in clinical applications such as hearing aids and cochlear implants or in commercial applications involving automatic speech recognition [31] . In hearing aids , various forms of noise reduction have been shown to offer an incremental improvement in the listening experience [32] , [33] though listening to speech in noisy environments remains the principal complaint of hearing aid users [34] . In addition , none of the current noise reduction algorithms have led to improvements in speech intelligibility [35] , [36] . Our ensemble of artificial neurons can be thought of as a modulation filter bank because the response of each neuron quantifies the presence and absence of particular spectral-temporal patterns as observed in a spectrogram and , contrary to a frequency filter bank , not solely the presence or absence of energy at a particular frequency band . In other words , the STRFs can be thought of as “higher-level” sound filters: if lower-level sound filters operate in the frequency domain ( for example removing low frequency noise such as the hum of airplane engines ) , these high-level filters operate in the spectral-temporal modulation domain . In this joint modulation domain , sounds that have structure in time ( such as beats ) or structure in frequency ( such as in a musical note composed of a fundamental tone and its harmonically related overtones ) are characterized by specific temporal and spectral modulations . A spectral-temporal modulation filter could then be used to detect sounds that contain particular time-frequency patterns while filtering out other sounds that might have similar frequency content but lack this spectral-temporal structure . Similar decompositions have also been proposed and used by others for the efficient processing of speech and other complex signals [37] , [38] , [39] . Noise filtering with such a modulation filter bank can be described as series of signal processing steps: i ) decompose the signal into frequency channels using a frequency filter bank; ii ) represent the sound as the envelope in each of the frequency channels , as it is done in a spectrogram; iii ) filter this time-frequency amplitude representation by a modulation filter bank to effectively obtain a filtered spectrogram; iv ) invert this filtered spectrogram to recover the desired signal . Although each of these steps involves relatively simple signal processing , two significant issues remain . First , one has to choose the appropriate gain on the modulation filters in order to detect behaviorally relevant signals over noise . Second , the spectrogram inversion step requires a computationally intensive iterative procedure [40] that would prevent such a modulation filtering procedure to operate in real time or with minimal delays . Our algorithm solves these two issues . We have eliminated the spectrographic inversion step and instead use the output of the modulation filter bank to generate a time-varying gain vector that can directly operate on the output of the initial frequency filter bank . Second , we propose to find optimal fixed gains on the modulation filter bank by minimizing the error between a desired signal and the output of the filtering process in the time domain . Then once the modulation filter weights are fixed , the algorithm can operate in real-time with a delay that is only dependent on the width of the STRF in the modulation filter bank . The various steps in our algorithm are illustrated on Fig . 5 . Both the analysis and synthesis steps of the algorithm use a complete ( amplitude and phase ) time-frequency decomposition of the sound stimuli . This time-frequency decomposition is obtained from a frequency filter bank of N-linearly spaced band-pass filter Gaussian shaped channels located between 250 Hz and 8 kHz . The amplitude of these N narrow-band signals is obtained using the Hilbert transform ( or rectification and low-pass filtering ) to generate a spectrogram of the sound . This spectrographic transformation is identical to the one that we use for the estimation of the STRFs ( see methods ) . The analysis step in the algorithm involves generating an additional representation of the sounds based on an ensemble of model neurons fully characterized by their STRF . These STRFs are designed to efficiently encode the structure of the signal and the noise , allowing them to be useful indicators of the time-course of signal in a noisy sound . For this study , we used a bank of STRFs that were designed to model the STRFs found throughout the auditory pallium , including STRFs not only from neurons in NCM but also the field L complex [22] . The log spectrogram of the stimulus is convolved with each STRF to obtain model neural responses: of dimension M . The crux of our algorithm is to transform these neural responses back into a set of time varying frequency gains , of dimension N . These frequency gains will then be applied to the corresponding frequency slices in the time-frequency decomposition of the sound to synthesize the processed signal . is a function of the sum of all model neural responses each scaled by an importance weighting , , and then multiplied by the frequency marginal of the corresponding neuron's STRF:The function f was chosen to be the logistic function in order to restrict the gains to lie between a lower bound , representing maximal attenuation , and 0 dB , representing no attenuation . Ki , j is the frequency marginal value of neuron i for the frequency band centered at j , and it was obtained from the frequency marginal of each STRF . Using these gains , we then synthesized a processed signal:where is the narrow-band signal from the frequency filter j obtained in the time-frequency decomposition of the song + noise stimulus , . The optimal set of weights , , was learned by minimizing the squared error through gradient descent . To assess the quality of our algorithm , we compared it to 3 other noise reduction schemes: the optimal classical frequency Wiener filter for stationary Gaussian signals ( OWF ) , a state-of-the-art spectral subtraction algorithm ( SINR ) used by a hearing aid company , and the upper bound obtained by an ideal binary mask ( IBM ) . The optimal Wiener filter is a frequency filter whose static gain depends solely on the ratio of the power spectrum of the signal and signal + noise . The state-of-the-art spectral subtraction algorithm uses a time variable gain just as in our algorithm but based on a running estimate of noise and signal spectrum . This algorithm was patented by Sonic Innovations ( US Patent 6 , 757 , 395 B1 ) and is currently used in hearing aids . The IBM procedure used a zero-one mask applied to the sounds in the spectrogram domain . The mask is adapted to specific signals by setting an amplitude threshold . Ideal binary masks require prior knowledge of the desired signal and thus can be considered as an approximate upper bound on the potential performance of general noise reduction algorithms [41] . As shown on Fig . 6A , with relatively little customization and exploration ( for example in the choice of the set of artificial STRFs ) our algorithm performed strikingly well: our algorithm performed significantly better than both the classical frequency Wiener filter and the SINR algorithm for a song embedded in ml-noise and similarly to the SINR algorithm for a song embedded in colony noise . The quality of the noise filtering can also be assessed by examining the time-varying gains shown on bottom row in Figs 6B and C: without any a priori knowledge of the location of the signal in time ( and contrary to the IBM ) , the time-varying gains can pick out when the signal occurs in the noise . Moreover , the gains are not constant for all frequencies but instead are also able to pick out harmonic structure in the sound . The quality of the reconstruction can also be visually assessed by examining the spectrograms shown in that figure or listening to the demos provided as supplemental material . We are now able to answer our questions . First , as quantified above , using an ensemble of physiologically realistic noise-invariant responses , we show that one is able to recover the distorted signal with remarkable accuracy . Second , we were also able to compare the properties of the STRFs in the model that had the biggest importance gains ( ) with those found in noise-invariant neurons in NCM . As shown on Fig . 7A &B , these STRFs are composed both of narrow band neurons with long integration times as observed in our data set and also broad band neurons with very short integration time . The eMTF shown in Fig . 7C&D further quantify these results . Thus , the noise invariant neurons found in NCM are well represented in by the model STRFs tuned for high spectral modulation and low temporal modulations . NCM also has neurons tuned to faster temporal modulations but the majority of these neurons had narrow band frequency tuning ( or high spectral modulations ) and these neurons are therefore not particularly effective at rejecting noise stimuli . Fast broad-band neurons are however found in the avian primary auditory forebrain [22] , [26] and could thus play a role , as part of an ensemble , in the signal and noise separation . Our third question regarded the sensitivity of noise-invariant neurons to the particular choice of signal and noise . The modeling shows that the importance weights obtained for filtering out ml-noise were slightly different that the weights obtained for filtering colony noise . This relatively small effect can be visually assessed by comparing the highest weighted STRFs for each noise class shown in Fig . 7A versus 7B . These results suggest that slightly different sets of invariant-neurons depending on the statistical nature of the signal and noise but that these effects might be rather small . In addition , we found no correlation between the magnitude of importance weights of the artificial neurons and their BF . Thus , we also predict that the modulation tuning properties of noise-invariant neurons that we described here would apply to a relatively large relevant set of natural signals and noise . This is in part possible because many forms of environmental noise , including noise resulting from the summation of multiple sound signals , have similar modulation structure characterized by a concentration of energy at very low spectral modulations and low to intermediate temporal modulations . In converse , communication signals can have significant energy in regions combining either high spectral modulations with low temporal modulations or high temporal modulations with low spectral modulations [17] [42] . Both in the model and in the biological system , given a complete modulation filter bank , the importance weights for a given signal and noise could be learned quickly through supervised learning . Moreover , after learning , the algorithm can easily be implemented in real-time with minimal delay . Thus , the algorithm is particularly useful with adaptive weights or if the statistics of the noise and signal are known , both of which are true in the biological system . Finally given its performance and the advantages described above , we also believe that this noise filtering approach could be useful in clinical applications , such as hearing aids or cochlear implants , or in consumer applications such as noise canceling preprocessing for automatic speech recognition . In summary , we have shown the presence of noise-invariant neurons in a secondary auditory cortical area . We show that a fraction of the noise-rejecting property can be explained by the spectral-temporal tuning of the neurons . However , tuning properties that are not well captured by the STRF can also both increase or decrease noise-invariance and these properties will have to be examined in future work . We have also described a novel noise reduction algorithm that uses a modulation filter-bank akin to the STRFs found in the avian auditory system . The performance of this algorithm in noise reduction was excellent and similar or better than the current state-of-the-art algorithms used in hearing aids . The model also illustrates some fundamental principles and allowed us to make stronger statements on the scope of our biological findings . The fundamental principles are , first , that signal and noises can have a distinct signature in the modulation space while overlapping in the frequency space and that therefore filtering in this domain can be advantageous . Second , that although modulation filtering is a linear operation in the spectrogram domain , that both the generation of a spectrogram and the re-synthesis of a clean signal require non-linear computations . We argue that the spectral-temporal properties that are found in higher auditory areas and that are particularly efficient at distinguishing noise modulations from signal modulations are the result of a series of non-linear computations that occurred in the ascending auditory processing stream . The model also shows that a real-time re-synthesis of a cleaned signal could be obtained with additional non-linear operations or , in other words , that a real-time spectrographic inversion is possible . Finally , our modeling efforts show that the noise-invariant findings described here for a song as a chosen prototypical signal and a modulation-limited noise as the chosen prototypical noise would also apply to other signals and noise . However , the involvement of neurons with slightly different tuning or adaptive properties would be needed to obtain optimal signal detection . Given the behavioral experiments that have shown that birds excel at auditory scene analysis tasks both in the wild [9] and in the lab [43] , [44] and given our increasing understating of the underlying neural mechanisms [45] , the birdsong model shows great promise to tackle one of the most difficult and fascinating problems in auditory sciences: the analysis of a sound scape into distinct sound objects .
All animal procedures were approved by our institutional Animal Care and Use Committee . Neurophysiological recordings were performed in four , urethane anesthetized adult zebra finches to obtain 50 single unit recordings in areas NCM and potentially field L ( see below ) . We used similar neurophysiological and histological methods to characterize other regions of the avian auditory processing stream and detailed descriptions can be found there [22] . The methods are summarized here and differences when they exist are noted . To obtain recordings from NCM , we used more medial coordinates than our previous experiments . With the bird's beak fixed at a 55° angle to the vertical , electrodes were inserted roughly 1 . 2 mm rostral and 0 . 5 mm lateral to the Y-sinus . We made extracellular recordings from tungsten-parylene electrodes having impedance between 1 and 3 MΩ ( A-M Systems ) . Electrodes were advanced in 0 . 5 µm steps with a microdrive ( Newport ) , and extracellular voltages were recorded with a system from Tucker-Davis Technologies ( TDT ) . In all cases , the extracellular voltages were thresholded to collect candidate spikes . Each time the voltage crossed the threshold , the timestamp was saved along with a high-resolution waveform of the voltage around that time ( 0 . 29 ms before and 0 . 86 ms after for a total of 1 . 15 ms ) . After the experiment , these waveforms were sorted using SpikePak ( TDT ) to assess unit quality . We sorted spike waveforms using a combination of PCA and waveform features ( maximum and minimum voltage , maximum slope , area ) . We assessed clustering qualitatively and verified afterwards that the resulting units had Inter-Spike-Interval distributions where no more than 0 . 5% of the intervals were less than 1 . 5 ms . In each bird , we advanced the electrode in 50 µm steps until we found auditory responses . At that point we recorded activity in 100 µm steps . When we no longer found auditory responses , we moved the electrode 300 µm further , made an electrolytic lesion ( 2 uA×10 s ) , advanced another 300 µm , and made a second identical lesion . These lesions were used to find the electrode track post-mortem and to calibrate the depth measurements . At the end of the recording session , the bird was euthanized with an overdose of Equithesin and transcardially perfused with 0 . 9% saline , followed by 3 . 7% formalin in 0 . 025 M phosphate buffer . The skullcap was removed and the brain was post-fixed in 30% sucrose and 3 . 7% formalin to prepare it for histological procedures . The brain was sliced parasagittally in 40 µm thick sections using a freezing microtome . Alternating brain sections were stained with both cresyl violet and silver stain , which were then used to visualize electrode tracks , electrolytic lesions and brain regions . All of our electrode tracks sampled NCM from dorsal to ventral regions . Some of the more dorsal recordings ( shallower depths ) could have been in subregions L or L2b of the Field L complex as the boundary between either of these two regions and NCM proper is difficult to establish [46] , [47] . It is possible therefore that the correlation between degree of invariance and depth also reflects lower invariance observed in the field L complex and higher invariance in NCM proper . Stimuli consisted of zebra-finch songs , roughly 1 . 6–2 . 6 seconds in length , recorded from 40 unfamiliar adult male zebra finches played either in isolation or in combination with a background of synthetic noise ( song+ml-noise stimuli in main text ) . The masking noise in the neurophysiological experiments was synthetic and obtained by low-pass filtering white noise in the modulation domain following the procedure described in [48] . This modulation low-pass filter had cutoff frequencies of ωf = 1 . 0 cycles/kHz and ωt = 50 Hz and gain roll off of 10 dB/ ( cycle/kHz ) and 10 dB/10 Hz . The cutoff modulation frequencies were chosen in order to generate noisy sounds with similar range of modulation frequencies found in environmental noise [17] . In addition , most of the modulations found in zebra finch song are well masked by this synthetic noise although it should be noted that song also includes sounds features with high spectral modulation frequencies ( above 2 cycles/kHz ) and high temporal modulation frequencies ( above 60 Hz ) . The frequency spectrum of the ml-noise was flat from 250 Hz to 8 kHz completely overlapping the entire range of the band-passed filtered songs we used in the experiments . Thus , although , different results could be found with noise stimuli with different statistics , we carefully designed our masking noise stimulus to both capture the modulation found in natural environmental noise while at the same time completely overlapping the frequency spectrum of our signal . The frequency power spectrum of these signals can be found in [20] . We have also shown that such ml-noise is an effective stimuli for midbrain and cortical avian auditory neurons in a sense that it drives neuron with high response rates and high information rates [20] . ML-noise is also very similar to the dynamic noise ripples described in [49] and used in many neurophysiological studies to characterize high-level mammalian auditory neurons . We also recorded responses to the ml-noise masker alone but these data were not analyzed for this study . All song and ml-noise stimuli were processed to be band limited between 250 Hz and 8 kHz and to have equal loudness using custom code in Matlab . The sounds were presented using software and electronics from TDT . Stimuli were played over a speaker at 72 dB C-weighted average SPL in a double-walled anechoic chamber ( Acoustic Systems ) . The bird was positioned 20 cm in front of the speaker for free-field binaural stimulation . Each of the combined stimuli consisted of a different ml-noise sound sample , randomly paired with one of the songs . The noise stimulus began five to seven seconds after the previous stimulus , and the song began after a random delay of 0 . 5 to 1 . 5 seconds after the onset of the noise . Thus for each trial the same song is paired with a different noise sample and at a different delay . In the combined presentations , the noise stimuli were attenuated by 3 dB to obtain a signal to noise ratio ( SNR ) of 3 dB . We played four trials at each recording location , each consisting of a randomized sequence of 40 songs , 40 masking noise stimuli , and 40 combined stimuli . Stimuli were separated by a period of silence with a length uniformly and randomly distributed between five and seven seconds . We used custom code written in MATLAB , Python and R for all of our analyses . We assessed responsiveness using an average z-score metric for each stimulus class . The z-score is calculated as follows:where μS is the mean response during the stimulus , μBG is the mean response during the background , σS2 is the variance of the response during the stimulus , and σBG2 the variance of the response during baseline . The background rates were calculated using the 500 ms periods preceding and following each stimulus . Using a cutoff of z≥1 . 5 for either ml-noise or song stimuli , 32 of the 50 single units were determined to be responsive . To measure invariance , we evaluated the similarity between the responses to song and song + ml-noise by computing two measures: 1 ) the correlation coefficient between the PSTH for each corresponding response and 2 ) the ratio of the SNR in the neural response to song+noise and the SNR in the response to song alone . If the PSTH for song is called and the PSTH obtained in response to song+noise is called , then the correlation coefficient is given by:where the <> are averages across time samples . We called this correlation coefficient , the correlation invariance or the invariance for short . The correlation coefficient is bounded between −1 and 1 and measures the linear similarity in the response after mean subtracted and scaling . Thus a response to song+noise with a deviation from its mean rate that is similar in shape but much smaller than the time-varying response to song alone will have a very high CC invariance . A better measure of invariance might therefore take into account both the mean PSTH rate as a proxy for noise and the deviations from this rate as a measure of signal . Thus , for the response to song alone , we define the signal power as and the noise power as for a signal to noise ratio of:For the response to the song+noise , we wanted to determine the fraction of the time varying-response that was related to the song . For that purpose , we used as a regressor to obtain an estimate of :where and are the coefficients obtained from the normal solution for linear regression . The signal to noise ratio for the response to song+noise is then:And the SNR invariance is given by the ratio of the two SNRs:As shown on Fig S1A , the two metrics ended up being highly correlated: the correlation coefficient between and the log of is r = 0 . 94 ( p<10−6 ) and we decided to use ICC in the main text . However , the calculation of also provides useful information in terms of the absolute magnitude of the invariance . For example , it shows that the SNR in the response for the seven most invariant cells is decreased by 5 to 10 dB when the song in presented in noise . Thus , even for these noise-robust neurons the loss of signal quality is present . Similarly , one can examine the value of the linear regression coefficient , on Fig S1B . This coefficient is always less than one showing that the responses to the song signal in the song+noise stimulus is always reduced . is also highly correlated with but always smaller . Together this shows that although the shape of the time-varying response is often very well preserved in noise-invariant neurons , that the magnitude of this response is decreased resulting in significant losses in signal power ( informative time-varying firing rate ) relative to noise power ( mean firing rate ) . In the calculations above , the PSTH was obtained by smoothing spike arrival times using a 31 ms Hanning window . The bias introduced by the small number of trials used to compute each PSTH was correcting by jackknifing . The single-stimulus results indicate a small but consistent negative bias in the four-trial estimates . We then computed the invariance as the mean of the individual bias-corrected correlations obtained for each 40 stimulus . For each responsive single unit , we estimated the neuron's STRF from their responses to song alone . The STRF were obtained using the strfLab neural data analysis suite developed in our laboratory ( strflab . berkeley . edu ) . The STRFs were estimated by regularized linear regression . The algorithm is implemented as a Ridge Regression in strfLab ( directfit training option ) . Because of the 1/f2 statistics of song , the ridge regression hyper parameter acts as a smoothing factor on the STRF . In addition , we used a sparseness hyper-parameter that controls the number of non-zero coefficients in the STRF . Optimal values of the two hyperparameters were found by Jackknife cross-validation ( see [21] , [50] for more details ) . The stimulus representation used for the STRF was the log of the amplitude of the spectrogram of the sound obtained with a Gaussian shaped filter bank of 125 Hz wide frequency bands . Time delays of up to 100 ms were used to assess the cross-correlation between the stimulus and the response . Performance of the estimated final best STRF was then quantified with a separate validation data set . We assessed the performance of each STRF using coherence and the normal mutual information as described in [51] , [52] . First , we compute the expected coherence between two single response trials; we then compute the coherence between the STRF prediction and the average response . The coherence is a function of frequency between zero and 1 that measures the correlation of two signals at each frequency . To obtain a single measure of correlation , one can compute the normal mutual information ( MI ) . We then computed the normal MI for the two coherences , calling the first the “response information” and the second the “predicted information” . The ratio of the predicted information to the response information is the performance ratio , and provides a measure of model performance that is independent of the variability of the neuron [52] . In all of our receptive field analyses , we used only STRFs that predict sufficiently well , defined here as having predicted information of at least 1 . 2 bits/second and a performance ratio of at least 20% . The STRF performance was not correlated with either the responsiveness of the neuron , as measured by their z-score , or the degree of invariance ( data not shown ) . To further examine the gain of the neuronal response as a function of temporal and spectral modulations , we also represented each STRF in terms of its Modulation Transfer Function ( MTF ) . The MTF is obtained by taking the amplitude of 2 dimensional Fourier Transform of the STRF [42] . For each neuron , we also computed the center of mass of its MTF to estimate its best spectral and temporal modulation frequencies To calculate the invariance metrics for the STRF model , we first obtained the predicted response to the song+ml-noise stimulus for each trial . Using these in place of the actual responses , we then computed an invariance metrics for the STRF model by comparing the predicted responses to the actual response obtained for song alone . In this manner , we were able to directly compare the STRF model invariance with the invariance calculated for the actual neuron . We used a two-tailed t-test to compare the distribution of similarity values for the 40 , four-trial linear predictions to the 40 actual four-trial responses . Fig . S2 illustrates the methodology and shows the STRF , MTF , neural responses and predictions to both song and song+ml-noise for two additional example neurons: one with relatively low noise-invariance and one with relatively high noise-invariance . Following directly from the premise that neurons in area NCM selectively respond to spectral-temporal modulations present in zebra finch songs , even in the presence of corrupting background noise , we developed a noise reduction scheme that would exploit this property . Our algorithm falls in the general class of single microphone noise reduction ( SMNR ) algorithms using spectral subtraction . The core idea in spectral subtraction is to estimate the frequency components of the signal from the short time Fourier components of the corrupted signal . The estimated signal frequency components are obtained by multiplying the Fourier components of signal+noise by a gain function . This is the synthesis part of the algorithm . The gain function can vary both in frequency and time . The form and estimation of the optimal gain function is the analysis step of the algorithm and its design is the principal focus of the novel development of the state-of-the art SMNR algorithms . Both the analysis and synthesis step in our algorithm used a complete ( amplitude and phase ) time-frequency decomposition of the sound stimuli ( Fig . 5 ) . This time-frequency decomposition was obtained from a frequency filter bank of N-linearly band-pass filter Gaussian shaped channels located between 250 Hz and 8 kHz ( BW = 125 Hz ) . N was set at 60 for all simulations . The amplitude of these N narrow-band signals could then be obtained using the Hilbert transform to generate a spectrogram of the sound . The analysis step in the algorithm involved generating an additional representation of the sounds based on an ensemble of M model neurons fully characterized by their STRF . The model STRFs were parameterized as the product of two Gabor functions describing the temporal and spectral response of the neuron: The parameters of these Gabor functions ( e . g . for time: , the temporal latency; , the temporal bandwidth; , the best temporal modulation frequency; and , the temporal phase ) were randomly chosen using a uniform distribution over the range of those found in area NCM ( present study ) and Field L [22] . The number of model neurons , M , was not found to be critical as long as the population of STRFs sufficiently tiled the relevant modulation space . M was set to be 140 for the results shown . To obtain the representation of sounds in this “neural space” , the log spectrogram of the stimuli was convolved by each STRF to obtain the model neural response: of dimension M . As explained in the main text , we then used these activation functions to obtain a set of optimal time varying frequency gains , of dimension N . These frequency gains are then be applied to the corresponding frequency slices in the time-frequency decomposition of the sound to synthesize the processed signal using:where is the narrow-band signal from the frequency filter j obtained in the time-frequency decomposition of the song + noise stimulus , . The optimal set of weights , , needed to obtain the optimal gains , ( see Results ) was learned by minimizing the squared error through gradient descent . For this purpose , training stimuli were generated by summing together a 1 . 5 s song clip and a randomly selected chunk of either ml-noise or zebra finch colony noise of the same duration . To match the experimental results , both the song , , and the noise , , were first high-pass filtered above 250 Hz and low-pass filtered below 8 kHz , and then resampled to a sampling rate of 16 kHz . The song and noise were weighted to obtain a SNR of 3 dB , although similar results were found with lower SNR's . Training was performed on all instances of the signal + noise samples . Weights were determined by averaging across values obtained through jack-knifing across this data set ten times with 10% of the data held out as an early stopping set . Noise reduction was then validated and quantified on a novel song in novel noise . Examples of noise corrupted signals and filtered signals that correspond to the spectrograms shown in Fig . 6 can be found in the supplemental online material: Audio S1 , Zebra finch song masked by ml-noise; Audio S2 , the recovered song signal; Audio S3 , the original song signal; Audio S4 , Zebra finch song masked by colony noise; Audio S5 , the recovered song signal; Audio S6 , the original song signal . To assess the performance of our model , we computed the cross-correlation between the estimate and the clean signal in the log spectrogram domain . We then took the ratio of this cross-correlation and the value obtained prior to attempting to de-noise the stimulus to obtain a performance ratio . As summarized in the text , we then compared our algorithm to other noise reduction schemes . For this purpose , we also estimated the performance ratio for three other spectral subtraction noise algorithms: the optimal Wiener filter ( OWF ) , a variable gain algorithm patented by Sonic Innovations ( SINR ) and the ideal binary mask ( IBM ) . The optimal Wiener filter is a frequency filter whose static gain depends solely of the ratio of the power spectrum of the signal and signal + noise . In our implementation , the Wiener filter was constructed using the frequency power spectrum of signal and noise from the training set and then applied to a stimulus from the testing set ( of the same class ) . The spectral subtraction algorithm for Sonic Innovations used a time variable gain just as in our implementation . Also , as in our implementation , the analysis step for estimating this gain was based on the log of the amplitude of the Fourier components . However , the gain function itself was estimated not from a modulation filter bank but estimating the statistical properties of the envelope of the signal and noise in each frequency band ( US Patent 6 , 757 , 395 B1 ) . We used a Matlab implementation of the SINR algorithm provided to us by Dr . William Woods of Starkey Hearing Research Center , Berkeley , CA . Optimal parameters for the level of noise reduction and the estimation of the noise envelope for that algorithm were also obtained on the training signal and noise stimuli and the performance was cross-validated with the test stimuli . The IBM procedure used a zero-one mask applied to the sounds in the spectrogram domain . The mask is adapted to specific signals by setting an amplitude threshold . Binary masks require prior knowledge of the desired signal and thus should be seen as an approximate upper bound on the potential performance of general noise reduction algorithms . Although these simulations are far from comprehensive , they allowed us to compare our algorithm to optimal classical approaches for Gaussian distributed signals ( OWF ) , to a very recent state-of-the-art algorithm ( SINR ) and to an upper bound ( IBM ) . For commercial applications , our noise-reduction algorithm is available for licensing via UC Berkeley's Office of Technology Licensing ( Technology: Modulation-Domain Speech Filtering For Noise Reduction; Tech ID: 22197; Lead Case: 2012-034-0 ) . | Birds and humans excel at the task of detecting important sounds , such as song and speech , in difficult listening environments such as in a large bird colony or in a crowded bar . How our brains achieve such a feat remains a mystery to both neuroscientists and audio engineers . In our research , we found a population of neurons in the brain of songbirds that are able to extract a song signal from a background of noise . We explain how the neurons are able to perform this task and show how a biologically inspired algorithm could outperform the best noise-reduction methods proposed by engineers . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"computational",
"neuroscience",
"biology",
"sensory",
"systems",
"neuroethology",
"neuroscience"
] | 2013 | Noise-invariant Neurons in the Avian Auditory Cortex: Hearing the Song in Noise |
Emerging insecticide resistance is a major issue for vector control . It decreases the effectiveness of insecticides , thereby requiring greater quantities for comparable control with a net increase in risk of disease resurgence , product cost , and damage risk to the ecosystem . Pyrethroid resistance has been documented in Puerto Rican populations of Aedes aegypti ( L . ) mosquitoes . In this study , topical toxicity of five insecticides ( permethrin , etofenprox , deltamethrin , DDT , transfluthrin ) was determined for susceptible ( Orlando—ORL ) and resistant ( Puerto Rico—PR ) strains of Ae . aegypti . Resistance ratios were calculated using LD50 values , and high resistance ratios for permethrin ( 112 ) and etofenprox ( 228 ) were observed for the Puerto Rico strain . Behavioral differences in blood-feeding activity for pyrethroid-resistant and pyrethroid-susceptible strains of Ae . aegypti when exposed to pyrethroid-treated cloth were also explored . Strains were exposed for 15 min to a range of concentrations of pyrethroid-treated uniform fabric in a cage that contained 60 female Ae . aegypti mosquitoes . Interestingly , the resistance ratios for blood-feeding were similar for permethrin ( 61 ) and etofenprox ( 70 ) , but were lower than their respective resistance ratios for topical toxicity , suggesting that knockdown resistance was the primary mechanism of resistance in the blood feeding assays . Results showed a rightward shift in the dose-response curves for blood-feeding that indicated higher concentrations of pyrethroids were necessary to deter blood-feeding behavior in the pyrethroid-resistant Puerto Rican strain of Ae . aegypti .
Insecticide resistance is a serious problem for vector control , and is associated with a higher cost and greater amounts of applied chemical to achieve a comparable level of control for a population of insects . Ultimately , resistance renders the insecticide being less effective for vector control , which unless other control methods are implemented , will increase both vector population size as well as disease transmission [1] . Levels of resistance lower than 10% will generally not affect disease control efforts [1] . It is important to identify and characterize developing resistance issues so that future control strategies can be optimized , or if there is evidence of resistance that adversely affects control , perhaps change or rotate the insecticide class [2 , 3] . Pyrethroids are acute neurotoxicants with low mammalian toxicity , low water solubility , and a high affinity to bind to sediment particles that are subdivided into two types , Type I and Type II , based on difference in structure and properties that relate to their intoxication [4 , 5 , 6] . Type I pyrethroids are more varied structurally with a wide variety of different alcohol groups . Etofenprox , which lacks the central ester group , is considered a Type I , non-ester pyrethroid [7] or a pseudopyrethroid [8] . Type II pyrethroids are characterized by the presence of an α-cyano group attached to the 3-phenoxybenzyl alcohol [9 , 4] . The inclusion of the α -cyano substituent produces greater insecticidal potency than Type I ( permethrin ) , but with similar photostability [7] . Pyrethroids bind to and prevent the inactivation of sodium channels in neuronal membranes [7] . They are commonly used to control mosquito vector populations in areas of the world that suffer from mosquito-borne diseases , such as dengue and malaria [10 , 11] . Through repeated treatments over multiple years , some populations of mosquitoes have developed broad cross-resistance to other chemicals in this group , since they have a similar mode of action . A study done in 1989 documented resistance to pyrethroids in Puerto Rican Aedes aegypti ( L . ) mosquitoes , but found no evidence for resistance to organophosphates or carbamates , despite exposure to ground and aerial ULV applications of malathion [12] . Dengue is endemic in Puerto Rico , as is Ae . aegypti , the principal mosquito vector of dengue , zika , chikungunya , and yellow fever viruses , all of which cause severe human morbidity and mortality [13] . Dengue control in Puerto Rico began in 1963 , when an epidemic of dengue-3 virus resulted in 27 , 000 reported cases [14 , 15] . Since 1963 , Puerto Rico has experienced epidemic dengue activity periodically and it continues to be a serious problem [16] . In this study , differences in blood-feeding activity for pyrethroid-resistant and pyrethroid-susceptible strains of Ae . aegypti when exposed to pyrethroid-treated cloth were explored and compared with the topical toxicity for five insecticides .
Adult mosquitoes used in all bioassays were female Ae . aegypti . The Orlando strain mosquitoes ( ORL ) were obtained from the colony maintained at the US Department of Agriculture , Agricultural Research Service , Center for Medical , Agricultural and Veterinary Entomology ( USDA-ARS-CMAVE ) laboratory in Gainesville , FL which originated in Orlando in 1952 . The pyrethroid-resistant Puerto Rican strain ( PR ) of Ae . aegypti ( NR-48830 , BEI Resources , Atlanta , GA ) was originally started from eggs collected in San Juan , Puerto Rico in June 2012 and this colony was also maintained at the USDA-ARS-CMAVE laboratory in Gainesville . This strain was challenged with permethrin as necessary to maintain the initial level of permethrin resistance ( 100 ppm exposure of permethrin to 3rd larval instars of approximately every 3rd generation ) and the 5-8th generations of mosquitoes were used for bioassays . Nulliparous mosquitoes aged 6–10 days were maintained ad libitum on a 10% sucrose solution at 25–28°C , 60–80% RH and a 14:10 ( L:D ) photoperiod . Nulliparous female mosquitoes aged 6–10 days were pre-selected for host seeking behavior from stock cages using a hand-draw box and a collection trap [17] . Anhydrous ethanol ( Acros , CAS#64-17-5 ) or acetone ( 99 . 7% , Fisher Chemical , CAS#67-64-1 ) was used as the solvent for all chemicals , as well as the negative control . The insecticides used for assays were technical grade permethrin ( 95 . 3% , AMVAC Chemical , CAS#52645-53-1 ) , etofenprox ( 97% , Landis Intl . , Inc . , CAS#80844-07-1 ) , deltamethrin ( 99 . 6% , Fluka Analytical , CAS#52918-63-5 ) , DDT ( 10 . 2% o , p’ , 88 . 5% p , p’ , ChemService , CAS#50-29-3 ) , and transfluthrin ( 98% , Bayer , CAS#118712-89-3 ) . Stock solutions of these insecticides were prepared , stored in a -8°C refrigerator , and at least five dilutions ( 102−10−3 ng/mL ) were made with ethanol within one week of testing . Mosquitoes were cold-anesthetized in a freezer at approximately -10°C , maintained anesthetized in a glass Petri dish on a portable chill table ( Bioquip , Rancho Dominguez , CA ) at about -4°C , separated into groups of 10 females , and each chemical dose was applied in triplicate ( n = 30 ) . Control group mosquitoes included both untreated mosquitoes as well as ethanol-treated mosquitoes ( n = 60 ) . Topical dosing of insecticides , from lowest concentration to highest ( 10−5–102 ng/mg mosquito ) , was applied using a Hamilton repeating dispenser ( PB600-1 1700 Series , Hamilton , Reno , NV ) and 10 μL syringe over a piece of filter paper that was replaced following each insecticide application . A 200 nL droplet of each chemical dose was applied topically to the thorax of each mosquito . Dosed mosquitoes were placed into clear , labeled 103 . 5 mL cups , covered with a square piece of gauze fabric and secured with a rubber band . The syringe was rinsed with 30 volumes of acetone between each insecticide application . Dosed mosquitoes were held overnight at 25°C , 60% RH and provided with a 10% sugar solution-soaked cotton ball that was replaced daily . Mortality was assessed for each dose replicate at 24 h and 48 h to determine the number of dead or impaired mosquitoes , characterized by twitching and erratic or upside-down flight . Control mortality above 20% resulted in that replicate being retested . Replicates with control mortality below 20% were corrected using Abbott’s formula [18] . A bolt of untreated Flame-Resistant Army Combat Uniform ( FRACU Type III ) fabric was provided by the US Army Natick Soldier Research , Development , and Engineering Center . The FRACU Type III cloth is currently the most common fabric construction used in US Army combat uniforms . Uniform fabrics were cut and sewn into sleeves with a surface area of approximately 690 cm2 for testing on the forearms of human volunteers . Six doses ( 101−10−5 mg/cm2 ) of each pyrethroid insecticide treatment were diluted in 12 mL of acetone . Sewn uniform sleeves were rolled , placed into a sealed 250 mL amber jar , and vortexed vigorously to absorb the full amount of pre-measured insecticide . The uniforms were removed from the jar and air dried under a fume hood for 15 min to allow the acetone to evaporate . For these assays , a volunteer’s hands were gloved and a dried sleeve was pulled tightly onto each arm , secured with masking tape at the wrist , and inserted into a stock cage filled with approximately 60 female Ae . aegypti mosquitoes for a 15 min duration test . An untreated control sleeve of the same fabric was paired with each treated uniform sleeve , in order to have a proper basis of comparison , as many uniform fabrics differ in weave tightness , which affects how easily a mosquito can penetrate the fabric ( Fig 1 ) . Blood-fed mosquitoes for each treatment concentration were recorded and compared to the total number of blood-fed mosquitoes for the fabric control . Assay results were averaged across 6 volunteers , with at least six treatment concentrations of insecticide per assay . Volunteers for all bioassays were adults and provided a written , informed consent for enrollment in the study , approved by the University of Florida IRB-01 ( Project # 69–2006 ) . Percent bite protection ( corrected for controls ) was calculated using Abbott’s formula: [ ( C-T ) /C]*100 , where C = # mosquitoes blood-fed on control and T = # mosquitoes blood-fed on a treated sleeve . Data for mortality and bite protection were log transformed and normalized using the GraphPad Prism 6 . 02 software package [19] . Nonlinear regression analysis was performed in GraphPad Prism 6 . 02 software using a sigmoidal , four-parameter dose-response fit with a variable slope to compare the dose-response curves for each strain . The equation used for the nonlinear regression fit was: Y = Bottom + ( Top-Bottom ) / ( 1+10^ ( ( LogED50-X ) *HillSlope ) ) , where Y = Abbott corrected mosquito mortality , and X = Dose in ng/mg mosquito . The four parameters for the model fit include the Y maximum ( Top ) , the Y minimum ( Bottom ) , the log X value at 50 percent response ( LogED50 ) , and the slope ( HillSlope ) . From these curve fits , ED50 estimates with 95% confidence limits , etc . were generated from Prism . The significance of the model term effects was evaluated using an F-test with a significance level of α = 0 . 05 . Resistance ratios were calculated by dividing PR resistant strain LD50 ( toxicity ) or ED50 ( blood feeding ) values by the corresponding values generated for the susceptible ORL strain .
Comparisons of topical treatments between ORL and PR mosquito strains were made using LD50 estimates , calculated from the nonlinear regression fit of sigmoidal variable-slope dose-response curves for permethrin , etofenprox , deltamethrin , DDT , and transfluthrin ( Table 1 ) . The rank order of toxicity ( Table 1 ) against ORL adult females by topical treatment was: deltamethrin >> permethrin > etofenprox > transfluthrin > DDT . Deltamethrin was 85-fold more active than permethrin , whereas the other differences in rank order of toxicity differed by only 2- to 3-fold ( Table 1 ) . For PR , the rank order of toxicity changed dramatically and was: deltamethrin 15-fold > permethrin = transfluthrin > DDT = etofenprox ( Table 1 ) . F tests comparing the logLD50 values indicated that the ORL strain LD50s were significantly lower than the PR strain . The following statistical values were calculated for permethrin , where F ( 1 , 35 ) = 175 . 1 , p < 0 . 0001 ( α = 0 . 05 ) ; for etofenprox , F ( 1 , 35 ) = 321 . 7 , p < 0 . 0001 ( α = 0 . 05 ) ; for deltamethrin , F ( 1 , 35 ) = 74 . 6 , p < 0 . 0001 ( α = 0 . 05 ) ; for DDT , F ( 1 , 32 ) = 19 . 00 p = 0 . 0001 ( α = 0 . 05 ) ; and for transfluthrin , F ( 1 , 32 ) = 110 . 09 , p < 0 . 0001 ( α = 0 . 05 ) . The LD50 values for permethrin on ORL and PR resulted in a resistance ratio of 112 , for etofenprox 228 , for deltamethrin 650 , for DDT 16 , and for transfluthrin 29 ( Table 1 ) . Comparisons in blood-feeding success following treatments with these same compounds to the ORL and PR strains were made using ED50 estimates , calculated from the linear regression fit of probit dose-response curves for permethrin , etofenprox , deltamethrin , and DDT ( Table 1 , Fig 2 ) . The rank order of bite protection performance ( Table 1 ) was much different from that observed for toxicity against PR adult females by treated sleeves: deltamethrin > permethrin > etofenprox > DDT . The difference between the pyrethroids was about 4-fold , while etofenprox was 24-fold more active than DDT ( Table 1 ) . For PR , the rank order for bite protection was permethrin > deltamethrin > etofenprox > DDT ( Table 1 ) . F tests comparing the logED50 values indicated that the ORL strain ED50s were significantly lower than the PR strain . Statistical values for blood feeding protection in the two strains were calculated for permethrin , where F ( 1 , 66 ) = 29 . 6 , p < 0 . 0001 ( α = 0 . 05 ) ; for etofenprox , F ( 1 , 66 ) = 23 . 0 , p < 0 . 0001 ( α = 0 . 05 ) ; for deltamethrin , F ( 1 , 69 ) = 77 . 9 , p < 0 . 0001 ( α = 0 . 05 ) ; and for DDT , F ( 1 , 66 ) = 4 . 23 , p = 0 . 044 ( α = 0 . 05 ) . The ED50 values for permethrin on ORL and PR resulted in a resistance ratio of 61 , for etofenprox 70 , for deltamethrin 695 , and for DDT 49 ( Table 1 ) . Transfluthrin was omitted from these analyses because its response was variable in this assay , with slope values that did not differ significantly from zero .
Results from both the topical toxicity assays and the blood-feeding assays showed a rightward shift in the dose-response curves for the Puerto Rican strain of Ae . aegypti , which indicated higher concentrations of pyrethroid chemicals are necessary to deter the pyrethroid-resistant mosquitoes . This observation was true of all the compounds investigated , although the size of the shift varied by chemical treatment . As expected , there was significant resistance to permethrin in the PR strain , as permethrin resistance was previously documented in wild Puerto Rican mosquitoes aged 1 day with the standard WHO paper test [12] . Reid et al . [20] found a topical resistance ratio of 73 for permethrin for this strain of Ae . aegypti aged 2–5 days , and 33 of 164 cytochrome P450s tested were found to be significantly upregulated . When pre-treated with piperonyl butoxide , the permethrin resistance ratio was reduced to 15 , suggesting that oxidative metabolic resistance accounted for about 5-fold of the resistance noted in the PR strain . Moreover , at least two kdr mutations were detected for this strain: Phe1534Cys and Val1016Iso [21] . These two mutations , associated with pyrethroid resistance in Ae . aegypti , are widespread in Latin America and the Caribbean [22] . More recent work with these strains by Estep et al . [21] found resistance ratios for permethrin ( 135 ) and DDT ( 19 ) which agree with the results found in this study for permethrin ( 112 ) and DDT ( 16 ) . Resistance ratios for blood-feeding with permethrin and etofenprox are similar , but much lower than for their respective topical toxicities . The insecticides were applied directly to the mosquitoes for the topical toxicity assay and evaluated for lethality 24 hrs later , whereas , in the blood-feeding assay , knockdown and excitorepellent action were thought to play the dominant role in blood-feeding deterrence . Consequently , it is logical to conclude that the resistance ratios in the blood feeding assay would primarily reflect the presence of kdr , while the much greater lethality ratios in the topical toxicity assay would include the additional impact of metabolism and perhaps other mechanisms , such as penetration . Etofenprox was overall a less effective insecticide than permethrin , as shown by the higher LD50 and ED50 results ( Table 1 ) . It also has a lower acute mammalian oral LD50 of >10 , 000 mg/kg compared to 500 mg/kg for permethrin [23] . Based on the acute toxicity of the active ingredients , the safety factor ( oral LD50 for a 10-kg child /amount required for the treatment of a single bednet ) is higher for etofenprox ( 133 ) compared to permethrin ( 0 . 7 ) [23] . As a fabric treatment , etofenprox may therefore have some advantages because of its lower mammalian toxicity , allowing for more chemical to be used safely . For surface contact exposures to the mosquito , an increase in the dosage rate would likely negate the reduced efficacy of etofenprox . However , since etofenprox had similar efficacy to permethrin in the blood-feeding assay , an increase in dosage as a fabric treatment may provide increased protection from blood-feeding for pyrethroid-resistant strains of mosquitoes . Resistance ratios for blood-feeding with deltamethrin were comparable to its respective topical toxicities . Despite having the largest resistance ratio between the two strains in both assays , deltamethrin had the lowest amount of chemical needed for the LD50 topical treatment against the resistant strain of all the treatments examined . On a per gram basis , deltamethrin was the most cost effective treatment against both the susceptible ORL strain and the resistant PR strain of all the treatments examined , based on the ED50 quantities and an active ingredient cost comparison [24] . The resistance ratios of deltamethrin were similar in both assays . As a Type II pyrethroid , deltamethrin is known to have some excitorepellent properties in Ae . aegypti and Anopheline mosquitoes , but less so when contact irritancy is absent [25 , 26 , 27 , 28 , 29] . Resistance ratios for both blood-feeding and topical toxicity were low for DDT , and of about the same magnitude . However , the negative public perception and current banned status of DDT limits its usefulness as either a spray or fabric treatment for mosquito control in the US , although it is World Health Organization Pesticide Evaluation Scheme ( WHOPES ) -approved for malaria control [10 , 11] . Both the greater mosquito activity , and lower mammalian toxicity of pyrethroids makes them more attractive than DDT for widespread use , and ideally , insecticides of a different chemical class and with a different mode of action should be used in areas with ongoing pyrethroid resistance , if possible . Although transfluthrin was initially examined for both topical toxicity and blood-feeding behavior , only the topical toxicity data are reported here . In the topical assay , transfluthrin was much less active than the other pyrethroids . This may be due to its rapid volatilization at ambient temperature , characteristic of a spatial repellent [30 , 31] , which may have volatilized the small volumes of active ingredient used in topical treatment off of the cuticle before it could be absorbed . Another study by Wagman et al . [32] found that Ae . aegypti mosquitoes insensitive to pyrethroid repellents and containing the Val1016Iso kdr mutation also displayed decreased toxicity to transfluthrin and that this trait was heritable . Typically , there is a biological fitness cost to an organism associated with maintaining resistance mechanisms in the absence of an exposure [33] . This is especially true for multiple resistance mechanisms , as in this case with pyrethroid and carbamate insecticides , unless there is consistent exposure to both of these chemical classes . Evidence for an increase in multi-resistance has been noted as control programs make sequential use of one chemical class after another [1] . Interestingly , a study by Saavedra-Rodriguez et al . [34] with Ae . aegypti showed that the lineages with the highest frequencies of the kdr mutation resulted in a lower number of altered detoxifying genes . These results strongly suggest that this kdr mutation had a lower fitness cost compared to the metabolic resistance genes [33] . Specific applications of this work would apply most directly to military uniforms , which currently use only permethrin as a clothing treatment [35] . However , commercially available clothing for outdoor use is also limited only to permethrin [35] , while insecticide-treated bednets are limited to permethrin , cypermethrin , and deltamethrin [36] . Future work should examine additional insecticides of different chemical classes and with different modes of action to be used in areas with ongoing pyrethroid resistance . | Aedes aegypti is a competent vector of mosquito-borne diseases and is the primary transmitter of yellow fever , zika , chikungunya , and dengue viruses . Through repeated insecticide treatments over the years , many populations of mosquitoes have developed resistance . Pyrethroid resistance is widespread in Aedes aegypti ( L . ) and has been problematic in Puerto Rico for decades . Using a pyrethroid-susceptible and a pyrethroid-resistant strain of Ae . aegypti , we created dose-response curves for feeding behavior using fabric treated with four distinct but related insecticides . Resistance ratios were calculated by dividing the LD50 values for the resistant strain by the susceptible LD50 values . We also calculated resistance ratios based on topical treatment , since the total amount of insecticides that the mosquitoes were exposed to in the topical assay could be controlled and compared to the blood-feeding assay . Interestingly , the resistance ratios for the blood-feeding were similar for permethrin and etofenprox , but lower than their respective resistance ratios for topical toxicity . Results from these assays showed a shift in the dose-response curves for blood-feeding amongst the susceptible and resistant strains , which indicated higher concentrations of pyrethroid chemicals necessary to deter blood-feeding behavior in the pyrethroid-resistant Puerto Rican strain of Ae . aegypti . | [
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"v... | 2017 | Pyrethroid resistance alters the blood-feeding behavior in Puerto Rican Aedes aegypti mosquitoes exposed to treated fabric |
Successful tumor development and progression involves the complex interplay of both pro- and anti-oncogenic signaling pathways . Genetic components balancing these opposing activities are likely to require tight regulation , because even subtle alterations in their expression may disrupt this balance with major consequences for various cancer-associated phenotypes . Here , we describe a cassette of cancer-specific genes exhibiting precise transcriptional control in solid tumors . Mining a database of tumor gene expression profiles from six different tissues , we identified 48 genes exhibiting highly restricted levels of gene expression variation in tumors ( n = 270 ) compared to nonmalignant tissues ( n = 71 ) . Comprising genes linked to multiple cancer-related pathways , the restricted expression of this “Poised Gene Cassette” ( PGC ) was robustly validated across 11 independent cohorts of ∼1 , 300 samples from multiple cancer types . In three separate experimental models , subtle alterations in PGC expression were consistently associated with significant differences in metastatic and invasive potential . We functionally confirmed this association in siRNA knockdown experiments of five PGC genes ( p53CSV , MAP3K11 , MTCH2 , CPSF6 , and SKIP ) , which either directly enhanced the invasive capacities or inhibited the proliferation of AGS cancer cells . In primary tumors , similar subtle alterations in PGC expression were also repeatedly associated with clinical outcome in multiple cohorts . Taken collectively , these findings support the existence of a common set of precisely controlled genes in solid tumors . Since inducing small activity changes in these genes may prove sufficient to potently influence various tumor phenotypes such as metastasis , targeting such precisely regulated genes may represent a promising avenue for novel anti-cancer therapies .
The accurate processing and integration of multiple external signals is a common feature of biological networks in normal health and complex disease . As illustrated by the examples of oxygen handling [1] , energy control [2] , and ion homeostasis [3] , such accuracy frequently involves the precise coordination of multiple cellular pathways , and mechanisms for regulating and balancing opposing activities . In cancer networks , many similar requirements for pathway balance are likewise found as successful tumorigenesis requires the robust integration of both pro- and anti-oncogenic pathways controlling cellular proliferation , apoptosis , motility , adhesion and senescence [4] , [5] . The importance of balancing opposing activities in cancer is illustrated by genes such as HEF1 ( NEDD9 ) , a metastasis-related gene [6] and HMMR , a gene involved in centrosome formation ( Pujana et al . 2007 ) . Either repression or overexpression of HEF1 can cause mitotic defects [7] , [8] , indicating that its activity in tumors requires tight regulation . Similarly , subtle alterations of HMMR expression in normal mammary tissues may promote breast tumorigenesis , underscoring the need to keep the HMMR gene tightly regulated [9] . Such findings support the notion that balancing the activity of positive and negative effectors is likely to be a central requirement of many cancers . At the systems-level , pathway balance is often facilitated through the use of network structures [10] conveying robustness to random fluctuations and errors [11]–[14] . However , the pivotal balancing role played by certain genetic components may at least partially explain why some networks also exhibit ultrasensitivity – a phenomena where small changes in activity at specific components can suffice to elicit qualitative changes in output [12] , [13] . Ultrasensitivity may contribute to a network's ability to rapidly respond to changing environmental and genetic conditions [15] , [16] . Intriguingly , there is emerging evidence that certain cancers can also display ultrasensitivity . Some remarkable examples include the dramatic responses of chronic lymphocytic leukemia cells to colchicines , occurring at concentrations 10 , 000-fold lower than that required for similar effects on normal lymphocytes [17] , [18] , and the striking clinical responses of certain solid tumors to targeted pathway inhibitors [19] . From a therapeutic perspective , such ultrasensitive components could prove particularly appealing as drug targets , as even small alterations might prove sufficient to induce potent effects on tumor phenotypes such as tissue invasion and metastasis . However , our current understanding of the role that ultrasensitivity plays in cancer is still far from complete . Identifying additional genetic components regulating pathway balance in tumors might thus improve our ability to target critical control nodes in cancer networks . As a general strategy to identify ultrasensitive components in tumors , we hypothesized that a ) such components should be precisely regulated and thus exhibit restricted levels of expression variation in cancers; and b ) subtle alterations in the expression levels of these components should induce or be associated with significant phenotypic changes . We then applied these criteria to determine if such precisely-regulated genes might be inferred from databases of tumor gene expression profiles . While several groups have compared the expression profiles of multiple tumor and non-malignant tissues [20] , [21] , to our knowledge , no study to date has systematically attempted to investigate the issue of precise gene regulation in tumors . Employing a genome-wide computational strategy , we identified and robustly validated a novel “Poised Gene Cassette” ( PGC ) of genes undergoing precise regulation in a microarray database of human tumors from diverse tissue types . Furthermore , subtle alterations in PGC expression were associated with significant and measurable alterations in important tumor phenotypes such as experimental metastasis and patient survival . Our results thus suggest the existence of a generalized homeostatic mechanism in solid tumors for maintaining precise levels of PGC transcription , which may be important for various cancer-associated phenotypes , such as tissue invasion and metastasis . Importantly , the approach described in this study is quite generalizable and can be applied to other diseases .
We hypothesized that genes precisely regulated in cancer should exhibit a highly restricted level of gene expression variation across a large database of individual tumor gene expression profiles . To investigate this , we generated gene expression profiles for 270 primary tumors from six tissue types ( breast , colon , liver , lung , oesophageal and thyroid ) using Affymetrix U133A Genechips . For every gene , we computed gene expression coefficient of variances ( CV ) , where genes with small CVs are considered more tightly regulated than genes with large CVs . We focused on the top 15% most tightly-regulated genes in tumors , corresponding to an empirical CV cut-off of 0 . 28 . To identify genes whose tight regulation was tumor-specific , we used a second database of 71 adjacent matched non-malignant tissues ( “control” tissues ) to eliminate from this 15% genes that were also tightly regulated in non-malignant samples ( CV>0 . 3 ) . The use of an absolute CV threshold is permissible , as the global distribution of expression CVs between tumors and controls were highly similar ( mean CVs were 0 . 46 and 0 . 45 for tumors and controls ) ( Figure 1A ) . Using this criterion , we identified a “Poised Gene Cassette” ( PGC ) of 48 genes exhibiting highly restricted levels of expression variation in tumors ( Figure 1B ) . The F-test , a statistical method for comparing the variation of different data sets , confirmed that each of the 48 PGC genes was indeed associated with significantly decreased expression variation in tumors relative to controls ( one tailed F-test , p = 0 . 0001 to 4×10−14 ) . We also varied the CV threshold between 0 . 26–0 . 3 ( +/−7% ) and repeated the analysis . Similar results were obtained ( Table S1 ) , indicating that the identification of PGC is not dependent on a particular CV threshold . We investigated whether the reduced expression variation of the PGC might be due to technical features of the Affymetrix platform or the composition of the initial training set . We considered the possibility that the reduced variance of the PGC might be due to an overabundance of ‘poor quality’ probes , which might be expected to cross hybridize with multiple genes and hence generate higher background signals [22] . However , an examination of a vendor provided list of questionable probes ( i . e . , with ‘_s_at’ and ‘_x_at’ suffixes ) , confirmed that the PGC was not significantly enriched in poor quality probes ( p = 0 . 4 ) . In addition , a comparison of the PGC genes against an in-house curated list of unreliable array probes based on sequence redundancy and repeat mapping [23] confirmed that unreliable probes were not overrepresented in the set of PGC genes ( p = 0 . 8 ) . To investigate the influence of normalization protocol on PGC discovery , we re-processed the training set using a different normalization method ( RMA , [24] , [25] ) . In the RMA-normalized data , we found that 90% of the original PGC genes still exhibited decreased expression variation in tumors relative to controls ( i . e . , CV ( control ) >CV ( tumors ) ) ( Figure 2A ) . Thus , the tumor-specific restricted expression variation of the PGC does not appear to be dependent upon a specific normalization technique . The reduced variation of the PGC is also not due to an overrepresentation of either high-expressing or low-expressing genes . As shown in Figure 2B , the PGC genes were equally distributed across a wide range of expression levels and not confined to either low or highly expressing genes in tumors or control tissues . Thus , the reduced expression variation of the PGC in cancers is unlikely to be due to the PGC genes simply being either highly expressed , rendering the PGC distinct from some studies suggesting an inverse correlation between expression variation and absolute expression levels [26] . Similarly , the PGC is also not biased in lowly expressed genes , consistent with our original selection criteria requiring these genes to be reliably detected in the majority of samples ( see Methods ) . It is also important to note that the PGC genes do not exhibit significant differences in their absolute mean expression levels between cancers and normal tissues ( Figures 1B and 2B ) , but instead only differ in their levels of expression variation between cancers and normal tissues . This observation , as well as others , also provides an argument that the PGC genes are unlikely to represent tissue-specific expression ( see Discussion ) . The discovery of the PGC is also not influenced by the overrepresentation of breast tumors in our initial training set ( breast tumors comprised 68% of the training set ) . Specifically , we removed all the breast tissues and repeated the PGC analysis . Even without inclusion of breast tissues , 83% ( 40/48 ) of the PGC genes still exhibited reduced variation in tumors compared to controls . Of 47 genes exhibiting tumor-specific tight regulation in the breast-excluded data ( CV<0 . 28 ) , 24 genes were part of the original PGC , an overlap far beyond random chance ( 50% , p = 1 . 3E-11 , hypergeometric test ) . Taken collectively , these results suggest that the identification of the PGC , and its restricted expression variation in cancers , is unlikely to be due to a technical artifact or the inclusion of a specific cancer type . To confirm that the restricted expression variation of the PGC was specifically associated with malignancy , we determined the frequency at which a member gene of the PGC could be re-identified in a series of class-permutation tests . When the class labels of the samples ( i . e . , tumor or control ) were shuffled to generate a series of 1000 permuted sets , almost all the PGC genes ( 46/48 , 96% ) could only be re-identified in less than 5% of the class-permuted signatures , consistent with the decreased expression variation of the PGC being tightly associated with tumor samples . We then evaluated the robustness of the PGC by repeated random sampling ( RSS ) , a stringent cross-validation strategy [27] . The original training set was randomly divided 1000 times into two parts , generating a large series of distinct training/test set combinations . For each of the 1000 derived RSS training sets , we identified new PGC signatures ( rPGC ) and compared them to the original PGC gene set . Following the guidelines of Michels et al [27] , 20 genes were repeatedly selected in more than half of the 1 , 000 new rPGC signatures . Of these 20 genes , 19 ( 95% ) are members of the original 48-gene PGC ( Figure 2C ) – the observation that only one gene not part of the original PGC signature was repeatedly selected in the RSS assay indicates that a substantial proportion of the PGC signature ( 40% ) is robust to training set selection . To evaluate the transportability of the PGC signatures , we then applied each of the 1000 rPGC signatures to their cognate test sets . In anticipation that most independent test sets are likely to contain either tumor or control samples but not both , we considered the tumors and controls separately from one another in this analysis . In each test set , we checked if the population of tightly regulated genes , defined using the original CVT threshold ( 0 . 28 ) , contained a significant enrichment of rPGC genes ( see Methods ) . The rPGC signatures were significantly enriched in the population of tightly regulated genes in 80% of the tumor test sets ( PGC→T , Figure 2D ) , and importantly were NOT significantly enriched in 100% of the control test sets ( PGC→N , Figure 2D ) , indicating that the PGC is robust in recapitulating its precise regulation in multiple tumor data sets , but not data sets of non-malignant samples . Together , these results confirm the specificity of the PGC for tumors . We then asked if the precision of PGC regulation in cancer could be observed in independent data sets of diverse tumors . We collected nine independent cancer cohorts , comprising in total 1105 cancer samples from >7 primary tissue types [28]–[32] , including I ) four tissues not represented in the original training data ( gliomas , gastric , NPC , and ovarian ) , II ) one data set ( Yu_Gastric&NPC ) representing a mix of two different tissues , and III ) a collection of cancer cell lines ( NCI60 ) from nine different tissues . A summary of these nine data sets can be found in Table S2 and the corresponding references . Using a similar strategy to the RSS test sets , a significant fraction of the PGC genes were tightly regulated in all nine primary tumor data sets ( p-value range: 0–0 . 002 ) ( Table 1A ) , confirming the existence of the PGC in a wide variety of solid tumors . In total , 19 out of 48 PGC genes repeatedly exhibited reduced expression variation in more than half of the 9 cancer test sets ( Table S3 ) . We also performed the reciprocal experiment and evaluated the regulation of the PGC in a series of independent non-malignant samples . Although such datasets are rarer in their availability and typically smaller than cancer datasets , we collected two distinct cohorts comprising 115 normal tissues from various organs [33] , [34] . Notably , these non-malignant samples were obtained from healthy donors , and are thus free of malignancy and representative of true normal samples . In stark contrast to the cancer data sets , the PGC genes exhibited either no or only a marginal degree of tight regulation in the normal data sets ( p = 0 . 07 and 0 . 01; Table 1B ) . Thus , these results indicate that the precise regulation of the PGC genes is largely restricted to cancer tissues , suggesting that diverse tumor types may harbor a general requirement for tightly regulating PGC expression . A pathway analysis revealed multiple highly significant interactions between the PGC genes and prevalent tumorigenic pathways . The top-scoring molecular network for the PGC comprised 11 PGC focus genes interacting either directly or indirectly with the well-known cancer-related transcription factors Myc and TP53 ( p = 10−19 , see Methods ) ( Figure S1 ) , and the most significantly enriched cellular functions in the PGC were cancer ( p<0 . 0045 ) , tumor morphology ( p<0 . 0045 ) and cell cycle control ( p<0 . 0045 ) . The PGC was also significantly enriched in components related to integrin signaling ( p = 2 . 33E−04; Figure S1 ) , a complex signaling pathway implicated in both positive and negative regulation of tumor cell growth and cancer metastasis . Besides integrin signaling , other individual PGC genes , such as RPS2 and RPL7A , have also been previously implicated in the control of cellular transformation , tumor growth , aggressiveness , and metastasis [35] , [36]; while the PGC gene MUS81 has recently been reported to interact with p53 to maintain genome stability [37] . Thus , an array of biological and functional evidences suggest that the PGC genes are likely to be involved in the activity of multiple cancer-related pathways , and not ubiquitous ‘housekeeping’ cellular functions . The full list of PGC genes is provided in Table S3 . The tightness of PGC regulation in tumors might be explained if small alterations in the expression levels of these components are sufficient to cause significant phenotypic changes in tumors . We employed three experimental assays to address this possibility . First , we analyzed a set of colon cancer cell lines derived from either primary tumors or distant metastases from the same patient ( SW480 and SW620 ) , which have been shown to exhibit several phenotypic differences including metastatic potential [38] , [39] . Using Gene Set Enrichment Analysis ( GSEA , [40] ) , we found that PGC expression was subtly yet significantly decreased in highly metastatic SW620 cells compared to poorly metastatic SW480 cells ( p<0 . 001 , Table S4 ) . Second , we then analyzed patterns of PGC expression in a cohort of 30 breast cancer cell lines , where the invasive capacity of each line had been previously measured by matrigel assays [32] . The PGC genes exhibited minimal expression variation across the lines when assessed using a standard range of expression variation , consistent with their being tightly regulated in cancers ( Figure 3A , left heat-map ) . However , when the scale of variation was amplified , we identified by hierarchical clustering two groups of cell lines showing either subtly higher or lower levels of PGC expression ( Figure 3A , right heat-map ) . Importantly , we again found that the majority of cell lines with high to moderate invasive abilities exhibited subtle yet significant decreased expression of the PGC genes compared to poorly invasive lines ( p = 0 . 04 , chi-square test , sample groups defined on the basis of the top-level branch point ) . To validate the robustness of this clustering by an alternative method , we then also performed independent k-means clustering ( k = 2 ) . Using k-means , 7 out of 8 highly invasive cell lines were clustered into one group together with 4 marginally or non-invasive cell lines ( p = 0 . 01 , chi-square test for high vs . marginal/non-invasive ) , consistent with the groupings observed by hierarchical clustering . Third , we conducted in vivo experiments using a murine xenograft model of metastasis , where poorly metastatic HCT116 colon cancer cells were injected into the spleens of nude mice , and metastatic liver tumor nodules were harvested 6 to 8 weeks later . The liver nodules were expanded in culture and re-passaged in mice to generate a panel of lines ( M1 , M2 , and M3 ) with increasing levels of metastatic capacity ( Figure 3B ) . Examining the gene expression profiles of these lines , we found that highly metastatic cells once again exhibited subtly decreased PGC expression compared to poorly metastatic HCT116 cells ( p = 0 . 03 , Figure 3B and Table S4 ) . These results , based on three different experimental models of metastasis , collectively suggest that small alterations in PGC expression in tumors may be associated with potent differences in tumor physiology , specifically metastatic and invasive capacity . To directly demonstrate the functional role of PGC genes in cellular invasion , we performed siRNA experiments where five PGC genes ( p53CSV , MAP3K11 , MTCH2 , CPSF6 , SKIP ) were silenced in poorly-metastatic AGS gastric cancer cells . While p53CSV is a gene required for p53-mediated cell survival [41] , its role in cancer is otherwise poorly understood . Furthermore , associations between MAP3K11 , MTCH2 and CPSF6 to cancer have also not been previously reported . The siRNA treatments reduced the expression levels of these five PGC genes from 45%–80% , as assessed by quantitative real-time PCR ( Figure 4A ) , and reductions in p53CSV , MAP3K11 , MTCH2 and CPSF6 resulted in a significant enhancement of in vitro invasive activity as measured in a matrigel assay ( p<0 . 01 , one-tailed t-test , Figures 4B and 4C ) . Furthermore , SKIP siRNA treatment resulted in a significant inhibition of cellular proliferation in AGS cells ( p<0 . 01 , Figure 4D ) . It is worth noting that for at least two genes ( p53CSV and CPSF6 ) , a partial reduction of gene expression of 45–60% was able to trigger a significant change in invasive phenotype . To further demonstrate the generality of this phenomenon , we then knocked down p53CSV in another poorly-metastatic colon cancer cell line , HCT116 which we previously utilized in the xenograft assay . Again , the partial silencing of p53CSV expression significantly increased the invasion activity of HCT116 cells ( Figure S3 ) . These results suggest that the PGC genes may play roles in regulating cancer invasion and metastasis . To extend the potential role of precise PGC regulation to the clinical context , we asked if similar small changes in PGC expression might be associated with significant differences in patient survival and clinical outcome . We employed hierarchical clustering to group the tumors in each of the six data sets with survival data available by their overall level of PGC expression . A representative example is shown in Figure 5A . Once again , the PGC genes exhibited minimal expression variation across the tumors when assessed on a standard scale of expression variation , consistent with their being tightly regulated in tumors ( Figure 5A , left heat-map ) . However , when the variation scale was amplified , we identified two groups of tumors showing either subtly higher or lower levels of PGC expression ( Figure 5A , right heat-map ) . Remarkably , a Kaplan-Meier survival analysis revealed that in all six data sets , patients with tumors expressing PGC levels below the population average experienced significantly worse survival outcomes compared to patients with high-PGC expressing tumors ( Figure 5B; all cases p<0 . 05 except in ovarian cancer set where p = 0 . 057 , see Figure S4 for clustering groupings ) . We only observed comparable survival stratifications across the six data sets in 46 out of 10 , 000 randomly selected 48-member gene sets , arguing that the prognostic ability of the PGC is statistically unique . In a multivariate analysis , PGC expression behaved as an independent prognostic factor compared to other clinical variables in the breast and colon cancer cohorts , and was associated with tumor stage in ovarian , lung and glioma cancer patients ( Table S5 ) . Importantly , the PGC exhibits very little overlap with other expression signatures reported to predict clinical behavior in multiple tumor types . A comparison of the PGC against a 128-gene metastasis signature ( MS ) [42] , a 70-gene chromosomal instability signature ( CIN70 ) [43] , a cell cycle module [44] , a wound response healing signature [45] , [46] , and multiple cell proliferation-related signatures ( 57–59 ) including a 874-gene cell cycle gene signature ( CPS ) [47] , revealed that there was no direct overlap in gene content between the PGC and these other “multi-tumor” gene signatures , except for a one-gene overlap with the CIN70 , and a four-gene overlap with the CPS , which was not statistically significant . This finding suggests that the specific gene content of the PGC is distinct from other previously described signatures . To ask if the PGC might target the same “poor prognosis” tumors as other published signatures capable of predicting clinical outcome in multiple tumor types , we then investigated the ability of the MS , CIN70 , and CPS to stratify patient survival in the six data sets - none of these signatures exhibited comparable prognostic significance to the PGC across the six patient cohorts ( data not shown ) . These observations suggest that the PGC is likely to target different molecular features and types of tumors than the aforementioned signatures . Previous gene expression studies comparing tumors and non-malignant tissues have typically employed microarray analysis algorithms such as t-tests with false positive correction or SAM [20] . Genes detected by such techniques typically require both differing mean expression levels and equivalent levels of variation between two cellular states ( Figure S5 ) . However , the PGC might not be detected by such conventional techniques , as PGC genes might not exhibit distinct mean expression levels between the two groups and only be associated with differing degrees of expression variation between tumors and controls ( Figure S5 ) . Indeed , performing SAM and t-tests on the training set only identified 27% of the original PGC , after multiple hypothesis correction , and the absolute mean expression levels of many PGC genes between tumors or non-malignant tissues were highly similar ( Figure S5 ) . To ask if the unequal distributions in expression variation might underlie the failure of the PGC genes to be identified by conventional techniques , we also analyzed the original training data set using Welch's test , an adaptation of Student's t-test intended for use with two groups having unequal variance . Again , 75% of the PGC genes failed to be detected as significant using Welch's test ( data not shown ) . These findings suggest that conventional algorithms would likely have failed to detect the PGC , thereby providing a partial explanation as to why the PGC might have been missed in previous studies .
In this study , we identified a novel cassette of genes exhibiting tumor-specific precise regulation in multiple cancer tissues . Our ability to discern the PGC was facilitated by the use of an analysis method focused on expression variance rather than expression levels . The reduced variance of the PGC in tumors is unlikely to be a technical artifact of the Affymetrix platform , as it was not related to probe selection , data normalization , absolute high or low expression levels in either tumors or non-malignant tissues , or sample set . Using both rigorous cross-validation ( RSS ) and multiple independent validations , we found the PGC to be robust to alterations in training set composition and repeatedly observed in diverse malignant tumor types , including several tissue types not present in the original training data . Importantly , the PGC failed to demonstrate tight regulation in several non-malignant tissue data sets , arguing that its control is cancer-specific . Interestingly , even though it was not a specific requirement in our initial analysis , the majority of PGC genes exhibited similar mean expression levels in both tumors and non-malignant tissues . This absence of a distinct difference in mean expression values resulted in the failure of standard microarray analysis methods ( e . g . , t-test ) to detect the majority of PGC genes when applied to the same training data set . Furthermore , a standard practice in microarray data processing is to filter out genes exhibiting low variation prior to clustering or statistical analysis - such filtering would inevitably lead to a bias towards differentially expressed genes and prevent the discovery of the PGC . One potential concern might be that the PGC genes simply reflect the activity of tissue-specific gene expression . However , five findings argue against this possibility . First , while dedifferentiated cancer cells frequently exhibit a loss of tissue-specific gene expression ( Rhodes et al . 2004 ) ; such a loss would typically result in tissue-specific genes being down-regulated in their absolute expression levels compared to normal tissues . In contrast , the PGC genes do not exhibit significant differences in their absolute expression levels between cancers and normal tissues ( Figure 2B ) . Second , the reduced variation of the PGC genes was consistently observed in multiple independent sets from diverse tissues ( e . g . gliomas , lung , breast ) , including a data set ( NCC ) that combined tissues from two different sources ( gastric and NPC tumors ) . Third , the PGC genes also showed reduced expression variation in the NCI60 test set - a mixture of cancer cell lines from 9 different tissue types . Fourth , the PGC genes consistently exhibited reduced expression variation in the repeated random sampling ( RSS ) cross-validation assay , where we tested 1000 distinct training set and independent test sets composed of mixed tissue types ( Figure 2D ) . Fifth , even within each of the six tissue types in the training set ( liver , colon , esophagus , thyroid , lung , and breast ) , the majority of the PGC genes ( 70% ) are not differentially expressed within tumors and normals ( p>0 . 01 , t-test ) ( YK , data not shown ) . Taken collectively , it is unlikely that the consistency of the PGC would have been observed if its reduced expression variation was solely due to tissue-specific expression , supporting the notion that the PGC genes are likely to be distinct from the conventional differentially expressed gene signatures described in most microarray studies . One possible explanation for why certain genes may require precise control is if they regulate or are involved in balancing disparate downstream pathways possessing mutually opposing activities . In cancers , the successful establishment of a malignant tumor involves multiple pro- and anti-oncogenic forces involved in cell proliferation , apoptosis , cell death , senescence , cell adhesion , and motility , all of which require delicate balance by different genetic components . For example , while loss of Ras signaling is lethal , aberrant signaling through this pathway is important for cancer development but can also drive cells into either senescence or cell proliferation , depending on cellular context [48] , [49] . Another good example is the anti-apoptotic gene Akt/PKB ( protein kinase B ) , which when constitutively activated reduced metastases in mice and inhibited the invasion of breast cancer cells [50] , [51] , indicating its involvement in multiple cancer pathways . Reassuringly , similar examples of balanced coordinator genes are also seen in the cohort of PGC genes . The PGC gene FUS1 ( also known as FUS ) has been reported as a tumor suppressor gene in lung and breast cancer [52] and a pro-oncogene in leukemia [53] . Oxidative stress , which may play an important role in cancer progression and the regulation of cancer metastasis [54] , is dependent upon the critical balance between intracellular hydrogen peroxide H2O2 and superoxide O2− . Two PGC genes - p53CSV and KIAA0247 have been reported to be induced in response to oxidative stress [55] , and may influence this balance and the response of tumor cells to apoptotic stimuli [56] . It is also worth noting that the PGC was significantly overrepresented in components of the integrin signaling pathway – a highly complex process involving multiple related family members with roles in many cellular functions , including ERK/MAPK and JNK/SAPK regulated gene expression , cell motility , cytoskeletal interactions , and PI3K and Wnt pathway signaling [57] . In metastasis , integrins are crucial for cell invasion and migration , not only for physically tethering cells to the matrix , but also for sending and receiving molecular signals regulating these processes [57] . Moreover , while some groups have proposed that increased integrin expression could promote malignant behavior by enhancing tissue stiffness [58] , other groups have suggested that loss of integrins may promote tumor invasion and metastasis [59] . The complexity of integrin family members and their pathway components also provides a plausible explanation for why even subtle alterations in PGC expression are associated with distinct and measurable changes in metastatic behaviour in both experimental models of metastasis and clinical outcome . What might be the mechanistic basis of precise PGC regulation ? At a general level , many precisely-regulated genes are likely to possess complex regulatory systems for tightly controlling expression levels , to rapidly sense and adapt to dynamic perturbations in both the internal and external environment [60] . Such mechanisms could involve the use of both positive and negative feedback loops , analogous to the circuitry utilized by the LacI/O bacterial system to ensure precise expression [61] , but in cancers could also involve eukaryote-specific mechanisms like epigenetic modifications ( DNA methylation or chromatin modifications ) , microRNA regulation , or transcription factor binding . Interestingly , in a preliminary analysis , we attempted to extend our observations from the pathway analysis showing an association of several PGC genes with both Myc and TP53 . Specifically , we investigated whole-genome transcription factor binding data for Myc and TP53 [62] , and found that the PGC genes were weakly but significantly associated with Myc binding sites under Myc-overexpressed ( tumorigenic ) conditions ( p = 0 . 04 ) but not under physiological conditions ( p = 0 . 3 ) ( Table S6 ) . These preliminary results raise the possibility that transcription factor binding , specifically Myc binding , may constitute one possible mechanism for PGC regulation in cancer cells . However , deciphering the mechanism of PGC regulation will undoubtedly require further research . Cancers have been proposed to possess robustness mechanisms for protection against various therapeutic perturbations and naturally occurring microenviromental ( e . g . , hypoxia ) and immune responses . However , many complex systems have evolved to exhibit a ‘robust yet fragile’ structure [63] , [64] , and it has been proposed that studying mechanisms of cancer-specific robustness and accompanying fragilities might prove useful for the development of novel targeted therapies [65]–[67] . The PGC gene cassette reported here may indicate such fragilities in the network of tumor cells , as subtle alterations on these components significantly affected the cellular behavior of cancer cells . Beyond cancer , this approach is conceptually applicable and easily transportable to other disease conditions where gene expression data is available . It will be interesting to explore if the approach will also prove informative in identifying genes and pathways with important roles in other human pathophysiologies .
The training data set contained 270 primary human tumors ( Lung = 18 , Thyroid = 35 , Liver = 9 , Esophagus = 16 , Colon = 9 , Breast = 183 ) and 71 adjacent non-malignant tissues ( Lung = 12 , Thyroid = 16 , Liver = 8 , Esophagus = 13 , Colon = 9 , Breast = 13 ) obtained from the Tissue Repository of the National Cancer Centre of Singapore ( NCCS ) . The phrase ‘non-malignant’ instead of ‘normal’ was used to describe the control tissues in the training set , as they were also obtained from cancer patients . Institutional approvals were obtained from the NCCS Tissue Repository and Ethics Committees . Descriptions of sample collection protocols , archiving , and histological assessments are presented in the Text S1 . RNA was extracted from the tissues using Trizol reagent ( Invitrogen , Carlsbad , CA ) and processed for microarray hybridizations on Affymetrix U133A Genechips according to the manufacturer's instructions ( Affymetrix Inc . , Santa Clara , CA ) . The expression data has been deposited into the Gene Expression Omnibus ( GEO ) database ( GSE5364 ) . Raw Genechip scans were processed using either the MAS5 algorithm ( Affymetrix ) normalized by median-centering ( GeneData , Basel , Switzerland ) , or by robust multiple chip analysis ( RMA ) [24] , [25] ( see Results ) . To identify reliably measured genes , we discarded probes with <80% present values ( P-call <80% ) across the training set samples . For genes with multiple probes , we selected the best-match probes ( to targets ) represented by a “_at” extension . For genes with multiple “_at” extension probes , the probe with the highest P-call rate ( i . e . , the highest valid value proportion ) was used . The final pre-processed training set comprises 5729 unique genes , each represented by a single probe . Gene expression CVs ( standard deviation divided by the mean expression level ) were used to compute the variability of expression for each gene . Based on the global distribution of CVs in the training set , we selected an empirical threshold of CVT = 0 . 28 below which a gene was considered to be tightly regulated ( see Results ) . Prior to comparing gene CVs between populations , we also confirmed that the global CV distributions for different sample cohorts ( i . e . , tumor or non-malignant ) were similar . To estimate the probability that the PGC signatures might be generated by chance , we randomly shuffled the class labels ( i . e . , tumor or non-malignant ) of the training set to generate multiple class-permuted sample sets and determined the frequency a particular PGC gene could be re-identified in situations where the sample labels were shuffled . Repeated Random Sampling ( RRS ) , a rigorous cross-validation strategy described in [27] , was also used to determine the influence of particular training set compositions on selecting specific signature genes . Detailed descriptions of the class permutation and RSS tests are provided in the Text S1 . The hypergeometric distribution was used to test if the PGC genes were significantly over represented in the population of tightly controlled genes in each test set . First , we identified genes exhibiting tightly controlled expression in the test set , using the CVT threshold cut-off ( CV ( Test ) <CVT ) . Second , we determined the overlap between the PGC gene signatures and the population of tightly regulated genes in the test set , and the hypergeometric distribution test was used to calculate the significance of the overlap . Significance was defined as p<0 . 01 . We used Ingenuity Pathway Analysis ( IPA , Ingenuity Systems ) to identify molecular networks , cellular functions , and signaling pathways associated with the PGC . The various networks were displayed as nodes ( genes ) and edges ( biological relationships between genes ) . We also used IPA to identify cellular functions and signaling pathways that were significantly enriched in the PGC . The significance of a pathway association is reflected by a Fisher's exact test p-value , indicating the likelihood that the pathway would have been identified by random chance . AGS gastric cancer cells and HCT116 colon cancer cells were cultured according to American Type Culture Collection ( ATCC ) recommendations . Cells were transfected with either siRNA pools of representative PGC genes p53CSV , MAP3K11 , MTCH2 , CPSF6 and SKIP ( Dharmacon , Lafayette , CO ) or non-targeting siRNA controls at 100 nM concentration , using oligofectamine reagent ( Invitrogen ) at 0 and 24 hr time points , in 6 well culture plates . Gene silencing was verified by Real time PCR . Invasion assays were performed using Biocoat matrigel invasion chambers ( BD Biosciences , Bedford , MA ) as recommended by the manufacturer . 48 hrs after siRNA transfection , equal numbers of target gene siRNA transfected cells and non-targeting siRNA transfected cells were placed in the invasion chambers , and after 24 hrs cells that successfully invaded through the matrigel invasion chambers were scored . Each experiment was repeated thrice and the percentage of invasion was calculated by comparing against the non-targeting siRNA transfected cells . To assay cell proliferation , AGS cells transfected with the PGC genes and non-targeting control siRNA in 6 well culture plates were harvested at 96 hrs after siRNA transfection and counted . Experiments were performed thrice . Total RNA was reverse transcribed using Taqman Reverse Transcription Reagent kit ( Applied Biosystems , Foster City , CA ) and quantitative PCR was performed using the following Taqman probes: p53CSV ( Hs00429934_g1 ) ; MAP3K11 ( Hs00176759_m1 ) ; MTCH2 ( Hs00819318_g1 ) ; CPSF6 ( Hs00199668_m1 ) ; SKIP ( Hs00273351_m1 ) , on a 7900HT Fast Real time system ( Applied Biosystems , Foster City , CA ) . Taqman GAPDH probes ( glyceraldehyde phosphate dehydrogenase ) ( Hs99999905_m1 ) were used as internal controls . All samples were run in triplicates . ( A ) Colorectal cancer model : this comprises two colon cancer cell lines derived from either primary or distant metastases from the same patient ( SW480 and SW620 ) . SW480 and SW620 cells exhibit several phenotypic differences including metastatic potential [38] , [39] . Gene Set Enrichment Analysis ( GSEA ) was performed as described in [40] . ( B ) Breast cancer panel: this comprises a panel of 51 breast cancer cell lines for which gene expression data is available [32] , and where the relative invasive capability of 30 lines has been measured using matrigel assays [32] . ( C ) Murine assay: this comprises an in vivo passage model where poorly metastatic HCT116 colon cancer cells were injected into mouse spleens , and subsequent hepatic metastases were harvested to generate increasingly metastatic cellular variants . Details of this model are provided in the Text S1 . The animal work performed was approved by the National University of Singapore Institutional Animal Care and Use Committee ( NUS IACUC ) . Cells obtained from the hepatic metastatic nodules after the first passage were named M1 , and the selection procedure was repeated twice to obtain the M2 and M3 cell lines . Three independent replicates were profiled for each cell line . Hierarchical clustering ( average linkage metric with Pearson correlation ) was used to cluster tumors into different groups on the basis of their PGC expression levels . Kaplan-Meier analysis ( SPPC , Chicago ) was used for survival comparisons between the tumor groups . P-values were calculated using the Log-rank test . | Successful carcinogenesis involves the integration of both pro- and anti-oncogenic pathways . We postulated that genes critical for balancing these opposing pathways are likely to be precisely controlled in tumors , since even subtle alterations in their activity might cause substantial alterations in tumor growth and survival . Using a novel genomic approach , we identified a 48-gene “Poised Gene Cassette” ( PGC ) showing tight regulation specifically in human cancers but not in corresponding nonmalignant tissues . We show , using a wide variety of in vitro and in vivo approaches , that small alterations in PGC expression are consistently associated with significant differences in experimental metastasis and patient survival , and we demonstrate a direct functional role for five PGC genes ( p53CSV , MAP3K11 , MTCH2 , CPSF6 and SKIP ) in cancer invasion . Our findings support the existence of a novel class of ultrasensitive genes that may regulate various cancer-associated phenotypes such as metastasis . Such precisely controlled genes could represent appealing drug targets , since even partial alterations in their activity should prove sufficient to induce potent effects on tumors . Besides cancer , our analytical approach is quite generalizable and likely to be applicable to other disease conditions . | [
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] | 2008 | A Precisely Regulated Gene Expression Cassette Potently Modulates Metastasis and Survival in Multiple Solid Cancers |
Astroviruses ( AstVs ) are positive sense , single-stranded RNA viruses transmitted to a wide range of hosts via the fecal-oral route . The number of AstV-infected animal hosts has rapidly expanded in recent years with many more likely to be discovered because of the advances in viral surveillance and next generation sequencing . Yet no study to date has identified human AstV genotypes in animals , although diverse AstV genotypes similar to animal-origin viruses have been found in children with diarrhea and in one instance of encephalitis . Here we provide important new evidence that non-human primates ( NHP ) can harbor a wide variety of mammalian and avian AstV genotypes , including those only associated with human infection . Serological analyses confirmed that >25% of the NHP tested had antibodies to human AstVs . Further , we identified a recombinant AstV with parental relationships to known human AstVs . Phylogenetic analysis suggests AstVs in NHP are on average evolutionarily much closer to AstVs from other animals than are AstVs from bats , a frequently proposed reservoir . Our studies not only demonstrate that human astroviruses can be detected in NHP but also suggest that NHP are unique in their ability to support diverse AstV genotypes , further challenging the paradigm that astrovirus infection is species-specific .
Astroviruses ( AstV ) are small , non-enveloped , positive-sense , single-stranded RNA viruses associated with significant morbidity , especially in the young , elderly and immunocompromised people as well as substantial economic losses in poultry [1 , 2] . Although most commonly associated with diarrhea , they can also cause a variety of clinical diseases including nephritis , hepatitis , and encephalitis or can be asymptomatic depending on the species . Since 2008 , the number of animal hosts shown to be infected with AstVs has quadrupled to include at least 30 mammalian and 14 avian species [3 , 4] with a correlative increase in genetic diversity resulting in division of the Astroviridae family into two genera , Mamastrovirus ( MAstVs ) and Avastrovirus ( AAstVs ) that are further sub-divided into genotypes or viral species based on the genetic differences within the complete viral capsid protein [5] . However , with the constant identification of new viral species and hosts , and the genetic diversity within the family , it is likely that the Astroviridae family will continue to diverge and taxonomy and nomenclature will have to be updated regularly . AstV infections are thought to be species-specific [2 , 3 , 5 , 6] . Yet , phylogenetic characterization suggests that a single host species may be susceptible to infection with divergent AstV genotypes . For example , humans can be infected with the “classical” serotypes HAstV1-8 or the recently identified HAstV-MLB1-3 , HMO AstVs A , B , and C , and HAstV-VA1-4 viruses [6 , 7] . These recently identified human AstVs are genetically much closer to AstVs from animals than they are to the canonical HAstVs . Similar observations were reported for AstVs detected in pigs , bats , California sea lions , sheep , mink , and turkeys [8] challenging the paradigm that AstV infections are species-specific . Indeed , recent studies have shown a mammalian-like virus in an avian host [9] . Yet to date , diverse MAstV and AAstV genotypes , especially viruses associated with human infections have not been detected in a single animal host . However , potential human-mammalian recombination events have been detected suggesting that the species barrier may have been crossed at some point [10 , 11] . Non-human primates ( NHP ) are highly susceptible to a variety of enteric viruses [12–15] . In Bangladesh , rhesus macaques , which are ubiquitous and often synanthropic ( i . e . species that thrive in human-altered habitats ) were shown to be infected with a variety of human enterovirus serotypes that shared considerable genetic overlap with viruses detected in closely associated humans , strongly suggesting interspecies transmission [13] . There is also serological evidence suggesting natural infection with rotavirus and norovirus among captive NHP [16] . No data are currently available on AstVs in NHP [13 , 15] . The objective of this study was to fill this gap in knowledge and determine the extent of AstV among NHP populations in Bangladesh and Cambodia . In Bangladesh and Cambodia , multiple species of NHP including rhesus macaques ( Macaca mulatta ) , Hanuman langurs ( Semnopithecus entellus ) , longtailed macaques ( M . fascicularis ) and pigtailed macaques ( M . nemestrina ) have for centuries thrived at the human-primate interface , ranging freely through villages and religious sites [17 , 18] . These macaques and langurs , as well as species of gibbons ( Hylobates spp . ) , are also found in captive settings . We have found evidence , based on analysis of sequences derived from the highly conserved RNA-dependent RNA polymerase ( RdRp ) gene , that these NHP harbor a variety of MAstV , including genotypes previously only associated with human infections , sequences with little similarity to currently identified AstVs , . AAstV genotypes , and what appears to be a recombinant between a human AstV and unique virus hitherto only detected in NHPs . We contrast this diversity with that observed in bats , which have been identified as having exceptionally diverse AstV populations ( 14–16 ) , but which our studies indicate is more phylogenetically isolated from AstV infecting other mammals . Importantly , the presence of antibodies to HAstVs further supports our hypothesis that NHP are susceptible to infection with human astrovirus genotypes . These studies provide important new evidence that primates can be infected with human astroviruses . They also directly challenge the paradigm that AstV infection is species-specific .
Fecal samples from NHP in Bangladesh and Cambodia were collected between 2007–2008 and 2011–2012 and RNA screened using a pan-astrovirus RT-PCR targeting a 422 nucleotide segment within the highly conserved RNA-dependent RNA polymerase ( RdRp ) gene [19] . Of the 879 fecal samples tested 68 ( 7 . 7% ) were AstV positive ( Table 1 ) . S1 Table contains the complete details on the positive samples including NHP species , percent similarity to closest identified sequence , and proposed nomenclatures . Sequence analysis unexpectedly revealed that HAstV , MLB , and VA genotypes were detected in NHPs ( Fig 1 ) . The majority of the positive samples ( 60 . 3% ) were 98–100% similar to HAstV-1 reference viruses ( Fig 2A ) and 11 . 7% of the samples were 79 to 84% similar to human VA and MLB reference viruses ( Fig 1 ) . Intriguingly , MLB and VA sequences were detected in NHP samples collected in 2007 prior to the official identification in 2008 and 2009 respectively [10 , 20 , 21] . Although most of the human-like sequences were closely related to human reference sequences , NHP FCB5 , MCB35 , MCB37 , which were collected in Cambodia in 2011–2012 , branched off the human VA/HMO subclade forming a unique clade . Numerous approaches including genome walking , 3’RACE , and deep sequencing were undertaken on all RdRp-positive samples to obtain more genomic information . We obtained ~300 nucleotides from the 5’ end of MAstV/Hoolock gibbon/Bangladesh/BG36/2007 ORF2 and confirmed that it was 84% similar to MLB1 capsid sequences ( Fig 2B ) . These results suggest that canonical ( HAstV1-8 ) and non-canonical ( MLB , VA/HMO ) viruses can be detected in NHP . Given that human AstVs have not been previously detected in mammals , we tested NHP sera for the presence of antibodies against HAstV-1 , and MLB capsid proteins by ELISA [22 , 23] . Turkey astrovirus type-2 ( TAstV-2 ) is genetically distant from the mammalian viruses and we found no evidence of TAstV-2-like sequences in our genetic analysis , thus it was used as an AstV “control” ( Fig 1 ) . Briefly , 96-well plates were coated with purified recombinant HAstV-1 , MLB1 , MLB2 , TAstV-2 capsid proteins or BSA and limiting dilutions of the NHP sera was incubated as described [22 , 23] . Positive controls included known positive polyclonal antisera and human sera . Unfortunately reagents to other AstV genotypes are unavailable . Of the sera samples available for testing , 90 from Bangladesh and 48 from Cambodia , 44 ( 31 . 9% ) samples were positive for AstV antibodies with the majority ( 72 . 7% ) specific for HAstV-1 and 27 . 3% positive for the non-canonical MLB capsids ( Table 2 ) . Cross-reactive MLB1 and MLB2 antibodies have been detected by ELISA so these results were combined although sera was run against MLB1 and MLB2 capsids [23] . None of the samples were positive for TAstV-2 . Eighty-five of the sera were from NHP that had also been tested for AstV by RT-PCR . Of the 22 serologically positive sera , 7 were from NHP that were also RT-PCR positive ( S1 Table ) . From these seven NHP , we identified three primates that were both serologically and RT-PCR positive: NHP MBG248 was serologically positive for MLB and RT-PCR positive for HAstV-1; MCB35 was serologically and RT-PCR positive for HAstV-1; and MCB43 was serologically HAstV-1 positive while RT-PCR positive for avian AstV . Combined with our genetic data , these studies strongly suggest that NHP not only harbor human AstV strains but have antibodies suggestive of previous exposures . In addition to the human-like sequences , 23 . 5% of the samples were similar to MAstVs isolated from diverse animal hosts including dogs , pigs , and sheep ( Fig 1 ) . NHP BG33 , BG113 , BG569 , and BG410 and its related subclade containing BG31 , BG41 , and MBG260 were collected in Bangladesh in 2007–2008 and 2012 ( MBG260 ) and appear to be part of a larger cluster of viruses identified in cows , pigs , and deer ( Fig 1 ) . We were able to obtain ~900 nucleotides of MAstV/Rhesus macaque/Bangladesh/BG31/2007 ORF2 and confirmed that it clusters within the same clade ( Fig 2C ) . NHP BG463 and BG469 cluster with a unique porcine AstV ( PAstV-5 ) [24] forming a poorly supported subclade off the HAstVs ( Fig 2A ) . Additionally , 4 . 4% of the positive samples clustered within the AAstVs though they formed a distinct and well supported subclade from previously identified AAstV genotypes ( Fig 1 ) . Although we demonstrated that people with occupational exposure to poultry can have antibodies against AAstVs [22] , only one prior study has successfully isolated AAstV from a mammal [25] . Overall , these data demonstrate that NHP can harbor a variety of mammalian including human and avian AstVs . Unfortunately , attempts to isolate the NHP viruses or obtain further genomic data either by traditional or deep sequencing methodologies were unsuccessful . Recombination events have been detected in numerous MAstV and AAstVs and are thought to be a major factor in the evolution of Astroviridae [4 , 5 , 10 , 11 , 21] . They can also confound phylogenetic reconstruction . Indeed , when constructing the RdRp phylogenetic tree , inclusion of the NHP BG35 sequence in the alignment resulted in a unique subclade that branched off the HAstVs ( Fig 2A ) . Included within this subclade were NHP BG31 , BG41 , and MBG260 , sequences that were shown to cluster within the cows , pigs , and deer RdRp ( compare Fig 1 ) when NHP BG35 is excluded from the alignment . Given that the BG31 capsid sequence was clearly shown to align within this larger cow , pig , deer clade ( Fig 2C ) , we hypothesized that NHP BG35 was a possible recombinant AstV . To test this hypothesis , the sequences , which upon visual inspection best matched the 5’ and 3’ end of the NHP BG35 , were identified as the canonical human AstVs and the NHP viruses phylogenetically close to BG31 , respectively . To represent these two putatively parental genotypes in recombination analyses , we chose the HAstV-2 sequence L23513 and the NHP AstV BG31 . The sequence from BG31 in particular was chosen because it provided the best overlap with the region of interest in BG35 . The global alignment was subset to these two sequences , together with the putative recombinant sequence and the duck AstV sequence FJ434664 as an out group . This alignment was further trimmed using trimal [26] with settings -gt 0 . 25 -sw 3 to reduce the effect of gap positions on the analysis , resulting in a 297 bp alignment . This resulting alignment tested positive for recombination via the Phi test [27] , as implemented in PhiPack ( P = 9 . 3x10-6 ) ( Fig 3 ) . To verify the specific nature of the recombination , a cBrother analysis was performed using the same alignment used in the Phi test . The duck , human and BG31 AstV sequences were used as genotype representatives in this analysis . Two independent cBrother runs of 1 . 1 million generations were run , with the initial 10% discarded as burn-in , and sampling every 1000 generations . Convergence was assessed using the Gelman–Rubin diagnostic [5] included with the cBrother distribution . This analysis found a recombinant breakpoint at base 209 of the subset alignment , with ancestry assigned to the BG31 genotype on the 5’ side , and to the human AstV on the 3’ side ( Fig 3 ) . Due to the unwanted effect of this recombination on phylogenetic analysis , BG35 was removed from the global alignment for the remaining analyses . These analyses suggest a possible HAstV-MAstV recombination event occurred either during or prior to infection in the NHP . Potential human-mammalian recombination events have also been reported in piglets in Colombia [10] and intriguingly in a California sea lion [11] . Based on the high prevalence rate and genetic diversity of AstV detected in bats compared to other species , bats have been proposed as a pimary natural reservoir for AstVs and possibly as host of the most recent conmon ancestor of the HAstVs[19 , 28 , 29] . However , with few exceptions , the bat sequences cluster within bat-specific genogroups ( MAstV 12 , 14–19 ) [6] . In contrast , the NHP-derived sequences are distributed throughout Astroviridae . Thus , to compare AstV diversity in NHP to that in bats , we performed phylogenetic analysis of sequences from these two hosts in comparison to sequences from other MAstV and AAstV hosts . These analyses took place on two distinct phylogenetic trees . Both trees were built in part from a core set of mammalian and avian AstVs reference sequences making up a reference community but differed in that one was built from the references sequences with the NHP AstV sequences , while the other was built with the bat AstV sequences . On each of these trees we computed three diversity metrics . The first is the phylogenetic diversity metric [30] , which measures the sum of branch lengths contained within the minimal subtree spanning all tips of interest as depicted in ( Fig 4 ) . Intuitively , the higher this value is , the more diverse the community . The remaining two metrics were chosen to characterize the distribution of host diversity in relation to the reference community . Of these , the first is the UniFrac [31] distance between sequences from a given host to the collection of reference sequences . This metric is computed as the sum of branch lengths unique to one community or the other , divided by the sum of all branch lengths as depicted in ( Fig 4 ) . This effectively gives us a measure of how different the viral community in question is from the reference community . Smaller or larger values indicate the community of interest is phylogenetically closer or more distant ( respectively ) to the reference community . Lastly , we evaluated the maximal monophyletic clade count of a given set of sequences on the tree as the number of unique places on the reference tree at which the sequences in question branch off ( Fig 4 ) . This measures how interspersed the sequences in question are among the reference community . The first column of Fig 5 shows that , assuming bat and NHP sequences are equally unbiased representations of the corresponding viral populations , the bat AstV population has considerably higher phylogenetic diversity . However , as we begin to account for novelty bias and sampling depth by clustering sequences at various thresholds and picking representatives ( as described in Materials and Methods and Discussion sections respectively ) , we find that NHP AstV diversity begins to match and even slightly exceed that of the bat AstVs ( Fig 5 ) . However , across all clustering and sampling depths explored , the UniFrac and MMC measurements indicate NHP AstVs are much closer to the rest of the phylogenetic tree than are the bat AstVs and more broadly distributed across the tree . In contrast , the bat AstV are less well integrated into the reference community , and more isolated from the rest of the AstV diversity . These data suggest that while bats and NHP harbor similarly diverse AstV , NHP AstV bears much greater resemblance to the community of other mammalian AstVs .
Our studies provide important new information that human AstVs can be detected in non-human primates . Several studies demonstrated that a single host species may be susceptible to divergent AstV genotypes including pigs , bats , California sea lions , sheep , mink , turkeys , and humans , which can be infected with strains genetically related to animal AstVs [7 , 8] . However , identification of mammals with AstV viruses associated with human infections has never been documented until now . Not only did we find evidence of diverse AstVs genotypes in fecal samples , we also detected AstV-specific antibodies in plasma samples . Although we did identify one NHP that was both serologically and RT-PCR-positive for HAstV-1 , most of the serologically positive animals were RT-PCR negative at the time of sample collection . This suggests a previous exposure to either the same or different AstV genotype . In support of this , we identified two primates that were serologically positive for different genotypes to those detected by RT-PCR; in one case serologically MLB-positive and while HAstV-1 positive by RT-PCR and in the second serologically HAstV-1 positive while an avian AstV was detected . Future studies will have to address whether detection of AstV in non-human primates is associated with actual infection , and if so , whether such infections are asymptomatic or associated with clinical disease; none of the NHP sampled in this study appeared to have clinical disease ( i . e . diarrhea ) at the time of sampling . One limitation of our study is that the genetic analysis is based on ~400 nucleotide region of the RdRp [19] . While this is the most conserved region of the AstV genome supporting the proposed genogroup assignments , precise genotyping is ideally done with full genome or at least the full ORF2 ( capsid ) sequence . Attempts to isolate viruses or construct full genomic sequences through a variety of methods including genome walking with degenerate primers , 3’RACE , and deep sequencing methodologies similar to those used for the novel human AstV strains [20] was unsuccessfully undertaken on all RdRp-positive samples . It is not surprising that virus isolation was unsuccessful given that very few AstVs can be cultured . However , the inability to identify further genetic sequences despite all efforts was frustrating and suggests that the amount of virus being “shed” by primates was at a very low level or that sample quality was not ideal . In spite of these limitations , we did sequence ~130 nucleotides from the conserved 5’ end of the MAstV/Hanuman langur/BG569/Bangladesh/2008 capsid that shared 77% similarity with bovine AstV ( S1 Fig ) and ~300 nucleotides from the conserved 5’ end of MAstV/Hoolock gibbon/Bangladesh/BG36/2007 capsid gene demonstrating that it was similar to human MLB viruses . We also obtained a larger capsid sequence from MAstV/Rhesus macaque/Bangladesh/BG31/2007 . Phylogenetic analysis suggests that while unique , it is part of a subclade comprised of diverse animal hosts including cows , pigs and deer . Bats are known to harbor a diversity of AstV species; although these viruses are unique to the bat host and haven’t been found in other species to date [19] . Nor have AstV typically associated with other animal hosts been identified in bats . In contrast , NHP harbor AstV associated with diverse animal hosts . Phylogenetic analyses comparing the sequence diversity observed in these two host groups revealed that NHP viruses exhibit a comparable level of diversity to those reported in bats . However , the distribution is quite different . In particular , AstV sequences from bats were found to be more phylogenetically isolated from AstV detected in known host species , while those of NHP are relatively well integrated within the greater phylogenetic tree . Note that these comparisons are complicated by two aspects of the sampling . The first is sampling depth . Ours is the first and only study of AstV diversity in NHP , from which 68 nucleotide sequences have been obtained . In contrast , there have been extensive studies of AstV in bats , from which hundreds of AstVs have been sequenced . To account for this , we compared randomly selected subsamples from the phylogenetic trees to see how sampling depth affected the diversity metrics , an analytic technique known as rarefaction [32] . Secondly , it is clear from the papers detailing studies in bats that there is diversity-selection bias regarding what nucleotide sequences are submitted to online repositories . For instance , in Zhu et al . AstV was detected in a total of 224 animals , while only 76 sequences were submitted to GenBank [28] . The authors did not indicate the criteria used in selecting these representatives , nor how many sequences obtained were identified with each representative submitted . As such , we have no way of precisely replicating this with our NHP AstV sequences . This problem is compounded by the fact that , in general , researchers from different studies are more likely to report sequences to GenBank if they appear to be novel . Thus , taking sequences from such a repository as a representation of the viral diversity found within these hosts will inevitably introduce a bias towards increased diversity . To account for this , we performed sequence clustering of both bat and NHP sequences at various thresholds as described in Materials and Methods , and picked one sequence per cluster for each of our comparisons . While this potentially leaves out fine grained information about abundance of AstV species or subspecies , as we increase the clustering threshold , it at least puts the communities closer to the same ground by being similarly biased towards novel selection . It is worth noting that the sequence rarefaction described above was performed after this clustering step . Cross-study comparisons would be significantly eased by thorough reporting and submission of sequence data . Despite these caveats , the trends that emerge are clear and suggest different ecological roles for bats and NHP in the maintenance of AstV diversity . While bats appear to have sustained a distinct and robust virus population for some time , the phylogenetic distance between these viruses and those of the reference community suggest transmission to and from other organisms is relatively rare . Thus , if bats are a significant reservoir of AstV diversity that occasionally spills over into other host groups , the significance of this role must be limited to a larger timescale . In contrast , the interspersedness of the NHP AstVs within the reference community suggests the net frequency of AstV transmission to and from NHP is much higher than in bats . However , the directionality of these transmission events is unknown , and it is unclear whether NHP are capable of sustaining AstV infections without periodic reintroduction from other host groups . Thus , whatever role NHP play in the ecology of AstVs , it appears their diversity is more pertinent to the recent history and dynamism of AstVs both on a whole , and specifically as is relevant to human AstV . We are currently working on analyses directly modeling the transmission of viruses between different host groups , which we hope will shed light on some of the details of this picture which remain unclear . The presence of recombination among the NHP RdRp sequences raises further questions about the role NHP play in the ecology of AstV . Unfortunately , it is impossible to determine what host species facilitated the recombination event responsible for the recombinant virus sampled from BG35 . However , the high prevalence of human AstV among NHP , coupled with the other parental strain most closely matching sequences only observed in other NHP , particularly BG31 , BG41 , and MBG260 suggests that the recombination event may have occurred in NHP . Even if the recombination event did occur in another host species , the role NHP may play in the emergence of novel AstV diversity is still called into question by the unique susceptibility of NHP to such diverse AstV genotypes . This study raises important questions as to the frequency of AstV recombination within NHP hosts , and highlights the importance of continued monitoring of AstV within NHP . In summary , a myriad of MAstV and AAstV genotypes can be detected in NHP . This further dispels the dogma that astroviruses are species-specific , and raises important questions about the role of NHP in astrovirus ecology , particularly those NHP thriving at the human-primate interface .
The study protocol was approved by the University of Washington Institutional Animal Care and Use Committee ( 4233–01 ) and adhered to the American Society of Primatologist Principles for the Ethical Treatment of Non-Human Primates . All non-human primates included in this study were free-ranging animals sampled in their natural habitats , as such housing , feeding and environmental enrichment were not part of this study . No animals were sacrificed as part of this study . All non-human primate handing , sedation and sampling was done by trained personnel , with animal safety and comfort as the first priority . As part of our ongoing longitudinal studies of synanthropic NHP populations in Bangladesh and Cambodia [13 , 17 , 33 , 34] , fecal material from freshly deposited stools from multiple NHP species were collected in Bangladesh ( n = 844 ) between 2007–2008 and 2011–2012 and Cambodia ( n = 68 ) between 2011 and 2012 . Sera samples were collected from a subset of animals in 2011–2012 . All of these animals were either at the animal-human interface in areas with substantial human population densities or sampled from limited numbers of zoo and wild NHP . Trapping and sampling protocols are reported in detail in [13 , 18] . Species , context of human contact , and global positioning system ( GPS ) coordinates were recorded for each sample and are described in [18] . The 2007–2008 samples were collected and processed as described [13 , 33] and provided as a fecal homogenate to St Jude . For the 2011–2012 samples , stool ( 100 μL ) was homogenized in 0 . 89% NaCl with 0 . 2 mm Zirconium Oxide beads ( Next Advance , Averill Park , NY , USA ) followed by centrifugation to form a fecal filtrate and RNA isolated from a 50μL of this filtrate on a Kingfisher Flex Magnetic Particle Processor ( Thermo Fisher Scientific , Waltham , MA , USA ) using the Ambion MagMAX-96 NI/ND Viral RNA Isolation kit ( Life Technologies Corporation , Grand Island , NY , USA ) and screened using a pan-astrovirus reverse transcription-PCR assay targeting the RdRp gene [19] . Sanger sequencing was performed by the St Jude Hartwell Center to identify the AstV genogroup . All RdRp-positive samples were then subjected to further sequencing to generate capsid sequence . Samples were tested against either strain-specific or random hexamer primers to attempt to obtain any capsid sequence using a variety of methods including genome walking with degenerate primers , 3’RACE and deep sequencing . Primers used on specific samples are described in S2 Table . Sera were obtained from 138 NHP in 2011–2012 , 85 of which were from animals where stool was collected for RT-PCR , and tested for antibodies to specific AstVs by ELISA as described [22] . Briefly , high binding–affinity polystyrene plates ( Corning Incorporated , Corning , NY ) were coated with 0 . 05 μg/well of purified recombinant HAstV-1 , MLB1 , MLB2 , TAstV-2 capsid protein or BSA ( negative control ) and incubated overnight at 4°C . Each plate contained an individual capsid protein or BSA . Plates were washed thrice with PBS in 0 . 05% Tween-20 ( PBST ) and then blocked with 4% BSA in PBST for 2 hour at room temp . Following extensive washing , limiting dilutions of the sera ( neat to 10−4 ) or rabbit polyclonal antisera to the different capsid proteins were added to the plates and incubated for 1 hour at room temp . After washing , plates were incubated with 0 . 05 μg/mL of HRP-conjugated anti-monkey or anti-rabbit secondary antibodies ( Jackson ImmunoResearch , West Grove , PA ) for 1 hour then reactivity was assessed by using an HRP substrate reagent kit ( R&D Systems , Minneapolis , MN ) . To reaction was stopped with 2N H2SO4 and absorbance read on a Multiskan Ascent microplate spectrophotometer ( ThermoFisher , Waltham , MA ) at 450 nm . Samples with capsid-specific absorbance greater than three times the absorbance of the sample binding to BSA were considered positive . All samples were tested in at least triplicate and experiments repeated at least twice . Bat AstV accession numbers used include HQ613157-HQ613171 , HQ613174-HQ613175 , HQ613178 , EU847144-EU847154 , EU847156 , EU847159-EU847173 , EU847175-EU847195 , EU847197-EU847215 , EU847217-EU847220 , FJ571065-FJ571068 , FJ571075 , FJ571077-FJ571080 , FJ571082 , FJ571085-FJ571086 , FJ571090-FJ571091 , FJ571093-FJ571108 , FJ571110-FJ571111 , FJ571113 , FJ571115 , FJ571117-FJ571119 , FJ571121-FJ571131 , FJ571133-FJ571135 , FJ571137 , FJ571139-FJ571140 , JQ814856-JQ814864 , JQ814866-JQ814868 , JQ814870-JQ814871 , HM368168-HM368172 , HM368174-HM368175 , KJ571377-KJ571391 , KJ571393-KJ571409 , KJ571411-KJ571431 . NHP AstV sequences are available at GenBank under accession numbers KT852380–KT852448 . | With the advances in next generation sequencing and pathogen discovery , astrovirus ( AstV ) , leading cause of diarrhea in children , the elderly and immunocompromised people , detection in diverse animal hosts has increased . Yet , to date there has been no detection of AstVs associated with human infections in animals suggesting these strains are specific to humans . In these studies we demonstrate that non-human primates ( NHP ) harbor a wide variety of AstVs including those previously only detected in people . Further , we identified an NHP with an AstV that is a recombination between human and animal genotypes . Our studies provide important new evidence that human astroviruses can be detected in animals directly challenging the paradigm that AstV infection is species-specific . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | Non-Human Primates Harbor Diverse Mammalian and Avian Astroviruses Including Those Associated with Human Infections |
Absence epilepsy is believed to be associated with the abnormal interactions between the cerebral cortex and thalamus . Besides the direct coupling , anatomical evidence indicates that the cerebral cortex and thalamus also communicate indirectly through an important intermediate bridge–basal ganglia . It has been thus postulated that the basal ganglia might play key roles in the modulation of absence seizures , but the relevant biophysical mechanisms are still not completely established . Using a biophysically based model , we demonstrate here that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata ( SNr ) to either the thalamic reticular nucleus ( TRN ) or the specific relay nuclei ( SRN ) of thalamus , through different biophysical mechanisms . Under certain conditions , these two types of seizure control are observed to coexist in the same network . More importantly , due to the competition between the inhibitory SNr-TRN and SNr-SRN pathways , we find that both decreasing and increasing the activation of SNr neurons from the normal level may considerably suppress the generation of spike-and-slow wave discharges in the coexistence region . Overall , these results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures , and might provide novel insights into the therapeutic treatments of this brain disorder .
Absence epilepsy is a generalized non-convulsive seizure disorder of the brain , mainly occurring in the childhood years [1] . A typical attack of absence seizures is characterized by a brief loss of consciousness that starts and terminates abruptly , and meanwhile an electrophysiological hallmark , i . e . the bilaterally synchronous spike and wave discharges ( SWDs ) with a slow frequency at approximately 2–4 Hz , can be observed on the electroencephalogram ( EEG ) of patients [1] , [2] . There is a broad consensus that the generation of SWDs during absence seizures is due to the abnormal interactions between cerebral cortex and thalamus , which together form the so-called corticothalamic system . The direct evidence in support of this view is based on simultaneous recordings of cortex and thalamus from both rodent animal models and clinical patients [3]–[5] . Recent computational modelling studies on this prominent brain disorder also approved the above viewpoint and provided more deep insights into the possible generation mechanism of SWDs in the corticothalamic system [6]–[13] . The basal ganglia comprise a group of interconnected subcortical nucleus and , as a whole , represent one fundamental processing unit of the brain . It has been reported that the basal ganglia are highly associated with a variety of brain functions and diseases , such as cognitive [14] , emotional functions [15] , motor control [16] , Parkinson's disease [17] , [18] , and epilepsy [19] , [20] . Anatomically , the basal ganglia receive multiple projections from both the cerebral cortex and thalamus , and in turn send both direct and indirect output projections to the thalamus . These connections enable the activities of the basal ganglia to influence the dynamics of the corticothalamic system . Therefore , it is naturally expected that the basal ganglia may provide an active role in mediating between seizure and non-seizure states for absence epileptic patients . Such hypothesis has been confirmed by both previous animal experiments [19] , [21]–[23] and recent human neuroimage data [20] , [24] , [25] . Nevertheless , due to the complicated interactions between basal ganglia and thalamus , the underlying neural mechanisms on how the basal ganglia control the absence seizure activities are still remain unclear . From the anatomical perspective , the substantia nigra pars reticulata ( SNr ) is one of the major output nucleus of the basal ganglia to thalamus . Previous experimental studies using various rodent animal models have demonstrated that suitable changes in the firing of SNr neurons can modulate the occurrence of absence seizures [21]–[23] , [26] . Specifically , it has been found that pharmacological inactivation of the SNr by injecting -aminobutyric acids ( GABA ) agonists or glutamate antagonists suppresses absence seizures [21] , [22] . Such antiepileptic effect was supposed to be attributed to the overall inhibitory effect of the indirect pathway from the SNr to thalamic reticular nucleus ( TRN ) relaying at superior colliculus [21] , [22] . In addition to this indirect inhibitory pathway , it is known that the SNr also contains GABAergic neurons directly projecting to the TRN and specific relay nuclei ( SRN ) of thalamus [27] , [28] . Theoretically , changing the activation level of SNr may also significantly impact the firing activities of SRN and TRN neurons [28] , [29] . This contribution might further interrupt the occurrence of SWDs in the corticothalamic system , thus providing an alternative mechanism to regulate typical absence seizure activities . To our knowledge , however , so far the precise roles of these direct basal ganglia-thalamic pathways in controlling absence seizures are not completely established . To address this question , we develop a realistic mean-field model for the basal ganglia-corticothalamic ( BGCT ) network in the present study . Using various dynamic analysis techniques , we show that the absence seizures are controlled and modulated either by the isolated SNr-TRN pathway or the isolated SNr-SRN pathway . Under suitable conditions , these two types of modulations are observed to coexist in the same network . Importantly , in this coexist region , both low and high activation levels of SNr neurons can suppress the occurrence of SWDs due to the competition between these two direct inhibitory basal ganglia-thalamic pathways . These findings clearly outline a bidirectional control of absence seizures by the basal ganglia , which is a novel phenomenon that has never been identified both in previous experimental and modelling studies . Our results , on the one hand , further improve the understanding of the significant role of basal ganglia in controlling absence seizure activities , and on the other hand , provide testable hypotheses for future experimental studies .
We build a biophysically based model that describes the population dynamics of the BGCT network to investigate the possible roles of basal ganglia in the control of absence seizures . The network framework of this model is inspired by recent modelling studies on Parkinson's disease [30] , [31] , which is shown schematically in Fig . 1 . The network totally includes nine neural populations , which are indicated as follows: excitatory pyramidal neurons ( EPN ) ; inhibitory interneurons ( IIN ) ; TRN; SRN; striatal D1 neurons; striatal D2 neurons; SNr; globus pallidus external ( GPe ) segment; subthalamic nucleus ( STN ) . Similar to other modelling studies [30]–[32] , we do not model the globus pallidus internal ( GPi ) segment independently but consider SNr and GPi as a single structure in the present study , because they are reported to have closely related inputs and outputs , as well as similarities in cytology and function . Three types of neural projections are contained in the BGCT network . For sake of clarity , we employ different line types and heads to distinguish them ( see Fig . 1 ) . The red lines with arrow heads denote the excitatory projections mediated by glutamate , whereas the blue solid and dashed lines with round heads represent the inhibitory projections mediated by and , respectively . It should be noted that in the present study the connections among different neural populations are mainly inspired by previous modelling studies [30] , [31] . Additionally , we also add the connection sending from SNr to TRN in our model , because recent anatomical findings have provided evidence that the SNr also contains GABAergic neurons directly projecting to the TRN [27]–[29] . The dynamics of neural populations are characterized by the mean-field model [9] , [11] , [33]–[35] , which was proposed to study the macroscopic dynamics of neural populations in a simple yet efficient way . The first component of the mean-field model describes the average response of populations of neurons to changes in cell body potential . For each neural population , the relationship between the mean firing rate and its corresponding mean membrane potential satisfies an increasing sigmoid function , given by ( 1 ) where indicate different neural populations , denotes the maximum firing rate , r represents the spatial position , is the mean firing threshold , and is the threshold variability of firing rate . If exceeds the threshold , the neural population fires action potentials with an average firing rate . It should be noted that the sigmoid shape of is physiologically crucial for this model , ensuring that the average firing rate cannot exceed the maximum firing rate . The changes of the average membrane potential at the position r , under incoming postsynaptic potentials from other neurons , are modeled as [9] , [33]–[36] ( 2 ) ( 3 ) where is a differential operator representing the dendritic filtering of incoming signals . and are the decay and rise times of cell-body response to incoming signals , respectively . is the coupling strength between neural populations of type and type . is the incoming pulse rate from the neural population of type to type . For simplicity , we do not consider the transmission delay among most neural populations in the present work . However , since the functions via second messenger processes , a delay parameter is introduced to its incoming pulse rate ( i . e . , ) to mimic its slow synaptic kinetics . This results in a delay differential equation in the final mathematical description of the BGCT model . Note that the similar modelling method has also been used in several previous studies [13] , [37] . In our system , each neural population gives rise to a field of pulses , which travels to other neural population at a mean conduction velocity . In the continuum limit , this type of propagation can be well-approximated by a damped wave equation [9] , [33]–[35] , [38]: ( 4 ) Here is the Laplacian operator ( the second spatial derivative ) , is the characteristic range of axons of type , and governs the temporal damping rate of pulses . In our model , only the axons of cortical excitatory pyramidal neurons are assumed to be sufficiently long to yield significant propagation effect . For other neural populations , their axons are too short to support wave propagation on the relevant scales . This gives ( ) . Moreover , as one of typical generalized seizures , the dynamical activities of absence seizures are believed to occur simultaneously throughout the brain . A reasonable simplification is therefore to assume that the spatial activities are uniform in our model , which has been shown as the least stable mode in models of this class [33] , [34] , [36] . To this end , we ignore the spatial derivative and set in Eq . ( 4 ) . Accordingly , the propagation effect of cortical excitatory axonal field is finally given by [33] , [34] , [36]: ( 5 ) where . For the population of cortical inhibitory interneurons , the BGCT model can be further reduced by using and , which is based on the assumption that intracortical connectivities are proportional to the numbers of synapses involved [9] , [13] , [33]–[36] . It has been demonstrated that by making these above reductions , the developed BGCT model becomes computationally more tractable without significant deteriorating the precision of numerical results . We then rewrite above equations in the first-order form for all neural populations . Following above assumptions , we use Eqs . ( 1 ) – ( 3 ) and ( 5 ) for modelling the dynamics of excitatory pyramidal neurons , and Eqs . ( 1 ) – ( 3 ) for modelling the dynamics of other neural populations . This yields the final mathematical description of the BGCT model given as follows: ( 6 ) ( 7 ) ( 8 ) ( 9 ) where ( 10 ) ( 11 ) ( 12 ) In Eq . ( 10 ) , the superscript T denotes transposition . The detailed expression of for different neural populations is represented by , and , given by ( 13 ) with ( 14 ) ( 15 ) ( 16 ) Here the variable in Eq . ( 15 ) denotes the delay and the parameter in Eq . ( 16 ) represents the constant nonspecific subthalamic input onto SRN . The parameters used in our BGCT model are compatible with physiological experiments and their values are adapted from previous studies [9] , [11] , [13] , [30] , [31] , [36] . Unless otherwise noted , we use the default parameter values listed in Table 1 for numerical simulations . Most of the default values of these parameters given in Table 1 are based on either their nominal values or parameter ranges reported in above literature . A small number of parameters associated with the basal ganglia ( i . e . , , and ) are adjusted slightly , but still within their normal physiological ranges , to ensure our developed model can generate the stable 2–4 Hz SWDs under certain conditions . Note that due to lack of quantitative data , the coupling strength of the SNr-TRN pathway needs to be estimated . Considering that the SNr sends GABAergic projections both to SRN and TRN and also both of these two nuclei are involved in thalamus , it is reasonable to infer that the coupling strengths of these two pathways are comparable . For simplicity , here we chose by default . In the following studies , we also change ( decrease or increase ) the value of several folds by employing a scale factor ( see below ) to examine how the inhibition from the SNr-TRN pathway regulates absence seizures . Additionally , during this study , several other critical parameters ( i . e . , , and ) are also varied within certain ranges to obtain different dynamical states and investigate their possible effects on the modulation of absence seizures . In the present study , several data analysis methods are employed to quantitatively evaluate the dynamical states as well as the properties of SWDs generated by the model . To reveal critical transitions between different dynamical states , we perform the bifurcation analysis for several key parameters of the model . For one specific parameter , the bifurcation diagram is simply obtained by plotting the “stable” local minimum and maximum values of cortical excitatory axonal fields ( i . e . , ) over changes in this parameter [11] , [39] . To this end , all simulations are executed for sufficiently long time ( 10 seconds of simulation time , after the system reaches its stable time series ) , and only the local minimum and maximum values obtained from the latter stable time series are used . Using the above bifurcation analysis , we can also easily distinguish different dynamical states for combined parameters . Such analysis technique allows us to further identify different dynamical state regions in the two-parameter space ( for example , see Fig . 2D ) . On the other hand , the power spectral analysis is used to estimate the dominant frequency of neural oscillations . To do this , the power spectral density is obtained from the time series ( over a period of 10 seconds ) by using the fast Fourier transform . Then , the maximum peak frequency is defined as the dominant frequency of neural oscillations . It should be noted that , by combining the results of both the state and frequency analysis , we can outline the SWD oscillation region that falls into the 2–4 Hz frequency range in the two-parameter space ( for example , see the asterisk region in Fig . 2E ) . Moreover , we calculate the mean firing rates ( MFRs ) for several key neural populations in some figures . To compute the MFRs , all corresponding simulations are performed up to 25 seconds and the data from 5 to 25 seconds are used for statistical analysis . To obtain convincing results , we carry out 20 independent simulations with different random seeds for each experimental setting , and report the averaged result as the final result . Finally , in some cases , we also compute the low and high triggering mean firing rates ( TMFRs ) for SNr neurons . In the following simulations , we find that the mean firing rate of SNr neurons is increased with the growth of the excitatory coupling strength , which serves as a control parameter to modulate the activation level of SNr in our work ( see the Results section ) . Based on this property , the low and high TMFRs can be determined by the mean firing rates of SNr neurons occurring at the boundaries of the typical region of 2–4 Hz SWDs ( for example , see the black dashed lines in Fig . 3B ) . All network simulations are written and performed under the MATLAB environment . The aforementioned dynamical equations are integrated by using the standard fourth-order Runge-Kutta method , with a fixed temporal resolution of [40] . In additional simulations , it turns out that the chosen integration step is sufficiently small to ensure the numerical accuracy of our developed BGCT model . The computer codes used in the present study are available on ModelDB ( https://senselab . med . yale . edu/ModelDB/showmodel . asp ? model=152113 ) . The fundamental implementation of the BGCT model is provided as supplementary information to this paper ( we also provide a XPPAUT code for comparison [41]; see Text S1 and S2 ) .
Previous studies have suggested that the slow kinetics of receptors in TRN are a candidate pathological factor contributing to the generation of absence seizures both in animal experiments and biophysical models of corticothalamic network [7] , [13] , [42] , [43] . To explore whether this mechanism also applies to the developed BGCT model , we perform one-dimensional bifurcation analysis for the inhibitory coupling strength and the delay parameter , respectively . The corresponding bifurcation diagrams and typical time series of are depicted in Figs . 2A–2C , which reveal that different dynamical sates emerge in our system for different values of and . When the coupling strength is too weak , the inhibition from TRN cannot effectively suppress the firing of SRN . In this case , due to the strong excitation from pyramidal neurons , the firing of SRN rapidly reaches a high level after the beginning of the simulation . Such high activation level of SRN in turn drives the firing of cortical neurons to their saturation states within one or two oscillation periods ( region I ) . As the coupling strength grows , the inhibition from TRN starts to affect the firing of SRN . For sufficiently long , this causes our model to successively undergo two different oscillation patterns . The first one is the SWD oscillation pattern , in which multiple pairs of maximum and minimum values are found within each periodic complex ( region II ) . Note that this oscillation pattern has been extensively observed on the EEG recordings of real patients during absence seizures [1] . The other one is the simple oscillation pattern , in which only one pair of maximum and minimum values appears within each periodic complex ( region III ) . However , if the coupling strength is too strong , the firing of SRN is almost completely inhibited by TRN . In this situation , the model is kicked into the low firing region and no oscillation behavior can be observed anymore ( region IV ) . Additionally , we also find that the model dynamics are significantly influenced by the delay , and only sufficiently long can ensure the generation of SWDs in the developed model ( see Fig . 2B ) . To check whether our results can be generalized within a certain range of parameters , we further carry out the two-dimensional state analysis in the ( ) panel . As shown in Fig . 2D , the whole ( ) panel is divided into four state regions , corresponding to those regions identified above . Unsurprisingly , we find that the BGCT model can generate the SWD oscillation pattern only for appropriately intermediate and sufficiently long . This observation is in consistent with our above finding , demonstrating the generalizability of our above results . To estimate the frequency characteristics of different oscillation patterns , we compute the dominant frequency based on the spectral analysis in the ( ) panel . For both the simple and SWD oscillation patterns , the dominant frequency is influenced by and , and increasing their values can both reduce the dominant frequency of neural oscillations ( Fig . 2E ) . However , compared to , our results indicate that the delay may have a more significant effect on the dominant oscillation frequency ( Fig . 2E ) . By combining the results in Figs . 2D and 2E , we roughly outline the SWD oscillation region that falls into the 2–4 Hz frequency range ( asterisk region ) . It is found that most of , but not all , the SWD oscillation region is contained in this specific region . Here we emphasize the importance of this specific region , because the SWDs within this typical frequency range is commonly observed during the paroxysm of absence epilepsy in human patients [1] , [2] . Why can the slow kinetics of receptors in TRN induce absence seizure activities ? Anatomically , the SRN neurons receive the TRN signals from the inhibitory pathway mediated by both and receptors . Under suitable condition , the double suppression caused by these two types of GABA receptors occurring at different time instants may provide an effective mechanism to create multiple firing peaks for the SRN neurons ( see below ) . Such firing pattern of SRN in turn impacts the dynamics of cortical neurons , thus leading to the generation of SWDs . It should be noted that , during the above processes , both and play critical roles . In each oscillation period , after the -induced inhibition starts to suppress the firing of SRN neurons , these neurons need a certain recovery time to restore their mean firing rate to the rising state . Theoretically , if this recovery time is shorter than the delay , another firing peak can be introduced to SRN neurons due to the latter -induced inhibition . The above analysis implies that our model requires a sufficient long delay to ensure the occurrence of SWDs . However , as described above , too long is also a potential factor which may push the dominant frequency of SWDs beyond the typical frequency range . For a stronger , the inhibition caused by is also strong . In this situation , it is obvious that the SRN neurons need a longer time to restore their firing rate . As a consequent , a relatively longer is required for the BGCT model to ensure the occurrence of SWDs for stronger ( see Fig . 2D ) . These findings provide consistent evidence that our developed BGCT model can replicate the typical absence seizure activities utilizing previously verified pathological mechanism . Because we do not change the normal parameter values for basal ganglia during above studies , our results may also indicate that , even though the basal ganglia operate in the normal state , the abnormal alteration within the corticothalamic system may also trigger the onset of absence epilepsy . Throughout the following studies , we set for all simulations . For this choice , the delay parameter is within the physiological range and modest , allowing the generation of SWD oscillation pattern while preserving its dominant frequency around 3 Hz in most considered parameter regions . It should be noted that , in additional simulations , we have shown that by slightly tuning the values of several parameters our developed BGCT model is also powerful to reproduce many other typical patterns of time series , such as the alpha and beta rhythms ( see Fig . S1 ) , which to a certain extent can be comparable with real physiological EEG signals [9] , [36] . Using the developed BGCT model , we now investigate the possible roles of basal ganglia in controlling absence seizure activities . Here we mainly concentrate on how the activation level of SNr influence the dynamics generated by the model . This is because , on the one hand , the SNr is one of chief output nucleus of the basal ganglia to thalamus , and on the other hand , its firing activity has been found to be highly associated with the regulation of absence seizures [21] , [22] . To this end , the excitatory coupling strength is employed to control the activation level of SNr and a three-step strategy is pursued in the present work . In this and next subsections , we assess the individual roles of two different pathways emitted from SNr to thalamus ( i . e . , the SNr-TRN and SNr-SRN pathways ) in the control of absence seizures and discuss their corresponding biophysical mechanisms , respectively . In the final two subsections , we further analyze the combination effects of these two pathways on absence seizure control and extend our results to more general cases . To explore the individual role of the SNr-TRN pathway , we estimate both the state regions and frequency characteristics in the ( ) panel . Note that during these investigations the SNr-SRN pathway is artificially blocked ( i . e . , ) . With this “naive” method , the modulation of absence seizure activities by the SNr-SRN pathway is removed and the effect caused by the SNr-TRN pathway is theoretically amplified to the extreme . Similar to previous results , we find that the whole ( ) panel can be also divided into four different regions ( Fig . 3A ) . These regions are the same as those defined above . For weak inhibitory coupling strength , increasing the excitatory coupling strength moves the model dynamics from the SWD oscillation state to the saturation state . Here we have to notice that the saturation state is a non-physiological brain state even though it does not belong to typical seizure activities . In strong region , the suppression of SWDs is observed by decreasing the excitatory coupling strength , suggesting that inactivation of SNr neurons may result in seizure termination through the SNr-TRN pathway ( Fig . 3A , right side ) . For strong enough inhibitory coupling strength , such suppression effect is rather remarkable that sufficiently low activation of SNr can even kick the network dynamics into the low firing region ( compare the results in Figs . 3C and 3D ) . The SNr-TRN pathway induced SWD suppression is complicated and its biophysical mechanism is presumably due to competition-induced collision . On the one side , the decrease of excitatory coupling strength inactivates the SNr ( Fig . 3E , top panel ) , which should potentially enhance the firing of TRN neurons . On the other side , however , increasing the activation level of TRN tends to suppress the firing of SRN , which significantly reduces the firing of cortical neurons and in turn inactivates the TRN neurons . Furthermore , the inactivation of cortical neurons also tends to reduce the firing level of TRN neurons . As the excitatory coupling strength is decreased , the collision caused by such complicated competition and information interactions finally leads to the inactivation for all the TRN , SRN , and cortical neurons ( Fig . 3E , bottom panel ) , which potentially provides an effective mechanism to destabilize the original pathological balance within the corticothalamic system , thus causing the suppression of SWDs . Indeed , we find that not only the dynamical state but also the oscillation frequency is greatly impacted by the activation level of SNr , through the SNr-TRN pathway . For both the simple and SWD oscillation patterns , increasing the excitatory strength can enhance their dominant frequencies . The combined results of Figs . 3A and 3B reveal that , for a fixed , whether the model can generate the SWDs within the typical 2–4 Hz is determined by at least one and often two critical values of ( Fig . 3B , asterisk region ) . Because the activation level of SNr is increased with the growth of , this finding further indicates that , due to effect of the SNr-TRN pathway , the model might exist the corresponding low and high triggering mean firing rates ( TMFRs ) for SNr neurons ( Fig . 3E , dashed lines ) . If the long-term mean firing rate of SNr neurons falls into the region between these two TMFRs , the model can highly generate typical 2–4 Hz SWDs as those observed on the EEG recordings of absence epileptic patients . In Fig . 3F , we plot both the low and high TMFRs as a function of the inhibitory coupling strength . With the increasing of , the high TMFR grows rapidly at first and then reaches a plateau region , whereas the low TMFR almost linearly increases during this process . Consequently , it can be seen that these two critical TMFRs approach each other as the inhibitory coupling strength is increased until they almost reach an identical value ( Fig . 3F ) . The above findings indicate that the SNr-TRN pathway may play a vital role in controlling the absence seizures and appropriately reducing the activation level of SNr neurons can suppress the typical 2–4 Hz SWDs . The similar antiepileptic effect induced by inactivating the SNr has been widely reported in previous electrophysiological experiments based on both genetic absence epilepsy rats and tottering mice [21]–[23] , [26] . Note that , however , in literature such antiepileptic effect by reducing the activation of SNr is presumed to be accomplished through the indirect SNr-TRN pathway relaying at superior colliculus [21] , [22] . Our computational results firstly suggest that such antiepileptic process can be also triggered by the direct SNr-TRN GABAergic projections . Combining these results , we postulate that for real absence epileptic patients both of these two pathways might work synergistically and together provide a stable mechanism to terminate the onset of absence epilepsy . We next turn on the SNr-SRN pathway and investigate whether this pathway is also effective in the control of absence seizures . Similar to the previous method , we artificially block the SNr-TRN pathway ( i . e . , ) to enlarge the effect of the SNr-SRN pathway to the extreme . Fig . 4A shows the two-dimensional state analysis in the ( ) panel , and again the whole panel is divided into four different state regions . Compared to the results in Fig . 3A , the suppression of SWDs appears in a relatively weaker region by increasing the excitatory coupling strength . This finding suggests that the increase in the activation of SNr can also terminate the SWDs , but through the SNr-SRN pathway . For relatively weak within the suppression region , the SNr-SRN pathway induced suppression is somewhat strong . In this case , the high activation level of SNr directly kicks the network dynamics into the low firing region , without undergoing the simple oscillation state ( Fig . 4C2 and compare with Fig . 4C3 ) . Note that this type of state transition is a novel one which has not been observed in the SWD suppression caused by the SNr-TRN pathway . For relatively strong within the suppression region , the double peak characteristic of SWDs generated by our model is weak . In this situation , as the inhibitory coupling strength is increased , we observe that the network dynamics firstly transit from the SWD oscillation state to the simple oscillation state , and then to the low firing state ( Fig . 4C3 ) . To understand how the SNr-SRN pathway induced SWD suppression arises , we present the mean firing rates of several key neural populations within the corticothalamic system , as shown in Fig . 4D . It can be seen that increasing the strength significantly improves the activation level of SNr ( Fig . 4D , top panel ) , which in turn reduces the firing of SRN neurons ( Fig . 4D , bottom panel ) . The inactivation of SRN neurons further suppresses the mean firing rates for both cortical and TRN neurons ( Fig . 4D , bottom panel ) . These chain reactions lead to the overall inhibition of firing activities in the corticothalamic system , which weakens the double peak shaping effect due to the slow kinetics of receptors in TRN . For strong , such weakening effect is considerable , thus causing the suppression of SWDs . Our results provide the computational evidence that high activation of SNr can also effectively terminate absence seizure activities by the strong inhibition effect from the SNr-SRN pathway . Compared to the SWD suppression induced by the SNr-TRN pathway , it is obvious that the corresponding biophysical mechanism caused by the SNr-SRN pathway is simpler and more direct . Moreover , our two-dimensional frequency analysis indicates that the dominant frequency of neural oscillations depends on the excitatory coupling strength ( see Fig . 4B ) . For a constant , progressive increase of reduces the dominant frequency , but not in a very significant fashion . Thus , we find that almost all the SWD oscillation region identified in Fig . 4A falls into the typical 2–4 Hz frequency range ( Fig . 4B , asterisk region ) . Unlike the corresponding results presented in previous subsection , the combination results of Figs . 4A and 4B demonstrate that the BGCT model modulated by the isolated SNr-SRN pathway only exhibits one TMFR for SNr neurons . For a suitably fixed strength , the generation of SWDs can be highly triggered when the mean firing rate of SNr neurons is lower than this critical firing rate ( Fig . 4D , dashed line ) . With the increasing of , we observe that this TMFR rapidly reduces from a high value to a low value ( Fig . 4E ) . Note that this decreasing tendency is in contrast with our previous finding based on the model modulated by the isolated SNr-TRN pathway ( compared to the results in Fig . 3F ) . Taken together , these observations suggest that increasing the activation level of SNr neurons also significantly influences the dynamics of the corticothalamic system and causes the suppression of absence seizure activities . To the best of our knowledge , this is a new finding that underscores the importance of the direct inhibitory SNr-SRN pathway in controlling and modulating absence seizure activities . It is reasonable to believe that several other external factors , which are able to enhance the activation level of SNr , may also result in the termination of absence seizures due to the similar mechanism . So far , we have confirmed that the absence seizure activities generated by the BGCT model can be inhibited by either the isolated SNr-TRN pathway or the isolated SNr-SRN pathway , through different biophysical mechanisms . In real brain , however , both of these pathways should be available and work together at the same time . Thus , an important and naturally arising question is whether these two types of seizure control can coexist in the same network , and if possible , whether this feature can be maintained in certain range of parameters . To address this issue , we introduce a scale factor and set the coupling strength . By utilizing this method , we can flexibly control the relative coupling strength between the SNr-TRN and SNr-SRN pathways and discuss the combination roles of these two inhibitory pathways in detail . Fig . 5A shows the state analysis in the ( ) panel with . Unlike the previous results , here we only discover three dynamical state regions , which correspond to: the SWD oscillation state ( II ) , the simple oscillation state ( III ) , and the low firing state ( IV ) . The disappearance of the saturation state is at least due to the following two reasons: ( 1 ) the double suppression from the SNr-TRN and SNr-SRN pathways and ( 2 ) the relatively strong inhibitory effect from TRN to SRN . As shown in Fig . 5A , the phenomenon of the SWD suppression appears in the strong region . For relatively weak within this suppression region , both increasing and decreasing the activation of SNr neurons from the normal level effectively inhibit the generation of SWDs ( see Fig . 5A ) . Such bidirectional suppression behavior can be attributed to the effective competition between the SNr-TRN and SNr-SRN pathways . As the scale factor is increased , the enhancement of the SNr-TRN pathway breaks the original competition balance between these two inhibitory pathways . During this process , the inhibition from the SNr-TRN pathway progressively dominates the model dynamics . Accordingly , for sufficiently strong , the suppression of SWDs is only found by lowing the activation of SNr ( Fig . 5A ) , which is consistent with our previous critical observation given in Fig . 3A . However , as described above , the increase in the excitatory coupling strength significantly reduces the dominant frequency of SWDs , through the SNr-TRN pathway . Therefore , although enhancing the activation level of SNr neurons cannot inhibit the SWDs directly , it tends to push the dominant frequency of SWDs below 2 Hz ( Fig . 5B ) . By combining the results of Figs . 5A and 5B , we successfully outline the SWD oscillation region that falls into the 2–4 Hz frequency range ( asterisk region ) . In this typical SWD region , the model exhibits both the low and high TMFRs of the SNr neurons for a fixed ( Figs . 5B and 5C ) . The generation of SWDs within the typical frequency range can be highly triggered if the mean firing rate of SNr neurons is between these two critical TMFRs ( Fig . 5C ) . Nevertheless , due to the competition between the SNr-TRN and SNr-SRN pathways , the firing activities of the TRN , SRN , and cortical neurons become more complicated , compared to the cases induced by any isolated pathway . For a constant , such competition creates a bell-shaped MFR curve for each key neural population by tuning the excitatory coupling strength , ( see Fig . 5C ) . To further investigate how the scale factor impacts the low and high TMFRs , we plot these two TMFRs as a function of in Fig . 5D . With the increasing of relative strength , both of these two critical TMFRs are rapidly changed but in different fashions . The high TMFR is increased from a relatively low value to saturation , whereas the low TMFR is reduced from a relatively high value to 0 ( see Fig . 5D ) . Obviously , such opposite tendencies of these two TMFRs are attributed to the combination effects of the SNr-TRN and SNr-SRN pathways . Furthermore , we also find that both the suppression of SWDs and the typical 2–4 Hz SWD region are shaped by the strength of inhibitory projections from the TRN to SRN . In Figs . 6A and 6B , we perform a series of two-dimensional state and frequency analysis in the ( ) panel for different values of . When the inhibitory coupling strength is too weak , the SWDs generated by the corticothalamic system is mainly controlled by the SNr-SRN pathway . In this case , the suppression of SWDs is observed in the intermediate region and only increasing the activation level of SNr can effectively inhibit the SWDs ( Fig . 6A ) . As the coupling strength is increased , the inhibition from SNr-TRN pathway starts to influence the model dynamics . This introduces the competition between the SNr-SRN and SNr-TRN pathways , leading to the emergence of the bidirectional suppression of SWDs for intermediate ( Fig . 6A ) . It is obvious that the higher the coupling strength , the stronger the inhibition effect caused by the SNr-TRN pathway . With increasing the value of , such strengthened inhibition significantly moves the boundary of the low TMFR toward higher values of , and thus notably shrinks the region of SWDs within the typical frequency range of 2–4 Hz ( Fig . 6B ) . These above observations emphasize the importance of the combination role of both the SNr-TRN and SNr-SRN pathways on the control of absence seizure activities . Quite remarkably , we observe that the bidirectional suppression of SWDs emerges under suitable conditions , that is , both increasing and reducing the activation levels of SNr neurons effectively suppress the SWDs . Such bidirectional suppression is determined and modulated by both the relative strength of these two inhibitory pathways and the strength of inhibitory projections from the TRN to SRN . This novel finding indicates the possible bidirectional control of absence seizures by the basal ganglia , which is induced by the competition between the SNr-TRN and SNr-SRN pathways . In additional simulations , we have found that several other factors , which can effectively change the activation level of SNr , can also lead to the bidirectional suppression of SWDs due to the similar mechanism , further demonstrating the generality of our results . In above subsections , we focused on one specific pathological factor and have computationally shown that the basal ganglia may bidirectionally control and modulate the typical absence seizure activities ( i . e . , the SWDs ) induced by the slow synaptic kinetics of in TRN . A natural question to ask is whether such bidirectional control feature caused by the basal ganglia is a generalized regulatory mechanism for absence seizures . We argue that this might be true . To check this postulation , at least one additional SWD generation mechanism should be introduced into our model , and we need to examine whether the bidirectional control of absence seizures by the basal ganglia is also available for the new pathological factor . In literature , there are several other theories that are associated with the generation mechanisms of SWDs . A boldly accepted one is related to the transmission delay between the cerebral cortex and thalamus and , specifically , it has been found that suitably choosing such transmission delay can drive the corticothalamic system produce the SWDs [11] , [33] . To apply this pathological factor in our model , here we block the pathway from TRN to SRN , and consider a bidirectional transmission delay between the cerebral cortex and thalamus , as that used in previous studies [11] , [33] . Additionally , several coupling strengths within the corticothalamic loop are also needed to be adapted , because such transmission delay induced SWDs require strong interactions between the cerebral cortex and thalamus [11] , [33] . The new added and modified model parameters that we used in this subsection are as follows [11] , [33]: , , , , and . For simplicity , we term the current model as the modified model in the following studies . Figs . 7A and 7B show an example pair of state analysis and frequency analysis in the ( ) panel , respectively . As expected , due to the competition between the SNr-TRN and SNr-SRN pathways , we observe the significant bidirectional control feature for intermediate scale factor ( Fig . 7A , the region between dashed lines ) . In this bidirectional region , both enhancing and lowing the excitatory coupling strength push the model dynamics from the SWD oscillation state into the simple oscillation state ( Fig . 7C ) , thus inhibiting the generation of SWDs . This finding supports our above hypothesis that under suitable conditions the basal ganglia may control and modulate the absence seizure activities bidirectionally . However , compared to the results in Fig . 5 , we find that the bidirectional region appears in a relatively larger region for the modified model , and increasing the coupling strength cannot kick the model dynamics into the low firing state as well . This is not so surprising because these two SWD generation mechanisms that we used are similar but not completely identical . As we introduced above , the SWDs induced by the corticothalamic loop transmission delay require relatively stronger interactions between the cerebral cortex and thalamus , which essentially weaken the inhibitory effect from the SNr neurons . This might lead that the firing of SRN neurons cannot be fully suppressed even when the activation level of SNr reaches its saturation state . Another important finding that we discover here is that the dynamics of the modified model become complicated for large and strong ( see Fig . 7A , upper right ) . In additional simulations , we further perform a series of state analysis for the modified model using the same group of parameter values but different random initial conditions ( see Fig . S3 ) . The corresponding results indicate that the modified model shows bistability ( the simple oscillation state or the saturation state ) in the large and strong region , and final model dynamics significantly depend on the initial conditions . In conclusion , these findings further stress the combination role of the inhibitory SNr-TRN and SNr-SRN pathways on the control of absence seizure activities . By combining all of our results , we postulate that the bidirectional control by the basal ganglia is possible a generalized regulatory mechanism for absence seizures and may be extendable to other pathological factors , even though the detailed bidirectional control behaviors may not be completely identical for different pathological factors .
Using a mean-field macroscopic model that incorporates the basal ganglia , cerebral cortex and thalamus , we presented here the first investigation on how the basal ganglia control the absence epilepsy through the projections directly emitted from the SNr to several key nuclei of thalamus . Through simulations , we demonstrated that the absence seizure activities induced by the slow synaptic kinetics of in TRN can be inhibited by either the isolated SNr-TRN pathway or the isolated SNr-SRN pathway via different biophysical mechanisms . More importantly , our results showed that under certain conditions these two types of seizure control can coexist in the same network , suggesting that both decreasing and increasing the activation levels of SNr may considerably suppress the generation of SWDs . Theoretically , such bidirectional control of absence seizures by basal ganglia is due to the effective competition between the SNr-TRN and SNr-SRN pathways , which might be a generalized mechanism for regulating absence seizure activities and can be extended to other pathological factors . In addition , our detailed frequency analysis also indicated that , depending on different system conditions , the developed model may exist low , high or both TMFRs for the SNr neural population for triggering the typical 2–4 Hz SWDs . These results are partly in agreement with former experimental observations . Previously , experimental studies based on electrophysiological recordings have established the linkage that reducing the activation of SNr neurons from the normal level can effectively suppress the SWDs in different rodent animal models [21]–[23] , [26] . Such antiepileptic effect was supposed to be attributed to the indirect pathway of the SNr to TRN relaying at superior colliculus . The results presented in this work also demonstrated that decreasing the SNr activity is an effective approach that terminates the SWDs . However , it is important to note that in our model the similar antiepileptic effect is triggered by the direct GABAergic projections from SNr to TRN . Presumably , this is because the SNr has overall inhibitory impacts on TRN via both indirect and direct pathways . In the brain of absence epileptic patients , both of these two pathways might work together and provide a stable and endogenous mechanism to terminate the paroxysm of absence epilepsy . Our model further makes prediction that increasing the activation of SNr from the normal level may also suppress SWDs . In previous experimental studies , there still lacks sufficient evidence to support this viewpoint . We speculate that this might be because the relative strength of the SNr-TRN and SNr-SRN pathways for rodent animals is generally high or at least not too low . Under this condition , the BGCT system for most rodent animals does not operate in the bidirectional control region , thus the suppression of SWDs caused by activating SNr is difficult to be observed in normal experiments . According to our results , the activation of SNr induced SWD suppression might appear by suitably tuning the relative strength between these two pathways . Further experiments based on animal models will be necessary to validate this prediction and characterize the detailed nature of the SWD suppression induced by the SNr-SRN pathway . Even so , the above prediction from our computational study might provide an alternative approach for terminating absence seizures . An interesting and important question is: can real brain utilize this bidirectional modulation mechanism to control the paroxysm of absence epilepsy ? Our answer is that it is possible , if the real brain has some mechanisms to automatically adjust the balance between the SNr-TRN and SNr-SRN pathways . Theoretically , there are several possible biological mechanisms and one of them is discussed as follows . Experimental data have uncovered that synapses conduct signals in an unreliable fashion , which is due to the probabilistic neurotransmitter release of synaptic vesicles [44]–[46] . It has been shown that the transmission failure rate at a given synapse generally tends to exceed the fraction of successful transmission , and in some specific cases it can be even higher than 0 . 9 [44]–[46] . Interestingly , recent studies indicated that such synaptic unreliability may play critical functional roles in neural processing and computation [47]–[49] . For patients with absence seizures , the suitable competition between the SNr-TRN and SNr-SRN pathways can be theoretically achieved by properly tuning the synaptic transmission rate of these pathways . This might be an important underlying mechanism and has significant functional advantages , because it does not require the changes of related anatomical structures and connection densities in the brain . However , we should notice that such tuning is not easy . Specifically , it requires the cooperation among neurons and needs to change the synaptic transmission rates collectively for most of relevant synapses . From the functional perspective , this mechanism may be associated with the self-protection ability of brain . After a long time of evolution , it is reasonable to suppose that our brain might have the abilities to use this type of plasticity-like mechanism to achieve complicated self-protection function during absence seizures [49] . Nevertheless , additional well-designed experiments are still needed to test whether our proposed hypothesis is correct . The current results highlight the functional roles of basal ganglia in the control of absence seizures and might offer physiological implications on different aspects . First , the termination of absence seizures by activating SNr neurons through the SNr-SRN pathway might inspire the treatment of refractory absence seizures . Although the absence epilepsy is one typical benign epilepsy , a considerable proportion of patients yet may fail to achieve freedom from absence seizures and become refractory to multiple antiepileptic drugs [50] , [51] . For those patients , one possible reason is that the strengths of their direct and indirect SNr-TRN pathways are somewhat weak , compared to the other patients . In this case , reducing the activation level of SNr may lose efficacy in the suppression of SWDs and , contrarily , increasing the firing of SNr neurons may stop the absence seizures . Second , our results might be generalized to the GPi neurons . In addition to the SNr , the GPi is also an important output structure of basal ganglia . The SNr often works in unison with GPi , since they have closely related inputs and outputs , as well as similarities in cytology and function [30]–[32] . It is thus reasonable to infer that the activation level of GPi might serve a similar bidirectional role in modulating the absence seizures . Third , the results presented in this study may provide new insight into the deep brain stimulation therapy on absence seizures [52] . In previous studies , it has been demonstrated that absence seizures can be inhibited by suitably applying the deep brain stimulation to STN . Our results not only support the traditional viewpoint , but also indicate that the SNr may be a more direct therapeutic target , compared to STN . We thus infer that the inhibition of typical absence seizure activities can also be accomplished by appropriately stimulating the SNr neurons . Finally , the main goal of this study was to explore the control role of basal ganglia in absence seizures , but not limited to this specific epilepsy . Several other types of epilepsies , such as the juvenile myoclonic epilepsy [53] and the generalized tonic-clonic epilepsy [54] , are also highly associated with the corticothalamic system and mediated by GABA receptors . If our above findings on absence seizures capture the real fact , such bidirectional control feature by basal ganglia may be also available for these types of epilepsies . Although our developed model is powerful enough to suggest some functional roles of basal ganglia in controlling and modulating absence seizures , we have to admit that this biophysical model is simplified and idealized . The limitations of this model and possible extensions in future studies need to be discussed . First , more detailed models based on the spiking neurons are able to introduce much more complexities , such as ionic dynamics , firing variability and connection property , to the system . In previous studies , it has been observed that ion concentrations inside the cell and in the extracellular space as well as the firing dynamics of neurons are changed during epileptic seizures [55]–[57] . It has been also found recently that the connection property of neural systems in different scales ( for example , neuronal networks at microscopic scales and brain region networks at macroscopic scales ) also contribute to the generation of seizure-like activities [58] , [59] . Therefore , we cannot exclude other possible interesting results by using more detailed modelling methodologies . Moreover , due to lack of necessary data , our model does not include the indirect pathway from the SNr to TRN , relaying at the intermediate and superficial layers of the caudal superior colliculus [60] . Although we have inferred that both the direct and indirect SNr-TRN pathways play the similar effects on the control of absence seizures , further studies based on detailed anatomical data need to be investigated in the future work . Finally , it should be emphasized that in the present study we mainly focus on the SWDs induced by the slow kinetics of receptors in TRN . Even though we also showed that the similar bidirectional control behavior can be observed for another pathological factor ( i . e . , the corticothalamic loop transmission delay ) , more detailed analysis on this aspect is still needed . As we know , several other pathological factors , such as the increased T-type Ca2+ current in thalamocortical neurons [2] , may also lead to the generation of SWDs . Nevertheless , it is still unknown and deserves to be further examined whether our proposed bidirectional control by basal ganglia is also available for these other pathological factors . Theoretically , a better choice for mimicking the slower dynamics of currents should use relatively smaller values of and at the relevant synapses . However , we have to notice that the mean-filed theory used in the present study is under an assumption of uniform and parameters for all incoming connections of one neural population ( see Eq . 3 ) . To our knowledge , introducing different and to different types of connections will make such simple form of differential operator presented in Eq . 3 become invalid , thus making the dendritic filtering of incoming signals mathematically intractable in the current mean-field framework . Considering this , we employed a delay parameter to represent the order of magnitude of rise and decay time of currents in our model , as the method used in a recent neural mean-field modelling study [13] . Essentially , more detailed models based on spiking neurons will allow us to consider different dynamical processes for different types of synapses [49] , [61] . We are currently trying to extend our above-identified results to a relevant spiking neural network and the corresponding results will be presented elsewhere . It should be also pointed out that traditional modelling studies on absence seizures mainly focus on the possible generation mechanism of the typical absence seizure activities , i . e . , SWDs , within the corticothalamic system , but little research addresses the regulatory mechanisms of absence seizures by basal ganglia . Inspired by previous experimental observations [21]–[23] , [26] and recent modelling studies on Parkinson's disease [30] , [31] , here we introduced the basal ganglia to the traditional mean-field model of corticothalamic system proposed by Robinson and his collaborators , and firstly explored how the basal ganglia control the SWDs through the computational approach . Theoretically , several other models that describe the macro-scale dynamics about the activity of neural ensembles , such as the Wilson-Cowan model [62]–[64] and the models based on dynamic causal modelling [65]–[67] , can be also used to deal with the similar problem but may need to re-estimate parameters according to different model assumptions . In summary , we have performed mechanistic studies and investigated the detailed roles of basal ganglia in the control of absence seizures . We have computationally demonstrated that the SWDs generated by the developed model can be terminated and modulated by both decreasing and increasing the activation levels of SNr . Our results provide the first evidence on the bidirectional control of absence seizures by basal ganglia . This finding might deepen our conventional understanding about the functional roles of basal ganglia in the controlling and modulating of absence seizures . For patients with absence epilepsy , these results in turn indicate that the loss of several basal ganglia functions , especially the functions related to the SNr neurons , might further aggravate the attacks of absence epilepsy . We hope that predictions from our systematic model investigation can not only inspire testable hypotheses for future electrophysiological experiments but also provide additional therapeutic strategies for absence seizures . | Epilepsy is a general term for conditions with recurring seizures . Absence seizures are one of several kinds of seizures , which are characterized by typical 2–4 Hz spike-and-slow wave discharges ( SWDs ) . There is accumulating evidence that absence seizures are due to abnormal interactions between cerebral cortex and thalamus , and the basal ganglia may take part in controlling such brain disease via the indirect basal ganglia-thalamic pathway relaying at superior colliculus . Actually , the basal ganglia not only send indirect signals to thalamus , but also communicate with several key nuclei of thalamus through multiple direct GABAergic projections . Nevertheless , whether and how these direct pathways regulate absence seizure activities are still remain unknown . By computational modelling , we predicted that two direct inhibitory basal ganglia-thalamic pathways emitting from the substantia nigra pars reticulata may also participate in the control of absence seizures . Furthermore , we showed that these two types of seizure control can coexist in the same network , and depending on the instant network state , both lowing and increasing the activation of SNr neurons may inhibit the SWDs due to the existence of competition . Our findings emphasize the bidirectional modulation effects of basal ganglia on absence seizures , and might have physiological implications on the treatment of absence epilepsy . | [
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] | 2014 | Bidirectional Control of Absence Seizures by the Basal Ganglia: A Computational Evidence |
Kawasaki disease ( KD ) is a pediatric vasculitis that damages the coronary arteries in 25% of untreated and approximately 5% of treated children . Epidemiologic data suggest that KD is triggered by unidentified infection ( s ) in genetically susceptible children . To investigate genetic determinants of KD susceptibility , we performed a genome-wide association study ( GWAS ) in 119 Caucasian KD cases and 135 matched controls with stringent correction for possible admixture , followed by replication in an independent cohort and subsequent fine-mapping , for a total of 893 KD cases plus population and family controls . Significant associations of 40 SNPs and six haplotypes , identifying 31 genes , were replicated in an independent cohort of 583 predominantly Caucasian KD families , with NAALADL2 ( rs17531088 , pcombined = 1 . 13×10−6 ) and ZFHX3 ( rs7199343 , pcombined = 2 . 37×10−6 ) most significantly associated . Sixteen associated variants with a minor allele frequency of >0 . 05 that lay within or close to known genes were fine-mapped with HapMap tagging SNPs in 781 KD cases , including 590 from the discovery and replication stages . Original or tagging SNPs in eight of these genes replicated the original findings , with seven genes having further significant markers in adjacent regions . In four genes ( ZFHX3 , NAALADL2 , PPP1R14C , and TCP1 ) , the neighboring markers were more significantly associated than the originally associated variants . Investigation of functional relationships between the eight fine-mapped genes using Ingenuity Pathway Analysis identified a single functional network ( p = 10−13 ) containing five fine-mapped genes—LNX1 , CAMK2D , ZFHX3 , CSMD1 , and TCP1—with functional relationships potentially related to inflammation , apoptosis , and cardiovascular pathology . Pair-wise blood transcript levels were measured during acute and convalescent KD for all fine-mapped genes , revealing a consistent trend of significantly reduced transcript levels prior to treatment . This is one of the first GWAS in an infectious disease . We have identified novel , plausible , and functionally related variants associated with KD susceptibility that may also be relevant to other cardiovascular diseases .
Kawasaki disease ( KD; MIM 611775 ) is an inflammatory vasculitis predominantly affecting young children [1] . It is characterized by a striking propensity for coronary artery damage , which occurs in approximately 25% of untreated and 3–5% of treated children . KD is the commonest cause of heart disease acquired in childhood in developed nations and in those who manifest coronary artery damage , KD may be associated with serious cardiovascular sequelae in adulthood [2] . The long-term cardiovascular implications of KD in those without overt coronary artery lesions are unclear . The etiology of KD is unknown , but it is thought to reflect an abnormal and sustained inflammatory response to one or more infectious triggers in genetically susceptible individuals [1] , [3] . No consistent etiologic agent for KD has been identified , hampering accurate and timely diagnosis and the development of optimal management strategies . The incidence of KD varies markedly in different ethnic groups , with the highest incidence in North East Asian populations . KD affects approximately 1 in 150 Japanese children [1] and is responsible for 1–2% of all pediatric hospital admissions in South Korea [4] . There are strong epidemiologic data to support a substantial genetic contribution to KD susceptibility . The Japanese incidence ( 135–200/100 000<5 years of age ) is 10–15 times greater than the Caucasian incidence ( 9–17/100 000<5 years of age ) [1] and this difference is maintained in American children of Japanese descent resident in the US [5] . Other Asian populations in the UK [6] , [7] and the US [8] also have a significantly higher incidence than non-Asians residing in the same geographic location . The familial inheritance pattern of KD is in keeping with a polygenic complex disease and in multi-case pedigrees , KD occurs in family members at different times and geographic locations [9] . Across all populations KD is approximately 1 . 6 times more common in males [3] . The sibling risk ratio for KD in the Japanese is approximately 10 during an epidemic [10] and 6 overall [11] . KD is over twice as common in the children of parents who themselves had KD in childhood , with multi-generational pedigrees often having more than one child affected , an earlier age of onset and a more severe phenotype [11] . A genome-wide linkage study identified three regions exhibiting modest linkage in Japanese KD sibling pairs [12] . Detailed association analysis of the linkage peak at chromosome 19q13 . 2 identified a significantly associated functional variant in ITPKC ( Gene ID:80271 ) , a negative regulator of T cell activation [13] . In both Japanese and US Caucasian children with KD , this variant was associated with an approximate doubling of KD risk [13] . Other investigations of KD susceptibility determinants to date have been candidate gene association studies . A number of immunologic and cardiovascular-related loci have revealed genetic associations of modest effect size , but studies have often been under-powered and the findings have rarely been replicated in independent populations . As these associated candidate loci are likely to account for only a small proportion of the overall genetic susceptibility to KD , we undertook a genome-wide association study ( GWAS ) to identify novel loci that might mediate susceptibility to KD . We performed the initial GWAS in a well-characterized Dutch Caucasian population and tested the most significantly associated SNPs and haplotypes in a large independent cohort of predominantly Caucasian trios from three countries . Fine-mapping of sixteen variants with a minor allele frequency ( MAF ) of >0 . 05 that lay within or close to known genes identified eight significantly associated variants , five of which may be functionally related .
We included 119 KD cases and 135 controls in the initial GWAS ( Table 1 ) . Ten non-Caucasian subjects were excluded following admixture analysis by Eigenstrat [14] . Three samples were excluded due to genotyping call rates <93% . The final GWA analysis therefore consisted of 107 KD subjects and 134 controls . Of the 262 , 264 SNPs on the Affymetrix 250 K NSP chip , 18 , 211 had a call rate of <93% , 18 , 981 were monomorphic ( MAF<0 . 1% ) and 1 , 150 deviated significantly from HWE in the control group . 223 , 922 SNPs were therefore available for analysis , of which 5 , 571 were on the X chromosome . A total of 14 , 065 SNPs were significantly associated ( p<0 . 05 ) . The quantile-quantile plot between observed and expected allele frequencies showed deviation from expected with p<∼10−4 , suggesting the presence of true associations [15] ( Figure 1 ) . We undertook a replication study in an independent cohort of KD cases and parental controls , using an exact replication strategy , genotyping only the most significantly associated variants by a different genotyping technology . After verifying family relationships and checking sample duplications , 63 samples were excluded from further analysis . Thus 1 , 903 members of 583 KD families , including 498 trios , were tested in the follow-up association analysis . The 1 , 148 most significantly associated SNPs variants ( corresponding to a minimum significance level of p<0 . 0024 ) were selected by a combination of Armitage trend test , recessive-dominant and allelic association analysis ( Figure 2 ) . SNPs were genotyped in the follow-up cohort by a custom Illumina Oligo Pool Assay , in which 1 , 116 SNPs were successfully genotyped ( Table S1 ) . Fifteen SNPs failed quality filters , leaving a total of 1 , 101 SNPs . Significant associations with KD susceptibility were replicated for 40 SNPs ( Table 2 ) . Twenty eight lay either in or within 50 kb of known genes . The most highly associated SNP was located in the gene for N-acetylated alpha-linked acidic dipeptidase-like 2 ( NAALADL2 , Gene ID:254827 ) , ( combined OR from case-control and family analyses = 1 . 43 ( 1 . 32–1 . 53 ) ; pcombined = 1 . 13×10−6 ) . Three SNPs were located in introns of the gene for AT-binding transcription factor 1 ( ZFHX3 , Gene ID:463 ) , all of which had a protective effect ( ORcombined = 0 . 64 ( 0 . 52–0 . 75 ) , 0 . 68 ( 0 . 57–0 . 79 ) , 0 . 73 ( 0 . 62–0 . 84 ) ; pcombined = 2 . 37×10−6 , 7 . 06×10−5 , 5 . 35×10−4 , respectively ) . Two of these SNPs , rs7199343 and rs10852516 , were only 647 bp apart and in high linkage disequilibrium ( LD ) ( r2 = 0 . 93 ) , whereas the third SNP , rs11075953 , was ∼10 kb distant and in less LD ( r2 = 0 . 46 ) . Two SNPs , rs1010824 and rs6469101 , which were 325 bp apart and in complete LD ( r2 = 1 ) , were situated ∼10 kb 3′ of the angiopoietin 1 gene ( ANGPT1 , Gene ID:284 ) ( ORcombined = 0 . 58 ( 0 . 43–0 . 73 ) and 0 . 6 ( 0 . 45–0 . 76 ) ; pcombined = 3 . 39×10−5 and 5 . 44×10−5 , respectively ) . 166 SNPs within the most highly associated thirty five haplotype blocks ( corresponding to p<2×10−3 ) were genotyped using a Sequenom iPLEX platform in the replication cohort ( Table S2 ) . Twenty-nine SNPs could not be genotyped , leaving 137 SNPs for further analysis . Significant associations were verified for six haplotypes , of which four were within known genes . Two haplotypes lay within the gene encoding M-phase phosphoprotein 10 ( U3 small nucleolar ribonucleoprotein ) , MPHOSPH10 ( Gene ID:10199 ) . One of these haplotypes was associated with increased susceptibility ( ORcombined = 3 . 52 ( 1 . 6–7 . 75 ) ; p = 3 . 69×10−3 ) and the other with protection from KD ( ORcombined = 0 . 39 ( 0 . 24–0 . 63 ) ; p = 3 . 91×10−4 ) ( Table 2 ) . In order to narrow the region of association within selected genes , we carried out fine-mapping of sixteen of the genes replicated in the family study ( Table S3 ) . We fine-mapped genes identified by variants with a MAF of >0 . 05 lying within 5 kb of known genes . Polymorphisms from eight of the sixteen fine-mapped genes showed significant combined p-values ( Table 3 ) . In three genes the tagging SNP showed the most significant genetic association; LNX1 ( Gene ID:84708 ) ( rs7660884 , pcombined = 1 . 8×10−3 ) , CAMK2D ( Gene ID:817 ) ( rs11728021 , pcombined = 1 . 3×10−2 ) and CSMD1 ( Gene ID:64478 ) ( rs2912272 , pcombined = 3 . 5×10−2 ) , indicating that the initial GWAS probably identified a disease-associated haplotype . Conversely within NAALADL2 , the most significantly associated gene identified in the replication study , a polymorphism ( rs1870740 ) located 23 kb away from the tagging SNP ( rs2861999 ) was the most significantly associated ( pcombined = 9×10−4 ) . A linkage disequilibrium plot of this region indicated that these two SNPs belong to distinct haplotype blocks ( Figure 3 ) . The transcription factor ZFHX3 had a number of SNPs with more significant associations than the variant tagging the replicated polymorphism ( Figure S2 ) . Linkage disequilibrium plots of all fine-mapped genes are presented in the online supplemental material ( Figure S3 , S4 , S5 , S6 , S7 , and S8 ) . In order to investigate differences in allele frequencies of associated SNPs between Japanese , where the incidence of KD is approximately twenty times higher than that of Caucasians , we compared MAF data for associated variants using HapMap . In addition we investigated the ancestral allele of each associated SNP from available data from higher primates ( Chimpanzee and Macaque ) ( Table S4 ) . All ( but one ) associated alleles of SNPs within ZFHX3 had higher frequencies in the Japanese population . The associated allele ( T ) from the most significantly associated SNP ( rs9937546 pcombined = 1 . 9×10−4 ) , had an allele frequency of 0 . 922 in the Japanese , compared to 0 . 633 in Caucasians . Despite the high frequencies in human populations , the rs9937546 T allele was not the ancestral allele , which might indicate rapid fixation in the population due to an unidentified evolutionary advantage . In contrast the associated allele of rs2912272 from CSMD1 , was absent in Japanese populations , which could indicate genetic heterogeneity in this gene , with other variants associated with susceptibility in the Japanese . In CSMD1 the ancestral allele is the major allele in humans . We explored possible functional relationships between the eight genes confirmed by fine-mapping using the Ingenuity Pathway Analysis ( IPA ) Knowledge Base . Unsupervised IPA network analysis identified a single cluster of 35 genes that included five of the eight associated genes and 26 additional genes , that was unlikely to occur by chance ( p = 10−13 ) . Highlights from the group are shown in Figure 4 , concentrating on the close connection between four of the associated fine-mapped genes and eight IPA identified genes that create a plausible biological network . We investigated the whole blood transcript levels of the eight fine-mapped associated genes , and three of the IPA identified genes , using TaqMan quantitative PCR , in 27 patients with paired samples from their acute KD stage ( prior to treatment ) and their convalescence , using the transcript levels of the gene 18S as a loading control . The blood cell profiles of these patients have been described previously [16] . Of the 54 RNA samples , two pairs were excluded because of inadequate RNA quality . Of the eight GWAS identified genes , five showed significantly lower abundance at the acute phase compared to convalescence , while two showed no change and expression of one was not detected in peripheral blood ( Table 4 ) . For the three IPA identified genes investigated , one showed significantly higher abundance , one showed significantly lower abundance and one showed no significant difference between acute and convalescence samples ( Table 4 ) . NAALADL2 ( NM_207015 ) showed the largest fold change ( FC ) ( FC = −6 . 3 , p = 1 . 55×10−4 ) , while CAMK2D ( NM_001221 ) showed the most significant difference ( FC = −2 . 5 , p = 1 . 53×10−7 ) ( Table 4 ) . Relative transcript abundance is represented in the network diagram ( Figure 4 ) , with red indicating greater abundance in acute samples compared to convalescent samples , green lower abundance and yellow no change .
To our knowledge , this is among the first GWAS of an infectious disease and the first GWAS of KD . We have identified a number of novel variants using a staged study design and subsequent fine-mapping that are associated with KD susceptibility . These include variants within or close to genes that are functionally inter-related and that are plausible biological candidates in the KD pathogenesis . The magnitude of the effect sizes for KD susceptibility is comparable to that reported from other GWAS [17] . Fine-mapping of associated and replicated SNPs has focused on more frequent variants that lie in known genes . In eight of these sixteen genes , fine-mapping confirmed the association and identified one or more associated haplotype ( s ) , which will form the basis of resequencing to identify the disease-modifying variants . The assertion that these variants are in ( or close to ) biologically relevant loci involved in KD susceptibility is supported by; ( i ) identification of eight loci containing one or more independently associated haplotypes identified by GWAS , replicated in an independent family-based study and subsequently fine-mapped , ( ii ) the significant differential gene mRNA transcript abundance of 5 of the 7 blood-expressed fine-mapped genes during acute versus convalescent KD , and ( iii ) the gene network analyses that suggest biologically plausible functional relationships , which are extremely unlikely to have occurred by chance , exist between five of the associated loci . We focused on fine-mapping of associated SNPs that lie either in or within 5 kb of known genes and had a MAF of >0 . 05 in HapMap . These data represent the most robust associations and we will therefore focus our discussion on those genes , where putative functional relationships were suggested by IPA . We used IPA in an unsupervised manner , allowing identification of gene-gene relationships without a priori assumptions . This analysis linked five of the eight genetically associated genes , of which four form a functionally closely related network linked to eight other nodes in a highly significant network . The gene network suggests possible mechanisms by which one or more infectious triggers may lead to dysregulated inflammation and apoptosis , and cardiovascular pathology . Central to the putative gene network is CAMK2D ( calcium/calmodulin-dependent protein kinase ( CaM Kinase ) II delta ) , whose expression was significantly down regulated during acute KD . CAMK2D encodes the δ-isoform of CaM kinase II ( NP_001212 ) , a ubiquitously expressed calcium sensitive serine/threonine kinase . The δ-isoform of CaM kinase II is the predominant form expressed in cardiomyocytes and vascular endothelial cells [18] and is involved in a number of pathophysiological processes that make it an attractive candidate in KD . In vascular endothelial cells CaM kinase II mediates nitric oxide ( NO ) production by endothelial synthase ( NOS3 , NP_000594 ) in response to changes in intracellular calcium and NO causes local vasodilatation [18] . In acute KD NO production is increased and NO metabolites decrease following successful treatment [19] . Following KD , especially where there has been overt CA damage , there is endothelial dysfunction and impaired vasodilatation , which can be restored after administration of antioxidants that may increase local availability of NO [20] . More chronically , NOS3 may become dysregulated ( ‘uncoupled’ ) and produce potentially harmful superoxide anions , resulting in chronic oxidant stress that is implicated in the pathogenesis of atherosclerosis [21] . In those with severe KD , NOS3 is expressed in coronary artery aneurysm tissue removed at surgery , and the tissue shows a pattern of senescence that is also typical of atherosclerosis [22] . Involvement of leukocyte expressed CaM kinase II in blood vessel damage and aneurysm formation , key features of KD , is also plausible . In human monocytes , CaM kinase II modulates tumor necrosis factor-induced expression of CD44 ( NP_000601 ) , which has a central role in leukocyte migration and extravasation at inflammatory sites [23] . CaM kinase II is also involved in disruption of the endothelial barrier following stimulation with agonists such as thrombin [24] , whose levels may be increased following KD [25] . Disruption of barrier integrity in coronary arteries may contribute to leukocyte infiltration into the vessel wall , proteolysis of extracellular matrix proteins and the internal elastic lamina and subsequent coronary artery aneurysm formation , that is pathognomonic of KD [1] . In addition , CaM Kinase II regulates endotoxin- and TNF-mediated apoptosis in human promonocytic cells by regulating the anti-apoptotic gene BIRC3 ( Gene ID:330 ) [26] . Delayed apoptosis of leukocytes is characteristic of acute KD and may contribute to pathogenesis [27] . Intravenous immunoglobulin ( IVIg ) , standard therapy for KD , induces apoptosis of neutrophils in acute KD [28] . In a genome-wide transcriptional study of KD , there was a marked over-representation of apoptosis regulatory genes [16] . Both CaM Kinase II and the product encoded by another fine-mapped gene LNX1 , i . e . ligand of numb-protein X1 ( NP_116011 ) , interact with the NUMB family of proteins [29] , [30] . Interestingly , one of the NUMB family members , NUMBL ( numb homolog ( Drosophila ) -like , Gene ID:9253 ) lies in the same small haplotype block that has recently been associated with KD susceptibility by a linkage study and subsequent fine-mapping [12] , [13] . The NUMB gene ( Gene ID:8650 ) showed significantly higher transcript abundance during acute KD . Both LNX1 and LNX2 ( Gene ID:222484 ) ( a closely related gene identified by the IPA network ) encode proteins that bind the coxsackievirus and adenovirus receptor ( CXADR , Gene ID:1525 ) [31] . CXADR is the receptor for coxsackievirus B3 , which causes myocarditis in humans . The myocarditis can be prevented in animal models by antagonizing viral binding to CXADR ( NP_001329 . 1 ) [32] . Coxsackievirus B3 has also been implicated in acute myocardial infarction [33] . Interestingly , the human endogenous retrovirus K protein Np9 also interacts with LNX1 [34] and therefore a number of viruses may theoretically bind the NUMB/CAR/LNX1 complex , leading to internalization and regulation of CAMK2D activity . This suggests a possible mechanism whereby more than one infectious trigger may result in cardiovascular damage in genetically susceptible individuals suffering from KD . Other fine-mapped IPA-networked genes include ZFHX3 ( also known as ATBF1 ) , which encodes a large enhancer-binding transcription factor that is known to be polymorphic [35] and interacts with a number of proteins , including PIAS3 ( protein inhibitor of activated STAT , NP_006090 ) that inhibits STAT3 ( signal transducer and activator of transcription-3 , NP_644805 ) [36] . STAT3 is activated by interleukin 6 ( IL6 , NP_000591 ) a pro-inflammatory cytokine that is involved in early innate immune reactivity , as indicated by the high fever , acute phase response with increased levels of CRP ( NP_000558 ) , complement factors and fibrinogen , in the blood as well as the myriad of cellular markers altered in acute KD [37] . ZFHX3 also interacts with MYH7 ( myosin , heavy chain 7 , cardiac muscle , beta , NP_000248 ) , in which mutations are known to cause an inherited form of cardiomyopathy [38] . CSMD1 ( CUB and Sushi multiple domains 1 ) , which is functionally related to CaM kinase II via histone deacetylase 4 ( HDAC4 , Gene ID:9759 ) , may be associated with dampening the early phase of KD . CSMD1 is located on chromosome 8 , in a region that is hypervariable in humans and which contains numerous immune-related genes [39] . Activation of the classical complement pathway occurs in acute KD [40] , and CSMD1 ( NP_150094 ) is a complement regulatory protein that blocks the classical but not alternate complement pathway [41] . The functions of other fine-mapped genes are generally poorly understood . The most significantly associated gene ( NAALADL2 , N-acetylated alpha-linked acidic dipeptidase-like 2 ) , which also showed the greatest change in transcript levels between acute and convalescent KD , is a large gene of 32 exons spanning 1 . 37 Mb . NAALADL2 undergoes extensive alternative splicing leading to multiple 5′ and 3′ untranslated regions and variable coding sequences . Its function is largely unknown , but mutations in the gene may contribute to Cornelia de Lange syndrome ( OMIM: 122470 ) [42] . Overall we have identified five genetically associated genes that also had significantly reduced transcript levels during acute KD , including three that are closely functionally related ( Figure 4 ) , suggesting that these genes may act together . This novel network may be distinct for KD as differences in transcript abundance in these genes have not been previously described as being part of a typical inflammatory response expressed in blood cells . Pathogen-specific host responses , identified by relative transcript abundance in the blood have been described for other infectious diseases [43] . Investigation of transcriptome abundance in whole blood rather than specific cell populations allows assessment of the entire peripheral blood transcriptome [43] and may be particularly informative in diseases such as KD , where an infectious trigger is implicated but remains unidentified [1] . In a genome-wide gene expression study of KD , variation in neutrophil and lymphocyte numbers , characteristic of acute KD [44] , [45] , were thought to account for approximately half of the variation in transcript abundance during the course of the KD illness [16] . Although we did not investigate individual cell populations in the current study , the data suggest that relative changes in transcripts reflect qualitative as well as quantitative differences . Given the enrichment of the expression profile with immune-related genes ( selected on the basis of associated loci ) , the changes in mRNA may not be more numerous than those expected by chance and do not provide definitive proof for the gene-specific associations . While the number of subjects in our expression study is large enough to identify overall trends in the host response during KD , we are unable to comment on expression-related allelic association , which will be investigated in future studies . There is a suggestion from peripheral blood expression data in KD that ‘person-specific’ gene expression patterns , possibly reflective of underlying genetic variation , may be present [16] . Further investigation of the relationship between genomic associations and gene expression will be undertaken , although clearly genetic variants may be significantly associated with disease without resulting in alterations in gene expression . Our sample of 893 cases represents a large genetic KD cohort drawn from a single ethnic group . KD shares many features of other infectious diseases of young children , including fever , rash and changes to the mucous membranes . There is no diagnostic test and laboratory parameters individually have insufficient sensitivity or specificity for diagnosis [1] . In all study cohorts we employed a conservative and widely accepted KD case definition in an attempt to maximize phenotypic homogeneity and diagnostic specificity . The similar ethnicity and ascertainment of KD cases in all cohorts reduces the risk of spurious associations [15] . Our methodological approach is consistent with current best practice recommendations in GWAS design and analysis , which are aimed to identify robust associations and reduce type 1 errors [15]: ( i ) the discovery and replication cohorts were recruited using very similar ascertainment techniques and drawn from predominantly Caucasian populations , with careful analysis to exclude cryptic population admixture in the discovery phase , which used a case-control design; ( ii ) the variants selected for replication were predominantly selected using single-point analysis , although we employed other models , including haplotypic analysis to maximize the informativeness of the initial GWAS data; ( iii ) we employed different genotyping technologies in each of the discovery , replication and fine-mapping stages to reduce spurious associations arising from genotyping errors; ( iv ) we limited our replication genotyping solely to variants identified in the discovery phase , as additional fine-mapping around associated variants in the replication phase may increase spurious associations [46]; ( v ) we used a staged study design to avoid conservative correction for multiple statistical comparisons that might mask associations of moderate effect size in this modestly sized sample; ( vi ) we present joint analysis of the discovery and replication data , rather than considering the replication data in isolation and; ( vii ) we have fine-mapped variants with a MAF>0 . 05 which lie within or close to known genes . We are aware that the genomic coverage and power of the discovery phase of the GWAS were limited and calculate that the initial GWAS had only approximately 50% power to detect an OR of 2 . 0 with alpha<0 . 05 . Our relatively modest sample size reflects the difficulties in recruiting for a relatively rare disease in which the phenotype is defined clinically . Our approach therefore aimed to reduce the risk of type I errors by ensuring that a large and independent replication cohort was included as part of the initial design , as we did not expect the associated variants to reach genome-wide significance , given the cohort size in the GWAS discovery phase [47] . It was therefore expected that neither previously reported and credible candidate gene associations in KD , such as IL4 ( Gene ID:3565 ) [48] , VEGFA ( Gene ID:7422 ) [49] , [50] , CCR5 ( Gene ID:1234 ) [51] , and MBL2 ( Gene ID:4153 ) [52] , nor the recently reported ITPKC variant [12] were replicated by the GWAS . Our study has failed to identify these and almost certainly other as yet unidentified variants that represent additional major determinants of KD susceptibility . We have identified a number of novel associated SNPs , confirmed by fine-mapping , which lie within or close to previously unrecognized candidates for KD . The effect sizes , independent verification in different populations , differential transcript abundance and network analyses all indicate that at least a proportion of these variants represent novel genetic risk factors for KD . Some of the associated genes may interact to mediate the deleterious effects of infection-driven inflammation on the cardiovascular system . Further characterization of the associated genes and their functional interactions may lead to the identification of novel diagnostic and therapeutic targets in KD and may be informative about early pathogenic processes in other cardiovascular diseases .
We used a staged study design with an initial GWAS , an exact replication phase in an independent cohort and subsequent fine-mapping of common variants lying within or near known genes . We performed the initial GWAS analysis in a Dutch Caucasian case-control sample ( the ‘discovery phase’ ) and re-tested the most significantly associated SNPs and haplotypes in an independent sample of KD trios from Australia , the US and the UK , using a different genotyping technology ( the ‘replication phase’ ) . Finally , a fine-mapping stage including sixteen replicated genes was performed in a subset of samples from discovery and replication phases , again using a different genotyping platform . KD was defined by the presence of prolonged fever , together with at least four of the five classical diagnostic criteria [53] . Children with at least five days of fever and two diagnostic criteria with echocardiographic changes of coronary artery damage during the acute and/or convalescent phases of KD were also included , as these coronary artery manifestations are pathognomonic for KD [53] . Details of clinical symptoms of our study group can be found on Table S5 . Cases of incomplete KD , who have fever , less than 4 diagnostic criteria and no coronary artery manifestations ( who constitute approximately 15% of KD cases receiving clinical treatment [54] ) , were excluded , to maximise the homogeneity of the clinical phenotype . In all cohorts clinical and laboratory data were obtained directly from patient medical files and supplemented with parental questionnaires . Phenotypic data were reviewed in all cases by experienced pediatricians . Ethical approval was obtained from the appropriate national and regional institutional review boards for each study population ( Academic Medical Center [AMC] , Amsterdam ( Dutch cohort ) , UK Multi-Centre Research Ethics Committee ( UK cohort ) , each participating tertiary pediatric hospital's ethics committee ( Australian cohort ) and the University of California at San Diego ( US cohort ) ) . Informed consent and assent as appropriate were obtained from participating families . The initial GWAS was performed on 119 Dutch Caucasian KD cases and 135 healthy controls . The cases were identified by collaborating pediatricians and sent for cardiological evaluation during the acute stage and subsequent follow-up to the AMC . The controls were unrelated adult Caucasian blood donors residing in the same geographical area . Ethnicity was determined by self or parental ethnic identification . Assessment for possible population stratification was performed with Eigenstrat [14] . Principal component analysis was applied to the genotype data to infer the axes of variation . We used the top two principal components to identify outliers . Any sample with principal component exceeding six standard deviations from the mean was identified as an outlier . This process was repeated five times . A lambda genomic control ( λGC ) , representation of stratification estimated after dividing the median ( chi-square ) by 0 . 456 , was calculated before and after running Eigenstrat . We observed a λGC = 1 . 18 before removal of potential outliers . After running Eigenstrat ( sigma 6 . 0 , 5 iterations , n = 10 individuals removed ) λGC dropped to 1 . 1 . Dividing chi-square values by λGC we were able to correct for possible existence of population admixture ( Table 2 ) . Sample duplication and family relationships were assessed by RelPair [55] . A second independent cohort , which consisted of 583 KD-affected families , including complete and incomplete trios , from Australia , the US and the UK , was used to replicate the most significantly associated variants identified in the GWAS . Family KD cases in each country were identified from pediatric hospital databases , through KD parent support groups and through media releases . Biological parents ( where available ) and unaffected siblings ( to reconstruct missing parental genotypes ) were recruited . A subset of samples from our GWA and follow up cohorts was genotyped in a fine-mapping experiment . Due to limitations in DNA template we could include ∼85% of the original samples . However , a new set of 493 samples were added in the case-control ( N = 247 ) and family-based cohorts ( N = 246 ) . A principal component analysis comparing Hapmap populations with our cohort was applied to the genotype data of the case-control cohort to infer the axes of variation ( Figure S1 ) . After removal of potential outliers ( N = 113 ) λGC was 1 . 06 . Allele chi-square values were divided by λGC and corrected p-values are reported on Table 4 . Blood was collected into EDTA ( Dutch , UK and US cohorts ) and ADC ( Australian cohorts , on whom Buffy coats were separated ) . Shed buccal cells were collected as previously described [51] . Genomic DNA was extracted by standard protocols . DNA quality was assessed by visual inspection after running 1 . 2% agarose gels and by calculating absorbance ratio OD260 nm/280 nm . DNA quantification was measured using Picogreen dsDNA reagent . Degraded samples or those with low DNA concentration were excluded . RNA samples from acute KD cases were obtained prior to intravenous immunoglobulin treatment and again during convalescence ( within one year n = 17 , after one year n = 10 ) from 27 US children who fulfilled the KD case definition . These KD patients were also analyzed as part of the replication cohort . Whole blood was collected into the PaxGene tubes , according to manufacturer's instructions and RNA was extracted and stored at −80°C for batch analysis . RNA quantification was performed by optical density ( 260 nM ) . We genotyped the Dutch case-control samples using the Affymetrix 250 K NSP chip in accordance with the manufacturer's instructions . Samples with call rates below 93% at p = 0 . 33 after running a BRLMM algorithm ( Bayesian Robust Linear Model with Mahalanobis distance classifier , Affymetrix ) , were re-hybridized and were excluded from further analysis if they failed to achieve the established threshold . Associated individual SNPs from the GWAS were ranked by nominal significance using a combination of allelic association , Armitage trend test and a recessive-dominant model . SNPs that deviated significantly from HWE in the control group after applying a Bonferroni correction , or failed genotyping QC ( call rate<93% , or monomorphic ) were excluded from further analysis . The 1176 most significantly associated SNPs were selected for genotyping in the Australian , UK and US trios by a custom Illumina Oligo Pool Assay . For 28 SNPs genotyping assays could not be designed , leaving 1148 SNPs for the follow-up study . Families were excluded if no familial relationship was detected by RelPair [55] , if there was sample duplication , if the proband genotype was unavailable or if they had more than 3 Mendelian errors . Analysis of the successfully genotyped SNPs was performed with Illumina BeadStudio software . In addition , haplotypic analysis of the GWA data was performed , using a multi-marker sliding window [56] . Genotyping of all 166 SNPs identified by haplotypic analyses was performed by Sequenom iPLEX in the family-based follow-up cohort . For each disease-associated polymorphism verified in the follow-up family study , we investigated whether the variant was within an annotated gene . When the SNP was in an inter-genic region , we analyzed the closest annotated genes situated within 50 kb up- and downstream . Replicated polymorphisms with allele frequencies over 5% , located within genes or five kb up or downstream of a gene were selected for fine-mapping . Sixteen genes fulfilled criteria ( Table S3 ) . Using a SNP-tagging approach ( r2≥0 . 8 ) we selected 1052 SNPs from Hapmap for genotyping with Illumina ISelect Infinium . After standard quality control ( call rate<93% , minor allele frequency below 0 . 01 ) 1 , 003 SNPs were included in the association analysis ( Table S3 ) . Associated genes were investigated for gene ontology information by Ingenuity Pathways Analysis ( IPA ) software ( Ingenuity Systems , www . ingenuity . com ) using an unsupervised analysis . To start building networks , IPA queries the Ingenuity Pathways Knowledge Base for interactions between identified ‘Focus Genes’ , in our case genes detected in the GWAS , and all other gene objects stored in the knowledge base , to generate a set of networks with a maximum network size of 35 genes/proteins . Networks are displayed graphically as genes/gene products ( ‘nodes’ ) and the biological relationships between the nodes ( ‘edges’ ) . All edges are supported by at least one reference from the literature , or from canonical information stored in the Ingenuity Pathways Knowledge Base . In addition , IPA computes a score for each network according to the fit of the user's set of significant genes . The score , representing the –log ( p-value ) , indicates the likelihood of the Focus Genes in a network from Ingenuity's knowledge base being found together due to random chance . Genes corresponding to associations or identified by IPA were investigated for differential expression ( between acute and convalescent KD ) using Taqman low density array ( TLDA; Applied Biosystems , Foster City , CA , USA ) on 2 µg total RNA as per manufacturer's instructions . Briefly , 2 µg total RNA was reverse transcribed using High-Capacity cDNA Archive Kit ( Applied Biosystems , Foster City , CA , USA ) . Reverse-transcriptase reaction was performed at 25°C for 10 min and then 37°C for 2 hours followed by 85°C for 5 seconds . cDNA converted from 0 . 1 ug RNA was resuspended in 50 µl buffer and was added to 50 µl TaqMan Universal Master Mix ( 2× ) ( Applied Biosystems ) then immediately loaded into a Micro Fluidic Card ( 3M Company , Applied Biosystems ) . The card was spun twice at 1200 rpm for 1 min each time to distribute the PCR mix into the wells of the card before sealing and loading into the ABI 7900HT sequence detection system . Default thermal cycling conditions were used ( 50°C for 2 min with 100% ramping , 94 . 5°C for 10 min with 100% ramping , and finally 40 cycles of 97°C for 30 sec with 50% ramping and 59 . 7°C for 1 min with 100% ramping ) and data were normalized for RNA loading levels by using 18 s quantitation as a reference and exported using SDS RQ Manager software ( Applied Biosystems , Foster City , CA , USA ) . Relative ( RQ ) levels were exported and analyzed for significance using the Wilcoxon Rank Sum Test ( assuming the data are non-normally distributed ) . Fold-change analysis was based on median levels for the acute stage samples over the convalescence samples . In the initial GWAS , HWE in the control group and allelic association analysis were calculated using HelixTree v4 . 4 . 1 ( GoldenHelix Inc . , Bozeman , MT , United States ) and Exemplar ( Sapio Sciences , LLC , York , PA , United States ) . Allelic P-values were calculated by means of a 2×2 chi-square table and an Armitage trend test was used to derive genotypic p-values . Genotype association analysis and odds ratios were calculated using Exemplar . A two-sided Fisher's exact test was used when counts in any cell fell below five . Allelic analysis of the X chromosome SNPs was performed with Haploview v3 . 31 [57] and a likelihood ratio test was applied to calculate genotypic associations . Quantile-quantile ( Q-Q ) plots were constructed by ranking a set of values of –log p-value and plotting them against their expected values . Deviations from the line of equality indicated either that the theoretical distribution was incorrect , or that the sample was contaminated with values generated in some other manner ( for example , by a true association ) . The family-based association analysis was performed using FBAT [58] , allowing transmission disequilibrium analysis in extended families . Combined p-values and odds ratios were calculated by Fisher's combined probability test which allows pooled information across several tests that share the same null hypothesis [59] . Analysis of haplotypic associations was performed using a recently described method , the VSSWRR ( Variable-Sized Sliding Window via Regularized Regression ) , a haplotype analysis method for population-based case-control association studies [56] . It uses a variable-size sliding window where the maximum window size is determined by local haplotype diversity and the sample size and a combined analysis of all the haplotypes of different lengths ( up to the maximum window size ) at the same starting position is performed using L1-regularized regression method , adjusting for the dependency and complementariness among the haplotypes . It allows efficient management of a large number of haplotypes and is powerful in the detection of disease associations [56] . We used the Haplotype Relative Risk ( HRR ) method for testing for LD between marker genotypes and disease phenotypes for the trios ( affected offspring and parents ) in the fine-mapping study design [60] . For testing LD this method compares the transmitted parental alleles to affected offspring to those which are not transmitted . In many of the genetic association studies complete genotypes for both the parent may not be available . Hence to use partial information available in the affected child in trios in which both single and two parent genotypes are missing , Guo et al ( 2005 ) extended the HRR methods using Expectation Maximization ( EM ) algorithm . Assuming that the parental genotypes are missing at random they use EM algorithm to estimate the proportion of parents who transmitted a specific allele and non-transmitted the other allele . If there is not a severe admixture among the families , through simulations Guo et al . ( 2005 ) have demonstrated that the EM-HRR gained power by including the families with the affected child for which single or both the parental genotypes are missing [61] . | Kawasaki disease is an inflammatory pediatric condition that damages the coronary arteries in a quarter of untreated patients and is the commonest cause of childhood acquired heart disease in developed countries . While the infectious trigger ( s ) remain unknown , epidemiologic evidence suggests that human genetic variation underlies the susceptibility . In order to identify novel mechanisms that may predispose to this disease , we undertook a genome-wide association study , which investigates genetic determinants without prior supposition regarding the loci of interest . This was amongst the first complex infectious diseases to be studied in this way and one of the largest genetic studies of Kawasaki disease with 893 cases . We identified and confirmed 40 SNPs and six haplotypes , identifying 31 genes , in an international cohort of Caucasian patients . We followed up 16 SNPs where the associated genetic variant was more common and was situated within a gene , confirming eight SNPs by fine-mapping across the entire gene . Of these eight genes , seven were expressed in blood and five showed significantly different gene expression in paired patient samples taken during acute and convalescent Kawasaki disease . Five of the eight genes also appear to be involved in a single putative functional network of interacting genes . These novel genes and pathways may ultimately lead to novel diagnostics and treatment for Kawasaki disease . | [
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] | 2009 | A Genome-Wide Association Study Identifies Novel and Functionally Related Susceptibility Loci for Kawasaki Disease |
The Staphylococcus aureus HrtAB system is a hemin-regulated ABC transporter composed of an ATPase ( HrtA ) and a permease ( HrtB ) that protect S . aureus against hemin toxicity . S . aureus strains lacking hrtA exhibit liver-specific hyper-virulence and upon hemin exposure over-express and secrete immunomodulatory factors that interfere with neutrophil recruitment to the site of infection . It has been proposed that heme accumulation in strains lacking hrtAB is the signal which triggers S . aureus to elaborate this anti-neutrophil response . However , we report here that S . aureus strains expressing catalytically inactive HrtA do not elaborate the same secreted protein profile . This result indicates that the physical absence of HrtA is responsible for the increased expression of immunomodulatory factors , whereas deficiencies in the ATPase activity of HrtA do not contribute to this process . Furthermore , HrtB expression in strains lacking hrtA decreases membrane integrity consistent with dysregulated permease function . Based on these findings , we propose a model whereby hemin-mediated over-expression of HrtB in the absence of HrtA damages the staphylococcal membrane through pore formation . In turn , S . aureus senses this membrane damage , triggering the increased expression of immunomodulatory factors . In support of this model , wildtype S . aureus treated with anti-staphylococcal channel-forming peptides produce a secreted protein profile that mimics the effect of treating ΔhrtA with hemin . These results suggest that S . aureus senses membrane damage and elaborates a gene expression program that protects the organism from the innate immune response of the host .
Staphylococcus aureus is a Gram-positive commensal bacterium that colonizes the skin and anterior nares of approximately 25 % of the population [1] . Upon breaching these initial sites of colonization , S . aureus is capable of causing a range of infections [2] . Staphylococcal infections affect almost every organ in the human body ranging from minor skin and soft tissue infections to more serious diseases such as endocarditis , septicemia , pneumonia and toxic shock syndrome [3] , [4] . In order to cause such a diverse array of pathologies , S . aureus employs an arsenal of virulence factors including proteins that contribute to immune evasion and alter immune system function [5] . During infection , S . aureus faces several barriers that interfere with its ability to replicate and colonize its host . One of these barriers is the paucity of free iron , which is a critical component of several reactions within the bacterial cell [6] . To circumvent this barrier , S . aureus can satisfy its iron needs through acquisition of the metalloporphyrin heme , which is a cofactor of host hemoglobin and myoglobin . S . aureus binds , transports , and releases heme into the cytoplasm through the combined action of the iron regulated surface determinant ( Isd ) system and the heme transport system ( Hts ) [7] , [8] , [9] , [10] , [11] . Although heme is a valuable nutrient iron source at low concentrations , high concentrations of heme are toxic and therefore heme acquisition necessitates the presence of heme detoxification systems . In this regard , S . aureus senses heme exposure through the HssRS two-component system [12] , [13] resulting in the up-regulation of the heme regulated transporter ( HrtAB ) . HrtAB is an ABC-type transporter that consists of an ATPase ( HrtA ) and a permease ( HrtB ) which work together to alleviate heme toxicity and protect the cell from the adverse effects of heme accumulation [13] , [14] . ABC transporters represent one of the largest protein super-families in both eukaryotes and bacteria [15] . They play a pivotal role in the transport of a diverse group of molecules across the lipid bilayer of the cell membrane either to import nutrients or to export waste and toxic products [16] . ABC transporters consist of four domains; two transmembrane domains ( TMD ) and two nucleotide binding domains ( NBD ) that couple ATP hydrolysis to the transport of solutes across the membrane [17] , [18] . The subcellular location of ABC transporters within the cell membrane makes it possible that alterations of ABC transporter structure or function induce membrane stress . S . aureus strains lacking HrtA ( ΔhrtA ) exhibit liver-specific hyper-virulence in an animal model of infection suggesting that heme toxicity is linked to staphylococcal virulence . Consistent with this supposition , hemin-exposed ΔhrtA exhibits increased expression and secretion of several immunomodulatory proteins that are modeled to interfere with immune cell migration to the site of infection . Taken together , these findings have led to a model whereby S . aureus strains unable to alleviate heme toxicity through HrtAB activate an immunomodulatory program resulting in decreased killing by immune cells and increased virulence [13] . In this manuscript , we propose an alternative model to explain the hypervirulence of ΔhrtA . We report that the stimulus which leads to the increased expression of immunomodulatory proteins in hemin-exposed ΔhrtA is membrane damage caused by permease over-expression rather than intracellular hemin accumulation . Consistent with this , we present a series of data suggesting that permease expression in the absence of a cognate ATPase produces dysregulated pores in the membrane . This membrane damage is somehow sensed by S . aureus leading to increased expression and secretion of immunomodulatory factors . This model is further supported by the observation that exposing wildtype S . aureus to the channel forming antimicrobial peptide ( AMP ) gramicidin results in a secreted protein profile that mimics that of ΔhrtA exposed to hemin . The pathological relevance of this response is demonstrated by results reported here which show that up-regulation of secreted proteins is responsible for the hyperviurlence of strains lacking hrtA . Taken together , these data suggest a model whereby S . aureus senses pore-formation in the cell membrane to elicit an immunomodulatory program that interferes with neutrophil recruitment to the site of infection .
In an effort to elucidate the mechanism responsible for the hypervirulence of S . aureus ΔhrtA , we analyzed the secreted protein profiles of staphylococcal strains with mutations in the Hss and Hrt systems . Upon exposure of ΔhrtA to hemin in concentrations of either 0 . 5 or 2 µM , up-regulation of the production and secretion of at least five proteins was observed ( Fig . 1A ) . These proteins have been previously identified through mass spectrometry to be immunomodulatory proteins that interfere with neutrophil recruitment to the site of infection [13] , [19] , [20] , [21] . This finding suggests that strains unable to alleviate hemin toxicity through HrtAB increase the expression of immunomodulatory proteins . However , a hemin-dependent increase in protein expression was not observed in either wildtype S . aureus or a strain lacking functional HssRS ( ΔhssR ) , the regulatory system which controls hemin-dependent hrtAB expression ( Fig . 1A ) [12] . In addition , the secreted protein profile of a hemin-exposed S . aureus strain containing a transposon insertion that inactivates the permease HrtB ( thrtB ) closely resembled that of wildtype and did not show changes similar to those observed in ΔhrtA exposed to hemin ( Fig . 1A ) . These data indicate that hemin-exposed S . aureus ΔhrtA increases the secretion of immunomodulatory proteins , but this immunomodulatory response does not occur upon loss of HrtB . One potential explanation for the discrepancy in secreted protein profiles observed between ΔhrtA and thrtB is that the ΔhrtA strain has accumulated secondary mutations that are responsible for altering the secretome . This possibility was eliminated by the demonstration that the secreted protein profile of hemin-exposed ΔhrtA can be complemented by providing a full-length copy of hrtA in trans ( Fig . 1B ) . HrtA is an ATPase which provides the energy required for hemin-detoxification by its cognate permease HrtB [13] , [14] . Based on the phenotype of the ΔhrtA mutant , we reasoned that strains producing a catalytically inactive HrtA would elaborate a similar secreted protein profile upon hemin exposure . To test this hypothesis , we transformed ΔhrtA with plasmids encoding HrtA proteins that are mutated in key residues in the conserved nucleotide-binding and hydrolysis motifs ( K45A , G145A , G145T , E167Q ) or partially catalytically active ( R76A ) [14] . Surprisingly , expression of catalytically inactive versions of HrtA complemented the secreted protein profile of hemin-exposed ΔhrtA ( Fig . 1B ) . These strains expressed comparable levels of HrtA , eliminating expression level as a possible factor in this analysis ( Fig . 1C ) . These data indicate that it is not the catalytic activity of the HrtAB system that is required to prevent up-regulation of the immunomodulatory proteins upon hemin exposure , but instead it is the physical presence of HrtA that is required to prevent this phenotype . Based on the genomic organization of the hrtAB locus , it is expected that hemin exposure should increase HrtB expression in strains lacking hrtA . One potential explanation for the phenomenon described above is that HrtB acts as an unregulated pore in the absence of HrtA , sending a stress signal to the cell that leads to the up-regulation of immunomodulatory proteins . This model assumes that hrtB and hrtA are co-transcribed and over-expressed when S . aureus is exposed to hemin . To address this , RT-PCR experiments were performed using two primers that bind to the 3′-end of the hrtB gene and the 5′-end of the hrtA gene , respectively ( Fig . 2A ) . The results of this experiment showed that hrtA and hrtB are indeed co-transcribed and transcription of the hrt operon increases upon hemin exposure ( Fig . 2B ) . These results demonstrate that hrtA and hrtB comprise a heme-regulated bicistronic operon . To further confirm that it is not inactivation of the HrtAB system that is responsible for the up-regulation of the immunomodulatory proteins , we constructed a strain containing a clean knock-out of hrtB ( ΔhrtB ) . Upon examining the secreted protein profile of ΔhrtB , no notable changes were observed upon hemin exposure ( Fig . 2C ) . We next sought to determine if the secreted protein profiles of ΔhrtA and ΔhrtB correlate with virulence levels in a murine model of systemic staphylococcal infection . Consistent with what has been reported previously , inactivation of hrtA leads to a significant increase in liver-specific virulence in this model [13] . However , when mice were infected with ΔhrtB , no significant difference was observed in liver colonization as compared to wildtype ( Fig . 2D ) . These data link the secreted protein profile of hemin-exposed ΔhrtA to increased virulence and suggest that inactivation of HrtAB activity is not responsible for this hypervirulence . A potential explanation for the results obtained above is that hemin-exposed ΔhrtA over-expresses HrtB which localizes to the membrane as a dysregulated permease and causes membrane damage . In turn , this membrane damage is sensed by S . aureus leading to changes in the secreted protein profile . As an initial test of this hypothesis , both wildtype and ΔhrtA were grown in ±1 µM hemin and membrane integrity was determined by propidium iodide ( PI ) staining . PI is a membrane impermeant cationic stain that produces strong red fluorescence when bound to nucleic acids , and hence can be detected using FACS [22] . When wildtype S . aureus was exposed to hemin there was a small shift in PI staining indicating minor changes in membrane integrity ( Fig . 3A ) . In contrast , ΔhrtA exposed to hemin exhibited a pronounced shift in PI staining indicative of substantial changes in membrane integrity ( Fig . 3B ) . Cell death was excluded as a possible cause of the increased permeability of hemin exposed ΔhrtA , as hemin exposure at this concentration did not affect viability of the tested strains ( Fig . 3C ) . These results indicate that exposure of ΔhrtA to 1 µM hemin compromises cell membrane integrity without affecting cellular viability . The data presented above are consistent with a model whereby HrtB expression in the absence of its cognate ATPase is responsible for increased secretion of immunomodulatory proteins . This phenotype can be complemented by catalytically inactive versions of HrtA suggesting that it is not HrtA ATPase activity but the physical presence of HrtA that prevents alterations in protein secretion . In keeping with this , it is predicted that discordant regulation of HrtB and HrtA would lead to a phenotype that mimics ΔhrtA exposed to hemin . To test this hypothesis , myc-tagged HrtB was constitutively expressed in wildtype , ΔhrtB and thrtB backgrounds . The three strains expressed comparable levels of HrtB as assessed by immunoblots using anti-c-Myc monoclonal antibody ( Fig . 3D ) . Upon comparing the secreted protein profiles of these three strains in the absence of hemin exposure to matched strains harboring empty vector , we observed alterations in protein abundance that closely resemble those produced by hemin-exposed ΔhrtA ( Fig . 3E ) . This result indicates that expressing disproportional ratios of HrtB:HrtA triggers a response similar to that obtained when HrtB is expressed in the absence of HrtA . To confirm that over-expressed HrtB localizes to the cell membrane , cell fractionation was performed on wildtype cells harboring the phrtB-myc plasmid grown in the absence of hemin . When equivalent protein amounts of cellular fractions were immuno-blotted using monoclonal antibody against c-Myc , a reactive band with the predicted molecular mass ( ∼37 kDa ) was detected exclusively in the membrane fraction ( Fig . 3F ) . A second band migrating at approximately 20 kDa was detected in the membrane fraction , however the identity of this band in unknown at this point . These observations support a model whereby HrtB expression in the absence of HrtA acts as an unregulated pore resulting in the production of membrane stress and the up-regulation of immunomodulatory proteins . To test if the secreted protein profile of hemin-exposed ΔhrtA is due to non-specific membrane damage , S . aureus was treated with sub-inhibitory concentrations of the anti-microbial peptide ( AMP ) LL37 . LL37 elicits antimicrobial activity through membrane disruption caused by widespread random intercalation into the bacterial membrane [23] . We chose to use 40 µg/ml LL37 for these experiments as this concentration slightly inhibited S . aureus growth but permitted growth to cell densities that were similar to untreated cultures ( data not shown ) . When the secreted protein profiles of S . aureus grown in the absence and presence of LL37 were compared to those of ΔhrtA grown in the absence and presence of 1 µM hemin , similar effects regarding the secreted proteins were not observed ( Fig . 4A ) . None of the five bands that were visibly up-regulated in hemin-exposed ΔhrtA were up-regulated in wildtype exposed to LL37 . In fact , some of the bands that were up-regulated in ΔhrtA exposed to hemin seemed to be down-regulated when wildtype was exposed to LL37 ( Fig . 4A ) . This result indicates that membrane damage caused by LL37 treatment does not induce the same response within S . aureus as over-expression of the HrtB permease without its cognate ATPase . Therefore , non-specific membrane damage is not responsible for alterations in the secretome observed upon HrtA and HrtB dysregulation . We next sought to test the impact of a pore-forming AMP on the staphylococcal secretome to more closely mimic the hypothesized membrane damage elicited upon HrtB over-expression . To this end , wildtype S . aureus was treated with sub-inhibitory concentrations of the pore-forming AMP gramicidin . The growth of S . aureus in the presence of 16 µg/ml gramicidin was slightly inhibited , but the treated cultures were able to reach similar optical densities to those of untreated cells ( data not shown ) . Upon comparing the secreted protein profile of wildtype treated with gramicidin to that of ΔhrtA treated with hemin a remarkable conservation in the expression patterns was observed ( Fig . 4B ) . Notably , all of the five bands that were up-regulated in hemin-exposed ΔhrtA were also up-regulated in gramicidin-treated wildtype , but to varying extents ( Fig . 4B ) . This result supports the contention that dysregulated pore formation through the staphylococcal membrane leads to a specific alteration in protein secretion . Gramicidin exposure induces significant alterations in protein expression across a panel of S . aureus isolates ( UAMS-1 , USA100 , USA300 , USA500 , and RN6390 ) ( Fig . 4C & D ) . Notably , a subset of these strains ( UAMS-1 , USA100 , and USA300 ) exhibit altered expression of proteins migrating to positions similar to those of the immunomodulatory proteins affected in the Newman strain ( Fig . 4C ) . It is interesting to point out that not all strains analyzed were affected by gramicidin treatment equally . More specifically , USA500 and RN6390 do not seem to increase expression of the immunomodulatory proteins ( based on migration pattern ) upon gramicidin exposure despite exhibiting significant changes in protein expression ( Fig . 4D ) . Taken together , these experiments suggest that treatment of distinct S . aureus strains with pore forming toxins produces changes in protein expression . The S . aureus Aps/Gra system is a two-component system responsible for resistance to antimicrobial peptides suggesting a potential involvement in the response to gramicidin reported here [24] , [25] . To evaluate the involvement of Aps/Gra in this response to gramicidin , we created a S . aureus strain inactivated for Aps/Gra ( ΔapsR ) and measured the impact of HrtB over-expression and gramicidin exposure on this strain . The secreted protein profile of ΔapsR did not differ noticeably from that of wildtype ( Fig . S1A & B ) . These experiments revealed that the Aps/Gra system is not involved in the response to gramicidin or HrtB overexpression . Gel based comparisons of protein secretion between gramicidin-treated wildtype and hemin-exposed ΔhrtA suggest that these distinct but similar stressors lead to analogous alterations in the staphylococcal secretome ( Figs . 3E and 4B ) . In an effort to increase the sensitivity and resolution of this comparison beyond Coomassie blue-stained SDS-PAGE analysis , a mass spectrometry-based approach known as shotgun proteomics ( see Materials and Methods ) was employed . Using shotgun proteomics we determined the proteomes of the culture supernatants of ΔhrtA grown in the presence and absence of hemin and wildtype S . aureus grown in the presence and absence of gramicidin . The analysis was performed on three independent samples from each condition . Quantitation of the proteins in each sample was performed using spectral counting of tandem spectra acquired for each protein normalized to the total number of spectra detected in the same sample . Analysis of the culture supernatants of ΔhrtA with and without hemin revealed the presence of at least 137 proteins exhibiting an average of at least 2 spectra in the three samples analyzed . Among these , 21 proteins were significantly up-regulated and 50 proteins were down-regulated in the presence of hemin ( Table S2 ) . Analysis of known secreted proteins demonstrated that there were 12 proteins significantly up-regulated and 18 proteins significantly down-regulated between these conditions ( Tables 1 & 2 , Figs . 5A & B ) . In the case of wildtype grown in the presence and absence of gramicidin , 101 proteins were detected among which 32 proteins were significantly up-regulated and 29 were down-regulated ( Table S3 ) . Among the 12 secreted proteins that were up-regulated in ΔhrtA exposed to hemin , 8 proteins ( i . e . 75% ) were also up-regulated in WT exposed to gramicidin ( Table 1 and Fig . 5A ) . All 8 of these proteins have potential immunomodulatory functions including seven Staphylococcal Superantigen-Like proteins ( Ssl1 , Ssl2 , Ssl4 , Ssl5 , Ssl6 , Ssl7 , and Ssl10 ) and the extracellular matrix and plasma binding protein ( Ssp , also known as Emp ) . In addition three other Ssls ( Ssl9 , Ssl8 and Ssl3 ) were up-regulated under both conditions but their up-regulation did not reach the level of significance in wildtype treated with gramicidin ( Table 1 ) . Among the 18 secreted proteins that were down-regulated in ΔhrtA exposed to hemin , 16 proteins ( i . e . 89% ) were also significantly down-regulated in wildtype treated with gramicidin ( Table 2 and Fig . 5B ) . This group of proteins included several hemolysins , leukotoxins , and other known virulence factors . Two additional proteins ( SspB and CHIPS ) that were significantly down-regulated in ΔhrtA exposed to hemin were also down-regulated in wildtype exposed to gramicidin but their down-regulation did not reach the level of significance ( Table 2 ) . Taken together , these results demonstrate that pore formation through gramicidin treatment or permease dysregulation results in alterations in the staphylococcal exoprotein profile highlighted by an increased abundance of immunomodulatory proteins with known anti-neutrophil functions [19] , [26] , [27] . In order to determine whether the changes observed in the secretomes of ΔhrtA treated with hemin and wildtype treated with gramicidin are occurring at the transcriptional level , quantitative real-time RT-PCR was performed on a representative sample of genes . Upon testing the transcript levels of ssl7 , ssl1 , and ssl2 as examples of genes encoding proteins that increase abundance upon pore formation , we noted a significant up-regulation in the level of all three transcripts in both ΔhrtA treated with hemin and wildtype treated with gramicidin ( Figs . 5C & D ) . The fold up-regulation in ΔhrtA treated with hemin was higher than that observed in wildtype exposed to gramicidin . Further , upon testing the transcript levels of lukS , hlgC , and hlgA as examples of genes encoding proteins that decrease abundance upon pore formation , there was a significant down-regulation in the level of all three transcripts in ΔhrtA treated with hemin . This down-regulation was only significant in lukS and hlgC in wildtype treated with gramicidin ( Figs . 5E & F ) . These findings demonstrate that the alterations in protein abundance that occur upon HrtB dysregulation or gramicidin treatment are occurring transcriptionally , consistent with the notion that S . aureus regulates an anti-neutrophil response upon sensing membrane damage elicited by pore forming toxins . Strains lacking hrtA exhibit liver-specific hypervirulence and increased secretion of immunomodulatory proteins such as Ssls and Ssp . To determine if the increased secretion of these immunomodulatory proteins is responsible for the hypervirulence of hrtA mutants , we created strains lacking ssl1-11 or ssp in a thrtA background ( thrtAΔssls , thrtAΔssp , thrtAΔsslsΔssp ) . Next we assessed the liver-specific virulence of S . aureus wildtype , thrtA , thrtAΔssls , thrtAΔssp , and thrtAΔsslsΔssp in the systemic model of staphylococcal infection described above . As expected , thrtA exhibited increased virulence in these studies as compared to wildtype . Inactivation of either ssl1-11 or ssp reduced the virulence of thrtA to levels approximately equivalent to wildtype despite the fact that these mutations had no adverse effect on growth in vitro ( Fig . 6 ) . Inactivation of ssp had a more pronounced effect on the hypervirulence of thrtA as compared to ssl1-11 underscoring the significant contribution of Ssp to the hypervirulence of thrtA . When the mutations of the ssls were combined with that of the ssp in the hrtA mutant background , the hypervirulence of the hrtA mutant was significantly reduced almost to the level of wildtype ( Fig . 6 ) . Taken together , these data demonstrate that the hypervirulence of hrtA mutants is due to the increased expression of Ssl1-11 and Ssp in response to dysregulated pore formation .
S . aureus HrtAB is an ABC-type transport system that is essential for alleviating hemin toxicity [13] , [14] . Upon hemin exposure HrtAB is up-regulated approximately 45-fold [28] . Inactivation of hrtA results in liver specific hyper-virulence that is associated with a hemin-induced over expression of immunomodulatory proteins [13] . Here we propose a revised model to explain the hyper-virulence of S . aureus ΔhrtA as summarized in Fig . 7A; when ΔhrtA is exposed to hemin , the HssRS system is activated leading to the over-expression of HrtB in the cell membrane without its cognate ATPase . In addition to an inability to relieve heme-toxicity [13] , this mutant experiences membrane stress caused by the over-expressed HrtB permease acting as an unregulated pore . This membrane stress is sensed by S . aureus through an as-yet-unidentified mechanism , leading to over-expression and secretion of several immunomodulatory molecules . The combined action of these immunomodulatory proteins inhibits neutrophil migration to the site of infection enabling ΔhrtA to exhibit hypervirulence during liver colonization . This model is supported by the observation that S . aureus exposed to a pore forming AMP exhibits a similar secreted protein profile to hemin-exposed ΔhrtA , potentially providing a physiologically relevant explanation for the existence of this response ( Fig . 7B ) . When S . aureus senses membrane damage in the form of pore formation , the organism appears to over-express and secrete immunomodulatory molecules that interfere with the recruitment of phagocytic cells to the site of infection . The observation that strains lacking hssR also exhibit hypervirulence suggest that disruption of HssRS signaling impacts HrtAB expression in a way that leads to a similar form of membrane damage [13] . The precise mechanism by which HrtB over-expression leads to membrane stress is not completely understood , but lack of expression of the HrtA protein may lead to misfolding of its cognate permease ( HrtB ) , or lock the HrtB pore in a conformation that no longer allows the passage of its substrate , and/or allows the passage of ions involved in maintaining membrane potential . These possibilities are in accordance with the observation that over-expression of membrane proteins usually leads to reduced growth rates that are generally assumed to be due to negative effects on membrane integrity [29] . An indication that ΔhrtA exposed to hemin experiences membrane damage is provided by the observation that this strain showed increased permeability to propidium iodide ( Fig . 3B ) [22] . During infection , S . aureus faces an array of membrane damaging peptides and small proteins that perturb the cell membrane leading to cell leakage and eventually death [30] , [31] . In fact , mammalian cells increase the production of several AMPs upon sensing S . aureus and its components [31] , [32] , [33] . In general , AMPs exert their antimicrobial activity through pore formation or membrane barrier disruption , however some AMPs mediate their antibacterial activity through altering septum formation or inhibiting cell-wall , nucleic-acid , or protein synthesis [34] , [35] , [36] . Few AMPs have been described that are considered channel-forming peptides that act through what is known as the “barrel-stave” mechanism [35] , [37] . On the other hand , several AMPs , including the widely expressed human LL37 , have been described to perturb membranes through what is known as the “carpet” or “detergent like” mechanism [23] , [37] . To counter the bactericidal effects of AMPs , bacteria express regulatory systems designed to sense these innate immune threats to generate an anti-AMP response . Among these systems , the Gram negative PhoPQ system is the best characterized [38] . Recently , the staphylococcal counterpart of the PhoPQ system was identified [25] , [39] , [40] . The Aps/Gra system is a three component bacterial sensing system that senses AMPs to induce a resistance response that includes modification of cell surface components and increased expression of putative AMP transporters [25] , [39] , [40] . The Aps/Gra system is not involved in the phenotypes reported here , implying the existence of an additional system responsible for sensing membrane damage in S . aureus [40] , [41] , [42] . The S . aureus immunomodulatory factors described here were found to be up-regulated upon exposure to the pore-forming peptide gramicidin which is produced by Bacillus brevis . In contrast , this response is not elicited upon exposure to the membrane-disintegrating mammalian peptide LL37 . Considering the apparent specificity of this secreted protein response to inhibit neutrophil migration , it is likely that as-yet-unidentified channel-forming AMPs elicit this response during mammalian infection . S . aureus regulation of virulence factors in response to phagocytes and phagocytosis-related signals have been previously reported and the response varies according to the setting of the experiment and the strain tested . For instance , the S . aureus virulence regulator Sae is highly activated upon exposure to α-defensins and H2O2 but not LL37 [43] . When S . aureus strain SG511 was exposed to human β-defensin 3 , the only pathogenic factors that were significantly up-regulated were fmtA and sdrE [44] . In a more comprehensive study [45] , different S . aureus strains were phagocytosed by human PMNs and transcriptome analyses of these bacteria showed up-regulation of genes encoding multiple virulence factors such as hlgA , hlgB , hlgC , extracellular matrix and plasma binding protein ( ssp ) , staphylocoagulase , and clumping factor . Interestingly , genes encoding several toxins , such as ssl7 , ssl11 , ssl10 ( set14 ) , lukD , and lukE were up-regulated only in strains causing community acquired ( CA ) infections . When the CA-MRSA strain MW2 was exposed to neutrophil azurophilic granule proteins [46] , there was an up-regulation of genes encoding numerous virulence factors including hemolysins ( hla , hld , hlgA , hlgB , and hlgC ) and leukotoxins ( lukS and lukF ) in addition to several immunomodulatory toxins belonging to the Ssl group ( ssl7 , ssl1 and ssl10 ) . In this manuscript we report that the S . aureus strain Newman response to either over-expression of the HrtB permease or the pore-forming AMP gramicidin is different from previous reports . We have observed up-regulation of the immunomodulatory Ssl exoproteins ( Ssl7 , 1 , 2 , 4 , 6 , 5 , and 10 ) and extracellular matrix and plasma binding protein ( Ssp ) while simultaneously observing a down-regulation of several hemolysins , cytotoxins and other secreted virulence proteins . As observed with S . aureus strain Newman , we found that UAMS-1 , USA100 , USA300 , USA500 and RN6390 undergo profound changes in protein expression upon exposure to gramicidin ( Fig . 4C and D ) . A subset of these strains ( UAMS-1 , USA100 , and USA300 ) exhibit altered expression of proteins migrating at the predicted size for the immunomodulatory proteins affected in the Newman strain . It is interesting to point out that not all strains analyzed were affected by gramicidin treatment equally . More specifically , USA500 and RN6390 do not seem to increase expression of the immunomodulatory proteins upon gramicidin exposure despite exhibiting significant changes in protein expression ( Fig . 4D ) . From these data , we conclude that S . aureus alters exoprotein expression in response to pore-forming toxins . The Ssl proteins have sequence and structural homology with superantigens; however they lack the superantigenic activity [19] . Functions have yet to be ascribed to each of the Ssl proteins , but those with known functions have been shown to have an immunomodulatory effect . For instance Ssl7 binds IgA and complement C5 , blocking IgA-FcR interactions and complement activation [26] . Both Ssl5 and Ssl11 inhibit neutrophil rolling while Ssl5 was recently shown to inhibit leukocyte activation by chemokines and anaphylatoxins [27] , [47] . In addition , Ssl10 inhibits CXCL12-induced human tumor cell migration [48] . However , very little is known about the regulation of these exotoxins [19] . It was shown that ssl4 ( set9 ) is up-regulated in a SarA-dependent manner while ssl3 ( set8 ) is up-regulated in an agr-dependent manner [49] . Combining these observations with the fact that many of the virulence factors that are down-regulated in our experiments such as Hla , HlgABC , Lip , and Map are also regulated through these two major S . aureus virulence regulators ( agr and SarA ) [49] , it is tempting to speculate that one or both of these regulators may play a role in orchestrating the immunomodulatory program elicited by membrane damage caused by pore formation . In conclusion , the data presented here shed light on a potentially new virulence regulatory circuit in S . aureus that can modulate the immune response in the host and the components of such a circuit represent potential therapeutic targets . The significance of this circuit stems from the fact that an inducer of this program , the antimicrobial agent gramicidin , is a component in preparations that have shown efficacy against S . aureus colonization and infections [50] , [51] . The identification of the factors involved in transducing the signal from the membrane which activates the expression and secretion of these immunomodulatory factors is critical for the full understanding of this program and is the focus of future research .
All procedures involving animals were approved by Vanderbilt University's Institutional Animal Care and Use Committee ( IACUC ) . All animal experiments were performed in accordance to NIH guidelines , the Animal Welfare Act , and US federal law . Staphylococcus aureus clinical isolate Newman [52] was used in all experiments as the wildtype strain ( unless explicitly stated ) and mutants were generated in its background . Isogenic mutants lacking the hrtA and hssR gene were previously described [13] . A strain containing a transposon insertion into hrtB ( thrtB ) was obtained from the Phoenix ( N ) library clone PhiNE 05560 ( SAV2360 ) [53] . Plasmids expressing WT and mutated hrtA genes were described previously [14] . S . aureus were grown on tryptic soy broth ( TSB ) solidified with 1 . 5 % agar at 37°C or in TSB with shaking at 180 rpm , unless otherwise indicated . When appropriate TSB was supplemented with chloramphenicol at a final concentration of 10 µg/ml . Escherichia coli were grown in Luria broth ( LB ) and when needed , the media were supplemented with ampicillin at a final concentration of 100 µg/ml . For the construction of a plasmid constitutively expressing Myc-tagged HrtB , a PCR amplicon was made containing the hrtB ORF into which the sequence encoding a c-Myc-epitope ( EQKLISEEDL ) was inserted just prior to the stop codon using the primers DS218 ( 5′- GGGGCATATGAAATTAGCGATAAAAGAG-3′ , NdeI site underlined ) for the 5′-end and AA494 ( 5′- TAGATCTTCTTCAGATATCAGTTTCTGTTCGCCGCCTTCTGCACCTCCAATTGCTTCGATAGGATCCACTTT-3′ ) together with AA495 ( 5′-CCTCGAGTTATAGATCTTCTTCAGATATCAGTTT-3′ , XhoI site underlined ) for the 3′-end . The PCR product was then digested with both NdeI and XhoI and ligated into the E . coli/S . aureus shuttle vector pOS1-plgt [56] which had been digested with the same restriction enzymes . Ligation mixtures were then transformed into E . coli DH5α and the resultant plasmid was designated phrtB-myc . After DNA sequence verification the plasmid was transformed into the restriction-deficient modification-positive S . aureus RN4220 [57] followed by transformation into the respective electrocompetent S . aureus strain using the protocol described [58] . S . aureus cells were grown to mid-log phase and sedimented by centrifugation at 3 , 200×g for 10 min . The supernatants were removed and the pellet was suspended in 500 µl TSM ( 100 mM Tris-HCl ( pH 7 . 0 ) , 500 mM sucrose , 10 mM MgCl2 ) supplemented with 100 µg lysostaphin and incubated for 1 hr at 37°C . The resulting protoplasts were sedimented by centrifugation at 16 , 000×g for 15 min; the supernatant was collected [cell wall ( CW ) fraction] , and the pellet was suspended in 500 µl membrane buffer ( 50 mM Tris-HCl ( pH 7 . 0 ) , 10 mM MgCl2 , 60 mM KCl ) and subjected to sonication . The membranes were sedimented by centrifugation at 100 , 000×g for 45 min . The supernatant was collected [cytoplasm ( Cyt ) fraction] and the pellet [membrane ( Mem ) fraction] was suspended in 200 µl membrane buffer . Equivalent amounts of protein from each fraction were separated by SDS-PAGE and analyzed by immunoblotting as described below . In order to monitor staphylococcal membrane integrity , bacterial cells were grown in Roswell Park Memorial Institute ( RPMI ) medium supplemented with 1% ( wt/vol ) Casamino Acids ( CAS ) ±1 µM hemin until the cultures reached mid-log phase ( OD600 ∼0 . 4 ) , washed and suspended in phosphate buffered saline ( PBS ) supplemented with 1% wt/vol bovine serum albumen ( BSA ) . All samples were adjusted to the same OD600 and 50 µl aliquots corresponding to approximately 107 cells were mixed with 2 µg of propidium iodide ( PI ) ( Sigma ) in 1 ml PBS/1%BSA and analyzed by fluorescence-activated cell sorting ( FACS ) . Analysis was performed on a FACSCalibur system , ( BD , Franklin Lakes , NJ ) using Cell Quest Pro software ( BD ) . Equal aliquots of each sample were serially diluted and plated on TSA for viability counting . The AMP LL37 was obtained from Phoenix Pharmaceuticals , Inc ( Burlingame , CA ) and it was dissolved directly in RPMI/CAS . The AMP gramicidin was obtained from Sigma ( St . Louis , MO ) and dissolved in absolute ethanol to a final concentration of 10 mg/ml . MIC determination was done by incubating 105 CFU/ml S . aureus with 2 fold serial dilutions of the tested peptide in RPMI/CAS at 37°C . The MIC was considered as the lowest concentration that prevented growth as indicated by lack of visible turbidity following incubation at 37°C for 24 hr . S . aureus cultures from a fresh streak on TSA plates were inoculated in 5 ml of RPMI/CAS overnight at 37°C . The overnight cultures were then used to inoculate a new 5 ml culture in a 15 ml concical tube to which hemin was added at final concentrations of 0 . 5 , 1 , or 2 µM . The AMP LL37 was added to a final concentration of 40 µg/ml and gramicidin was added to a final concentration of 16 or 32 µg/ml . As a negative control , cells were grown in RPMI/CAS with equivalent volume of ethanol ( WT – gramicidin ) . All the cultures were allowed to grow overnight ( ∼18 hrs ) then the samples were normalized to the same OD600 . Bacterial cells were sedimented by centrifugation and proteins in the culture supernatants were precipitated using 10% ( vol/vol ) TCA at 4°C overnight ( ∼16 hrs ) . The precipitated proteins were sedimented by centrifugation , washed with absolute ethanol , dried , resuspended in 1X SDS-loading buffer , and boiled for 10 min . Proteins in the samples were resolved using 15% wt/vol SDS-PAGE , and stained with Coomassie blue . At times , we observed slight variations in protein expression from identical strains used in distinct experiments as shown in Figures 1 , 2 , 3 , 4 , and S1 . This variation correlated with changes in the source of medium , suggesting that batch-to-batch variation in media preparations affects this phenotype . Despite these minor variations , similar patterns of protein expression were observed across all experiments . TCA precipitated proteins from filtered culture supernatants of ΔhrtA ±1 µM hemin and WT ±32 µg/ml gramicidin were prepared as described above . Higher concentration of gramicidin than what was used above was aimed to increase the sensitivity of the assay . Proteins were resuspended in 1X SDS-digestion buffer and loaded into 12% SDS-PAGE gel ( without a stacking gel ) and electrophoresed 2 cm into the gel . The gel was stained with Colloidal Blue ( Invitrogen , Carlsbad , CA ) then destained with distilled water over night . Proteins were then subjected to in-gel trypsin digestion and peptide extraction and the resulting peptides were then analyzed using a Thermo Finnigan LTQ ion trap instrument and separated as described [59] . Tandem spectra were acquired using a data dependent scanning mode with a one full MS scan ( m/z 400–2000 ) followed by 9 MS-MS scans . The SEQUEST algorithm was then used to search the tandem spectra against the Newman strain of the S . aureus subset of the UniRef100 database . To determine false positive rates , the database was concatenated with the reverse sequences of all proteins in the database . The SEQUEST outputs were filtered through the ID Picker suite with a false positive ID threshold of 5% and proteins were required to be identified by 2 or more unique peptides . Protein reassembly from identified peptide sequences was done as previously described [60] . The number of spectra identified for each protein under a given condition were normalized to the number of total spectra detected in the same injection . S . aureus cultures where grown in RPMI/CAS with the appropriate additive to an OD600 ∼1 . 0 . Cultures were then mixed with equal volume of 1∶1 ethanol: acetone mixture and frozen at -80°C . For RNA extraction , frozen samples were allowed to thaw on ice; cells were then sedimented and washed two times with TE buffer . Cells were broken mechanically using fastprep bead beater ( MP Biomedicals , Solon , OH ) then RNA was isolated using the RNeasy mini kit ( Qiagen , Valencia , CA ) according to the manufacturer's recommendation . On-column DNase digestion was performed and after RNA elution , the samples were cleaned from any residual DNA contamination using the MessageClean kit ( GenHunter , Nashville , TN ) . RT-PCR was performed to determine if the hrtB and hrtA genes were transcriptionally linked . 2 µg of RNA isolated as described above was used as template for a reverse transcriptase reaction using random hexmer ( Promega , Madison , WI ) and the M-MLV reverse transcriptase ( Promega ) . The synthesized cDNA was then used as a template for a PCR reaction using primers AA492 ( 5′-TCCTATCGAAGCAATTGGAGGTGC-3′ ) which binds near the 3′-end of the hrtB gene and AA493 ( 5′-TCCCAGAACCAGAGGCACCATTTA-3′ ) which binds near the 5′-end of the hrtA gene . Control reactions were carried out where DNA and RNA without reverse transcriptase treatment were used as templates . For real time RT-PCR , 50 ng , or 5 ng in case of 16S , of total RNA was reverse transcribed and PCR-amplified by the MultiScribe enzyme ( Applied Biosytems , Foster City , CA ) in the presence of sybr green PCR mix ( Applied Biosytems , ) using primers specific for each of the analyzed transcripts ( Table S1 ) . 16S RNA was utilized at 5 ng in order to prevent the reaction rapidly reaching saturation which would hinder normalization . The levels of all transcripts were normalized to the level of the ribosomal RNA 16S . The normalized transcript levels of the respective strain without treatment ( either hemin or gramicidin ) were used as calibrators . All samples were analyzed in triplicate and the data were then analyzed by the iQ5 standard edition software ( Bio-rad , Hercules , CA ) using the ΔΔCT method . S . aureus cultures were grown in RPMI/CAS overnight , cells were then treated with lysostaphin to digest the cell wall and the resulting protoplasts were pelleted and resuspended in BugBuster Protein Extraction Reagent ( Novagen , Gibbstown , NJ ) with proteinase inhibitor and sonicated for 10 s . Proteins in protoplasts were then resolved in 15% SDS-PAGE , transferred to nitrocellulose membranes , immunoblotted with 9E10 anti-C-Myc monoclonal antibody as a primary antibody and AlexaFluor-680-conjugated anti-mouse as secondary antibody . Membranes were then scanned using an Odyssey Infrared Imaging System ( LI-Cor Biosciences , Lincoln , NE ) . Six- to eight-week-old BALB/c female mice ( Jackson Laboratories , Bar Harbor , Maine ) were infected retro-orbitally with approximately 1×107 CFU of S . aureus strains . Ninety-six hours post-infection , mice were euthanized with CO2 , livers were removed , homogenized in sterile PBS , serially diluted and plated on TSA for colony forming unit ( CFU ) counts . At least seven mice were infected with each strain and statistical analyses were performed using the Student's t test , where p values <0 . 05 were considered statistically significant . | Staphylococcus aureus infects almost every tissue within the human body utilizing a range of virulence factors to combat host defenses . The expression of these virulence factors is a tightly regulated process; however , the signals sensed by S . aureus during infection remain elusive . It has been hypothesized that heme toxicity is a signal sensed by S . aureus during infection . This hypothesis is based on the observation that S . aureus mutants which are incapable of relieving heme-toxicity due to inactivation of the ATPase HrtA elicit an immunomodulatory program that interferes with neutrophil recruitment to the site of infection . In keeping with this , S . aureus hrtA mutants exhibit liver-specific hypervirulence . Herein , we provide evidence for an alternative model to explain the hypervirulent phenotype of S . aureus ΔhrtA . We demonstrate that instead of accumulation of heme toxicity being the trigger for the observed immunomodulatory program , dysregulated pore formation caused by the HrtB permease triggers the anti-neutrophil response . In support of this model , over-expression of HrtB in wildtype S . aureus or exposing S . aureus to channel-forming antimicrobial peptides induces a similar immunomodulatory program . Our work provides evidence that S . aureus senses membrane damage and induces an immunomodulatory circuit that helps the pathogen evade immune-mediated clearance . | [
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"immunology/immun... | 2010 | Membrane Damage Elicits an Immunomodulatory Program in Staphylococcus aureus |
Enteropathogenic E . coli ( EPEC ) and related enterobacteria rely on a type III secretion system ( T3SS ) effector NleE to block host NF-κB signaling . NleE is a first in class , novel S-adenosyl-L-methionine ( SAM ) -dependent methyltransferase that methylates a zinc-coordinating cysteine in the Npl4-like Zinc Finger ( NZF ) domains in TAB2/3 adaptors in the NF-κB pathway , but its mechanism of action and other human substrates are unknown . Here we solve crystal structure of NleE-SAM complex , which reveals a methyltransferase fold different from those of known ones . The SAM , cradled snugly at the bottom of a deep and narrow cavity , adopts a unique conformation ready for nucleophilic attack by the methyl acceptor . The substrate NZF domain can be well docked into the cavity , and molecular dynamic simulation indicates that Cys673 in TAB2-NZF is spatially and energetically favorable for attacking the SAM . We further identify a new NleE substrate , ZRANB3 , that functions in PCNA binding and remodeling of stalled replication forks at the DNA damage sites . Specific inactivation of the NZF domain in ZRANB3 by NleE offers a unique opportunity to suggest that ZRANB3-NZF domain functions in DNA repair processes other than ZRANB3 recruitment to DNA damage sites . Our analyses suggest a novel and unexpected link between EPEC infection , virulence proteins and genome integrity .
NF-κB signaling plays a central role in defending against bacterial infection [1] , [2] . The NF-κB signaling initiates innate immune responses and inflammation via a myriad of pathogen-recognition or cytokine receptors . These receptors generate ubiquitin-chain signals that are directly recognized by the TAB2/3 adaptors , thereby activating the TAK1 and IKK kinase cascade , leading to transcription of genes involved in immune defense . EPEC and the related enterohaemorrhagic E . coli ( EHEC ) block NF-κB signaling using virulence effector proteins injected into host cells by the type III secretion system ( T3SS ) . The NleE effector , conserved in Shigella and Salmonella , plays a major role in EPEC suppression of the NF-κB signaling in cell culture infection [3] , [4] , [5] . We recently discovered that NleE is a SAM-dependent methyltransferase that modifies a cysteine in the NZF domains of TAB2/3 , thereby disrupting ubiquitin-chain sensing of TAB2/3 and abolishing NF-κB-mediated proinflammatory responses [6] . Protein methylation is of great importance in a plethora of cellular processes including biosynthesis , signal transduction , protein repair , chromatin regulation and gene silencing [7] . SAM-dependent methyltransferases are diverse in their primary sequence , three dimensional structure and SAM-binding mode , and have been classified into five different families ( Class I-V ) [8] . The five families of methyltransferases generally catalyze lysine or arginine methylation . NleE-catalyzed cysteine methylation of TAB2/3 is the first example of enzyme-catalyzed protein cysteine methylation , representing a novel mechanism in regulating signal transduction in eukaryotes . NleE harbors no sequence homology to known methyltransferases . The structural basis for NleE methyltransferase activity and substrate specificity are unknown . Here we determine the crystal structure NleE-SAM complex , which reveals a novel methyltransferase fold and a unique mode of SAM binding . Molecular dynamic simulation of the docked NleE-SAM-NZF complex indicates that Cys673 in TAB2-NZF is structurally and energetically favorable for attacking the SAM . Profiling of a large number of zinc fingers identifies ZRANB3 as a new NleE substrate . ZRANB3 is recruited to damaged DNA replication forks and functions in maintaining genome integrity [9] , [10] , [11] . NleE-methylated ZRANB3-NZF domain lost the ubiquitin chain-binding activity , suggesting an unexpected link between EPEC infection , virulence proteins and genome integrity . These structural and functional analyses suggest that NleE may target ZRANB3 or other zinc-finger proteins for cysteine methylation in promoting bacterial virulence .
Due to the lack of an antibody capable of recognizing methylated cysteine , we developed a back-methylation assay by examining the sensitivity of TAB2 purified from NleE-transfected mammalian cells to in vitro re-methylation by NleE ( Figure 1A ) . Flag-TAB2 from 293T cells was efficiently re-methylated , whereas that from cells co-transfected with wild-type NleE resisted further in vitro methylation by NleE , suggesting full methylation of cellular TAB2 by transfected NleE . Furthermore , tandem mass spectrometric analysis of TAB2/3 from infected 293T cells confirmed the methylation modification as a Cys673-methylated peptide from Flag-TAB2 was detected upon infection with wild-type EPEC but not the ΔnleE strain ( Figure 1B ) . Complementation of ΔnleE strain with NleE expressed from a high-copy plasmid resulted in complete methylation of Cys673 ( Figure 1B ) . This provides direct evidences that NleE carries out cysteine methylation of TAB2/3 during EPEC infection . In addition to TAB2/3 , components of the linear ubiquitin chain assembly complex ( LUBAC ) , HOIP , HOIL-1L and Sharpin , also contain NZF domains and play important roles in NF-κB signaling [12] , [13] , [14] . Consistent with our previous in vitro data , the HOIL-1L and Sharpin-derived tryptic peptides bearing the cysteine corresponding to Cys673 in TAB2 were not methylated even when the infection was performed with the NleE-proficient EPEC strain ( Figure 1C and 1D ) . These data support that NleE inhibition of NF-κB signaling results from its specific targeting TAB2/3-NZF domains for cysteine methylation . To understand the mechanism of NleE function , we attempted to determine its crystal structure . Wild-type NleE yielded poor crystals , but NleE K181A protein , out of 15 Lys-to-Ala mutants designed to improve the crystallization [15] , produced sufficient-quality crystals in the C2 space group . A model obtained from 2 . 6-Å diffraction data collected on the selenomethionine ( Se-Met ) protein was further refined and a final resolution of 2 . 3 Å was achieved ( Table S1 ) . The structure shows that the mutated K181A is exposed and located at the interface of crystal contacts ( Figure S1 ) . Despite the presence of four NleE in an asymmetric unit ( chain A–D ) ( Figure S2A ) , NleE was exclusively a monomer in solution as judged by gel filtration chromatography analysis . The size of the buried surface area formed between different chains ranged from 590 Å2 to 1722 Å2 ( Figure S2B ) . PISA ( http://www . ebi . ac . uk/pdbe/pisa/ ) analysis of protein interface present in the crystal also suggested that the NleE tetramer is unlikely to be stable in solution ( Figure S2C ) , indicating that the tetrameric assembly of NleE in the asymmetric unit results from crystal packing likely with no physiological relevance . No meaningful structural difference was found among the four molecules and therefore only chain A was analyzed hereinafter . The structure of NleE ( residues 21–220: residues 1–20 and 220–224 lacked electron density ) adopts α/β doubly wound topology with a central three-stranded anti-paralleled β-sheet ( β1–β3 ) sandwiched by α-helices ( α1–α10 ) ( Figure 2A ) . β1–β3 are arranged in the left-right-middle order , which , together with the flanking α-helices ( α8–α10 ) , generates a deep and narrow cavity on the left side of NleE . The SAM molecule fits snugly into the cavity ( Figure 2B ) , which buries 2374 Å2 solvent accessible surface area , corresponding to 76% of the total surface area of SAM and leaving the rest of 24% exposing to the solvent . We first analyzed the structural details of SAM binding in NleE . The interior of the SAM-binding cavity is filled with hydrophobic side chains , but polar interactions appear to play a key role in riveting the SAM into the cavity ( Figure 2C ) . Specifically , the α-carboxylic group of the amino acid moiety of the SAM is coordinated by the side chain of Arg107 situated on a loop connecting β1 and α6 . The hydroxyl group of the ribose of the SAM is hydrogen-bonded with the side-chain carboxylic group of Glu191 . The adenine ring of SAM is oriented by the aromatic ring of Tyr212 residing at α10 ( residues 206–219 ) through a π-π stacking interaction . This explains the complete functional loss of the NleEΔ6 mutant [4] , [6] as deletion of 209IDSYMK214 is expected to disrupt the α10 . The interactions embed the SAM at the bottom of the cavity with a narrow opening slit , through which the buried ligand presents its methylthio in a direction favorable for the SN2 methyltransfer . Mutation of Arg107 , Glu191 or Tyr212 in NleE all abolished in vitro methylation of GST-TAB2-NZF , revealed by native gel mobility analysis of the NZF domain ( Figure 3A ) . When exogenous SAM was added , NleE-E191A and Y212A showed activity equal to wild-type NleE , whereas NleE-R107A and NleEΔ6 remained completely inactive ( Figure 3A ) . Consistently , NleE-Y212A and NleE-E191A were partially impaired in transferring 3H-methyl from radiolabeled SAM onto TAB2-NZF; NleE-R107A showed absolutely no activity in this assay ( Figure 3B ) . Thus , Arg107 is most critical for SAM binding while NleE-Y212A and NleE-E191A are severely impaired . The in vivo activity of NleE-R107A , E191A or Y212A mutants in inhibiting NF-κB in transfected 293T cells was concordant with their in vitro methylation activity ( Figure 3C ) . Further supporting the structural analysis , the NleE-R107A mutant , when complemented into EPEC ΔnleE strain , failed to restore methylation of TAB2 in bacteria infected cells ( Figure 3D ) . Among the five families of SAM-dependent methyltransferases [8] , the most abundant Class I has a central seven-stranded β-sheet and a GxGxG SAM-binding motif; Class II has long β-strands and a shallow groove with a RxxxGY SAM-binding motif; Class III is a homodimer with each monomer adopting an αβα structure and the SAM moiety bound between the two monomers; Class IV ( the SPOUT family of RNA methyltransferases ) bears a C-terminal SAM-binding knot structure; Class V contains a SET-domain SAM binding motif composed of three small β-sheets . Remarkably , the overall architecture of NleE does not resemble any of the five families ( Figure 4A ) , thus representing a completely novel class of methyltransferases . Moreover , the conformation of the SAM in NleE is much different from other methyltransferases as reflected in the adenosine and methionine conformations ( Figure 4B and 4C ) . The adenine base in NleE-bound SAM is characterized by a C4′-C1′-N9-C4 dihedral angle of 60° , significantly smaller than that in other methyltransferase-bound SAM or S-adenosylhomocysteine ( SAH ) ( Figure 4D ) . The O4′-C4′-C5′-Sδ dihedral angle in NleE-bound SAM is 160° , comparable to the ∼180° in that in Class I methyltransferases , whereas this dihedral angle is approximately −90° in Class II-IV and 80° in Class V methyltransferases ( Figure 4D ) . According to the C4′-C5′-Sδ-Cγ dihedral angle , the SAM/SAH molecules in NleE , Class I and II methyltransferases adopt a relatively extended conformation while those in other three classes adopt a more compact structure ( Figure 4C ) . NleE specifically methylates Cys673 in TAB2 ( Cys692 in TAB3 ) among the four Zn-coordinating cysteines in TAB2/3-NZF domains despite that they are predicted to be chemically inert due to protection by hydrogen bonds [16] ( Figure S3A ) . In the TAB2-NZF structure ( PDB ID code: 3A9J ) , Cys673 and Cys687 are largely exposed , whereas Cys670 and Cys684 are completely buried ( Figure S3B ) . To understand the mechanism of site-specific methylation by NleE , a hierarchical protein-protein docking approach with enforced distance restraints between the methyl group of SAM and the sulfur of Cys673/Cys687 was employed and molecular dynamics ( MD ) simulation was performed . The Cys687-restricted simulation showed a dramatic motion with pronounced root-mean-square deviation ( RMSD ) values , high interaction energy and a large distance from Cys687 to the methyl donor ( Figure 5A and Figure S3C ) . In contrast , the Cys673-restricted simulation showed a relatively limited motion and lower energy with a close distance from Cys673 to the methyl donor . Thus , a most energetically favorable and structurally stable NleE-SAM-NZF complex was in silico modeled ( Figure 5B ) , which clearly showed that Cys673 is the most favorable substrate residue . We previously observed that deletion of the NZF from TAB3 ( TAB3ΔNZF ) does not affect its binding to NleE [6] ( Figure 6A , and Figure S4A and S4B ) . This suggested that specific recognition by NleE requires another region in TAB3 . Progressive truncations from both the C and N termini of TAB3ΔNZF identified residues 52–194 as the minimal fragment sufficient for binding to NleE in the yeast two-hybrid interaction assay ( Figure 6A and 6B , and Figure S4 ) . Co-immunoprecipitation assay in transfected 293T confirmed that residues 52–194 of TAB3 , in contrast to the NZF alone , were competent in efficient binding to NleE ( Figure 6C ) . Thus , binding of the N-terminal region in TAB3 ( possibly also TAB2 ) may serve as a docking mechanism for recognition and methylation of TAB2/3-NZF by NleE . However , it is worth noting here that this region , involved in docking TAB3 onto NleE , does not appear to be sufficient for NleE methylating of other NZF domain as an NleE-resistant ZRANB2-NZF ( see below ) remained unmodified by NleE even when positioned in place of TAB3-NZF in the TAB3 ΔNZF construct ( Figure S5 ) . Given that Zn coordination is required for cysteine methylation by NleE , we investigated whether other Zn fingers could also be a substrate of NleE . Among a total of more than 50 Zn fingers including C2H2 , RING , RBCC/TRIM , FOG , PHD , as well as all the 13 NZF C4 fingers ( Table S2 and Figure S6 ) , NleE efficiently methylated the NZF domain of ZRANB3 with similar efficiency to that of the NZF domains of TAB2/3 and yeast Vps36 ( Figure 7A ) . NleE did not modify Npl4 , Sharpin , HOIP , HOIL-1L , Trabid-NZF1/2/3 and ZRANB2-NZF among the NZF subfamily [6] ( Figure 7A and Table S2 ) . Tandem mass spectrometry analysis identified the second cysteine in ZRANB3 ( Cys630 ) being the methylation site , which echoes the situation with TAB2/3-NZF domains ( Figure S7 ) . Full-length ZRANB3 purified from 293T cells was also a robust substrate in the in vitro methylation assay ( Figure 7B ) . In the back-methylation assay , recombinant NleE failed to methylate ZRANB3 purified from NleE-expression 293T cells ( Figure 7B ) , suggesting a full methylation of ZRANB3 in transfected mammalian cells . Agreeing with that reported in previous studies [9] , [11] , GST-ZRANB3-NZF could bind to polyubiquitin chains of Lys63 , Lys48 , as well as tetra ubiquitin with linear linkage ( Figure 7C ) . However , methylation by NleE was found to abolish the binding of ZRANB3-NZF to all ubiquitin chains . NleE could also abolish the ubiquitin chain binding of full-length ZRANB3 in transfected 293T cells , whereas the methyltransferase-deficient NleEΔ6 mutant failed to do so ( Figure 7D and 7E ) . In EPEC-infected cells , the majority of Flag-ZRANB3 appeared to be methylated in an NleE-dependent manner ( Figure 7F ) . The diminished ZRANB3 methylation in ΔnleE EPEC-infected cells could be fully restored by wild-type NleE but not the SAM-binding deficient R107A mutant ( Figure 7F ) . Consistently , NleE could completely disrupt the ubiquitin chain-binding ability of Flag-ZRANB3 during EPEC infection , which also required Arg107 in NleE ( Figure 7G ) . Thus , ZRANB3 , like TAB2/3 , is a bona fide target of NleE methyltransferase activity under physiological conditions . Overexpression of ZRANB3 in the presence or absence of NleE did not affect NF-κB activation ( Figure S8 ) . ZRANB3 has 1 , 077 amino acids; its N-terminal half is a helicase domain and the C-terminus harbors multiple domains including the NZF domain . Recent studies suggest that ZRANB3 is localized in nucleus and functions in DNA replication stress response to maintain genome stability [9] , [10] , [11] . ZRANB3 is recruited to damaged replication forks to promote fork restart . We also observed that EGFP-ZRANB3 was recruited to laser-generated stripes where DNA damage occurred ( Figure S9 ) . Notably , co-expression of NleE , which was found distributed in both the cytoplasm and nucleus ( Figure S10 ) and resulted in complete methylation of ZRANB3 ( Figure 7B ) , did not affect ZRANB3 recruitment to DNA damage sites ( Figure S9 ) . It has been proposed that damage-induced recruitment of ZRANB3 is mediated by its binding to K63-linked polyubiquitin chains on PCNA , a protein playing a central role in promoting faithful DNA replication [9] , [11] . Our results suggest that another structural region in ZRANB3 is more likely responsible for its recruitment to DNA damage sites and the NZF domain-mediated polyubiquitin-chain binding probably participates in other aspects of ZRANB3 function that remains to be defined . It is also worth noting here that the activity of NleE offered us a unique approach to achieve functional disruption of a single domain within a large multiple-domain protein .
NleE is a unique SAM-dependent methyltransferase in catalyzing cysteine methylation . The structure of NleE bears an overall Rossmann-like fold and more resembles that of Class I SAM-dependent methyltransferase , but its SAM-binding mode and conformation are completely different . This indicates an independent evolution of the two sub-lineages within the methyltransferase family and highlights the convergent evolution of bacterial virulence activity . The unique fold of NleE expands the repertoire of SAM-dependent methyltransferases and highlights the convergence on methylation chemistry from different three dimensional folds . Cysteine methylation is rare; a recent example is methylation of Cys39 in Rps27a , a nonessential yeast ribosomal protein [17] . Rps27a is structurally similar to the N-terminal domain of Ada protein and Cys39 is also one of the four Zn-coordinating cysteines , suggesting a similar non-enzymatic methyl transfer . This supports that Zn coordination facilitates methyl transfer onto the cysteine thiol . The high abundance of zinc finger [18] also indicates that other zinc-finger motifs might be potential methylation targets of some methyltransferases . A recent study on a radical SAM methyltransferase RlmN shows methylation of a cysteine not bound to the zinc [19] , [20] , further highlighting a chemical diversity of cysteine methylation . In addition TAB2/3 , we now also identify as ZRANB3 as another efficient methylation substrate of NleE ( EPEC 2348/69 strain ) . As NleE homologues are also present in other pathogenic E . coli strains as well as Salmonella and Shigella spp . , it is possible that different NleE homologues , produced by different bacterial pathogens , may target different host substrates . NleE efficiently methylates ZRANB3-NZF and abolishes its ubiquitin-chain binding but does not affect ZRANB3 recruitment to DNA damage sites . Proper function of ZRANB3 depends on its interaction with PCNA [9] , [10] , [11] and three domains , the PCNA-interacting protein motif ( PIP-box ) , the AlkB2 PCNA-interaction motif ( APIM ) , and the NZF domain , are proposed to be involved . These studies are all based on arbitrary deletion of an internal fragment in ZRANB3 , which might complicate data interpretation . NleE offers an unprecedented opportunity for specifically inactivating the NZF in ZRANB3 in situ without interfering with other domain functions , which reveals a dispensable role of the NZF domain in DNA damage recruitment . Thus , the NZF either plays little role in the recruitment or is functionally redundant to other domains . It is also plausible that NZF-mediated poly-ubiquitin chain binding may regulate the activity of ZRANB3 itself or fulfill functions as yet undefined .
The cDNA expression constructs for NleE , TAB2/3 , TAB2/3-NZF , LUBAC-NZFs and NEMO-NZF were described previously [6] . NleE point mutations were generated by QuickChange Site-Directed Mutagenesis Kit ( Stratagene ) . cDNA for mTrabid was kindly provided by Dr . Paul Evans ( University of Sheffield , UK ) . IMAGE clones for ZRANB2 and ZRANB3 were purchased from Source BioScience LifeSciences Inc . NZFs of Trabid , ZRANB2 and ZRANB3 were PCR-amplified and inserted into the pGEX-6p-2 vector for recombinant expression in E . coli , and the full-length ZRANB3 was inserted into pCS2-Flag for transient expression in mammalian cells . pEF-Flag-TAB3ΔNZF-ZRANB2-NZF chimera were constructed as previously described [6] , [21] . Luciferase plasmids were also described previously [22] . All the plasmids were verified by DNA sequencing . Antibodies for EGFP ( sc8334 ) , GAL4 AD ( C-10 , sc-1663 ) and ubiquitin ( P4D1 , sc8017 ) were purchased from Santa Cruz; Anti-Flag ( M2 ) antibody , anti-tubulin antibody and EZview Red ANTI-FLAG M2 Affinity Gel were from Sigma . Antibody for c-Myc ( 9E10 , MMS-150R-200 ) was purchased from Covance . Cell culture products were from Invitrogen , and all other reagents were Sigma-Aldrich products unless noted . His-SUMO-NleE was expressed in E . coli BL21 ( DE3 ) Gold strain . Se-Met labeled NleE was expressed in E . coli B834 ( DE3 ) as previously described [23] . NleE was purified sequentially by nickel affinity chromatography , Ulp1 digestion and Mono Q+ Superdex 75 chromatography . Expression and purification of NleE mutants and GST-NZFs were essentially the same as previously described [6] . Purified NleE was concentrated to 20 mg/ml in a buffer containing 20 mM Tris-HCl ( pH 8 . 0 ) and 100 mM NaCl . Crystals were grown using vapor-diffusion hanging-drop method at 19°C for one week against a reservoir buffer containing 22% PEG3350 and 0 . 2 M ammonium citrate dibasic . Se-Met crystals were obtained using Se-Met labeled NleE plus 1 mM SAM against 20% PEG3350 , 0 . 125 M ammonium citrate dibasic and 0 . 1 M sodium malonate ( pH 7 . 0 ) . Crystals were cryo-protected in the well buffer supplemented with 25% glycerol and flash-freezed in liquid nitrogen . Diffraction data were collected at Shanghai Synchrotron Radiation Facility ( SSRF ) BL-17U at the wavelength of 0 . 9789 Å for Se-Met crystals and 0 . 9792 Å for native crystals . All data were processed in the HKL2000 [24] . The phase for NleE was determined from the Se-Met crystal data using the single wavelength anomalous dispersion method [25] . Phasing and initial model building were accomplished using the AutoSol function of PHENIX . Automatic model building was performed using the 2 . 3-Å native data in PHENIX . Autobuild [26] . The autobuild model was manually adjusted in Coot [27] . The final model was refined in PHENIX . Refine . All the structural figures were prepared using the PyMol program ( http://www . pymol . org ) . Mammalian cell culture , transfection , immunoprecipitation and luciferase assays were basically the same as those described previously [6] . Rhodamine-Phalloidin staining of F-actin also follows that described the previous literature [28] . EPEC strains and infection protocols were described previously [6] . Yeast whole cell extracts were prepared as previously described with some minor modifications [29] . 20 OD600 units of yeast cells were harvested and freezed in liquid nitrogen . 600 µl of yeast lysis buffer ( 1 . 85 M NaOH and 7 . 4% β-mercaptoethanol ) were added and cells were kept on ice for 10 min . Trichloroacetic acid ( TCA ) was then added to a final concentration of 25% and cell lysates were incubated on ice for another 10 min . After centrifugation at 4°C for 30 min , the pellet was washed with cold acetone for four times . The air-dried pellet was solubilized in the SDS loading buffer and the supernatant was loaded onto an SDS-PAGE for further immunoblotting analysis . Flag-TAB2/HOIL-1L-V195R/Sharpin-S346R immunopurified from infected 293T cells , Flag-TAB3/TAB3ΔNZF-ZRANB2-NZF co-expressed with NleE in 293T cells , and GST-Vps36/GST-ZRANB3-NZF treated with NleE are subjected to in-gel trypsin digest and subsequent tandem mass spectrometry analysis similarly as previously described [6] . The V195R and S346R mutations in HOIL-1L and Sharpin , respectively , were introduced to facilitate mass spectrometry identification of the target tryptic peptides . For mass spectrometry analysis of ZRANB3 methylation by NleE , Flag-ZRANB3 was digested by Glu-C in solution . 293T cells transfected with Flag-ZRANB3-expressing plasmid and infected with EPEC were first harvested in buffer A ( 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 20 mM n-octyl-β-D-glucopyranoside ( INALCO ) and 5% glycerol ) supplemented with an EDTA-free protease inhibitor mixture ( Roche Molecular Biochemicals ) . Cells were lysed by ultrasonication . The supernatant was pre-cleared by protein G–Sepharose at 4°C for 1 h and subjected to anti-Flag immunoprecipitation . Following 4-h incubation , the beads were washed once with buffer A and then five times with TBS buffer ( 50 mM Tris-HCl , pH 7 . 5 , and 150 mM NaCl ) . Bound proteins were eluted with 600 mg/ml Flag peptide ( Sigma ) in the TBS buffer . The eluted protein was diluted 8 times with 50 mM Tris-HCl ( pH 8 . 5 ) and then concentrated to 20∼30 µl using the Vivaspin ( 30 , 000 MWCO , 500 µl , Sartorius ) . Tris-HCl ( pH 8 . 5 ) and Urea were then added to the final concentrations of 0 . 1 M and 0 . 8 M , respectively . Ultrasonication was performed to facilitate solubilization of the denatured proteins . The proteins were reduced in 5 mM TCEP ( Tris- ( carboxyethyl ) phosphine hydrochloride ) at 55°C for 20 min and then alkylated in 10 mM iodoacetamide at room temperature in dark for 15 min . The alkylated proteins were digested with the sequencing grade Glu-C ( Roche Molecular Biochemicals ) at 25°C overnight . An aliquot of peptide solution was analyzed by tandem mass spectrometry as previously described [6] . The NZF domain of TAB2 ( PDB code 3A9J ) [30] was used to model the NleE-NZF complex . The cysteine residues coordinating the Zn ( Cys670 , Cys673 , Cys684 and Cys687 ) were deprotonated and hydrogen atoms were added using the Protein Local Optimization Program [31] , [32] , [33] . Protein-protein docking was carried out using the RosettaDock program ( Rosetta 3 . 1 ) [34] , [35] with distance restraints enforced between the carbon atom of donor methyl group in SAM and the sulfur atoms of Cys673 or Cys687 in NZF ( cutoff value of 10 Å ) . Distance restraints between the Zn and sulfur atoms of the four cysteines were also added to ensure the correct spatial geometry of the zinc finger motif . The docking poses were clustered using the NMRCLUST program [36] according to the RMSD values of Cα atoms of TAB2-NZF using the NleE structure as the reference . Representative models of the largest four clusters were selected for further MD simulation refinement . All the MD simulations were set up by employing the Gromacs 4 . 07 package [37] with amber 99SB force filed [38] in the TIP3P explicit water model [39] . The tetrahedron-shaped zinc parameters were applied in the MD simulation [40] , [41] . After minimization and equilibration , the production run was performed in NVT for 15 ns ( 300 K ) without positional restraints . The short-range electrostatic and Lennard-Jones interactions in the simulations were calculated using a force-shifted cutoff value of 12 Å and 10 Å , respectively . The Long-range electrostatic interactions were computed by the Particle Mesh Ewald method [42] . The covalent bonds involving hydrogen atoms were constrained with the LINCS algorithm [43] . The non-bound interaction energy between TAB2-NZF and NleE was computed by accounting the sum of electrostatic ( Ecoul-SR ) and vander Waals ( ELJ-SR ) interaction terms in short range . The surface accessible area is calculated in DSSP program [44] , and the related solvent accessibility is measured on the ASA-View Server [45] . The trajectories of last 10-ns MD simulations were saved every 100 ps and further analyzed . The interaction energy between TAB2-NZF and NleE , the RMSD , and the distance of polarized “CH3” group of SAM to the sulfur of Cys673 and Cys687 were measured and compared to obtain the near-native complex structure . The model derived from the cluster 1 of distance restraint sampling was the best model , on which another 15-ns MD simulation was carried out for further optimization . 8 µg of GST-TAB2-NZF was incubated with 6 µg of NleE or its mutants ( without exogenous SAM ) or 2 µg of NleE ( with 0 . 8 mM exogenous SAM ) for 30 min at 37°C in 30 µl of buffer containing 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 5 mM DTT and 0 . 1% NP-40 . The reaction mixtures were separated on a 12% native-PAGE gel , followed by Coomassie blue staining . 3H-SAM labeling of different GST-NZFs were carried out as previously described [6] . To examine NleE methylation of TAB2/ZRANB3 in vivo , Flag-TAB2/ZRANB3 co-expressed with an empty vector or NleE was immunopurified from 293T cells and subjected to in vitro methylation using 0 . 6 µg of recombinant NleE and 0 . 55 µCi of 3H-SAM . To examine the effect of NleE modification on the ubiquitin-chain binding activity of ZRANB3 in vitro , 20 µg of GST-ZRANB3-NZF was incubated with 3 µg of NleE for 30 min at 37°C in a 40-µl reaction containing 0 . 8 mM SAM . The GST-tagged proteins were then immobilized onto Glutathione Sepharose 4B beads ( GE Healthcare ) for GST pulldown of Lys48 , or Lys63-linked ubiquitin chains or linear tetra-ubiquitin similarly as that described previously [6] . To assay NleE modification and inactivation of cellular ZRANB3 , 293T cells , co-transfected with Flag-ZRANB3 and EGFP-NleE as indicated , were harvested and re-suspended in 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 0 . 1% Triton X-100 and 5% glycerol . Cells were lysed by ultrasonication . The supernatant was pre-cleared using Protein G Sepharose ( GE Healthcare ) and then subjected to overnight pulldown by Lys63-linked ubiquitin chains or SBP ( streptavidin binding peptide ) -tagged linear tetra-ubiquitin chains [6] . U2OS cells cultured in 35-mm glass bottom culture dish ( MatTek ) were co-transfected with EGFP-ZRANB3 and RFP-NleE expression plasmids as indicated by using the Vigofect reagent ( Vigorous ) . Cells were sensitized by addition of 10 µM BrdU for 16 h and then transferred to the environmental chamber ( 5% CO2 , 37°C ) in the spinning disk confocal imaging system ( PerkinElmer UltraVIEW VOX ) . Following visualization under Nikon Eclipse Ti inverted microscope , cells with both EGFP and RFP fluorescence were subjected to laser microirradiation using the FRAP ( Fluorescence recovery after photobleaching ) module and live images were then taken at indicated times points after the microirradiation . The coordinates of the NleE structure together with the structure factors have been deposited in the Protein Data Bank with the accession code 4R29 . | Pathogens often manipulate host functions by posttranslational modifications such as ubiquitination and methylation . The NF-κB pathway is most critical for immune defense against infection , thereby frequently targeted by bacterial virulence factors . NleE , a virulence effector from EPEC , is a SAM-dependent methyltransferase that modifies a zinc-finger cysteine in TAB2/3 in the NF-κB pathway . NleE is not homologous to any known methyltransferases . We present the crystal structure of SAM-bound NleE that shows a novel methyltransferase fold with a unique SAM-binding mode . Computational docking and molecular dynamics simulation illustrate a structural and chemical mechanism underlying NleE recognition of the NZF and catalyzing site-specific cysteine methylation . Subsequent substrate specificity analyses identify an N-terminal region in TAB3 required for efficient NleE recognition as well as another NZF protein ZRANB3 being a new substrate of NleE . NleE-catalyzed cysteine methylation also disrupts the ubiquitin chain-binding of ZRANB3-NZF domain , providing new insights into ZRANB3-NZF functioning in DNA damage repair . These results reinforce the idea of harnessing bacterial effectors as a tool for dissecting eukaryotic functions . | [
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"microbiology"
] | 2014 | Structure and Specificity of the Bacterial Cysteine Methyltransferase Effector NleE Suggests a Novel Substrate in Human DNA Repair Pathway |
Ascaris lumbricoides and Ascaris suum are socioeconomically important and widespread parasites of humans and pigs , respectively . The excretory-secretory ( ES ) molecules produced and presented at the parasite-host interface during the different phases of tissue invasion and migration are likely to play critical roles in the induction and development of protective immune and other host responses . The aim of this study was to identify the ES proteins of the different larval stages ( L3-egg , L3-lung and L4 ) by LC-MS/MS . In total , 106 different proteins were identified , 20 in L3-egg , 45 in L3-lung stage and 58 in L4 . Although most of the proteins identified were stage-specific , 15 were identified in the ES products of at least two stages . Two proteins , i . e . a 14-3-3-like protein and a serpin-like protein , were present in the ES products from the three different larval stages investigated . Interestingly , a comparison of ES products from L4 with those of L3-egg and L3-lung showed an abundance of metabolic enzymes , particularly glycosyl hydrolases . Further study indicated that most of these glycolytic enzymes were transcriptionally upregulated from L4 onwards , with a peak in the adult stage , particularly in intestinal tissue . This was also confirmed by enzymatic assays , showing the highest glycosidase activity in protein extracts from adult worms gut . The present proteomic analysis provides important information on the host-parasite interaction and the biology of the migratory stages of A . suum . In particular , the high transcriptional upregulation of glycosyl hydrolases from the L4 stage onwards reveals that the degradation of complex carbohydrates forms an essential part of the energy metabolism of this parasite once it establishes in the small intestine .
Ascariasis is the most prevalent internal macro-parasite of humans ( Ascaris lumbricoides ) and pigs ( Ascaris suum ) worldwide . Approximately 1 . 2 billion people infected , with a prevalence that is highest in children of the tropics and subtropics [1] . Infected children show signs of malnutrition , growth stunting , intellectual retardation , and cognitive and educational deficits [2] . Ascaris also causes major production losses in pigs , including reduced growth rates associated with a decrease in feed conversion efficiency [3] . In addition , lesions in pig livers ( i . e . ‘milk spots’ ) caused by migrating larvae represent considerable losses as such livers are condemned [4] . Traditionally , ascariasis is usually controlled by mass treatment with anthelmintics . However , due to the short activity of the anthelmintics and an environment often highly contaminated with Ascaris eggs , reinfections can occur rapidly . Hosts become infected by the oral ingestion of Ascaris eggs containing infective third-stage larvae ( L3s ) . After hatching in the gastrointestinal tract , the larvae penetrate mainly the caecal wall and undergo a hepatopulmonary migration , after which , ultimately , the adult females and males establish and develop in the small intestine . During a primary infection , migrating larvae cause pathological lesions in the gut , liver and lungs . A short-lived immunological reaction against the migrating L3s is seen in the liver 7 days after infection , and is characterized by the production of B cells and CD4+ T cells in the local lymph nodes [5] . Two weeks after the infection , the immunological reaction changes from a liver to a lung response , in which the local lymph nodes are enlarged [5] . After the hepatopulmonary migration of the larvae , an intestinal hypersensitivity reaction is seen in the gut , characterized by an accumulation of mast cells , eosinophils and IgA- and IgE-producing cells in the gut mucosa . Pathophysiological changes in the gut , such as increased mucus secretion and mucosal permeability , caused by enhanced secretion of IL-4 and IL-13 , have also been observed [6] . After a prolonged exposure , pigs develop a strong protective immunity in the gut , which prevents new incoming larvae from penetrating the intestinal wall . Recently , Masure et al . [7] showed that eosinophils play a crucial role in generating this immune barrier . The proteins produced and presented at the parasite-host interface during these different phases of tissue invasion and migration are inferred to play a critical role in the induction and development of immune responses [8] . Such proteins can be present on the outermost layers of the cuticle and in the excretory-secretory ( ES ) products , which are mainly released from the cuticular surface , specialized excretory/secretory organs and the worm intestine [8] , [9] . To date , little is known about these components from A . suum . Limited by technical and practical constraints , earlier studies of ES products from A . suum were mainly focused on exploring their chemical composition , ultrastructure and immunological role [10]–[14] . Recently , with major developments in mass spectrometry and genomic technologies , many of the previous challenges and limitations in the proteomic analysis of parasite ES proteins have been overcome , and have led to the characterisation of ES proteomes for parasitic nematodes including Ancylostoma caninum , Brugia malayi , Haemonchus contortus , Teladorsagia circumcincta and Trichinella spiralis [15]–[22] . Nonetheless , there has been no profound proteomic analysis of Ascaris ES products at critical stages of development . The aim of this study was to characterize the ES proteins of three different larval stages of A . suum ( i . e . L3-egg , L3-lung and L4 ) using tandem mass-spectrometry combined with the recently completed A . suum genome for annotation [23] . In addition , transcriptomic datasets of the larval stages [23] were used to investigate transcription of genes encoding some of the proteins identified in the ES products from the three larval stages .
All animal experiments were conducted in accordance with the E . U . Animal Welfare Directives and VICH Guidelines for Good Clinical Practice , and ethical approval to conduct the studies were obtained from the Ethical Committee of the Faculty of Veterinary Medicine at Ghent University ( Identification number EC2011/176 ) who have also approved the document . Adult worms of A . suum were collected from naturally infected pigs at the local slaughterhouse as part of the normal work at the abattoir . Subsequently , male and female worms were dissected and the intestine , reproductive system and cuticle collected and stored at −80°C until use . Eggs of A . suum were obtained from the uteri of female worms , and cultured in 0 . 1% K2Cr2O7 for 28–30 days at 25°C . After 90% of the eggs had become fully embryonated , the infective L3s were hatched from the eggs as described previously by Urban and Douvres [24] and then separated from eggshell fragments and other debris by baermannization . Two groups of two pigs were experimentally infected with larvated eggs of A . suum by gavage . Pigs of group one were each inoculated with 500 , 000 eggs and euthanized seven days post infection ( pi ) in order to collect the lung stage larvae ( L3-lung ) , whereas pigs of group two each received 30 , 000 eggs and were euthanized 14 days pi to collect intestinal stage larvae ( L4 ) . L3-lung and L4 were separated from lung tissue and small intestinal contents of host by baermannization , respectively . All three larval stages ( L3-egg , L3-lung and L4 ) were cultured for five days in RPMI 1640 medium with 10 mM L-Glutamine ( GIBCO , Invitrogen ) containing 0 . 2 mg/ml gentamycin ( 10 mg/ml GIBCO , Invitrogen ) , 1% amphotericin B ( 250 µg/ml , Sigma ) , 1 mg/ml streptomycin ( Sigma ) and 1 , 000 U/ml penicillin ( Kela pharma ) . The viability of larvae was checked daily and the culture fluid was collected every 24 h and filtered through a 0 . 22 µm filter ( PALL Corporation ) . After 5 days , the filtrates were pooled and then concentrated and dialysed against phosphate-buffered saline ( PBS ) at 4°C using filters ( Amicon , YM-10 membranes , Millipore ) . Proteins were precipitated through the addition of 6 volumes of cold acetone for 18 h at −20°C . The proteins were pelleted by centrifugation at 13 , 000 rpm for 15 min at 4°C . The pellet was resuspended in PBS and stored in aliquots at −80°C . For SDS-PAGE analysis , protein samples ( 20 µg per lane ) were mixed with loading buffer ( 2% SDS , 50 mM Tris HCl and 5% β-mercaptoethanol ) , boiled for 5 min and then separated on 12% SDS-PAGE gels using a standard procedure [25] . After staining with Coomassie Brilliant Blue ( Invitrogen ) , the entire gel lane was sliced in 10 equal pieces ( horizontally ) and used for subsequent liquid chromatography-tandem mass spectrometric ( LC-MS/MS ) analysis . Tryptic in-gel digestion was performed as described previously [26] . In brief , to ensure better transfer of buffers , each protein band was cut into 1 mm2 portions , washed twice in 50% acetonitrile with 25 mM ammonium bicarbonate , reduced with 10 mM dithiothreitol in 25 mM ammonium bicarbonate , alkylated with 100 mM iodoacetamide in 25 mM ammonium bicarbonate and digested with trypsin ( 200 ng per band ) at 37°C for 18 h . Peptides were extracted with acetonitrile and dried in a Speedvac . The in-solution digestion was performed as previously described [27] . In brief , 10 µg of the acetone-precipitated ES proteins were resuspended in 20 µl of 0 . 5 M triethylammonium bicarbonate buffer , reduced with 2 µl of 10 mM dithiothreitol and incubated at 60°C for 1 h . Subsequently , 1 µl of 200 mM methyl methanethiosulfonate in isopropanol was added and incubated for 10 min at room temperature . The solution was digested with trypsin ( resuspended in triethylammonium bicarbonate ) in at a ratio of 1/50 ( amount trypsin/protein ) overnight at 37°C . Dried peptides were dissolved in 40 µl 0 . 1% formic acid ( FA ) and 20 µl was desalted for 10 min on a C-18 pre-column ( C18 PepMap100 , 5 µm×5 mm , i . d . 300 µm Dionex ) with 0 . 1% FA . Separation was performed by means of reversed phase nano-HPLC ( 25 cm PepMap C18 analytical column , Dionex ) at 60°C using a linear gradient of H2O: ACN ( 97∶3 , 0 . 1% FA ) to H2O: ACN ( 20∶80 , 0 . 1% FA ) at 300 nl/min over 70 min . The different peptides were analyzed on an ESI Q-TOF Premier ( Waters , Wilmslow , UK ) in a data dependent mode , with automatic switching between MS and MS/MS for up to 7 higher charge ions , when the intensity of the individual ions rose above 50 counts per sec . Fragmentation of the precursors was performed by means of CID . The capillary voltage was set at 1 . 9 kV , and the cone voltage was set at 100 . M/z ratios for MS ranged between x and y and for MS/MS between x and y . M/z ratios selected for MS/MS were excluded for 150 sec . A custom collision energy profile was used . Data were searched against an in-house Ascaris sequence database ( 18 , 542 protein entries ) , which is based on the recently published A . suum genome [23] , using the search engine Mascot Daemon ( v . 2 . 3 , Matrix Science , London , UK ) , allowing a maximum of one miscleavage . Carbamidomethyl ( C ) was specified as fixed modification and carbamidomethyl ( N-term ) , deamidated ( NQ ) and oxidation ( M ) were considered as variable modifications for in-gel digest . For in solution digests , methylthio ( C ) was selected as the fixed modification , and deamidated ( NQ ) and oxidation ( M ) as variable modifications . An error-tolerant Mascot search was performed as well . The peptide tolerance and MS/MS tolerance were set to 0 . 35 Da and 0 . 45 Da , respectively . Only the most parsimonious group of protein identifications were reported from the identified proteins , and the identification threshold was set at p<0 . 01 . For the proteins that were annotated based on only one peptide , the identification threshold was set at p<0 . 0001 . An estimate of the relative abundance of the predicted proteins in the trypsin digestion was assessed using the Exponentially Modified Protein Abundance Index ( emPAI ) [28] together with the MS score , sequence coverage , detected peptides numbers . For redundant identifications , the emPAI value from the hit with the highest score was considered . The Gene Ontology ( GO ) database was used for inferring the molecular function of individual proteins identified . The protein sequences were analysed for the presence of signal peptides and transmembrane regions with SignalP 3 . 0 and TMHMM 2 . 0 ( http://www . cbs . dtu . dk/services/TMHMM/ ) , respectively . The subcellular localization was predicted with SecretomeP 2 . 0 . The sequences of the identified proteins were then used to BLAST search the A . suum genome to identify homologous sequences . This was done through the WormBase ( http://www . wormbase . org/ ) ( E-value threshold = 1E-16 ) . Amino acid sequences of selected eukaryotic glycosyl hydrolases listed in the CAZy database ( http://www . cazy . org/ ) were downloaded and used for multiple alignment and consecutive phylogenetic analyses . These sequences included: Homo sapiens alpha acid glycosidase ( AAG ) ( P10253 ) , dual catalytic sucrase-isomaltase ( SUIS ) ( P14410 ) , maltase-glucoamylase ( MGA ) ( O43451 ) , alpha glucosidase AB ( GANAB ) ( Q14697 ) , alpha glucosidase C ( GANC ) ( Q8TET4 ) ; Bos taurus AAG ( Q9MYM4 ) ; Mus musculus AAG ( P70699 ) ; Coturnix japonica AAG ( O73626 ) ; Oryctolagus cuniculus SUIS ( P07768 ) ; Suncus murinus SUIS ( O62653 ) ; Rattus norvegicus SUIS ( P23739 ) ; Sus scrofa GANAB ( P79403 ) ; Drosophila melanogaster AAG-like ( Q7KMM4 ) and Caenorhabditis elegans AAGR1-4 . The protein sequences were subjected to MUSCLE alignment ( http://www . ebi . ac . uk/Tools/msa/muscle/ ) , and alignments verified and visually checked and edited , as required , in Jalview ( http://www . jalview . org/ ) . The program ClustalX 2 . 0 . 10 was used to generate phylogenetic tree following analysis using the neighbour-joining method ( 1000 replicates ) [29] . Finally , the program WebLogo application ( http://weblogo . threeplusone . com/create . cgi ) was used to provide a graphical representation of the amino acid homology around the catalytic sites of some of the glycosyl hydrolases of A . suum and C . elegans . Total RNAs from larvae and adult worm tissue samples were isolated using TRIzol ( Invitrogen ) , followed by further purification with the RNeasy Mini kit ( Qiagen ) , according to the manufacturer's instructions . An on-column DNase digestion was performed using the RNase-free DNase set ( Qiagen ) to remove any possible genomic DNA . The RNA concentrations were determined ( NanoDrop ND-1000 spectrophotometer , NanoDrop Technologies ) and its quality was verified ( Experion Automated Electrophoresis System , Bio-Rad ) . For all samples , the RNA quality indicator ( RQI ) calculated ( Experion™ software , Bio-Rad ) was 8 . 0 , demonstrating high RNA integrity . The qPCR analyses were performed as described previously [30] . Tubulin and glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) were selected as housekeeping genes . The primer sets used were designed by Primer3 software ( http://frodo . wi . mit . edu/primer3/ ) and are listed in Table S1 . A transcriptome dataset was generated from the L3-egg , the L3-liver , the L3-lung and the intestinal L4 stages as part of a previous study [23] . Briefly , following RNA-seq , all paired-end reads for each library constructed were aligned to the predicted A . suum gene set using TopHat . Levels of transcription ( reads per kilobase per Million mapped reads ( RPKM ) ) were calculated using Cufflinks [31] . To obtain the RPKM values for genes of interest , accession numbers from the A . suum genome were used to search the transcriptomic datasets . Protein extracts of larval stages or adult worm tissues were produced by grinding the frozen material to a fine powder in a liquid nitrogen-cooled pestle and mortar . The powder was sequentially subjected to a two-step process with reagents of increasing solubilising power [32] . For the water-soluble protein fraction , 4 ml of PBS , pH 7 . 4 , were used to resuspend the powder for 2 h at 4°C by gentle ‘head-over-head’ mixing . The insoluble material was pelleted by centrifugation at 120 , 000×g for 15 min and the supernatant retained . For the water-insoluble protein fraction , the pellet was incubated at 22°C for 3 h using an extraction buffer consisting of 5 M urea ( Sigma ) , 2 M thiourea ( Sigma ) , 2% CHAPS ( Sigma ) and 2% SB3-10 ( Sigma ) in 40 mM Tris , pH 7 . 4 . The supernatant was collected , as described for the water-soluble protein fraction . A general use cocktail of protease inhibitor ( Sigma ) was added to each extracts to avoid proteolytic degradation . Protein concentrations were measured with the Bradford reagent ( Sigma ) , and proteins stored at −80°C . The glycosidase assays were conducted by incubating 5 µg of protein extract with 30 mM of substrate at pH 6 . 5 for 40 min at 37°C . Reactions were quenched by the addition of 3 M Tris . The glucose was quantified using the Glucose Assay Kit ( Sigma ) . The substrates used in the assays included maltose , lactose and sucrose . Each analysis was performed three times , and the results presented as the average of the three readings . For statistical analysis , the unpaired student t-test was used to test differences in activity between the different protein homogenates . The level of significance for analyses was set at P≤0 . 05 .
The protein profiles of the ES products from each of the three larval stages of A . suum , displayed by SDS-PAGE and Coomassie staining , are shown in Figure 1 . The analysis revealed a complex and distinct banding pattern for the ES of three individual stages . Most ES proteins from L3-egg were distributed between 10–120 kDa , whereas those of L3-lung were mainly between 30 and 100 kDa , with a smear above 40 kDa . L4 ES represented a complicated profile , with major bands between 37 and 150 kDa , and some fainter bands in the 20–30 kDa range . Mascot searches of the MS/MS spectra for both the in-gel and in-solution approaches yielded 20 , 45 and 58 protein identities within ES products of L3-egg , L3-lung and L4 stages , respectively . The full lists of proteins identified are provided in Tables 1 ( L3-egg ) , 2 ( L3-lung ) and 3 ( L4 ) . Most ES proteins detected were inferred to be stage-specific [85% ( n = 17 ) for L3-egg , 69% ( n = 31 ) for L3-lung and 74% ( n = 43 ) for L4] , and 15 proteins identified in ES products were shared by at least two larval stages . The identities of proteins shared by all three stages are given in Figure 2 . ES products from L3-lung and L4 shared 14 proteins , representing 31% and 24% of their sub-total , respectively , whereas the L3-egg shared only 2 and 3 proteins with L3-lung and L4 , respectively . Finally , two proteins shared by all three ES samples included a 14-3-3-like protein and a serpin ( Figure 2 ) . In silico prediction of classical and non-classical secretion showed that 9 ( 45% ) , 25 ( 56% ) and 42 ( 72% ) of the identified proteins from L3-egg , L3-lung and L4 ES products , respectively , were predicted to be either a classical or non-classical secreted protein ( Tables 1–3 ) . All proteins identified were subsequently categorized based on their molecular function , according to information from the GO database . Assigned were: metabolic pathway , structural , motor activity , binding , other functions and proteins of unknown function ( Figure 3 ) . From the entire annotated ES protein dataset , 24% ( n = 38 ) of proteins did not have any known function or known homologues in other organisms . Comparison of the results obtained for the three larval stages indicated an increase in the number of proteins involved in metabolic pathways from the L3-egg stage to the L3-lung and L4 stage larvae , whereas only two endochitinase homologues were identified from the L3-egg . In contrast , motor activity proteins , including proteins such as myosin-4 , paramyosin and tropomyosin , were unique to L3-egg . Finally , 9% of proteins identified in L3-lung ES products , including cuticlin-1 , cuticle collagen 12 and 13 , represented ‘structural’ proteins , whereas those belonging to this category were less represented in L3-egg ( none ) and L4 ( 2% ) . Of the 17 binding proteins identified 82% of them were ATP- , ion- , carbohydrate- and DNA-binding proteins . The most frequently identified proteins in ES products were glycosyl hydrolases belonging to family 31 ( GH31 ) . In total 16 GH31 proteins were identified in the ES products of L3-lung and L4 larvae with homology to maltase-glucoamylases and sucrase-isomaltases . Six and 5 GH31 proteins were identified in L3-lung and L4 , respectively , and another 5 for both of these larval stages . In order to obtain more information on these proteins , we subsequently BLAST searched the A . suum genome for additional members of this GH31 family . In total , 32 protein sequences were identified , all showing homology to GH31 proteins ( Table 4 ) . The length of the protein sequences ranged from 80 to 1772 amino acids ( aa ) , suggesting that some of the sequences were not full length . Twenty of the predicted GH31 proteins were predicted as either secreted through a classical or non-classical pathway . The GH31 protein sequences ( ≥700 aa ) representing Ascaris were aligned with those of homologous proteins from other species for subsequent phylogenetic analysis ( Figure 4 , panel A ) . The unrooted tree indicated clustering of the majority of the GH31 proteins of A . suum with acid-active GH31 enzymes ( i . e . AAG , SUIS , MGA , AAGR1-2 ) , whereas only one ( i . e . GS_18807 ) clustered with neutral-active GH31 enzymes ( i . e . GANAB and GANC ) . The results of a comparative analysis of the amino acid sequence homology around the catalytic site of 13 A . suum GH31 proteins ( codes GS_0471 , GS_05082 , GS_06701 , GS_08447 , GS_13054 , GS_17123 , GS_17323 , GS_18807 , GS_19777 , GS_20796 , GS_22047 and GS_23879 ) and the 4 GH31 proteins present in C . elegans ( AAGR1- AAGR 4 ) ( Figure 4 , panel B ) indicated that the signature motifs around the catalytic nucleophile are largely conserved between these two nematode species . In the transcriptomic analysis , the RPKM values for all GH31 proteins identified here showed that most of them are transcriptionally upregulated in the late larval stages ( L3-lung and L4 ) of A . suum ( Table 4 ) . Based on the RPKM values , GH31 proteins with the highest transcription were GS_18934 , GS_13054 and GS_19777 , with RPKM values of >500 in L4 . A qPCR analysis of genes encoding GH31 proteins ( codes GS_18934 and GS_19777 ) was conducted to ( 1 ) verify the transcriptomic data and ( 2 ) to analyse their transcription profiles in different tissues of adult A . suum ( Figure 5 panel A ) . Indeed , transcription levels of both genes were higher in L4 compared with other stages . In addition , the transcription linked to these GH31 was in the intestine of both female and male adults of A . suum , whereas almost no transcription was detected in either the reproductive system or the cuticle of both sexes ( Figure 5 , panel A ) . To confirm the intestinal location of the GH31 proteins , enzymatic assays were performed to measure glycolytic activity in protein homogenates from different adult A . suum tissues ( Figure 5 , panel B ) . Particularly maltose and sucrose were degraded following incubation with homogenates from the intestinal tracts of both adult male and female worms . The glycolytic activity measured was markedly higher in the water-insoluble protein fractions compared with the water-soluble fraction ( P<0 . 05 ) . In addition , the intestinal homogenates from males showed higher activity compared with females ( P<0 . 05 ) . The degradation of lactose was only observed after incubation with the water-insoluble protein fraction produced from the adult male intestines .
The goal of this study was to identify the ES proteins produced and released by the larval stages of A . suum in vitro and to infer the functions of these molecules during the migratory phase of the parasite through the body of the host animal . In total , 106 proteins were identified , of which 62% were predicted to either contain a signal peptide , suggesting secretion through a classical pathway , or predicted to be secreted via a non-classical pathway . The other 38% of proteins lacked a detectable signal sequence . Although no changes were observed in the motility or physical appearance of the larvae during the in vitro culture , some atypical secreted proteins were detected . The highest number of ‘non-secreted’ proteins for L3-egg was 55% compared with 45% and 26% for L3-lung and L4 , respectively . Some of these ‘atypical secreted’ proteins , including 14-3-3 and serpin , may include their secretion in extracellular vesicles as described for other helminths , such as C . elegans [33] , Fasciola hepatica and Echinostoma caproni [34] . However , the presence of some typical intracellular proteins in the ES material , such as histones , for example , suggests that there was some cellular damage in the larvae leading to leakage of intracellular proteins into the medium . The precise reason for this is unclear , but it is possible that the hatching procedure , and the subsequent washing steps have a role . Moreover , keeping the in vitro culture as short as possible may help reducing the possibly invisible leakage of intracellular proteins into the medium . Therefore , in the future it would be interesting to analyse ES material that has been collected after only few hours of in vitro culture . Among the 106 ES proteins identified in this study , two ( i . e . a serpin-like and a 14-3-3 protein ) were released by all three larval stages investigated . Serpins are serine protease inhibitors with a wide spectrum of functions in numerous biological systems , such as blood coagulation , complement activation and inflammation [17] , [19] , [35] , [36] . Analysis of the A . suum genome and transcriptomes showed that they contain 10 serpin-encoding genes [23] , whereas 8 and 3 serpin genes have been identified in the genomes of C . elegans and B . malayi [35] , respectively . A number of studies have previously reported on the presence of serpins in nematode ES products and experimental evidence indicates that many of them can have an immune-evasive function [36] . Interestingly , earlier studies of A . suum have shown that the activities of host proteases , such as trypsin and chymotrypsin , were greatly decreased from the micro-environment of live worms with a functioning gastrointestinal system [37] . Subsequently , Martzen et al . [38] , [39] showed that inactive chymotrypsin complexes were formed in the muscle sarcolemma and in the epithelial surface of the gut of adult A . suum as well as in developing eggs and larvae of this nematode . In this way , the serine protease inhibitors may not only protect the worms from degradation in host digestive environment but might also mask the surface of developing larvae , permitting them to evade the host's immune system as they migrate from the intestine to the liver and the lungs . Whether the serpins detected in the ES products from A . suum are involved in these processes is still unclear . In addition to the serpin , a 14-3-3 protein was also detected in the ES material of all three larval stages . Such 14-3-3 proteins represent a family of relatively conserved regulatory proteins , which can bind a range of functionally diverse signaling proteins . In C . elegans , a 14-3-3 protein regulates daf-2/insulin-like signaling pathway , which is critical for regulating development , longevity , metabolism and stress resistance [40] . Although the 14-3-3 proteins have been isolated and characterized recently as molecules with a significant role in the parasite biology and immunology within the context of the host–parasite relationship [41]–[43] , currently , little information is available on their actual role in parasites . Further comparison of the protein composition of the larval ES proteins showed that more overlap existed between L3-lung and L4 compared with L3-egg . Glycosyl hydrolases belonging to family 31 ( GH31 ) were particularly prominent in ES products from L3-lung and L4 . The identification of 16 GH31 proteins is an intriguing outcome of this study , particularly since no other studies have reported the presence of such enzymes in the ES products from nematodes . An analysis of the A . suum genome and transcriptomes revealed 32 putative GH31 protein encoding genes/sequences . Although the exact number of GH31 protein genes in A . suum is less than 32 , because of short or incomplete sequences in the current dataset , it is still clear that this gene family has undergone a large expansion compared with other nematode species . A preliminary analysis indicated the presence of only 4 GH31 protein genes in the genomes of C . elegans , B . malayi and T . spiralis ( results not shown ) . The results presented in the present study also indicated that most of the GH31 proteins were transcriptionally upregulated from the L4 larval stage onwards , with a peak in the adult stage of Ascaris , in particular in intestinal tissues . This finding was also confirmed by enzymatic assays , showing the highest glycosidase activity in intestinal protein extracts from adult worms . It has been suggested [44] that Ascaris takes most of its nutrients from the partially digested host food in the intestine . The present findings suggest that the degradation of complex carbohydrates forms an essential part of the energy metabolism of this parasite once it is established in the small intestine . The highest level of glycolytic activity was consistently found in the water insoluble protein fraction , suggesting that the enzymes are associated or directly bound to a cell membrane . In mammals , it has been shown that the sucrose-isomaltase complex is anchored to the small intestinal brush border through a highly hydrophobic segment in the N-terminal region of the isomaltase subunit [45] , [46] . However , none of the A . suum GH31 protein sequences were predicted to contain such a transmembranic region . Therefore , further research is needed to determine the exact cellular location of the GH31 proteins within the intestinal tissues of A . suum . If the results would show that they are actually located on the intestinal surface , it would make them interesting drug and/or vaccine targets . Apart from the shared proteins , most of the proteins identified were unique to a particular larval stage . Amongst the ES proteins identified in L3-egg , there were at least two different endochitinases . Chitinases are enzymes that catalyze the hydrolysis of beta-1 , 4-N-acetyl-d-glucosamine linkages in chitin polymers . Studies of B . malayi ( a filarioid nematode ) showed that a chitinase was secreted during the exsheathment process of the microfilariae in the mosquito vector and from the eggshell during hatching of the larvae within the reproductive tract of the adult stage [47] . RNAi studies of Acanthocheilonema viteae ( also a filarioid ) showed that chitinase was also critical in the moulting process of the nematode [48] . Interestingly , Geng et al . [49] previously reported on the abundant secretion of a chitinase in the perivitelline fluid surrounding the infective A . suum larva just prior to hatching from the egg . This chitinase is however different from that identified here . During the larval cultivation , many of the larvae need to lose the L2 cuticle , which is usually still present around the infective L3 larvae when they hatch from the egg . Therefore , it is possible that the chitinases identified herein are involved in the exsheathment process . Analysis of ES products from L3-lung and L4-ES resulted in the identification of various proteins that have consistently been found in ES material of other parasites , such as the transthyretin-like proteins , C-type lectins and venom allergens [15] , [17] , [18] , [50] . The transthyretin-like proteins are one of the largest conserved nematode-specific protein families of which the function is still largely unclear . Recent data published by Wang et al . [51] on TTR-52 , one of the 57 transthyretin-like proteins present in C . elegans suggest that these proteins act extracellulary to mediate cell-cell interactions . C-type lectins belong to a type of carbohydrate-binding protein family , known as lectins . These molecules are widely distributed throughout the animal kingdom and have a diverse range of functions , including cell-cell adhesion , immune responses to pathogens and apoptosis [15] , [52] . Notably , C-type lectins were also particularly abundant in the secretions from T . canis and hookworms [50] , [53] . The recent report of the sequence similarity of C-type lectins from A . suum to host to dendritic cell receptors suggests that the parasites may utilize lectins to bind to carbohydrate moieties on the surface of host cells to avoid pathogen recognition mechanisms in hosts [54] . The identification of several C-type lectins in the current study could indeed indicate that they play an important , yet undiscovered , role at the parasite-host interface . Venom allergens belong to the SCP/TAPS protein family and are basically found in every species investigated so far . Despite the fact that the exact function of SCP/TAPS proteins remains unknown , various studies have shown that they are amongst the most abundant proteins expressed and secreted during the transition from the free-living to the parasitic life stages , suggesting an important role in the onset of parasitism [55] . Compared with the number of SCP/TAPS proteins identified in the ES material of some other species [20] , [56] , [57] , it is surprising that only 2 were identified in the current study . However , this finding is concordant with previous evidence from genomic and transcriptomic datasets [23] indicating the presence of only 12 SCP/TAPS-encoding genes in the A . suum with relatively limited transcription levels ( results not shown ) . In conclusion , this study provides the first in-depth characterization of the ES products from the larval stages of A . suum , a crucial step in enhancing our knowledge and understanding of the biology of this parasite and its interactions with its mammalian host . The study provides a basis for further molecular investigations aimed at exploring the biological role of the proteins identified and their potential as vaccine and/or therapeutic targets . | The gastro-intestinal nematodes Ascaris lumbricoides and Ascaris suum are amongst the most prevalent parasites of humans and pigs , respectively . To date , little is known about A . suum excretory-secretory proteins , which are present at the parasite-host interface and likely to play a critical role in the induction and development of the immune response . The aim of this study was to identify the excretory-secretory proteins of the migratory stages of A . suum utilizing LC-MS/MS . In total , 106 proteins were identified , some of which are known as important players in the parasite-host interface . Interestingly , an abundance of glycosyl hydrolases was observed in the ES material of the intestinal L4 stage larvae . By combining the proteomic analysis with in depth genomic , transcriptomic and enzymatic analyses we could show that the glycosyl hydrolase protein family has undergone a massive expansion in A . suum and that most of the glycolytic activity is present in the intestinal tissue of the adult parasites . This could suggest that the degradation of complex carbohydrates forms an essential part of the energy metabolism of this parasite once it establishes in the small intestine . These findings provided useful information on the host-parasite interaction and the biology of this parasite , which can support the concerted efforts to develop better intervention strategies . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Proteomic Analysis of the Excretory-Secretory Products from Larval Stages of Ascaris suum Reveals High Abundance of Glycosyl Hydrolases |
Many genes are expressed in bursts , which can contribute to cell-to-cell heterogeneity . It is now possible to measure this heterogeneity with high throughput single cell gene expression assays ( single cell qPCR and RNA-seq ) . These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting , namely the rate that genes turn on , the rate that genes turn off , and the rate of transcription . We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples , specifically an algorithm for clustering cells by their bursting behavior ( Simulated Annealing for Bursty Expression Clustering , SABEC ) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells ( Estimation of Parameter changes in Kinetics , EPiK ) . We applied these methods to hematopoiesis , including a new single cell dataset in which transcription factors ( TFs ) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated . We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells . In addition , we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation . Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data , which should be widely applicable given the rapid expansion of single cell genomics .
Many genes are expressed in stochastic bursts: there are time periods where many transcripts are quickly produced , interspersed randomly with gaps of little or no transcriptional activity . Bursting gene expression was initially proposed as a mechanism to explain why cells in a seemingly uniform cell culture responded heterogeneously to steroids [1] . Two decades later , new live imaging technologies enabled researchers to observe transcriptional and translational bursting in real-time , finally confirming that bursting gene expression is a widespread phenomenon [2–4] . In fact , Dar et al . [5] tested 8 , 000 human genes and found that all of them were expressed in episodic bursts . Ko et al . [6] described bursting gene expression using a two-state model of gene expression , depicted in Fig 1A . In this model , each gene can either be in an on or an off state , and the gene stochastically transitions between these states , with transcription only taking place when the gene is on . The distribution of mRNA across a population of cells is determined by the following three kinetic parameters: the rate the gene turns on ( Kon ) , the rate the gene turns off ( Koff ) and the rate of transcription when the gene is on ( Kt ) , all normalized to the rate of mRNA degradation [7] . The values of these three kinetic parameters determine the distribution of mRNA transcripts within a population of cells ( Fig 1B ) . Predicting the rate at which genes turn on , turn off , and transcribe mRNA can provide insights into how genes are regulated . For instance , TFs which are necessary for transcription would control the rate at which the genes turn on ( Kon ) . One example of such a TF would be a pioneering factor , which opens up the chromatin to allow transcription . Brown et al . [8] found that switching between active/inactive states corresponded to distinct chromatin changes in PHO5 . This is consistent with the results in Dadiani et al . [9] , which show that manipulating nucleosome-disfavoring sequences in yeast can influence the burst frequency . On the other hand , TFs that control Kt are responsible for modulating the levels of gene expression of genes that are already on [10] . For instance , they may be involved in polymerase II ( PolII ) recruitment or transcriptional elongation . Therefore , estimating these kinetic parameters could help generate hypotheses for gene regulation mechanisms . Until now , the study of transcriptional bursting has been limited by the available experimental approaches . The most common high-throughput strategies ( conventional RNA-seq or qPCR ) for measuring gene expression require biological material from thousands of cells . These bulk strategies only measure the average levels of gene expression in populations of cells , data that cannot be used to make functional predictions about the bursting dynamics of transcription . While transcriptional bursting can be visualized in real-time in single cells , this is a low-throughput approach which can only measure expression for a single gene per cell [3 , 4] . Recently , there has been an emergence of single cell resolution RNA-seq and qPCR technologies , which can observe the full profile of gene expression in a population of cells . However , these are snapshot methods , which can only measure gene expression at a single point in time , because they involve lysing the cells . Nevertheless , preliminary studies have shown that it is possible to utilize the shape of the distribution of gene expression at a single point in time to estimate the kinetic parameters of the two-state model . Raj et al . [11] used a variation of the mathematical analysis done by Peccoud and Ycart [7] to estimate the kinetic parameters in fluorescent in situ hybridization ( FISH ) -based expression studies , and Kim and Marioni [12] developed a strategy to estimate kinetic parameters in RNA-seq data . In addition , Teles et al . [13] applied a similar approach to a single cell qPCR dataset in the context of a hematopoietic developmental system . As it is now accepted that bursting dynamics can in principle be resolved from single cell snapshot datasets , we can take advantage of this type of analysis to develop new algorithms that can answer a wide range of biological questions . In this paper , we apply our understanding of bursting gene expression to develop a more effective clustering algorithm for single cell gene expression data , which we call Simulated Annealing for Bursty Expression Clustering ( SABEC ) . This can help researchers identify previously uncharacterized sub-populations of cells within their single cell data . Secondly , we develop a statistical tool for identifying whether the burst frequency ( Kon or Koff ) or burst magnitude ( Kt ) are the source of gene expression variations between experimental samples . Instead of simply observing whether gene expression increases or decreases across two populations of cells , this Estimation of Parameter changes in Kinetics ( EPiK ) toolkit allows researchers to make inferences about the underlying regulatory mechanisms affecting gene expression . We provide a complete pipeline in R for analyzing single cell qPCR data , including data normalization steps , SABEC clustering and EPiK cluster comparisons . This pipeline can be utilized in a wide range of biological contexts . We apply it to study how some of the key transcription factors in hematopoiesis are being regulated , discovering that the hematopoietic stem cells studied in Moignard et al . [14] formed two distinct sub-populations , distinguishable by Tel and Gata1 expression levels . Then , by manipulating the expression levels of two of the TFs involved in early differentiation ( Gfi1 and Gata2 ) , we predict that the primary mechanisms by which these TFs influence their downstream targets is by manipulating Kon . Finally , we compare the mechanisms by which cell surface markers are regulated in healthy and leukemic cells based on data previously reported in Guo et al . [15] .
We have developed a pipeline for analyzing single cell qPCR data in order to determine the mechanism by which TFs were regulated during differentiation ( Fig 2 ) . The input data consists of single cell qPCR data for a set of genes , where the cells have been sorted into their expected cell populations , using fluorescence-activated cell sorting ( FACS ) . There are two main outputs of the pipeline . Firstly , cells are divided into populations ( clusters ) based on their bursting behaviour– each gene has the similar rates of activation ( Kon ) , rates of inactivation ( Koff ) and rates of transcription ( Kt ) across all cells within each cluster . Secondly , the pipeline compares between each pair of cell populations and predicts the mechanism by which each gene changes its expression– by changing the rate the gene turned on and off or by changing the rate of transcription once the gene is already active . A key component in the pipeline is our strategy for estimating the kinetic parameters . Previous approaches for estimating the kinetic parameters were slow to compute , because they involved iterative algorithms , such as simulated annealing [13] , expectation maximisation [11] , or Gibbs Sampling [12] . Meanwhile , we have a pre-computed look-up table for P ( x|Kon , Koff , Kt ) , the probability of seeing x mRNA transcripts , given a set of kinetic parameters ( Fig 2A and Eq 1 ) . This table can be used to compute the most likely set of kinetic parameters using a single matrix multiplication– a very efficient computation ( Eq 2 ) . In the pipeline , first we normalize the measurements from the qPCR and transform them into estimated mRNA counts ( See Eq 6 ) . Next , these measurements are input into a newly developed unsupervised clustering algorithm ( SABEC ) which identifies populations of cells with uniform bursting kinetics . SABEC is an iterative algorithm that starts by randomly assigning cells to clusters; next , the algorithm alternates between estimating the kinetic parameters for each gene within each cluster ( using Eq 2 ) and probabilistically re-assigning cells to clusters based on their likelihood of belonging to each cluster ( using Eq 9 ) , until the algorithm converges– i . e . when fewer than 5% of cells swap clusters . SABEC is run fifty times using different initial sets of randomly assigned clusters , and the results are summarised as a consensus matrix– a matrix that records the number of times two cells were grouped together . The number of clusters can be determined by one of the following methods: the proportion ambiguously clustered ( PAC ) , Variable Information ( VI ) or the corrected Rand score ( See Fig 2B ) . Finally , each pair of populations is compared using a statistical tool we developed called EPiK , in order to identify whether each gene is regulated by modifying the rate the gene turns on ( burst frequency ) or the level of transcription once the gene is active ( burst magnitude ) . EPiK is a compilation of three different methods: the Bayesian Information Criteria ( BIC ) , a Marginal Probability ( MP ) Score , and a subsampling method . Since for each gene there can be between 0 and 3 parameters that can differ across a pair of populations , BIC identifies the set of parameters most likely to differ after penalising parameter sets that are larger ( See Eq 12 ) . The MP score separately calculates the likelihood of each parameter changing , independent of whether the other parameters change or stay the same ( See Eq 15 ) . In the subsampling method , the kinetic parameters are calculated for small random subsets of cells , then the distributions of estimated kinetic parameters for the subsets are compared by the Kolmagorov-Smirnov statistic ( Fig 2C ) . All together , our R data processing scripts , SABEC , and EPiK come together to form a pipeline that utilizes the mathematics of bursting gene expression to determine how genes are differentially regulated across populations of cells ( Fig 2D ) . These methods were all tested in silico using data sets that have similar structures to the experimental datasets . Each of the three methods introduced in this paper ( ML approach for kinetic parameter estimation , SABEC and EPiK ) required a different set of simulated data . The ML method for kinetic parameter estimation was benchmarked populations of cells with known kinetic parameters to allow us to quantify the accuracy of the method . 3 , 000 parameter sets were randomly selected ( uniformly distributed ) with Kon between 0 and 5 , Koff between 0 and 20 , and Kt between 0 and 600 . We randomly sampled 10% of the transcripts from each of the simulated cell , to represent the technical noise caused by the loss of 90% of the starting material during sample preparation . We included a stochastic loss of mRNA transcripts to account for material loss during cDNA library construction– Islam et al . [33] estimates that only 48% of transcripts are reverse transcribed into cDNA , and Wu et al . [34] could capture 42% of the total unique transcripts that were identified in bulk RNA-seq . The simulated datasets for testing the SABEC method were chosen to be as similar to the experimental datasets as possible . 100 simulation sets were generated , with each completely parallel to the Moignard dataset; Each simulation set consisted of five populations of 124 cells with 18 genes each , with their kinetic parameters equal to those estimated by the ML method for the experimental data . Finally , to test the final method of comparing kinetic parameters between two populations of cells , 1600 simulated datasets were generated , each one consisted of a pair of populations of 124 cells with 1 gene each . There are eight combinations of kinetic parameters that can change ( none , all , three ways one parameter can change and three ways two parameters can change ) . Two hundred pairs of populations were selected for each of these eight scenarios , with 100 simulations with 0 < Koff < 5 and 100 simulations with 5 < Koff < 10 . The range of the other parameters were 0 < Kon < 5 and 0 < Kt < 600 . In these simulations we also simulated the random loss of 90% of the mRNAs . Even after simulating 90% loss of biological material , the predicted parameters correlated well with their real values , although Koff was the most difficult parameter to predict ( See Fig 3A–3C ) , especially at wider parameter ranges ( S1 and S2 Figs ) , but it still performed better than the Kim and Marioni Method ( S3 Fig ) . These in silico results emphasize that the cDNA library efficiency can have a large impact on the absolute values of predicted parameters , which suggests that raw parameter values are not comparable across different experiments . SABEC also accurately predicted clusters of simulated datasets , with similar population structures and parameter ranges to the experimental dataset ( Fig 3D–3F ) . Finally , we found that the EPiK method was very conservative , with approximately a 0 . 05% false positive rate when the methods were intersected ( Fig 3G and 3H ) . Further details about the validations and comparisons with alternative methods are described in depth in the Methods . Next , we applied these methods to experimental datasets . The dataset we use to test our pipeline comes from Moignard et al . [14] , which is a high quality single cell qPCR dataset that includes approximately 124 cells each from five different populations of cells during hematopoeisis . Hematopoiesis is the process by which hematopoietic stem cells ( HSC ) in the bone marrow differentiate into different types of red and white blood cell types . This process can be depicted as a differentiation tree , in which each cell must make multiple “decisions” at each branching point in the tree that will determine its final cell fate . Moignard et al . [14] includes two key branching points: i ) HSC cells can become lymphoid-primed multipotential progenitors ( LMPPs ) or premegakaryocytes ( PreMegEs , also referred to as PreMs in figures ) and ii ) LMPP cells can become granulocyte-macrophage progenitors ( GMPs ) or common lymphoid progenitors ( CLP ) . The focus of this study is on the densely interconnected TF regulatory network that has been shown to contribute to cell fate decisions . There is strong evidence that at least seven of these TFs directly interact with one another , potentially forming a SCL/Lyl1/Gata2/Runx1/Lmo2/Fli-1/Erg heptad , with some TFs directly binding to the DNA ( such as Gata2 and SCL ) and other TFs serving a bridge between the DNA bound components ( such as LMO2 ) [16] . Other key TFs that were profiled in Moignard et al . [14] were PU . 1 , Meis1 , Hhex , Tel , Nfe2 , Eto2 , Mitf and Ldb1 . There are a number of open questions about the differentiation of HSC into the various progenitor cell populations . Firstly , although the cells were assigned to their populations via FACS , it is unclear whether these sub-populations are truly uniform . For instance , some HSC cells may be biased towards self-renewal or producing cells in the lymphoid or myeloid lineages . Secondly , the specific mechanisms by which these TFs are being regulated are as yet uncharacterized . Influencing the rate at which genes turns on ( Kon ) could increase the proportion of cells with an active copy of a gene; whereas manipulating the transcription rate ( Kt ) would result in there being higher concentrations of mRNA , while maintaining the same proportion of cells with an active gene . In hematopoiesis , cellular TF concentrations help determine which branch the cell will take as it differentiates towards its final cell fate . Therefore , choosing to manipulate Kon instead of Kt could influence the proportion of cells that enter a certain differentiation trajectory . In addition , since the expression of these TFs is tightly linked , one gene’s bursting dynamics could have repercussions on the dynamics of the entire network . Although the cells that we study have been sorted into distinct subpopulations using FACS , this does not guarantee that the populations are indeed transcriptionally uniform . Some cells may be misclassified by FACS ( expected misclassification rate is 1% for the Moignard dataset ) , and some of the known populations may be composed of as yet unidentified subpopulations . In addition , extrinsic variability , such as having cells in different stages of the cell cycle , could cause the populations to be heterogeneous . It can be difficult to accurately identify homogenous subpopulations using standard clustering approaches like K-means or hierarchical clustering ( S5 Fig ) . For instance , K-means is most effective when each cluster is normally distributed and has similar variances . Due to bursting , gene expression is unlikely to come from such a distribution; gene expression distributions can look like a Poisson distribution , a negative binomial distribution , or even a bimodal distribution , depending on Kon , Koff , and Kt . Therefore , we developed a simulated annealing strategy ( Simulated Annealing for Bursty Expression Clustering , SABEC ) , which takes into account bursting gene expression , in order to have more robust clustering . SABEC was rigorously tested against simulated datasets that were designed to be as close as possible to the structure of the experimental data ( S6 Fig ) . Additionally , the algorithm was tested on a wide range of simulated datasets , to see if this method could perform well under varied conditions , such as on data with different numbers of genes measured in each cell and different numbers of cells in each population ( S7 Fig ) . Next , SABEC was applied to the experimental single cell qPCR data from Moignard et al . [14] . The final clustering is depicted in Fig 4A . Although the predicted hematopoiesis differentiation tree is expected to look like Fig 4Bi , we found that HSC is divided into two distinct subpopulations . One of these populations had 12 . 8% of cells expressing Gata1 , while the other population had none . Since Gata1 is uncommon in HSC cells , but often found in multi-potent progenitor populations ( MPP , the earliest progenitor population formed by HSCs [17] ) , we hypothesized that the populations of cells without Gata1 may be self-renewal HSCs and the other may be HSCs poised for differentiation . Our clustering with SABEC was based solely on the expression of 18 TFs . However , Moignard et al . [14] also profiled cell surface protein tyrosine kinase c-Kit . It is known that low levels of c-Kit correlate to greater self-renewal potential in HSCs [18] . Even though all of the HSC cells were positive for the c-Kit protein according to FACS , not all the cells had high levels of c-Kit transcripts . Our hypothesized self-renewal HSC population had significantly lower levels of c-Kit than our poised-to-divide population ( p-value 9 . 117e-06 with Kolmogorov-Smirnov test ) . Even though c-Kit was not one of the genes used to cluster the cells , there was substantial difference in expression levels , suggesting the tree topology depicted in Fig 4Bii . Further evidence for this arrangement is in S9 Fig . The SABEC algorithm has provided us with subpopulations that appear to have uniform bursting kinetics . In the next section we will identify which kinetic parameters were most likely adjusted across each pair of cell populations , using EPiK . EPiK works best when cell populations have uniform bursting dynamics; S11 Fig shows how having mixed cell types can influence estimates of kinetic parameters . Therefore , we remove cells that do not cluster well with other cells of the same type , with cutoff thresholds designated by the vertical lines in Fig 4C , which is determined by the region of the curve with the steepest slope . However , this pruning protocol could bias parameter estimation if it were to eliminate cells that are falsely identified as outliers ( S10 Fig ) . Therefore , we run EPiK both on the pruned and unpruned datasets , and only consider parameters that are consistently found to have changed under both conditions . It is important to note that the proposed outlier cells may be of biological interest– for instance , they may be rare cell types or cells in the process of differentiation . The pruned dataset is only for use to boost the accuracy of predictions with EPiK , but all cells should be observed for other types of analysis . EPiK incorporates three different metrics for evaluating whether kinetic parameter changes are significant . By taking the intersection of these three prediction methods , the false positive rate decreases without a significant drop in the true positive rate . This gives us a very conservative list of probable kinetic parameter changes ( a 0 . 05% false positive rate with our simulated dataset ) . The first method is the Bayesian Information Criterion ( BIC ) ( S12 Fig ) , and the second method is the marginal probability ( MP ) , which is the log likelihood of a certain parameter being varied , independent of whether the other two parameters vary or stay the same ( S13 Fig ) . In the third method , we repeatedly subsample cells from each population and estimate the kinetic parameters for each subset , comparing the maximum distance between the cumulative density functions of the distributions ( S14 Fig ) . These three methods were applied to the experimental data from Moignard et al . [14] . Each of the methods is based on slightly different assumptions , so they each have different distributions of parameters being varied ( See S15 Fig ) . For instance , BIC predicts that Koff is adjusted in a few cases , but this is not deemed significant by either the MP or subset methods . In addition , there are more significant parameter changes in the case of the pruned populations compared to the complete populations , because these populations are more distinct from one another . We can now take a closer look at a few examples of TFs that have predicted kinetic parameter changes during differentiation ( Fig 5 ) . The predictions from each method are drawn along the branches of the hematopoiesis differentiation tree ( Fig 5A–5E ) . To demonstrate the magnitude and direction of these kinetic parameter changes , Fig 5E–5H illustrates the kinetic parameter estimates for each population of cells . In Fig 5A and 5F , four out of the six methods predict that Eto2 is up-regulated by increasing Kon ( red ) during the HSC to PreM transition , which is consistent with Eto2 having a role in increasing the proportion of cells that differentiate into PreM [19] . One striking feature of Fig 5A–5E is that Eto2 , Mitf , and PU . 1 are regulated by different kinetic parameters in different stages of blood differentiation . PU . 1 has known cell-type-specific enhancer elements [20] , and our results may suggest that each of these may regulate PU . 1 through different mechanisms . On the other hand , Nfe2 is consistently regulated by Kt throughout differentiation . In some instances , all six methods come to a consensus as to which kinetic parameter was the source of gene expression variability for a particular TF , and these are shown in Fig 6A . Throughout hematopoiesis , most of the differences in gene expression come from changes of Kon , but Lmo2 , Nfe2 and Meis1 are regulated by Kt in the transition from LMPP to GMP . Recall that in the previous section , we identified that HSC forms two distinct sub-populations . We compared the two HSC sub populations with their child populations ( LMPP and PreM ) ( see Fig 6 ) . Between HSC1 and HSC2 only Tel seemed to consistently change its kinetic parameter ( Kon ) . As expected , the cell population with higher Gata1 and c-Kit expression has more in common with LMPP and PreM cells than the other HSC subpopulation . In summary , our methods have allowed us to not only identify which genes have changed their expression , but also what physical mechanism caused that change . In the previous section , we identified TFs that were regulated by Kon or Kt during blood differentiation , but it is unclear which TFs were controlling these changes . It may be possible to discover the specific mechanistic role of a TF by manipulating its expression experimentally and then calculating the change in kinetic parameters of its downstream targets . We decided to focus on the Gfi1-Gfi1b-Gata2 subnetwork that was identified as being important at the first branching point of HSCs to LMPP and PreMegEs [14] . Primary HSCs are difficult to isolate in large quantities , culture and manipulate , so we turned to HPC7 cells , a model cell line for hematopoietic stem and progenitor cells which has some differentiation potential towards more mature blood cells [16 , 21] . Similarly to HSCs , HPC7 cells express Gata2 and Gfi1b , but little or no Gfi1 . We therefore up-regulated Gfi1 expression and down-regulated Gata2 expression and performed single cell gene expression analysis for the gene set described by Moignard et al . [14] , as well as some additional genes involved in HSC differentiation . We analysed 81 cells expressing an shRNA against Gata2 and 77 cells control cells expressing an shRNA against Luciferase , and 72 cells overexpressing Gfi1 and 45 control cells expressing an empty vector . There are a number of strategies by which a TF could reduce gene expression: by decreasing the rate a gene turns on , by increasing the rate a gene turns off , or by decreasing the rate of transcription of an active gene , and each of these strategies would result in different temporal dynamics of gene expression bursting . All six methods came to a consensus that up-regulating Gfi1 seemed to significantly alter Erg , Gfi1b , Hhex and Mpl , by lowering Kon . Previous research suggests that Gfi1 is usually a repressor that either keeps the chromatin in a condensed state or actively competes for binding with activators [22] . Both of these mechanisms of action are consistent with Gfi1 down regulating its targets by lowering Kon . Based on ChIP-seq experiments from Sanchez-Castillo et al . [23] , Gfi1 binds in or near all four of these potential targets ( see S1 Table ) . In the other experiment , Gata2 was down-regulated; however , this was not a complete knockdown , with only a slight overall decrease in expression ( S20 Fig ) . All six methods suggest that Gfi1b expression was decreased via a change in Kon as Gata2 levels decreased . In the pruned population of cells , Procr ( also known as EPCR , a known target of Gata2 ) had higher Kon after the knockdown [24] . When Gata2 and Gfi1 were down- and up-regulated , our methods could only detect changes in Kon . Therefore , we can hypothesize that this could be the mechanism of action of these two TFs . Our pipeline for the identification of differential kinetic parameter values can also be applied to compare healthy and diseased cell populations . In particular , we apply it to four of the cell populations isolated in Guo et al . [15]: two cell types from a healthy mouse ( GMP , Lin+ ) and two from a leukemic mouse ( LGMP , LLin+ ) , whose leukemic cells came from a MLL-AF9 fusion protein [25] . The most significant kinetic parameter changes are shown in Fig 7 . Some of the genes profiled by Guo et al . [15] were found in fewer than 10 cells in one or more cell population , and these were excluded from kinetic parameter comparisons . Some of the genes are enriched in a single cell population; for instance , TLR9 , a factor whose expression influences the prognosis of leukemia [26] , is found predominantly in the LLin+ cells , and appears to be regulated by Kon . Most of the identified kinetic parameter changes were in Kon or a combination of Kon and Kt . However , MUC13 and SIGLEC5 were predicted to have been regulated by only Kt and IL3RA was predicted to be regulated by Koff . Interestingly , IL3RA is the only example where all methods predict changes in Koff , which suggests that this is a promising gene to focus on in future research .
In this study , we exploit established mathematical models of bursting gene expression to develop a new pipeline for analyzing single cell qPCR data to more robustly cluster biological samples and provide insight into the mechanics of gene regulation . We apply these methods to study gene regulation in hematopoietic stem cell and progenitor populations . Even though single cell qPCR data can only provide snapshots of gene expression in a population of cells across different time points , we can infer the temporal dynamics of gene expression in these cells , and use this information to infer the population substructure ( via SABEC ) or regulatory mechanisms ( via EPiK ) . This pipeline can be applied to study how genes are regulated during the natural process of differentiation or as cells progress into a diseased state . In addition , by manipulating the expression of a TF within its cell culture , we can infer its specific regulatory role . These algorithms perform well in simulated datasets ( S1 , S2 , S6 , S7 , S8 , S12 , S13 and S14 Figs ) , performing better than similar computational tools ( Figs 8 , S3 and S5 ) . Instead of simply observing how much gene expression heterogeneity there is in a sample , it is now possible to predict the specific regulatory mechanisms that contributed to heterogeneity . Most crucially , this type of analysis can be done in a high-throughput manner . In addition , commonplace clustering algorithms like K-means and hierarchical clustering are not meant to cluster data drawn from Poisson , negative binomal , and bimodal distributions , as is the case for single cell gene expression data . For instance , K-means performs best on data that comes from normal distributions with similar standard deviations . For this reason , it is critical to use a clustering algorithm that incorporates information about the shape of the gene expression distributions when analyzing single cell resolution datasets . However , it is unclear how the cell cycle could influence our results , so future experiments ought to include cell cycle markers as controls [27] . In addition , our approach assumes that the gene expression distributions are close to equilibrium . Fortunately , Peccoud and Ycart [7] demonstrated that the two state model approaches the equilibrium distribution very rapidly ( at an exponential rate ) , so this assumption likely holds . Other researchers have attempted to fit a multi-state model to data , instead of a two state model [28] , but unless there is strong evidence for a more complex model , it is wise to use the simplest approach to avoid over-fitting the data . In the future , it may be possible to modify the model to detect cells that are in the process of transitioning between cell populations– for instance , these may be cells that have estimated kinetic parameters that are between two other populations . In addition , Teles et . al . [13] estimated the probability of transition between cell populations using machine learning methods , but this is beyond the scope of this paper . One particularly useful application of this pipeline is the validation of assumptions used to model specific sub-networks of genes important in differentiation . Often , these models assume certain mechanisms by which the TFs influence one another . For instance , Narula et al . [29] constructed a mathematical model of a hematopoietic sub-network under the assumption that Koff was the parameter that is biologically regulated , in the absence of any experimental data . Instead of arbitrarily selecting a modeling strategy , we can now choose one that fits the data best . In addition , we have discovered that different TFs are regulated through different mechanisms in each stage of differentiation , implying that a single model of a gene network might not universally apply . Therefore , these strategies would allow us to make more biologically plausible models of gene subnetworks . Having a better understanding of gene regulation processes is important in order to learn how these are perturbed in disease , and also to develop protocols that produce desired cell types for cell therapy . A modified version of this pipeline for RNA-seq data would be an important future development . The kinetic parameter estimation strategy and EPiK may be applied to single cell RNA-seq , as long as there is sufficient sequencing depth to capture the population-wide distribution of gene expression . Both these methods scale approximately linearly with the number of cells and genes under study , and could also be run in parallel for very large datasets . However , SABEC would not scale well with the large number of genes analysed in RNA-seq experiments , so alternative clustering approaches must be tested in an RNA-seq context . The transcription process is a multi-step chemical reaction: chromatin must enter the correct state , TFs must bind in the right places , the general transcription machinery must be recruited and initiated , etc . It is currently impossible to distinguish the effects of all of these mechanisms in a high-throughput way . Our pipeline provides a first attempt to understand how the kinetic parameters underlying complex transcriptional processes influence heterogeneity within and across cell populations , through the analysis of single cell gene expression data .
HPC7 cells [21] were grown in suspension in Iscove’s Modified Eagle’s Medium ( IMDM , Gibco ) with 10% FCS , 10% stem cell factor-conditioned medium , 1% penicillin/streptomycin ( Sigma ) and 1 . 5 × 10−4 M monothioglycerol ( MTG ) at 37°C and 5% CO2 . Cells were passaged every two days to maintain a concentration of 0 . 5 − 2 × 106 cells/ml . For knockdown experiments , shRNA fragments were cloned into pMSCV/LTRmiR30-PIG ( pLMP , Open Biosystems ) : Luciferase ( control , 5’ CACGTACGCGGAATACTTCGAA 3’ , [30] ) , Gata2 ( 5’ CGCCGCCATTACTGTGAATATT 3’ , [31] ) . For overexpression experiments , the mouse Gfi1 cDNA was inserted into pMSCV-ires-GFP ( Addgene plasmid 20672 ) , with the empty vector used as a control . Retrovirus was produced using the pCL-Eco Retrovirus Packaging Vector ( Imgenex ) in 293T cells . HPC7 cells were infected with retrovirus by centrifugation at 800 xg at 32°C for 1 . 5 hours with 4 μg/ml polybrene ( Sigma ) , after which the retroviral supernatant was replaced with fresh media and cells were cultured as normal . Transduction efficiency was monitored by flow cytometry for GFP . Single GFP+ cells were sorted by FACS into individual wells of 96 well plates and single cell RT-qPCR was carried out as described previously [14] . Cells were captured 48 hours after retroviral transduction for Gfi1 overexpression and 72 hours after transduction for Gata2 knockdown ( S1 Table ) . Bursting gene expression can lead to a number of different distributions of gene expression in a population of cells ( See Fig 1B ) , ranging from a bimodal distribution ( when Kon and Koff are low ) to a Poisson distribution ( when Kon is much higher than Koff ) to an exponential decay-like distribution ( when Koff is much higher than Kon ) . The probability of having x mRNA molecules in a cell with kinetic parameters Kon , Koff and Kt is given by the analytical solution developed by [11]: P ( x | K o n , K o f f , K t ) = Γ ( K o n + x ) Γ ( K o n + K o f f ) K t x Γ ( x + 1 ) Γ ( K o n + K o f f + x ) Γ ( K o n ) 1 F 1 ( K o n + x , K o n + K o f f + x , - K t ) ( 1 ) In this equation , 1F1 represents the confluent hypergeometric function of the first kind , a summation over an infinite series that is time intensive to compute . To improve our runtimes , we precomputed an extensive lookup table of values of P ( x|Kon , Koff , Kt ) . Let us say that we have n cells , each with an mRNA molecule count ( for a particular gene ) of xi , and let X = {x0 , x1 , …xn} . Given this list of mRNA counts from a semi-uniform population , we can assess the log likelihood of each possible set of parameters: L ( K o n , K o f f , K t | X ) = ∑ X ( ln ( P ( x i | K o n , K o f f , K t ) ) ) ( 2 ) The set of kinetic parameters that has the maximum likelihood is chosen . However , it is important to note that some areas of the parameter space are more sensitive to parameter changes than others . For instance , [11] notes that at large values of Koff the equation of P ( x|Kon , Koff , Kt ) approaches: P ′ ( x | K o n , K o f f , K t ) = 1 + K t K o f f - K o n Γ ( K o n + x ) Γ ( K o n ) Γ ( x + 1 ) K t K o f f 1 + K t K o f f x ( 3 ) This equation depends of the ratio of Kt/Koff rather than Kt and Koff separately , which means that Koff and Kt are more difficult to distinguish as Koff increases . The practical implication of this observation is illustrated in Fig 9A and 9B , which depict the log likelihoods at different kinetic parameter values for GFI1b in HSC cells , with regions that have log likelihoods close to the peak value coloured in ( specifically , within 0 . 5 of the maximum likelihood ) . Although there is only a narrow range of possible Kon values ( A ) , there is a range of values where Koff and Kt can partially compensate for one another ( B ) . A specific example is illustrated with simulated data in Fig 9C: while the original distribution of gene expression ( grey ) varies visibly when Koff ( blue ) or Kt ( red ) are varied , it is possible to change both variables ( purple ) and almost recover the original distribution . Furthermore , this analysis suggests that it would be difficult to incorporate any additional parameters into the system and find unique estimates for each of them . In S1 Fig , we test the performance of this strategy of kinetic parameter estimation on simulated data , including artificial technical noise . Although Koff cannot be accurately identified when Koff > 5 , the ratio of Koff to Kt is accurately predicted ( S2 Fig ) . Our method also performs better than the Gibbs Sampling based approach developed by Kim and Marioni , 2012 ( S3 Fig ) . Next , we applied this maximum likelihood approach for kinetic parameter estimation on hematopoietic stem cell and progenitor populations ( CLP , GMP , CLP , LMPP and PreM cells ) from [14] . The values for Kon and ln ( Koff/Kt ) for the Moignard data are shown in Fig 9F . Note that many of the TFs were particularly chosen due to their importance in early differentiation of HSCs , so one would expect a lower level of gene expression ( and therefore a higher Koff/Kt ratio ) in later stage progenitor populations such as CLP and GMP . There is a wide range of estimated kinetic parameter values across the TFs in each cell population; however , we need to ensure that these kinetic parameter estimates are not being skewed by mis-classified cells before we can evaluate whether these differences are statistically significant . It is crucial to select a table of appropriate range and point density for applying to the experimental data . The criteria for selecting this table were: i ) fewer than 5% of the experimental points were at the maximum parameter value for the Kon and Kt parameters . ii ) Koff had to be high enough in order to include the parameter range in which only the ratio of Kt to Koff matter ii ) the density of points was sufficient to minimise artifacts arising from the discretization of the parameter space . Given these constraints , the range of parameters was chosen to be 0 < Kon < 5 , 0 < Koff < 20 and 0 < Kt < 200 and possible mRNA counts as 0 < x < 200 . The sampling density for Kon was every 0 . 1 , for Koff every 0 . 4 and for Kt every 5 for a total of 395000 possible parameter sets . We deposited the look-up table , the code used to generate the table in Mathematica and the code for calculating the maximum likelihood in R on Github: https://github . com/ezer/SingleCellPipelineOverview . The main source of data analysed in this paper comes from Moignard et al . [14] , and contains single cell qPCR data from five populations of hematopoietic stem cell and progenitor cells ( CLP , GMP , HSC , LMPP , PreM ) , as determined by FACS , with approximately 124 cells in each population . The genes profiled by the qPCR include 18 TFs with crucial roles in cell fate , which were normalised to two “housekeeping” genes ( PolII and Ubc ) , as described in Moignard et al . [14] . The outcome of a PCR experiment is a normalised Ct value , which relates to mRNA molecules ( x ) as follows: x = i n t ( b · 2 a - C t ) ( 4 ) where a and b are constants . For each TF , we chose b: b T F = x max 2 a - min ( X T F ) ( 5 ) where xmax = 200 is the maximum number of mRNA pre-calculated in our lookup table and XTF is the set of mRNA counts for the particular TF . This choice of bTF stretches the values of x to have as wide a range as possible . It also removes the need to set an a parameter , since the equation for calculating x can be simplified to: x = i n t ( x m a x · 2 min ( X T F ) - C t ) ( 6 ) It is important to note that while a different b parameter was chosen for each TF , this b factor is consistent across all five populations of cells , which is crucial for our later attempts to compare kinetic parameter values between cell populations . Even though the choice of b changes the absolute value of the kinetic parameters that are estimated , it has minimal effects on the strength of the linear correlation between the known and estimated values . SABEC begins by assigning each cell to a random population 1 to K . Note that the total number of clusters K must be set at the start of the algorithm . Next , SABEC iteratively calculates the kinetic parameters of each of the K populations , and the cells are reassigned to new populations probabilistically . In a standard Expectation Maximisation clustering algorithm , the probability of assigning a cell to a population is proportional to the ratio of the likelihoods of the cell coming from each population . Let the kinetic parameter set for a single population be Si ( g ) = ( Kon ( g ) , Koff ( g ) , Kt ( g ) ) , where g is the gene ( between 1 and G ) and i labels the population ( between 1 and K ) , and let x ( g ) be the vector of mRNA counts for each gene , for a particular cell . The log likelihood for a certain population can be calculated as follows: L ( S i | x ) = ∑ g = 1 G ( ln ( P ( x ( g ) | S i ( g ) ) ) ( 7 ) If the sum of the likelihoods for each population is Ltot , this value can be scaled as such: L ′ ( S i | x ) = L ( S i | x ) L t o t ( 8 ) Since it would take a long time for this algorithm to converge if the clusters are close to one another , we add a temperature parameter , so that initially it is easy for cells to be assigned to different clusters , but it becomes harder and harder to swap clusters over time: L ″ ( S i | x ) = L ′ ( S i | x ) τ t ( 9 ) where τ is the temperature parameter and t is the iteration number of the algorithm ( a counter that increments each time the cells are reassigned to new clusters ) . A cell is probabilistically assigned to a new cluster based on the relative values of L″ ( Si|x ) for each population . The algorithm terminates when fewer than 5% of the cells swap clusters in an iteration , or after 100 iterations . S4 Fig shows how the accuracy of the algorithm depends of the temperature parameter , τ . Since this algorithm is randomized and since it is possible for certain runs of the algorithm to fall into local optima , we run this algorithm 50 times and conduct a secondary consensus clustering step . In this step , a consensus matrix is produced , in which each cell of the matrix represents the number of times that two cells are found in the same cluster . A plot of the cumulative density function of the values of this matrix can help visualise the robustness of the clustering ( See Fig 8A ) . For comparison and to show the necessity for SABEC , consensus clustering of K-means was also conducted ( See Fig 8B ) . Fig 8 shows the cumulative density functions for the number of times two cells cluster together for SABEC ( A ) and K-means ( B ) . [32] determined that the most robust metric for comparing the consistency of consensus clustering methods is the Proportion Ambiguously Clustered ( PAC ) score , which is defined as the proportion of cell pairs that cluster together in 10% and 90% of the repeated runs of the algorithm . This corresponds to the proportion of cell pairs that lie between the vertical lines in the cumulative distribution functions in Fig 8 ( A ) and 8 ( B ) . The PAC scores for SABEC and K-means are compared in C , illustrating that SABEC usually has more consistent outcomes than K-means , with the fewest ambiguously clustered cells at K = 7 . In addition , to estimate the accuracy of our method , we can assume that the expected cluster assignment ( as determined by FACS ) is our gold standard . By comparing the results of our clustering approach with the gold standard , we can estimate the accuracy of our method on the experimental data . To do this , we cluster each of our consensus matrices into five clusters using partitioning around medoids ( PAM ) , a clustering approach similar to K-means ( but more consistent since it uses data points as centres ) . We can then compare our results to the gold standard labels using metrics such as VI ( See Fig 8D ) and the corrected Rand index ( See Fig 8E ) . SABEC performs better than K-means by these two metrics , with the estimated number of clusters equal to 6 . The one exception is that the corrected Rand index suggests that K-means performs slightly better than SABEC when K = 5; however , SABEC provides substantially better outcomes at K = 6 . Based on simulated datasets with values similar to the experimental data , we determined that these latter two methods provide more accurate estimates of the number of clusters than the PAC method , which can sometimes overestimate the appropriate number of clusters ( S8 Fig ) . Figs 8F and 2G compare the consensus matrices for K = 6 for SABEC ( F ) and K-means ( G ) , with the cells sorted by their FACS-determined labels . SABEC results appear more consistent , with cells frequently clustering with other cells of the same type . In addition , any cells that are “misclassified” tend to cluster instead with cell populations of their parent or children populations . For instance , some HSC cells cluster with their child populations ( PreM and LMPP ) , and some GMP and CLP cells cluster with their parent population ( LMPP ) . The R script for SABEC is available in Github: https://github . com/ezer/SingleCellPipelineOverview , including a sample input file to run in parallel on a Condor cluster , and appropriately merge the outputs . The SABEC method was compared to hierarchical clustering and K-means approaches . The hierarchical clustering approach used was the default one associated with the heatmap function in R ( Euclidean distance metric and complete clustering ) . The K-means approach used the default algorithm in R ( Hartigan and Wong ) , but the maximum iterations was increased to 100 in order to be more comparable to the SABEC approach . K-means was repeated 50 times and an additional consensus clustering step was taken , in order to provide a fairer comparison to SABEC . Note that the input to both the hierarchical clustering and K-means algorithms were the normalised Ct values , while the input to SABEC is the scaled mRNA counts . These algorithm and distance metrics were chosen since they are the most commonly used . Other variations of hierarchical clustering and K-means were tested , but none of the results were significantly better or different than the ones shown . The R script for determining which kinetic parameters vary across populations is available in Github: https://github . com/ezer/SingleCellPipelineOverview . Three methods were tested on simulated datasets of genes with randomly selected kinetic parameters . We conducted these simulation tests for two different ranges of the kinetic parameters to illustrate that there is different sensitivity to parameter changes in different regions of the parameter space ( S12 , S13 and S14 Figs ) . These results suggest that changes in Koff cannot be accurately detected when Koff > 5 . In addition , the methods often performs better when fewer kinetic parameters change at once ( S12 and S17 Figs ) . The correctly identified simulated cases were those that have the largest magnitude of kinetic parameter change ( S18 Fig ) and had average kinetic parameter values closer to the origin , where the kinetic parameters are more accurately estimated ( S19 Fig ) . Two of the methods ( MP and Subset methods ) , have continuous-valued outputs , and so a threshold must be set for determining whether or not a change in a kinetic parameter is likely significant . The thresholds were set to have approximately a 2% false positive rate , based on the in silico validation tests . The threshold values for the MP method when Koff < 5 are −6 . 3 , −8 . 5 and −6 . 8 for Kon , Koff and Kt , and −4 . 9 and −6 . 0 for Kon and Kt when Koff > 5 . For the subset method , the thresholds are 0 . 77 , 0 . 91 and 0 . 86 ( Kon , Koff and Kt , respectively ) when Koff < 5 , and 0 . 681 and 0 . 870 ( Kon and Kt ) when Koff > 5 . | Many genes are expressed in bursts , which can contribute to cell-to-cell variability . We construct a pipeline for analyzing single cell gene expression data that uses the mathematics behind bursty expression . This pipeline includes one algorithm for clustering cells ( Simulated Annealing for Bursty Expression Clustering , SABEC ) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells ( Estimation of Parameter changes in Kinetics , EPiK ) . We applied these methods to blood development , including a new single cell dataset in which TFs involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated . | [
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"re... | 2016 | Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data |
Changes in gene expression are commonly observed during evolution . However , the phenotypic consequences of expression divergence are frequently unknown and difficult to measure . Transcriptional regulators provide a mechanism by which phenotypic divergence can occur through multiple , coordinated changes in gene expression during development or in response to environmental changes . Yet , some changes in transcriptional regulators may be constrained by their pleiotropic effects on gene expression . Here , we use a genome-wide screen for promoters that are likely to have diverged in function and identify a yeast transcription factor , FZF1 , that has evolved substantial differences in its ability to confer resistance to sulfites . Chimeric alleles from four Saccharomyces species show that divergence in FZF1 activity is due to changes in both its coding and upstream noncoding sequence . Between the two closest species , noncoding changes affect the expression of FZF1 , whereas coding changes affect the expression of SSU1 , a sulfite efflux pump activated by FZF1 . Both coding and noncoding changes also affect the expression of many other genes . Our results show how divergence in the coding and promoter region of a transcription factor alters the response to an environmental stress .
Transcriptional regulation plays a key role in development and an organism's response to physiological and environmental changes . However , changes in gene regulation that occur over the course of evolution are more difficult to interpret . Genome-wide patterns of gene expression divergence show that while many aspects of regulation are conserved between distantly related species [1]–[3] , there is also extensive variation in gene expression levels within and between closely related species [4] . In many , but not all instances , gene expression divergence is consistent with a neutral model of evolutionary change [5]–[8] . Yet , understanding regulatory divergence requires identifying the genetic basis of divergence in gene expression and knowing which changes in gene expression translate into changes in phenotype and fitness . Substantial progress has been made in understanding the genetic basis of regulatory divergence . Changes in gene expression are influenced by both cis-regulatory sequences and trans-acting factors , with cis-regulatory changes being enriched in interspecific comparisons [9] , [10] . Expression changes caused by cis-regulatory elements frequently involve gain or loss of transcription factor binding sites , e . g . [11] , [12] , although other changes , such as nucleosome position , can also play an important role [13] . Even when changes in gene expression can be attributed to specific cis-regulatory elements , the phenotypic consequences of such changes are hard to know , especially if they depend on the combined effects of many cis-regulatory changes . While changes in trans-acting factors can simultaneously influence the expression of many genes , significant efforts are needed to identify the genetic basis of trans-acting changes in gene expression . The phenotypic effects of changes in gene expression have in some cases been identified [14] . This has primarily been accomplished by mapping , association and transgenic studies that identify genetic changes underlying a phenotype . While these approaches typically identify changes in protein coding sequences , cis-regulatory changes are more frequently found to underlie interspecific compared to intraspecific differences [14] . Furthermore , changes in protein coding sequences can affect the expression of many genes [15] , [16] , and in some cases their phenotypic effects depend on multiple differentially expressed genes [17] . What has been more difficult to investigate is the combined influence of multiple regulatory changes . Multiple changes of small effect may frequently go undetected , at least individually , but together could have a substantial impact on divergence [18] . Evidence for adaptive evolution via multiple cis-regulatory changes has been found based on concerted changes in the expression of genes that function in the same pathway or biological process [19]–[21] . Multiple cis-regulatory changes at a single locus have also been found to make substantial contributions to phenotypic divergence between species [22]–[26] . Statistical tests of neutrality are particularly well-suited to identifying multiple adaptive substitutions at a single locus since multiple substitutions are often needed to detect a significant deviation from a neutral pattern of molecular evolution . Rapidly evolving noncoding sequences have been identified in a number of species [27]–[30] , and in some instances are known to cause notable changes in gene expression [31] , [32] . Although tests of neutrality rely on the concentration of multiple changes at single loci , clustering of changes may occur if there are genetic , developmental or selective constraints at other loci [33] . One mechanism by which multiple , coordinated changes in gene expression may arise is through changes in transcriptional regulators . However , changes in transcription factors can also be constrained by their pleiotropic effects on gene expression . The negative effects of pleiotropy may in some cases be eliminated by altering the regulation of a transcription factor; thereby limiting downstream changes in gene expression to specific times during development , within particular cells or tissues , or to certain environmental conditions [33] , [34] . In this study , we investigated changes in gene expression and phenotype caused by a rapidly evolving transcription factor , FZF1 . To directly target genes that have potentially accrued multiple cis-regulatory changes , we screened four Saccharomyces genomes for noncoding sequences with non-neutral patterns of divergence . FZF1 was among the genes identified and it also shows a non-neutral pattern of amino acid divergence [35] . To examine the phenotypic consequences of FZF1 divergence we used cross-species complementation assays and found divergence in both its coding and upstream noncoding sequence affect sulfite resistance . Whereas divergence upstream of FZF1 affects its expression in response to sulfites , divergence in the coding region of FZF1 affects the expression of SSU1 , an efflux pump that mediates sulfite resistance [36]–[38] . Coincident with their effects on sulfite resistance , both the coding and noncoding regions of FZF1 affect the expression of many other genes . Our results show how divergence in the coding and promoter region of a transcription factor affect the response to an environmental stress .
To identify promoter sequences likely to have diverged in function , we screened the noncoding sequences of four Saccharomyces species for accelerated substitution rates . We used a likelihood ratio test to compare a model of sequence evolution where the ratio of the noncoding to synonymous substitution rate , dNC/dS , is constant across lineages versus a model where dNC/dS is free to vary across lineages . Out of 2 , 539 noncoding regions tested , we identified 145 that showed significant variation in the noncoding substitution rate across species ( Likelihood ratio test , P<0 . 05 , Bonferroni corrected , Dataset S1 ) . In these regions , a higher noncoding substitution rate in one or more lineages may be the result of loss of constraint , or in some cases , positive selection . One of the noncoding regions that we identified lies upstream of the transcription factor FZF1 . We selected FZF1 for further analysis because it is known to function in sulfite resistance , a hypothesized adaptation to vineyard environments [39] , and its potential role in gene expression divergence . The substitution rate upstream of FZF1 is characterized by an accelerated rate along the lineages leading to Saccharomyces cerevisiae and Saccharomyces paradoxus relative to that along the lineages leading to Saccharomyces mikatae and Saccharomyces bayanus ( Figure 1 ) . However , previous studies have shown that signals of selection are highly dependent on the alignment [40] , [41] . To determine whether the evidence for rate heterogeneity upstream of FZF1 is dependent on the alignment used , we generated additional alignments using alternative alignment parameters and algorithms , and tested each for substitution rate heterogeneity . Both the alignment parameters and the algorithm affected the evidence for rate heterogeneity , with 9 out of 18 alignments showing evidence of rate heterogeneity ( Table S1 , Likelihood ratio test , P<0 . 05 , Bonferroni corrected ) . Although the high substitution rate combined with uncertainty in the placement of insertions or deletions makes it difficult to know the correct alignment , dNC/dS along the S . cerevisiae and S . paradoxus lineage was consistently estimated to be greater than or equal to one ( Figure 1 ) . The protein coding sequence of FZF1 also shows evidence for non-neutral evolution based on a sliding window analysis of the nonsynonymous to synonymous substitution rate ratio ( dN/dS ) between S . cerevisiae and S . paradoxus [35] . However , caution should be taken when interpreting the results of the dN/dS test in the context of a sliding window analysis since dS can vary for a number of reasons [42] . Upon re-examination of divergence in FZF1 , we found that the window with the signal of positive selection , dN/dS = 1 . 95 , is characterized by a synonymous substitution rate of 0 . 18 , which is lower than the average of 0 . 46 across the entire gene , and a nonsynonymous substitution rate of 0 . 34 , which is higher than the average of 0 . 14 across the entire gene . Despite some uncertainty regarding the evidence for non-neutral evolution , we decided that FZF1 was a reasonable candidate to test for functional divergence . FZF1 encodes a five zinc finger transcription factor that activates the plasma membrane sulfite pump , SSU1 [37] . Gain of function mutations in FZF1 result in hyperactivation of SSU1 and increased sulfite resistance [36] , [38] . To determine whether FZF1 has diverged in its ability to confer sulfite resistance , we tested FZF1 alleles from four Saccharomyces species: S . cerevisiae , S . paradoxus , S . mikitae , and S . bayanus , for their ability to complement a deletion of FZF1 in S . cerevisiae . The S . cerevisiae allele of FZF1 showed nearly complete complementation of the FZF1 deletion , as measured by the delay in exponential growth following sulfite treatment ( Figure S1 ) . In comparison , FZF1 alleles from the other three species all showed a shorter delay in growth relative to that of S . cerevisiae , indicating that these FZF1 alleles confer greater resistance to sulfites ( Figure 2 , Kruskal-Wallis test , P = 5 . 3×10−13 ) . To determine whether divergence in FZF1 activity resulted from changes in its protein coding sequence or upstream noncoding sequence , we also tested chimeric constructs containing each species' FZF1 upstream noncoding sequence combined with the S . cerevisiae FZF1 coding sequence . These FZF1 5′ noncoding chimeras conferred significant differences in sulfite resistance ( Figure 2 , Kruskal-Wallis test , P = 2 . 5×10−18 ) , indicating that the 5′ noncoding region alone makes a significant contribution to FZF1 divergence . Both the S . paradoxus - S . cerevisiae and S . mikatae - S . cerevisiae chimeric alleles showed sulfite resistance intermediate to that of their full length parental alleles , although only the former chimera was significantly different from both parent alleles ( Wilcoxon rank sum test , P = 1 . 9×10−14 for the S . cerevisiae parent and P = 4 . 2×10−8 for the S . paradoxus parent ) . In contrast , the S . bayanus 5′ noncoding region upstream of an S . cerevisiae coding sequence conferred greater resistance than either of the two full length parent alleles ( Figure 2 , Wilcoxon rank sum test , P = 4 . 6×10−16 for the S . cerevisiae parent and P = 2 . 4×10−8 for the S . bayanus parent ) . The S . cerevisiae and S . paradoxus alleles of FZF1 confer the largest difference in sulfite resistance . This phenotypic divergence corresponds to the lineages showing the highest noncoding to synonymous substitution rates and the elevated nonsynonymous to synonymous substitution rate within a portion of the coding region . Thus , we further mapped the differences in sulfite resistance between the S . cerevisiae and S . paradoxus FZF1 alleles . The S . cerevisiae FZF1 protein is 900 amino acids long and has 195 bases in the 5′ noncoding region . Between the S . cerevisiae and S . paradoxus FZF1 alleles there are 67 amino acid differences and 82 differences in the 5′ noncoding region , 31 of which are insertion/deletion differences . To delineate which subset of these differences are responsible for divergence in sulfite resistance , we generated ten sets of reciprocal chimeric constructs between the two species ( Figure 3 ) . The FZF1 chimeric breakpoints were located ( 1 ) in the middle of the 5′ noncoding region , ( 2 ) at the junction between the 5′ noncoding and the coding region , ( 3 ) in the coding region between the first zinc finger domain , known to bind DNA [37] , and the region under positive selection [35] , and ( 4 ) at the junction between the coding and 3′ noncoding region . Five sets of chimeric constructs contain a single region in the opposite background and the remaining sets of constructs contain five of the ten possible pairwise combinations of each region . Including the full length S . cerevisiae and S . paradoxus alleles of FZF1 , the 22 constructs show a nearly continuous distribution of sulfite resistance ( Figure 4 ) . Using an additive model , the estimated effects of the first three FZF1 regions individually account for 8 . 2% , 39 . 0% , and 49 . 5% , respectively , of the difference in sulfite resistance between the S . cerevisiae and S . paradoxus alleles ( Table 1 ) . The latter two regions are not statistically significant . Some of the variation in sulfite resistance can be attributed to non-additive interactions among regions . The additive model explains a total of 66% of the variance among alleles , significantly less than a model that allows for pairwise epistatic interactions , which explains 70% of the variance ( Likelihood ratio test , 2Δln ( L ) = 56 . 48 , 10 d . f . , P = 1 . 7×10−8 ) . However , out of all the pairwise interactions , only the interaction between the two coding regions is individually significant after correcting for multiple tests ( Table 1 ) . The interaction indicates that the two coding regions have a smaller effect in combination compared to that expected from each region individually . FZF1-dependent changes in sulfite resistance may be mediated by changes in the expression of FZF1 or the expression of other genes . To characterize changes in gene expression caused by FZF1 divergence , we measured expression of FZF1 and SSU1 , a sulfite efflux pump activated by FZF1 [37] , [38] . Using quantitative PCR , we measured the expression of both genes before and after sulfite treatment of strains carrying an S . cerevisiae , S . paradoxus , or two reciprocal chimeric FZF1 alleles , which divide the coding and 5′ noncoding regions of the S . cerevisiae and S . paradoxus FZF1 allele . All of the FZF1 alleles increased in expression following sulfite treatment . However at time-points 15 , 30 and 60 minutes after sulfite treatment , the FZF1 alleles with an S . paradoxus promoter were expressed at higher levels than those containing an S . cerevisiae promoter ( Wilcoxon rank sum test , P = 6 . 7×10−9 , P = 1 . 7×10−4 , P = 0 . 008 , respectively , Figure 5A ) . No significant differences were found due to the FZF1 coding region alone from the two species . Yet , 30 minutes after sulfite treatment , the two FZF1 alleles with the S . paradoxus promoter showed significant differences in expression; the allele with an S . cerevisiae coding region remained at a higher level relative to the allele with an S . paradoxus coding region ( Wilcoxon rank sum test , P = 0 . 0012 ) . Similarly , the FZF1 allele with an S . cerevisiae promoter and S . paradoxus coding region showed higher expression at the 30 minute time-point relative to the full length S . cerevisiae allele , although this difference was not significant ( Wilcoxon rank sum test , P = 0 . 15 ) . Differences in gene expression that depend on changes within a coding region have previously been found in yeast [43] and could result from feedback regulation . The FZF1 alleles also caused an increase in SSU1 expression after sulfite treatment ( Figure 5B ) . Unlike FZF1 expression , SSU1 expression primarily depended on the origin of the FZF1 coding region . For both the 15 and 30 minute time-points , FZF1 alleles containing the S . paradoxus coding region caused higher levels of SSU1 expression relative to those containing the S . cerevisiae coding region ( Wilcoxon rank sum test , P = 1 . 15×10−5 , P = 8 . 94×10−6 , respectively ) . No significant differences in SSU1 expression were found as a result of the FZF1 5′ noncoding region alone . If FZF1-dependent differences in sulfite resistance are mediated by activation of FZF1 and SSU1 , they may also be influenced by levels of FZF1 and SSU1 expression prior to sulfite treatment . Immediately prior to sulfite treatment , FZF1 alleles with the S . cerevisiae coding sequences were expressed at 1 . 5-fold higher levels than those with the S . paradoxus coding sequence ( Wilcoxon rank sum test , P = 1 . 3×10−6 ) . The 5′ noncoding region caused no significant differences in FZF1 expression prior to sulfite treatment . In comparison , expression of SSU1 prior to sulfite treatment was 1 . 09-fold higher for FZF1 alleles containing the S . cerevisiae coding region and 1 . 12-fold higher for FZF1 alleles containing the S . cerevisiae 5′ noncoding region relative to the corresponding S . paradoxus regions ( Wilcoxon rank sum test , P = 0 . 011 , P = 6 . 5×10−4 , respectively ) . Because the S . paradoxus allele of FZF1 causes higher levels of sulfite resistance , levels of FZF1 expression prior to sulfite treatment do not appear to be related to sulfite resistance . The effect of FZF1 divergence on SSU1 expression suggests that FZF1 may also affect the expression of other genes . To examine this possibility , we measured genome-wide changes in expression caused by the S . cerevisiae and S . paradoxus FZF1 alleles and the two reciprocal 5′ noncoding chimeras . Gene expression was measured using microarrays before and 15 minutes after addition of sulfites . Out of 6127 open reading frames queried , 655 showed FZF1-dependent differences in expression across both time-points and 648 showed FZF1-dependent differences in expression that varied by time-point ( ANOVA , P<0 . 01 for both ) . For both tests , permutation resampling of the data indicated a false discovery rate of 9 . 8% . Out of the combined set of 1 , 096 genes that showed FZF1-dependent differences in expression , 87% showed significant changes following sulfite treatment ( ANOVA , P<0 . 01 ) , of which 219 and 271 showed a >2-fold decrease and increase , respectively , in expression following sulfite treatment . Consistent with other studies of the stress response [44] , [45] , many of the genes that decreased in expression are involved in ribosome biogenesis ( 64 genes ) and many of the genes that increased in expression are involved in oxidation reduction ( 51 genes ) and response to abiotic stimulus ( 49 genes ) ( Dataset S2 ) . Overall , strains carrying the S . cerevisiae FZF1 allele showed more pronounced changes in expression than those carrying the S . paradoxus allele ( Figure S2 ) , consistent with the possibility that many of the expression differences are not due to direct differential activation or repression by FZF1 , but rather a consequence of downstream differences in sulfite resistance initiated by FZF1 . A small number of genes , including SSU1 , showed a larger increase in expression in strains carrying the S . paradoxus compared to the S . cerevisiae FZF1 allele . Excluding two putative genes , SSU1 showed the largest differences in expression between the S . cerevisiae and S . paradoxus alleles at 15 minutes and was one of the most significant FZF1-dependent differences across both time-points . FZF1-dependent changes in gene expression may be caused by protein coding changes or by regulatory changes in the FZF1 5′ noncoding region . To distinguish between these possibilities , we classified FZF1-dependent expression changes into those that can be attributed to the 5′ noncoding region , coding region , or an interaction between the two regions . Most of the genes that showed FZF1-dependent differences in gene expression across both time-points were characterized by an interaction between the coding and 5′ noncoding regions ( ANOVA , P<0 . 01 , Figure 6 ) . Interestingly , in many cases , the chimeric alleles caused these genes to be expressed at higher or lower levels compared to both of the full length alleles of each species . In contrast , most of the genes showing allele-specific differences in gene expression that varied by time-point were characterized by effects that depended on the coding region of FZF1 ( ANOVA , P<0 . 01 , Figure 6 ) . Together , these results suggest that both the FZF1 coding and 5′ noncoding region contribute to downstream changes in gene expression .
We identified FZF1 based on a genome-wide screen for patterns of non-neutral divergence . FZF1 shows evidence of non-neutral divergence in its promoter region based on an accelerated substitution rate in some lineages but not others . In the coding region , evidence of non-neutral divergence is also present and is based on an elevated ratio of nonsynonymous to synonymous substitutions . However , upon closer examination we found a number of uncertainties regarding the evidence for non-neutral patterns of divergence . In the noncoding region , the evidence for substitution rate heterogeneity depends on the alignment . In the coding region , the cause of the elevated nonsynonymous to synonymous substitution rate is ambiguous because the synonymous substitution rate decreases in the same region that the nonsynonymous substitution rate increases . Interestingly , the strongest evidence for non-neutral evolution comes from divergence between the S . cerevisiae and S . paradoxus alleles , which also show the greatest difference in sulfite resistance . Thus , the pattern of divergence for FZF1 is at least consistent with non-neutral evolution . With respect to a potential cause of non-neutral divergence , both positive selection and loss of constraint can result in elevated substitution rates . However , loss of constraint by itself does not provide a good explanation for the loss of sulfite resistance along the S . cerevisiae lineage and the gain of sulfite resistance along the S . paradoxus lineage relative to the intermediate levels of sulfite resistance in S . mikatae and S . bayanus . While patterns of non-neutral divergence led us to test FZF1 alleles for functional divergence , the value of such an approach remains difficult to assess . First , the evidence for non-neutral evolution is not definitive . Second , we only tested a single candidate . Third , the coding region with the largest effect on sulfite resistance does not include the region with evidence for non-neutral evolution . One factor that may be critical in selecting candidates is whether there is a clearly defined phenotype to test . Many of the other genes that exhibit substitution rate heterogeneity are known to impact a variety of phenotypes , making it difficult to know which one to test . Testing FZF1 was facilitated by its narrowly defined function in sulfite resistance . Thus , while some fascinating examples have emerged , e . g . [46] , further work is needed to evaluate whether non-neutral patterns of divergence provide an effective screen for genes that have diverged in function . Chimeric FZF1 alleles from S . cerevisiae and S . paradoxus indicate that both upstream noncoding regions and the first coding region make additive contributions to divergence in FZF1 activity . A second coding region , including the region with the elevated nonsynonymous to synonymous substitution rate , contributes an epistatic effect through interaction with the other coding region . The number of regions underlying sulfite resistance is likely dependent on how we identified FZF1 . Tests of neutrality based on rate heterogeneity and dN/dS only indicate deviations from expected rates of divergence based on multiple substitutions . The accumulation of multiple changes at a single locus has also been found in other studies of interspecific differences [22]–[26] , so it may not be an uncommon result when multiple regions are individually tested . A limitation of our study is that we only quantified the effects of five regions and did not narrow their effects to individual substitutions . This limitation is in part due to the sensitivity of our sulfite resistance assay . As such , we did not determine whether the regions with the largest effect are caused by single or multiple substitutions , and whether there are epistatic effects between substitutions within a region . Further dissection of FZF1 divergence is needed to more accurately quantify the number , effect size and interactions among mutations affecting sulfite resistance . Transcription factors are often posited to be highly constrained during evolution due to their pleiotropic effects on the expression of other genes [47] . As such , many efforts to understand the evolution of gene regulation have focused on the evolution of cis-regulatory sequences rather than on trans-acting factors , e . g . [12] , [48] . While changes in the expression of transcriptional regulators is hypothesized to be an important mode of evolutionary change [34] , protein coding changes may also be important , e . g . [49] . We find that divergence in both the regulatory and coding sequence of FZF1 affects sulfite resistance and causes numerous downstream changes in gene expression . This raises the question of whether there have been any constraints on FZF1 divergence due to pleiotropy . If FZF1 has been constrained by pleiotropy there must , at least under certain circumstances , be negative consequences to changes in FZF1 activity . Increased levels of FZF1 activity could reduce fitness in the absence of sulfites or after other exposures that activate FZF1 , such as nitric oxide treatment [50] . The observation that the more potent S . paradoxus FZF1 allele is expressed at lower levels in the absence of sulfites provides some support for the idea that high levels of FZF1 activity may not always be advantageous . Assuming that there is some cost to constitutive increases in FZF1 activity , there are a number of ways in which this cost could be small enough to overcome or even eliminated . One consideration is that SSU1 expression is likely the major determinant of sulfite resistance and so the benefit of increased SSU1 expression may outweigh any costs . In support of this possibility , SSU1 overexpression is able to rescue the effect of an FZF1 deletion ( Figure S3 ) [38] . However , the expression of other genes may also be involved in sulfite resistance since divergence upstream of FZF1 affects sulfite resistance but only has a small , insignificant effect on SSU1 expression . Thus , coding changes in FZF1 that increase SSU1 expression may have outweighed any costs under other conditions , or may have been facilitated by lower levels of FZF1 expression in the absence of sulfites . Another explanation for the lack of constraints on FZF1 divergence is compensatory changes in genes regulated by FZF1 . In this scenario , slight changes in FZF1 activity may be compensated by cis-regulatory mutations in those FZF1 regulated genes where changes in gene expression are deleterious . A number of empirical studies have shown that transcription factors bind different targets between closely related species and even within species due in part to cis-regulatory sequence changes [11] , [51]–[53] . Thus , it is also possible that cis-regulatory sequence evolution may have accommodated divergence in FZF1 activity . A third explanation , suggested by the finding that transcription factors with few targets are less likely to be constrained by pleiotropy [54] , is that FZF1 has few transcriptional targets and so is not greatly constrained by pleiotropy . In response to exogenously supplied nitric oxide , activation of only a small set of five genes , including SSU1 , was found to specifically depend on the presence of FZF1 [50] . Another study found 21 upregulated and 37 downregulated genes two hours after sulfite treatment [55] . We found 1 , 096 FZF1-dependent expression changes , most of which showed the same direction of response to sulfite and only differed in magnitude . The observation that the sulfite-sensitive S . cerevisiae FZF1 allele caused more pronounced changes in gene expression relative to the S . paradoxus allele ( Figure S2 ) is consistent with FZF1 causing indirect changes in gene expression mediated by its effects on sulfite resistance rather than by direct activation or repression of these genes . Furthermore , we found no enrichment of the FZF1 motif identified in the SSU1 promoter ( TATCGTAT and CAACAA , [37] ) , defined by protein microarrays ( CTGCTA , [56] ) , or by promoter bashing and response to nitrosative stress ( YGSMNMCTATCAYTTYY , [50] ) within the 271 genes showing a 2-fold significant increase in expression following sulfite treatment . Thus , most of the changes in gene expression that we observed may be an indirect consequence of a sulfite-induced stress response rather than a consequence of changes in direct targets of FZF1 . Regardless of the mechanism , the concentration of multiple sequence changes in FZF1 suggests that it may have evolved without many genetic , functional or evolutionary constraints . However , the apparent absence of constraints could be a consequence of low basal levels of FZF1 expression . Under this scenario , changes in FZF1 regulation may have facilitated changes within its protein coding sequence . Even though FZF1 has diverged in its ability to confer resistance to sulfites , its impact on the evolution of sulfite resistance is hard to know . While there is substantial variation in sulfite resistance within and between species ( Figure S4 ) , divergence at other loci may be responsible for most differences in sulfite resistance and could compensate for any changes in FZF1 . Within S . cerevisiae , variation in sulfite resistance is associated with a reciprocal translocation upstream of SSU1 that is more frequent in vineyard and wine strains than strains derived from other sources [39] , [57] . The inferred loss of sulfite resistance conferred by changes in FZF1 along the lineage leading to S . cerevisiae , combined with the gain of sulfite resistance due to the translocation within some strains of S . cerevisiae , suggests that the evolution of sulfite resistance among species is not simple and compensatory changes may be involved . In this study we find substantial divergence in function within the coding and upstream noncoding region of FZF1 . Our finding that multiple regions underlie divergence in sulfite resistance is not unexpected given the patterns of non-neutral evolution , but differs from other studies that identify single changes of large effect based on genetic mapping or candidate gene approaches [14] . The contribution of both noncoding and coding regions to differences in sulfite resistance suggests that the distinction between evolution in noncoding and coding regions may be less important than the degree to which a gene has the capacity to evolve , unencumbered by constraints on its other functions [33] . In conclusion , our work supports a model whereby both gene expression and phenotypic divergence can be attributed to multiple mutations throughout the regulatory and protein-coding region of a single gene .
S . cerevisiae , S . paradoxus , S . mikatae , and S . bayanus noncoding regions [58] were tested for substitution rate heterogeneity using a likelihood ratio test implemented using HyPhy [59] . The likelihood ratio test was used to compare a constrained model with a single substitution rate across lineages to an unconstrained model where each lineage was allowed to have a different substitution rate . For both models we used the HKY85 substitution model implemented in HyPhy , the known phylogenetic relationship among the species , and either a single parameter ( constrained ) or branch-specific parameters ( unconstrained ) for the ratio of the noncoding substitution rate at the locus of interest to the substitution rate at four-fold degenerate sites across the genome . Noncoding alignments were removed if the total length of insertion/deletions was more than 15% of the length of the entire alignment . While this filter eliminated the noncoding region upstream of FZF1 , we had already initiated our functional analysis of FZF1 based on preliminary rate heterogeneity results and so retained it in our list of candidates . To examine whether substitution rate heterogeneity upstream of FZF1 depends on the alignment , we aligned the 5′ noncoding region using 6 alignment programs: Clustalw [60] , MUSCLE [61] , TCOFFEE [62] , MAFFT [63] , PRANK [64] , and DCA [65] . The resulting alignments were tested for rate heterogeneity using the likelihood ratio test described above . For the coding sequence of FZF1 , a sliding window analysis of dN/dS was performed for FZF1 using the K-estimator software [66] as described in Sawyer and Malik ( 2006 ) . K-estimator uses Monte Carlo simulations to estimate the confidence intervals for estimates of dN/dS . FZF1 was deleted in YJF173 ( S288c-background , Mat a , ho , ura3-52 ) using the KANMX deletion cassette [67] . FZF1 alleles were integrated into this strain at the ura3 locus by amplifying the entire FZF1 gene region , including the entire 5′ and 3′ noncoding regions along with 25 bases of ZRT1 and 45 bases of HXK2 , using primers with homology to pRS306 and transforming the product along with the yeast integrative plasmid , pRS306 [68] . Integration of these constructs at the ura3 locus was achieved by selection on plates lacking uracil and each transformant was confirmed by PCR . Chimeras were generated using the same procedure but with FZF1 regions amplified from different species . The aligned ATG start site was used for all chimeras divided between the 5′ noncoding region and the coding region . A mutation of an alternate FZF1 start site in the S . paradoxus FZF1 allele did not significantly alter sulfite resistance compared to the non-mutated counterpart ( data not shown ) . A subset of 2–5 transformants were sequenced to ensure that at least one transformant per construct contained no mutations . All experiments were conducted using YPD+TA ( 1% yeast extract , 2% peptone , 2% dextrose , 75 mM L-tartaric acid buffered to pH 3 . 5 ) [69] . Sulfite resistance was measured by comparing growth in the presence and absence of sodium sulfite . Strains were grown overnight in YPD+TA , diluted 1∶1000 in YPD+TA , grown for 3 hours , treated with either water or sodium sulfite ( final concentration 0 . 7–0 . 9 mM sodium sulfite ) , and then grown for 20 hours in an iEMS plate reader at 30° with 1200 rpm shaking ( model no . 1400; Thermo Lab Systems , Helsinki , Finland ) . For each strain , the sulfite-dependent delay in growth was determined by comparing the time at which maximum growth rate was observed for strains treated with sulfite relative to a water-treated control [70] . For each FZF1 construct , 4 to 8 independent transformants were phenotyped . To compare the sulfite-dependent delay in growth within and between yeast species , three replicate measurements were obtained for 6 S . cerevisiae strains: S288c ( source: laboratory , obtained from: D . Botstein ) , YPS163 ( source: oak exudate , United States , obtained from: P . Sniegowski ) , M8 and M33 ( source: vineyard , Italy , obtained from R . Mortimer ) , YJM440 ( source: clinical , United States , obtained from: J . McCusker ) , K9 ( source: saké , Japan , obtained from: Nami Goto-Yamamoto ) , and five S . paradoxus strains: YPS138 ( source: oak soil , United States ) , N17 ( source: oak exudate , Russia ) , N44 ( source: oak exudate , Russia ) , Y7 ( source: oak bark , United Kingdom ) , and NRRL Y-17217 all obtained from G . Litti and E . Louis . Additional yeast species included: S . mikatae ( IFO1815 , obtained from: E . Louis ) , S . bayanus ( NRRL Y-11845 , obtained from: C . Kurtzman , ARS Culture Collection ) , Saccharomyces castellii ( NRRL Y-12630 , obtained from: M . Johnston ) , Saccharomyces kluyverii ( NRRL Y-12651 , obtained from: M . Johnston ) , and Kluyveromyces lactis ( FM423 , a haploid MAT á strain obtained from M . Johnston ) . All strains are diploid except as noted . Differences in sulfite resistance between species and species' chimeras were normalized for day effects and tested for significance using the nonparametric Kruskal-Wallis test . Pairwise differences between constructs were examined using the nonparametric Wilcoxon rank sum test with Bonferroni correction . Differences in sulfite resistance among S . cerevisiae - S . paradoxus chimeric constructs of FZF1 were measured using linear mixed effect ( lme ) models to account for repeated measurements of the same construct . Sulfite resistance of each construct was measured three times and measurements on different days were standardized by a Z-score transformation . Sulfite resistance was fit to two models . The first model assumes each region from S . cerevisiae or S . paradoxus makes an additive contribution to differences in sulfite resistance: sulfite resistance = region1+region2+region3+region4+region5+ ( error | batch ) +error , where each region has an effect that depends on the species the region came from and ( error | batch ) models random effects due to measurement of the same construct in different batches ( 96-well plates ) . The second model builds on the first model but adds in all pairwise interactions between regions: sulfite resistance = ( region1+region2+region3+region4+region5 ) ∧2+ ( error | batch ) +error . The fit of the two models was compared using a likelihood ratio test with 10 degrees of freedom since the first and second models have 8 and 18 degrees of freedom , respectively . The percent variance explained by each model was calculated by R2 = 1−exp ( −LR/n ) , where n is the sample size and LR is the likelihood ratio statistic defined by twice the difference in the log likelihood of the alternative relative to the null model [71] . The null model was fit using only an intercept: sulfite resistance = ( error | batch ) +error . For lme P-values , we tested whether the assumptions of the test were violated and resulted in inaccurate P-values by repeatedly permuting the data labels to obtain the distribution of P-values expected by chance . The permuted data showed no evidence for inaccurate P-values . Gene expression was measured using four independent transformants of each FZF1 construct . Strains were resuspended in YPD+TA at an OD600 of 0 . 25 from an overnight YPD+TA culture and grown in 100 mL cultures at 30°C , 200 rpm . After 3 hours , each culture was sampled at 0 , 15 , 30 and 60 minutes after addition of sodium sulfite to a final concentration of 1 mM . Cells were centrifuged , washed and frozen in a dry ice/ethanol bath and stored at −80°C . RNA was isolated and cDNA prepared using Qiagen's RNaeasy Mini Kit and Omniscript RT Kit , respectively ( Valencia , CA ) . Quantitative PCR was used to measure expression of FZF1 and SSU1 . A 20-fold dilution of cDNA reactions was used for the real-time PCR assays with gene specific primers and Strategene's Brilliant II SYBR Green QPCR Master Mix ( Santa Clara , CA ) . Expression was assayed on Stratagene's MX3000P QPCR machine . For FZF1 , species-specific primers were used and a plate specific correction factor , estimated for each plate from quantitative PCR measurements of DNA extracted from a heterozygous strain containing both the S . cerevisiae and S . paradoxus FZF1 alleles , was used to account for the difference in PCR efficiency between the S . cerevisiae and S . paradoxus primers . Data were mean normalized for day and batch effects and expression levels were measured relative to ACT1 . The Wilcoxon rank sum test with Bonferroni correction was used to identify significant differences in expression due to FZF1 alleles . Genome-wide measurements of gene expression were obtained using Agilent Technologies ( Santa Clara , CA ) yeast ( V2 ) gene expression microarrays ( 8×15K , Catalog number: G4813A-016322 ) following the manufacturers protocols . Sample labeling , hybridization and microarray scanning was conducted by the Expression and Genotype Core at Washington University's Genome Center . Gene expression was measured for three independent replicates at the 0 and 15 minute time-points . Each sample was compared to a reference made up of a pool of all RNA samples . Expression data was deposited in the GEO database under accession GSE35308 . After median normalization of each microarray , differences in gene expression were tested using an analysis of variance ( ANOVA ) with the model: expression = allele*time+technical replicate+error , where allele measures the effect of the different FZF1 alleles , time measures the effect of each time-point , and technical replicate accounts for differences between replicated features on the microarray . The rate of false positives was estimated by permuting the sample labels 100 times and repeating the analysis . For each gene showing a significant difference in expression , a second ANOVA was performed to identify expression changes that could be attributed to the coding or 5′ noncoding region or an interaction between the two regions . For genes showing expression differences that depended on the FZF1 construct we used the model: expression = noncoding*coding+error , and for genes showing differences that depended on an interaction between the FZF1 construct and time we used the model: expression = noncoding*coding*time+error . Gene sets enriched for gene ontology ( GO ) categories were identified using DAVID [72] . | Changes in gene regulation are thought to play an important role in evolution . While variation in gene expression between species is common , it is hard to identify the phenotypic consequences of this variation since many changes in gene expression may have subtle or no phenotypic effects . In this study , we investigate changes in sulfite resistance and gene expression caused by the transcription factor , FZF1 , that has evolved rapidly during the divergence of related yeast species . We find that divergence in the ability of FZF1 to confer sulfite resistance is mediated by changes in its expression as well as changes in its protein structure , both of which cause changes in the expression of other genes . Our results show how the combination of multiple changes within a transcription factor can produce substantial changes in phenotype and the expression of many genes . | [
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] | 2012 | Divergence of the Yeast Transcription Factor FZF1 Affects Sulfite Resistance |
Fungal pathogens elicit cytokine responses downstream of immunoreceptor tyrosine-based activation motif ( ITAM ) -coupled or hemiITAM-containing receptors and TLRs . The Linker for Activation of B cells/Non-T cell Activating Linker ( LAB/NTAL ) encoded by Lat2 , is a known regulator of ITAM-coupled receptors and TLR-associated cytokine responses . Here we demonstrate that LAB is involved in anti-fungal immunity . We show that Lat2−/− mice are more susceptible to C . albicans infection than wild type ( WT ) mice . Dendritic cells ( DCs ) express LAB and we show that it is basally phosphorylated by the growth factor M-CSF or following engagement of Dectin-2 , but not Dectin-1 . Our data revealed a unique mechanism whereby LAB controls basal and fungal/pathogen-associated molecular patterns ( PAMP ) -induced nuclear β-catenin levels . This in turn is important for controlling fungal/PAMP-induced cytokine production in DCs . C . albicans- and LPS-induced IL-12 and IL-23 production was blunted in Lat2−/− DCs . Accordingly , Lat2−/− DCs directed reduced Th1 polarization in vitro and Lat2−/− mice displayed reduced Natural Killer ( NK ) and T cell-mediated IFN-γ production in vivo/ex vivo . Thus our data define a novel link between LAB and β-catenin nuclear accumulation in DCs that facilitates IFN-γ responses during anti-fungal immunity . In addition , these findings are likely to be relevant to other infectious diseases that require IL-12 family cytokines and an IFN-γ response for pathogen clearance .
Fungal infections with pathogens such as Candida albicans are a significant health risk for immunocompromised individuals [1] . There is a high degree of mortality in these cases even with treatment , highlighting the need for a better understanding of the immune response involved in controlling fungal infections in order to develop improved treatments [2] , [3] . Responses to fungal infections involve both innate and adaptive immunity [4] . The host response relies on the recognition , ingestion and elimination of C . albicans by phagocytic cells . During fungal infections , various pro-inflammatory cytokines such as TNF , IL-12p70 , IL-23 and IL-6 , produced by the activated leukocytes , result in the promotion of a sustained Th1 and Th17 response [5] , [6] , [7] . The requirement for these cytokines and pathways has been demonstrated by increased susceptibility of several knockout mice to C . albicans infections . For example , mice deficient in genes associated with Th1 responses such as Il12a , Ifng or Ifngr1 are more susceptible to systemic C . albicans infection [8] , [9] . In addition , Il18−/− mice display enhanced susceptibility to disseminated C . albicans due to their inability to produce sufficient IFN-γ [10] . More recently , fungal responses have been shown to involve the Th17 pathway; Il23a−/− , Il17ra−/− and Il17a−/− mice are more susceptible to oral and/or systemic candidiasis [6] , [11] , [12] . Therefore , the level of inflammatory cytokine production in response to C . albicans infection is important in determining whether the host will eliminate or succumb to the infection . Stimulation of host immune cells to produce pro-inflammatory cytokines occurs through the recognition of PAMPs by pathogen-recognition receptors ( PRRs ) [13] . Various PRRs such as the mannose receptor , TLR2/4 , CR3 , Dectin-1 and Dectin-2 are involved in fungal recognition and responses [14] . Dectin-1 and Dectin-2 are type II , C type lectin-like receptors expressed mainly on myeloid cells [15] , [16] . Fungal cell walls are mostly composed of β-glucans , chitins and mannans . Dectin-1 recognizes β-glucans in the fungal cell wall while Dectin-2 binds mannans . Mice lacking either Dectin-1 ( Clec7a−/− ) or Dectin-2 ( Clec4n−/− ) exhibit increased susceptibility to fungal infections supporting the role of these proteins in fungal immunity [5] , [6] , [17] , [18] . Furthermore , identification of a CLEC7A single nucleotide polymorphism in humans , which encodes a non-functional form of Dectin-1 , confirms the role of Dectin-1 in anti-fungal responses , as carriers of this polymorphism are more susceptible to mucocutaneous infections with C . albicans , in part due to reduced IL-17 production [19] , [20] . Consistent with these data , polymorphisms resulting in IL-17RA and IL-17F deficiency were recently identified in some patients with chronic mucocutaneous candidiasis [21] . A wide variety of immunoreceptor tyrosine-based activation motif ( ITAM ) -coupled receptors are centrally involved in mediating the inflammatory response . The Dectin-1 cytoplasmic domain includes an ITAM-like sequence ( known as a hemiITAM , YxxxI/Lx7YxxL ) while Dectin-2 couples to the signaling chain FcεRIγ , which signals via a canonical ITAM ( YxxLx7–12YxxL ) [13] . Engagement of ITAM-associated receptors such as Dectin-2 ( FcεRIγ ) , or hemiITAM-containing receptors such as Dectin-1 , results in phosphorylation of the tyrosines within ITAMs or hemiITAMs . Src homology domain-2 containing protein tyrosine kinases of the Syk/Zap70 family are then recruited to the ITAM/hemiITAM and activated , resulting in CARD9- and MAPK-dependent pro-inflammatory cytokine production [4] . Surprisingly however , the biochemical adaptor ( s ) involved in coupling Dectin-1 or Dectin-2 proximal phosphorylation to downstream effectors have yet to be identified and despite the established importance of these receptors , little is known about the regulation of their signaling . Two closely related transmembrane adaptor proteins , the Linker for Activation of T cells ( LAT/Lat1 ) and Linker for Activation of B cells/Non-T cell Activation Linker/Linker for Activation of T cells family member 2 ( LAB/NTAL/Lat2 , hereafter referred to as LAB ) facilitate signaling downstream of various ITAM-coupled receptors [22] . These adaptor proteins activate signaling pathways such as the MAPK cascade and regulate production of cytokines including IL-12p40 [23] . As a consequence we hypothesized that LAT and/or LAB would mediate or regulate cytokine production during fungal responses downstream of Dectin-1 and/or Dectin-2 due to the similarities in hemiITAM and canonical ITAM signaling . Here we demonstrate that LAB is involved in anti-fungal immunity . Mice deficient in LAB ( Lat2−/− mice ) are more susceptible to systemic C . albicans infection than WT mice . However , Lat2−/− neutrophil cytokine production is not impaired suggesting a defect in another cell type . We show that LAB but not LAT , is expressed by DCs and that both the M-CSF/DAP12 and mannan-Dectin-2/FcεRIγ pathways promote LAB phosphorylation . Conversely , the β-glucan-Dectin-1 or TLR pathways do not . We also define a novel role for LAB in suppressing β-catenin nuclear translocation , which in turn permits efficient IL-12 production from bone marrow-derived DCs ( BMDCs ) stimulated with a range of PAMPs . Furthermore , through this novel mechanism LAB promotes NK and T cell-mediated IFN-γ production , which is deficient in vivo during C . albicans infection of Lat2−/− mice . Thus LAB provides a molecular bridge between β-catenin activation and the cytokine production required for fungal clearance during systemic infection .
As LAB has previously been shown to mediate/regulate signaling and/or cytokine responses downstream of ITAM-coupled receptors and TLRs [23] , [24] , we hypothesized that LAB would play a role in anti-fungal immunity . To investigate this possibility , we systemically infected WT and Lat2−/− mice with C . albicans . Lat2−/− mice displayed increased susceptibility to high ( 1 . 5×105 ) and low ( 5×104 ) dose C . albicans infection compared to WT mice ( Fig . 1A–B ) . The reduced survival was paralleled by increased fungal burden in the kidneys of Lat2−/− mice , nine days after infection ( i . v . ) with C . albicans ( Fig . 1C ) . The kidney depicted from a Lat2−/− mouse that succumbed to infection displays a marked proliferation of fungal hyphae within the pelvis , which was surrounded by neutrophilic inflammation , consistent with an inability to clear the infection ( Fig . 1D ) . Together , these data demonstrate that LAB is important for the host response to C . albicans infection . Neutrophil function is important for anti-fungal immunity . They produce cytokines such as TNF and IL-6 , both of which play a role in determining susceptibility to fungal infections [25] , [26] . As LAB is expressed in neutrophils [24] , we examined the effect of LAB deficiency on C . albicans-recruited neutrophil cytokine production following restimulation with heat-killed C . albicans yeast ( HKY ) . TNF and IL-6 production from C . albicans-recruited neutrophils was not impaired in Lat2−/− cells , in fact they were enhanced ( Fig . 2A ) , similar to the findings of Tessarz et al [24] . Consistent with these data , we observed enhanced TNF and IL-6 production from bone marrow purified neutrophils following stimulation with zymosan and LPS ( Fig . 2B ) . In addition , serum TNF and IL-6 levels were enhanced in Lat2−/− mice systemically infected with C . albicans ( Fig . 2C ) . These data indicate that neutrophil cytokine production is not impaired in Lat2−/− mice suggesting that LAB plays additional roles in other cell types during systemic C . albicans infection . In addition to neutrophils , DCs are important for anti-fungal immunity . In previous studies , we have shown that there is a switch from LAT to LAB expression as monocytes differentiate into macrophages and that LAB is involved in regulating ITAM-mediated signals in macrophages [23] . To extend these studies to DCs we examined the expression of LAB in human monocytes and monocyte-derived DCs and murine GM-CSF-cultured BMDCs . Similar to macrophages , LAB is expressed in DCs while LAT expression is lost ( Fig . 3A–B ) . To determine whether LAB is involved in fungal-induced signaling , we stimulated BMDCs with the yeast cell wall extract zymosan and found that many proteins were tyrosine phosphorylated following stimulation ( Fig . 3C ) . Consistent with previous findings [5] , [27] we found that Syk and PLCγ2 were prominently phosphorylated following zymosan stimulation ( Fig . 3D ) . This response was also associated with the phosphorylation of LAB and one of its binding partners , c-Cbl [23] ( Fig . 3D ) . Phosphorylation of these proteins was unaffected by the absence of LAB ( Fig . S1 ) . Accordingly , we found that activation of the Erk and NFκB pathways were unaffected by the absence of LAB ( Fig . S1 ) . Similar to our findings with zymosan , we also observed that many proteins , including LAB , were phosphorylated following stimulation with heat-killed C . albicans yeast ( HKY ) ( Fig . 3E ) . The zymosan- and C . albicans-induced phosphorylation of LAB in DCs suggests that this adaptor protein may be important in regulating downstream pathways in DCs that are instrumental in combating fungal infections . Given that LAB is phosphorylated following zymosan stimulation in DCs , we next assessed the ability of LAB to regulate zymosan-induced cytokine responses in DCs . We found that zymosan-induced IL-12p40 production from BMDCs is partially dependent on LAB ( Fig . 4A ) . As zymosan is a complex ligand that engages multiple receptor systems , including Dectin-1 , Dectin-2 , TLR2 and others , we examined the effect of LAB on various fungal and TLR ligands . WT and Lat2−/− BMDCs were stimulated with heat-killed C . albicans yeast , LPS , particulate β–glucan ( Sigma ) , curdlan , Pam3CSK4 and CAWS ( mannans ) . IL-12p40 production was significantly reduced in Lat2−/− BMDCs following stimulation with each of these ligands suggesting that LAB plays an important role in IL12-p40 production ( Fig . 4B–C ) . To underline the importance of these findings to fungal infection , we next stimulated BMDCs with the yeast form of live C . albicans . These results showed that induction of Il12b , Il12a and Il23a mRNA were significantly impaired in Lat2−/− cells compared to WT controls ( Fig . 4D ) . Subsequently , after a twenty-four hour stimulation of BMDCs with live C . albicans , cytokine levels in the supernatants were measured . These data showed that production of IL-12p40 and IL-12p70 were both greatly reduced , and IL-23 levels were also partially suppressed , in Lat2−/− BMDCs following stimulation with live yeast . However , IL-10 , IL-1β and TNF levels were not impaired by the absence of LAB ( Fig . 4E ) . We next stimulated BMDCs with increasing doses of LPS . Consistent with our findings with C . albicans we demonstrated that LAB plays a significant role in mediating the production of IL-12 family cytokines downstream of LPS , while production of IL-10 , IL-1β and TNF levels were not impaired by the absence of LAB ( Fig . 4F ) . These data indicate that LAB plays an important and selective role in facilitating fungal/PAMP-induced IL-12 family cytokine production . C . albicans contains ligands for many receptors ( including Dectin-1 , Dectin-2 and TLRs 2 and 4 ) and systemic infection with C . albicans involves both Th1 and Th17 responses [5] . In vitro however , the T cell response to C . albicans is dominated by Th17 polarization [6] . In order to assess the capacity of Lat2−/− DC to direct both Th17 and Th1 responses in the context of reduced IL-12 production , heat-killed C . albicans and the alternate TLR4 selective ligand , LPS , were used respectively . Supernatants from WT and Lat2−/− BMDCs cultured in the presence of heat-killed C . albicans yeast or LPS were added to naïve WT CD4+ T cells stimulated with anti-CD3 and anti-CD28 . IFN-γ and IL-17A production were measured by flow cytometry and ELISA following 4 days of culture . As expected , conditioned media from BMDCs stimulated with heat-killed yeast induced a robust Th17 response while stimulation with LPS induced a Th1 response ( Fig . 5A-B ) . HKY-stimulated WT and Lat2−/− BMDCs induced comparable Th17 responses ( Fig . 5A–B ) , likely due to the production of similar levels of IL-1β and only a minimal defect in IL-23 production from Lat2−/− BMDCs . Interestingly , the proportion of IFN-γ-secreting CD4+ T cells arising from LPS stimulated Lat2−/− BMDCs was significantly reduced compared to WT BMDCs ( Fig . 5A–C ) , which could be attributed to the significant reduction in IL-12p70 production from Lat2−/− BMDCs ( Fig . 4E–F ) . These data indicate that LAB-facilitated cytokine production is important for inducing Th1 responses but not Th17 responses in vitro . As both Th1 and Th17 responses are important for in vivo protection against C . albicans infections [6] , [9] , our data suggests that defective Th1 responses in Lat2−/− mice may be responsible for the increased susceptibility of these mice to C . albicans infection . As LAB facilitates fungal/PAMP-induced cytokine production in DCs and subsequent Th1 polarization , we next sought to determine which signaling pathways were involved . As LAB phosphorylation is a documented feature of ITAM-coupled receptor signaling [23] , [28] , we hypothesized that β-glucans signaling through the hemiITAM-containing Dectin-1 or mannans signaling through the ITAM-coupled Dectin-2 would induce LAB phosphorylation . After resting the cells to remove basal phosphorylation , we found , surprisingly , that stimulation of DCs with washed zymosan particles ( insoluble components of zymosan such as the β-glucans ) or purified particulate β-glucans does not lead to LAB phosphorylation while stimulation with zymosan extract ( soluble components of zymosan such as the mannans ) or purified mannans caused rapid and robust LAB phosphorylation ( Fig . 6A–B ) . LAB phosphorylation was not observed in response to TLR2/4 ligands Pam3CSK4 or LPS ( Fig . 6A–C ) . These data suggest that LAB may be phosphorylated downstream of the mannan-Dectin-2/FcεRIγ pathway rather than via Dectin-1 or the TLR pathways . To further address this possibility , we examined LAB phosphorylation in DCs lacking Myd88 or Fcer1g . Consistent with a role for Dectin-2 , LAB phosphorylation was intact in the absence of MyD88 ( Fig . 6D ) and ablated in Fcer1g−/− cells ( Fig . 6E ) . We next stimulated RAW-264 macrophage cell lines , engineered to express either Dectin-1 or Dectin-2 [29] , with zymosan and found that LAB was only phosphorylated in cells expressing Dectin-2 ( Fig . 6F ) . This was in spite of high zymosan recognition by Dectin-1 over-expressing RAW-264 macrophages and their potential to signal via TLRs [29] , [30] . To confirm this selective role for Dectin-2 , we conducted Dectin-2 specific shRNA-knockdown experiments ( Fig . 6G–H ) . We found that LAB phosphorylation was attenuated in DCs with partial Dectin-2 knockdown as a result of infection with shRNA against Dectin-2 compared to BMDCs infected with a scrambled shRNA control ( Fig . 6H ) . Taken together , these data clearly demonstrate that zymosan stimulates LAB phosphorylation through a mannan-driven Dectin-2-FcεRIγ signaling pathway and not through the β-glucan-Dectin-1 or TLR pathways . The phosphorylation of LAB downstream of Dectin-2 could potentially explain the reduced IL-12p40 in response to C . albicans , zymosan and mannans . However , the reduced IL-12p40 in response to β-glucan , LPS or Pam3CSK4 implies the involvement of another LAB activating pathway . Given the broad effect of LAB deletion , we hypothesized that LAB may exert its effect prior to stimulation . Therefore , we next examined whether LAB phosphorylation occurs in DC during their expansion in vitro . For this experiment we compared WT BMDCs harvested directly from culture to those rested in cytokine/serum free conditions for 30 min with or without subsequent stimulation with zymosan for 1 min ( Fig . 7A ) . These data demonstrate readily appreciable basal phosphorylation of LAB in WT BMDCs during culture ( Fig . 7A ) . LAB phosphorylation in response to SCF [31] has been described previously , suggesting that a growth factor may be responsible for this basal LAB phosphorylation . Therefore , we stimulated BMDCs with GM-CSF and M-CSF . Interestingly , M-CSF induced LAB phosphorylation ( Fig . 7B ) and examination of the BMDC cultures demonstrated that while M-CSF is not added to these cultures , the cells are producing appreciable levels of M-CSF ( Fig . 7C ) during culture . Crosstalk between M-CSF and the ITAM-containing signaling chain , DAP12 has recently been described [32] . Thus , we tested whether basal LAB phosphorylation in BMDC cultures was DAP12-dependent . Basal levels of LAB phosphorylation were almost completely abolished in the absence of DAP12 ( Fig . 7D ) . We then sought to determine whether M-CSF-induced LAB phosphorylation was dependent on Syk . Pre-treatment of WT cells with the Syk inhibitor , Piceatannol , inhibited M-CSF- and zymosan-induced LAB phosphorylation ( Fig . 7E ) . These data indicate that LAB is phosphorylated through an M-CSF/DAP12-Syk pathway during culture . This basal M-CSF/DAP12 pathway may control IL-12p40 production in response to all of the PAMPs in this study . M-CSF is constitutively present in naïve mice in vivo and it is also induced during infection with C . albicans [33] . Moreover , M-CSF crosstalks with DAP12 to induce activation of β-catenin [32] . Importantly , β-catenin represses pro-inflammatory cytokine production in DCs [34] , [35] . Therefore we examined nuclear β-catenin levels in WT and Lat2−/− BMDCs . Interestingly , Lat2−/− BMDCs showed increased β-catenin activation as evidenced by increased nuclear localization in response to zymosan , heat-killed yeast/hyphae ( HKH ) or LPS ( Fig . 8A–C ) . Importantly , basal nuclear β-catenin levels are also increased in Lat2−/− BMDCs ( Fig . 8A–C ) , and the physiological relevance of this was confirmed by the increased mRNA levels of the β-catenin target genes Axin2 and Wisp1 in non-stimulated cells ( Fig . 8D ) . We next examined whether this increasedβ-catenin nuclear accumulation in Lat2−/− cells was responsible for the decreased cytokine production by these cells . In the absence of Wnt signaling β-catenin is phosphorylated by GSK-3β and targeted for proteasomal-mediated destruction [36] . We induced β-catenin stabilization by stimulating the Wnt pathway with the canonical ligand Wnt3a or with an inhibitor of GSK-3β ( SB-216763 ) . Following activation of β-catenin through Wnt3a or SB-216763 , the difference in IL-12p40 production between WT and Lat2−/− cells was reduced ( Fig . 8E–F ) resulting in WT cells more closely resembling those deficient in LAB . In addition , we stimulated β-catenin degradation by stabilizing axin , a component of the destruction complex , with XAV939 . This again resulted in alleviation of the difference in IL-12p40 production between WT and Lat2−/− cells with Lat2−/− cells more closely resembling WT cells ( Fig . 8G ) . In all of the above cases , the two-way ANOVA indicates a clear interaction between the effect of the inhibitors/agonists and LAB-deficiency as predicted by the proposed role of β-catenin in LAB-facilitated IL-12p40 production . TNF levels were largely unaffected by treatment with inhibitors/activators of this pathway ( Fig . 8H–I ) . As the basal and PAMP-induced β-catenin levels are different between WT and Lat2−/− BMDCs , we hypothesized that modulating β-catenin activation would normalize IL-12p40 production in response to a variety of PAMPs including those that do not directly stimulate LAB phosphorylation . Consistent with this hypothesis , activation of β-catenin through SB-216763 reduced the difference in IL-12p40 production between WT and Lat2−/− cells stimulated by LPS ( TLR4 ) ( Fig . 8J ) or WGP ( Dectin-1 ) ( Fig . 8K ) . TNF levels were largely unaffected by treatment with SB-216763 in response to either LPS ( Fig . 8L ) or WGP ( Fig . 8M ) . These data indicate that constitutive LAB phosphorylation maintains limited basal β-catenin nuclear accumulation , thus facilitating robust cytokine production , consistent with previous reports demonstrating that β-catenin represses pro-inflammatory cytokine production [34] , [35] . As Lat2−/− mice were more susceptible to systemic C . albicans infection and Lat2−/− DCs displayed impaired IL-12 production and subsequent Th1 responses in vitro , we wanted to determine whether these defective cytokine responses occurred in vivo . In correlation with the reduced IL-12p40 production in vitro we observed reduced Il12b mRNA levels in the spleens of both uninfected ( Fig . 9A ) and C . albicans infected Lat2−/− mice ( Fig . 9B ) . Additionally , Ifng mRNA levels were reduced in both uninfected and C . albicans infected Lat2−/− mice and Tbx21 mRNA levels were also reduced in the C . albicans infected Lat2−/− mice ( Fig . 9A–B ) . In contrast Il12a , Il23a and Rorc mRNA levels were normal in the Lat2−/− mice and Il17 mRNA levels were undetected . Consistent with reduced RNA levels of Il12b , we also observed reduced IL-12p40 in Lat2−/− splenic DCs following stimulation with LPS ( Fig . 9C and Fig . S2 ) . In order to further examine whether Lat2−/− mice displayed reduced IFN-γ responses , we injected WT and Lat2−/− mice intraperitoneally with C . albicans and collected cells by peritoneal lavage 72 h post injection . Following restimulation with PMA/Ionomycin there were reduced percentages of IFN-γ producing cells in Lat2−/− lavages ( Fig . 9D and Fig . S2 ) . The IFN-γ producing cells consisted of CD3+NK1 . 1−CD4+ T cells , CD3+NK1 . 1−CD4− T cells , CD3−NK1 . 1+ NK cells and CD3+NK1 . 1+ NKT cells . Not surprisingly , IL-12 , alone or in combination with IL-18 , is known to induce IFN-γ production in T , NK and NKT cells and it appears that each of these cell types contributes to the reduction in IFN-γ production with the largest reduction in NK cell IFN-γ production ( Fig . 9E ) [37] , [38] , [39] , [40] . Taken together , these data indicate that LAB facilitates IL-12 production from DCs and subsequent IFN-γ production mainly from NK and T cells in vivo . These data demonstrate that LAB is important for DC and NK , T and NKT cell-mediated cytokine production during the host response to C . albicans infection .
Here we identify for the first time an important role for LAB in anti-fungal immunity . Lat2−/− mice displayed increased susceptibility to C . albicans infection and the fungal burden was significantly increased in these mice , similar to Clec4n−/− mice and Clec7a−/− mice [6] , [17] . Lat2−/− mice displayed reduced NK and T cell-mediated IFN-γ production which reflects the attenuated production of IL-12 by Lat2−/− DCs , while Lat2−/− neutrophil cytokine responses were not impaired . Moreover , we defined a novel role for LAB in the repression of β-catenin nuclear translocation in DCs , resulting in regulated cytokine production from a range of PAMPs , which is important in mounting an effective immune response in an infectious disease model . These data also suggest that LAB will be important in other diseases where IL-12 is involved in disease outcome . The adaptor protein LAB mediates signals downstream of multiple ITAM-coupled receptors including BCR , FcRγ and TREM1/2 and the growth factor Stem Cell Factor [22] . LAB , similar to LAT , is targeted to lipid rafts where it nucleates signaling complexes [41] . Erk is activated downstream of ITAM-coupled receptors through the recruitment of a Grb2-Sos complex to LAT or LAB while PI3K is thought to be recruited through the formation of a LAB-Grb2-PI3K ( p85 ) complex [22] , [23] . Activation of these signaling pathways downstream of ITAM-coupled receptors results in cytokine production . However , while LAT and LAB can mediate cytokine production by facilitating formation of these signaling complexes , LAB has also been shown to negatively regulate cytokine production in certain circumstances . In some cases , this is due to competition with LAT which signals more efficiently than LAB due to the presence of a PLCγ binding site in LAT . Alternatively , in the absence of LAT , LAB binds the E3 ubiquitin ligase c-Cbl which targets multiple substrates for proteasomal degradation [23] , [41] . LAB mediates the production of IL-12p40 and attenuates the production of IL-8/CXCL8 , TNF and IL-10 downstream of LPS or ITAM-coupled receptors in macrophages and DCs [23] , [24] , [42] . Similarly , here we demonstrate that LAB partially mediates the production of IL-12p40 , IL-12p70 and IL-23 in response to multiple PAMPs . Previous reports have shown that the levels of Th1 and Th17 inducing cytokines ( IL-12 , IL-23 and IL-1β ) downstream of Dectin-1 and Dectin-2 are regulated by different NFκB subunits [43] , [44] . However , while fungal and TLR-induced IL-12 family cytokine production is inhibited in the absence of LAB , IκB degradation is unaffected ( Fig . S1 and data not shown ) . In addition , previously identified targets of LAB regulation such as Syk , c-Cbl or MAPK [23] , [42] are also unaffected in the absence of LAB ( Fig . S1 ) . This suggests a role for LAB in the regulation of a distinct pathway . Otero et al [32] recently identified an exciting link between M-CSF , DAP12 and β-catenin . The authors demonstrated that the ITAM-containing signaling chain DAP12 was required for the phosphorylation and accumulation of nuclear β-catenin during MCSF-induced signaling in macrophages . β-catenin is found in various locations in the cell . Under resting conditions , β-catenin is part of a complex found at the cell membrane bound to E-cadherin , which controls cell-cell adhesion [45] . In addition to membrane bound β-catenin , β-catenin is also found in the cytosol and cytosolic levels are tightly regulated by Wnt signaling . In the absence of a Wnt ligand β-catenin is phosphorylated by GSK-3β and CK1 and subsequently targeted for proteasomal degradation . In the presence of a Wnt ligand , GSK-3β activity is inhibited and β-catenin levels accumulate in the cytoplasm and the protein translocates to the nucleus to induce gene expression in conjunction with the TCF/LEF transcriptional activators [46] . Our data indicate that LAB is phosphorylated by two pathways , ( M-CSF/DAP12 and Dectin-2 ) ( Fig . S3 ) , and that LAB inhibits basal β-catenin nuclear translocation mediated by the M-CSF/DAP12 pathway and also PAMP-induced β-catenin nuclear translocation . M-CSF is present in serum and organs of naïve mice and C . albicans infection further increases its levels [33] and provides the PAMPs necessary to induce these pathways in vivo . β-catenin and LAB have never before been associated and our data demonstrate that this is an important component of PAMP-induced cytokine production in DCs . DCs expressing constitutively active β-catenin have previously been shown to display a diminished IL-12p40 response [35] . Additionally , ablation of β-catenin in DCs was recently shown to enhance the production of pro-inflammatory cytokines translating into increased Th1 and Th17 polarization [34] . Our results show that LAB plays a critical role in repression of the β-catenin pathway . In accordance with these findings , Lat2−/− DCs display high levels ofnuclear β-catenin accumulation and produce reduced levels of IL-12 resulting in reduced NK and T cell-mediated IFN-γ production . Activation of β-catenin by Wnt3a or SB-216763 results in a reduction of IL-12p40 production in WT DCs so that they now resemble those lacking LAB . Various PAMPs ( LPS , Pam3CSK4 , β-glucan ) do not promote LAB phosphorylation , however , basal LAB phosphorylation appears to be sufficient to inhibit β-catenin activation and to facilitate PAMP-induced IL-12 production . The Dectin-2 pathway enhances LAB phosphorylation in response to various ligands ( zymosan , C . albicans , CAWS ) in addition to basal M-CSF/DAP12-induced LAB phosphorylation . These data indicate that basal LAB phosphorylation is sufficient to facilitate PAMP-induced IL-12 production and subsequent IFN-γproduction independently of Dectin-2 . However , PAMPs that engage Dectin-2 likely play a cumulative role in promoting IL-12 production ( Fig . S3 ) . While the mechanism for LAB-mediated regulation of β-catenin activation is currently unknown , it is possible that LAB exerts its effects through regulation of the PI3K pathway . Akt phosphorylation promotes nuclear β-catenin accumulation [47] and we have previously shown in macrophages that LAB binds to the p85 subunit of PI3K , likely via Grb2 [23] suggesting that this may be a possible mechanism for LAB-mediated regulation of β-catenin translocation . The LAB/β-catenin pathway in DCs exerts a specific effect on IL-12 family cytokines while minimally affecting other cytokines ( Fig . 4E–F ) . There are some possible explanations for this specificity . Firstly , NFκB and Erk activation , critical components of IL-1β/TNF [48] , [49] and IL-10 production , respectively [50] are normal in Lat2-/- DCs ( Fig . S1 ) . Secondly , β-catenin induces gene expression in conjunction with the TCF/LEF transcriptional activators [46] and examination of candidate TCF/LEF binding sites in the promoters of Il12b , IL10 and Tnf revealed interesting differences . While Il10 ( −1 kb ) and Tnf ( −300 bp ) contain one candidate TCF/LEF binding site , Il12b has two such sites ∼60 bp apart , ∼600 bp upstream of the transcription start site . Moreover an AML-1 binding site is located between these two TCF/LEF binding sites in Il12b and AML-1 has previously been shown to cooperate with TCF in the TCRá enhancer [51] . Thirdly , Jiang et al [45] demonstrated that cluster disruption of DCs , a process involving β-catenin , selectively increased IL-12p40 production in response to LPS ( other cytokines such as TNF and IL-10 were reduced in response to LPS ) . All of these observations indicate a definite specificity of β-catenin for IL-12 family cytokines; however , separate studies would be required to dissect this . Here we demonstrated a novel LAB-mediated pathway for regulation of IL-12 production and subsequent IFN-γ production in response to C . albicans . Collectively our data indicate the importance of this for controlling susceptibility to C . albicans infection . However , as LAB is expressed in other cell types , it may be involved in additional immune responses . For example , we have observed reduced IL-12p40 production from Lat2−/− macrophages ( data not shown ) similar to DCs suggesting that the reduced Il12b mRNA levels in Lat2−/− mice is likely a combined effect of DCs and macrophages . Additionally , neutrophils and associated cytokines ( TNF , IL-6 ) are important for overcoming C . albicans infection [25] , [52] . Interestingly , Lat2−/− neutrophils display enhanced TNF and IL-6 production , demonstrating unimpaired neutrophil activation and cytokine production in Lat2−/− mice . High levels of TNF and IL-6 are associated with septic shock [53] , [54] , however , the levels in C . albicans-infected Lat2−/− mice are much lower than those in murine sepsis models making it an unlikely cause for the increased mortality . In addition , LAB is expressed in B and activated T cells and its deficiency , similar to Lat2−/− neutrophils , results in “enhanced” rather than impaired responses . Lat2−/− mice have increased levels of natural antibodies and T cells from aged Lat2−/− mice are hyperactivated and produce more cytokine than WT T cells [55] . These data indicate that reduced DC/macrophage IL-12 production and subsequent NK and T cell-mediated IFN-γ production is the predominant impairment found in Lat2−/− mice , however , the role of LAB in other cell-types and functions during anti-fungal immunity needs to be addressed with future studies . In conclusion , we have shown that LAB is paramount for robust pro-inflammatory cytokine production from DCs downstream of multiple PAMPs . LAB facilitates production of these essential cytokines through novel regulation of the β-catenin pathway . LAB represses β-catenin nuclear accumulation in DCs , thereby facilitating cytokine production . Through this mechanism , LAB plays an important role in the host defense against systemic C . albicans infection by inducing NK and T cell-mediated IFN-γ production . These data are the first demonstration of LAB as a prominent target for diseases dependent on IL-12 and further elucidation of these pathways may be important for the development of new therapeutics .
Lat2−/− mice ( the product of the Lat2 locus is the adaptor protein LAB ) , described previously [56] , have been backcrossed onto the C57BL/6 background for at least 10 generations . These mice were screened and found to be 99% C57BL/6 . Lat2−/− , Myd88−/− , Fcer1g−/− , Tyrobp−/− and age , weight and gender matched control C57BL/6 mice were maintained under specific pathogen-free conditions at the NCI–Frederick , MD . Animal care was provided in accordance with the procedures in , “A Guide for the Care and Use of Laboratory Animals” . Ethical approval for the animal experiments detailed in this manuscript was received from the Institutional Animal Care and Use Committee ( Permit Number: 000386 ) at the NCI-Frederick . Bone marrow ( BM ) cells were removed from the femurs and tibiae of mice and erythrocytes were lysed in ACK buffer . Bone marrow-derived dendritic cells ( BMDCs ) were generated by culturing cells for 6–9 days in RPMI 1640 medium containing 10% fetal bovine serum , 2 mM L-glutamine , penicillin/streptomicin , HEPES , NEAA , Sodium pyruvate , 2-mercaptoethanol and 10 ng/ml GM-CSF . Zymosan , particulate β-glucan , LPS ( Escherichia coli 0111:B4 ) and purified mannan were purchased from Sigma-Aldrich ( St . Louis , MO ) . CAWS ( C . albicans water soluble mannans ) were provided by Prof . Naohito Ohno [6] . Pam3CSK4 was purchased from Invivogen ( San Diego , CA ) , Wnt3a and anti-CD3 ( clone 145-2C11 ) were purchased from R&D ( Minneapolis , MN ) . Anti-CD28 ( 37 . 51 ) , anti-CD4 ( RM4 . 5 ) , anti-CD25 ( PC61 . 5 ) , anti-CD44 ( IM7 ) , anti-CD62L ( MEL-14 ) , anti-Ly6G ( 1A8 ) , anti-CD11b ( M1/70 ) , anti-IL-6 ( MP5-20F3 ) , anti-IFN-γ ( XMG1 . 2 ) and anti-IL-17A ( TC11-18H10 . 1 ) were purchased from BD Biosciences ( San Jose , CA ) . Anti-TNF ( MP6-XT22 ) was purchased from eBioscience ( San Diego , CA ) . GM-CSF , M-CSF and M-CSF ELISA were purchased from Peptotech ( Rocky Hill , NJ ) . The GSK-3β inhibitor SB-216763 and the Tankyrase inhibitor XAV939 were purchased from Tocris Bioscience ( Ellisville , MO ) . The Syk inhibitor Piceatannol was purchased from Millipore ( Billerica , MA ) . Anti-phosphoErk , anti-Erk , anti-phosphoAkt , anti-Akt , anti-β-catenin were purchased from Cell Signaling Technology ( Beverley , MA ) . Anti-LAT antibody [28] was as previously described . Anti-phosphotyrosine ( clone 4G10 , Millipore ) , anti-Syk ( Novus Biologicals , Littleton , CO ) , anti-LAB , anti-c-Cbl ( sc-170 ) , anti-PLCγ ( Santa Cruz , CA ) , anti-Actin ( Chemicon International , Temecula , CA ) , anti-Lamin B1 ( Abcam , Cambridge , MA ) and anti-Dectin-2 ( AbD Seotech , Raleigh , NC ) were used in this study . Zymosan was resuspended in 10% ethanol in endotoxin free water . Zymosan extract was prepared by resuspending zymosan in endotoxin free water . The supernatant was filtered and used as zymosan extract . The zymosan particles were washed 3× with endotoxin free water and used as washed zymosan . BM cells were removed from the femurs and tibiae of mice and erythrocytes were lysed in ACK buffer . Neutrophils were purified through percoll gradient or by MACS separation ( Miltenyi , Auburn , CA ) . BMDCs were harvested and CD11c+ DCs were sorted using a FACS ARIA or bead purified by MACS separation routinely giving purities of >98% . BMDCs were serum starved for 30 min at 37°C ( except for Figs 7A&D ) . 1×107 BMDCs were resuspended in 100 µl DPBS and stimulated at 37°C with 1 mg/ml zymosan , zymosan extract , washed zymosan , β-glucan , mannan , Pam3CSK4 , 10 ng/ml LPS , 100 ng/ml GM-CSF or M-CSF for the indicated times . Cells were lysed with Lauryl-maltoside lysis buffer ( 1% laurylmaltoside in 20 mM Tris [pH 7 . 5] , 100 mM NaCl , 10% glycerol , 0 . 4 mM Na3VO4 , aprotinin , leupeptin and phenylmethylsulfonyl fluoride ) . Lysates were clarified by centrifugation and protein levels were normalized using a BCA protein assay . Cytosol and nuclear extracts were prepared as previously described [57] . 4× non-reducing or reducing Nupage sample buffer was added to lysates and heated for 10 min at 70°C . Lysates were separated by SDS-PAGE ( Nupage , Invitrogen , Carisbad , CA ) , transferred to PVDF membrane ( Millipore , Billerica , MA ) and analyzed by Western blot . Cells were resuspended in Trizol and RNA was extracted using RNeasy Mini Kit ( Qiagen , Valencia , CA ) . cDNA was synthesized from total RNA using Superscript III First Strand Synthesis System for RT-PCR ( Invitrogen ) . Quantitative RT-PCR was performed using ABI Taqman Primer and Probe sets and normalization was performed against Hprt1 . An shRNA construct for mouse Dectin-2 ( TRCN0000066785 ) and a scrambled shRNA control in the pLKO . 1 lentiviral vector were used to infect WT and Lat2−/− cells . 293FT cells were transfected with the pLKO . 1 construct and with the Invitrogen packaging constructs pLP1 , pLP2 and pVSV-G and viral supernatants were collected at 48 and 72 h post-transfection . BM cells were plated on Day 0 at 6×105 cells/ml in complete media containing GM-CSF . On Days 1 and 2 , the media was replaced for 8 h with viral supernatants containing 10 µg/ml hexamethrine bromide . On Day 3 , fresh complete media containing GM-CSF and 5 µg/ml puromycin was added to the cells . The cells were harvested 2 days later and stimulated as described . Dectin-2 surface expression was examined by flow cytometric analysis . BMDCs were plated at a density of 1×105 cells/well in a 96-well plate in RMPI containing 10% fetal bovine serum and 10 ng/ml GM-CSF . BMDCs were stimulated with zymosan , zymosan extract , washed zymosan , Pam3CSK4 , LPS ( Escherichia coli 0111:B4 ) , β-glucan , curdlan , yeast/hyphae or heat-killed yeast/hyphae for 24 h . BMDCs were stimulated with CAWS for 48 h . BM neutrophils were plated at a density of 2×105 cells/well in RPMI containing 10% fetal bovine serum and stimulated with zymosan or LPS for 24 h . Cell culture supernatants were recovered and assayed for cytokine by ELISA ( eBioscience , San Diego , CA ) or cytometric bead array ( CBA ) ( BD Biosciences , San Jose , CA ) , according to the manufacturer's protocol . Unpurified spleen cells or CD11c+ MACS bead purified spleen cells were stimulated with 100 ng/ml LPS for 6 h in the presence of Brefeldin A . IL-12p40 producing cells were determined by flow cytometry . CD4+ T cells were purified from WT spleens by negative selection MACS separation ( Miltenyi , Auburn , CA ) and naïve CD4+ T ( CD4+CD25−CD44loCD62L+ ) cells were sorted using a FACS ARIA giving purities of >98% purity . Naïve CD4+ T cells were stimulated with plate-bound anti-CD3 and soluble anti-CD28 antibodies for 4 days and supplemented with conditioned supernatants from BMDCs stimulated with heat-killed C . albicans yeast or LPS . The supernatants were collected and analyzed for IL-17A and IFN-γ production by ELISA . The cells were restimulated with PMA ( 50 ng/ml ) and Ionomycin ( 0 . 5 µg/ml ) in the presence of monensin ( 3 µM ) and IL-17A and IFN-γ producing cells were determined by flow cytometry . C . albicans SC5314 ( ATCC , Manassas , VA ) was cultured for 24 h in YEPD broth , washed three times with PBS and resuspended at the required concentration in PBS . Mice were matched by gender , weight and age ( 10–15 weeks old ) and 100 µl of C . albicans in PBS was injected i . v . Mice were monitored and weighed daily . Mice were euthanized by CO2 asphyxiation when they were moribund or had lost 20% of their body weight . Experiments were continued for a maximum of 55 days at which point all surviving mice were euthanized . Mice were bled by cardiac puncture after CO2 administration and kidneys and spleens were harvested . The kidneys were either placed in 10% formalin , embedded in paraffin wax blocks and stained for H&E and PAS or they were placed in PBS , dounce homogenized and serial dilutions were plated on YEPD agar containing 50 µg/ml chloramphenicol . The plates were cultured for 24 h and CFU were calculated/organ . The spleens were dounce homogenized in trizol followed by RNA and cDNA preparation . Serum samples were analyzed by CBA for cytokine levels . Mice were injected intraperitoneally with 1×105 live C . albicans and euthanized 72 h later . The inflammatory infiltrate was collected by peritoneal lavage with 5 ml 5 mM EDTA in RPMI . The cells were plated and restimulated with PMA ( 50 ng/ml ) and Ionomycin ( 0 . 5 µg/ml ) in the presence of monensin ( 3 µM ) for 4 h and IL-17A and IFN-γ producing cells were determined by flow cytometry . Mice were injected intraperitoneally with 1×105 live C . albicans and euthanized 4 h later . The inflammatory infiltrate was collected by peritoneal lavage with 2 rounds of 5 ml 5 mM EDTA in RPMI . The cells were plated and restimulated with media or heat-killed C . albicans yeast for 12 h in the presence of monensin ( 3 µM ) . The % of TNF and IL-6 producing neutrophils were determined by flow cytometry . Data are presented as means +/− s . e . m . and are representative of 2–3 independent experiments . Survival data was analyzed by log-rank test . One-way ANOVA followed by Bonferroni's post-test or two-way ANOVA were used for statistical analysis when multiple groups were analyzed . Student t test or Mann-Whitney test were used for statistical analysis when two groups were analyzed . When data did not follow a Gaussian distribution , it was transformed by Y = sqrt ( Y+0 . 5 ) [58] and analyzed by Student's t test . If data still did not follow a Gaussian distribution after transformation , then significance was tested by Mann-Whitney test . Statistical significance was set at *p<0 . 05 **p<0 . 005 ***p<0 . 0005 . | Fungal infections are a major healthcare problem and the incidence of fungal infections has increased significantly in recent years . Mortality rates are high even with treatment , highlighting the need for a better understanding of anti-fungal immunity in order to develop improved therapies . Adaptive T-helper 1 and T-helper 17 ( Th1 and Th17 ) responses are important mediators of anti-fungal immunity . Dendritic cells express Dectin-1 , Dectin-2 and Toll-like receptors , which interact with fungal pathogens to induce these adaptive immune responses . Here we identify LAB as an important facilitator of IFN-γ production by regulating β-catenin activation . Susceptibility to fungal infections is increased in the absence of LAB , in association with reduced IFN-γ production . β-catenin activation in dendritic cells inhibits the IL-12 production required for IFN-γ production . Thus targeting β-catenin therapeutically could help to promote efficient IFN-γ production in patients suffering from fungal infections . These findings are important for fungal infections and potentially for other diseases where IFN-γ production is important for disease outcome . | [
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] | 2013 | LAB/NTAL Facilitates Fungal/PAMP-induced IL-12 and IFN-γ Production by Repressing β-Catenin Activation in Dendritic Cells |
Prion diseases are fatal neurodegenerative disorders characterized by a long pre-symptomatic phase followed by rapid and progressive clinical phase . Although rare in humans , the unconventional infectious nature of the disease raises the potential for an epidemic . Unfortunately , no treatment is currently available . The hallmark event in prion diseases is the accumulation of a misfolded and infectious form of the prion protein ( PrPSc ) . Previous reports have shown that PrPSc induces endoplasmic reticulum stress and changes in calcium homeostasis in the brain of affected individuals . In this study we show that the calcium-dependent phosphatase Calcineurin ( CaN ) is hyperactivated both in vitro and in vivo as a result of PrPSc formation . CaN activation mediates prion-induced neurodegeneration , suggesting that inhibition of this phosphatase could be a target for therapy . To test this hypothesis , prion infected wild type mice were treated intra-peritoneally with the CaN inhibitor FK506 at the clinical phase of the disease . Treated animals exhibited reduced severity of the clinical abnormalities and increased survival time compared to vehicle treated controls . Treatment also led to a significant increase in the brain levels of the CaN downstream targets pCREB and pBAD , which paralleled the decrease of CaN activity . Importantly , we observed a lower degree of neurodegeneration in animals treated with the drug as revealed by a higher number of neurons and a lower quantity of degenerating nerve cells . These changes were not dependent on PrPSc formation , since the protein accumulated in the brain to the same levels as in the untreated mice . Our findings contribute to an understanding of the mechanism of neurodegeneration in prion diseases and more importantly may provide a novel strategy for therapy that is beneficial at the clinical phase of the disease .
Prion diseases or transmissible spongiform encephalopathies ( TSEs ) are neurodegenerative disorders affecting humans and animals alike , which are characterized by the presence of PrPSc , an abnormal , protease-resistant isoform of the cellular prion protein , called PrPC [1] . The most common TSE in humans is Creutzfeldt-Jakob disease ( CJD ) affecting on average one new patient per million people each year [2] . In animals the most common TSE is scrapie , which is an endemic disease affecting sheep and goats for centuries . However , it is the recent appearance of new TSEs that have put prions in the spotlight . These new diseases include variant CJD ( vCJD ) in humans , bovine spongiform encephalopathy ( BSE ) in cattle and chronic wasting disease ( CWD ) in elk and deer . vCJD is the newest and most frightening member of the TSE group . Its appearance in 1996 has been undoubtedly linked to the BSE outbreak and despite that the number of new cases of vCJD seems to be decreasing , there is an enormous concern of secondary transmission of vCJD among humans [3]–[5] . Indeed , recent reports have indicated the propagation of the disease through blood transfusion [6] , [7] . At this time there is no effective treatment or cure for TSE [8] , [9] . The disease is inevitably fatal and affected people usually die within months of the appearance of the first clinical symptoms . Assuming that the hallmark event in the disease is the conversion of PrPC into PrPSc , a reasonable therapeutic target would be to prevent PrP misfolding and prion replication . This approach has been extensively attempted by many research groups and some compounds have been identified with the capability to decrease prion replication and delay the onset of the clinical disease [1] , [9] , [10] . However , these compounds produce benefit only when they are administered during the pre-symptomatic stage of the disease , long before the appearance of clinical symptoms . It is likely that compounds interfering with prion replication would have little or no benefit to patients with already established clinical disease , since at the time clinical symptoms appear there is substantial brain damage . It seems that a treatment aimed at patients with established symptoms of CJD would need to attack the cellular pathways implicated in brain damage . This is precisely the major goal of this study , which comes from our recent studies of the mechanism of neurodegeneration in prion diseases . We have previously shown that purified PrPSc from scrapie-infected brains is able to induce cell death in primary neuron cultures [11] . Our studies demonstrated that the cellular pathway controlling the induction of apoptosis involves stress of the endoplasmic reticulum ( ER ) [11] . The first alteration observed when cells were exposed to PrPSc consisted of the sustained release of calcium from the ER followed by the induction of the unfolded protein response ( UPR ) . The UPR consists of the up-regulation of several molecular chaperones and clearance mechanism to attempt correcting the protein misfolding problem [12] . In support of these in vitro observations , histological and biochemical analysis of brains from scrapie-sick mice and from humans affected by sCJD and vCJD demonstrated the presence of activated caspases and the induction of ER-stress inducible chaperones in brain areas exhibiting extensive neuronal death [11] , [13] , [14] . In the present study we show that an additional consequence of calcium release from the stressed ER is the hyperactivation in vitro and in vivo of a key protein , termed calcineurin ( CaN ) . CaN is a phosphatase of type 2B highly abundant in the brain that has been implicated in the regulation of synaptic plasticity , memory and neuronal death [15] . CaN immunoreactivity is observed exclusively in neurons in various brain regions , but not in glial cells , including astrocytes , oligodendrocytes , microglia and ependymal cells both in humans and rodents [16] , [17] . Subcellular localization studies by electron microscopy showed that calcineurin was found in dendrites including postsynaptic densities , cell bodies , spines , axons and terminals [16] . The activity of this enzyme is regulated by the Ca2+-calmodulin complex . Optimum activity of CaN is required to maintain the proper phosphorylation state of different important targets , like apoptosis inducer BAD or transcription factor CREB . Hyper-activation of CaN reduces the phosphorylation of BAD [18] , which then disassociate from the scaffolding protein 14-3-3 and interacts with Bcl-Lx or other Bcl2 family proteins located in the mitochondrial membrane . As a result cytochrome C is released in the cytoplasm , leading to caspase activation and finally causing apoptosis . On the other hand dephosphorylated by hyper-activated CaN , CREB is not able to translocate into the nucleus to act as a transcription factor to regulate expression of different genes required for synaptic plasticity [19] . To study the potential role of CaN in prion-induced neurodegeneration and to assess the possibility of inhibiting this phosphatase as a putative target for therapeutic intervention , we treated prion infected animals with the FDA-approved CaN inhibitor FK506 . FK506 ( also known as Tacrolimus and Prograf ) is a natural product produced by the fungus Streptomyces tsukubaensis , marketed to prevent transplant rejection [20] . By inhibiting CaN , the drug suppresses both humoral and cellular immune responses [21] . Our results show that mice receiving FK506 after the onset of the clinical phase of prion disease exhibited improved motor activity and coordination , lived longer and had a decreased level of brain degeneration when compared to untreated prion infected animals . These findings suggest that inhibition of CaN activity in the brain may be a promising new approach for the treatment of prion diseases .
Our previous results showed that PrPSc accumulation induced ER stress [11] . One of the alterations produced by ER stress is the change of calcium homeostasis . Indeed , exposure of N2A mouse neuroblastoma cells to brain-isolated PrPSc results in the increase of cytoplasmatic calcium ( Fig . 1A ) , which as shown before is coming from the ER [11] . It is well-established that CaN activity is modulated by cytoplasmic calcium concentration . To assess whether neuroblastoma cells exposed to PrPSc indeed have a higher level of CaN activity , we measured the phosphatase activity in N2A cells treated with 200 nM of PrPSc . The results show that CaN activity is significantly elevated in cells exposed to PrPSc , but not to the same concentration of the natively folded recombinant prion protein ( PrPC ) ( Fig . 1B ) . As positive control we used Thapsigargin , an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase that increases cytoplasmatic calcium concentration [22] . FK506 ( also known as Tacrolimus and Prograf ) is a FDA approved drug able to inhibit CaN activity [21] . The PrPSc-induced elevation of CaN activity was efficiently blocked by concomitant addition of FK506 ( Fig . 1B ) . To assess the effect of PrPSc and subsequent CaN activation in cell damage , we measured cell death by release of lactate dehydrogenase ( LDH ) and cell viability by the MTT ( 3- ( 4 , 5-Dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ) assay in N2A cells exposed to 200 nM of PrPSc in the presence or absence of FK506 ( Fig . 1C ) . Cell death produced by treatment with PrPSc or thapsigargin was significantly decreased by addition of the CaN inhibitor FK506 ( Fig 1C , left panel ) . Indeed , the rate of neuroblastoma cell death after treatment with PrPSc and FK506 was not significantly different from the control untreated cells , indicating that FK506 completely protected cells from PrPSc toxic activity . A similar protective effect of FK506 against PrPSc neurotoxicity was found when cell viability was measured by MTT reduction ( Fig . 1C , right panel ) . Treatment of cells with FK506 in the absence of PrPSc did not change cell death or viability ( data not shown ) . To study whether the increase in CaN activity observed in vitro as a consequence of PrPSc formation also occurs under in vivo conditions , we measured CaN activity in brain of animals infected with prions . Groups of mice intra-peritoneally infected with the RML prion strain were sacrificed at various time points until the onset of the clinical signs , which under these conditions occurred between 210 and 230 days post-inoculation . A basal CaN activity in brain was detected at all time points during the pre-symptomatic phase , which was not different from the activity found in non-inoculated animals ( Fig . 1D ) . However , at the beginning of the clinical phase of the disease , a ∼3-fold higher CaN activity was found in brain , indicating that the activity of this phosphatase is significantly elevated at the time brain damage and clinical disease occur . To study whether CaN activity is implicated in the progression of prion disease and whether the inhibition of this phosphatase could be a good target for therapy , groups of mice were treated with FK506 during the clinical phase of the disease . Clinical onset of the disease was carefully measured every other week by two different researchers monitoring the appearance of hunch and trail rigidity , which is the first alteration clearly associated to the disease . When the clinical symptoms were unambiguously observed during 3 consecutive days , infected mice were injected i . p . daily with 5 mg/kg of FK506 ( dissolved in saline , containing 1 . 25% PEG40 Castor oil and 2% ethanol ) ( n = 14 ) or with vehicle ( n = 14 ) . This dose was chosen based on a toxicology study showing that administration of FK506 at concentrations higher than 10 mg/Kg produced side-effects detectable by behavioral tests ( data not shown ) . As a control we used another immunosuppressant drug , Rapamycin ( n = 8 ) , which does not interfere with CaN activity . Treatments lasted until animals died or were sacrificed for analysis . Progressive decline in motor coordination and activity is a well documented clinical feature of prion disease [23] , which likely is the result of loss of synaptic integrity . Therefore motor alterations during the clinical phase of the disease in animals treated with FK506 , rapamycin or vehicle were assessed by using rota-rod ( Fig 2A ) and open field tests ( Fig 2B ) . The motor coordination in the rota-rod test was measured once a week in a 3 min time interval and was expressed as a percentage of the activity at day 0 ( day in which treatment was started ) . The results showed that prion infected animals treated with vehicle or rapamycin experienced a progressive and significant decline on motor coordination , whereas animals treated with FK506 showed no significant decrease on performance over de 5 weeks period ( Fig . 2A ) . Indeed , the rota-rod performance of the FK506 treatment group was similar to that of non-infected animals ( either treated or untreated with FK506 ) , used as control . The slight increase on performance over the time period in control animals probably reflects that animals become more familiarized with the test . The locomotor ability in open field test was measured at day 21 after starting the treatment , by the rearing activity during 20s and corresponds to the time animals spent only on the hind limb . As shown in figure 2B , the rearing activity in prion infected animals was dramatically reduced compared to control non-inoculated animals . Treatment with FK506 , but not rapamycin , prevented significantly the locomotor deficiencies ( Fig . 2B ) . Next we investigated the survival time of the animals under treatment . Infected mice were left to die naturally and time of death was recorded . Figure 3 shows the survival curve of the group treated with FK506 ( n = 10 , red line ) , treated with rapamycin ( n = 4 , blue line ) or the vehicle group ( n = 10 , black line ) . The results showed a 35% increase in survival of the group treated with CaN inhibitor compared to the vehicle treated control ( P = 0 . 039 , as estimated by the Log-rank Mantel-cox test ) . To study the effect of FK506 treatment on neurodegeneration , 4 random animals from each treatment group were sacrificed at the time in which the first mice injected with vehicle reached the stage 5 of the clinical disease ( around 20 days after the beginning of the treatment ) , their brain collected and one hemisphere was kept frozen and the other fixed for histological analysis ( see below ) . Frozen brains were homogenized in TBS with protease inhibitors . The level of CaN activity in total brain was measured as described in Methods . Prion inoculated mice had substantially higher CaN activity in brain than non-inoculated mice ( Fig . 4A ) , supporting the result shown in Fig 1D . The levels of CaN activity in prion inoculated mice were normalized upon treatment with FK506 , indicating that the drug is acting as expected in the brain and that the dose used was appropriate . To measure PrPSc accumulation in the different group of animals , samples were treated with PK ( 50 µg/ml for 1h at 37°C ) and 1/5 and 1/25 dilutions from the 10% brain homogenate were evaluated by western blot . The result shows that FK506 treatment did not alter PrPSc accumulation ( Fig . 4B ) , a result consistent with our hypothesis that CaN activation is a downstream event of prion replication . To study the influence of CaN inhibition in the phosphorylation stage of some of the key substrates of the phosphatase involved in controlling synaptic plasticity and neuronal apoptosis , we measured the brain levels of pCREB , pBAD as well as total CREB and BAD . The western blot in figure 4C shows the results for CREB of two representative animals per group and the graph in the right panel represents the quantification of the ratio pCREB/total CREB . The results indicate that scrapie-affected mice receiving vehicle have a 2-fold reduction of pCREB ( normalized to total CREB ) compared to non-infected animals . Strikingly , treatment with FK506 , but not rapamycin , restored the concentration of this phosphorylated transcription factor to reach levels indistinguishable from healthy controls ( Fig . 4C ) . A similar result was observed when the pro-apoptotic BAD protein was analyzed . Indeed , prion affected mice have a 3–4 fold reduction of pBAD/total BAD ratio compared to controls . Again , the phosphorylation state of this important protein was restored upon treatment with FK506 ( Fig . 4D ) . To further assess the effect of FK506 treatment on prion induced neurodegeneration , fixed brains from 4 animals per group were stained and analyzed . The histopathological alterations observed in prion affected animals , include spongiform degeneration , astroglyosis , neuronal death and synaptic dysfunction . Analysis of the extent of vacuolation revealed no significant differences between treated and un-treated animals ( Figure S1 ) . Although , spongiosis is the most characteristic brain alterations observed in TSEs , its role in brain dysfunction and clinical disease is mostly unclear [24] , [25] . Brain inflammatory changes in the form of reactive astrocytes and activated microglia are also a typical alteration associated to prion diseases [26] . Since CaN has been implicated in immunological and inflammatory pathways [27] , we wanted to analyze in detail the effect of FK506 treatment on the extent of astrocytosis and microglial alteration . Astrogliosis was studied by staining the tissue with the anti-GFAP ( Glial fibrillary acidic protein ) antibody and reactive microglia with the anti-AIF1 ( allograph inflammatory factor 1 ) antibody ( Fig . 5A ) . Quantification of the area of the thalamus containing reactive astrocytes ( Fig . 5B ) and activated microglia ( Fig . 5B ) revealed a pronounced difference between samples coming from non-infected and prion infected animals . However , no statistically significant differences on the extent of brain inflammation were observed among prion infected animals treated or untreated with FK506 or rapamycin . These results suggest that the therapeutic effect of FK506 is not mediated by a neuroimmune pathway . To study the number of neurons present in the brain , we stained tissue slides with NeuN , a specific and well-established neuronal marker [28] . The data shows a substantially higher quantity of neurons in the thalamus of FK506 treated-mice , compared with prion affected animals treated with vehicle or rapamycin ( Fig . 6 ) . Indeed , mice treated with the CaN inhibitor have almost twice the number of neurons as their untreated-mates . However , still the treated animals have around 50% less neurons than normal animals not affected by prion diseases ( Fig . 6 ) , indicating that the treatment only stop in part the neurodegeneration process . In order to further study the influence of the treatment on neuronal damage , we stained the tissue with Fluoro-Jade , a well-established method to detect degenerating neurons [29] . As shown in Figure 7 , the brain of mice treated with FK506 has a substantially lower level of Fluoro-Jade stained cells as compared with animals treated with vehicle or rapamycin . Again , the effect of the treatment was not complete , since FK506 treated mice still have significantly more degenerating neurons than control non-infected animals ( Fig . 7 ) .
TSEs are dreadful diseases that produce a 100% fatality rate and a progressive and rapid deterioration which leads to complete disability . Currently , there is no therapy available against prion disorders [1] . The self-propagating protein misfolding process that features prion diseases amplifies the toxic and infectious prion in a logarithmic scale making it difficult for development of an efficient therapy at the symptomatic phase . Since there still not available a test to diagnose prion disease at the pre-symptomatic stage [30] , the top priority is to develop strategies that could be beneficial after the patients show the first clinical signs of the disease . Considering that a central event in TSEs is the conversion of PrPC into PrPSc , a widely pursued therapeutic strategy has been to disrupt PrPSc formation . This approach has been extensively explored by many groups and some compounds have been identified with activity in vitro and in vivo . The list of compounds studied include: Polyanionic molecules , dextran sulphate , pentosan polysulphate , heparin sulphate mimetics , Phosphorothioate oligonucleotides , congo red analogs , suramin , curcumin , quinacrine , dendritic polyamines , tetracycline , amphotericin B , beta-sheet breaker peptides , anti-PrP antibodies , etc ( for reviews , see [8] , [9] , [31] ) . However , these compounds produce a benefit mostly when they are administered during the pre-symptomatic stage of the disease , long before the appearance of clinical symptoms . Despite of the lack of positive results in animal models at the clinical phase of the disease , the unmet need for a medicine to treat patients resulted in human clinical trials with at least 3 of these drugs: quinacrine , amphotericin B and pentosan polysulphate [9] . Quinacrine , chlorpromazine , and some of their tricyclic derivatives were described as efficient inhibitors of PrPSc formation in murine neuroblastoma cells chronically infected with scrapie [32] , [33] . However , subsequent animal experiments failed to demonstrate efficacy in the treatment of TSEs [34] , even after intraventricular infusion [35] . Because quinacrine and chlorpromazine have been used in human medicine as anti-malarial and anti-psychotic drugs , respectively , they were tested in small clinical trials . No therapeutic effect was seen following quinacrine treatment in two independent trials , although some transient improvement occasionally occurred [36] , [37] . Amphothericin B and some of its analogues inhibited prion replication in infected cell cultures [38] and delayed the appearance of spongiosis , astrogliosis , and PrPSc accumulation in the brain of scrapie-infected hamsters [39] , [40] . However , an attempt to treat a CJD patient with amphothericin B was unsuccessful [41] . In view of its high systemic toxicity , these results decrease hopes that amphothericin B will prove useful in prion disease therapy . Several in vitro and in vivo studies have suggested pentosan polysulphate may be useful in prion diseases [35] , [42]–[44] . Pentosan polysulphate is marketed in some countries as a treatment for interstitial cystitis and as anticoagulant , although its side effects include hemorrhage and hypersensitivity reactions . The main problem for using this drug for TSEs is that it does not cross the blood-brain barrier , so it has to be administered either early ( during the peripheral prion replication phase ) or directly into the brain . Several small observational trials by intra-cerebroventricular infusion of pentosan polysulphate have been conducted , some of them showing promising results [45]–[48] . However , the fact that the drug has to be administered directly into the brain makes its routine use very complicated . Our approach provides a novel molecular target down-stream of the prion misfolding process and aims to prevent the signaling pathways leading to synaptic alterations and neuronal death . Our data suggests that the pathway by which PrPSc induces neurodegeneration involves ER-stress , alterations in calcium homeostasis and hyperactivation of CaN , a key brain phosphatase that controls important signaling events modulating neuronal fate and functioning [49] . These findings indicate that down regulation of CaN activity may be a promising target for prion disease therapy . Fortunately , there are known CaN inhibitors extensively studied , such as FK506 and Cyclosporin [21] . FK506 is a FDA approved drug that is used to prevent transplant regression [20] . The drug is sold under the name of Tacrolimus or Prograf . FK506 is produced by Streptomyces tsukubaensis [50] . By inhibiting CaN , the drug suppresses both humoral and cellular immune responses . As an FDA approved drug , the pharmacological properties of the compound are very well-known . Our results show that administration of FK506 during the symptomatic phase of the disease produced a significant delay of the disease progression manifested as an improvement on behavioral abnormalities and increase survival time compared to controls treated with vehicle . The effect is not dependent on the immunosupresant activity of FK506 , since rapamycin ( a drug with similar immunosupressive effect , but not acting through CaN ) [51] did not produce any change on prion disease . Moreover , administration of FK506 did not alter the pattern of brain inflammation , suggesting that the beneficial effect of this compound is not mediated by neuroimmune pathways . Treatment with FK506 led to a significant increase of pCREB and pBAD levels in the brain , which paralleled the decrease of CaN activity . Importantly , we observed lower degree of neurodegeneration in animals treated with the drug , which was revealed by a higher number of neurons and a lower quantity of degenerating nerve cells . These changes were not dependent on PrPSc formation , since the protein accumulated in the brain to the same levels as in the untreated mice . Interestingly , it has been reported that neurodegeneration in other brain diseases associated to protein misfolding also involves ER-stress , changes on calcium homeostasis and CaN activation [52]–[55] . Recent studies have shown an increase in calcineurin signaling during the early clinical symptoms of Alzheimer's disease [56] . Strikingly , administration of FK506 to transgenic mice models of Alzheimer's disease restore memory deficits associated to the accumulation of amyloid-beta oligomers [57] , [58] and reduced long-term potentiation deficits produced by aggregated amyloid-beta [59] . These findings indicate that CaN may play a general role in neurodegenerative diseases and could serve as a novel target for therapeutic intervention in these devastating diseases .
All animal experiments were approved by and conducted in strict accordance with guidelines of the Animal Care and Use Committee of the University of Texas Medical Branch in Galveston and the University of Texas Health Science Center in Houston and complied with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . FK506 was purchased as a powder from LC Laboratories . Purity was >95% . F506 stock solution ( 0 . 5 mg/ml ) was prepared by dissolving the compound in saline ( 0 . 9% NaCl ) containing 1 . 25% PEG40 Castor Oil ( Spectrum ) and 2% ethanol . FK506 stock solution was stored frozen . Rapamycin ( LC Laboratories ) was prepared in the same conditions . The phosphatase activity of CaN was measured using the Calcineurin Cellular Activity Assay kit from Calbiochem as previously described [57] . The brain homogenate were prepared in the assay buffer and the residual phosphate was removed by passing through a desalting column . A final concentration of 1ug/ul of the homogenate was used for the enzyme assay in presence of bovine calmodulin . The reaction mixture was incubated with a final concentration of 150mM RII peptide ( substrate ) at 37°C for 20 min and reactions were terminated by the addition of 100 µl malachite green reagent . Product formation was measured by recording the absorbance at 635 nm . As a source of PrPC we used mouse PrP recombinantly expressed in bacteria , purified and folded into the native structure using a previously described protocol [60] . Briefly , the murine prnp gene ( coding residues 23–230 ) was PCR-amplified from mouse blood , inserted in a vector and used to transform BL21-Star E . coli cells ( Invitrogen ) . For purification , cell pellets were thawed and resuspended , cells were lysed by adding 0 . 5 mg/mL Lysozyme and subsequently sonicated . The released inclusion bodies were pelleted by centrifugation and solubilized in buffer containing 10 mM B-Mercaptoethanol and 6M GdnCl . PrPC was purified by using Ni Sepharose 6 Fast Flow resin ( GE Healthcare ) in batch-mode . Recombinant PrPC was on-column refolded for 6 h and eluted with 500 mM Imidazole . The main peak was collected and quickly filtered to remove aggregates . The protein was confirmed to be monomeric and folded by size exclusion chromatography , Western blotting and Circular Dichroism . PrPSc was purified from the brain of RML scrapie sick mice , following a previously described protocol [11] . Briefly , brains were homogenized in PBS and after a low speed centrifugation , samples were mixed with 1 volume of 20% sarkosyl . Samples were centrifuged at 100 , 000×g for 3 hr at 4°C . Supernatant was discarded; pellets were resuspended , layered over a 20% sucrose cushion and centrifuged for 3h at 4°C . The supernatant was discarded and the pellet resuspended , sonicated and incubated with PK ( 100 µg/ml ) for 2 h at 37°C and shaking . The digested sample was layered over PBS containing 20% Sarkosyl , 0 . 1% SB 3–14 and 10% NaCl and centrifuged for 1h 30 min at 100 . 000×g . The final pellet was resuspended in 100 µl of PBS and sonicated . The sample was stored at −80°C . Purity was >90% as analyzed by silver staining and amino acid composition analysis . N2A cells were cultured in DMEM supplemented with 10% fetal calf serum and antibiotics ( 10'000U/ml Penicillin , 10µg/ml streptomycin ) , at 37°C and 5% CO2 . For cell viability analysis , cells were grown in collagen IV coated 96-well plates for 24h in cell culture medium containing 1% serum before addition of the agonist . Cell viability was quantified using 3- ( 4 , 5-dimethylthazol-2-yl ) -5-3-carboxymethoxy-phenyl ) -2- ( 4-sulfophenyl ) -2H-tetrazolium ( MTS ) and phenazine methosulfate ( PMS ) according to the recommendations of the supplier ( Promega ) . Cell death was determined measuring the amount of LDH released to the culture media using the Cytotox 96 LDH kit , following the manufacturer specifications ( Promega ) . The changes in intracellular calcium levels were measured 20 minutes after adding the reagents using the Fluo-4 Direct Calcium Assay kit ( Invitrogen ) in 96 well plate according to manufacturer's protocol . The infectious model of prion disease in mice is a very good model for TSEs , since it reproduces many of the clinical , neuropathological and biochemical aspects of the disease in humans and other mammals [61] . Wild type C57Bl6 mice were injected intraperitoneally ( i . p . ) with 100µl of brain homogenate containing infectious prions ( RML strain ) . Approximately 210 days after inoculation , animals begin to show signs of scrapie . The disease onset was monitored weekly by visual inspection , using the following scale [62]: 1 , normal animal; 2 , roughcoat on limbs; 3 , extensive roughcoat , hunckback , and visible motor abnormalities; 4 , urogenital lesions; and 5 , terminal stage of the disease in which the animal presented with cachexia and lies in the cage with little movement . Usually animals die few days after reaching stage 5 . The period between the appearance of the first disease symptoms and death range between 20–40 days without treatment . Some of the animals were left to die to determine survival time and others were sacrificed at the indicated time by exposure to CO2 inhalation to assess brain alterations . After animal sacrifice half of the brain was fixed for histological analysis and the other half was kept frozen for biochemical assays . Treatment with FK506 , rapamycin or vehicle was started when animals exhibit the first signs of prion disease ( stage 1 in our scale ) . Animals were injected intra-peritoneally with 0 . 12 mg of the drug ( 5 mg/Kg ) dissolved in 100 µl of the vehicle solution mentioned above . Administration was done daily until animals die or were sacrificed for experiments . To evaluate if treatment with FK506 alters clinical signs we performed Open field and rotarod tests . The Open field test monitor exploratory behavior and locomotor activity . Animals were placed in a corner of the field box and all activity during various 20s intervals was recorded by a video camera mounted above the open field and scored in real-time . We measured and analyzed total distance , average speed , time spent in various parts of the field , rearing activity and inactive time . Testing was carried out in a temperature , noise and light controlled room . Rota-rod test is used to measure the motor activity and coordination . Animals were placed on the rotating rod with an accelerated speed ( initial velocity 5 RPM; acceleration of 3 ) and the total time spent on the rod was measured . The animal falls from a high of about 6 inches into a plastic platform that automatically counts the time spent in the rod . The presence and quantity of PrPSc in brain homogenates of sick animals was measured by a standard assay consisting of the ability of the misfolded protein to resist proteolytic degradation . Samples were incubated in the presence of proteinase K ( 50 µg/ml ) during 60 min with shaking at 37°C . The digestion was stopped by adding electrophoresis sample buffer and protease-resistant PrP was detected by western blotting , as previously described [63] . Briefly , proteins were fractionated by sodium dodecyl sulphate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , electroblotted onto nitrocellulose membrane and probed with 6D11 antibody at a 1∶5 , 000 dilution . The immunoreactive bands were visualized by enhanced chemoluminesence assay ( Amersham ) and densitometric analysis done by using a UVP image analysis system . Frozen brain samples were homogenized in RIPA buffer containing a cocktail of protease inhibitors and were sonicated for 15 s , and then centrifuged at 20 , 000 g for 5 min . The supernatants were collected and protein concentration measured using BCA assay ( Pierce ) . 50µg of protein extracts were subjected to SDS-PAGE , and western blotting . The membrane was immunoblotted with pBAD , pCREB ( Cell signaling; 1∶1000 ) antibodies and the target proteins were subsequently detected using horseradish-peroxidase conjugated anti-IgG secondary antibodies ( Amersham Biosciences; 1∶2000 ) . Then the membrane was stripped and reblotted with BAD , CREB ( cell signaling; 1∶1000 ) to determine the total protein level . In all cases the membrane was reprobed with β-actin ( Cell signaling;1∶5000 ) to ensure equal protein loading . As before gels were densitometrically analyzed by the UVp image analysis system . Histological studies were done to assess the effect of the treatment on brain damage . For this purpose half of the brain was fixed in 10% formaldehyde solution , embedded in paraffin and cut in sections using a microtome . Serial sections ( 8 µm thick ) from each block were stained to assess PrP deposition , spongiform degeneration , brain inflammation , neuronal degeneration and neuronal loss . The following studies were done: a ) Brain Vacuolation . One of the neuropathological hallmarks of prion diseases is the presence of spongiform degeneration in the brain . Vacuolation was assayed by staining of the tissue with Hematoxilin and eosilin . Then , the number of vacuoles was counted in cerebellum , hippocampus , inferior culliculum , occipital cortex , frontal cortex and thalamus of each animal , as described [62] . b ) Brain inflammation . Astrocytosis was assayed by immunohistochemistry utilizing antibodies against Glial Fibrillary Acidic Protein ( GFAP ) expressed in abundance in activated astrocytes , following a previously described protocol [62] . Staining for activated microglia was done with the AIF-1 antibody . AIF-1 is a 17 kDa interferon-gamma inducible calcium binding protein , associated with chronic inflammatory processes , which has been previously used to assess microglial activation in CJD patients [64] . Digital images were collected on a Leica Microscope fitted with an apotome for optimal sectioning . To calculate the extent of astrocytosis and microglial activation , six 20× sections of Cortex , Thalamus , Hippocampus and Cerebellum were collected per animal and the stained area compared to the total tissue area was determined using the image analysis program Image J from NIH . c ) Neurodegeneration . To evaluate neuronal degeneration and death we used Fluoro-JadeB staining to detect degenerative cells and NeuN , a specific marker for neurons . The Fluoro-Jade B staining was done following the protocol described by Liu et al [65] . Briefly , 8 µm sections were cut from paraffin embedded brains and spread on microscope slides and allowed to air dry followed by mounting on microscope slides and placed in 70% ethanol . The sections were washed and oxidized by soaking in a solution of 0 . 06% KMnO4 for 15min . After washing , they were stained with 0 . 001% Fluoro-JadeB ( MiliPore ) in 0 . 1%acetic acid for 20min . Slides were washed again and dried overnight at room temperature . Digital images were collected on a Leica Microscope fitted with an apotome for optimal sectioning . Six 20× sections of Cortex , Thalamus , Hipocampus and Cerebellum were collected per animal . Fluoro-Jade B positive cells were counted from each field . The number of total neurons was counted after staining with the monoclonal anti-NeuN antibody ( Chemicon ) at 1/1000 dilution . NeuN is a specific neuronal marker for a DNA-binding protein present in the nucleus of postmitotic neurons [28] . Brain sections were mounted onto gelatin-coated coverslips and allowed to air dry . Air dried sections were blocked and permeabilized in 0 . 1MPB with 0 . 3% TX-100 ( Sigma ) and 10% goat serum ( PBTGS ) for 1 h . Following permeabilization , the mouse monoclonal anti-NeuN antibody ( Chemicon International ) was applied at a 1∶200 dilution and incubated overnight at room temperature . After washing , the secondary antibody and Hoechst ( Molecular Probes ) were applied for 1 h at room temperature followed by 3 consecutive washes . Slides were visualized under the microscope by two different researchers blinded to the treatment who counted the number of neurons in different brain areas . For the in vitro studies of the effect of PrPSc on calcium , CaN activity and cell death , the data was analyzed by one-way ANOVA , followed by the Tukey's Multiple Comparison post-test to estimate the significance of the differences . The in vivo survival study was assessed by the Log-rank ( Mantel-cox ) test . The effect of treatment on the rotarod performance was evaluated by two-ways ANOVA using time and treatment as the variables . The behavioral study by open field test and the differences on CaN activity in animals at different times post-inoculation were evaluated by unpaired t-test ( two-tailed ) . Finally , the effect of treatment on the CaN activity , CREB phosphorylation , BAD phosphorylation , astroglyosis , microglial activation , number of neurons and degenerating cells was analyzed by one-way ANOVA , followed by the Tukey's Multiple Comparison post-test to estimate the significance of the differences . All statistical analysis was done with the GraphPad Prism , version 5 . 0 software . | Prion diseases are a group of infectious neurodegenerative disorders producing a rapid and devastating clinical deterioration . The disease is 100% fatal and affected patients usually die within one year from the appearance of the first clinical alterations . Currently there is no treatment available for these diseases . The main goal of this study was to show that inhibition of the brain phophatase calcineurin is a therapeutic target for prion diseases . We show that calcineurin is hyperactivated as a consequence of endoplasmic reticulum stress produced by accumulation of misfolded prion protein . Treatment of animals showing the symptoms of prion disease with the calcineurin inhibitor FK506 resulted in a substantial decrease in the severity of clinical signs , an increase in animal survival and a reduction in brain degeneration . These findings indicate that calcineurin might play an important role in prion-induced neurodegeneration and inhibition of this phosphatase might represent a promising novel approach for prion disease therapy . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"neurological",
"disorders/prion",
"diseases"
] | 2010 | Calcineurin Inhibition at the Clinical Phase of Prion Disease Reduces Neurodegeneration, Improves Behavioral Alterations and Increases Animal Survival |
Reactivation of chronic Chagas disease , which occurs in approximately 20% of patients coinfected with HIV/Trypanosoma cruzi ( T . cruzi ) , is commonly characterized by severe meningoencephalitis and myocarditis . The use of quantitative molecular tests to monitor Chagas disease reactivation was analyzed . Polymerase chain reaction ( PCR ) of kDNA sequences , competitive ( C- ) PCR and real-time quantitative ( q ) PCR were compared with blood cultures and xenodiagnosis in samples from 91 patients ( 57 patients with chronic Chagas disease and 34 with HIV/T . cruzi coinfection ) , of whom 5 had reactivation of Chagas disease and 29 did not . qRT-PCR showed significant differences between groups; the highest parasitemia was observed in patients infected with HIV/T . cruzi with Chagas disease reactivation ( median 1428 . 90 T . cruzi/mL ) , followed by patients with HIV/T . cruzi infection without reactivation ( median 1 . 57 T . cruzi/mL ) and patients with Chagas disease without HIV ( median 0 . 00 T . cruzi/mL ) . Spearman's correlation coefficient showed that xenodiagnosis was correlated with blood culture , C-PCR and qRT-PCR . A stronger Spearman correlation index was found between C-PCR and qRT-PCR , the number of parasites and the HIV viral load , expressed as the number of CD4+ cells or the CD4+/CD8+ ratio . qRT-PCR distinguished the groups of HIV/T . cruzi coinfected patients with and without reactivation . Therefore , this new method of qRT-PCR is proposed as a tool for prospective studies to analyze the importance of parasitemia ( persistent and/or increased ) as a criterion for recommending pre-emptive therapy in patients with chronic Chagas disease with HIV infection or immunosuppression . As seen in this study , an increase in HIV viral load and decreases in the number of CD4+ cells/mm3 and the CD4+/CD8+ ratio were identified as cofactors for increased parasitemia that can be used to target the introduction of early , pre-emptive therapy .
Chagas disease is endemic in Latin America , where fewer than 8 million people , many of whom live in urban centers , are infected by T . cruzi [1] . In Brazil , the control of the Chagas disease insect vector Triatoma infestans and prevention of the transmission of T . cruzi parasitosis by blood transfusion have led to epidemiologic changes , shifting the predominant T . cruzi transmission routes to oral , congenital , and organ transplant transmission . HIV/T . cruzi coinfection has been found in urban centers , and HIV infection [2] has spread to regions in which Chagas disease is endemic . In addition , Chagas disease is now an emerging disease in developed countries , with active congenital and organ transplant transmission and reactivation of the chronic disease [3] , [4] . Acute Chagas disease is characterized by high levels of parasitemia , which is detected by direct microscopy of fresh buffy coat , a quantitative buffy coat ( QBC ) test , or a microhematocrit test [5] , [6] . In the chronic disease , low-level parasitemia is observed and can be detected only by indirect parasitological methods ( xenodiagnosis and blood culture ) [7] . Anti-T . cruzi IgG antibodies are found in almost 100% of these patients [8] . Most chronically infected patients do not develop clinical symptoms of Chagas disease , but approximately 20–30% suffer from heart and or digestive tract disease [8] . T . cruzi parasites are detected more frequently and with higher parasitemia levels in HIV coinfected patients than in those with chronic Chagas disease alone [9] , [10] . Reactivation of chronic Chagas disease , which occurs in approximately 20% of individuals coinfected with HIV/T . cruzi , is characterized by high parasitemia levels , similar to an acute infection [11] . More severe disease ( meningoencephalitis and/or myocarditis ) has been commonly described in patients infected with HIV/T . cruzi [12]–[15]; the involvement of other organs , such as the skin [16] , gastrointestinal tract , and pericardium , has also been reported [14] . The diagnosis of Chagas disease reactivation is based on direct observation methods [5] , [6] . However , this diagnosis is not usually made during the early phase of reactivation , and many patients die soon after diagnosis or during treatment [11] . Case fatality is higher in patients with late diagnosis of reactivation because they die soon after the introduction of the therapy [6] , [8]–[12] . Sensitive and rapid methods are required to monitor parasitemia in immunosuppressed patients with Chagas disease . Xenodiagnosis and blood culture are highly sensitive for the acute disease but are labor-intensive and time-consuming methods and the results take 30–120 days to be analyzed . In addition , technical expertise is required to manipulate live parasites , due to the risk of infection of laboratory staff [7] , [9] , [17] . In HIV/T . cruzi-infected patients , semi-quantitative xenodiagnosis that indicates the percentage of nymphs per assay may predict the occurrence of Chagas disease reactivation . Episodes of reactivation have been observed in 50% of patients who show ≥20% nymphs per assay in a follow-up period of 5 years [11] . A competitive polymerase chain reaction ( C-PCR ) method [18] , [19] has been reported for monitoring the treatment of children with congenital Chagas disease , patients with chronic Chagas heart disease , and a patient with HIV and meningoencephalitis . It was used to demonstrate clearance or early detection of the parasite [19]–[21] . Another molecular method , quantitative real-time PCR ( qRT-PCR ) , has been used to diagnose congenital [22] , [23] and chronic Chagas disease and showed 41% positive detection of the chronic disease [22] . Using this method , low parasitemia was found in mothers ( 93 . 3%<10 parasites/mL ) and higher parasitemia was found in neonates ( 76 . 3%>1 , 000 parasites/mL ) [23]; parasitemia correlated negatively with age ( 0 . 01–640 parasites/mL ) [24] . The aim of this study was to evaluate the use of a new quantitative molecular method ( qRT-PCR ) to monitor T . cruzi parasitemia in HIV-infected patients with or without Chagas disease reactivation . In addition , the sensitivities of different molecular and parasitological tests were compared .
The study included 91 samples that were collected between 1996 and 2008 from patients ≥18 years old with Chagas disease who were admitted to the AIDS Clinic and/or Clinic of Infectious and Parasitic Diseases at the Hospital das Clinicas , a tertiary hospital attached to the School of Medicine of the University of São Paulo , Brazil . The patients were classified into two groups: ( 1 ) 57 immunocompetent patients with chronic Chagas disease ( CR ) and ( 2 ) 34 patients with chronic Chagas disease coinfected with HIV , of whom 29 lacked reactivation ( CO ) and 5 had reactivation of Chagas disease ( RE ) . The inclusion criterion for patients with Chagas disease with or without HIV infection was the presence of antibodies in two or three conventional serological tests for Chagas disease ( indirect immunofluorescence ( ≥1/40 ) , indirect hemagglutination ( ≥1/40 ) or Enzyme linked immunoassay ( ELISA ) ) [24] . HIV patients were included after detection of anti-HIV antibodies by ELISA and confirmation by immunoblot [25] . Chagas disease reactivation was diagnosed if at least one of the following tests was positive: direct blood microscopy or QBC for T . cruzi ( two patients ) or direct cerebrospinal fluid ( CSF ) examination for T . cruzi ( three patients ) . A control group of 58 healthy individuals without Chagas disease ( indicated by negative conventional serological tests for Chagas disease ) was used to check for contamination during the sample extraction process; the control samples were paired with samples from patients . Trypomastigotes were identified by direct microscopy of peripheral blood mononuclear cells ( PBMCs ) or through QBC analysis [6] . For QBC , the blood was collected in a microhematocrit tube containing acridine orange ( BD Biosciences ) . After centrifugation , the parasites remaining in the platelet layer at the top of the buffy coat were identified by immunofluorescence microscopy . The blood culture assay was performed as previously described [17] . Six culture tubes were examined after 10 , 20 , 30 , 60 , 90 and 120 days of culture . The results are expressed as the number of positive tubes divided by the total number of tubes examined ( % positive tubes ) ; the result was considered positive if any tube was positive and negative if all were negative . Xenodiagnosis was performed with 20–40 nymphs of T . infestans fed in vitro with 10 mL of patient blood . The search for T . cruzi in the gut contents of each triatome was performed 30 , 60 and 90 days later and the results are expressed as the percentage of positive insects ( semi-quantitative xenodiagnosis ) ; or a positive result if at least one insect was positive and negative if all of them were negative [7] , [26] . DNA was extracted with QIAamp™ DNA Mini Kit ( Qiagen , Hilden , Germany ) from whole blood collected in 6 M guanidine HCl plus 0 . 2 M EDTA buffer ( pH 8 ) ; in a few cases , DNA was extracted from blood collected in EDTA ( PBMC ) or from CSF , which was collected from two patients with central nervous system reactivation , as previously reported [27] , [28]; samples were stored at −20°C . The quantity and purity of the DNA were determined with a spectrophotometer ( Gene-Quant , Pharmacia Biotech , Cambridge , England ) , and only samples with high purity were used in the experiments . PCR was performed using the S35 and S36 primer pair , which amplifies a 330-bp minicircle sequence from T . cruzi ( Gibco™ Life Technologies , CA , USA ) [29] . The reactions contained Taq polymerase , 0 . 2 µM of each primer , 1 . 4 mM MgCl2 and 50–150 ng of DNA . Negative controls for the master mix preparation and DNA addition and a positive control , which consisted of 2×10−15 µg of DNA from the Y strain of T . cruzi , were used . The presence of inhibitors of DNA amplification was verified by β-actin amplification and by amplification of duplicate patient samples containing parasite DNA . To assess the analytical sensitivity of the qualitative PCR assays , 10-fold dilutions from 0 . 2 pg to 0 . 002 fg of DNA parasites were processed; the detection limit of the assay was 0 . 2 fg of T . cruzi , which corresponded to 0 . 01 parasite/assay in an agarose gel . A 280 bp DNA fragment with binding sites for the S34/S35 oligonucleotides was used for competitive PCR with the kDNA 330 bp product and was cloned into the pT7 Blue vector ( kindly provided by the Laboratório Multidisciplinar de Pesquisa em Doença de Chagas , Universidade de Brasília ) . The assay was performed in the Laboratory of Immunology , Faculdade de Medicina da USP , as previously described [18] . Known concentrations of the competitor ( 150 , 15 , 1 . 5 and 0 . 15 fg ) , or no competitor , were mixed with four aliquots of DNA from patient samples that had previously shown positive PCR results for kDNA . For each competitive PCR analysis , we included five samples per patient . The equivalence point was determined by visually comparing the intensities of the 280 and 330 bp products . The number of T . cruzi/mL of blood was calculated based on the blood volume used for extraction , the dilution of the sample , and the amount of patient DNA used in the PCR reaction . PCR for the microsatellite sequence TCZ3/TCZ4 ( TGCTGCAGTCGGCTGATCGTTTTCGA/CAAGCTTGTTTGGTGTCCAGTGTGTGA ) , which was previously described by Ochs et al . ( 1996 ) [30] as internal primers for TCZ1 and TCZ2 ( Gibco™ Life Technologies , CA , USA ) , was performed using 20 µl SYBR™ Advantage™ qRT-PCR Premix ( Clontech , CA , USA ) , according to the manufacturer's instructions . The mixture contains Taq polymerase , 0 . 2 µM of each primer , 1 . 4 mM MgCl2 and 50–150 ng of DNA . We amplified a 149 bp sequence using 45 PCR cycles with a denaturation temperature of 94°C , an annealing temperature of 64°C and an extension temperature of 72°C on a RotorGene 3000™ ( Corbett Research , Australia ) . All DNA extractions and amplification reactions were performed with the appropriate negative controls to detect contamination at any stage of the procedure and with positive controls that gave reproducible results during all of the experiments . The standard amplification curve was prepared from 10-fold dilutions of DNA from blood spiked with 5×105 to 5×10−3 T . cruzi epimastigotes/mL . Y strain ( human , Brazil ) . The detection limit was found at 0 . 005 parasite/mL ( Figure 1 ) . The initial number of parasites ( 107/mL ) was counted by microscopic examination ( Neubauer camera ) . The first blood aliquot mixed with Guanidine HCl-EDTA ( v/v ) was spiked with 5 . 105 parasites cells/mL . After homogenization , this tube was used as starter for preparing 10 ten-fold serial dilutions ranging from 5×105 to 5×10−3 T . cruzi epimastigotes/mL . The final concentration of the patient sample was calculated based on the volume of the blood extracted , the amount of DNA amplified and the volume and dilution of the sample analyzed . Dilution of the samples was necessary for patients with reactivation and for some coinfected patients with large numbers of parasites . High levels of parasitemia were used for comparison among the different methods or groups but were not necessarily indicative of reactivation . Three trained individuals performed and read the tests ( one was responsible for the xenodiagnosis , one for the hemoculture and the other for the molecular tests ) . The results were read blind . The HIV plasma viral load was determined by reverse-transcriptase ( RT ) -PCR using an Amplicor™ HIV-1 Monitor Test ( Roche Diagnostic Systems , NJ , USA ) in the Central Laboratory of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo , which had a lower detection limit of 200 copies of HIV/mm3 . CD4+ and CD8+ T lymphocyte counts were determined by flow cytometry ( FACSCalibur , BD Biosciences , CA , USA ) using antibodies to CD4 and CD8 ( BD Biosciences ) . All protocols were approved by the ethical committee ( Comissão de Ética para Análise de Projetos de Pesquisa - CAPPesq ) , Hospital das Clínicas , Faculdade de Medicina of University of São Paulo . Written informed consent was obtained from all participants . SPSS version 17 was used for the statistical analyses . The chi-squared test and Fisher exact test were used to compare the qualitative results of the tests and the differences between the patient groups . The McNemar test was used for comparisons between paired proportions . For non-parametric data , Spearman's rank correlation coefficient was used for quantitative variables ( P<0 . 01 ) [31] . Kruskal–Wallis one-way analysis of variance was also applied to non-parametric data to compare the three groups of patients , followed by the comparison of independent groups , two by two , by the Dunn's Multiple Comparison post test ( Prism 3 . 0 , Graph Pad Software Incorporation , CA , California ) . P values<0 . 05 were considered significant .
There were no significant differences in gender ( CR male frequency = 38 . 6%; CO male frequency = 48 . 3%; RE male frequency = 60 . 0% ) among the groups with and without HIV infection . The five individuals with chronic Chagas disease , HIV infection and reactivation of Chagas disease were younger than those without HIV infection ( CR mean = 46 . 1±12 . 2 years , median = 43; CO mean = 42 . 0±10 . 1 , median = 38; RE mean = 32 . 7±3 . 93 , median = 33 ) ( P = 0 . 014 ) . A comparison of the different tests for the HIV-positive and HIV-negative patients is shown in Table 1 . PCR with the S35/36 primers was more sensitive than blood culture , xenodiagnosis and both tests combined ( P<0 . 001 ) . Patients with HIV infection ( CO+RE ) showed higher rates of positive results by PCR ( P<0 . 001 , Fisher exact test ) , xenodiagnosis ( P<0 . 001 , Fisher exact test ) or both tests ( P = 0 . 002 , χ2 ) compared with patients without HIV infection . The highest mean percentage of positive nymphs per assay ± SD ( Table 2 ) was observed in the RE group ( Kruskal-Wallis , P = 0 . 001 ) . Table 3 shows the Spearman correlation indices ( rs ) of the quantitative molecular , parasitological and immunological tests . A strong positive correlation between the number of parasites/mL detected by C-PCR and qRT-PCR was shown in Figure 4 . To study the influence of CD4+ and CD8+ T cells on the level of parasitemia , we calculated the Spearman correlation coefficient for 30 samples from patients infected with HIV/T . cruzi with and without parasite reactivation and found negative correlations with the number of CD4+ T cells ( data not shown ) and the CD4+/CD8+ ratio ( Figure 5 ) . However , no correlation was observed with the number of CD8+ T cells ( data not shown ) . A positive correlation was found between the HIV viral load and the level of T . cruzi parasitemia in 20 samples from individuals infected with HIV/T . cruzi with and without Chagas disease reactivation ( Figure 6 ) .
In this study , the utility of the molecular methods C-PCR and qRT-PCR for the diagnosis and quantification of the number of parasite DNA copies in the blood of patients with HIV/T . cruzi coinfection was demonstrated for the first time . In addition , we have shown that qRT-PCR was able to distinguish between the groups of HIV/T . cruzi–infected patients with and without Chagas disease reactivation and between groups of patients with chronic Chagas disease and those coinfected with HIV/T . cruzi , with or without Chagas disease reactivation ( Figure 2 B and Table 2 ) . High levels of parasitemia have been reported in three chronic heart disease patients after heart transplantation [24] , but no report has compared parasitemia in reactivated and non-reactivated HIV/T . cruzi-infected groups . The inclusion of the latter group indicated that there are different levels of parasitemia in these patients . The majority had similar levels to the chronic cases , less than 10% had the highest levels of the group and the remainder showed intermediate levels of parasitemia . The patients with higher parasitemia might be targeted for therapy . Additionally , unlike what we would expect based on the immunopathogenesis of HIV/T . cruzi coinfection , we found that one HIV-infected patient with reactivation had much lower parasitemia than the majority of the RE group . This episode of reactivation was characterized by the presence of trypomastigotes , as detected by direct microscopy of the blood [32] . The patient only presented mild symptoms without meningoencephalitis , myocarditis or other tissular lesions . The level of CD4+ T cells was more than 300/mm3 , and , during this period , the patient showed >20% of nymphs on xenodiagnosis and an increased viral load . The lineage type of the parasite was no different from the majority of the cases . It is possible that the level of parasitemia and the level of CD4+/mm3 did not change because the reactivation was diagnosed in the initial phase of the disease and early therapy with benznidazole was administered . Although the number of patients with reactivation was small , the high number of DNA copies observed in the blood ( Figure 2B ) or cerebrospinal fluid of the remaining RE patients with myocarditis or meningoencephalitis is impressive . In our study , qRT-PCR showed high performance with a low detection limit ( 0 . 005 parasite/mL ) and good efficiency , as previously described [32]–[34] . We suggest that increasing parasitemia in subsequent examinations and/or stabilization at levels higher than previously seen in the same patient should be carefully monitored for CD4+ counts and viral load . The two quantitative molecular methods , C-PCR and qRT-PCR , used in the present study were strongly correlated by Spearman's correlation coefficient ( 0 . 725 ) , showing that both could be used for diagnostic purposes . However , C-PCR is more labor-intensive and time-consuming and requires more DNA , post-PCR processing of the amplified products and subjective analysis of the results . The previous result of 41% positivity for chronic Chagas disease by qRT-PCR [22] is similar to the results observed in our study . In addition , qRT-PCR with the S35/S36 primers was used to measure parasitemia in neonates with congenital Chagas disease [23]; this analysis yielded similar data to those observed here in the RE group compared with the CR group using satellite sequences . Those authors reported a higher level of parasitemia ( >1000 copies/mL blood ) in neonates compared with their mothers with chronic Chagas disease ( <10 copies/mL blood ) [23] . In our study , the level of parasitemia in the RE group ( median 1428 . 90 parasites ) was higher than in the CO group ( median 1 . 57 parasites ) and CR group ( median 0 . 00 parasite ) . A previous study on the reactivation of Chagas disease in heart transplant patients with positive Strout tests [24] reported a lower concentration of parasites in the blood than that shown in our study ( 9 . 07 and 468 . 0 parasites/mL ) . Our data show that C-PCR and qRT-PCR had higher sensitivities than the parasitological tests ( xenodiagnosis and blood culture ) and confirmed the previously described higher sensitivity of S35/S36 PCR [35]–[37] . Moreover , PCR takes less time ( a few hours ) and has a low risk of infection , in contrast to the labor-intensive and time-consuming parasitological tests ( 30–120 days ) , which have high specificity but require the manipulation of live parasites . The risk of DNA contamination in molecular tests needs to be minimized by using negative controls at each stage of the analysis . The risk of contamination is lower with qRT-PCR because it employs extraction kits and excludes post-processing PCR , although the high cost is a disadvantage . Analyses of the demographical characteristics of the different clinical groups showed no differences in terms of gender , but RE patients were younger than the other groups , possibly due to the epidemiological characteristics of HIV-infected patients in Brazil [2] . A limitation of our study was that the small number of patients with Chagas disease reactivation did not allow for age-matched controls . An analysis of the level of parasitemia in different groups represents a good strategy to monitor the host protozoan/virus imbalance . Our data were not influenced by the lineage of the parasite , which was similar for most of the isolates ( data not shown ) , as previously observed [24] . In our study , the parasite level was lower in the CR group and higher in the CO and RE groups , possibly due to ability of the parasite to evade the host immune response in patients without HIV infection . Cellular immunity and macrophage deficiencies in HIV infection could explain the increased parasitemia observed in HIV/T . cruzi-infected patients . The highest level of parasitemia was seen in the RE group , which was associated with increased HIV viral load , a decreased number of CD4+ cells , and a decreased CD4+/CD8+ cell ratio; these results confirm the failure of immune mechanisms in the RE group . These data are corroborated by clinical studies that showed a relationship between the reactivation of trypanosomiasis , increased HIV viral load , and decreased CD4+ counts in peripheral blood [11] . The correlation between HIV viral load and the concentration of parasites was demonstrated for the first time in our study , although it has been previously suggested by the relationship observed between an increased viral load and an increased rate of positive xenodiagnosis in HIV/T . cruzi-infected patients [11] . These data are also consistent with the rapid evolution of murine leukemia virus in mice infected by T . cruzi [38] . Although no relationship was found between CD8+ cells and the concentration of parasites , their ability to control infection via IFN-γ secretion [39] has been demonstrated previously . The strong correlation between the number of parasites and CD4+/CD8+ cells suggests that both cells play a role in the control of parasitemia . The data from a previous report [40] in mice , which showed high parasitemia in CD4−/CD8+ animals and low parasitemia and high survival in CD4+/CD8+ mice , help to explain the results of this study . Reactivation of chronic Chagas disease or increased parasitemia has been reported in T . cruzi-infected patients with hematological malignancies or autoimmune diseases receiving cytotoxic or anti-inflammatory therapy with corticosteroids [4] , [24] , [41] . Severe reactivation of trypanosomiasis has been described in about 20% [11] of patients infected with HIV ( although these data are possibly overestimated by the inclusion of one referral center ) . In these cases , if treatment is delayed for at least 30 days , the mortality rate of such patients is 80% , but mortality decreases to 20% for patients treated within 30 days , indicating that earlier diagnosis and treatment increase patient survival [8]–[16] . One of the limitations of this study is the cross-sectional design , which does not allow for an investigation of the evolution of parasitemia . Another limitation is the low number of HIV-infected patients with Chagas disease reactivation , which constitutes an important challenge for prospective studies . Nevertheless , we observed that the high levels of parasitemia seen in the majority of HIV-infected patients with reactivation were not found in coinfected patients without reactivation . Considering the imbalance of host-parasite interactions in HIV/T . cruzi-coinfected patients and the fact that HIV infection might favor parasite growth by itself , therapy might be considered in these coinfected patients on the basis of high parasitemia and low CD4+ count and decreased CD4+/CD8+ ratio , even though symptoms were absent . The adverse effects of the drugs , previous immunosuppression and the immunosuppressive effects of Chagas disease , poor surveillance against neoplasia and the therapy efficacy , which is lower in the presence of low levels of parasitemia , must be taken into consideration when recommending universal therapy for any coinfected patient . We propose that prospective multicenter studies are warranted to address important questions regarding the management of HIV/T . cruzi coinfection , including determining why Chagas disease reactivation occurs in some individuals with lower levels of parasitemia , characterizing the influence of parasite lineage and immune responses on reactivation , and evaluating the outcome of initiating therapy on the basis of serology [42] versus treating with pre-emptive therapy ( parasitemia versus uniquely or persistently high levels of parasitemia ) . Finally , in the present study , we demonstrated for the first time that qRT-PCR shows different levels of parasitemia in groups of HIV/T . cruzi-infected patients with and without Chagas disease reactivation . The highest concentrations of parasites were found in the latter , followed by coinfected patients and , finally , patients with chronic Chagas disease . We propose that this new test be evaluated under standardized conditions in prospective controlled studies to determine the importance of parasitemia ( persistent and/or increased ) as a criterion for initiating pre-emptive therapy in chronic Chagas disease patients with HIV infection or immunosuppression . The association of increased parasitemia with increased viral HIV load and a decreased CD4+ count and CD4+/CD8+ ratio in peripheral blood suggests that these could be analyzed as cofactors of increased parasitemia to further support any intervention . In addition , this association ( increased parasitemia , increased HIV viral load and decreased number of CD4+ cells/mm3 and decreased CD4+/CD8+ ratio ) reinforces the need to monitor parasitemia using quantitative methods to determine when to start therapy for the better management of Chagas disease in patients with immunosuppression . | Chagas disease is endemic in Latin America and is caused by the flagellate protozoan T . cruzi . The acute phase is asymptomatic in the majority of the cases and rarely causes inflammation of the heart or the central nervous system . Most infected patients progress to a chronic phase , characterized by cardiac or digestive involvement when not asymptomatic . However , when patients are also exposed to an immunosuppressant ( such as chemotherapy ) , neoplasia , or other infections such as HIV , T . cruzi infection may develop into a severe disease ( Chagas disease reactivation ) involving the heart and central nervous system . The current microscopic methods for diagnosing Chagas disease reactivation are not sensitive enough to prevent the high rate of death observed in these cases . Therefore , we propose a quantitative method to monitor blood levels of the parasite , which will allow therapy to be administered as early as possible , even if the patient has not yet presented symptoms . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biology"
] | 2011 | Real-Time PCR in HIV/Trypanosoma cruzi Coinfection with and without Chagas Disease Reactivation: Association with HIV Viral Load and CD4+ Level |
Stereo “3D” depth perception requires the visual system to extract binocular disparities between the two eyes' images . Several current models of this process , based on the known physiology of primary visual cortex ( V1 ) , do this by computing a piecewise-frontoparallel local cross-correlation between the left and right eye's images . The size of the “window” within which detectors examine the local cross-correlation corresponds to the receptive field size of V1 neurons . This basic model has successfully captured many aspects of human depth perception . In particular , it accounts for the low human stereoresolution for sinusoidal depth corrugations , suggesting that the limit on stereoresolution may be set in primary visual cortex . An important feature of the model , reflecting a key property of V1 neurons , is that the initial disparity encoding is performed by detectors tuned to locally uniform patches of disparity . Such detectors respond better to square-wave depth corrugations , since these are locally flat , than to sinusoidal corrugations which are slanted almost everywhere . Consequently , for any given window size , current models predict better performance for square-wave disparity corrugations than for sine-wave corrugations at high amplitudes . We have recently shown that this prediction is not borne out: humans perform no better with square-wave than with sine-wave corrugations , even at high amplitudes . The failure of this prediction raised the question of whether stereoresolution may actually be set at later stages of cortical processing , perhaps involving neurons tuned to disparity slant or curvature . Here we extend the local cross-correlation model to include existing physiological and psychophysical evidence indicating that larger disparities are detected by neurons with larger receptive fields ( a size/disparity correlation ) . We show that this simple modification succeeds in reconciling the model with human results , confirming that stereoresolution for disparity gratings may indeed be limited by the size of receptive fields in primary visual cortex .
Human 3D depth perception is highly precise , with the ability to detect disparities between the two retinal images of less than the width of one photoreceptor [1] . However , it has very poor spatial resolution [2]–[4] . This can be demonstrated , for example , by using random-dot patterns to depict a corrugation in depth . An example is shown in Figure 1 , depicted in red/green anaglyph stereo for illustration . The disparities between dots visible to the left eye ( red ) and right eye ( green ) vary sinusoidally as a function of vertical position in the image . Accordingly , when viewed with red/green 3D glasses , the dots appear to lie on an undulating surface rather like a sheet of corrugated iron , with the bars of the corrugations horizontal on the page . We shall refer to this kind of stimulus , pioneered by Tyler [3] , as a sinusoidal disparity grating , by analogy with the luminance gratings pioneered by Schade [5] . The upper frequency limit at which such disparity gratings can be perceived has been found to be around 3–4 cycles per degree [3] , [6]–[8] which is much lower than the limit found for luminance gratings . This low spatial stereoresolution has been explained in terms of a model , based on the known properties of cells in primary visual cortex ( V1 ) , where disparity is measured by the use of local cross-correlation between the two eyes' images [6] , [8] , [9] . Banks et al . found that the spatial stereoresolution of the model depended on the size of the correlation window , roughly corresponding to the receptive field size of the V1 cells modelled , such that the resolution was higher for smaller windows , up to a limit set by the highest useful dot density which in turn depends on the level of optical blur [6] , [8] . For realistic levels of blur , they found that the smallest useful window size was roughly 6 arcmin . They also found that the performance of the model with this window size showed the most similar dependence on the level of blur to that of human observers , suggesting that “the smallest mechanism in humans has a diameter of roughly 3–6 arcmin , which is the smallest useful size given the optics of the human eye” [8] . Based on the success of this model , these authors made the interesting and plausible suggestion that spatial stereoresolution may be set in primary visual cortex , reflecting the size of receptive fields there [6] , [9] . In this view , the better spatial resolution for luminance gratings occurs because V1 receptive fields are divided into ON and OFF subregions; the effective window reflects the size of V1 subregions . Because V1 neurons respond best to locally uniform disparity [9] , the effective window for disparity is the entire receptive field . This is an intriguing and attractive model , which relates human perception to the properties of neurons early in visual cortex . However , we recently raised an observation which potentially presents a challenge to this view [10] . Almost all previous empirical results relating to stereo resolution were obtained using sine-wave disparity gratings like that depicted in Figure 1 . Because the model uses detectors which are tuned to locally uniform patches of disparity it would be expected to perform better on detection of square-wave gratings , which consist of regions of locally constant disparity . We recently confirmed this with simulations using the model of Banks et al [6] , [8] with the optimal window size of 6 arcmin . As expected , the model does indeed perform better with square-wave gratings , in particular at high disparity amplitudes . However , Tyler [4] had found using line stereograms that performance was similar in both square- and sine-wave disparity gratings . We therefore tested human observers on dense random-dot stereograms depicting square-wave gratings . We found that the model's prediction was not borne out: humans never showed significantly better ability to detect square-wave than sine-wave gratings [10] . Figure 2 shows example human and model data near the upper frequency limit , illustrating the marked qualitative difference between the model and the human observers . For humans ( Figure 2 , top row ) , performance rises rapidly to a peak and thereafter declines as the grating's disparity amplitude increases , for both sine-wave gratings ( red circles ) and square-waves ( blue squares ) . The model ( bottom row ) performs similarly for sine-waves , but for square-waves , the model's performance remains at its peak value as disparity amplitude increases , in disagreement with the human data . This failure of the model raises the possibility that spatial stereoresolution may not be limited by the smallest receptive field size in V1 after all but rather at a later stage , perhaps by detectors in extra-striate areas tuned to disparity slant or curvature [11]–[14] . However , there is also the possibility that minor modifications to the model may make it consistent with these new human results . In this paper , we examine a modified version of the model , where larger disparities are detected using larger correlation windows . There is considerable psychophysical evidence for such a size/disparity correlation [2]–[4] , [15]–[17] , and some physiological evidence has also been found in favour of it [18] . We show that this new version can capture human performance on both sine- and square-wave depth corrugations .
The cross-correlation coefficient used in the present paper as well as by Banks et al . differs in a number of ways from the cross-correlation implemented by the energy model . First , it is normalized to lie between 1 ( for perfect interocular correlation ) and −1 ( for anti-correlated stimuli ) . Second , it operates on the retinal images directly , not the images after filtering by a bandpass receptive field . Finally , the multiplication of the two images is performed first , followed by integration over space , unlike the energy model where the images are integrated over space first and the results are then multiplied together . This has the consequence that the cross-correlation model used here depends more critically on the exact relative positioning of visual features in the two images compared to an energy model unit of the same window-size , and that its disparity tuning is finer and independent of window size . Given that we are claiming our results show that disparity resolution is limited by activity in primary visual cortex , it is important to be clear how the idealized cross-correlation computed in our model relates to more realistic models of individual neurons . To this end , we begin the Results section by showing that the output of a Banks-style cross-correlator can be approximated by suitably combining the responses of many complex cells tuned to different orientations and frequencies . In the standard energy model the response of a stereo energy unit is described by the equation:whereand IL is the left eye's image , the wavenumbers kx and ky together specify the spatial frequency and orientation of the cells receptive field , xL and yL specify the position of the center of the left eye's receptive field , φL is the phase of the receptive field , and σ is the standard deviation of the Gaussian envelope of the receptive field . SR1 and SR2 are defined analogously . We assume that , due to adaptation at lower levels of the visual system , the image is defined relative to the overall mean luminance , so that averaged across the whole image , . Let us assume there are also monocular complex cells which computeThe response of the energy model unit can be split into a binocular part B and monocular parts L and R:whereNow we compute the total response of all cells at this location which have phase disparity zero and position disparity Δx , summing over cells tuned to a range of spatial frequencies and orientations . In Text S3 , we show that integrating B in this way over all spatial frequencies and orientations gives usApproximating the integrals with a sum over pixels , and using LW to represent the image after multiplication by the window function , this isThis is simply the covariance of the weighted image-patches , plus a term reflecting the average pixel-value within the window:where n is the total number of pixels included in the sum . Similarly , integrating the monocular terms over all spatial frequencies and orientations , we obtainNow we use the monocular terms to normalise the binocular term [19]–[21]:The normalisation ensures that Cint remains between +1 ( for units tuned to the stimulus disparity , where Lw = Rw ) and −1 ( for anti-correlated stimuli , where Lw = −Rw ) . For random-dot patterns where the correlation window is large compared to the dot-size , the average pixel-value within each eye's window will be very nearly the same as the average pixel-value across the whole eye's image , which is zero by definition . For such images , Cint reduces immediately to C as defined in Equation 1 . For natural scenes or other images where the luminance undergoes large-scale changes across the image , this would not be the case , and Cint would not be zero for binocularly uncorrelated images . Real neurons have not been studied with such images , so it is not possible to say whether Cint or C as defined in Equation 1 would be more appropriate in that case . This analysis shows that the key features of the Banks model – units sensitive to the precise location of features within the window , isotropic windows , disparity tuning curves whose width is independent of window size – can be produced within a more physiologically-realistic model , simply by combining the outputs of energy-model units tuned to many spatial frequencies and orientations . Essentially , the Banks model is a computational short-cut which enables us to approximate the properties of a much larger population of energy-model units at vastly reduced computational cost . This is somewhat analogous to how the energy-model itself uses a quadrature pair of units with 0 and π/2 phase to approximate the output of a large number of subunits tuned to a range of phases . This derivation gives us confidence that the encoding stage of our model , while clearly highly idealised , is nevertheless consistent with the physiology of early visual cortex . We now move on to examine how the model performs when its outputs are used to perform our psychophysical task , under various different decision models . Figure 5 shows the results of the model . Panels A–H show the model's performance ( percent correct judgments ) as a function of disparity amplitude for different grating frequencies and the final panel shows the maximum performance , i . e . that at the optimal disparity amplitude for each frequency , as a function of frequency . Red circles show results for sine-wave gratings; blue squares those for square-wave gratings . Throughout , error bars show 95% confidence intervals . Critically , the results are now very similar for both sine- and square-wave disparity gratings – like human observers and unlike the original model ( Figure 2 ) . Like human observers , as disparity amplitude increases beyond its optimal value , performance for both grating waveforms decays back to chance . Similar figures are given in Text S1 and Text S2 for alternative decision models ( Figure S1-1 in Text S1 and Figure S2-2 in Text S2 ) . Unsurprisingly , there are quantitative differences between the results from different decision models , especially in the percent correct at the lowest disparity amplitude . This amplitude , 0 . 3 arcmin , is below the step size of 0 . 6 arcmin in the range of correlation detectors , and the decision models vary in how efficient they are at extracting information at this sub-step-size disparity . Similarly , the decision models vary somewhat in the frequency at which peak performance first starts to decline . We know in principle how to match human performance on both of these . Capturing sensitivity to small disparity amplitudes would require the right minimum spacing in the population of cross-correlators , plus the addition of noise to limit the ability to discriminate tiny disparities . Capturing the correct frequency at which performance declines would require us to tweak the minimum window-size , i . e . the value of the first term in Equation 3 , as done by Banks et al [6] , [8] . Given the long simulation run-time and the fact that these issues are solved in principle , we have not here attempted to chase down these parameters further . In Figure S2-2 in Text S2 , showing results for a decision model based on auto-correlation , there are a couple of frequencies where performance starts dropping for the sine-waves at slightly lower amplitudes than for the square-waves . Interestingly , 2 of our 4 human observers also displayed this tendency ( Figure 10 of [10] ) , while neither humans nor model ever displayed an earlier drop for square-waves than for sine-waves . The results in Figure 5 assumed a quadratic relationship between a correlator's window-size and its preferred disparity . The psychophysical data suggests there may be noticeable inter-subject variation in the relationship between spatial scale and disparity correlation , with Smallman & McLeod's two subjects showing linear and quadratic relationships respectively . However , all our subjects showed near-identical performance on sine- and square-wave gratings [10] . We therefore wanted to check that the precise form assumed for the size-disparity correlation was not critical for our results . To this end , we also tested the model with a linear size/disparity correlation ( Equation 3 ) . The results ( Figure 6 ) are similar to those obtained with the second order polynomial size/disparity correlation ( Equation 2 ) , and in particular the key result holds: differences between the sine-wave and square-wave results remain negligible . This suggests that several different forms of the size/disparity correlation may be consistent with the human data in our previous paper [10] . Many previous studies have suggested that human depth perception is limited in the disparity gradients it can detect [4] , [6] , [8] , [15] , [22] , [23] . For example , Tyler found that , for sinusoidal disparity gratings , the highest disparity amplitude which can be perceived is inversely proportional to grating frequency ( i . e . lies on a line with a slope of minus one in log-log coordinates [4]; black symbols in Figure 7 ) , as if perception is limited by the maximum gradient present in the grating . This observation does not require a size-disparity correlation; for example , Filippini & Banks [8] successfully reproduced it with their local cross-correlation model which incorporates no relationship between size and disparity tuning of detectors ( Figure 7A ) . However , Tyler also found the same relationship between upper depth limit and frequency in square-wave disparity gratings . He argued that this does imply a size-disparity correlation . No computational model has yet reproduced this observation . To examine this , we re-ran our simulations using a larger range of correlation detectors , including detectors tuned to disparities up to 140 arc min . This enabled us to probe the model's upper depth limit even at frequencies <1 cpd , where performance remains perfect up to tens of arc min . The coloured symbols in Figure 7 shows the upper limit of disparity amplitude , defined as the maximum amplitude for which performance exceeds 80% on our grating detection task , as a function of grating frequency . For comparison , Tyler's results are replotted in black . Figure 7A shows our results with the original , constant window-size model . For sinusoidal disparity gratings , the upper limit falls as a power-law with frequency , replicating the finding of Filippini & Banks . However , the model fails completely for square-wave gratings . No results are shown since the model has no upper depth limit for square-wave gratings; performance remains optimal at all amplitudes up to Panum's fusional limit , with no trade-off between upper depth limit and frequency . This is inconsistent with Tyler's data showing that , for human subjects , the upper depth limit for square-waves falls with increasing frequency in the same way as it does for sine-waves [4] , as well as with our own data [10] . Figure 7B shows the results of the new model using a linear size/disparity correlation ( Equation 3 ) . For both square-wave and sine-wave gratings , the upper depth limit is inversely proportional to frequency , in agreement with the human data . However , in the model results the sine- and square-wave curves overlap almost perfectly while they are offset by a constant amount in Tyler's data . Tyler's data were obtained using a different stimulus , line stereograms rather than random dot stereograms , and while similar results have also been obtained with random dot stereograms for sine-waves [3] , to our best knowledge the frequency dependence of the upper depth limit for square-waves has only been measured with line stereograms , making it hard to say whether this difference reflects a real problem with the model or if it is just a consequence of using a different stimulus . In the human data in our previous paper [10] , some subjects seem to show a difference in the same direction as Tyler , though smaller , while others show almost no difference . But our paper only looked at high frequencies and the experiments were not designed specifically to test the upper disparity limit . Clearly , more data on the upper disparity limit for sine- vs . square-wave disparity gratings in random dot stereograms would be needed to test whether the lack of an offset between the sine- and square-wave results reflects a remaining problem with the model . Figure 7C shows the results of the new model using a quadratic size/disparity correlation ( Equation 2 ) . The results for sine-waves and square-waves are again very similar , but now the upper depth limit rises less steeply as frequency is reduced , or put another way , the highest frequency detectable for a given amplitude decreases at an accelerating rate as the amplitude increases .
Correlation-based models are built of disparity detectors which respond maximally , i . e . with correlation output 1 , to uniform stimulus disparity at their preferred value . Stimulus disparities away from the preferred value cause a decline in the reported correlation output . In this type of model , the rate of the decline is ultimately limited by the point-spread function of the eye , with an SD of around 2 arcmin . In the old , fixed-window-size model , the quality of the correlator output declines with increasing amplitude for the sine-waves , but not for the square-waves . Figure 8 shows examples of the old model's correlator output for sine- and square-waves with low and high amplitude , for a frequency of 3 . 8 cpd . The white lines show which disparity was actually presented at each vertical position . The black lines show the extent of a correlation window , defined as the 1SD contour of the Gaussian . For the low amplitude gratings ( Figure 8AB ) , the correlator output is of high quality for both waveforms . It is maximal at the front and back surfaces of each waveform , where the range of stimulus disparities within the correlation window is smallest . In this example , the grating half-period is 7 arcmin , so for the square-wave , detectors positioned at the center of the grating's front and back surfaces experience uniform stimulus disparity everywhere within their 6-arcmin correlation window . Detectors tuned to the stimulus disparity will therefore respond close to their maximum possible value of 1 . Even at the edges of the square-wave the window will only experience two disparities , each covering half the window , allowing the correlation to be relatively high ( close to 0 . 5 ) for detectors tuned to either of these two disparities . For the sine-wave , the stimulus disparity is constantly varying . However , detectors positioned at the peak and trough of the gratings experience only a small ( 0 . 8-arcmin ) range in disparity within their correlation window , so the response is still high at the front and back surfaces . Even detectors at the centre of the grating ( zero disparity ) experience a range of only 2 . 4-arcmin disparity , and so give a clear , though reduced , response . For the high-amplitude sine-wave grating , Figure 8C , the situation is very different . Detectors at the centre of the grating now experience a 14-arcmin range of stimulus disparities . There is thus almost no visible response to the slanting regions of the grating which can be distinguished from chance responses to particular random dot patterns within the stimulus . Detectors centred on the peaks and troughs of the sine-wave experience a lower disparity range of 4 . 8 arcmin , and periodic blobs of higher activation are still just visible here . Thus overall , the high-amplitude sine-wave grating is barely visible in the correlator output . For the high-amplitude square-wave , Figure 8D , little is changed compared to the low-amplitude case , Figure 8B . Detectors in the center of the grating's front and back surfaces still experience uniform disparity , and so their response is undiminished . Detectors at the edges of the square-waves still only experience two disparities . That these are now further apart makes no difference: each disparity is still seen by half the window allowing correlations of about 0 . 5 even close to the edges . This is why the old model performed so much better with high-amplitude square-waves than with sines ( Figure 2 , bottom row ) . How does the size/disparity correlation change things ? Figure 9AB shows correlator output for our new model , for high amplitude sine- and square-waves at 3 . 8 cpd , the same frequency that was used in Figure 8 . For the low amplitude gratings , the correlator output remains almost exactly the same as shown in Figure 8AB , since the window-size remains close to that used in the fixed- window-size model . For high-amplitude gratings on the other hand , considerably larger windows will be used to detect the large disparities , as indicated by the black lines . For sine-wave gratings , this has relatively little effect . Detectors at the peaks and troughs of the grating now have a window-size of 2σ = 10 arcmin . The range of disparity they experience within their correlation window is therefore larger , at 10 . 7 arcmin as compared to 4 . 8 in Figure 8C . The correlation output in Figure 9A is therefore somewhat reduced compared to the old model , Figure 8C ( note slightly different colorscale ) , but the grating is still visible in the periodic “blobs” of higher correlation . For the square-wave , on the other hand , the increase in window-size has a more serious effect . The window now exceeds the grating half-period , meaning that correlation detectors at the middle of the front or back surfaces no longer sample only their preferred disparity , but also some disparities 15 arcmin away from their preferred value . Detectors at different vertical positions now vary only in the proportion of dots which are at their preferred disparity . Accordingly , not only are the “blobs” marking each front and back surface now lower in amplitude , but critically , they are no longer separated by clear regions of low activation ( compare Figure 9B vs Figure 8D ) . This is very damaging to the model's performance . Recall that , in order to assess spatial resolution , observers were asked to discriminate stimuli in which disparities were arranged as a periodic function of position ( gratings ) from those in which the same disparities were scattered at random ( noise ) . Figure 9CDEF shows the mean correlator output for both types of stimuli: that is , the grating templates for this frequency and amplitude ( Figure 9CD ) , and the noise templates for this amplitude ( Figure 9EF ) . The model's task , then , is essentially to decide whether the output to a given stimulus , Figure 9A and B , is a better match to the grating templates in Figure 9CD or to the noise templates in Figure 9EF . These are distinguished only by their periodicity . For the square-wave grating , the periodicity was perfectly clear with the fixed-window-size model ( Figure 8CD ) , and is much less obvious with the size-disparity correlation model ( Figure 9AB ) , thanks to the larger window sizes at the relevant disparities . In the new model , both the sine-wave and the square-wave output is now hard to distinguish from the noise patterns . This is why all our decision models gave similar results for both square-wave and sine-wave gratings . For the frequency and amplitude used in this example , the template matching decision model with known frequency performed at about 80% correct for both . Although we have concentrated on the template-matching decision model when explaining why the size-disparity correlation has the effect it does , qualitatively similar results were obtained from all four decision models examined ( see Text S1 and Text S2 ) . We conclude that stereoresolution is limited by the initial encoding of disparity , not by the particular read-out we have adopted . Similar conclusions were reached by Banks [6] , [8] and Harris et al [27] . Previous studies have suggested that our perception of depth patterns containing a large range of disparities may be limited by disparity gradient rather than the large disparities as such [6] , [8] , [15] , [22] , [23] . In particular a study by Tyler [4] found that the maximum depth limit , the disparity amplitude at which depth differences are no longer perceived in sinusoidal and square-wave disparity gratings , depends on corrugation frequency in a way that approximately corresponds to a straight line with slope −1 in log-log coordinates . Banks et al . [8] had previously shown that a constant window size local cross-correlation model performed in a qualitatively similar way when tested with sinusoidal disparity gratings . Here , we have replicated this finding and shown that when a size/disparity correlation is incorporated into the model it performs in the same way for square-wave disparity gratings , consistent with Tyler's results . The model achieves this despite lacking any sensors tuned to non-zero disparity gradients . Banks et al . suggested that the disparity gradient limit was a by-product of using local cross-correlation to estimate disparity [6] , [8] . However , as Tyler [4] recognized , this alone cannot explain why the frequency dependence of the upper depth limit exists for square-waves as well as for sine-wave gratings . We have found that incorporating a size/disparity correlation into a correlation-based model makes it perform consistently for random-dot patterns depicting both square-wave and sine-wave disparity gratings . This supports Tyler's conclusion [4] that the disparity gradient limit reflects a size/disparity correlation , rather than being solely a by-product of local cross-correlation . Models of stereopsis based on cross-correlation of local patches of the two eyes' images have a long history [23] , [28]–[30] . They are widely used in computer vision as a fast and relatively reliable approach of achieving stereo correspondence . They have often been used to model human vision [6] , [8] , [27] , [31] . Local cross-correlation is closely related to the “stereo energy” computation performed by cells in primary visual cortex [32]–[35] , although cells spectrally filter the local image patches before cross-correlating them . Models based on stereo energy units have also been used as models of human vision [19] , [21] , [35]–[38] . All these implementations have recognized that useful disparity estimates require the outputs of many stereo energy units to be combined in some way . For example , models have estimated disparity by combining the outputs of stereo energy units with different spatial locations [35] , [39] , or different spatial frequencies and/or orientations [21] , [36] , [40] . As we show in this paper , combining stereo energy units tuned to many different spatial frequencies and orientations can produce something which is formally identical to local cross-correlation of the unfiltered image . Stereo energy units based on phase disparity [32] , [41] naturally incorporate a size-disparity correlation . In this type of disparity encoding , the unit's preferred disparity Δx is roughly Δφ/2πf , where Δφ is its preferred phase and f its preferred spatial frequency . If the largest phase disparity and bandwidth are the same for all spatial scales , then the largest preferred disparity is inversely proportional to frequency and thus proportional to size . Tsai & Victor [19] used stereo energy units with phase disparity which therefore incorporated a size-disparity correlation . They showed that this model , with template-matching , was able to account for stereoacuity as a function of frequency in sine-wave luminance gratings ( NB these are luminance gratings at a constant depth , not random-dot patterns depicting sinusoidal depth modulation as in the present paper ) . Our model uses position disparity , in which size-disparity correlation does not arise naturally , but has been built in by design . This leads to an important difference between the two implementations . Our size-disparity correlation links disparity to the size of the window across which disparities are sought , but not to spatial frequency . Our correlation-based model includes information from all spatial frequencies , independent of window-size . Thus , the meaning of “size-disparity correlation” is somewhat different in the two cases . Our model suffers from many limitations , most of which were forced on us by the difficulty of running simulations with large numbers of neurons . Most previous studies have either used stimuli with a uniform disparity profile , meaning that it suffices to model neurons at only one location in the visual field [19]–[21] , or have modelled neurons at several locations but with only one spatial frequency and orientation [37] . In order for the model to detect gratings that vary in depth , we needed to compute responses in many locations in the visual field . It would have been very costly also to model the responses of stereo energy units tuned to many different spatial frequencies and orientations . We therefore used the cross-correlation technique [6] , [8] , [27] , [37] as a convenient short-cut to approximate the responses of many stereo energy units tuned to all possible frequencies and orientations . Our analysis showing how local cross-correlation can be implemented exactly by stereo energy units is clearly idealized . Most notably , we integrated the response over all spatial frequencies , while keeping the receptive field size constant . Extending the integration to infinite spatial frequency is obviously unrealistic , although in practice will not greatly affect the results , since unrealistically high spatial frequencies will be removed from the images by the optical blurring and pre-processing . Keeping the receptive field size constant is a more serious limitation . Of course , primary visual cortex contains cells with a range of receptive field sizes . We have included only one window-size ( receptive field size ) at each preferred disparity . Once again , this was for reasons of computational economy . We regard the window-size within our model as representing the smallest receptive field sizes which contribute significantly to disparity detection . Ideally , we would have included a range of window-sizes at every disparity , with the smallest window-size at each disparity increasing as a function of disparity . However , since stereoresolution is limited by the smallest windows present , we would not expect this to alter our results substantially . Keeping the receptive field size constant corresponds to postulating that bandwidth declines with spatial frequency , as it does in the macaque [42] . Assuming Gabor receptive fields , a Gaussian envelope with standard deviation 3 arcmin implies a bandwidth of 0 . 5 octaves at 15 cpd; at 5 cpd the bandwidth ranges from 1 . 5 octaves ( sine phase ) to 2 . 0 octaves ( cosine phase ) , while at 0 . 5 cpd the bandwidth is 1 . 8 octaves for sine phase ( cosine-phase cells are low-pass ) . These values are consistent with those reported in macaque [42] . At a given frequency , the bandwidth will be narrower for large RFs than for small ones . As mentioned in the previous section , our correlation-based model includes information from all spatial frequencies , independent of window-size . This is a consequence of the mathematical trick we have used to integrate over frequencies . In fact , several lines of evidence suggest that larger disparities are detected predominantly by mechanisms tuned to lower spatial frequencies in the luminance domain [16] , [43] , [44] . Thus , it would be more realistic to include a weight term in the integration over luminance spatial frequency , weighting the integral towards lower frequencies at the larger disparities/window-sizes , and towards higher frequencies at the smaller disparities/window-sizes . We have not included any neuronal noise within our model , nor have we attempted to reproduce human stereoacuity for gratings , i . e . the smallest disparity amplitude detectable at each frequency . In principle , it would be simple to add this . Stereoacuity is limited by the spacing of disparity detectors , and by neuronal and stimulus-dependent noise ( random correlations between non-corresponding parts of the dot pattern , for example ) . However , stereoacuity is also clearly limited by processing in higher cortical areas and not solely by the information available in V1 [45] , [46] . This means that the model's assumptions about extra-striate processing would probably play a much more critical role in reproducing stereoacuity data than they have done here in reproducing stereoresolution . We have only modeled the detection of horizontally-oriented disparity gratings . Humans find these easier to detect than vertically-oriented gratings [7] , [47]–[49] . It is currently unclear what model features would be required to match this feature of stereo vision . However , a clue may be that the disparity tuning surfaces of real cortical neurons are extended horizontally and are relatively narrow vertically [50] . In any stereo algorithm , the choice of window-size represents a trade-off between resolution and accuracy . Large windows collect support over a wider region of the image , enabling greater accuracy and robustness against false matches . However , they also lose the ability to track rapid changes in depth . For this reason , disparity steps are detected most accurately by windows which are elongated parallel to the edge and narrow orthogonal to the edge [23] . Thus , the horizontally-elongated disparity tuning surfaces of real neurons would be expected to give greater sensitivity to changes in depth along a vertical direction in the image , as observed in humans . Further modelling work is required to examine whether models which incorporate this known anisotropy in V1 neurons can reproduce the anisotropy in human depth perception . A great deal is now known about how disparity is encoded within V1 . Much less is known about how this activity is read out in higher areas to result in depth perception and judgments on tasks such as our grating detection [51] . Thus , our model is necessarily much more speculative here . Is it realistic to assume that our brains have access to “templates” representing the expected V1 output for different stimuli ? Physiologically , these templates could be represented as the synaptic weights between V1 and “grating detector” units in a higher visual area ( see [20] for a more detailed account ) . While neurons specifically tuned for disparity gratings have not been reported , “grating detector” units would also respond preferentially to disparity curvature and slant , and such neurons are known to exist in areas IT and MT [11] , [12] . Alternatively , such neurons might be constructed as required . In areas such as LIP , neurons quickly adapt their responses to the particular task requirements at hand [52] . In this view , participants may be able to construct adequate templates simply from the few disparity gratings they are shown as demonstration stimuli . Local cross-correlation within a fixed window has been postulated as a model of human stereo vision . This model accounts for stereoresolution when depth is modulated sinusoidally , but gives incorrect predictions for square-waves . We have shown that introducing a size/disparity correlation , such that larger disparities are detected within coarser windows , reconciles the local cross-correlation model with human stereoresolution on both square- and sine-wave disparity gratings . This supports the original conclusion of Banks et al . [6] that the limit on spatial stereoresolution is set by the smallest receptive field size of V1 neurons , which respond best to locally frontoparallel surfaces [6] , [8] . There is thus no need to invoke further limits imposed by cells in extrastriate cortex tuned to more complicated aspects of disparity such as slant and curvature . Such cells can be created by combining the outputs of V1 neurons with different preferred disparities , but in this view , they inherit a fundamental limit on stereoresolution , set in primary visual cortex . | Stereo depth perception requires the brain to detect displacements of features between the two eyes' images . Several current models use local cross-correlation between the two eyes' images , looking for small patches that are the most similar between the two images . There is evidence that cells in primary visual cortex are doing something very similar . This model captures many aspects of human depth perception , notably why we can see depth variation on much coarser scales than luminance variation . This suggests that the spatial resolution for depth perception is set in primary visual cortex . However , the model as currently implemented cannot explain why humans are as good at detecting sine-waves in depth as they are at detecting square-waves , a fact that we have previously raised as a challenge to the model . Here we show that if we introduce a size/disparity correlation , such that larger patches are used when searching for larger displacements of features between the two images , then simple models based on local cross-correlation can explain human performance for both sine- and square-wave depth corrugations , without needing to invoke more complicated disparity processing . This supports the proposal that spatial resolution for depth perception is set in primary visual cortex . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"visual",
"system",
"computational",
"neuroscience",
"biology",
"sensory",
"systems",
"neuroscience"
] | 2011 | Spatial Stereoresolution for Depth Corrugations May Be Set in Primary Visual Cortex |
Contractile function of striated muscle cells depends crucially on the almost crystalline order of actin and myosin filaments in myofibrils , but the physical mechanisms that lead to myofibril assembly remains ill-defined . Passive diffusive sorting of actin filaments into sarcomeric order is kinetically impossible , suggesting a pivotal role of active processes in sarcomeric pattern formation . Using a one-dimensional computational model of an initially unstriated actin bundle , we show that actin filament treadmilling in the presence of processive plus-end crosslinking provides a simple and robust mechanism for the polarity sorting of actin filaments as well as for the correct localization of myosin filaments . We propose that the coalescence of crosslinked actin clusters could be key for sarcomeric pattern formation . In our simulations , sarcomere spacing is set by filament length prompting tight length control already at early stages of pattern formation . The proposed mechanism could be generic and apply both to premyofibrils and nascent myofibrils in developing muscle cells as well as possibly to striated stress-fibers in non-muscle cells .
Several groups have proposed polarity sorting of actin filaments by myosin activity [30] , [31] . However , those mechanisms localize myosin filaments close to actin filament plus-ends , which is opposite to the myosin localization observed in striated stress fibers and myofibrils , where myosin resides in the mid region between neighboring crosslinks that attach to the actin plus-ends , see figure 1 . In simulations of a generic bundle of polar filaments crosslinked by populations of both plus- and minus-end directed motors , Zemel et al . demonstrated sarcomeric ordering with correct polarity sorting if applied to actin bundles [32] , see also [33] . However , in the context of actin bundles , there is little evidence for an unconventional , minus-end directed myosin [34] . The concept of a plus-end tracking crosslinker as put forward here has been introduced earlier in the framework of a mean field description [35] . Recently , the group of Joanny proposed a description for the establishment of striated order by stress-induced polarity sorting in terms of a one-dimensional , active gel [36] . However , this mechanism relies on a phenomenological coupling term and as such does not provide insight into the microscopic mechanisms that eventually underlie this coupling .
To describe the transition from an unstriated actin bundle to a striated one , we consider in our simulations a single , long bundle that consists of parallel actin filaments aligned with the long axis of the fiber ( chosen to be the -axis ) . In biological cells , striated fibers have an extension in the transverse direction of only a few hundred nanometers . In our computational model , we therefore ignore the transverse position of the individual actin filaments and assume that each filament can interact with any other provided their projections on the fiber axis overlap . This assumption corresponds to a mean-field treatment of the transverse degrees of freedom . For simplicity , filaments are assumed to be rigid and incompressible with respective lengths , . For figures 2 , 3 , 4 , filament lengths are monodisperse with for all ; whereas for figure 4 filament length are chosen from a log-normal distribution that satisfies and , see also the Supporting Information ( SI ) . Actin filaments are structurally polar and filaments ends are referred to as either the plus-end or the minus end , see figure 2A . We distinguish actin filaments with plus-ends that face either the positive -direction ( orientation , blue in figures ) , or the negative -direction ( , red in figures ) . Actin filament polymerization is a non-equilibrium process and polymerization and depolymerization rates differ for the plus- and minus-ends , respectively . In a deterministic description of filament polymerization dynamics at steady state , we assume that the individual actin filaments possess a net polymerization speed at their plus-ends whose absolute magnitude is equal to the net depolymerization speed at their minus ends . ( The corresponding polymerization rate is thus , where denotes actin monomer length . ) The broken symmetry of the polymerization dynamics results in a velocity difference between the current plus-end position of the -th filament ( with a lab-frame velocity ) and its individual monomers ( velocity ) . This phenomenon is commonly referred to as filament treadmilling [2] , see figure 2A . For an actin filament that is subject only to a friction force for motion relative to the cytosol , the plus-end advances with velocity , while the monomers are at rest , , and the friction force is zero due to force balance . Here , is an effective friction coefficient that accounts for rapid binding and unbinding interactions with the surrounding actin gel , and , possibly , integrin-mediated interactions with a substrate . This situation changes , if rigid crosslinks between actin filaments constrain their motion . In addition to treadmilling actin filaments , the second key ingredient of our model is a processive , plus-end tracking actin crosslinker that effectively describes the concerted action of several Z-body proteins , see figure 2A . In our simulations , actin filaments become irreversibly crosslinked with a rate , if their respective plus-end positions and are close . The precise functional form of affects results only slightly and we chose with and ( measured in units of ) . A case of reversible plus-end crosslinking for which actin filaments can spontaneously dissociate again is considered in the SI text S1 . Subsequent crosslinking results in the formation of ‘actin filament clusters’ that consist of many actin filaments whose respective plus-ends are aligned and which are permanently crosslinked by effective plus-end tracking crosslinkers . Such an actin cluster will move as a whole subject to the sum of forces acting on its constituent actin filaments . These crosslinked actin clusters can grow by fusion . If two actin filaments belonging to two small clusters establish a new crosslink , the new -coordinate of the merged cluster is taken as the weighted average of the respective -coordinates of the two clusters . In real nascent striated fibers , the longitudinal alignment of plus-ends of crosslinked filaments supposedly involves a dynamic reorganization of the crosslinking Z-band on a time-scale of several minutes [27] , which is not included in our minimal model . Importantly , the proposed plus-end tracking crosslinkers are assumed to be processive , i . e . they always remain locally attached to the filament plus-ends , even in the presence of actin treadmilling of the crosslinked filaments , see figure 2A . As a consequence , the center of an actin cluster is subject to polymerization forces of its constituent actin filaments and moves with a velocity that is determined by a local force-balance of cytosolic friction forces . This force balance is spelled out below in the paragraph ‘Active motion of a single actin cluster’ . For figure 2 only , a generic friction force for the relative sliding of two actin filaments is introduced , which is proportional to the mutual length overlap of the two filaments . Here , denotes a friction coefficient . Finally , the motion of actin clusters is determined in each time-step in a self-consistent manner by a balance of forces . We employ periodic boundary conditions with a system size ; a case of static boundary conditions is discussed in the SI text S1 . Total filament numbers were for actin filaments and for myosin filaments ( for figure 2 ) . In the premyofibrils of developing muscle cells as well as in stress fibers of non-muscle cells , the molecular motor myosin II polymerizes into bipolar filaments of a few hundred nanometers length that have numerous myosin heads at either end [37] . Individual myosin heads change conformations via ATP-dependent cycles , while synchronously attaching to ( and pushing on ) actin filaments . Despite the low duty ratio of individual myosin heads , the large number of these heads ensures a processive and significant myosin-actin interaction . In our simulations , we employ a coarse-grained description of bipolar myosin filaments of length , in which the individual myosin heads at the two ends of a myosin filament are described as a pair of ‘actin binding sites’ , see figure 3D . Each of these two actin binding sites can bind one actin filament in a polarity-specific way . Attachment and detachment to actin filaments are described as simple Poisson processes with constant rates . Once a myosin filament is attached to an actin filament , we assume a linear force-velocity relation for myosin walking past the actin filament , see also SI text S1 for details . Myosin walking speed is directly related to an active myosin force ( that also equals the myosin stall force ) . While myosin filaments tend to walk towards actin filament plus-ends , a strong backward force acting on the actin filament can push both the actin and myosin filaments in the opposite direction . In our simulations , actin treadmilling and associated polymerization forces indeed cause such a motion of myosin filaments towards actin filament minus-ends . For sake of illustration , consider an isolated actin cluster that comprises a total number of filaments of positive orientation that treadmill towards the -direction ( blue in figures ) as well as a number of filaments of negative orientation ( treadmilling towards the -direction , red in figures ) . In our deterministic description of filament treadmilling , the monomers of the filaments with positive orientation all move with the same velocity , whereas those of the filaments of negative orientation all move with velocity . Here is treadmilling speed and the ( yet unknown ) velocity of the crosslinking Z-band . The two sets of filaments exert respective friction forces on the cytosol , and , where is actin filament length and a cytosolic friction coefficient per actin filament unit length , see above . By Newton's third law , the counter forces of these cytosolic friction forces act on the Z-band and amount in this case exactly to the polymerization forces of the treadmilling actin filaments . Local force balance at the Z-band , , determines the velocity of this single cluster as . The structure factor is a standard measure used in condensed matter physics to quantify the regularity of periodic order [38]; it is defined as the squared amplitude of the Fourier transformed density-density correlation function . We can adopt the structure factor to quantify sarcomeric order in our simulations: We characterize the crosslinked clusters by their respective plus-ends positions and total filament number . We then define . Examples of this structure factor as a function of wave vector are shown in figure 3A . Periodic order is characterized by a series of very sharp , so-called Bragg peaks . The height of the principal Bragg peak ( red point ) defines a sarcomeric order parameter . Our computational model primarily serves as a proof of physical principle . The emergence of striated order in the framework of this model is a robust process that is not sensitive to the parameter choices . A sensitivity analysis can be found in the SI text S1 . Since the parameters in the model represent effective quantities ( which , in particular , average out transverse degrees of freedom ) , numerical estimation of these parameters is difficult . Therefore , our simulation results are presented assuming specific ratios of parameters only , without specifying their absolute values in physical units . Nevertheless , we now present a rough guide to these parameter values . In unstriated stress fibers , actin filament length range from , myosin filaments have a length of about [39] . Thus , the length-scale , which sets the mean length of actin filaments in our simulations , may be chosen as . Actin polymerization speeds of up to about have been observed in vitro , while filopodia protrusion driven by actin polymerization can be as fast as , see [40] and references therein . In stereocilia , actin polymerization is highly regulated and polymerization speeds can be as low as [41] . While in general the polymerization speed of an actin filament is force-dependent with a stall force in the pico Newton range [37] , [42] , we assume here a constant mean polymerization speed . The ratio sets the primary time-scale of sarcomeric pattern formation in our simulations , and it is shown below that sarcomeric ordering in established within for typical parameter choices . Experimentally , sarcomeric pattern formation evolves on a time-scale of hours [5] , which corresponds to an actin polymerization speed in our simulations . This estimated actin polymerization speed would be lower than that in filopodia , but significantly larger than the speed measured e . g . in stereocilia . Myosin filaments may exert pico Newton forces on actin filament at full activation . Decoration of actin filaments with troponin/tropomyosin reduces myosin walking , which would correspond to lower values for the active myosin force in our simulations . Below , we argue that myosin walking towards actin filaments impedes the correct , sarcomeric polarity sorting , which is established in our model by actin treadmilling . The effective friction for an actin filament moving within a dense bundle is presumably dominated by binding-unbinding interactions with the surrounding actin gel as well as integrin-mediated interactions with the substrate . The corresponding effective friction coefficient is expected to be orders of magnitude larger than the hydrodynamic friction coefficient for motion in water [43] , . Assuming a friction coefficient for single actin filaments ( per unit length ) in the range , we would find for a filament of length moving at a speed of friction forces in the range , i . e . well below both the stall force of actin polymerization and the buckling force of single actin filaments . We did not incorporate filament diffusion explicitly in our model , as thermal motion will be small in a dense bundle . Note , however , that dynamic myosin forces with short correlation time can induce stochastic , bidirectional motion of filaments . Several studies pointed out the effect of integrin-mediated anchorage of Z-lines for myofibrillogenesis [44]: Although , initial I-Z-I complexes did form even in the presence of RNAi against integrin , Z-body stability was apparently reduced and bundle integrity was impaired in these experiments [28] . Presumably , integrins play multiple roles starting with the stabilization of I-Z-I-complexes , which corresponds in our model to a reduced rate of dissociation of single filaments from an actin cluster ( see also SI text S1 ) . Secondly , anchorage reduces the mobility of I-Z-I complexes , which would correspond to an increased total friction coefficient of actin clusters . As anchored I-Z-I complexes still showed some residual mobility , anchorage must be dynamic and allow for slippage . Thus , dynamic anchorage affects the effective parameters in our model , but does not change its basic , qualitative features . Finally , stable anchorage at the two terminal ends of an acto-myosin bundle specifies its boundary conditions; a simulation case of static boundary conditions is shown in the SI to mimic a bundle whose terminal ends are grafted by focal complexes to a substrate .
In our simulations , we consider a minimal , one-dimensional model of a bundle of treadmilling actin filaments . Actin filaments with nearby plus-ends can form a stable crosslink by a complex of molecules ( that eventually become the Z bodies ) that holds the plus-end of the two actin filaments , but still allows for actin polymerization at the plus-end , see section ‘The computational model’ and figure 2A . Subsequent crosslinking gives rise to the formation of actin clusters that consist of several actin filaments whose respective plus-ends are aligned and which are permanently crosslinked by effective plus-end tracking crosslinkers . Each actin cluster will move as a whole subject to the sum of forces acting on its constituent actin filaments . These crosslinked actin clusters can grow by fusion and eventually self-organize into sarcomeric order , thus representing precursors of the I-Z-I complexes observed during early myofibrillogenesis [45] . To gain basic insight into the process of actin cluster formation and coalescence , we first simulated bundles of treadmilling actin filaments and crosslinks without myosin filaments; the effect of myosin filaments is discussed in the next section . We observe the formation and coalescence of clusters of crosslinked actin filaments , see figure 2B . In each actin cluster , the constituent actin filaments polymerize at their plus-ends , thereby pushing against the processive crosslinkers of the Z-band . The growing actin filaments themselves move away from the Z-band in a form of ‘local retrograde flow’ . The polymerization forces exerted by the polymerizing actin filaments on the Z-band are counter-balanced by friction forces that constrain the motion of the actin filaments . Any imbalance in the number of filaments of the two orientations will result in a net polymerization force and thus net motion of the cluster . The collision of two clusters can result in their mutual coalescence and the formation of a larger cluster . If actin filaments slide past each other without any friction , all filaments would eventually coalesce into a small number of very large clusters , see figure 2B . If we assume , however , a hypothetical , effective friction between moving actin filaments , coalescence of actin clusters above a critical size is dynamically impeded and sarcomeric order results . The arrest of actin cluster coalescence due to our proposed inter-filament friction can be understood on qualitative grounds as follows: The active motion of a single actin cluster is driven by an imbalance of polymerization forces acting on the Z body that can arise from an imbalance between the respective numbers of the constituent filaments of the two different filament orientations . This net polymerization force is balanced by the total friction force of the actin cluster ( and possibly additional forces due to interactions with neighboring clusters ) . Since this total friction is proportional to the total number of filaments in the actin cluster , whereas the net polymerization force ( due to statistical imbalance ) roughly scales only as the square root of this number , smaller actin clusters move faster than larger clusters . Furthermore , the mutual friction force between two overlapping actin clusters adds a friction term to the force balance that scales as the product of the respective filament numbers and therefore will eventually stall the approach of actin clusters above a certain size . In the more complex case of an actin bundle , the force balance for all actin clusters has to be considered . Friction between sliding actin filaments may be provided by fast , dynamic crosslinking along the entire lengths of the actin filaments by a second set of crosslinkers . Next , we discuss the possibility that myosin filaments serve as such a dynamic actin crosslinker , which mediates an effective repulsion between neighboring actin clusters . We now augment the simple actin bundle model by adding bipolar myosin filaments that can dynamically attach to actin filaments in a polarity-specific way , see figure 3D . The relative motion of actin and myosin filaments is governed by a linear force-velocity relation for myosin walking , see section ‘The computational model’ . While myosin activity leads to ‘walking’ of the myosin towards the actin plus-ends , the local retrograde flow of treadmilling actin filaments transports the myosin in the opposite direction as in figure 3A . For the case shown , actin treadmilling outpaces active myosin walking towards actin plus-ends , resulting in highly regular sarcomeric patterns with myosin localized near the actin minus-ends . Any actin filament , which is grafted at its plus-end in a Z-band has to polymerize against this obstacle , and is pushed away from the cluster center in a form of ‘local retrograde flow’ , see figure 3C . For weak active myosin forces and thus slow active myosin walking , myosin filaments attached to such an actin filament are dragged along with this retrograde flow towards the depolymerizing minus-end of the actin filament . This ‘actin conveyor belt’ not only transports myosin filaments to the future A-band , but also generates an effective repulsion between neighboring I-Z-I clusters mediated by crosslinking actin filaments , which ensures a regular sarcomeric spacing of actin clusters . Stronger active myosin forces drive the myosin towards the actin plus-ends and therefore slow down sarcomeric ordering , see figure 3D . Above a critical force level , active myosin walking dominates actin treadmilling , and a wrong polarity sorting results that localizes myosin at the plus-ends and thus impedes sarcomeric ordering . To account for a distribution of actin filament lengths , we simulated bundles comprising actin filaments of different lengths . For simplicity , we chose a static polydispersity for the actin length given by a unimodular distribution of fixed mean length and tunable width . Remarkably , sarcomeric ordering occurred even for considerable length variability , though with a sarcomeric order parameter that decreased monotonically with , see figure 4 . Sarcomeric spacing increased as a function of length variability , showing that the longest actin filaments set sarcomere spacing . Using an exponential distribution for actin filament length instead of a unimodular distribution resulted in no apparent sarcomeric ordering ( not shown ) . Assuming static filament lengths allows us to study separately the mechanisms that result in actin filament length control and actin turnover , which we now discuss . Actin filament length control and turnover of filaments both depend crucially on the polymerization and depolymerization dynamics of actin filaments . Thus , length control and filament turnover are in principle inseparable . This being said , we nonetheless aimed at isolating the qualitative effect of actin turnover . To this end , we augmented our computational model by including prototypical actin dynamics that differentiates between idealized dynamic regimes of either ( i ) steady-state treadmilling with constant actin filament length , ( ii ) ‘actin catastrophies’ characterized by fast and complete depolymerization of filaments that occur with rate , and ( iii ) rapid de novo polymerization of new actin filaments [46] . These simple limits are not intended to realistically depict actin dynamics . Rather they allow us to study the qualitative effects of actin filament turnover , without changing the filament length distribution . As expected , actin filament turnover interferes with the formation of large actin clusters and results in reduced sarcomeric order , see figure 5 . Surprisingly , myosin is still sorted into regular A-bands even for considerable actin turnover rates . We conclude that partial polarity sorting of actin filaments is sufficient to sort myosin into A-bands . This may provide an explanation for experimental observations in which myosin ordering was observed to precede the formation of large , periodically spaced I-Z-I complexes . Our simulations suggest that sarcomere spacing is set by the length of actin filaments at early stages of striated ordering . How is actin filament length controlled within a pool of highly dynamic actin filaments ? Capping proteins regulate filament polymerization and depolymerization rates . However , on their own , these proteins do not provide a means to tune the average filament length to a set point since they act locally in a manner that is not sensitive to the total length of a filament . Energetically favorable crosslinking or attraction of actin filaments all along their length can result in a unimodular length distribution as this ensures maximal mutual overlap of filaments [47] . However , to allow for filament sliding and sorting , such crosslinking would have to be highly dynamic . Alternatively , severing agents ( such as ADF/cofilin-like UNC-60B [23] ) are recruited by actin filaments in a length-dependent manner and can provide a generic feedback mechanism that controls actin filament length [48]–[50] . We consider a simple implementation of actin filament severing assuming that filaments elongate by polymerization at their plus-end with constant polymerization speed , whereas the minus-end is stable . A generic severing agent can bind with constant rate anywhere along the filament and cut it there . Since the minus-end facing fragment of a cut actin filament comprises mainly ADP-bound actin monomers and thus is less stable , we assume that this fragment rapidly depolymerizes after severing , see figure 6A . This simple severing mechanism results in a unimodular length distribution at steady state , see figure 6B as well as SI text S1 . For an intuitive explanation for this length control mechanism , note that longer filaments with more monomers have a higher probability to recruit a severing agent within a certain time interval compared with shorter filaments: In this scenario , filaments act as ‘binding antennas’ for severing agents . Figure 6 shows the emergence of sarcomeric order from an initially unstriated bundle for which actin filaments polymerize and are cut by severing agents .
Here , we proposed a simple , generic , and robust mechanism for striated pattern formation in a crosslinked bundle of aligned actin filaments . This physical mechanism of sarcomeric ordering is based on the formation of small actin clusters by the plus-end crosslinking of single actin filaments and the subsequent coalescence of these smaller actin clusters into larger ones , which are reminiscent of the I-Z-I complexes observed during early myofibrillogenesis [45] . This mechanism represents a way to establish cytoskeletal order on length-scales of tens of microns from micron-size building blocks independent of any external scaffolding . Termination of cluster coalescence and stabilization of sarcomeric units requires a repulsive force between actin clusters . In mature myofibrils , the giant protein titin acts like an elastic spring and could serve this function . However , it is questionable if titin could play its role as a spacer between Z-bodies already at these early stages . While the N-terminal domain of titin is involved in early Z-body formation [28] , the M-line epitope of titin associated to its C-terminal domain is established only after a delay [51] and ligand binding may be required to stretch the titin protein so that it spans the sarcomere; thus , at early times , titin may not set the initial sarcomere spacing [20] . Here , we studied polymerization forces from polymerizing actin filaments as a possible mechanism to generate repelling forces between actin clusters . A similar mechanism may apply to stress fibers in adherent , non-muscle cells as well as to stress-fiber like structures in developing muscle cells . The assembly of mature myofibrils in striated muscle cells has been proposed to be a multi-step process [8] that starts with the formation of unstriated , stress fiber-like acto-myosin bundles near the plasma membrane , followed by the establishment of sarcomeric order within these bundles [10] , possibly by actin cluster formation and coalescence as proposed here . These striated bundles represent an important intermediate in the assembly of mature myofibrils and are termed nascent myofibrils . Nascent myofibrils can grow by incorporating free actin and myosin filaments in a mechanism of “self-templating” . Additionally , they can fuse with each other into a single fiber of increased diameter after aligning their respective periodic patterns [5] , [52] . Finally , maturation processes and actin length fine-tuning regularizes sarcomeric order resulting in mature myofibrillar “crystals” . This myofibrillogenesis pathway represents a succession of hierarchical ordered states . We speculate that the assembly of striated stress fibers in non-muscle cells may follow a partial sequence of myofibrillar steps . Initial sarcomeric pattern formation in unstriated bundles would be a key step in this pathway and could rely on similar physical mechanisms both in muscle and non-muscle cells . Experimental visualization of early sarcomeric pattern formation including actin filament length distribution , polymerization dynamics and their associated forces is technically challenging , but may be essential to test theoretical models of sarcomere formation . Little is known about the dynamics of actin filaments at early stages of sarcomeric pattern formation . In mature myofibrils , actin polymerization dynamics has been observed at both the plus- and the minus end [6] , [29] . These experiments show that actin filaments are highly dynamic even in these apparently stable striated bundles and that Z-bodies may act as plus-end tracking actin crosslinkers . It should be noted that at these late stages , actin filament treadmilling was not observed; thus , actin treadmilling may be limited to the early stages of striated ordering . In vitro experiments with reconstituted actin stress fibers [53] might serve as an accessible experimental system to study sarcomeric pattern formation and actin polarity sorting . Additionally , filament treadmilling in the presence of crosslinkers is a source of expansive stress and should reduce any contractile prestress in the bundle , or even give rise to an overall expansive stress . This prediction could be tested in future experiments , possibly by laser nano-surgery of unstriated bundles . Myosin filaments walk towards actin plus-ends . Unless counter-acted by other mechanisms , myosin walking would result in a wrong localization of myosin at nascent Z-bodies and thus impair sarcomeric ordering . In our model , actin treadmilling counter-acts myosin walking and transports myosin towards the future M-band , provided active myosin forces are not too strong . It has been suggested that in some species , the early establishment of sarcomeric patterning involves a non-muscle isoform of myosin II , which is later replaced by muscle-specific myosin II [8] . It is tempting to speculate that muscle myosin allows for maximal force generation , whereas non-myosin filaments play a role as structural elements during the early establishment of striated order , for which , according to our model predictions , strong myosin forces could be obstructive . Alternatively , the decoration of actin filaments with tropomyosin may limit myosin walking during the early stages of sarcomeric pattern formation and thus prevent the active myosin forces from disrupting the treadmilling imposed myosin localization as we suggest . This is consistent with a recent study by Rui et al . , which showed that sarcomeric pattern formation was impaired in the presence of RNAi against tropomyosin and troponin [28] . In conclusion , we put forward a model that includes a minimal number of generic mechanisms that results in sarcomeric polarity sorting in in silicio acto-myosin bundles . We acknowledge the possibility that the mechanism presented here is only partial and that other mechanisms also contribute to sarcomeric pattern formation that can be tested experimentally . In particular , details of our computational model can differ from the genesis of sarcomeres in developing muscle cells: Actin filament buckling as observed in reconstituted in vitro systems [12] , [53] may reduce the myosin mediated repulsion force between neighboring actin clusters . Also , adhesive linkage of nascent Z-bodies to an extra-cellular substrate could reduce actin cluster motility [7] , [44] . We believe , however , that our theoretical study helps identify key elements of sarcomeric pattern formation . We propose that the length of sarcomere constituents such as actin filaments must be tightly controlled as it is expected to set sarcomere length at early stages of striated ordering . The emergence of sarcomeric order from the active condensation of actin clusters fits into the general framework of cytoskeletal pattern formation by active self-organization , which provides an alternative to external templating mechanisms . | Muscle contraction driving voluntary movements and the beating of the heart relies on the contraction of highly regular bundles of actin and myosin filaments , which share a periodic , sarcomeric pattern . We know little about the mechanisms by which these ‘biological crystals’ are assembled and it is a general question how order on a scale of 100 micrometers can emerge from the interactions of micrometer-sized building blocks , such as actin and myosin filaments . In our paper , we consider a computational model for a bundle of actin filaments and discuss physical mechanisms by which periodic order emerges spontaneously . Mutual crosslinking of actin filaments results in the formation and coalescence of growing actin clusters . Active elongation and shrinkage dynamics of actin filaments generates polymerization forces and causes local actin flow that can act like a conveyor belt to sort myosin filaments in place . | [
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] | [
"physics",
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"cell",
"mechanics",
"biophysics",
"simulations",
"biophysics",
"biomechanics",
"computational",
"biology"
] | 2012 | Sarcomeric Pattern Formation by Actin Cluster Coalescence |
Antiviral resistance in influenza is rampant and has the possibility of causing major morbidity and mortality . Previous models have identified treatment regimes to minimize total infections and keep resistance low . However , the bulk of these studies have ignored stochasticity and heterogeneous contact structures . Here we develop a network model of influenza transmission with treatment and resistance , and present both standard mean-field approximations as well as simulated dynamics . We find differences in the final epidemic sizes for identical transmission parameters ( bistability ) leading to different optimal treatment timing depending on the number initially infected . We also find , contrary to previous results , that treatment targeted by number of contacts per individual ( node degree ) gives rise to more resistance at lower levels of treatment than non-targeted treatment . Finally we highlight important differences between the two methods of analysis ( mean-field versus stochastic simulations ) , and show where traditional mean-field approximations fail . Our results have important implications not only for the timing and distribution of influenza chemotherapy , but also for mathematical epidemiological modeling in general . Antiviral resistance in influenza may carry large consequences for pandemic mitigation efforts , and models ignoring contact heterogeneity and stochasticity may provide misleading policy recommendations .
The use of chemotherapy in the treatment of pathogenic disease places selective pressures on the pathogen to develop resistance to the treatment [1] . Since failure of chemotherapeutic agents in the treatment of influenza can cause large morbidity and mortality , much work has been done to understand the biology of – and assess the public policy regarding – resistance [2]–[5] , this is especially important in the light of recent studies on the evolution of transmissibility of highly pathogenic avian influenza ( H5N1 ) [6]–[9] . The most widely used antiviral agents , neuraminidase inhibitors ( NIs ) oseltamivir and zanamivir have demonstrated beneficial effects on pandemic and seasonal influenza strains , and thus play key roles in the planning of mitigation of epidemics [3] , [5] , [10]–[13] . Though fundamentally important to the transmission dynamics of infectious disease , the bulk of current studies examining the effects of treatment on resistance to therapies have ignored contact structure [14] and timing of treatment [15] , [16] . Given the surprising and largely unpredictable evolutionary trajectories exhibited by influenza [6] , the role of structure in populations may have significant effects on these trajectories . Here we employ network models of influenza transmission extending previous work [2] to incorporate the effects of contact structure and timing of antiviral treatment . Network models are a robust framework for studying the transmission dynamics of infectious diseases in structured populations [17] , [18] . Read & Keeling ( 2003 ) [14] examined the evolution of a pathogen on networks with varying contact structures , without the effects of treatment . They find differential levels of virulence depending on the clustering of the contact network . Previous studies have examined the role of treatments on networks of disease transmission . Pastor-Satorras ( 2002 ) [19] suggested targeting vaccination by node degree . While extremely effective in theory , identifying high degree individuals a priori is practically impossible . Cohen et al . ( 2003 ) [20] extended this idea to vaccinate an individual and one of the individual's contacts at random . Thus by design , the probability of identifying high degree individuals is greatly increased . This method has been shown empirically to be more effective at detecting influenza transmission early than by using a randomly selected group [21] . In addition to the problem of identifying individuals for efficient treatment , the timing of treatment plays directly into the evolution of resistance . Wu et al . ( 2009 ) [15] found that in a pandemic scenario with limited supplies of antivirals , it was beneficial to use a small amount of a secondary drug early in the epidemic to ‘hedge’ against the evolution of resistance . Hansen and Day ( 2011 ) [16] use optimal control theory to explore the effects of changing treatment over the course of an epidemic . They find that in a well-mixed , homogenous population it is optimal to fully treat a population as long as the timing is correct as they derive . While much important work has been done , the bulk of studies to this point have either ignored stochasticity [22] , [23] or contact structure [14] , [24] or both [25] , the effects of which have been previously shown to be significant [26] . The goal of the present work is to combine network simulation models of evolution of pathogen resistance under chemotherapy and explore the effects of treatment timing and treatment regimes ( targeted versus non-targeted ) on the development and persistence of resistance . We focus on influenza and as we model resistance explicitly , we wish to answer three questions: one , to minimize resistance , should treatment be initiated at all in epidemics ? two , if treatment is initiated , how does its timing affect the emergence and persistence of resistance in structured populations ? and three , which treatment regime , targeted by degree or not , leads to the least amount of resistance ? The approach taken here is novel in that our model combines stochasticity and population structure in assessing the role of treatment , and find results contrary to previous studies .
We extend an ordinary differential equation ( ODE ) model of treatment and resistance to influenza antivirals developed by Lipsitch et al . ( 2007 ) [2] . Whereas they considered both prophylactic and therapeutic treatment in well-mixed , homogenous populations , we consider only reactive treatment in structured populations . We limit our exploration to treatment because current guidelines suggest limiting prophylactic use of antivirals to individuals at high risk [5] . Our model features five possible states for individuals: susceptible ( ) , infectious and untreated ( ) , infectious and effectively treated ( ) , infectious with a resistant strain ( ) , or recovered ( ) . The dynamics then obey the following rules: susceptibles become infected at rates , , and from untreated , treated and resistant individuals , respectively; wild-type infection ( from or individuals ) is treated with probability ; those treated develop de novo resistance with probability ; resistant infections ( transmitted by ) transmit only this strain ( i . e . , no reverse mutation ) ; and infectious individuals recover at rates , respectively . We assume treatment reduces transmissibility but does not affect the rate of recovery . Disease propagation has been the subject of massive modeling efforts in recent network theory spanning multiple approaches and disease models [17] , [27]–[29] . While the standard ODE treatment of epidemics is essentially a coarse-grained mean-field model of disease propagation in a population with homogeneous mixing , it has two main shortcomings in relation to realistic models of disease transmission: It neglects individual heterogeneity ( i . e . , the variance of the node degree distribution ) [27] as well as state correlations between neighboring nodes ( i . e . , an infectious node is more likely to be connected to other infectious nodes ) [30] , [31] . To include individual heterogeneity we employ a network model of disease transmission . Here , in contrast to the standard 5-states modeled in the ODE system , one typically needs to introduce a higher-order compartmentalization where nodes are distinguished not only by their state , but also by their degree . Hence , instead of one equation for the fraction of susceptible individuals at time , an infinite number of equations describes the fraction of susceptible nodes of degree , , at time . Correlations between nodes are then taken into account by coupling this system of equations to another system describing the evolution of the density of links stemming from susceptible nodes . To accurately reproduce features of real networks , we consider networks with heavy-tail degree distributions [32] , [33] . Specifically , we use a binomial distribution leading into a power-law tail with exponential cut-off to avoid unrealistically high degree and infinite average excess degree ( see Text S1 ) . Such a heterogeneous distribution is more realistic in modeling influenza pandemics where there exists large variation in numbers of individual contacts across a population [34] . This is opposed to modeling outbreaks within small communities or schools , where there are natural lower and upper bounds to the numbers of possible contacts , not representing the variation seen across an entire population . Even so , in modeling transmission within small communities , it is still debated whether contact structure should feature heavy-tailed degree distributions [35]–[37] or not [38]; and , while several studies have indicated that networks with low coefficients of variation may be better for modeling influenza [38] , others have not [34] , [36] . Finally , heterogeneous distributions as employed here have been shown to influence the outcome of epidemics [27] and the efficiency of targeted treatment [19] , [20] . The full mean-field model and ODE model equations and details of the degree distributions are given in . Integrating the ODEs resulting from the mean-field analysis yields the possible final states of the dynamics . But such an analysis neglects the inherent stochastic nature of disease transmission . Standard epidemic models often only consider stochastic extinctions of a disease . When the contact structure of the population is known , the probability of extinction can be calculated [17] . However , in addition to stochastic extinction , our model dynamics also depend on the probability of treatment and mutation . Thus even though the mean-field model predicts a final state dominated by the resistant strain , a randomly picked trajectory will reach this state only if a mutation occurs ( with probability ) , i . e . , infections must occur , then resistance is able to appear . This becomes especially important if the resistant strain has a higher force of infection than the treated wild-type strain ( e . g . , ) [39] , [40] . In this case , even below the epidemic threshold of the treated wild-type strain , the development of resistance can occur and propagate . From the expected number of secondary infections caused by a quantity of initial infectious individuals and the total probability of transmission , [17] , one can calculate the probability , , that an individual infected with a wild-type strain develops de novo resistance ( details in Text S1 ) : ( 1 ) where and are the average degree and excess degree of the network , respectively [41] . Hence , Eq . ( 1 ) equals the probability of reaching a state where the resistant strain has emerged ( assuming such a state is possible according to our mean-field analysis ) . Since the epidemic threshold is given by , we set for . Note that Eq . ( 1 ) assumes that is such that , but . Finally , we note the generality of our model: parameter values chosen here are to illustrate and exaggerate the phenomena observed .
We are interested in assessing the effects of the timing of antiviral treatment . If the resistant strain is less transmissible than the treated wild-type strain ( ) , treatment will always be a good option and one must then concentrate on optimizing treatment efficiency ( Figure 1 ) . If the resistant strain is at least as transmissible as the treated wild-type strain ( ) , timing of treatment is crucial [15] . Figure 2 shows the final epidemic size ( proportion recovered ) as a function of the untreated force of infection , , and corresponds to a situation when the resistant strain is more transmissible than the treated wild-type infections . For increasing values of we see an expected increase in final epidemic size . However , the first bifurcation creates a regime of bistability where two final states can be reached for the same in stochastic simulations . Between the two possible branches , there exists a critical manifold corresponding to the curve of initial conditions ( initial number infected , ) yielding equal expected epidemic sizes whether treatment is implemented or not ( details in Text S1 ) . Thus , depending on the number of infected individuals when treatment is initiated , we encounter one of three scenarios: one , where treatment is effective , de novo resistance is unlikely and there are few infections which eventually die out ( this is the green area – “Efficient Treatment” – in Figure 2 , panel b ) . In the second and third scenario ( the red area – “Dangerous Treatment” – in Figure 2 ) , treatment will most likely fail and result in either large incidence of resistant infections or a small outbreak of resistance in a depleted susceptible population ( depending on the timing of this dangerous treatment ) . The derivation of the critical manifold is detailed in Text S1 . Figure 3 demonstrates the behavior of the system in the regimes defined by this critical manifold . We see similar behavior for epidemics from both regimes when no treatment is applied ( panels b and e ) . As observed in previous work [16] , late treatment can be somewhat efficient if implemented after the peak of infections , such that the wild-type strain has depleted the pool of susceptibles to limit propagation of the resistant strain ( panels d and g ) . However , since this implies that the bulk of the original epidemic has passed , this does not qualify as a truly efficient treatment regime . On the other hand , simulations ( Figure 3 , points ) for early treatment of an epidemic with low initial number of infectious individuals appear significantly more efficient than predicted by the ODEs ( Figure 3 , solid lines , panels c and f ) . This discrepancy is caused by the stochasticity of this system , or more precisely , by the mutation probability , . Such mathematical models based on mean-field approximations consider infinite populations in which a finite fraction of infectious individuals cause an infinite number of infections , resulting in an infinite number of treatments and an inevitable emergence of resistance . In finite populations , early treatment with low initial infections will cause only a small number of interventions resulting in a small probability of resistance emergence , . This is why the expected value of the prevalence of resistance is below one individual for all time in the simulations . Importantly , models without stochasticity would have not indicated treatment and failed to identify this efficient treatment regime ( Figure 3 ) . We note that presenting the per-epidemic average number of cases would have allowed the mean-field approximations to better align with simulations . This however would have ignored the role of stochastic extinctions including those due to successful treatment . Assuming treatment is expected to be efficient , we can explore two different forms of treatment: non-targeted , where is a percentage of the population selected at random for treatment , and targeted , where is a function of node degree ( ) , similar to Cohen et al . where an individual's probability of being treated depends on its degree [20] . We focus on scenarios where treatment would be indicated a priori; i . e . , when there is a fitness cost to resistance ( ) . In the case when there is no cost of resistance ( as explored above ) treatment may or may not be optimal , however the results are qualitatively similar . Similar to previous studies [2] , [4] , we see a transition from wild type to resistant infections as treatment levels increase , and find a minimum in disease prevalence at intermediate levels of treatment . Interestingly , we see higher levels of resistance at lower treatment percentages in the targeted treatment regime . Figure 4 shows that under the non-targeted treatment regime , the resistant strain dominates when , whereas under the targeted treatment regime , resistance is dominant when . This happens because targeted treatment increases the chances of resistance occurring in high-degree nodes . Once resistant mutants arise in highly connected nodes , they will have a high probability of being widely transmitted . In addition to the take over of the resistant strain in the targeted treatment regime , we see high levels of total infection with increasing percentage treated due to treatment failure in the resistant cases . Finally , we find the effects of treatment targeting to be robust to the network structure . Under a more homogenous degree distribution ( binomially distributed ) we find the difference between high- and low-degree individuals to be less than in the heterogeneous network , and thus targeting treatment by degree has a smaller effect . However , the results are qualitatively the same , with targeted treatment leading to higher levels of resistance at lower levels of treatment than non-targeted treatment ( see Text S1 ) . This finding is reassuring given the uncertainty in actual contact structures relevant to influenza transmission [34] , [36] , [38] .
In the current study we wanted to answer three questions: one , to minimize resistance , should treatment be initiated at all in epidemics ? two , if treatment is initiated , how does its timing affect the emergence and amount of resistance in structured populations ? and three , which treatment regime , targeted by degree or not , leads to the least amount of resistance ? We find potential bistability in the final epidemic size and deviations from mean-field approximations which would have misidentified optimal treatment timing . We find two scenarios: one , when the initial number infected is low ( early in an epidemic ) , early treatment is preferable to late treatment , and two , when the initial number infected is high , treatment after the peak of epidemic is optimal to keep resistance low . Interestingly , this occurs at identical values of the force of infection ( values of ) , and indicates a strong dependence on initial conditions ( number of cases at the onset of treatment ) and thus on the timing of treatment . Given the uncertainty inherent in estimating epidemic prevalence , especially in emerging infections [42] , caution must be taken when deciding to implement mass treatment . In addition to the presence of this bifurcation and strong dependence on initial conditions we find large differences depending on the method used to allocate treatment . In accordance with previous results , we find a minimum in the total number of infections at intermediate levels of antiviral use . Surprisingly however , we find higher levels of resistance at lower levels of treatment in the targeted treatment case . This is due to the heterogeneity in contact structure wherein if those that are preferentially targeted for treatment ( due to their high number of secondary contacts ) develop de novo resistance , they have a large opportunity to spread the resistant strain . This is counter to previous results demonstrating that targeted treatment is optimal to keep absolute numbers of infecteds low . Thus , in structured populations , non-targeted treatment is preferable if resistance is to be minimized . This implies that in populations where the development of resistance is of concern , resources do not need to be spent on targeting treatment . We note two things: first , in cases where drugs are scarce , the amount of resistance expected to appear is low ( Figure 4 ) and treatment targeted by node degree and factors not considered here ( i . e . , treating teachers , healthcare workers , first-responders , etc . ) is preferable to no treatment or non-targeted treatment . Second , non-targeted , or random treatment may be complicated by additional clinical factors also not considered here ( i . e . , age , severity of illness , pregnancy , etc . ) ; however , our results indicate that in cases where antivirals can be provided to a large fraction of the infected population , resource-intensive targeting by degree need not be employed and treatment should be initiated based on clinical factors alone . The current work highlights the importance of including stochasticity and contact structure in epidemic models . Due to the bistability in final epidemic sizes , the mean-field approximation overestimated the number of resistant cases when treatment was initiated early and missed the efficient treatment when the initial numbers of infected are low . Additionally , we have shown that targeted treatment is not optimal due to the heterogeneous contact structure of the population . This is contrary to earlier studies demonstrating the efficiency of targeted treatment . While our results are qualitatively valid , and hold over multiple network types ( see Text S1 ) , more detailed models can and should be developed to study the effects of contact structure heterogeneity on the development of resistance . Parameters were chosen to be general , and give qualitative results , more accurate statistical estimation could be employed to improve the realism of the model . The timing and targeting of antivirals for the treatment of influenza has important policy implications . Recent studies have demonstrated the facility with which highly pathogenic H5N1 can mutate to spread efficiently from human-to-human [6]–[9] . The development of resistance of H5N1 to common antiviral treatments , could have devastating consequences . We have demonstrated the danger of initiating treatment when the number of infected cases have surpassed a certain threshold ( above and below the critical manifold ) , but have also demonstrated that spending resources on targeting treatment may not be necessary . | Resistance of influenza to common antiviral agents carries the possibility of causing large morbidity and mortality through failure of treatment and should be taken into account when planning public health interventions focused on stopping transmission . Here we present a mathematical model of influenza transmission which incorporates heterogeneous contact structure and stochastic transmission events . We find scenarios when treatment either induces large levels of resistance or no resistance at identical values of transmission rates depending on the number initially infected . We also find , contrary to previous results , that targeted treatment causes more resistance at lower treatment levels than non-targeted treatment . Our results have important implications for the timing and distribution of antivirals in epidemics and highlight important differences in how transmission is modeled and where assumptions made in previous models cause them to lead to erroneous conclusions . | [
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"evolution... | 2013 | The Timing and Targeting of Treatment in Influenza Pandemics Influences the Emergence of Resistance in Structured Populations |
Calcium ions ( Ca2+ ) are important mediators of a great variety of cellular activities e . g . in response to an agonist activation of a receptor . The magnitude of a cellular response is often encoded by frequency modulation of Ca2+ oscillations and correlated with the stimulation intensity . The stimulation intensity highly depends on the sensitivity of a cell to a certain agonist . In some cases , it is essential that neighboring cells produce a similar and synchronized response to an agonist despite their different sensitivity . In order to decipher the presumed function of Ca2+ waves spreading among connecting cells , a mathematical model was developed . This model allows to numerically modifying the connectivity probability between neighboring cells , the permeability of gap junctions and the individual sensitivity of cells to an agonist . Here , we show numerically that strong gap junctional coupling between neighbors ensures an equilibrated response to agonist stimulation via formation of Ca2+ phase waves , i . e . a less sensitive neighbor will produce the same or similar Ca2+ signal as its highly sensitive neighbor . The most sensitive cells within an ensemble are the wave initiator cells . The Ca2+ wave in the cytoplasm is driven by a sensitization wave front in the endoplasmic reticulum . The wave velocity is proportional to the cellular sensitivity and to the strength of the coupling . The waves can form different patterns including circular rings and spirals . The observed pattern depends on the strength of noise , gap junctional permeability and the connectivity probability between neighboring cells . Our simulations reveal that one highly sensitive region gradually takes the lead within the entire noisy system by generating directed circular phase waves originating from this region .
Calcium ions ( Ca2+ ) play a crucial role for almost every aspect in the biology of organisms . Cells possess sophisticated machinery to precisely regulate the free Ca2+ concentrations in the cytoplasm ( ccyt ) , the endoplasmic reticulum ( cER ) and the mitochondria ( cmito ) . Maintaining the low concentrations of Ca2+ in the cytoplasm against a 10 , 000-fold higher extracellular Ca2+ concentration , i . e . the strong trans-membrane electrochemical gradient of Ca2+ ions needed for proper cell signaling [1] requires energy . Upon agonist stimulation , cytoplasmic Ca2+ levels are elevated from two sources: ( i ) Ca2+ influx from the extracellular space across the plasma membrane and ( ii ) Ca2+ release from stores , mostly the endoplasmic reticulum ( ER ) . Different types of Ca2+ channels are responsible for the Ca2+ influx across the plasma membrane including: voltage-dependent Ca2+ channels , receptor-operated Ca2+ channels including transient receptor potential channels ( TRP ) , store-operated Ca2+ channels , etc . [2] . The release of Ca2+ from the ER results from activation of either the ryanodine receptors ( RyR ) or the inositol 1 , 4 , 5-trisphosphate ( InsP3 ) receptors ( InsP3R ) . Previously , it was assumed that RyR are of primary importance for Ca2+ release in excitable cells , while InsP3R were presumed to govern Ca2+ release in non-excitable cells . However , both InsP3R and RyR are expressed in excitable as well as in non-excitable cells [3 , 4] , indicating cooperation between the two types of receptors in all cell lines . RyR have structural and functional similarities with InsP3R , but show no sensitivity to InsP3 [5] . One of the roles of RyR is to amplify the InsP3-mediated release of Ca2+ [6] . Ca2+ signals are often organized in specific temporal patterns . The rhythmic changes in ccyt are called Ca2+ oscillations . Several ligand/receptor interactions together with the involvement of components of the intracellular Ca2+-signaling toolkit induce Ca2+ oscillations [7 , 8] and as a result Ca2+ oscillations can act as integrators of different stimuli [9] . The stimuli intensity was often found to be proportional to the oscillation frequency , which in turn was proportional to the evoked down-steam cellular response , e . g . histamine-dependent fluid secretion of blowfly salivary gland [10] or glucose-dependent insulin secretion of pancreatic islets of Langerhans [11] . An identical genotype , differentiation state and moreover a stable environment are not sufficient to guarantee the same phenotype for neighboring cells within a tissue or organ . Indeed , single cell analysis of genetically identical cells grown in vitro revealed rather large cell-to-cell variability [12] . The transcription of DNA to mRNA followed by translation to protein occurs stochastically , as a consequence of the low copy number of DNA and mRNA molecules involved [13–15] . Therefore , each cell is expected to have a stochastic number of receptors for a certain ligand or agonist . This leads to an individually different sensitivity to agonists and individually different cellular response . For instance , Ca2+ responses in individual mesothelial cells show a wide range of different oscillatory patterns within a genetically homogenous cell population [16] . Nevertheless , in many organs , the neighboring cells have to overcome their individually different sensitivity and produce a synchronized response for instance , smooth muscle cells , in order to generate the contractile waves of the uterus or the peristaltic movement in the gastrointestinal tract . Gap junctions are integral membrane structures that enable the direct exchange of cytoplasmic constituents ( ions and low molecular weight metabolites ) between neighboring cells . The core proteins of these channels are the connexins [17] . Gap junctions are permeable to small molecules including both Ca2+ and InsP3 . Thus , gap junctions are involved in intercellular Ca2+ signaling . Besides forming gap junctions between the same cell types , numerous gap junctions are also known to exist between different cell types . For example , gap junction channels ensure heterocellular Ca2+ waves between glia and neurons [18] . Intercellular Ca2+ waves spreading via gap junctions occur in rat liver epithelial cells upon mechanical stimulation [19] . Besides of gap junctional transport of Ca2+ and/or InsP3 , ATP may serve as a coupling messenger . ATP is thought to be released into the extracellular space and subsequently activating adjacent cells through purinergic receptors [20] . The ATP-mediated Ca2+ spreading within cell populations is slower than direct gap junctional coupling , but allows a connection between cells not connected by gap junctions [19] . Two types of Ca2+ waves can be distinguished associated with gap junctional coupling: i ) Ca2+ “diffusion” or trigger waves and ii ) Ca2+ phase waves . Ca2+ trigger waves arise , when a single cell is stimulated in the network . In this case the gap junctional transport of InsP3 originating from the stimulated cell drives the Ca2+ wave . In this case , local increases in ccyt may be considered as an indicator of intercellular diffusion of InsP3 molecules and not of the bulk movement of Ca2+ ions [21] . Ca2+ trigger waves are slow and due to the dilution of InsP3 , the intensities of the Ca2+ signal decrease in distant cells and finally Ca2+ waves fade out . In the case of Ca2+ phase waves , all cells are stimulated in the network , yet to different extents . Ca2+ phase waves are generated by a small shift in the phase between individual cells oscillating with the same or nearly the same frequencies . Evidently , a coupling agent is required that synchronizes the ensemble of cells . A Ca2+ phase wave differs from a trigger wave , since the spreading of Ca2+ phase waves can be much faster than diffusion and moreover can travel long distances without annihilation [22] . Many different models have been built to understand the versatile patterns of travelling Ca2+ waves [23 , 24] . However , these models do not take into account the heterogeneous sensitivity of cells to stimulation by agonists . This heterogeneity is assumed to substantially contribute to different individual temporal patterns observed in physiological settings . In this article , we address this issue . In order to decipher the presumed function of Ca2+ waves spreading among neighboring cells , a general , not cell-type specific mathematical model was developed . The concept of this general approach is based on the hypothesis of Fewtrell [25]: “Since a single cell type may exhibit most , if not all , of the different types of oscillatory patterns , it seems unlikely that each cell type has developed its own specific mechanism for the generation of Ca2+ oscillations . Instead , each cell may either contain a number of different mechanisms or a single , rather complex mechanism that is capable of generating the full range of oscillatory patterns” . Our model , although containing several simplifications related to the machinery implicated in Ca2+ oscillations , allows to numerically modifying ( i ) the connectivity probability between neighboring cells , ( ii ) the permeability of gap junctions and ( iii ) the sensitivity of a single cell to a particular agonist . The intercellular heterogeneity in agonist sensitivity can manifest in the onset of a Ca2+ response , as alterations in the steady-state levels of InsP3 and/or changes in the Ca2+ handling . The main goal of this work is thus to provide a coherent model for intercellular Ca2+ waves propagation and analyze the effects of gap junctions on networks of cells with inhomogeneous properties . This model highlights possible situations leading to the formation of typical signaling wave patterns ( such as rings or spirals ) . In order to conduct our investigations , we analyze them in a first step within a deterministic framework , where the parameter values on the network are fixed . In such a manner , prominent parameters can be isolated . In a second step , noise is added to the system to stick to more realistic situations and its influence is investigated .
We generalize the model of Pecze and Schwaller [16 , 26] by embedding it into a network of N = n m cells on a two-dimensional graph of size n × m . Each cell vij , 1 ≤ i ≤ n , 1 ≤ j ≤ m is composed of the endoplasmic reticulum ( ER ) and the cytosol and whose dynamics is similar as the one depicted in [16] . We consider that cytosolic Ca2+ ions can pass from one cell to another via gap junction [17] , but that ER lumen from neighboring cells are not connected , thus not allowing direct transfer of ER luminal Ca2+ . The parameter d denotes the strength of gap junctional coupling; the stronger the gap junctional coupling , the faster the diffusion of Ca2+ ions between linked cells . For simplicity , only the diffusion of Ca2+ ions is considered in the models reported in the Main Text , in line with the study of Hofer [27] or Harris and Timofeeva [28] . In the supplemental S1 Text , we extend these models by also considering InsP3 diffusion through gap junction and show that InsP3 can also function as the molecule involved in synchronization of Ca2+ oscillation in cell types , where Ca2+ spikes are connected to InsP3 fluctuations [29] . Synchronization means here that two randomly selected neighboring cells tend to adjust the times at which they produce Ca2+ peaks ( not the amplitude of these peaks ) . This is known as phase-synchronization [30 , 31] . First we select two neighboring cells in the network , u ~ v and estimate the phases of the time series Xu and Xv ( Ca2+ concentrations within cytosol ) using the interpolation technique described in [32] . Denote by τ1 , τ2 , … the times at which Xu attain its maxima . Between two maxima , the phase increases by 2π and in between a linear interpolation is used , so that the phase of Xu for τn ≤ t < τn+1 is defined by Φu ( t ) = 2π ( t−τnτn+1−τn+n − 1 ) ( 1 ) We consider now the differences of the phase of Xu ( t ) and Xv ( t ) and rescale these angles in the interval [0 , 2π] Δϕu , v ( t ) = ( Φu ( t ) − Φv ( t ) ) mod 2π ( 2 ) In a phase-locked situation , this difference would always be the same over time and would result in a constant . If this constant is 0 , it means that Xu ( t ) and Xv ( t ) are perfectly synchronized , that is , all their peaks occur simultaneously . When the phases vary over time , so do their differences . Therefore , in order to quantify synchronization , one has to consider the distribution over time of the phase differences . The more the distribution is concentrated around 0 , the more synchronized Xu ( t ) and Xv ( t ) are . A uniform distribution corresponds to the null case of no synchronization . To quantify the concentration of the phase differences distribution Tass et al . [30] and Cazelles and Stone [32] use the following synchronization index , based on the Shannon entropy , Qu , v = Smax , u , v − Su , vSmax , u , v ( 3 ) where the Shannon entropy Su , v is estimated by −Σk=1nb ( u , v ) pk ( u , v ) logpk ( u , v ) and the maximal Shannon entropy is Smax , u , v = log ( nb ( u , v ) ) . The number of class intervals is nb ( u , v ) and pk ( u , v ) denotes the relative frequency that Δϕu , v lies in the kth interval . The index Qu , v lies between 0 ( no phase-synchronization ) and 1 ( perfect synchronization ) . Finally , we define a synchronization measure of the network by taking the mean of Qu , v over all neighboring cells u ~ v msync=1|E|∑u~v Qu , v ( 4 ) where |ε| is the number of edges , i . e . pairs of neighbors in the network . When msync = 1 , the synchronization between all cells is perfect and in particular no wave nor any pattern would appear . For medium ranges of msync , synchronization between neighboring cell is efficient enough to enable waves to emerge . When msync is close to zero , all cells tend to behave independently from each other . Our synchronization measure msync indicates the degree of phase synchrony focusing on time periods at which peaks are produced within the whole network . Amplitudes of the peaks are not taken into account for the parameter msync . Two graph topologies were used: homogeneous , fully dense graph and heterogeneous sparse graph topologies ( Fig 1 ) . I ) The homogeneous , fully dense graph topology consists of a grid graph to which diagonals were added ( G0 , Fig 1A ) . In this setting , all neighboring cells are connected to each other . The set of nodes is V0 = {vij , 1 ≤ i ≤ n , 1 ≤ j ≤ m} and let ε0 denote the set of links . Let vi1j1 and vi2j2 be two members of set V0 . These nodes are connected in G0 , if and only if they are neighbors , that is , {vi1j1 , vi2j2 } ∈ E0 ⇔ |i1−i2|2 + |j1−j2|2 ≤ 2 . ( 5 ) II ) Heterogeneous sparse graph ( G ) is a random sparse version generated from the homogeneous fully dense graph ( G0 ) . In this situation , some links and nodes were randomly removed , exemplified in Fig 1B . Decreased gap junction coupling can be achieved by decreasing the permeability or numbers of the specific channels involved [21 , 33] . The latter situation can be modeled by link removal . Changes in the densities of gap junctions ( strong decrease and recovery ) was observed after partial hepatectomy [34] . Random sparse graphs were obtained by the following procedure . The link-removal probability p ( v ) is built with a colored noise in space , in order to create spatial correlations among the network . Let F be a Gaussian random field on the graph with standard deviation set to one and with a correlation function of the Gaussian form , that is Cor ( F ( x ) , F ( y ) ) ∝ exp ( − ∥x−y∥2c0 ) , x , y∈ℝ2 , ( 6 ) where c0=n2+m2 is a correlation parameter . When randomized , the probabilities p ( v ) are as follows p ( v ) =p max ( F ) − F ( v ) max ( F ) −min ( F ) ( 7 ) for some p ∈ [0 , 1] in a way that in regions with nodes having low values of F , the probability to remove links is high , creating sparse areas , whereas regions with large values of F tend to be more strongly linked . Choosing a small quantile q of F , it is possible to create holes in the network by excluding nodes v from V0 whenever F ( v ) < q . The resulting graph is denoted by G= ( V , E ) , ( Fig 1B ) . Denote by ( Xv ( t ) , Yv ( t ) ) the free Ca2+ concentrations in the cytosol ( ccyt ) and in the ER ( cER ) of the cell v at time t . The corresponding dynamical system on the graph G is driven by the following system of differential equations: {dXvdt = JINF ( v , t ) +f ( Xv , Yv , v , t ) +d∑{v , u}∈E ( Xu−Xv ) dYvdt = g ( Xv , Yv , v , t ) , ( 8 ) for all v ∈ V with initial conditions Xv ( 0 ) = ccyt , ini = 110nM and Yv ( 0 ) = cER , ini = 260μM according to [16] . At the boundaries or in the case of holes , fluxes follow only existing directions , and there are no fluxes via the missing links . The function JINF ( v , t ) comprises the following movements of Ca2+ ions that increase the free Ca2+ concentration in the cytoplasmic compartment: ( i ) Ca2+ ions entering the cytoplasm from the extracellular space or from organelles ( mitochondria , Golgi apparatus , lysosomes ) and ( ii ) Ca2+ ions released from cytoplasmic Ca2+-binding proteins , which are considered as Ca2+ buffers in a broad sense . In a stricter sense , Ca2+ buffers comprise specific subsets of mobile Ca2+-binding proteins such as calretinin , calbindin D-28k and parvalbumin . The particular case of calretinin is presented in the S2 Text ) . The diffusion of Ca2+ through gap junctions from a cell’s cytosol to the one of an adjacent cell is given by the term d Σ{v , u}∈ε ( Xu − Xv ) , with d the gap junctional coupling strength . The functions f and g of concentrations x , y regulate the Ca2+ exchanges between cytosol and ER and are defined by f ( x , y , v , t ) = −JEFF ( x ) −JSERCA ( x ) +JEREFF ( x , y , v , t ) +JERLEAK ( 9 ) g ( x , y , v , t ) = γ ( JSERCA ( x ) −JEREFF ( x , y , v , t ) −JERLEAK ) , ( 10 ) where the constant γ is the ratio between the changes in Xv and Yv caused by the same amount of Ca2+ ions transported through the ER membrane . This value is derived from the difference in the effective volume of the ER lumen and the cytosolic volume and from the different fractions of free and protein-bound Ca2+ in these compartments . The function JEFF represents a combination of Ca2+ fluxes: ( i ) from the cytosol to the extracellular space , ( ii ) from cytosol to organelles and ( iii ) Ca2+ ions temporarily sequestered by cytosolic Ca2+ buffers . The common features of these Ca2+-ion movements are their dependence on ccyt . The higher ccyt is , the more Ca2+ ions are removed by mitochondria or plasma membrane extrusion systems , or are bound to cytosolic Ca2+ buffers . We simulated JEFF by a linear equation , in line with the work of Fink et al [35] and based on the experimental results of Herrington et al . [36] . The different dissociation constant ( Kd ) values of individual components ( plasma membrane Ca2+ ATPase’s , exchangers , mitochondria ) ensure that the extrusion fluxes will never reach their saturation points in the range of biologically relevant values of ccyt . JEFF ( x ) = ( re1x−re2 ) 1{re1x−re2>0} ( 11 ) where the indicator function 1{x>0} returns 1 if x > 0 and 0 otherwise . The function JSERCA denotes the Ca2+ flux from the cytosol to the ER . For simplicity , we assume that it depends only on ccyt , in line with our previous study [16] . Here , we also assumed that the different SERCA variants with different Kd values [37] ensure that this flux will never reach its saturation point in the range of biologically relevant values of ccyt . It is given by JSERCA ( x ) = ( rs1x−rs2 ) 1{rs1x−rs2>0} ( 12 ) Modeling of JSERCA and JEFF fluxes with saturating Hill equations , did not modify qualitatively the behavior of our model . The simulations are presented in S3 Text . The parameter JERLEAK represents a small flux of Ca2+ ions diffusing from the ER to the cytosol . The origin of this flux is unknown as well as its main characteristics [38] . For simplicity , we considered this flux to be constant , JERLEAK=β ( 13 ) JEREFF describes the flux of Ca2+ passing from the ER to the cytosol through InsP3R . In our model , InsP3R are influenced both by ccyt and by cER , however without an allosteric regulation between the two . We consider JEREFF as the sum of two individual functions . The first one depends on ccyt and has a bell-shaped form when considering concentrations in logarithmic scale [39] , Jcytdep ( x , v , t ) = Ri , max ( v , t ) exp ( − ( log ( x ) −M ( v , t ) ) 2σ2 ) ( 14 ) where M is the concentration mean on a logarithmic scale , Ri , max the maximum and where the variance σ2 is a positive constant . The second function expresses the experimental fact that an increase in [InsP3] causes significant Ca2+ release from the ER even if ccyt = 0 [40] . JERdep ( y , v , t ) = ri , 1 log ( y ) −Ri , 2 ( v , t ) ( 15 ) where ri , 1 and Ri , 2 are positive . We thus define JEREFF ( x , y , v , t ) = ( Jcytdep ( x , v , t ) +JERdep ( y , v , t ) ) ⋅ 1{Jcytdep ( x , v , t ) +JERdep ( y , v , t ) >0} ( 16 ) The shape of JEREFF depends on [InsP3] and is encapsulated in the functions M , Ri , max and Ri2 . Indeed , [InsP3] modifies the sensitivity of InsP3R to changes in ccyt and in cER . Elevating [InsP3] mainly changes the mean ( M on a logarithmic scale ) and the maximum ( Ri , max ) of the bell-shaped curve of ccyt dependence [41] . In our model , we did not consider that the open probability curve at high [InsP3] is not precisely bell-shaped [41] . For simplicity , we didn’t consider that the Ca2+ flux from the ER , either through leak channels or InsP3R depends on the driving force originating from an electrochemical gradient across the ER membrane . We used stationary open probabilities , not considering the binding/unbinding kinetics of Ca2+ to activating or inhibitory sites of InsP3R . Taking the abovementioned factors into account did not modify critically the behavior of the system . The simulations are presented in S3 Text . Based on the experimental data presented in [42 , 43] , elevating [InsP3] also has an effect on the loading of the ER . Increased [InsP3] reduces the amount of Ca2+ ions stored in the ER . We simulated this effect by changing the parameter Ri2 . The functions M , Ri , max and Ri , 2 have similar forms , given by M ( v , t ) =μmin+ ( μmax−μmin ) KbKb+InsP3 ( v , t ) ( 17 ) Ri , max ( v , t ) = rim , min + ( rim , max − rim , min ) KbKb+InsP3 ( v , t ) ( 18 ) Ri , 2 ( v , t ) = ri2 , min + ( ri2 , max − ri2 , min ) KbKb+InsP3 ( v , t ) ( 19 ) We assume that within cell populations , each cell has a different sensitivity to agonists . A stimulation induces two processes that are important for Ca2+ oscillations: ( i ) it increases the levels of InsP3 by G-protein-regulated phospholipase C and ( ii ) it increases the Ca2+ flux characterized by JINF , mainly due to the opening of plasma membrane Ca2+ channels . We assume that both processes are positively related to the stimulus intensity , but two cells with the same [InsP3] can have different JINF values . Hence , we used two input parameters: InsP3 ( v , t ) ={0 . 015 , if t<t1 ( v ) iIP3 , max ( v ) t−t1 ( v ) KIP3+ ( t−t1 ( v ) ) , if t≥t1 ( v ) ( 20 ) and JINF ( v , t ) = {0 . 1 , if t<t1 ( v ) iJINF , max ( v ) t−t1 ( v ) KJINF+ ( t−t1 ( v ) ) , if t≥t1 ( v ) ( 21 ) where t1 is the time point of the onset of increase in intracellular InsP3 levels and of the increase of the Ca2+ flux characterized by JINF . KIP3 and KJINF are positive constants . The functions InsP3 ( v , t ) and JINF ( v , t ) can be inhomogeneous in space , hence depending on the cell v . Initially , they are set to small values , which are constant in time and identical for all cells . At time t1 ( v ) , the processes start and both functions rapidly increase to approach limiting values iIP3 , max ( v ) and iJINF , max ( v ) , respectively . This leads to different frequencies of single cell Ca2+ oscillations . In accordance with experiments [44] , we assumed that stronger activation results in smaller t1 value . Thus , the values of iJINF , max ( v ) and iIP3 , max ( v ) are positively correlated among cells , but the values of iJINF , max ( v ) and t1 ( v ) are negatively correlated , as exemplified in Fig 1D and 1E . In our model , we did not consider that JINF is partially sensitive to changes in ccyt . All parameter values are presented in Table 1 . All these values were set to reproduce as closely as possible the Ca2+ concentration changes in cytosol and ER measured experimentally by fluorescent Ca2+ indicators . We present and analyze four particular types of models and networks , each type allowing to highlight different phenomena and to isolate the key features leading to the various patterns arising .
An isolated cell ( model G0 ) oscillates in response to a given input encapsulated into the functions JINF and InsP3 for well-chosen parameters ( see [16] ) . Unless specified , all parameters are set according to Table 1 and the ranges of parameters leading to oscillations are depicted in Fig 2 . The period of oscillations ( inverse of the frequency ) is determined by iJINF , max and iIP3 , max; three zones can be distinguished . In zone I , the Ca2+ concentration oscillates with a constant frequency as exemplified in Fig 2B . In zone II ( the boundary of zone I ) any small oscillation rapidly vanishes after the initiating stimulation signal ( Fig 2C ) . Finally , in zone III , no oscillations occur and Ca2+ concentrations saturate ( Fig 2D ) . The model for each cell has a Hopf bifurcation that separates region III from I and a homoclinic one that separates region I from region II . The system oscillates in a certain range of iJINF , max and iIP3 , max values . We thus differentiate two types of cells: self-oscillating ones and non-self-oscillating ones . Consider a noise-free graph ( model GD ) with gap junctional coupling d > 0 and one particular region at the center of the homogenous , fully dense graph G0 , where the sensitivity is increased ( iJINF , max , 1 >iJINF , max , 0 , iIP3 , max , 1 > iIP3 , max , 0 and t1 , 1 < t1 , 0 ) . In this setting , a Ca2+ wave emerges from the center of the graph , indicating that the most sensitive cells are the wave initiators ( Fig 3 and S1 Movie ) . The wave front is initiated in the ER as illustrated in Fig 4A . Hence , intercellular Ca2+ waves are driven by ER Ca2+ release as reported in [29] . This is also in accordance with the findings in [16] , where in single cells , oscillations are initiated from within the ER . With a strictly positive gap junctional coupling , single oscillations thus propagate through all connected cells with a velocity controlled by the strength of the gap junctional coupling d , as illustrated in Fig 4C . In Fig 4B , we report that iJINF , max , 0 and iIP3 , max , 0 additionally contribute to the wave speed . Large values of the parameter d diminish the period of the wave and thus increase the oscillation frequency . Note however that if parameters iJINF , max , 0 and iIP3 , max , 0 are too large , the network homogenizes rapidly , so that ccyt reaches saturating values in every cell , thus dampening any oscillatory behavior . Indeed , parameters iJINF , max , 0 and iIP3 , max , 0 negatively correlate with the period of oscillation in all cells of the graph as illustrated in Fig 4B . In this figure , the period of oscillation has been calculated for various parameter values of iJINF , max , 0 , iIP3 , max , 0 and a moderate d . Both parameters participate to the homogenization of the patterning through the graph . If both parameters iJINF , max , 0 and iIP3 , max , 0 are sufficiently large , no oscillation is observed . All cells fill up with Ca2+ and remain on a stationary state . In Fig 4 , we chose iJINF , max , 1iJINF , max , 0=iIP3 , max , 1iIP3 , max , 0=t1 , 0t1 , 1=k>0 , which is seen as an added percentage of sensitivity in the sub-graph G˜ . This parameter k has no remarkable global effect except that for sufficiently large values of iJINF , max , 0 , it slightly negatively contributes to the wave speed ( Fig 4D ) . In the S2 Text , an extension of this model , which explicitly takes into account the presence of calretinin , shows that this specific buffer has an effects on wave propagation: an increase in calretinin tends to increase the oscillation frequency and the wave speed , as illustrated in Fig . A in S2 Text . Interestingly , the synchronization of neighboring cells occurs even if the network contains non-self-oscillating cells . To verify this , we add to the previous network a single region Z of cells with parameters ( iJINF , max , 2 , iIP3 , max , 2 ) outside of the range that would allow them to oscillate independently ( zone III in Fig 2D ) . Any oscillations in those cells would rapidly die out ( Fig 2C ) . But since they are coupled to neighboring oscillating cells , they synchronize and oscillate in response to the behavior of their neighbors ( Fig 5 and S2 Movie ) . Ca2+ ions transported to non-self-oscillating cells thus evoke changes in their oscillations-properties . This phenomenon is accelerated by higher gap junctional coupling values . In such a configuration , we observe that the wave initiators are shifted from the center to cells adjacent to the region Z . When considering noise in the system , the same local effects depicted previously ( Figs 4 and 5 ) occur . Neighboring cells , although having different sensitivities , try to synchronize their operation . This is illustrated in Fig 6 and S3–S5 Movies , where we used the random model GR described in the previous section for a system with strong noise and with p = 0 , i . e . no links were removed ( graph G0 ) . The extreme case of no coupling ( d = 0 ) leads to independent Ca2+ oscillations in each cell ( Fig 6A and S3 Movie ) . For low and moderate couplings , small bursting phenomena are visible before synchronization of two neighboring cells ( Fig 6B and S4 Movie ) . By “burst” we mean that a cell changes its phase during the development of a Ca2+ spike resulting in a prolonged irregular Ca2+ spike . Moreover , no coherent behavior or typical wave patterns emerge from such situations . Incorporating calretinin into the model has the very interesting effect of promoting the formation of coherent wave patterns . This phenomenon is exemplified in S18 Movie , where the same framework as in Fig 6B is used , with the addition of calretinin ( see S2 Text ) . More generally in Fig . B in S2 Text , we illustrate that a deficiency in coupling can be generally compensated by sufficiently high levels of calretinin from the point of view of synchronization . Notice that strong coupling leads to well-synchronized oscillations , even in the presence of strong noise ( Fig 6C and S5 Movie ) . We observed that when phase synchronization occurs , the initial differences in the amplitudes of individual cells decrease , i . e . the harmonization of the network is also visible as a harmonization of the amplitudes . Highly sensitive cells still initiate Ca2+ waves travelling through the network . In such noisy systems , waves can arise from different random places . When they collide , they aggregate or split depending on their velocity and on local properties of the graph . In the S1 Text , we also consider the effect of InsP3 coupling within this random model . In these particular settings , Fig . A in S1 Text illustrates how InsP3 and Ca2+ oscillations synchronize in an arbitrary cell . S14–S16 Movies show the evolution of Ca2+ concentrations when ( i ) Ca2+ coupling is active , but there is no InsP3 coupling ( S14 Movie ) , ( ii ) InsP3 coupling is active but there is no Ca2+ coupling ( S15 Movie ) and ( iii ) Both couplings are active ( S16 Movie ) . There are two important phases in order to compare the three situations . In the beginning , around t1 ( v ) , InsP3 coupling is very efficiently involved in the synchronization process , but afterwards acts poorly as synchronizing agent ( see Fig . B in S1 Text ) . The reverse is true for Ca2+ coupling ( Fig . B in S1 Text ) . All results are reported in Table B in S1 Text . Next we examined the effect of link removal on Ca2+ wave propagation i . e . the heterogeneous sparse graph was used ( G ) . Besides the gap junctional coupling ( d ) , the density of the connection p ( v ) influences the wave propagation . Remember , the parameter p ( v ) indicates , whether two neighboring cells are connected or not , while the parameter d represents the strength of the coupling if such a connection exists . Hence it is not surprising that increasing the link removal probability p ( v ) has a similar effect on the wave propagation as decreasing the strength of the gap junctional coupling d . Highly connected cells ( regions ) enable waves to spread easily , while poorly linked regions will act as a barrier deviating the wave front to another direction . In the extreme case , holes were added to the network ( Fig 7 and S6–S8 Movies ) . The most interesting feature occurring in random systems is the waves-patterning transitions . As explained above , waves initially emerge from highly sensitive cells and spread radially away from them . Such travelling waves commonly appear in many oscillating systems [45] . In inhomogeneous systems built with our model , such patterns do not necessarily stabilize with time . Circle centers move , some waves die out as other gain in strength , collide and even spiraling phenomena are observed ( S8 Movie ) . In this section the different frameworks resulting to spiraling phenomena are explored . In our settings , surprisingly , even homogenous noise-free networks can exhibit transitions from circles to spiral waves . The cause of this has to be looked for in the inhomogeneity of cell sensitivities . Indeed , if we consider the same framework as in [46] , by considering model GD , with three additional small regions where cells are more sensitive , spiral waves appear ( see Fig 8A and S9 Movie ) . These spirals are of transitory nature and the system finally ends up with stable circular waves emanating from the three sensitive zones . Notice that defining two particular zones is sufficient to create spirals as demonstrated in Fig 8B and S2 Movie . In this figure , the central region is very sensitive and a second area ( upper-left ) is low sensitive ( with parameters in zone III of Fig 2A ) . After a sufficiently long time , a spiral develops around this second area and moves along the network . This sheds light on the effect of gap junctional coupling . Here its value ( d = 0 . 0045 ) is moderate . An identical framework , but with a high gap junctional coupling , would prevent spiraling behaviors . Considering two highly sensitive areas leads to the same conclusion , as illustrated in S10 Movie . Beyond collisions of different travelling circular waves and topological considerations , gap junctional coupling and inhomogeneous activation ( as defined by e . g . noise strength ) play key roles in spiral generation . Independently of its strength , noise acts as a catalyst for producing spirals . For a low level of noise , moderate gap junctional coupling enables the appearance of transitions between circles and spirals . In highly disordered systems , the level of gap junctional coupling needed to smooth the spirals and obtain stable circles increases . Indeed , a high gap junctional coupling allows waves to spread rapidly among the cells and thus enables a fast synchronization of their behavior ( Fig 6C ) . As such , any inhomogeneity that can be generated from different traveling waves breaks up . To explore the particular effect of inhomogeneous activation , the noise is set to a low value . In Fig 9A ( and S11 Movie ) the random model GR is used with a relatively high sensitivity . Due to the noise , the system is inhomogeneous enough to produce spirals , as locally explained in Fig 8A–8B . Turning to model GR , C , by adding a small central zone with higher sensitivity regulates the behavior of the system and concentric circles develop ( Fig 9B and S12 Movie ) . In this case the sensitive center enables the homogenization of the whole network . This occurs in a similar way as what is shown in the noise-free framework of Fig 3 . Increasing simultaneously the two main frequency-determining parameters , μiIP3 , max and μiJINF , max , results in similar concentric circles , but with higher frequencies ( see S19 Movie , where the same parameters as in Fig 9C are used with μiJINF , max , 0=0 . 9 and μiJINF , max , 1=1 ) . However , very high values for one parameter ( e . g . μiIP3 , max as in Fig 9C and S13 Movie ) shifts the model to non-organized wave propagation and creates spirals again; the sensitive center is no more sufficient to homogenize the whole network . Our explanation for this phenomenon is the following: increasing one parameter excessively increases the number of non-self-oscillating cells ( Fig 2A Zone III ) , but when increasing simultaneously both parameters , most cells remain in the oscillating Zone I and the network is able to produce rhythmic concentric circles The effect of specific buffers ( calretinin ) on μiIP3 , max− overstimulated systems is investigated in S2 Text . Our general finding is that Ca2+ buffers promote coherent behavior in these situations .
Over the last 25 years , many different models have been developed to describe Ca2+ oscillations in cells [23 , 24 , 47]; new methods including high-resolution spatiotemporal recordings of Ca2+ signals enabled the reconsideration and further development of the different models . As an example , the simultaneous monitoring of ccyt and InsP3 production has revealed that Ca2+ oscillations are not the direct consequence of fluctuations in [InsP3] [29] . Simultaneous monitoring of ccyt and cER showed a time shift between the maximum of ccyt and the minimum in cER during a Ca2+ spike [48–51] , findings that have allowed for a better understanding of the mechanisms implicated in oscillations . The continuous loading of the ER with Ca2+ during the interspike periods followed by a rapid release during the Ca2+ spike in ccyt results in sawtooth-like Ca2+ oscillations in cER [16] . This indicates that the loading state of the ER is an essential parameter to understand Ca2+ oscillations in the cytosol . A next Ca2+ spike can be generated only , if cER reaches a certain threshold value [52] . This threshold is determined by the prevailing InsP3 concentration [42] . The replenishment of the ER Ca2+ store is modulated by a constant Ca2+ influx across the plasma membrane . An increase in the Ca2+ influx rate leads to a higher frequency of Ca2+ oscillations , while decreasing the Ca2+ influx reduces the frequency [16 , 26 , 53] . In some conditions , mitochondrial Ca2+ transport ( uptake and release ) was found to substitute for the plasmalemmal Ca2+ exchange function , thus rendering the oscillations independent of extracellular Ca2+ [26] . The magnitude of the Ca2+ transport into mitochondria was also found to influence the Ca2+oscillation frequencies [54] . Often it is the refilling of the ER that sets the oscillation period ( frequency ) , not the InsP3R dynamics . The model proposed in our previous works [16 , 26] has demonstrated to be coherent with the above-mentioned experimental findings at the single-cell level ( zero dimension ) . In this study , we showed that this single-cell model in a 2D framework is a useful tool for the prediction and understanding of several phenomena in naturally-occurring multicellular noisy systems . Among existing models for intercellular Ca2+ waves , the key limitations are the size of the system ( two [27] or three cells [55] ) , restriction of cell activation to a single cell [56] or the fact that cells with altered sensitivities are restricted to few areas in an otherwise noise-free system [46] . To the authors' best knowledge , the model that we propose in the present study is the first to address simultaneously those limitations . Our model revealed the ER to be the initiation site of the Ca2+ phase wave front ( Figs 3A and 4A ) . Thus , intercellular Ca2+ phase waves are driven by an initiative wave front starting in the ER; even if the neighboring cells might communicate with each other by exchanging their cytosolic but not luminal Ca2+ ions via gap junctions . This model prediction has already been proven experimentally . Keller and coworkers have found in guinea heart myocytes that Ca2+ waves in ccyt are driven by “sensitization” wave fronts in cER [57] . Differently from Ca2+ phase waves , the initial wave front of Ca2+ trigger waves starts from within the cytosol . Another difference between the two types of waves is that two Ca2+ phase waves annihilate each other when they collide , while two Ca2+ trigger waves add up to generate a new wave of greater amplitude . It has been known for a long time that gap junctions are permeable both to InsP3 and Ca2+ ions [58] . However , because Ca2+ but not InsP3 is strongly buffered by cytoplasmic proteins and/or unidentified immobile buffers , Ca2+ movement within a cell is very restricted and slower than that of InsP3 [59] . Thus InsP3 is more likely to diffuse to greater distances and subsequently to mediate intercellular Ca2+ waves [21] . Although this concept is quite attractive , one has to take into account that ( i ) Albritton et al . [59] measured the Ca2+ diffusion in cell extracts , where the Ca2+ pumps and InsP3-metabolizing enzymes were blocked , i . e . not in physiological conditions . ( ii ) To generate a long distance Ca2+ wave , Ca2+ ions do not need to travel for long distances; it is enough to diffuse to the next InsP3R or RyR . More precisely , since these receptors are organized in clusters , Ca2+ ions only have to diffuse from one cluster to the neighboring cluster . Since the ER consists of a network in the cytoplasm filling almost the entire cell , the cluster-to-cluster distances between adjacent cells should be of similar magnitude than that of intracellular cluster-to-cluster distances , i . e . based on the images presented in Chalmers et al . [60] , approximately 1 μm . ( iii ) Ca2+ buffer proteins not only take up Ca2+ ions , but to the same extent , also release Ca2+ ions . Depending on their Ca2+-binding parameters ( fast or slow kinetics ) they can promote or inhibit intracellular Ca2+ wave formation [61] . Most probably , they have similar effects on intercellular wave formation , yet no experimental data are available . As a preliminary result in the S2 Text , we have already simulated the effect of a specific Ca2+ buffer , calretinin , on wave propagation . Our model predicts that calretinin promotes intercellular synchronization . ( iv ) As presented in the introduction , there are two types of Ca2+ waves: Ca2+ trigger waves and Ca2+ phase waves . If we complete our model with the gap junctional transport of InsP3 , this results in Ca2+ trigger waves , since only few cells get activated before activation of the large majority of the other cells . In this case the gap junctional transport of InsP3 is the main agent harmonizing the initial Ca2+ signal , in agreement with previous experimental results [21] . However , the existence of individual Ca2+ oscillatory machinery and the individual stimulation of each cell , as in our model , allows the generation of phase waves in which the emergence of the waveform is due to Ca2+ release from adjacent oscillating cells slightly differing in their oscillation phase . In summary , our model shows that both Ca2+ ions and InsP3 are likely the synchronizing agents , possibly in a synergistic way . InsP3 is more involved in the formation of the initial Ca2+ trigger waves , while Ca2+ ions serving as a coupling agent are more implicated in the later ones indicating that Ca2+ phase waves are dominant at the later stages of a model experiment ( See S1 Text ) . Of note , in other studies , the authors have concluded that only InsP3 may serve as the coupling agent [55 , 62] , reporting that the pertaining actual [InsP3] is the only frequency-determining factor . In our model , we identified two factors determining the Ca2+ oscillation frequencies , i . e . iJINF , max and iIP3 , max . Stimulation of primary mesothelial cells induces Ca2+ responses showing a wide range of different oscillatory patterns within a single , supposedly homogenous cell population . Since a single cell type may exhibit most , if not all , of the different types of oscillatory patterns , most likely each cell contains all components of the Ca2+ signaling toolkit ( possibly to different extents ) required to generate the full range of oscillatory patterns and spreading Ca2+ waves [25] . Our model indicates that different spatiotemporal patterns of intercellular Ca2+ signals are mostly the consequence of different strengths of coupling via gap junctions . Non-synchronous oscillations in individual cells is favored when gap junctional coupling is weak as was observed in cultured primary mesothelial cells [16] . Moderate levels of gap junctional coupling led to temporally and spatially restricted Ca2+ bursts ( Fig 6 ) . Nevertheless , other mechanisms can also be involved in the formation of Ca2+ bursts [63–65] . This type of oscillations and waves was found in pancreatic beta cells upon glucose stimulation [66] . Strong gap junctional coupling resulted in Ca2+ waves , even if individual cells had different sensitivities with respect to stimulation . Synchronous smooth muscle cell-mediated contractions of the uterus driven by Ca2+ waves [67 , 68] are a typical example of strong coupling . The influence of gap junctional coupling on the generation and spreading of intercellular Ca2+ waves was experimentally revealed by analysis of GT-1 cells , a cell line derived from immortalized Luteinizing Hormone-Releasing Hormone neurons . GT-1 cells were then further subcloned to result in lines GT1-1 , GT1-3 and GT1-7 cell lines differing in expression levels of connexin 26 ( Cx26 ) [69 , 70] . Low Cx26-expressing GT1-7 cells mostly displayed frequent spontaneous asynchronous Ca2+ oscillations , while high Cx26-expressing GT1-1 cells showed spontaneous intercellular Ca2+ waves , completely in line with our model . The occurrence of Ca2+ waves strongly depends on cell-cell contact probability and moreover the strength of gap junctional coupling . Our model also predicts that in a noisy system , in which each cell has an individual sensitivity to stimulation , the most sensitive cells act as the wave initiator cells . Physiological cellular responses to evoked Ca2+ signaling are cell-type dependent: Ca2+ signals elicit contraction in muscle cells [71] , neurotransmitter release in neurons [72] and e . g . insulin secretion in pancreatic beta cells [73] . Ca2+ signals in immune cells participate in the regulation of cell differentiation , gene transcription and effector functions [74] . They are also involved in the regulation of cell proliferation of cancer cells [75] . Intracellular Ca2+ signaling , frequently in the form of Ca2+ oscillations , activates specific enzymes and transcription factors in a cell , which are often involved in cell proliferation [76] . Ca2+ signals are usually not restricted to individual cancer cells , but are propagated to neighboring cells in the form of intercellular Ca2+ waves , usually Ca2+ trigger waves are observed [77] . Assuming that intra- and intercellular Ca2+ signaling is the major way by which cells encode and transmit information , it is likely that during the passageway of a Ca2+ wave , several Ca2+-dependent targets in affected cells would be activated and/or deactivated . Our model predicts that Ca2+ waves play an important role in the harmonization of evoked responses i . e . Ca2+ wave initiator cells are capable of activating neighboring cells , even when those cells would not oscillate by themselves; however they start to oscillate and hence support waves when coupled to other cells . Thus , a highly sensitive cell may trigger cell division even if the neighboring cells are not sensitive enough to mitogenic stimuli . The exploration of the details on Ca2+ wave-dependent harmonization of Ca2+-related cellular responses remains an interesting topic to be investigated experimentally in the future . Our model is capable of producing both circular waves and spirals within a specific range of parameters . Systems producing spirals , such as the famous Fitzhugh-Nagumo equations have been mathematically analyzed [45 , 78–80] and several ways to create spirals in oscillating systems was proposed in the work of Mckenzie [81] . In biological systems , Ca2+ spirals are observed in “overstimulated” conditions . Frog oocyte overexpressing muscarinic acetylcholine receptor produce spiral waves in ccyt upon stimulation [82] . During normal physiological function of many organs , directed rhythmic Ca2+ waves are required , i . e . Ca2+ phase waves propagating along a certain direction . For instance , rhythmic Ca2+ phase waves are required for the contraction of the gastric pylorus [83] , uterus [67 , 68] , intestine [84] or urinary bladder [85] . Heart contraction is even more orchestrated with very fast directed Ca2+ phase waves , but in this case the gap junctional transmission of action potentials is thought to be the relevant synchronizing process [22] . Nevertheless , it is not excluded that gap junctional transport of Ca2+ and InsP3 also plays role in the synchronization . This might explain the phenomena of delayed after-depolarization [86] or arrhythmias associated with altered operation of InsP3R [87] . In our model , directed waves observed as circular rings can be generated even in noisy systems , if there is one highly sensitive , wave initiator region . Incorporating one highly sensitive region is an effective way to harmonize networks and to determine the wave direction within a broad frequency range . However , the regulated network collapses , if the number of non-oscillating “signal-plateau” cells increase . Cells usually show signal-plateau response at high stimulation intensity , e . g . by strongly elevated levels of InsP3 [29] . This corroborates the experimental observations in biological systems , i . e . spirals appear as a consequence of very extensive stimulation in spatially connected systems [82 , 88] . From a general viewpoint , noise can arise in any other excitable oscillating network , in which the individual units are connected by different synchronizing agents . Examples for coupled excitable units exist in the field of Biology ( synchronization of cellular clocks in the suprachiasmatic nucleus [89] ) , Physics ( Josephon junction circuits [90] ) , Chemistry ( e . g . Belousov–Zhabotinsky reaction [91] or catalytic oxidation of carbon monoxide on a platinum surface [92] ) or Social Sciences ( social interaction such as waving of a human crowd during a football match[93] , an audience clapping in synchrony [94] , flashing of fireflies [95] or cricket chirps [96] ) . We propose that phenomena that we observed in our model might be extrapolated to other systems . For example the Kuramoto model [97] predicts a transition with increasing global coupling strength , at which the oscillators with originally different frequencies become coherent . Also , the global coupling strength was investigated in the limit of large number of oscillating sites [98 , 99] . Critical coupling was found to differentiate coherent and non-coherent macroscopic behaviors . Their general methods allowed to reducing the involved dynamical description of coupled equations to a finite number of differential equations for the macroscopic state of a system . We demonstrated how local coupling is involved as a globally synchronizing agent in our model , quantified by our synchronization index . Locally , this transition would occur around the “highly sensitive region” and this region would be the initiator ( pacemaker ) of the subsequent phase waves in originally non-synchronized noisy systems . The “highly sensitive region” might represent the area of higher frequencies or the area with a higher density of links depending on the system . Based on our observation that in all cases , a highly sensitive region gradually determined the behavior of the entire system , as is also observed in many model structures ( S1–S3 Texts ) , one can deduce some general conclusions . This phenomenon may occur if I ) each unit has its own machinery for the generation of oscillations , II ) there is a certain level of noise within the system , III ) there is one or more coupling agents modifying the oscillation frequencies of the coupled units and IV ) there is a highly sensitive region allowing for a global synchronization . | The calcium ion ( Ca2+ ) , a universal signaling molecule , is widely recognized to play a fundamental role in the regulation of various biological processes . Agonist–evoked Ca2+ signals often manifest as rhythmic changes in the cytosolic free Ca2+ concentration ( ccyt ) called Ca2+ oscillations . Stimuli intensity was found to be proportional to the oscillation frequency and the evoked down-steam cellular response . Stochastic receptor expression in individual cells in a cell population inevitably leads to individually different oscillation frequencies and individually different Ca2+-related cellular responses . However , in many organs , the neighboring cells have to overcome their individually different sensitivity and produce a synchronized response . Gap junctions are integral membrane structures that enable the direct cytoplasmic exchange of Ca2+ ions and InsP3 molecules between neighboring cells . By simulations , we were able to demonstrate how the strength of intercellular gap junctional coupling in relation to stimulus intensity can modify the spatiotemporal patterns of Ca2+ signals and harmonize the Ca2+-related cellular responses via synchronization of oscillation frequency . We demonstrate that the most sensitive cells are the wave initiator cells and that a highly sensitive region plays an important role in the determination of the Ca2+ phase wave direction . This sensitive region will then also progressively determine the global behavior of the entire system . | [
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"organelles",... | 2016 | The Effect of Gap Junctional Coupling on the Spatiotemporal Patterns of Ca2+ Signals and the Harmonization of Ca2+-Related Cellular Responses |
Bacteria use a variety of stress-sensing systems to sense and respond to diverse stressors and to ensure their survival under adverse conditions . The gram-positive bacterium Bacillus subtilis responds to energy stress ( ATP depletion ) and to environmental stressors using two distinct stress-sensing pathways that converge on the alternative sigma factor σB to provoke a general stress response . Past efforts to study the σB stress response in bulk culture and on agarose pads were unable to visualize the responses of individual cells under tightly controlled conditions for extended periods of time . Here we use a microfluidics-based strategy to discern the basic features of σB activation in single cells in response to energy and environmental stress , both immediately upon stressor exposure and for tens of generations thereafter . Upon energy stress at various levels of stressor , cells exhibited fast , transient , and amplitude-modulated responses but not frequency modulation as previously reported . Upon environmental stress , which is mediated by the stressosome complex , wild-type cells primarily exhibited a transient and amplitude-modulated response . However , mutant cells producing only one of the four paralogous RsbR stressosome proteins showed striking and previously unseen differences . Whereas RsbRA-only cells mimicked the wild type , RsbRC-only cells displayed a slower but sustained overall response composed of repeated activation events in single cells .
Microorganisms respond to stressful conditions by activating genes that facilitate cell survival . These stress responses often involve the activation of an alternative sigma factor , such as in Escherichia coli the heat shock factor σ32 , the cell envelope stress factor σE , and the general stress-response factor σS [1 , 2] . In the gram-positive bacterium Bacillus subtilis , the general stress response is mediated by the alternative sigma factor σB [3 , 4] . We chose σB as the subject of this investigation , as its relatively well-understood signaling pathway facilitates specific manipulations . Under non-stress conditions , σB is held inactive by the anti-sigma factor RsbW , which binds to σB and prevents it from binding to RNA polymerase ( Fig 1 ) [5] . Upon stress , the anti-anti-sigma factor RsbV binds to RsbW , thereby freeing σB to bind to RNA polymerase and activate stress-response genes [6–8] . RsbV is itself regulated at the level of phosphorylation and is only able to bind to the anti-sigma factor RsbW in its dephosphorylated state [9 , 10] . Although σB activity responds to relatively low levels of stress that have minimal effects on cell growth , σB has an important role in cell survival at higher stress levels [11 , 12] . Activation of σB is controlled by two distinct upstream pathways that detect different types of stress but converge on the common activation system described above ( Fig 1 ) [13] . When cells undergo energetic or nutritional stress ( i . e . , ATP depletion ) , the proteins RsbQ and RsbP are both required to sense this stress by an unknown mechanism , but it is RsbP that then dephosphorylates RsbV [14 , 15] . Environmental stress signaling , which is activated by such factors as ethanol , salt , acid , and heat , instead occurs via a more complex pathway that activates RsbU , which like RsbP desphosphorylates RsbV [10 , 16–19] . The pathway begins with the stressosome , a 1 . 8-MDa complex primarily composed of RsbR and RsbS proteins [20–22]; there are an estimated 10–20 stressosomes per cell [23] . When cells are unstressed , RsbT proteins are bound to stressosomes via RsbS , keeping RsbT inactive . Environmental stress is thought to be sensed by the RsbR proteins , a contention supported by their N-terminal non-heme globin domains that extend out from the stressosome core [23–25] . B . subtilis produces five RsbR paralogs ( further discussed in the Results below ) that are thought to be mixed among stressosomes [21] . Upon stressor exposure , RsbR activates the kinase activity of RsbT , which then phosphorylates RsbS and RsbR during its release from the stressosome [26] . The freed RsbT then activates RsbU phosphatase activity as described above [10 , 16–19] . The phosphorylation of RsbR and RsbS is reversed by the phosphatase RsbX [10 , 27] to recapture RsbT and reset the environmental stress-response system . Because of their small size , bacterial cells experience rapid changes in their natural local environments , and their survival depends on their ability to mount stress responses rapidly and with appropriate magnitude and duration . Stress responses also represent a circumstance in which heterogeneity across a population can be particularly important , as cell-to-cell variation can permit "bet hedging" , in which a community of cells can benefit from some cells being better adapted to unpredictable future changes in local conditions . Therefore , an understanding of the basic features of bacterial stress responses requires immediate observation of cells upon stress exposure , so as to reveal the speed and magnitude of the initial response; observations of individual cells , so as to evaluate cell-to-cell variations across a population; and long-term observation , so as to uncover any response trends beyond the initial response to stressor onset . However , these basic features are not yet well understood , primarily because of technical limitations to previously employed methods . Classic bulk-culture experiments in flasks have a limited observational window ( typically under one hour ) and do not yield single-cell information . Agarose pad-based experiments give single-cell data , but it is difficult to observe immediate responses to stress onset , and the observational window is limited . With both methods , cellular metabolism continuously changes the local environment in uncontrolled ways . Here , we use a microfluidics-based approach [28] , which we previously adapted for B . subtilis [29] , to observe bacterial stress responses under constant exponential-phase growth conditions . This experimental strategy permits us to add a defined stressor to the medium flow and then to observe lineages of single cells both as they first encounter the stressor and over the course of many cell generations thereafter as they adapt to the presence of the stressor; hence , our observational window is much longer than for bulk culture- or agarose pad-based approaches . The microfluidic device also maximizes spatial and temporal uniformity so as to highlight true cell-to-cell heterogeneity . Using this approach , we discern the basic features of the B . subtilis σB stress response to different levels of energy and environmental stress . We also find previously unappreciated distinctions among the environmental stress-response profiles mediated by the four paralogous RsbR proteins encoded by the B . subtilis genome .
We began by using the microfluidic platform to investigate the response of individual cells to energy stress . A previous investigation based on the use of agarose pads gave rise to a frequency-modulation model that posited that energy-stress responses were pulsatile , with pulses getting more frequent ( but not greater in amplitude ) as stressor levels increased [30] . Such a frequency-modulated response might in principle create a broad distribution of cellular states in which only some cells would be responding at any given time; such heterogeneity across a cell population might then represent a bet-hedging strategy [30] . Because the previously reported pulse frequency was on the order of a few hundred minutes , only 2–3 pulses were observable over the course of an experiment . Also , the use of agarose pads only allowed observations to be made in a steady-state condition , well after cells had already been exposed to the stressor . We reasoned that our microfluidic platform would be an ideal system to observe frequency modulations , not only because we could observe cell lineages for longer ( and hence see a greater number of response pulses ) , but also because we could add the stressor to unstressed cells and watch the initial response to stress in addition to observing the steady state behavior of cells under stress . We reasoned that the initial response of cells to the sudden introduction of an energy stressor might be relatively synchronous , with the amplitude of the response tailored to the level of stress . Indeed , earlier bulk-culture studies showed initial responses in tens of minutes but did not examine the response trends for longer than 60 minutes or across different stressor concentrations [31 , 32] . We chose as an energy stressor the oxidative phosphorylation inhibitor CCCP ( carbonyl cyanide m-chlorophenyl hydrazone ) , a direct ATP-depleting agent that elicited a relatively fast and robust energy-stress response in previous work [31 , 32] . Our results showed a clear , rapid , and transient energy-stress response upon exposure to 40 μM CCCP ( Fig 2A and S1 Movie ) . Switching growing cells into medium containing only the dimethyl sulfoxide ( DMSO ) vehicle that we used to deliver the CCCP induced no visible response , whereas exposing cells to increasing concentrations of CCCP elicited progressively greater response amplitudes ( Fig 2B–2D and S1–S4 Movies ) . To ensure that the observed responses did not result from crosstalk between the energy and environmental stress signaling branches , we used a ΔrsbP mutant to inactivate energy signaling . Importantly , the ΔrsbP mutant showed no response , even at the highest tested CCCP concentration ( Fig 2B–2D and S5 Movie ) . Moreover , the amplitude of the response appeared to plateau above 40 μM CCCP , as 55 μM CCCP did not induce a significantly stronger response ( Fig 2D ) but did show additional , closely spaced response peaks ( Fig 2B and 2C and S3 Movie ) . This broadening of the overall response peak once the maximal response amplitude has been reached may represent a strategy to enhance energy-stress protection at near-toxic stressor levels ( in preliminary experiments , exposure to CCCP concentrations above 60 μM resulted in widespread cell death ) . Strikingly , and in contrast to a previous report [30] , we observed virtually no additional long-term response pulses after the initial response to CCCP onset , even when observing cells for over 1 , 000 minutes in the presence of CCCP ( Fig 2B ) . We saw occasional peaks in cell lineages exposed to 55 μM CCCP , but such peaks were extremely infrequent , much weaker than the initial response peak , and absent from cells treated with lower CCCP concentrations ( Fig 2B ) . We conclude that , under our experimental conditions , the energy-stress response is amplitude-modulated rather than being frequency-modulated , suggesting that cells prioritize a more-stereotyped initial response to energy stressor onset over more broadly distributed , sustained responses in a stressor-exposed state . Finally , we investigated the response to mycophenolic acid , which was used as an energy stressor in the study of Locke et al . [30] . S1 Fig shows that cells responded strikingly slower to mycophenolic acid than was seen in the experiments with CCCP ( 190 min to reach a half-maximal response for 60 μg/ml of mycophenolic acid versus 20 min for 40 μM CCCP ) . Also , the response to mycophenolic acid was sustained for the duration of the experiment , unlike the response to CCCP , which was transient . These differences may reflect the fact that mycophenolic acid acts indirectly to trigger energy stress by inhibiting GTP synthesis rather than directly to block ATP production as in the case of CCCP [32] . In any event , although the sustained response exhibited some noise in single cells ( S1B Fig ) , we saw little evidence of pulsatile behavior . While we cannot exclude the possibility that slight or fast pulsing was obscured by fluorescent protein maturation , we conclude that any pulsatile component is not a major contributor to the overall energy-stress responses we observed . We next turned to the environmental branch of the stress-response pathway . In contrast to the relatively simple energy-stress branch consisting only of the RsbQ and RsbP proteins ( Fig 1 ) , environmental stress is mediated by large stressosome complexes consisting of RsbR proteins along with RsbS and RsbT ( Fig 3A ) [20–23] . As described above , the stressosome functions by sequestering RsbT until stress is sensed , at which time RsbT is freed to activate the downstream steps of stress signaling ( Fig 3A ) [10] . Notably , B . subtilis encodes four paralogous RsbR proteins known as RsbRA , RsbRB , RsbRC , and RsbRD [24] . A fifth paralog , YtvA , is a blue-light sensor [33 , 34] that differs from the other four in that it evidently cannot form stressosomes on its own in vivo [22 , 24 , 35]; because it had only minor effects on the magnitude and not the shape of RsbRA-mediated stress responses in preliminary experiments , we used a ytvA-deleted strain background for all the experiments in this study to avoid spurious activation by fluorescence imaging . The RsbR paralogs share with RsbS conserved C-terminal STAS domains that compose the stressosome core , whereas the more-divergent N-terminal regions form non-heme globin dimers that extend out from the stressosome core [23 , 24] . Wild-type stressosomes are regarded as containing stochastic mixtures of the RsbR paralogs [21] , as indicated by different shades of blue in Fig 3A . We first sought to observe the responses of cells to different concentrations of ethanol , a classic environmental stressor well-known to provoke a strong general stress response . Challenging cells with 1% ethanol did not induce a readily discernible response , whereas 2% ethanol provoked a strong and transient response spike that was synchronous across the cell population ( Fig 3B and 3C and S7 and S8 Movies ) . This response profile resembled the canonical response previously seen in bulk-culture experiments [16 , 22 , 24] and in microcolonies on agarose pads or under microfluidic conditions [36] , suggesting that growth in our microfluidic device does not appreciably change the general principles of the cellular response to environmental stress . The response was also specific to the environmental-stress pathway , as cells deleted for rsbU showed a minimal response to 2% ethanol ( S10 Fig and S9 Movie ) . When we further challenged wild-type ( rsbU+ ) cells with 4% ethanol , we observed a similar synchronous response spike that was only slightly stronger than that elicited by 2% ethanol ( Fig 3B–3E and S10 Movie ) , implying amplitude modulation resembling that of the energy-stress response ( Fig 2 ) and in agreement with previous results from bulk culture [16] and agarose pads [36] . Moreover , there was a sharp increase in the mean output amplitude ( approximately 8-fold ) in response to a twofold change in the input stressor ( from 1% to 2% ethanol; Fig 3E ) , in accord with an ultrasensitive , switch-like system . The modest further increase at 4% ethanol suggested that the system was nearing saturation . Exposure to 4% ethanol also increased the rate of cell death , resulting in a smaller number of high-quality cell lineages ( Fig 3C ) . Notably , 4% ethanol , in contrast to 2% ethanol , elicited a sustained response that endured for the duration of the experiment , as individual cells seldom returned to their pre-stress reporter levels ( Fig 3B–3D and S10 Movie ) . As the cells were continuously exposed to ethanol after the medium switch , this result suggests that wild-type cells experience a relative insensitivity to ethanol stress after their initial exposure and that a higher ethanol concentration ( e . g . , 4% ) can partially overcome the insensitivity to provoke a more-sustained stress response . This sustained response at 4% ethanol represents a previously unappreciated response feature that was only uncovered by the long-term observation of cells well after the initial response peak . As with the peak-broadening that we observed at high levels of energy stress ( Fig 2B and 2C ) , this sustained response may enhance cellular stress tolerance once the maximum response amplitude is reached . The coexistence of four RsbR paralogs in the stressosome with their variable N-terminal regions extending from the stressosome core makes it attractive to hypothesize that each paralog fulfils a distinct cellular function . For instance , the RsbRs might sense different specific stressors or respond with different sensitivity . However , it has been a persistent challenge to identify functional differences among the RsbRs [22 , 35] . We thus asked whether individual RsbRs might display different responses , reasoning that the long observation window , constant environment , and single-cell resolution of our microfluidic platform might uncover previously unappreciated differences . To examine each RsbR paralog individually , we constructed sets of triple-deletion strains that would produce only one of the four RsbR proteins and then challenged these strains with 2% ethanol . We observed striking differences in their stress-response profiles . Cells containing only RsbRA exhibited a transient and synchronous response spike that almost perfectly resembled the wild-type response ( Fig 4A–4C and S11 Movie ) . In sharp contrast , RsbRC showed a slower , progressive response that reached a high average level and was sustained for the duration of the experiment ( Fig 4A–4C and S12 Movie ) . The response mediated by RsbRB resembled the RsbRC response but was substantially weaker ( Fig 4A–4C and S13 Movie ) . RsbRD , meanwhile , showed a hybrid response composed of an initial response spike that resembled the wild-type response in magnitude ( Fig 4D and S14 Movie ) and was then followed by a sustained response . Single-cell traces revealed that the sustained mean responses observed for the RsbRB- , RsbRC- , and RsbRD-only populations were composed of repeated stress-activation peaks in single cells ( Fig 4A and 4B; S7 and S8 Figs highlight the greater standard deviations and coefficients of variance for these populations ) . Together , our results reveal that the RsbR paralogs have different response characteristics when challenged with an identical 2% ethanol stress . The different RsbRs differed in the speed of their initial response , in their overall and single-cell response magnitudes , and in the duration of the overall response . The repeated σB activity peaks in single cells during sustained responses give rise to a broad activity distribution over a cell population , resembling the proposed bet-hedging strategy that would be facilitated by frequency-modulated responses [30] . Having observed distinct response profiles for each of the four RsbR paralogs , we focused our attention on RsbRA and RsbRC , as these paralogs showed the greatest contrast in their response profiles ( and are known to be produced at similar levels [37] ) . Whereas RsbRA-only cells responded to ethanol stress with a fast , sharp , and transient response , RsbRC-only cells displayed a slower initial response that progressively reached a higher mean level and was then sustained for the duration of ethanol exposure ( Fig 4A–4C ) . We thus asked how these response profiles would change in response to lower or higher concentrations of ethanol . When challenged with 1% ethanol , both strains showed a modest response ( Fig 5A–5C and S15 and S16 Movies ) , in accord with our results for wild-type cells ( Fig 3B and 3C ) . RsbRA-only cells showed a small but detectable spike in the population average trace ( Fig 5C and S5B Fig ) , whereas the RsbRC-only strain responded with a broadening of the variation across the cell population that was caused by more-frequent response events in individual cells ( Fig 5B and S7 Fig ) . Hence , even at stress levels close to the response threshold , RsbRA and RsbRC maintain their strikingly distinct response profiles but at substantially lower amplitudes . The modest responses of both strains to 1% ethanol also support the idea that both RsbRA and RsbRC appear to exhibit some degree of ultrasensitivity between 1% and 2% ethanol . When stressed with 4% ethanol , both strains also largely preserved their characteristic response profiles but substantially increased their response magnitudes . RsbRC-only cells again manifested a progressively increasing response that was faster and reached a higher sustained level than with 2% ethanol ( Fig 5C and S17 Movie ) . In contrast , RsbRA-only cells showed a transient response spike that was notably stronger than that elicited by 2% ethanol and that was followed by a clear sustained response ( Fig 5C , S5B Fig and S18 Movie ) characterized in single-cell traces by greater variability and more-frequent response spikes ( Fig 5B ) . The sustained phase of the RsbRA response in 4% ethanol thus resembled the sustained RsbRC response yet was clearly distinguished by its narrower variability across the cell population , as indicated by coefficient-of-variance plots ( Fig 5D and S8 Fig ) . The long-term sustained responses of the RsbRA- and RsbRC-only strains at 4% ethanol were only distinguishable thanks to the long observation times and single-cell resolution of a microfluidics-based approach . Meanwhile , the immediate response of RsbRA-only cells showed strong amplitude modulation with different ethanol concentrations ( S5B Fig ) , partially mimicking the wild type but being distinguished by its greater increase in magnitude between 2% and 4% ethanol . Thus , both the RsbRA- and RsbRC-only responses to different ethanol concentrations appear to be less switch-like than that of the wild-type . Finally , we examined the responses of individual RsbRA-only and RsbRC-only cells to observe the similarities and differences among single cells in a stress-responding population . It was clear from our overlaid single-cell traces that the transient initial response spikes observed for RsbRA-only cells were synchronous across the population but heterogeneous in amplitude ( Figs 4B and 5B ) , an observation that was borne out in traces considered individually ( Fig 6A ) . After the initial response spike , the RsbRA-only responses varied from cell to cell within a relatively narrow range but with no discernible pattern , suggesting stochastic fluctuations in individual cells . In contrast , RsbRC-only cells showed greater variation both in time and amplitude . The progressively increasing mean response of RsbRC appears to be due to temporal heterogeneity across the population , with some cells responding early ( e . g . , Cells 2 and 4 in Fig 6B ) and other cells not responding until later ( e . g . , Cells 1 , 3 , and 5 in Fig 6B ) . The sustained mean RsbRC response , as implied by our population overlays ( Figs 4B and 5B ) , was composed not of sustained responses in single cells but rather by repeated , pulsatile activation events that varied widely in frequency and amplitude , although the amplitudes of individual events were generally greater in 4% ethanol than in 2% ethanol ( Fig 6B ) . We were unable to discern any patterns or characteristic frequencies in the single-cell response profiles , leading us to conclude that the pulsatile responses are governed by stochastic processes rather than oscillatory mechanisms . To test whether the response peaks were associated with cell division or other obvious morphological cues , we also visually examined cells as they underwent response events . We observed no such cues in cells exhibiting weak or strong responses; cells appeared to have normal morphology and to grow and divide normally ( Fig 6C ) . Moreover , the response peaks often lasted for more than one cell cycle ( Fig 6C ) , and we did not observe any obvious correlation between pulsatile responses and cell-division events ( S9 Fig ) , although activation of the general stress response under stress generally slowed the cell growth rate ( S6 Fig ) . Such a growth slowdown is expected because of the substantial metabolic burden of activating the general stress response . Notably , in both RsbRA- and RsbRC-only cells , the heterogeneity across the cell population during sustained long-term responses could in principle represent a bet-hedging strategy . In this scenario , individual cells with a weaker response might be better prepared to take advantage of future decreases in stress , whereas cells with a stronger response might be poised to handle future increases in stress .
The use of a microfluidics-based platform to characterize the general stress response of B . subtilis cells has important advantages over earlier approaches . Unlike the use of agarose pads , the platform allows us to observe the response of cells to a stressor immediately after its application and under uniform conditions . Cells growing on agarose pads are in a potentially heterogeneous environment of progressively increasing crowding and must be exposed to a stressor before the cells are placed on pads and measurements of responses can be taken . Moreover , in contrast both to bulk ( shaking liquid ) culture-based experiments and to agarose pad experiments , the platform allows us to monitor the behavior of individual cells under tightly controlled conditions for long periods of time . We used microfluidics to study the activation of σB in response to energy and environmental stress , aiming to discern general features of the bacterial stress response in the absence of extrinsic variability . To further focus on the intrinsic responses of cells , we used relatively low levels of stress that elicited a σB-mediated response but did not strongly affect cell growth ( S2B and S6 Figs ) or survival . Our study uncovered at least three general features . First , we were able to distinguish the initial response upon stressor exposure from the long-term responses of cells that were continuously exposed to a stressor . In the case of energy stress , we observed amplitude-modulated initial responses but minimal long-term responses . With environmental stress , different ethanol concentrations produced different response patterns , as moving from 2% to 4% ethanol elicited a long-term sustained response without substantially increasing the amplitude of the initial response . Moreover , individual RsbR paralogs showed differing initial and long-term responses to identical stress conditions . For example , RsbRA-only cells had a rapid initial response but a relatively weak sustained response , whereas RsbRC-only cells were slower to initially respond but had a much stronger sustained response . Second , for environmental stress , we observed a relatively switch-like response in which the response went from barely detectable to nearly saturated over a roughly twofold increase in stressor concentration . The stressosome has been proposed as a cooperative complex [38] , possibly explaining why the environmental-stress response was more switch-like than the energy-stress response . Third , we observed response heterogeneity both over time and across cell populations that was especially apparent in the pulsatile responses of single cells during long-term responses . Such heterogeneity may be especially important in a stress-response context , as some cells are ( randomly ) poised to take advantage of future changes in growth conditions , either for the better or for the worse . To trigger energy stress we used the uncoupler of oxidative phosphorylation CCCP , which has been traditionally used in the field as a direct inhibitor of ATP synthesis [31 , 32] . Our principal finding is that the response is amplitude-modulated with increasing levels of the uncoupler . Our results with CCCP are in contrast with the observation using mycophenolic acid as a stressor that increasing energy stress increases the frequency of responses rather than their amplitude [30] . When we tested mycophenolic acid , we saw that cells were tenfold slower to reach a half-maximal level than for CCCP and exhibited a sustained rather than transient response that provided little evidence for pulsing ( S1 Fig ) . We note that only the sustained phase would have been visualized on agarose pads as previously used [30] . A slow σB response of cells to mycophenolic acid was also observed in bulk culture , where it was attributed to guanine nucleotide depletion [32] . Our results are in accord with the idea that mycophenolic acid provokes a different energy-stress response than CCCP because guanine nucleotide depletion , which indirectly affects ATP levels , has additional effects on cell physiology . Nonetheless , and at a minimum , our results with the microfluidic platform show that frequency modulation is not a general feature of the response to energy stress . A longstanding question has been whether the four RsbR paralogs make distinct contributions to the environmental-stress response of B . subtilis . A few distinctions have been uncovered; for example , the blue-light sensor YtvA requires RsbRA for its light-sensing function but is negatively influenced by RsbRB [35] , whereas RsbRC and RsbRD reportedly respond to nutritional stress in strains deleted for rsbRA and rsbRB [39] . However , differences among the RsbR paralogs with respect to their response profiles , even when examined individually , have been difficult to discern [22 , 24 , 35] . In contrast , we observed clear differences among the RsbR proteins , with each showing a different response profile to identical ethanol stress conditions . We attribute our ability to make such distinctions in large part to the long observation window and constant conditions of microfluidic devices , which permit the sustained phase of the response to be observed . In contrast , previous stress experiments in bulk culture typically tracked the response for 90 minutes or less to ensure that the cells remained in exponential phase . In retrospect , some shorter-term experiments hint at the differences we observed , for instance the sustained response of RsbRC-only cells [22] . The distinct responses of the different RsbR paralogs to the relatively low levels of ethanol stress we used in this study raise the possibility that different stressors , or more-extreme stress conditions under which the σB response becomes important for cell survival [11 , 12] , will reveal additional functional distinctions among the RsbR paralogs . The close similarity between the wild-type and RsbRA-only responses , characterized by a fast but transient response ( Fig 4C ) , suggests that the wild-type response is dominated by RsbRA . How might RsbRA dominate the overall response ? Stressosomes deactivate or activate the environmental-stress response by sequestering or releasing RsbT molecules , respectively . In the stressosome structure , each sequestered RsbT protein is associated with an RsbS protein and an RsbR dimer [23] Importantly , RsbRA appears to be present at a roughly tenfold excess over RsbT [37] , suggesting that there is more than enough RsbRA distributed among wild-type stressosomes to sequester all cellular RsbT . We hypothesize that RsbRA , following its initial activation by the onset of environmental stress , becomes refractory to further activation . Refractory RsbRA molecules distributed among the cellular stressosomes would then be able to recapture on their associated RsbS proteins free RsbT molecules but would be unable to re-release them . RsbT proteins freed from other RsbR paralogs would tend to accumulate on RsbS proteins associated with refractory RsbRA molecules , irrespective of the abundance or activation state of the other RsbR proteins . The sequestration of most of the cellular RsbT by refractory RsbRA molecules would give rise to the transience of the wild-type environmental-stress response and would mask the activation of the other RsbR paralogs . Therefore , only when present as the sole source of RsbR in the cell does each of the other RsbR proteins manifest its distinct response . What is the basis for such a refractory state ? A refractory state of RsbRA could be induced by RsbT-mediated phosphorylation of RsbRA on its conserved C-terminal threonine residue ( Fig 3A ) . Such phosphorylation has been suggested as a means of negative feedback under strong environmental stresses [40] . Moreover , a phosphomimetic substitution of this residue in an otherwise wild-type context markedly suppresses the environmental-stress response [17 , 22] , thereby resembling a refractory state . The dominance of RsbRA in the wild-type response to ethanol argues that RsbRA may be a cellular control point in this environmental stress response; altering its levels by controlling its synthesis and/or degradation could in principle change the overall response . Activity-attenuating threonine phosphorylation of RsbRA was only detected under strong heat and ethanol stresses [40] . It is possible that in the presence of other stressors RsbRA does not become refractory , thereby permitting a different paralog to dominate the response . It will be interesting in future work to determine whether cells modulate the relative abundances and/or stabilities of the different RsbR proteins under different stress conditions to generate a particular response profile . For instance , rsbRD expression is stimulated by stress , and , as mentioned above , RsbT levels are approximately 10-fold lower than RsbRA levels despite being co-transcribed [37] , suggesting that cells employ both transcriptional and post-transcriptional regulatory mechanisms . In sum , the use of a microfluidic platform has proven to be an effective tool for monitoring the response to individual cells under tightly controlled conditions to energy and environmental stress . We were able to discern general stress-response features , namely that cells have distinct initial and long-term stress responses , show a relatively switch-like response to increasing stressor levels , and show response heterogeneity in time and across a population . We also conclude that energy stress is amplitude-modulated rather than frequency-modulated under the conditions of our experiments and that , in extension of earlier investigations based on bulk measurements , different RsbR proteins are capable of making decidedly different contributions to the response to environmental stress .
The bacterial strains used in this study are listed in Table 1 and in the S1 Text . B . subtilis strains were routinely grown in Lennox broth ( 10 g/L tryptone , 5 g/L yeast extract , 5 g/L NaCl ) or on Lennox agar plates fortified with 1 . 5% Bacto agar at 37°C . When appropriate , antibiotics ( 5 μg/ml chloramphenicol , 100 μg/ml spectinomycin , or 10 μg/ml tetracycline ) were added to select for markers . All strains used for microfluidic analysis contained the hagA233V point mutation to render cells immotile , thereby preventing cell loss from side channels without interfering with motility regulation [29] . Markerless deletions in B . subtilis were generated using the pMiniMAD vector ( a gift of Daniel Kearns ) for allelic replacement . The details of plasmid and strain construction are described in the Supplemental Text . For energy-stress experiments , cells were grown in salt-free LB medium buffered with potassium phosphate ( 10 g/L tryptone , 5 g/L yeast extract , 21 mM K2HPO4 , and 11 mM KH2PO4 ) [3] , as this medium reportedly enhances the response of cells to CCCP [41] . For environmental-stress experiments , standard Lennox broth was used . CCCP and mycophenolic acid were added from 100-mM stocks in DMSO , and absolute ethanol was added to the desired final concentration . The polydimethyl siloxane ( PDMS ) microfluidic devices were dimensionally identical to and prepared essentially as those previously described [29] . Briefly , cured PDMS devices ( 10:1 Sylgard 184 ) prepared from a silicon wafer master were punched with a 0 . 75-mm biopsy punch to create holes to connect the fluidics using 21-ga blunt needles . The devices were bonded to isopropyl alcohol-cleaned glass cover slips by oxygen-plasma treatment at ~200 mtorr O2 for 15 sec at 30 W and baked at 65°C for at least 1 hour before use . The devices were passivated with growth medium containing 1 mg/ml bovine serum albumin ( BSA ) before cell loading . Cells were grown in shaking culture to stationary phase ( OD600 = ~4–5 ) , filtered through a 5-μM filter to remove cell chains , concentrated by centrifugation at 5 , 000 g for 10 min , and loaded into the device using gel-loading tips . Cells were then spun into the side channels of the device in a custom-designed microcentrifuge adaptor at 6 , 000 g for 10 min . The fluidics were then connected to the device and run at 35 μl/min for approximately 20 min to flush out excess cells before being run at 1 . 5 μl/min for imaging . Imaging was not initiated until cells in the device had resumed uniform exponential growth . The media used for fluidics always contained 1 mg/ml BSA as a passivation agent to limit cell adhesion to the device during flow . The fluidics were fed by 20-ml syringes in 6-channel syringe pumps ( New Era Pump Systems , Farmingdale NY ) that were connected by 21-ga blunt needles to Tygon flexible tubing with an inner diameter ( ID ) of 0 . 02" . To permit medium switches , 2 banks of syringes were used , one for the one for the pre-stress phase containing plain medium , the other for the stress phase containing the stressor . Each pair of syringes ( minus and plus stressor ) was joined with a polypropylene 1 . 6-mm ID Y-connector with 200-series barbs; 2-cm lengths of flexible silicone tubing ( 0 . 04" ID , 0 . 085" outer diameter ) were used to connect the Tygon lines to the 2 input branches Y-connector . The lengths of silicone tubing facilitated the placement of small binder clips to one or the other branch to make pinch valves . A 1-cm length of silicone tubing was used to connect a 10-cm length of Tygon tubing to the output of the Y connector . The output Tygon tubing was directly connected to a bent 21-ga blunt needle that was then connected to the PDMS device , and a similarly constructed needle-tube combination was used to carry the outflow of the device to a waste beaker . In initial tests , we noticed that a higher concentration of CCCP was necessary to elicit a response under microfluidic conditions than under flask-grown conditions . We attribute this difference to the continuous flow and "open" nature of the microfluidic system , which likely attenuates nutritional stress because cells are continuously supplied with nutrients . Experiments were always initiated in stressor-free medium , and this initial growth phase typically lasted approximately 10–12 h before the switch . In the initial phase , pinch valves were closed on the stressor-containing branch of the fluidics , and the corresponding syringe pump was paused . At the switch , the syringe pump with stressor-free medium was paused , the binder clips were carefully moved to the stressor-free branch of the Y-connectors , and the other ( stressor-containing ) syringe pump was activated ( at 1 . 5 μl/min ) . Initial tests with marker beads and dyes indicated that the second medium took approximately 50 minutes to reach the cells in the device . The switch apparatus was housed within a temperature-controlled microscope enclosure during imaging ( see below ) . Imaging was performed with a Nikon Eclipse Ti inverted microscope equipped with an Orca R2 ( Hamamatsu ) camera , a 60X Plan Apo oil objective ( NA 1 . 4 , Nikon ) , an automated stage ( Ludl ) , a Lumencor SOLA fluorescent illumination system , and a custom-designed temperature-controlled Plexiglas enclosure in which the temperature was maintained at approximately 37°C during imaging . Image acquisition was performed using MATLAB scripts interfacing with μManager , as previously described [29] . Semrock filter cubes for GFP ( GFP1828A ) and mKate2 ( mCherry-B ) were used to image mNeonGreen and mNeptune , respectively . mNeonGreen ( used for σB reporters ) was imaged with 2x2 pixel binning at approximately 20% illumination power with 200-ms exposures , and mNeptune ( used for cell segmentation ) was imaged with 1x1 binning at approximately 24% power with 400-ms exposures ) . Images were captured at 10-minute intervals . Automated cell segmentation and lineage tracking was performed as previously described [29] using a constitutively expressed cytoplasmic mNeptune reporter . The average mNeonGreen average intensity in mother cells was used to generate σB reporter traces . After lineage tracking , lineages were filtered to retain only lineages that were tracked for >150 continuous frames . Because spontaneous cell death events and other anomalies ( e . g . , overcrowding of side channels ) were associated with spurious peaks in reporter intensity , the full filtered set of lineages was then manually curated to remove spurious events . The average traces for the full and curated lineage sets showed close concordance , as shown in S2–S5 Figs , implying that the curation process did not bias the overall observed trends . In all other figures , curated lineage sets were used to plot average traces and overlaid single-cell traces . As part of the automated cell-segmentation process , cell lengths and division times are calculated . For display , we averaged the observed division times over a 10-frame sliding window ( an example of a scatter plot and the corresponding averaged trace is shown in S11 Fig ) . In all cases , the data from the full set and the curated set were virtually indistinguishable ( S11 Fig ) , so only full-set data are shown for simplicity . | All living things must sense and respond to stress in order to survive . Because bacteria are often subjected to rapidly changing conditions in nature , they have evolved stress-response mechanisms that are poised to respond to harsh environmental conditions . Many of the proteins that mediate bacterial stress responses are known , but technical limitations have made it difficult to discern how individual cells respond to stress at short and long time scales . By using a microfluidic device in which we can continuously observe individual bacteria as we expose them to different stresses , we have overcome previous limitations and uncovered basic features of bacterial stress responses . Knowledge of these features will help us to understand how different stress-response profiles may benefit cells under stressful circumstances and how cell-to-cell variability may enhance the survival of a population of cells experiencing harsh conditions . Our results from a relatively simple bacterial model system may also yield insights into how higher cells effectively respond to stress , as many stress-response principles are broadly conserved throughout biology . | [
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"... | 2017 | Use of a microfluidic platform to uncover basic features of energy and environmental stress responses in individual cells of Bacillus subtilis |
Cyclin T1 is a regulatory subunit of a general RNA polymerase II elongation factor known as P-TEFb . Cyclin T1 is also required for Tat transactivation of HIV-1 LTR-directed gene expression . Translation of Cyclin T1 mRNA has been shown to be repressed in human monocytes , and this repression is relieved when cells differentiate to macrophages . We identified miR-198 as a microRNA ( miRNA ) that is strongly down-regulated when monocytes are induced to differentiate . Ectopic expression of miR-198 in tissue culture cells reduced Cyclin T1 protein expression , and plasmid reporter assays verified miR-198 target sequences in the 3′ untranslated region ( 3′UTR ) of Cyclin T1 mRNA . Cyclin T1 protein levels increased when an inhibitor of miR-198 was transfected into primary monocytes , and overexpression of miR-198 in primary monocytes repressed the normal up-regulation of Cyclin T1 during differentiation . Expression of an HIV-1 proviral plasmid and HIV-1 replication were repressed in a monocytic cell line upon overexpression of miR-198 . Our data indicate that miR-198 functions to restrict HIV-1 replication in monocytes , and its mechanism of action appears to involve repression of Cyclin T1 expression .
Productive transcription of eukaryotic protein-coding genes requires a processive form of RNAP II to overcome pauses resulting from negative elongation factors . The positive transcription elongation factor , P-TEFb , plays a critical role in converting RNAP II to a processive enzyme through phosphorylation of the C-terminal domain of RNAP II and negative elongation factors [1] , [2] . P-TEFb is composed of Cyclin-dependent kinase 9 ( CDK9 ) as the catalytic subunit and Cyclin T1 , T2 , or K as the regulatory subunit [3] , [4] . Although there are multiple Cyclin partners for CDK9 , Cyclin T1-containing P-TEFb ( Cyclin T1/P-TEFb ) is the major cellular form in cell types examined thus far and it has been studied extensively because of its involvement in HIV-1 gene expression [5] . The HIV-1 Tat transactivator protein recruits Cyclin T1/P-TEFb to the TAR RNA structure of viral transcripts , resulting in a switch from an abortive transcription process to a highly processive one , and this greatly enhances viral gene expression and is essential for viral replication [6] . Unlike Cyclins involved in cell cycle progression , the expression level of Cyclin T1 is usually independent of cell cycle stages . However , Cyclin T1 has been shown to be regulated in human peripheral blood lymphocytes ( PBLs ) , primary CD4+ T cells , and monocytes/macrophages . In PBLs and CD4+ T cells , activation from a resting state results in a strong up-regulation of Cyclin T1 protein expression through a mechanism that involves post-transcriptional regulation [7]–[9] . Cyclin T1 expression is low in freshly isolated monocytes and increases significantly when cells are induced to differentiate into macrophages [10] . The induction of Cyclin T1 in macrophages correlates with a permissive state for HIV-1 replication , as monocytes do not support HIV-1 replication [11] . Additionally , Cyclin T1 protein expression is shut-off at late times of macrophage differentiation by proteasome-mediated proteolysis , but it can be re-induced by macrophage activation or HIV-1 infection [10] , [12] . The increase in Cyclin T1 protein expression in monocytes plays an important role in macrophage differentiation , as an shRNA depletion of Cyclin T1 in a monocytic cell line prevents the up-regulation of over 20% of mRNAs normally induced when these cells are stimulated to differentiate into macrophages [13] . We have previously observed that although Cyclin T1 protein expression is very low in freshly isolated primary monocytes , Cyclin T1 mRNA levels are high , suggesting that translation of this mRNA may be actively suppressed in monocytes [10] , [14] . Given the approximate 5 kb length of the Cyclin T1 3′UTR [15] , a miRNA ( s ) may be responsible for this repression . MicroRNAs are non-coding small RNAs of about 22 nucleotides in length that function in metazoans through base-pairing preferentially with the 3′UTR of target mRNAs , resulting in translational inhibition or in some cases mRNA degradation [16]–[18] . A number of studies have linked miRNAs to the regulation of specific gene functions , cell fate transition , malignant transformation , and especially hematopoietic lineage commitment [19]–[21] . In this study , we examined the miRNA expression profile during the early stage of primary human monocytes differentiation into macrophages . This analysis identified miR-198 as a negative regulator of Cyclin T1 protein expression through targeting sequences in the 3′UTR of Cyclin T1 mRNA . We found that inhibition of miR-198 function in primary monocytes resulted in increased Cyclin T1 protein levels , and overexpression of miR-198 in differentiating monocytes repressed the normal Cyclin T1 up-regulation . Inhibition of Cyclin T1 up-regulation by miR-198 in a monocytic cell line also resulted in decreased HIV-1 proviral gene expression and HIV-1 replication , indicating that miR-198 possesses an anti-HIV-1 function that appears to function through the repression of an essential cellular cofactor .
Cyclin T1 protein expression is induced by a post-transcriptional mechanism when human primary monocytes are cultured in vitro under conditions that allow macrophage differentiation [10] . Given the ∼4 . 6 kb 3′UTR in the Cyclin T1 mRNA ( see below ) , we wished to examine changes in miRNAs expression during monocyte to macrophage differentiation , as miRNAs that are down-regulated during differentiation are candidates for repressors of Cyclin T1 protein expression . We isolated total RNA from both monocytes and macrophages allowed to differentiate in vitro from two healthy blood donors . A microarray platform was used to examine expression levels of 321 validated human miRNAs in these RNA preparations ( see Materials and Methods ) . Using criteria and statistical analysis described in Material and Method , we identified several miRNAs that were differentially expressed in monocyte and macrophages from both donors ( Figure 1A ) . Of these miRNAs , nine were up-regulated ( red in heat map ) and 13 were down-regulated ( green in heat map ) in macrophages relative to monocytes . Thus , a total of 22 out of 321 miRNAs examined showed reproducible differential expression during monocyte to macrophage differentiation . To monitor the reliability of the miRNA microarray data , we selected four miRNAs for end-point RT-PCR analysis with commercially available primer sets . As shown in Figure 1B , increased amounts of PCR products for miR-155 were detected in RNA samples from macrophages relative to monocytes , in agreement with the microarray data which showed an induction of miR-155 . Reductions in the levels of miR-26a and miR-223 in macrophages were detected in the PCR assays , again in agreement with the microarray data in which both of these miRNAs were down-regulated . Finally , the levels of miR-24 were similar between monocyte and macrophages in the PCR assays , in agreement with microarray data that showed a constant level of miR-24 expression . These results indicate that the microarray data are likely to be reliable in general . To identify miRNAs that might repress Cyclin T1 mRNA translation , we wished to search Cyclin T1 mRNA sequences for potential target sites for the 13 miRNAs found to be down-regulated during monocyte differentiation . Cyclin T1 mRNA is ubiquitously expressed as a ∼8 kb transcript and because the coding sequence of Cyclin T1 only requires 2178 nucleotides and the 5′UTR is approximately 330 nucleotides in length , it has an extensive 3′ untranslated region ( 3′UTR ) that has not been characterized [4] , [22] . The alignment of human EST sequences and the full-length goat Cyclin T1 mRNA sequence suggest that the 3′UTR of human Cyclin T1 mRNA is contained within a single exon [15] . An analysis of the 8 kb sequence downstream of the Cyclin T1 stop codon revealed seven potential poly ( A ) signals ( AATAAA ) ( Figure 2A ) . To determine the 3′UTR sequences for miRNA target predictions , we first characterized the poly ( A ) signal usage in the Cyclin T1 mRNA by RT-PCR . RNA samples from cell lines , primary CD4+ lymphocytes , and monocytes/macrophages were examined to determine if alternative poly ( A ) signals are utilized for the Cyclin T1 3′UTR . Using an RT primer that anneals to the start of the poly ( A ) sequences and PCR primers specific for each poly ( A ) signal ( P1 to P5 , Figure 2A ) , we were able to amplify a discrete band of ∼400 bp only with primer P4 ( Fig 2B ) . Extension times in PCR reactions were 45 seconds to maximize the expected ∼400 bp product that would be produced by the primer nearest to the major poly ( A ) site . These data suggest that the fourth poly ( A ) signal is utilized to produce the majority of Cyclin T1 mRNA in the cell types examined . The absence of PCR products in reactions without reverse transcription excludes the possibility of genomic DNA contamination in PCR reactions . A BLAST search revealed two cDNA clones with only three or four nucleotide differences with the P4-PCR product ( Figure 2C ) , further supporting the conclusion that the fourth poly ( A ) signal is used predominantly . Based upon these results , we conclude that Cyclin T1 mRNA arises predominantly from utilization of the fourth poly ( A ) signal and its length is ∼7 . 1 kb ( plus poly ( A ) sequences ) . Additionally , this analysis found no evidence of alternative poly ( A ) site selection in CD4+ lymphocytes and monocyte/macrophages . This deduced Cyclin T1 3′UTR was used for miRNA target site predictions . We selected several down-regulated miRNAs , including miR-15b , miR-26a , and miR-198 , for initial experiments . These three miRNAs were down-regulated >2-fold in our microarray data with monocytes/macrophages and each are predicted to have >five target sites in the Cyclin T1 mRNA 3′UTR with minimum free energies ( mfe ) less than -20 kcal/mol . In gain-of-function experiments , precursors for these miRNAs were transfecting into 293T cells and Cyclin T1 protein expression was examined in immunoblots . Cyclin T1 protein levels were reduced in 293T cells transfected with the miR-198 precursor ( pre-miR-198 ) relative to mock-transfected cells , while miR-15b precursor ( pre-miR-15b ) had no significant effect ( Figure 3A ) . Similar to the results with the pre-miR-15b transfection , the miR-26a precursor did not affect Cyclin T1 protein expression ( data not shown ) . SiRNAs that target the Cyclin T1 coding region were used as a positive control for transfection , and these siRNAs successfully knocked down Cyclin T1 protein expression . To further validate the effect of miR-198 on Cyclin T1 protein expression , increasing amounts of pre-miR-198 were transfected into HeLa cells , and a dose-dependent reduction in Cyclin T1 protein level was seen ( Figure 3B ) . A control miRNA precursor , pre-miR-Control , did not affect Cyclin T1 expression . Additionally , miR-198 did not affect Cyclin T1 mRNA levels , which remained constant in cells transfected with pre-miR-198 ( Figure 3C ) . The levels of β-actin and GAPDH mRNAs also remained unchanged following transfection of pre-miR-198 , suggesting that miR-198 overexpression has no impact on overall mRNA levels . These data demonstrate that miR-198 , a miRNA which is down-regulated during monocyte to macrophage differentiation , is capable of down-regulating Cyclin T1 protein expression . Because the ectopic expression of miR-198 reduces Cyclin T1 protein levels without affecting Cyclin T1 mRNA levels , it is likely that miR-198 acts through translational inhibition as is common for miRNAs . We found that miR-198 is predicted to have >10 potential target sites in the 3′UTR of Cyclin T1 mRNA with mfe lower than −20 kcal/mol ( see Materials and Methods ) . To determine if the Cyclin T1 3′UTR can be regulated by miR-198 , we inserted the full length 3′UTR between the luciferase coding sequence and the poly ( A ) signal of the pGL3 firefly luciferase vector driven by CMV immediate early promoter ( pU3-full , Figure 4B ) . An shRNA vector was generated to express miR-198 ( shmiR-198 ) from the pCL-based retroviral vector . An shRNA designed to target green fluorescence protein , shGFP , was also generated as a control . Pools of HeLa cells expressing shGFP or shmiR-198 were generated by infection of cells with shRNA retroviruses and subsequent puromyocin selection . Reporter plasmids were then transfected into shGFP- or shmiR-198-expressing HeLa cells along with a renilla luciferase reporter plasmid ( pTK-RL ) as an internal control for transfection efficiency . Additionally , p198T , which contains sequences with perfect complementarity to miR-198 , was included as a positive control that should be repressed through an siRNA pathway . As shown in Figure 5A , the relative expression of the positive control p198T was repressed in cells expressing shmiR-198 relative to cells expressing shGFP , showing more than a 60% reduction . The parental pVector expressed at a slightly higher level in shmiR-198-expressing cells . Expression of pU3-full was also repressed by ∼40% , suggesting that there are miR-198 target sites in the Cyclin T1 3′UTR . To identify the locations of these target sites , we divided the full-length 3′UTR into four overlapping fragments ( U3-1 to U3-4 ) and inserted them into to the pGL3 vector ( pU3-1 to pU3-4 , Figure 4A and 4B ) . Only expression from the pU3-1 and pU3-4 reporters showed a statistically significant repression in shmiR-198-expressing cells , demonstrating a 30% and a 20% reduction for pU3-1 and pU3-4 , respectively ( Figure 5A ) . It appears therefore that target sites for miR-198 reside in these two fragments , consistent with recent findings that miRNA target sites are generally locate away from the center of a 3′UTR [23] . There were three predicted target sites in fragment U3-1 and U3-4 with mfe<−25 kcal/mol , and they were designated site 2478 , site 2867 , and site 6502 according to their locations in the Cyclin T1 mRNA ( Figure 4A , first nucleotide in coding sequence defined as +1 ) . All three predicted targets show extensive of complementarity with miR-198 at their 5′ ends . However , site 6502 contains additional eight nucleotides at its 3′ end complementary to the miR-198 seed sequence ( Figure 4C ) , which closely resembles the structure of the 5′-dominant canonical miRNA target site [24] . Expression of reporter plasmids containing two or three copies of site 2478 ( p2478 ) , site 2867 ( p2867 ) , or site 6502 ( p6502 ) in the 3′UTR of the luciferase gene was assayed in cells expressing either shmiR-198 or shGFP ( Figure 5A ) . A control reporter plasmid , pCtrl , which contains three copies of unrelated sequences , expressed at similar levels in both shmiR-198- and shGFP-expressing cells . Expression of p2867 was reduced a relatively modest 20% in shmiR-198-expressing cells , while expression of p2478 was unaffected perhaps due to a sub-optimal seed sequence . Expression of p6502 was significantly repressed by shmiR-198 , showing a 40–50% reduction in shmiR-198-expressing cells relative to control cells . Therefore , site 2867 in fragment U3-1 and especially site 6502 in fragment U3-4 appear to be responsible for repression mediated by miR-198 in Cyclin T1 mRNA 3′UTR . Although both of the sites have similar binding energies , site 6502 showed greater sensitivity to shmiR-198 when compared to site 2867 . When site 6502 along with its adjacent sequences was clone into the reporter plasmid ( p6502g-WT , Figure 4B ) and assayed , a ∼50% reduction was observed in shmiR-198-expressing cells relative to shGFP-expressing cells ( Figure 5A ) . In addition , mutation of three nucleotides in the site 6502 which are complementary to the mRNA seed sequence abolished the inhibition by shmiR-198 ( Figures 4C and 5A ) , strongly suggesting the direct targeting of miR-198 to site 6502 and highlighting the importance of the seed match present in site 6502 during miRNA-mediated repression . To verify that repression of Cyclin T1 protein expression by miR-198 requires target sequences in the Cyclin T1 3′UTR , we carried out a plasmid transfection experiment with a HA-tagged Cyclin T1 expression plasmid lacking the Cyclin T1 3′UTR . The HA-Cyclin T1 plasmid was co-transfected with a precursor for either miR-198 ( pre-miR-198 ) or a pre-miR-Control . To verify that the transfected pre-miR-198 was biologically active , we co-transfected the p198T luciferase control plasmid ( Figure 4C ) that is repressed by miR-198 by a siRNA pathway . Additionally , a renilla luciferase reporter plasmid was co-transfected to monitor transfection efficiency and siRNAs against Cyclin T1 were included in a control co-transfection . The siRNAs against the HA-Cyclin T1 were very effectively in reducing protein expression but had little effect on expression of the p198T reporter plasmid ( Figure 5B ) . Although the pre-miR-198 was able to reduce expression of the p198T reporter plasmid about 20-fold relative to the pre-miR control , it had no inhibitory effect on Cyclin T1 expression and even appeared to stimulate Cyclin T1 expression . These data indicate that the repression of Cyclin T1 protein expression by miR-198 requires target sequences in the Cyclin T1 mRNA 3′UTR . The data presented above demonstrate that miR-198 can target sequences in the Cyclin T1 mRNA 3′UTR and can repress expression of endogenous Cyclin T1 protein expression when ectopically expressed . To examine whether miR-198 regulates Cyclin T1 expression in primary monocytes , we isolated primary monocytes and macrophages from two donors ( Donor3 and Donor4 ) and first re-examined the correlation between miR-198 expression and Cyclin T1 protein levels . In agreement with our previous results [10] , [14] , Cyclin T1 protein levels were strongly up-regulated in macrophages relative to monocytes ( Figure 6A ) . Additionally , although Cyclin T1 protein expression is induced in macrophages , Cyclin T1 mRNA levels were reduced from 20 to 60% when compared to levels on monocytes ( Figure 6B ) , similar to our previous results [10] , [14] . In agreement with the microarray data shown in Figure 1A , miR-198 levels were reduced in macrophages relative to monocytes , showing a 14-fold reduction in Donor3 and a 19-fold reduction in Donor4 . Although both Cyclin T1 mRNA and miR-198 levels were decreased in macrophages , the ratio between Cyclin T1 mRNA and miR-198 increased in macrophages . This observation indicates that in macrophages there are more copies of Cyclin T1 mRNA for each copy of miR-198 . In loss-of-function experiments , we examined the effects of a miR-198 inhibitor on Cyclin T1 expression in monocytes . We transfected a chemically modified single-stranded nucleic acid inhibitor of miR-198 ( anti-miR-198 ) into freshly isolated monocytes . As shown in Figure 6C , anti-miR-198 was able to induce Cyclin T1 levels from two- to six-fold in two donors examined ( Donor 3 , 4 ) . In an additional gain-of-function experiment , transfection of pre-miR-198 to overexpress miR-198 in monocytes induced to differentiate with GM-CSF treatment resulted in about a 3-fold decrease in Cyclin T1 protein levels ( Figure 6C , right panel ) . The effects of miR-198 on Cyclin T1 expression are consistent with recent quantitative proteomic studies that have shown that in most cases miRNAs influence proteins levels about 1 . 5- to 2-fold [25] , [26] . These data in primary monocytes further support the proposal that miR-198 plays a role in repressing Cyclin T1 protein expression in monocytes , and the reduction in miR-198 levels during macrophage differentiation contributes to induction of Cyclin T1 protein expression . The induction of Cyclin T1 has been shown to be important for the program of gene expression in macrophages [13] . In addition to cellular genes that are dependent on Cyclin T1 for expression , HIV-1 Tat-mediated gene expression is highly dependent upon Cyclin T1 . We therefore examined if down-regulation of Cyclin T1 by miR-198 could result in decreased HIV-1 proviral expression . We used a promonocytic cell line , Mono Mac 6 ( MM6 ) for these experiments , as we previously determined that this cell line is a useful model to investigate the role of Cyclin T1 function in monocyte differentiation [13] . Cyclin T1 is up-regulated in MM6 cells treated with PMA for 48 hours ( Figure 7A , left panel ) , similar to its induction during primary monocyte to macrophage differentiation . Ectopic expression of miR-198 in MM6 cells from a transfected shmiR-198 plasmid , followed by PMA treatment , largely prevented Cyclin T1 up-regulation ( Figure 7A , right panel ) . In contrast , transfection of a control shGFP plasmid resulted in similar Cyclin T1 levels as those seen in mock-transfected cells . To examine if HIV-1 proviral gene expression is affected by the down-regulation of Cyclin T1 by miR-198 , a HIV-1 NL4-3 luciferase proviral reporter plasmid was co-transfected with shGFP or shmiR-198 plasmid into MM6 cells . A renilla luciferase reporter plasmid , pTK-RL , was also co-transfected as an internal control . Cells were treated with PMA , harvested 48 hours after transfection , and divide into two equal portions for luciferase assays and immunoblot analysis . The results of two independent experiments are shown in Figure 7B; in one of these experiments , duplicate transfections were performed . Luciferase assays revealed that expression of the HIV-1 proviral reporter plasmid was approximately nine-fold lower in shmiR-198-expressing cells than in shGFP-expressing cells , and the results of immunoblots indicated that this reduction correlated with decreased Cyclin T1 protein levels . These data suggest that miR-198 is capable of repressing HIV-1 proviral expression by targeting its cellular cofactor Cyclin T1 . To examine if HIV-1 replication is also repressed by ectopic expression of miR-198 , MM6 cells were transfected with a shGFP control or shmiR-198 plasmid and cells were treated with PMA immediately after transfection . Cultures were infected with M-tropic HIV-1 strain SF162 after one day of PMA treatment and p24 expression in culture supernatants was measured at either three or four days post-infection in two independent experiments ( Figure 7C ) . A 25% reduction in p24 expression was observed when expression was examined at three days post-infection , while a 50% reduction was observed when p24 expression was examined at four days post-infection . The greater inhibition of proviral reporter plasmid expression by miR-198 in Figure 7B than in the HIV-1 infection in Figure 7C is likely the result of transfection efficiency . In the proviral reporter plasmid experiment , both the reporter plasmid and the miR-198 shRNA vector were co-transfected and it is therefore likely that the majority of transfected cells contained both plasmids . In the HIV-1 infection experiment , it is likely that a significant portion of infected cells were not transfected with the miR-198 shRNA vector . The data in Figure 7C further suggest that miR-198 is capable of repressing HIV-1 replication through down-regulation of the essential cofactor Cyclin T1 . It has been reported that miRNA expression shows distinct patterns in HIV-1 provirus plasmid-transfected HeLa cells [27] . Therefore , we wished to examine if HIV-1 infection affects miR-198 expression in macrophages . Macrophages isolated from healthy blood donors were allowed to differentiate for four days , and were infected with a M-tropic HIV-1 SF162 strain for seven days , and RNA was isolated for miR-198 quantification . Measurements of p24 expression were performed at day four and seven post-infection to verify productive infections ( data not shown ) . As shown in Figure 8A , miR-198 levels increased modestly to 1 . 9-fold in Donor 6 and 3 . 8-fold in Donor 7 when normalized to U6B snRNA . These data suggest that HIV-1 infection up-regulates miR-198 levels in macrophages . Cyclin T1 mRNA levels also increased 1 . 8-fold in Donor 6 and 1 . 6-fold in Donor 7 , while α-tubulin mRNA levels remained the same ( Figure 8B ) . Cyclin T1 protein expression is induced in HIV-1-infected macrophages , and this has been shown to involve an inhibition of proteasome-mediated proteolysis of Cyclin T1 [12] . The data shown on Figure 7B indicates that a relatively modest increase in Cyclin T1 mRNAs may contribute to the induction in Cyclin T1 protein expression in infected macrophages . The significance in the modest increase in miR-198 levels in HIV-1 infected macrophages remains to be elucidated .
Freshly isolated monocytes do not support viral replication and must undergo a program of macrophage differentiation to become permissive for HIV-1 replication [11] . Viral entry is not limiting in monocytes , but reverse transcription and nuclear import of the pre-integration complex are defective [32] , [33] . Because Tat is essential for HIV-1 replication [6] and Cyclin T1 is a critical Tat cofactor , miR-198 can function in monocytes as an additional repressor of viral gene expression and replication ( Figure 6B and 6C ) . MiRNAs have previously been reported to regulated Tat function , as the miRNA cluster miR-17/92 is capable of inhibiting HIV-1 replication through repression of the histone acetyltransferase PCAF that also participates in Tat function [34] . We observed that HIV-1 infection of macrophages modestly induces both miR-198 and Cyclin T1 mRNA expression levels ( Figure 7 ) . Because HIV-1 infection induces Cyclin T1 protein expression in macrophages [12] , it is possible that following up-regulation of Cyclin T1 mRNA and protein by infection , a cellular negative-feed back loop is activated that results in elevated levels of miR-198 and a subsequent dampening of the induction of Cyclin T1 . The up-regulation of Cyclin T1 protein expression is an important event during monocyte differentiation , as shRNA depletions and transcriptional profiling have shown that Cyclin T1 is required for expression of more than 20% of the mRNAs that are induced during the differentiation program [13] . Because miR-198 restricts Cyclin T1 expression in monocytes , this miRNA may contribute to the prevention of a transcriptional program of differentiation . The reduction of expression of miR-198 is therefore likely to be important for monocyte differentiation . Furthermore , genes involved in immune responses are overrepresented in the set of Cyclin T1-dependent mRNAs in macrophages [13] , suggesting that proper macrophage function may require the down-regulation of miR-198 . A topic for future research will be the study of signals and mechanisms that lead to the down-regulation of miR-198 in macrophages . It is notable that Cyclin T1 mRNA levels are reduced ∼30–60% when Cyclin T1 protein levels are up-regulated following monocyte differentiation ( Figure 5 ) [10] , [14] . The explanation for this phenomenon is not clear , but it is possible that transcription of the Cyclin T1 gene is not increased during differentiation , as the promoter for Cyclin T1 appears to be constitutively active [22] . The active translation of Cyclin T1 mRNA following the relief of repression by miR-198 may reduce the mRNA half-life , and if a transcriptional induction of the Cyclin T1 gene does not occur , a decrease in the amount of Cyclin T1 mRNA will result . miR-198 is in the 3′UTR ( exon 11 ) of FSTL1 , a gene that has recently been shown to enhance inflammatory cytokine production in a monocytic cell line after mitogen stimulation [35] . The correlation between the FSTL1 transcript and miR-198 levels has not yet been established , but FSTL1 transcripts are not detectable in peripheral blood leukocytes [36] , suggesting that the processing of the FSTL1 primary transcript for protein production or miR-198 might be mutually exclusive . Little information is available concerning potential functions and mRNA targets for miR-198 . Although expression in monocytes was not examined in a previous study in 40 normal human tissues , miR-198 expression was found to be generally low and restricted to only a few tissues [37] . In hepatic tumors , expression of miR-198 was found to be down-regulated relative to normal liver parenchyma [38] . A genetic analysis identified a SNP in the miR-198 gene that has a nominally significant allelic association with schizophrenia [39] . We used both PicTar [40] and TargetScan [41] , [42] to identity mRNAs that are predicted to be targets of miR-198 . This analysis identified 41 mRNAs in common between the two programs that are potential targets for miR-198 . However , no common features from the proteins encoded by these mRNAs were apparent . An intriguing predicted target for miR-198 is in the 3′UTR of PTEN , a negative regulator of the PI3-kinase pathway , and PTEN is involved in LPS signaling in monocytes/macrophages [43] . Interestingly , the HIV-1 Tat protein has been shown to decrease expression of PTEN in primary human macrophages [44] , which is consistent with our findings that HIV-1 infection induces miR-198 expression in macrophages ( Figure 8 ) . Because overexpression of PTEN enhances HIV-1 expression [45] , it is possible that the anti-HIV-1 function of miR-198 involves targeting cellular cofactors in addition to Cyclin T1 . We note that miR-223 , which has been shown to target HIV-1 in the 3′ end of the viral genome and repress its expression [46] , was identified in our miRNA profiling as a miRNA that is down-regulated during macrophage differentiation ( Figure 1 ) . This further emphasizes the power of miRNA profiling in identifying miRNAs associated with specific processes . Finally , given the ∼5 kb of 3′UTR sequence in Cyclin T1 mRNA , it is possible if not likely that miRNAs in addition to miR-198 are involved in repression of Cyclin T1 expression in monocytes and other cell types . Our miRNA profiling only assayed 321 miRNAs and given that it is estimated that there are >800 human miRNAs , it is possible that additional miRNAs that regulate Cyclin T1 in monocytes and other cell types await discovery .
Peripheral blood mononuclear cells ( PBMCs ) were isolated from healthy blood donors ( Gulf Coast Regional Blood Center , Houston , TX ) by Isolymph density gradient centrifugation ( Gallard-Schlesinger ) . Primary monocytes were isolated from PBMCs by negative selection using monocyte Isolation Kit II ( Miltenyi Biotec ) . Macrophages from the same donors used for monocyte preparations were obtained by adherence of PBMCs to dishes in RPMI medium supplemented with 1% human serum for one hour as described previously [10] . Adhered cells were washed three times with phosphate buffered saline ( PBS ) , incubated with complete RPMI medium ( 10% fetal bovine serum and 1% Penicillin-Streptomycin liquid , GIBCO ) for two hours , washed three times with PBS , and cultured with complete RPMI medium containing GM-CSF ( 10 units/ml ) for four to five days before RNA isolation . Purities of monocyte and macrophage preparations were evaluated by flow cytometry using CD14 as a monocyte marker and CD71 as a macrophage marker . The purity of monocyte and macrophage preparations was determined to be ≥ 92% ( data not shown ) . Resting CD4+ T cells were isolated and activated as previous described [9] . To prepare RNA samples for miRNA microarray or miRNA quantification , total RNA enriched with small RNAs was isolated using mirVana miRNA isolation kit ( Ambion ) according to the manufacturer's protocol . RNA preparations from Donor1 and Donor2 were sent to LC Sciences ( Houston , TX ) for miRNA microarray analysis using the μParaflo microfluidic chips and detailed process can be found at http://www . lcsciences . com . Briefly , probes for miRNAs were in situ synthesized on chips using photogenerated reagent chemistry with repeats for each probe to allow statistical analysis . Version 7 . 1 arrays were used to detect 321 unique mature miRNAs . RNA samples from the same donor for comparison were labeled with Cy3 or Cy5 for hybridization , and the labeling was reversed between the same cell populations from the two donors . Quality control was done by hybridizing a group of control oligos to evaluate array quality as well as by mixing a fixed amount of 20-mer RNA oligos to samples as external controls . Data were processed by subtracting background and normalized the signals using a LOWESS filter [47] . A detectable transcript was defined by fulfilling the following criteria: ( 1 ) signal intensity >3×background standard deviation; ( 2 ) spot CV ( standard deviation/signal intensity ) <0 . 5; ( 3 ) signals from at least 50% of the repeating probes are above detection level . The ratio of the two colors of detected signals and p-values of the t-test were calculated . A cut-off at p-value < 0 . 01 was used to define a differentially expressed miRNA . A heat maps for all differentially expressed miRNAs was generated by Cluster [48] . The primary data can be accessed at http://www . bcm . edu/molvir/labs/herrmann-rice-lab/Rice-Herrmann_Pages-Data . htm . End-point PCR analysis for miR-26a , miR-155 , miR-223 , and miR-24 were used to verify microarray results of Donor1 and Donor2 by mirVana qRT-PCR detection kit and primer sets ( Ambion ) . A 25-cycle of PCR was preformed and the PCR products were resolved by gel electrophoresis using a 3 . 5% agarose gel to examine the ∼90 bp products . Quantification of band intensity was done by analysis of the gel pictures with ImageQuant ( Molecular Dynamics ) . TaqMan MicroRNA assays ( Applied Biosystems ) were use to quantify mature miR-198 , U6B snRNA , and U3B snRNA . Target prediction was done using primarily RNAhybrid [49] and also microInspector [50] . Total RNA was isolated from cells using RNeasy mini kit ( Qiagen ) and treated with DNase I ( Invitrogen ) for reverse transcription using ThermoScript RT-PCR system ( Invitrogen ) and primer T7- ( TTT ) 6-V ( 5′-TTGTAATACGACTCACTATAGGGC-TTTTTTTTTTTTTTTTTT-A/G/C-3′ ) . PCR was performed using reverse primer for T7 and forward primers specific for each poly ( A ) signal ( P1: 5′-gggccaatggtcacaacacg-3′ , P2: 5′-TGTATATCCGAAGGGAAACAGC-3′ , P3: 5′-GCTGAACTGTAGTGAAGCATCG-3′ , P4: 5′-CAGCATATTATTCTGCCATTGC-3′ , and P5: 5′-ATTGTTACATGAATACCTGG-3′ ) . PCR products were resolved on a 2% agarose gel and bands of ∼400bp were extracted from the gel , purified with QIAquick gel extraction kit ( Qiagen ) for TOPO TA cloning ( Invitrogen ) for DNA sequencing . An shRNA strategy was utilized to express miR-198 ( shmiR-198 ) or a control shRNA designed to target GFP ( shGFP ) from a pCL-based retroviral vector [51] . BOSC cells were used to generate viruses by co-transfecting shGFP or shmiR-198 plasmid and an amphotropic packaging vector using lipofectamine 2000 ( Invitrogen ) . Viruses were harvested 24 hours after transfection for infection of HeLa cells for another 24 hours . Infected HeLa cells were then selected by puromyocin treatment ( 1 µg/ml ) for two days to obtain cell pools expressing shGFP or shmiR-198 . As quantified by TaqMan MicroRNA assays ( Applied Biosystems ) , miR-198 was expressed >400-fold in cell pools expressing shmiR-198 relative to pools expressing shGFP . Full-length or fragments of Cyclin T1 3′UTR , or copies of three predicted target sequences were inserted between the luciferase gene and the poly ( A ) signal of a pGL3-based firefly luciferase reporter driven by CMV immediate early promoter ( Promega ) . The full-length Cyclin T1 3′UTR was cloned by PCR reactions using BAC clones as templates and extra ∼500bp genomic sequence downstream of the poly ( A ) signal was included for cloning into the luciferase reporter ( pU3-full ) . Fragments of Cyclin T1 3′UTR were cloned by PCR reactions using pU3-full as template and primers containing XbaI site at the 3′ ends for subsequent cloning . Individual predicted target sequence was obtained by annealing complementary oligos containing copies of the predicted sequence and 3′ overhang for XbaI site ligation . Mutagenesis was done using QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) according to manufacturer's instructions . For reporter plasmid experiments , 25 ng of each firefly luciferase reporter plasmid was co-transfected into shGFP- or shmiR-198-expressing HeLa cell pools along with 10 ng of thymidine kinase promoter-driven renilla reporter plasmid ( pTK-RL , control for transfection efficiency ) using Lipofectamine 2000 ( Invitrogen ) . To further boost miRNA expression , 1ug of shGFP- or shmiR-198 plasmids were also co-transfected . Cells were harvested 24 hours after co-transfection and luciferase activities were measured using Dual-Luciferase Reporter Assay System ( Promega ) . Relative luciferase activity was calculated by dividing firefly luciferase activity by renilla luciferase activity ( FL/RL ) . Relative expression was obtained by normalization to FL/RL in shGFP-expressing cells transfected with the same firefly reporter plasmid . For the transfection experiment to examine the requirement of the Cyclin T1 3′UTR for repression by miR-198 , pHA-Cyclin T1 [4] ( 50 ng ) , p198T ( 50 ng ) and pTK-renilla luciferase ( 10 ng ) were co-transfected into HeLa cells with pre-miR-Control ( 50 nmol ) , pre-miR-198 ( 50 nmol ) or siCyclin T1 ( 50 nmol ) in a 24-well culture dish using Lipofectamine ( Invitrogen ) . Cells were harvested at 24 hours post-transfection and split into two portions for immunoblot analysis and dual-luciferase assays . miRNA precursors ( 6 . 25 pmole to 100 pmole , Applied Biosystems ) or an siRNA targeting Cyclin T1 coding region ( Dharmacon ) were transfected into HeLa or 293T cells ( ∼30% confluent at times of transfection ) using oligofectamine ( Invitrogen ) . Cells were harvested by direct lysis with 1×loading buffer for immunoblot analysis at 72 hours post-transfection . Independent transfections were performed in HeLa cells for total RNA isolation using RNeasy mini kit ( Qiagen ) and RNA were subjected to RT-real-time-PCR assays for quantification of Cyclin T1 , β-actin , GAPDH , and α-tubulin mRNA levels using the Bio-Rad MyIQ single color detection system as previously described ( Sung and Rice , 2006 ) . Primer sequences designed by the Beacon Designer 2 . 0 ( Premier Biosoft ) are: β-actin-F 5′-AGCAAGCAGGAGTATGACGAGTC-3′ , β-actin-R 5′-AGAAAGGGTGTAACGCAACTAAGTC-3′ , GAPDH-F 5′-CGCCAGCCGAGCCACATC-3′ , GAPDH-R 5′-AAATCCGTTGACTCCGACCTTCAC-3′ . miR-198 inhibitor ( anti-miR-198 ) or control miRNA inhibitor ( anti-miR-Control ) was transfected into primary monocytes ( 250 pmole inhibitors/3×106 cells ) obtained from healthy donors using the Amaxa Nucleofector system . Cells were harvested by direct lysis at 18 or 24 hours after transfection for immunoblot analysis to detect Cyclin T1 and β-actin protein expression . Pre-miR-198 or pre-miR-Control was also transfected with the Amaxa system into primary monocytes ( 250 pmole inhibitors/3×106 cells ) and cells were treated with GM-CSF to induce differentiation for 48 hours . Cyclin T1 protein levels were quantified by scanning X-ray films with Personal Densitometer SI and analyzed with ImageQuant ( Molecular Dynamics ) , followed by normalization to β-actin protein levels . Antibodies for immunoblot were purchased from Santa Cruz Biotechnology . The promonocytic cell line MM6 was cultured and transfected according to the protocols described at http://www . monocytes . de . Briefly , MM6 cells were propagated in RPMI 1640 ( Invitrogen ) supplemented with L-glutamine ( 2 mM , Invitrogen ) , penicillin-streptomycin ( 400 U/ml and 400 µg/ml , Invitrogen ) , 1×non-essential amino acids ( Invitrogen ) , OPI media supplement ( Sigma ) , and 10% certified fetal bovine serum tested for endotoxin ( Invitrogen ) . Low passage MM6 cells were used and cells were kept at a concentration of no more than 5×106/ml to maintain a low endogenous Cyclin T1 protein expression level . For HIV-1 wild type LTR-luciferase reporter assays , 5×106 MM6 were washed with RPMI 1640 and incubated with 3 µg shGFP- or shmiR-198-expressing plasmids , 1 µg HIV-1 luciferase reporter plasmid , 1 µg pTK-RL , and 250 µg/ml DEAE dextran ( Sigma ) for 90 minutes . The HIV-1 luciferase reporter plasmid contains a deletion in the env gene , a replacement of the nef gene with firefly luciferase gene , and a deletion in the vpr gene . Cells and plasmids were mixed every 30 minutes by gently rocking the tube during incubation and then treated with 10% DMSO for 3 minutes . After washing out DMSO , cells were treated with PMA ( 10 µg/ml final concentration ) , harvested 48 hours later , and divided into two equal portions for dual-luciferase assays and immunoblot analyses as described above . HIV-1 infections with strain SF162 ( 100 TCID50 units per culture in 6 well dishes ) were performed one day after PMA treatment . Measurements of p24 expression ( RETROtek , ZeptoMetrix Corporation ) in culture supernatants were performed at day three or four post-infection as indicated . Macrophages were allowed to differentiate for five days from monocytes and incubated with HIV-1 SF162 strain at 6000 TCID50 per 10 cm2 dish for two hours ( 37°C , 5% CO2 ) with intermittent shaking . Following virus adsorption , cells were washed three times with PBS . Cells were then supplied with complete RPMI medium and incubated for seven days to allow at least two rounds of infection . Measurements of p24 expression ( RETROtek , ZeptoMetrix Corporation ) at day four and day seven of infection were performed to verify productive infections . At day seven post-infection , cells were washed with PBS three times for total RNA isolation for miR-198 quantification and mRNA quantification as described above . | Monocytes do not support HIV-1 replication , in part because they do not express adequate levels of essential cellular cofactors that mediate steps in the viral replication cycle . Monocytes become permissive for viral replication upon differentiation to macrophages , indicating that cellular cofactors are induced during the differentiation process . One such cofactor is Cyclin T1 , which is not expressed in monocytes and is expressed at high levels following macrophage differentiation . Cyclin T1 functions to greatly stimulate the amount of HIV-1 produced in the infected cell . We identified a microRNA ( miRNA ) named miR-198 that represses the expression of Cyclin T1 in monocytes . miRNAs block expression of proteins by binding to messenger RNAs and preventing their translation by ribosomes . The expression levels of miR-198 are greatly reduced in macrophages , and this appears to allow translation of Cyclin T1 mRNA and expression of Cyclin T1 protein . Our study indicates that this miRNA restricts HIV-1 replication in monocytes . We think that it is possible , if not likely , that additional miRNAs in monocytes also restrict HIV-1 replication by repressing other essential cellular cofactors . | [
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] | 2009 | miR-198 Inhibits HIV-1 Gene Expression and Replication in Monocytes and Its Mechanism of Action Appears To Involve Repression of Cyclin T1 |
Observability of a dynamical system requires an understanding of its state—the collective values of its variables . However , existing techniques are too limited to measure all but a small fraction of the physical variables and parameters of neuronal networks . We constructed models of the biophysical properties of neuronal membrane , synaptic , and microenvironment dynamics , and incorporated them into a model-based predictor-controller framework from modern control theory . We demonstrate that it is now possible to meaningfully estimate the dynamics of small neuronal networks using as few as a single measured variable . Specifically , we assimilate noisy membrane potential measurements from individual hippocampal neurons to reconstruct the dynamics of networks of these cells , their extracellular microenvironment , and the activities of different neuronal types during seizures . We use reconstruction to account for unmeasured parts of the neuronal system , relating micro-domain metabolic processes to cellular excitability , and validate the reconstruction of cellular dynamical interactions against actual measurements . Data assimilation , the fusing of measurement with computational models , has significant potential to improve the way we observe and understand brain dynamics .
A universal dilemma in understanding the brain is that it is complex , multiscale , nonlinear in space and time , and we never have more than partial experimental access to its dynamics . To better understand its function one not only needs to encompass the complexity and nonlinearity , but also estimate the unmeasured variables and parameters of brain dynamics . A parallel comparison can be drawn in weather forecasting [1] , although atmospheric dynamics are arguably less complex and less nonlinear . Fortunately , the meteorological community has overcome some of these issues by using model based predictor-controller frameworks whose development derived from computational robotics requirements of aerospace programs in 1960s [2] , [3] . A predictor-controller system employs a computational model to observe a dynamical system ( e . g . weather ) , assimilate data through what may be relatively sparse sensors , and reconstruct and estimate the remainder of the unmeasured variables and parameters in light of available data . The result of future measured system dynamics is compared with the model predicted outcome , the expected errors within the model are updated and corrected , and the process repeats iteratively . For this recursive initial value problem to be meaningful one needs computational models of high fidelity to the dynamics of the natural systems , and explicit modeling of the uncertainties within the model and measurements [3]–[5] . The most prominent of the model based predictor-controller strategies is the Kalman filter ( KF ) [2] . In its original form , the KF solves a linear system . In situations of mild nonlinearity , the extended forms of the KF were used where the system equations could be linearized without losing too much of the qualitative nature of the system . Such linearization schemes are not suitable for neuronal systems with nonlinearities of the scale of action potential spike generation . With the advent of efficient nonlinear techniques in the 1990s such as the ensemble Kalman filter [6] , [7] and the unscented Kalman filter ( UKF ) [8] , [9] , along with improved computational models for the dynamics of neuronal systems ( incorporating synaptic inputs , cell types , and dynamic microenvironment ) [10] , the prospects for biophysically based ensemble filtering from neuronal systems are now strong . The general framework of the UKF differs from the extended KF in that it integrates the fundamental nonlinear models directly , along with iterating the error and noise expectations through these nonlinear equations . Instead of linearizing the system equations , UKF performs the prediction and update steps on an ensemble of potential system states . This ensemble gives a finite sampling representation of the probability distribution function of the system state [3] , [11]–[15] . Our hypothesis is that seizures arise from a complex nonlinear interaction between specific excitatory and inhibitory neuronal sub-types [16] . The dynamics and excitability of such networks are further complicated by the fact that a variety of metabolic processes govern the excitability of those neuronal networks ( such as potassium concentration ( ) gradients and local oxygen availability ) , and these metabolic variables are not directly measurable using electrical potential measurements . Indeed , it is becoming increasingly apparent that electricity is not enough to describe a wide variety of neuronal phenomena . Several seizure prediction algorithms , based only on EEG signals , have achieved reasonable accuracy when applied to static time-series [17]–[19] . However , many techniques are hindered by high false positive rates , which render them unsuitable for clinical use . We presume that there are aspects of the dynamics of seizure onset and pre-seizure states that are not captured in current models when applied in real-time . In light of the dynamic nature of epilepsy , an approach that incorporates the time evolution of the underlying system for seizure prediction is required . As one cannot see much of an anticipatory signature in EEG dynamics prior to seizures , the same can be said of a variety of oscillatory transient phenomena in the nervous system ranging from up states [20] , spinal cord burst firing [21] , cortical oscillatory waves [22] , in addition to animal [23] and human [24] epileptic seizures . All of these phenomena share the properties that they are episodic , oscillatory , and have apparent refractory periods following which small stimuli can both start and stop such events . It has recently been shown that the interrelated dynamics of and sodium concentration ( ) affect the excitability of neurons , help determine the occurrence of seizures , and affect the stability of persistent states of neuronal activity [10] , [25] . Competition between intrinsic neuronal ion currents , sodium-potassium pumps , glia , and diffusion can produce slow and large-amplitude oscillations in ion concentrations similar to what is observed physiologically in seizures [26] , [27] . Brain dynamics emerge from within a system of apparently unique complexity among the natural systems we observe . Even as multivariable sensing technology steadily improves , the near infinite dimensionality of the complex spatial extent of brain networks will require reconstruction through modeling . Since at present , our technical capabilities restrict us to only one or two variables at a restricted number of sites ( such as voltage or calcium ) , computational models become the “lens” through which we must consider viewing all brain measurements [28] . In what follows , we will show the potential power of fusing physiological measurements with computational models . We will use reconstruction to account for unmeasured parts of the neuronal system , relating micro-domain metabolic processes to cellular excitability , and validating cellular dynamical reconstruction against actual measurements .
Model inadequacy is an issue of intense research in the data assimilation community – no model does exactly what nature does . To deal with inadequate models , researchers in areas such as meteorology have developed various strategies to account for the inaccuracies in the models for weather forecasting [4] , [5] , [29] . In complex systems such as neuronal networks , the need to account for model inadequacy is critical . To demonstrate that UKF can track neuronal dynamics in the face of moderate inadequacy , we impaired our model by setting the sodium current rate constant instead of using the actual complex function of , ( see equation ( 2 ) for the functional form of ) , and tracked it as a parameter ( Figure 3 ) . That is , we deleted the relevant function for from the model and allowed UKF to update it as a parameter . The model with fixed is by itself unable to spike , but when it is allowed to float when voltage is assimilated through UKF using the data from hippocampal pyramidal cells ( PCs ) , it is capable of tracking the dynamics of the cell reasonably well . The tracked by the filter is sufficiently close to its functional form values ( within 25% ) so that spiking dynamics can be reconstructed ( Figure 3C and 3D ) . This occurs because Kalman filtering constantly estimates the trade off between model accuracy and measurements , expressed in the filter gain function [2] , [3] . This is an excellent demonstration of the robustness of this framework . Looking at the estimated values of it also becomes clear that in fact should be assigned the functional form rather than a constant value . Despite decades of effort neuroscientists lack a unifying dynamical principle for epilepsy . An incomplete knowledge of the neural interactions during seizures makes the quest for unifying principles especially difficult [30] . Here we show that UKF can be employed to track experimentally inaccessible neuronal dynamics during seizures . Specifically , we used UKF to assimilate data from pairs of simultaneously impaled pyramidal cells and oriens-lacunosum moleculare ( OLM ) interneurons ( INs ) in the CA1 area of the hippocampus [23] . We then used biophysical ionic models to estimate extra- and intracellular potassium , sodium , and calcium ion concentrations and various parameters controlling their dynamics during seizures ( Figure 4 ) . In Figure 4A we show an intracellular recording from a pyramidal cell during seizures , and plot the estimated extracellular potassium concentration ( ) in Figure 4B . As is clear from the figure the extracellular potassium concentration oscillates as the cell goes into and out of seizures . The potassium concentration begins to rise as the cell enters seizures and peaks with the maximal firing frequency , followed by decreasing potassium concentration as the firing rate decreases and the seizure terminates . Higher makes the PC more excitable by raising the reversal potential for currents ( equation 7 ) . The increased reversal potential causes the cell to burst-fire spontaneously . Whether the increased causes the cells to seize or is the result of seizures has been an old question [31] whose resolution will likely take place from better understanding of the coupled dynamics . For present purposes , it is known that increased in experiments can support the generation , and increase the frequency and propagation velocity of seizures [32] , [33] . Changes in the concentration of intracellular sodium ions , , are closely coupled with the changes of ( Figure 4C ) . As shown in panels ( 4D–F ) we reconstructed the parameters controlling the microenvironment of the cell . These parameters included the diffusion constant of in the extracellular space , buffering strength of glia , and concentration in the reservoir of the perfusing solution in vitro ( or in the vasculature in vivo ) during seizures . Note that the ionic concentration in the distant reservoir is different from the more rapid dynamics within the smaller connecting extracellular space near single cell where excitability is determined . We were also able to track other variables and parameters such as extracellular calcium concentration and ion channel conductances . In Figure 5 , we show an expanded view of a single cell response during a single seizure from Figure 4 . Extracellular potassium concentration increases several fold above baseline values during seizures [31] . During a single seizure , starts rising from a baseline value of 3 . 0mM as the seizure begins and peaks at 7mM at the middle of the seizure ( Figure 5 ) . Interestingly the estimated by UKF matches very closely the measured seen in vitro studies [34] . Considering the slow time scale of seizure evolution ( period of more than 100 seconds in our experiments ) , we test the importance of slow variables such as ion concentrations for seizure tracking . As shown in Figure 6 , we found that including the dynamic intra- and extracellular ion concentrations in the model is necessary for accurate tracking of seizures . Using Hodgkin-Huxley type ionic currents with fixed intra- and extracellular ion concentration of and ions fails to track seizure dynamics in pyramidal cells ( Figure 6C ) . We used physiologically normal concentrations of 4mM and 18mM for extracellular and intracellular respectively for these simulations . The conclusion remains the same when higher and are used . A similar tracking failure is found while tracking the dynamics of OLM interneurons during seizures ( not shown ) . To further emphasize the importance of ion concentrations dynamics for tracking seizures we calculate the Akaike's information criterion ( AIC ) for the two models used in Figure 6 , i . e . the model with and without ion concentration dynamics . AIC is a measure of the goodness of fit of a model and offers a measure of the information lost when a given model is used to describe experimental observations . Loosely speaking , it describes the tradeoff between precision and complexity of the model [35] . We used equation ( 29 ) for the AIC measure . The AIC measure for the model without ion concentration dynamics is . The model with ion concentration dynamics on the other hand has AIC value equal to , indicating the importance of ion concentration dynamics for tracking seizures . Pyramidal cells and interneurons in the hippocampus reside in different layers with different cell densities . To investigate whether there exist significant differences in the microenvironment surrounding these two cell types we assimilated membrane potential data from OLM interneurons in the hippocampus and reconstructed and ion concentrations inside and outside the cells . As shown in Figure 7 , both the baseline level and peak near the interneurons must be very high as compared to that seen for the pyramidal cells ( cf . Figure 4B ) . This is an important prediction in light of the recently observed interplay between pyramidal cells and interneurons during in vitro seizures [23]; in these experiments pyramidal cells were silent when the interneurons were intensively firing . Following intense firing the interneurons entered a state of depolarization block simultaneously with the emergence of intense epileptiform firing in pyramidal cells . Such a novel pattern of interleaving neuronal activity is proposed to be a possible mechanism for the sudden drop in inhibition during seizures – it may be permissive of runaway excitatory activity . The mechanism leading to such interplay , specifically the reasons for differential firing patterns in pyramidal cells and interneurons are unknown . Our results here indicate the potential role of the neuronal microenvironment in producing such interplay . Our findings suggest that the buffering mechanism in the OLM layer is weaker as compared with the pyramidal layer , thus causing higher in the OLM layer . The higher surrounding the interneurons causes increased excitability of the cell by raising the reversal potential for currents ( higher than the pyramidal cells , see equation 7 ) . The higher reversal potential for currents causes the interneuron to spontaneously burst fire at higher frequency and eventually drives the interneuron to transition into depolarization block when firing is peaked . As the INs enter the depolarized state , the inhibitory synaptic input from the INs to the PCs drops substantially , releasing PCs to generate the intense excitatory activity of seizures ( equation 8 , Figure S3 ) . The collapse of inhibition due to the entrance of INs into a depolarized state also helps explain the sudden decrease in inhibition at seizure onset in neocortex described by Trevelyan , et al . [36] as the loss of inhibitory veto . As shown in Figure S1 , we also tracked the remaining variables for the INs . Since the interaction of neurons determines network patterns of activity , it is within such interactions that we seek unifying principles for epilepsy . To demonstrate that the UKF framework can be utilized to study cellular interactions , we reconstructed the dynamics of one cell type by assimilating the measured data from another cell type in the network . In Figure 8 we only show the estimated membrane potentials , but we also reconstructed the remaining variables and parameters of both cells ( Figures S2 and S3 ) . We first assimilated the membrane potential of the PC to estimate the dynamics of the same cell and also the dynamics of a coupled IN ( Figure 8A–D ) . Conversely , we estimate the dynamics of PC from the simultaneously measured membrane potential measurements of the IN ( Figure 8D–F ) . As is evident from Figure 8 the filter framework is successful at reciprocally reconstructing and tracking the dynamics of these different cells within this network . In Figure S2 , we show intracellular concentration and gating variables of and channels in PCs for simulation in Figure 8A–D . The variables modeling the synaptic inputs for both INs and PCs in Figure 8A–D are shown in Figure S3 . As clear from Figure S3 ( D ) , the variable ( equation 8 ) reaches very high values when the INs lock into depolarization block , shutting off the inhibitory inputs from INs to PCs .
In conclusion , we have demonstrated the feasibility for data assimilation within neuronal networks using detailed biophysical models . In particular , we demonstrated that estimating the neuronal microenvironment and neuronal interactions can be performed by embedding our improving biophysical neuronal models within a model based state estimation framework . This approach can provide a more complete understanding of otherwise incompletely observed neuronal dynamics during normal and pathological brain function .
We used two-compartmental models for the pyramidal cells and interneurons: a cellular compartment and the surrounding extracellular microenvironment . The membrane potentials of both cells were modeled by Hodgkin-Huxley equations containing sodium , potassium , calcium-gated potassium ( after-hyperpolarization ) , and leak currents . For the network model , the two cell types are coupled synaptically and through diffusion of potassium ions in the extracellular space . A schematic of the model is shown in Figure 9 . To estimate and track the dynamics of the neuronal networks , we applied a nonlinear ensemble version of the Kalman filter , the unscented Kalman filter ( UKF ) [8] , [9] . The UKF uses known nonlinear dynamical equations and observation functions along with noisy , partially observed data to continuously update a Gaussian approximation for the neuronal state and its uncertainty . At each integration step , perturbed system states that are consistent with the current state uncertainty , sigma points , are chosen . The UKF consists of integrating the system from the sigma points , estimating mean state values , and then updating the covariance matrix that approximates the state uncertainty . The Kalman gain matrix updates the new most likely state of the system based on the estimated measurements and the actual partially measured state . The estimated states ( filtered states ) are used to estimate the experimentally inaccessible parameters and variables by synchronizing the model equations to the estimated states . To estimate the system parameters from data , we introduced the unknown parameters as extra state variables with trivial dynamics . The UKF with random initial conditions for the parameters will converge to an optimal set of parameters , or in the case of varying parameters , will track them along with the state variables [11]–[13] . Given a function describing the dynamics of the system ( equations 1–10 in our case ) , and an observation function contaminated by uncertainty characterized in the covariance matrix , for a -dimensional state vector with mean the UKF generates the sigma points , … , so that their sample mean and sample covariance are and . The sigma points are the rows of the matrix ( 11 ) The index on the left-hand side corresponds to the row taken from the matrix in the parenthesis on right-hand side . The square root sign denotes the matrix square root and indicates transpose of the matrix . Sigma points can be envisioned as sample points at the boundaries of a covariance ellipsoid . In what follows , superscript tilde ( ) represents the a priori values of variables and parameter , i . e . the values at a given time-step when observation up to time-step are available , while hat ( ) represents the a posteriori quantities , i . e . the values at time-step when observations up to time-step are available . Applying one step of the dynamics to the sigma points and calling the results , and denoting the observations of the new states by , we define the means ( 12 ) where and are the a priori state and measurement estimates , respectively . Now define the a priori covariances ( 13 ) of the ensemble members . The Kalman filter estimates of the new state and uncertainty are given by the a posteriori quantities ( 14 ) and ( 15 ) where is the Kalman gain matrix and is the actual observation [3] , [8] , [9] , [11]–[13] . Thus and are the updated estimated state and covariance for the next step . The a posteriori estimate of the observation is recovered by . Thus by augmenting the observed state variables with unobserved state variables and system parameters , UKF can estimate and track both unobserved variables and system parameters . | To understand a complex system such as the weather or the brain , one needs an exhaustive detailing of the system variables and parameters . But such systems are vastly undersampled from existing technology . The alternative is to employ realistic computational models of the system dynamics to reconstruct the unobserved features . This model based state estimation is referred to as data assimilation . Modern robotics use data assimilation as the recursive predictive strategy that underlies the autonomous control performance of aerospace and terrestrial applications . We here adapt such data assimilation techniques to a computational model of the interplay of excitatory and inhibitory neurons during epileptic seizures . We show that incorporating lower scale metabolic models of potassium dynamics is essential for accuracy . We apply our strategy using data from simultaneous dual intracellular impalements of inhibitory and excitatory neurons . Our findings are , to our knowledge , the first validation of such data assimilation in neuronal dynamics . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience/theoretical",
"neuroscience",
"computational",
"biology/computational",
"neuroscience",
"neurological",
"disorders/epilepsy"
] | 2010 | Assimilating Seizure Dynamics |
The hallmark of Philadelphia chromosome positive ( Ph+ ) leukemia is the BCR/ABL kinase , which is successfully targeted by selective ATP competitors . However , inhibition of BCR/ABL alone is unable to eradicate Ph+ leukemia . The t ( 9;22 ) is a reciprocal translocation which encodes not only for the der22 ( Philadelphia chromosome ) related BCR/ABL , but also for der9 related ABL/BCR fusion proteins , which can be detected in 65% of patients with chronic myeloid leukemia ( CML ) and 100% of patients with Ph+ acute lymphatic leukemia ( ALL ) . ABL/BCRs are oncogenes able to influence the lineage commitment of hematopoietic progenitors . Aim of this study was to further disclose the role of p96ABL/BCR for the pathogenesis of Ph+ ALL . The co-expression of p96ABL/BCR enhanced the kinase activity and as a consequence , the transformation potential of p185BCR/ABL . Targeting p96ABL/BCR by RNAi inhibited growth of Ph+ ALL cell lines and Ph+ ALL patient-derived long-term cultures ( PD-LTCs ) . Our in vitro and in vivo stem cell studies further revealed a functional hierarchy of p96ABL/BCR and p185BCR/ABL in hematopoietic stem cells . Co-expression of p96ABL/BCR abolished the capacity of p185BCR/ABL to induce a CML-like disease and led to the induction of ALL . Taken together our here presented data reveal an important role of p96ABL/BCR for the pathogenesis of Ph+ ALL .
The Philadelphia chromosome ( Ph ) is the cytogenetic correlate of der22 formed by the t ( 9;22 ) ( q34;q11 ) . The t ( 9;22 ) is a reciprocal translocation [1] . Two principal breaks occur in the BCR ( breakpoint cluster region ) gene locus on chromosome 22: the ( major ) M-BCR , between exons 12 and 16 , and the ( minor ) m-BCR , in the first intron of BCR . On der22 , M-BCR leads to the creation of p210BCR/ABL and m-BCR to that of p185BCR/ABL . The breakpoint in the ABL ( Abelson tyrosin protein kinase 1 ) gene on chromosome 9 falls within the intron between the exons 1 and 2 . Therefore the ABL-part of the t ( 9;22 ) fusion proteins is constant [1] . The breakpoint on der22 is decisive for the determination of the phenotype of the Ph+ leukemias . In fact M-BCR p210BCR/ABL is associated with primarily myeloid leukemia . p210BCR/ABL is pathognomonic for the chronic myeloid leukemia ( CML ) . In the very rare cases of Ph+ acute myeloid leukemia ( AML ) the great majority of the patients harbors the p210BCR/ABL [2 , 3] . In contrast , p185BCR/ABL is nearly exclusively detected in Ph+ acute lymphatic leukemia ( ALL ) [4] . On the other hand about 30% of patients with Ph+ ALL harbor the M-BCR p210BCR/ABL . There are differences regarding the prognosis between Ph+ ALL patients harboring either the p185BCR/ABL or p210BCR/ABL [5 , 6 , 7] . Furthermore the progression of chronic phase ( CP ) CML , if untreated , leads in most of the cases to a myeloid blast crisis ( BC ) . Only 30% of patients develop lymphatic BC [1 , 8 , 9] . The development of lymphatic BC has been attributed to an increased kinase activity of p210BCR/ABL as compared to patients with myeloid BC [10] . Factors able to modify the kinase activity of p210BCR/ABL are still completely unknown . The fusion proteins p185BCR/ABL and p210BCR/ABL are mutant ABL kinases . The native ABL kinase is finely regulated in response to growth factors and other stimuli [11] . Through the fusion to BCR , ABL constitutively activates its “down-stream” signaling pathways , including RAS , JAK/STAT and PI-3 kinase [1 , 12] . In primary murine hematopoietic models we have shown that both p210BCR/ABL and p185BCR/ABL allow only a myeloid commitment/differentiation of hematopoietic stem cells ( HSCs ) . Both suppress the lymphatic commitment of HSCs by the suppression of the B-cell signaling [13] . Even on leukemic blasts of patients with Ph+ ALL there is a high frequency of myeloid marker expression such as CD33 and CD13 [14] . The inhibitory effect of BCR/ABL on the B-cell signaling is counteracted by the ABL/BCRs , the reciprocal t ( 9;22 ) fusion proteins [13] . The ABL/BCR fusion gene on der9 differs between m-BCR-p185BCR/ABL and M-BCR-p210BCR/ABL . In the case of M-BCR the reciprocal ABL/BCR gene encodes a “small” ABL/BCR with an approximate molecular mass of 40 kDa ( p40ABL/BCR ) [13] , whereas in the case of m-BCR it encodes a “larger” ABL/BCR of 96 kDa ( p96ABL/BCR ) [13] . The p40ABL/BCR transcript is detectable in 65% of the CML patients [15] and the p96ABL/BCR transcript is present in 100% of examined patients with m-BCR Ph+ ALL [16] . The p96ABL/BCR and p40ABL/BCR are BCR mutants [17] . Native BCR acts as a negative regulator of proliferation and oncogenic transformation by a down-regulation of RAS-mediated signaling [18] . Furthermore , it inhibits Wnt signaling by blocking TCF-1/β-catenin-mediated transcription [19] . BCR harbors both RHO-GEF and RAC-GAP functions and controls cytoskeleton modeling by regulating the activity of RHO-like GTPases [17 , 20] . Most likely by the loss of regulatory domains both p96ABL/BCR and p40ABL/BCR activate RAC , a key player in the leukemogenesis of Ph+ leukemia [21] , which contributes to their leukemogenic potential in vivo and increases the proliferation of the stem cell capacity of murine HSC [13] . The aim of this study was to disclose the functional interplay of p96ABL/BCR and p185BCR/ABL in the induction and maintenance of Ph+ ALL . Therefore we studied the role of p96ABL/BCR for i . ) the transformation potential of p185BCR/ABL; ii . ) the survival of Ph+ ALL cells; iii . ) the stem cell capacity of p185BCR/ABL-positive HSCs; iv . ) the drug response of Ph+ ALL cells and v . ) the lineage commitment of HSCs in vivo .
All p185BCR/ABL-positive Ph+ ALL patients express p96ABL/BCR at both mRNA and protein levels [13 , 16] , but experimental models only focus on the role of p185BCR/ABL alone . Thus , we investigated the effect of p96ABL/BCR on the capacity of p185BCR/ABL to confer factor independent growth in Ba/F3 cells . Co-expression of p96ABL/BCR and p185BCR/ABL in Ba/F3 cells was achieved by expressing the two transgenes from a p2a peptide-linked multi-cistronic retroviral vector ( Fig 1A ) . The expression of the transgenes was further confirmed by immunoblotting in transduced Ba/F3 cells ( Fig 1C ) . Proliferation and cell growth were assessed by XTT-proliferation assay or dye exclusion using trypan blue and followed for up to 3 or 5 days , respectively ( Fig 1B ) . As reported previously [13] , p96ABL/BCR alone did not induce factor-independent growth of Ba/F3 cells ( Fig 1B ) . However , the presence of p96ABL/BCR protein enhanced the proliferation of p185BCR/ABL-expressing cells following factor withdrawal ( Fig 1B ) . These results were confirmed by dye exclusion assays ( Fig 1B ) . Factor independent growth of BCR/ABL-transduced Ba/F3 cells reflects the capacity of BCR/ABL to substitute for IL-3 survival signaling . In order to investigate the influence of p96ABL/BCR on the transformation potential of p185BCR/ABL , we performed classical transformation assays in non-transformed Rat-1 fibroblasts . These assays consist of focus formation assays for the determination of contact inhibition and colony assays in semi-solid medium ( soft agar ) for the determination of anchorage-independent growth . Thus Rat-1 cells were retrovirally transduced with the constructs indicated in Fig 1A . As presented in Fig 1D , p96ABL/BCR alone was not able to transform the Rat-1 cells; however , in combination with p185BCR/ABL , it profoundly enhanced the number of foci as compared by the Rat-1 cells expressing p185BCR/ABL alone . The number of foci was so high that they became early confluent and were not anymore scorable at day 15 , when the foci of the other samples were counted . In fact at this time point all the “carpet” of foci already was detached from the dish and fluctuating in the medium ( Fig 1D ) . A similar effect was seen in the colony formation in soft agar . In fact upon co-expression of p96ABL/BCR , p185BCR/ABL-positive Rat-1 cells formed a significantly higher number of colonies than with p185BCR/ABL alone ( p<0 . 05 or p = 0 . 01 ) ( Fig 1D ) . Next we sought to analyze the effects of p96ABL/BCR and p185BCR/ABL on the growth of human HSCs . To do this , we transduced CD34+/CD38- PB cells obtained from G-CSF stimulated volunters with lentivirus harboring the indicated constructs ( Fig 1A ) . Proliferation was measured at 72h after transduction . Neither empty vector nor p185BCR/ABL alone increased the proliferation of CD34+/CD38- cells . In contrast p96ABL/BCR alone and the co-expression of both p96ABL/BCR and p185BCR/ABL increased proliferation ( Fig 1E ) . In order to disclose the molecular mechanism by which p96ABL/BCR promotes the proliferative and transformative potential of p185BCR/ABL we investigated the influence of p96ABL/BCR on the kinase activity of p185BCR/ABL . Kinase activity was assessed by the autophosphorylation of p185BCR/ABL as well as the p185BCR/ABL-dependent substrate phosphorylation and signaling pathway activation in Ba/F3 cells in the presence/absence of p96ABL/BCR . The autophosphorylation at Y177 in the BCR-portion as well as at Y245 in the ABL kinase domain of the p185BCR/ABL fusion protein was strongly increased in cells co-expressing p96ABL/BCR ( Fig 1C ) . Expression of total p185BCR/ABL was not affected by co-expression of p96ABL/BCR excluding an increased p185BCR/ABL levels as a plausible mechanism for the increased p185BCR/ABL autophosphorylation ( Fig 1C ) . Substrate phosphorylation was addressed on the phosphorylation status of Crkl and endogenous Bcr . Whereas Crkl phosphorylation did not increase , phosphorylation of Bcr was increased in the presence of p96ABL/BCR ( Fig 1C ) . BCR/ABL dependent down-stream signaling activation in the presence of p96ABL/BCR was assessed by the phosphorylation of Stat5 and Erk1/2 . The activity of BCR/ABL-dependent signaling was further enhanced by p96ABL/BCR as evidenced by an increase of the Stat-5 activation . Also Erk 1/2 was activated as revealed by a higher amount of phosphorylated Erk 1/2 due to an up-regulation of total Erk 1/2 ( Fig 1C ) . These findings are in accordance with both an increased phosphorylation of Bcr and an enhanced autophosphorylation in the BCR-portion of p185BCR/ABL , which are up-stream of Ras/Erk signaling . In summary , these data show a prominent functional crosstalk between p185BCR/ABL and p96ABL/BCR resulting in a p96ABL/BCR-directed enhanced autophosphorylation of p185BCR/ABL and activation of MAP-kinase pathway leading to accelerated proliferation of p185BCR/ABL-positive Ba/F3 cells and enhanced transformation potential of p185BCR/ABL . Based on our findings that p96ABL/BCR exerts pro-proliferative and pro-transformative effects on p185BCR/ABL , we aimed to determine the role of p96ABL/BCR in the growth and survival of Ph+ ALL cells . Thus we targeted p96ABL/BCR in the Ph+ ALL cell line SupB15 cells by RNAi . The breakpoint sequence of p96ABL/BCR , the only specific sequence , was inaccessible for a rational siRNA design . Therefore we used shRNA directed against the 3'UTR of the BCR gene , which targeted both alleles , the der9 encoding the p96ABL/BCR and the native BCR on chromosome 22 . The K562 cell line was used as a control , since it harbors the M-BCR with p210BCR/ABL and does not express the reciprocal ABL/BCR [13] . As shown by immunoblotting , lentiviral transduction of these shRNAs ( efficiency of ~75% ) resulted in a pronounced reduction ( ~80% ) of p96ABL/BCR expression as compared to non-targeting control shRNA ( siNTC ) whereas the expression of BCR was decreased of 40–60% ( Fig 2A ) . No effect was seen on the expression of p185BCR/ABL ( S1A Fig ) . The proliferation was assessed by XTT-proliferation assay . As shown in Fig 2A , down-regulation of p96ABL/BCR significantly reduced the proliferation rate of SupB15 cells as compared to the siNTC-transduced controls , whereas no effect was seen in K562 despite an efficient down-regulation of BCR . In order to further investigate the functional interplay between p96ABL/BCR and p185BCR/ABL , we studied the influence of the p96ABL/BCR knock-down on the p185BCR/ABL-dependent signaling in SupB15 cells . Down-regulation of p96ABL/BCR almost completely abolished ERK1/2 activation , whereas no effect on the activation of STAT5 was observed ( Fig 2B ) . These data confirmed a functional crosstalk between p96ABL/BCR and p185BCR/ABL in human Ph+ ALL cells , which seems to be fundamental for proliferation of these cells . The biology of Ph+ ALL in adults is not fully represented by cell lines such as SupB15 . Therefore , we investigated the effect of p96ABL/BCR knock-down in different primary PD-LTCs from Ph+ ALL patients . We selected two different PD-LTCs: one , the PH , fully responsive to TKIs and one , BV , exhibiting a nearly complete resistance to TKIs not attributable to mutations in the TKD [22 , 23] . As a negative control we used a PD-LTCs , HP , from a Ph- ALL patient . In order to provide additional evidence that the effects seen were due to down-regulation of p96ABL/BCR and not to that of endogenous BCR , we utilized a TEL/ABL-expressing PD-LTC ( VG ) derived from a patient with t ( 12;9 ) ( p13;q34 ) -positive ALL , previously described [24] . As the biological consequences of TEL/ABL are similar to those of BCR/ABL [25 , 26 , 27] , the effects of targeted down-regulation of BCR in these cells should allow to estimate the contribution of BCR down-regulation to the effects of targeting p96ABL/BCR in Ph+ ALL cells . Proliferation of the cells was measured using XTT-proliferation assay . Efficient down-regulation of p96ABL/BCR and/or BCR in the PD-LTCs was assessed by immunoblotting and in PH and BV also by q-RT-PCR for p96ABL/BCR ( Fig 2C ) . The down-regulation of p96ABL/BCR efficiently inhibited proliferation of BV ( 50–67% ) and PH ( 40–45% ) , but had no effect on HP cells ( max . 10% ) and the effect on VG cells was weak ( 8–20% ) ( Fig 2C ) . These findings not only show that the down-regulation of p96ABL/BCR and not that of BCR is responsible for the block of proliferation but also indicate that endogenous BCR does not play any role for the leukemogenic potential of either TEL/ABL or BCR/ABL . This confirms several studies showing that BCR has even a negative impact on BCR/ABL activation and its oncogenic potential [28 , 29 , 30 , 31] . In order to investigate whether apoptosis is important for the inhibition of proliferation , we stained the Ph+ PD-LTCs cells with 7-AAD and measured the apoptosis rate by flow cytometry . As shown in S1B Fig , both specific shRNAs induced a high rate of apoptosis ( about 40% and 50% , respectively ) in the Ph+ PD-LTCs whereas no effect ( 10–15% ) was seen with the siNTC transduced controls ( S1B Fig ) . In summary our data implicate a role for p96ABL/BCR in the survival of Ph+ PD-LTCs with m-BCR which harbors both p185BCR/ABL and the reciprocal p96ABL/BCR fusion proteins . If the crosstalk between p96ABL/BCR and p185BCR/ABL leads to an increased kinase activity , in a reverse conclusion the down-regulation of p96ABL/BCR should increase the responsiveness of Ph+ ALL cells towards selective ABL-kinase inhibitors . In order to test this hypothesis , we used two different classes of TKIs , imatinib , a classical ATP-competitor , and GNF-2 an allosteric inhibitor which binds to the myristoyl binding pocket of ABL [32 , 33] . The PH and the resistant BV PD-LTCs were exposed to increasing concentrations ( 0 . 1 to 2μM ) of imatinib or GNF-2 in the absence/presence of the shRNA targeting p96ABL/BCR . Proliferation was measured by XTT-assay . As shown in Fig 3A in the presence of the NTC control shRNA , PH cells exhibited a full response to imatinib whereas the BV cells only weakly responded to imatinib even at higher dosages . The presence of the specific shRNA not only increased the response of PH cells but fully restored to the growth inhibition by imatinib in BV cells ( Fig 3A ) . Very similar results were obtained by using the allosteric inhibitor GNF-2 ( Fig 3B ) . These results indicate that targeting p96ABL/BCR in primary PD-LTCs of Ph+ ALL increases response to selective ABL inhibitors . Ph+ ALL has been proposed to originate from a BCR/ABL-mediated transformation at the level of committed progenitor cells . In addition , inactivation of BCR/ABL by TKIs seems to be ineffective regarding the eradication of the disease . In order to disclose a role for p96ABL/BCR in a leukemic stem cell model , we compared the effects of p96ABL/BCR and p185BCR/ABL on the biological characteristics of immature HSCs in serial replatings in semi-solid medium . Therefore we isolated Sca1+/lin- cells from murine fetal liver and transduced them retrovirally with the constructs ( schematic procedure in Fig 4A ) . PML/RARα was used as a positive control , due to its well-known effect on the self-renewal of HSPCs [34 , 35 , 36] . The successful transduction was verified by flow cytometry ( S2 Fig ) . As shown in Fig 4B , the replating efficiency of the empty vector and p185BCR/ABL-transduced cells was limited to three cycles of replating . In contrast , p96ABL/BCR alone increased the number of serial replatings with an increase in the number of CFUs in each round of replating similar to PML/RARα ( Fig 4B ) . The co-expression of p185BCR/ABL seemed to inhibit the serial replating capacity of HSPC expressing p96ABL/BCR alone , without suppressing it to the level of control and only p185BCR/ABL expressing HSPC ( Fig 4B ) . Our observation show an autonomous role of p96ABL/BCR in immature HSPCs , which is stronger than that of p185BCR/ABL alone and is not suppressed by the co-expression of p185BCR/ABL . Our previous data suggest that ABL/BCR fusion proteins target immature HSPCs thereby influencing their commitment [13] . In order to determine the effect of both p96ABL/BCR and p185BCR/ABL on the self-renewal of early HSPCs and to address the question whether there is a functional hierarchy between p96ABL/BCR and p185BCR/ABL regarding their role in the maintenance and proliferation of subpopulations in the stem cell compartment , we performed CFU-S12 assays . The CFU-S12 performed in combination with a 9 days culture in vitro ( in which normal control cells lose their CFU-S12 potential by spontaneous differentiation ) reveals a combined effect of a given transgene on the proliferation/self-renewal and the differentiation of HSPCs . Only a differentiation block maintains the potential of the HSPCs to give origin to colonies in a CFU-S12 [37] . Therefore Sca+/lin- HSPCs were isolated and retrovirally transduced with the transgenes ( schematic procedure in Fig 5A ) . After 9 days in culture , cells were transplanted into lethally ( 11Gy ) irradiated recipients . The spleens were isolated after 12 days and colonies were counted ( Fig 5A ) . As shown in Fig 5B , p96ABL/BCR increased number of spleen colonies as compared to empty vector controls or mice transplanted with cells expressing p185BCR/ABL . Co-expression of p96ABL/BCR and p185BCR/ABL resulted in additional significant increase of both number and size of colonies ( Fig 5B ) . Taken together , these data show a differential effect of p96ABL/BCR as compared to p185BCR/ABL , most likely due to a differentiation block of HSPCs , which is further increased by the co-expression of both p96ABL/BCR and p185BCR/ABL . In order to disclose the mechanisms underlying the different effects of p96ABL/BCR and p185BCR/ABL alone and in combination on HSPCs , we compared the gene expression profiles induced by p96ABL/BCR , p185BCR/ABL and the combination of both using CFU-S12 spleens by microarray analysis ( S1 Text ) . The analysis was performed in triplicates for each construct , empty vector ( control ) , p96ABL/BCR , p185BCR/ABL and p96ABL/BCR-p185BCR/ABL . An unsupervised clustering grouped the profiles of the triplicates together ( Fig 5C ) . Due to the wide range of differentially expressed genes between p185BCR/ABL and p96ABL/BCR-p185BCR/ABL samples , we analyzed in greater detail those signaling pathways known to be important for the pathogenesis of Ph+ leukemia in which at least 5 genes were differentially regulated . These pathways were related to cell cycle regulation , proliferation and apoptosis ( Fig 5D ) . The schematic representation of genes involved in these pathways is represented in S3–S8 Figs . This analysis revealed an increase of Tp53 expression upon co-expression of p96ABL/BCR and p185BCR/ABL . The up-regulation of Tp53 was accompanied by an up-regulation of Gadd45α , but not of Cdkn1a , both main Tp53 target genes in the DNA-damage response ( Fig 5D ) . In order to validate the microarray data , q-RT-PCR was performed on the RNA derived from CFU-S12 and the results were in agreement with those obtained in the expression profiling ( Fig 5E ) . To confirm the significance of these findings for the human Ph+ ALL we studied the GADD45α expression in PD-LTCs of Ph+ ALL . Therefore we targeted p96ABL/BCR in PH and BV PD-LTCs by the above described shRNA and revealed a significant reduction of GADD45α expression by q-RT-PCR ( Fig 5F ) . Up-regulation of Tp53 and Gadd45α is related to DNA-repair processes [38] . In order to disclose a relationship between co-expression of p96ABL/BCR and p185BCR/ABL and increased DNA-repair we compared the γH2AX foci in p185BCR/ABL-positive and p96ABL/BCR-p185BCR/ABL-positive leukemic spleens as a marker for DNA-repair at sites of double-strand breaks ( DSB ) ( S1 Text ) [39] . We found in p185BCR/ABL-positive spleens an increased number of signals , many of them abnormal [40] and most likely related to the high number of apoptotic granulocytes ( S9 Fig ) . In contrast p96ABL/BCR-p185BCR/ABL-positive leukemic spleens showed a high number of cells with γH2AX foci , indicating an increased number of DSBs with ongoing DNA repair ( S9 Fig ) . These data strongly suggest that the up-regulation of Tp53/Gadd45α is related to increased number of DSBs upon co-expression of p96ABL/BCR in p185BCR/ABL-positive leukemia . Given the fact that Gadd45α can be regulated also independently of Tp53 [38] we wondered whether the up-regulation of Gadd45α may contribute by itself to the biological effects of p96ABL/BCR . Therefore we lentivirally expressed Gadd45α in early primary Sca1+/lin- HSPCs ( S1 Text ) . We kept these cells either in liquid culture or plated them in semi-solid medium both supplemented with mIL-3 , mIL-6 and mSCF . The cells in semi-solid medium were harvested , counted and replated as described above . In both conditions the expression of Gadd45α led to an increased proliferation which led to an increased colony number as compared to empty vector transduced controls in the serial plating rounds ( S10 Fig ) . In order to further investigate the consequences of the co-expression of p96ABL/BCR and p185BCR/ABL for leukemogenesis , we addressed the question of whether the presence of p96ABL/BCR affects the phenotype or initiation of p185BCR/ABL-mediated leukemia . Therefore we transduced Sca1+ murine HSPCs with p96ABL/BCR , p185BCR/ABL and the provirus encoding both proteins and inoculated the transduced HSPCs into sub-lethally ( 4 , 5 Gy ) irradiated recipients . Recipients inoculated with empty vector transduced Sca1+ cells served as controls ( Fig 6A ) . As already known , all the mice inoculated with p185BCR/ABL-transduced cells rapidly developed a CML-like myeloproliferative disease defined by splenomegaly ( 400–1200 mg spleen weight ) and high numbers of Mac1 ( monocytes- macrophage ) and Gr1 ( granulocytes ) and a low number of B220 ( mature B-cell ) expressing cells ( Fig 6C and 6D ) . Notably , the co-expression of p96ABL/BCR and p185ABL/BCR induced a lymphoid-like leukemia phenotype in 37% of the mice , with the majority of BM cells expressing the B220 , and only few myeloid cells and associated with moderate splenomegaly ( 90–300mg spleen weight ) ( Fig 6B–6D ) . As compared to the CML-like disease induced by p185ABL/BCR alone , the onset of the B-cell leukemia was delayed . In contrast to the CML-like disease the p96ABL/BCR and p185ABL/BCR induced lymphoid-like leukemia was re-transplantable , giving origin to a full blown leukemia within 38–60 days ( Fig 6B ) . The secondary leukemia exhibited a surface marker phenotype identical to that of the primary leukemia characterized by a great majority of B220/CD19-positive blasts confirming the B-cell origin of these leukemias ( Fig 6E ) . In summary , these data indicate that the co-expression of p96ABL/BCR and p185BCR/ABL shifts the leukemia from a myeloproliferative disease upon p185BCR/ABL alone to ALL .
Our understanding of the pathogenesis of Ph+ leukemias has improved , but several key features remain unexplained , such as the association of the m-BCR with Ph+ ALL and different responses of ( m-BCR ) p185BCR/ABL and ( M-BCR ) p210BCR/ABL-positive leukemia to TKIs . In contrast to CML-CP , which is maintained by p210BCR/ABL alone [41] , many findings suggest that in Ph+ ALL BCR/ABL needs additional factors for the induction and maintenance of the disease . Although the t ( 9;22 ) is a balanced translocation , one of the most obvious factors , the reciprocal ABL/BCR fusion protein was early abandoned , because no relationship between the presence of the transcripts and clinical features , like prognosis , therapy response , or others was seen [42] . These observations , in a still pre-TKI era , were exclusively based on CML , but until today nothing is known about a role for p96ABL/BCR in Ph+ ALL , where the transcript is present in 100% of the cases with m-BCR and efficiently translated [13 , 16] . Our here presented data further confirms that the ABL/BCR contributes to the maintenance of the disease . This is supported by the fact that a siRNA-mediated targeting of ABL/BCR strongly reduced the proliferation not only of an ALL cell line , but also of primary Ph+ PD-LTCs , which was accompanied by the induction of apoptosis . The cell growth arrest and apoptosis upon down-regulation of ABL/BCR strongly suggests a functional interplay between the two fusion proteins in Ph+ ALL . This is further confirmed by our findings that ABL/BCR increases transforming kinase activity of BCR/ABL leading to a higher proliferation rate in factor-dependent Ba/F3 cells , primary human CD34+/CD38- HSCs , a tremendously increased colony formation in the classical transformation assays in untransformed fibroblasts and an increased CFU-S12 formation . The functional interplay between the two fusion proteins seems to be based on an enhanced kinase activity of BCR/ABL in the presence of ABL/BCR leading to an enhanced activity of Erk kinase responsible for an increased transformation potential of BCR/ABL . MAP kinase pathway activates the expression of its downstream target genes; therefore it still has to be investigated if this alters the phosphorylation of BCR/ABL upstream of this protein . The functional interplay enhances the expression of Tp53 and one of its target genes , Gadd45α , but not that of Cdkn1a most likely as a response to an increased number of DSBs in cells co-expressing BCR/ABL and ABL/BCR leading to DNA-repair but not to apoptosis or senescence . Why the up-regulation of Tp53 activated only Gadd45α but not Cdkn1a , is still unclear . An explanation could be the capacity of Tp53 to regulate long intergenic non coding RNAs ( lncRNA ) which may play a role in the regulation of Cdkn1a expression [43 , 44] . On the other hand the consequences of an up-regulation of Tp53 in hematopoietic cells seem also to be dependent on their differentiation status . Only in committed progenitors the X-ray induced up-regulation of Tp53 leads to apoptosis , whereas in short-term hematopoietic stem cells the induction of Tp53 is not followed by apoptosis , suggesting a different DSB responses in stem cell and progenitor populations with a different differentiation potential [45] . Given the fact that Cdkn1a is a an attenuator of BCR/ABL-mediated cell proliferation [46] , its down-regulation in the presence of ABL/BCR together with activation of Gadd45α may contribute to the enhanced transformation potential of BCR/ABL . To which extent the Gadd45α activation is involved in the pathogenesis of Ph+ ALL remains to be disclosed . On the other hand Gadd45α does not exhibit growth suppressing functions , as suggested by our findings that Gadd45α is able to increase proliferation of murine HSPCs . This is in accordance to findings showing that up-regulation of GADD45 does not only protect hematopoietic progenitors from UV-induced apoptosis by the activation of p38-NFκB signaling [47 , 48] , but it represents together with an activation of ERK-1 a negative prognostic factor in malignant lymphoproliferative diseases [49] . Our data indicate that Ph+ ALL may be one of the malignant diseases in which Gadd45α exhibits a pro-oncogenic function [50] . The functional interplay between BCR/ABL and ABL/BCR may promote the disease in the absence of BCR/ABL inhibition , whereas the functional independence of ABL/BCR as a self-standing leukemogenic factor may contribute to the maintenance of the disease upon an efficient BCR/ABL inhibition and thus to the only transient response of Ph+ ALL patients to the TKI treatment . The functional independence of ABL/BCR is given not only by the increased serial replating efficiency and CFU-S12 , as compared to BCR/ABL and controls , but also by its capacity to induce a leukemic phenotype in syngenic mice [13] . One could hypothesize that there may exist a functional hierarchy with BCR/ABL active in committed progenitors and ABL/BCR active at an earlier stage of differentiation , which contributes to the maintenance of the leukemia even upon an efficient BCR/ABL inhibition and the following selection of subclones with BCR/ABL harboring resistance mutations . A functional hierarchy between the t ( 9;22 ) fusion proteins could play an important role for the fate decision of leukemia . From the Sca1+ compartment BCR/ABL is able to induce nearly exclusively myeloproliferative disease , which is characterized by a rapid onset of the disease , accumulation of mature myeloid linage cells in the spleen and BM and splenomegaly . However an ALL-like phenotype appeared when we co-expressed both p96ABL/BCR and p185BCR/ABL even with a lower incidence and a much longer latency as compared to BCR/ABL alone . The shift from myeloid to lymphatic phenotype may be due to the increased BCR/ABL kinase activity in the presence of p96ABL/BCR as it has been already shown by Jones and co-worker for the development of myeloid or lymphatic BC in patients with CML [10] . The fact that not all of these mice develop the disease can be attributed most likely to the fact that the target cells for leukemic transformation followed by an acute leukemia phenotype by p96ABL/BCR and p185BCR/ABL are rarer , as compared to that targeted by BCR/ABL for induction of myeloproliferation . In addition , a different grade of proliferative advantage between the two scenarios may account for the differences in latency . Taken together , our study provide clear evidence of a functional interplay between BCR/ABL and ABL/BCR in the pathogenesis of Ph+ ALL , which suggest an additional target to be hidden by molecular therapy approaches in order to achieve an efficient treatment of this high risk subgroup of ALL .
The cDNAs encoding p185BCR/ABL PML/RARα and p96ABL/BCR were described previously [17 , 51 , 52] . For the simultaneous expression of genes , p2a peptide-linked multi-cistronic retroviral vector was used , which allows the expression of multiple proteins from a single open reading frame ( ORF ) [53] . The p2a sequence was kindly provided by Frank Schnütgen ( University Clinic , Frankfurt , Germany ) . In order to construct pEp96ABL/BCR-p2a-p185BCR/ABL the p2a fragment was amplified by PCR using Pr1 , 5′- gcg gcc gcg agc cac gaa ctt ctc-3′and Pr2 , 5′- ggt cag taa att gga tat cgg ccc-3′ and transferred via TA cloning into the pCR2 . 1 vector ( Invitrogen , Karlsruhe , Germany ) and controlled by Sanger sequencing . Then it was transferred by EcoRV/NotI into EcoRV/NotI digested pEp96ABL/BCR . As next step for a continuous ORF , stop codon of the p96ABL/BCR sequence was deleted using the “quick change site-directed mutagenesis” kit ( Stratagene , La Jolla , CA , USA ) using Pr3 , 5′-ttc tcc acc gaa gtc aag aat tcg cgg ccg-3′ and Pr4 , 5′-cgg ccg cga att ctt gac ttc ggt gga gaa-3′ . FseI and SacII sites were introduced in p96ABL/BCR-p2a fragment at the 5' and 3' , respectively , by PCR using the following primers: Pr5 , 5′-acc cgc gga tgt tgg aga tct gc-3′ and Pr6 , 5′-ggc cgg cct tcg gcc cgg ggt ttt-3′ . The resulting construct was then subcloned into the FseI/SacII-digested pEp185BCR/ABL . All PCR-products were controlled by Sanger sequencing . Thus the final construct was available in the Gateway® entry-vector ( pENTR1A ) for recombination into destination Gateway® vectors according to the manufacturer's instructions ( Invitrogen ) . All retroviral expression vectors used in this study were based on PINCO as previously described [54] . Lentiviral vectors expressing short hairpins against human ABL/BCR and non-targeting control lentiviral vectors were based on the PLKO-1 vector ( Sigma , Steinheim , Germany ) . The shRNAs were designed as siR961: ccg gca gat cca gat acc taa gct cga gct tat tag gta tct gga tct gtt ttt tg , and siR962: ccg gca aga gtt aca cgt tcc tga tct cga gat cag gaa cgt gta act ctt gtt ttt . Ecotropic Phoenix , 293T packaging cells , and Rat-1 fibroblasts were cultured in Dulbecco’s modified Eagle medium ( DMEM; sigma ) supplemented with 10% FCS ( Invitrogen , Karlsruhe , Germany ) . K562 and SupB15 cells were kept in RPMI 1640 containing 10% or 15% FCS , respectively . Ba/F3 cells were grown in RPMI + 10% FCS supplemented with 10ng/mL mIL-3 ( Cell Concepts , Umkirch , Germany ) . Ph+ ALL patient-derived long-term cultures ( PD-LTCs ) ( PH , BV , VG and HP ) were maintained in a serum-free medium as described previously [24 , 55] . Imatinib ( kindly provided by Novartis , Basel , Switzerland ) and GNF-2 ( Sigma ) were dissolved in DMSO for a stock solution and diluted to the appropriate concentrations . All animal studies were performed in accordance with international animal protection guidelines and approved by the Regierungspräsidium Darmstadt ( approval number F39/08 ) . Sca1+ and Sca1+/lin- HSCs were isolated from 8 to 12 week-old female C57BL/6J mice ( Janvier , St . Berthevin , France ) as described . The cells were ‘‘lineage depleted” by labeling the cells with biotin-conjugated lineage panel antibodies against B220 , CD3e , Gr1 , Mac1 and Ter-119 ( Miltenyi , Bergisch-Gladbach , Germany ) . Labeled cells were removed using ‘‘MACS” cell separation columns according to the manufacturer's instructions . Sca1+ cells were purified by immunomagnetic beads using the ‘‘MACS” cell separation columns according to the manufacturer’s instructions ( Miltenyi ) . Prior to further use , the purified cells were pre-stimulated in medium containing mIL-3 ( 20 ng/mL ) , mIL-6 ( 20 ng/mL ) and mSCF ( 100 ng/mL ) ( Cell Concepts ) . The source of CD34+/CD38- was residual peripheral blood ( PB ) of healthy donors stimulated with G-CSF for the mobilization for stem cell transplantation kindly provided by Halvard Bönig ( German Red Cross Blood Donor Centre , Institute of Transfusion Medicine and Immunohematology , Goethe University , Frankfurt , Germany ) after informed consent . CD34+ cells were isolated using the CD34-Multisort Kit ( Miltenyi ) followed by the CD38 depletion by labeling the cells with FITC-conjugated anti CD38 antibody which allowed the immunomagnetic isolation by a MACS separation column according to the manufacturer's instructions ( Miltenyi ) . Retro- and lentiviral supernatant using ecotropic Phoenix and 293T packaging cell lines were obtained as described [56] . 24-well plates were first coated with retronectin ( Takara Bio Inc . , Otsu , Japan ) followed by the retro- or lentiviral supernatant . Then target cells ( 105 cells/mL ) were plated and incubated overnight by 37°C . Subsequently another two rounds of infection were performed by adding viral supernatant and centrifuged at 2 . 200 rpm for 45 minutes by 32°C . Infection efficiency was measured after 48h by determining the percentage of GFP-positive cells by fluorescence-activated cell sorting ( FACS ) . Differences in the infection efficiency between samples did not exceed 10% . Proliferation was assessed by using XTT proliferation kit ( Roche , Mannheim , Germany ) , according to the manufacturer’s instructions . Cell growth was assessed by dye exclusion using Trypan-blue according to widely used protocols . Apoptosis was measured by the 7-amino-actinomycin D ( 7-AAD ) method as described before [52 , 57] . After retroviral transduction 5 x 103 transduced Rat-1 cells were suspended in ‘top-agar’ , DMEM supplemented with 10% FCS and 0 . 25% bacto-agar ( DIFCO Laboratories , Detroit , USA ) , and stacked in six-well plates filled with DMEM supplemented with 10% FCS and 0 . 5% bacto-agar ( 2 ml per well ) . After 15 days incubation at 37°C and 5% CO2 colonies were counted . The focus-formation assays were performed in 24-well plates . 4 x 104 transduced Rat-1 cells/well were plated . Unstained foci were photographed at day 15 using an AxioCam HRc system ( Zeiss , Goettingen , Germany ) with 10x magnification . Immunoblot analyses were performed according to widely established protocols using the following antibodies: anti-ABL ( α-ABL ) , anti-BCR ( α-BCR ) ( St . Cruz Biotechnology , Santa Cruz , USA ) , anti- phosphorylated ABL ( α-p-ABL-Y245 ) , anti-CRKL ( α-CRKL ) , and anti-phosphorylated CRKL ( α-p CRKL-Y207 ) , anti-STAT5 ( α-STAT5 ) , and anti-phosphorylated STAT5 ( α-p STAT5-Y694 ) , anti-ERK ( α-ERK ) , and anti-phosphorylated ERK ( α-p ERK-T202-Y204 ) and anti-phosphorylated BCR ( α-p BCR-Y177 ) ( Cell Signaling , Boston , USA ) , and anti-Tubulin ( α-Tubulin ) ( Neo Markers , Asbach , Germany ) . Membrane blocking and antibody incubation were performed in 5% low-fat dry milk , followed by washing in Tris-buffered saline ( TBS ) ( 10 mM Tris-HCl pH 8 , 150 mM NaCl ) containing 0 . 1% Tween20 ( TBS-T ) . The membrane was then incubated with the secondary horse-radish-peroxidase-conjugated antibody . After extensive washing with TBS-T Signal was detected by chemiluminescence using the ECL detection system ( Thermo Scientific , Schwerte , Germany ) . Blots were “stripped” using Restore Western blot Stripping Buffer Pierce ( Perbio Science , Bonn , Germany ) . The quantification of the protein bands was performed with Image Studio 4 . 0 Imaging Software ( LI-COR Biosciences , Lincoln , NB , USA ) . At day 2 post-infection , Sca1+/lin- cells were plated at 5 x 103 cells/mL in methyl-cellulose supplemented with mIL-3 ( 20 ng/mL ) , mIL-6 ( 20 ng/mL ) and mSCF ( 100 ng/mL ) ( Stem-Cell Technologies , Vancouver , Canada ) . On day 10 after plating , the number of colony forming units ( CFUs ) was determined . After washing out from the methyl-cellulose , the cells were stained with specific antibodies for the detection of surface marker expression by FACS . 5 x 103 cells/ml were replated in methyl-cellulose , thus permitting determination of the serial replating potential . Sca1+/lin- cells were retrovirally transduced and plated in 24 well in the presence of mIL-3 , mIL-6 and mSCF . After 9 days of culture 1 x 104 infected cells were inoculated intravenously into lethally ( 11Gy ) irradiated recipient mice . At day 12 spleens of 3/6 mice/group were fixed in Tellesnizky’s fixative for counting the colonies and 3 were used for RNA isolation and subsequent gene expression and RT-PCR analysis . Female C57BL/6J mice 8–12 weeks of age ( Janvier ) were used as recipients and donors . 1x105 transduced Sca1+ cells were inoculated into sub-lethally irradiated ( 4 . 5 Gy ) recipient mice via tail vein injection . The mice were sacrificed at the first appearance of morbidity ( weight loss >10% , neurological abnormalities , failure to thrive or diarrhea ) . Statistical relevance was determined by the Log-rank test . Cytospins of whole bone marrow and spleen cells were stained with May-Grünwald-Giemsa stain . For secondary transplantation the frozen spleen cells from the primary leukemic mice were thawed and 104 cells/mouse were inoculated into sublethally ( 4 . 5Gy ) irradiated recipients . For surface marker analysis freshly thawed cells ( 5x105/sample ) were stained with B220 ( V450 ) , CD19 ( PE ) , Gr1 ( PerCP-Cy5 . 5 ) and Mac1 ( APC ) according to manufacturer's instructions ( Beckton Dickinson Biosciences , Heidelberg , Germany ) . Analysis were performed on a FACS Canto II ( Beckton Dickinson ) . RNA was isolated from CFU-S12 spleens using RNeasy kit according to the manufactures protocol ( Qiagen , Düsseldorf , Germany ) . The cDNA synthesis was performed using standardized protocols ( Applause WT-Amp Plus ST Systems and Encore Biotin Module , NuGEN ( Bemmel , Niederlands ) . Microarray hybridization to GeneChip MoGene 1 . 0-ST-V1 ( Affymetrix , Santa Clara , CA , USA ) , washing steps and scanning of the microarray were performed according to Affymetrix protocol . Heatmaps were done with the Spotfire software ( Spotfire Decision Site 9 . 1 . 2 , TIBCO Spotfire , Boston , MA , USA ) . The statistical analysis was done with the statistical computing environment R version 2 . 12 [58] . Additional software packages were taken from the Bioconductor project [59] . Total RNA and first strand cDNA were obtained from CFU-S12 spleens as described above . The TaqMan PCR was conducted in triplicates in total of two times following standard protocols using the ABI PRISM 7700 ( Applied Biosystems , Darmstadt , Germany ) . For the quantification of Tp53 , Gadd45α , and Cdkn1a mRNA , gene expression quantification using ‘‘Assay-on-demand” was performed according to the manufacturer's instructions ( Applied Biosystems , Foster City , CA , USA ) . For the detection of ABL/BCR-transcripts on PH and BV cells the following primers probes were used: AB-a-fw: 5'-cct cgt cct cca gct gtt a-3'; AB-a rev: 5'-gcc gta tcc agg tgg tgt-3'; AB-a probe: 5'Fam—tcc gaa cga gcc atc ttc cag a—3'Tamra; AB-b-fw: 5'-gaa tca tcg agg cat ggg-3'; AB-b-rev; 5'-ccg tat cca ggt gtt c-3'; AB-b probe 5'Fam—cga acg agc cat gtt cca ca-3'Tamra . Normalization to glyceraldehyde-3-phosphate dehydrogenase ( Gapdh ) was done for each sample . Ct values were exported into a Microsoft Excel worksheet for calculation of fold changes according to the comparative Ct method . The amount of target , normalized to endogenous Gapdh is given by 2-ΔΔCt [60] . All statistical analyses were performed using Student’s-t-test and p ≤ 0 . 05 was considered as significant . All experiments were performed at least 3 times and the results were taken only if the replicates of independent experiments indicated the same results . GraphPad Prism 5 . 0 was used to provide the statistical calculations . All animal studies were performed in accordance with international animal protection guidelines and approved by the Regierungspräsidium Darmstadt ( approval number F39/08 ) . Human cells were used in agreement with the Declaration of Helsinki with the approval of the local ethic committee ( approval number 329–10 ) . | The t ( 9;22 ) is a reciprocal translocation , which causes chronic myeloid leukemia ( CML ) and a subset of high risk acute lymphatic leukemia ( ALL ) . The derivative chromosome 22 is the so called Philadelphia chromosome ( Ph ) which encodes the BCR/ABL kinase . Targeting BCR/ABL by selective ATP competitors , such as imatinib or nilotinib , is a well validated therapeutic concept , but unable to definitively eradicate the disease . Little is known about the role of the fusion protein encoded by the reciprocal derivative chromosome 9 , the ABL/BCR . In models of Ph+ ALL we show that the functional interplay between ABL/BCR and BCR/ABL not only increases the transformation potential of BCR/ABL but is also indispensable for the growth and survival of Ph+ ALL leukemic cells . The presence of ABL/BCR changed the phenotype of the leukemia most likely due to its capacity to influence the stem cell population as shown by our in vivo data . Taken together our here presented data reveal an important role of p96ABL/BCR for the pathogenesis of Ph+ ALL . | [
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] | [] | 2015 | The Functional Interplay Between the t(9;22)-Associated Fusion Proteins BCR/ABL and ABL/BCR in Philadelphia Chromosome-Positive Acute Lymphatic Leukemia |
Hepatitis B virus ( HBV ) core protein ( HBc ) contains an N-terminal domain ( NTD , assembly domain ) and a C-terminal domain ( CTD ) , which are linked by a flexible linker region . HBc plays multiple essential roles in viral replication , including capsid assembly , packaging of the viral pregenomic RNA ( pgRNA ) into nucleocapsids , viral reverse transcription that converts pgRNA to the genomic DNA , and secretion of DNA-containing ( complete ) virions or genome-free ( empty ) virions . The HBc linker is generally assumed to act merely as a spacer between NTD and CTD but some results suggest that the linker may affect NTD assembly . To determine its role in viral replication , we have made a number of deletion and substitution mutants in the linker region , in either the presence or absence of CTD , and tested their abilities to support capsid assembly and viral replication in human cells . Our results indicate that the linker could indeed impede NTD assembly in the absence of CTD , which could be partially relieved by partial linker deletion . In contrast , when CTD was present , the linker deletions or substitutions did not affect capsid assembly . Deletion of the entire linker or its C-terminal part resulted in a partial defect in pgRNA packaging and severely impaired viral DNA synthesis . In contrast , deletion of the N-terminal part of the linker , or substitutions of the linker sequence , had little to no effect on RNA packaging or first-strand DNA synthesis . However , the N-terminal linker deletion and two linker substitution mutants were defective in the production of mature double-stranded viral DNA . Secretion of empty virions was blocked by all the linker deletions and substitutions tested . In particular , a conservative linker substitution that allowed mature viral DNA synthesis and secretion of complete virions severely impaired the secretion of empty virions , thus increasing the ratio of complete to empty virions that were secreted . Together , these results demonstrate that the HBc linker region plays critical and complex roles at multiple stages of HBV replication .
Hepatitis B virus ( HBV ) , a major cause of viral hepatitis , liver cirrhosis , and hepatocellular carcinoma [1] , replicates a small ( ca . 3 . 2 kb ) , partially double-stranded ( DS ) , relaxed circular ( RC ) DNA via reverse transcription of an RNA intermediate , the pregenomic RNA ( pgRNA ) [2 , 3] . Virus assembly begins with the formation of an immature nucleocapsid ( NC ) incorporating the pgRNA and the viral reverse transcriptase ( RT ) , which then undergoes a process of maturation defined as the conversion of the pgRNA first to a single-stranded ( SS ) DNA and subsequently to the RC DNA , catalyzed by the RT protein [4] . The RC DNA-containing NC is defined as the mature NC , which can be enveloped by the viral envelope proteins and secreted extracellularly as complete virion . HBc is a small ( 183 or 185 amino acids depending on the strains , ca . 21 kd ) protein that forms the shell of the NC and also plays a critical role at multiple other stages of HBV replication [2 , 5 , 6] . It is composed of three regions , an N-terminal domain ( NTD ) , a C-terminal domain ( CTD ) , and a linker that connects the NTD and CTD . NTD encompasses amino acid residues 1–140 and forms the classical assembly domain , generally thought to be necessary and sufficient for capsid assembly [7–9] . CTD encompasses residues from 150 to the C-terminal end , is highly basic ( enriched in R , protamine-like ) , displays non-specific nucleic acid-binding activity [7 , 10] , and is functionally important in pgRNA packaging and reverse transcription but generally thought to be dispensable for capsid assembly [11–14] . Furthermore , CTD is known to undergo dynamic phosphorylation and dephosphorylation , which regulate HBc functions in pgRNA packaging and reverse transcription [15–22] . In between the NTD and CTD is a “linker” peptide with a conserved sequence , 141STLPETTVV149 ( Fig 1 ) [23] . The linker is routinely included together with NTD ( as in HBc149; Fig 1 ) for recombinant expression and capsid assembly in bacterial systems and in vitro assembly reactions using HBc proteins purified from bacteria under high protein and/or salt concentration conditions [5] . Under these assembly conditions , the linker clearly does not interfere with NTD assembly . Indeed , deletion of most of the linker ( from 143–149 ) in the context of the full-length HBc , resulting in the fusion of CTD directly to NTD , abolished capsid assembly when expressed in E . coli , suggesting a positive role for the linker in capsid assembly by the full-length HBc [24] . Furthermore , permutation of the last 7 residues of the linker in the context of HBc149 also prevented capsid assembly but replacement of these same seven residues by the seven N-terminal residues of HBc ( MDIDPYK ) maintained assembly [24] . These results thus further indicated that the specific sequence of the linker can modulate capsid assembly by the full-length HBc under those conditions . On the other hand , the linker can be removed entirely and NTD alone is able to assemble into capsids under those conditions , in the absence of both the CTD and the linker [8 , 24 , 25] . Interestingly , truncation of the linker , in the complete absence of the CTD , affected the ratio of the T = 3 ( with 90 HBc dimers ) or T = 4 ( with 120 dimers ) capsids assembled under these conditions [24 , 25] . Thus , whereas most capsids formed when the linker is present belong to the T = 4 class , most capsids formed when the linker is removed belong to the T = 3 class . Other than this apparent effect on the dimorphism of capsid assembly , the mechanism of which remains elusive , the linker is not known to have any other specific functions in HBV replication . As a para-retrovirus , HBV is selective in virion morphogenesis in that only mature NCs containing the DS , RC DNA , but not immature NCs containing either pgRNA or the SS DNA , are selected for envelopment and secretion as complete virions [26 , 27] . Situated between the genome and the envelope , the capsid plays an integral role in this selective virion formation process . Within the NTD and spatially located on the surface of the capsid shell , a so-called matrix binding domain ( MBD ) has been defined , through elegant genetic analysis , that is thought to interact with a short segment in the preS1 region of the viral large envelope protein ( L ) , the so-called matrix domain ( MD ) , for complete virion formation [28–30] . L is one of three HBV surface or envelope proteins ( HBs ) ( the other two being the middle or M and small or S surface protein ) , which are also secreted as the classical HBsAg particles ( the Australian antigen ) that contain no capsid or genome , in huge excess over complete virions ( by up to 100 , 000-fold ) [27] . Surprisingly , recent studies have revealed that HBV also secretes very high levels ( ca . 1011/ml ) of genome-free ( empty ) virions , which contain the envelope and capsid but no DNA or RNA and are found at ca . 100-fold excess over complete ( i . e . , RC DNA-containing ) virions in cell culture supernatant and in the blood of experimentally infected chimpanzees and naturally infected humans [27 , 31 , 32] . Neither the capsid nor the envelope requirements for empty virion formation are clear at present . Naked ( non-enveloped ) capsids are also released in cell cultures via an unknown mechanism that appears to be different from that for the secretion of virions [33] . However , the release of naked capsids seems to be a phenomenon in transformed cell lines , and has not been observed in vivo during HBV infection [31 , 32 , 34] . We have recently demonstrated that contrary to expectation , the HBc CTD is apparently needed for capsid assembly in living human cells and in the rabbit reticulocyte lysate ( RRL ) , where both the protein concentration and salt conditions mimic more closely the conditions in authentic human host cells than the previous assembly systems using bacterial expression and purified HBc proteins [35] . An HBc construct containing both the NTD and the linker ( i . e . , HBc149 ) but no CTD was unable to assemble under these ( near ) physiological conditions . On the other hand , other CTD-lacking HBc constructs that also lack part of the linker attached to the NTD ( truncated at position 147 , 145 , or 144 ) accumulated and assembled to varying but detectable levels [11 , 36–38] , indicating that the exact truncation point within the linker region affects the capacity of NTD to assemble in the absence of CTD . These results raise the possibility that the linker sequence can somehow interfere with the assembly by NTD in the absence of CTD under ( near ) physiological conditions in RRL and in human cells , and this inhibitory effect of the linker on the NTD assembly function is somehow overcome by CTD in the full-length HBc . Given the unexpected role of CTD , and potentially of the linker , in capsid assembly in RRL and in human cells , it is now important to further assess the role of the CTD , the linker and the interplay between the CTD and the linker , in capsid assembly under physiological conditions . Also , these results bring about the possibility that the linker may have potential roles in the other functions of HBc beyond capsid assembly , which has never been tested so far . Therefore , we have carried out a genetic analysis to test the role of the linker in capsid assembly , both in the presence and absence of CTD , under near physiological conditions in vitro and in cells . Furthermore , the effects of a panel of linker deletion and substitution mutants on pgRNA packaging , DNA synthesis , and virion secretion were assessed . Our results have revealed that the linker indeed can affect capsid assembly in a manner that is dependent on CTD , and furthermore , it plays a critical role in multiple stages of HBV replication beyond capsid assembly .
Since previous reports have found that HBc mutants with truncation of the linker region , in addition to CTD removal ( i . e . , C-terminal truncation beyond 149 ) , could be expressed and assemble at appreciable levels [11 , 36–38] whereas NTD plus an intact linker ( also without CTD ) ( i . e . , HBc149 ) failed to assemble and accumulate [35 , 38] , we reasoned that deletion of the linker may restore HBc expression and/or assembly , when the CTD was absent . Thus , we deleted the entire linker ( from 141 to 149 ) or only part of the linker ( from 144–149 ) , both in the absence of CTD , to make HBc140 and HBc143 ( Fig 1 ) , respectively , and determined their expression and assembly in human hepatoma cells ( HepG2 and Huh7 ) . A second plasmid expressing the HBV pgRNA and all viral proteins except HBc ( HBV-C- ) was co-transfected in a trans-complementation assay to assess the ability of the mutant HBc proteins to carry out the other functions of HBc including pgRNA packaging , DNA synthesis , and virion secretion . We selected the plasmid pSVHBV1 . 5 to derive the HBc-defective genomic construct , as our pilot experiments showed that this plasmid secreted significantly higher levels of HBsAg than another genomic construct pCMVHBV ( S1 Fig ) . Since the secreted HBsAg is known to be in great excess over virions during natural HBV infection [27 , 31] , the higher levels of HBsAg produced from pSVHBV1 . 5 helped to ensure that the complementation experiment mimicked better the natural infection in terms of HBsAg expression and to avoid the potential situation where the expression of the envelope proteins might become limiting for virion secretion . Even though the HBc sequence used here was from HBV genotype D , and the complementing ( HBV-C- ) construct was from HBV genotype A , they complemented each other efficiently in all aspects of viral replication assayed here , as shown below . In support of a negative effect of the linker on NTD expression/assembly as hypothesized above , the expression levels of HBc140 and HBc143 , as assessed by SDS-PAGE and western blot analysis , approached those of the WT HBc in both HepG2 and Huh7 cells ( Fig 2A and 2C , 3rd and bottom panels ) , much better than that of HBc149 , which retains the entire linker [35 , 38] . As shown in Figs 2 and S2 , the mAb T2221 , recognizing an epitope towards the end of the HBc NTD [39] , detected the WT and CTD- ( and linker- ) deleted HBc proteins very well , in comparison with two other mAbs targeted to the beginning of NTD , 10E11 [40] ( commercially available ) and the anti-WHc made against the very N-terminal sequences of the woodchuck hepatitis virus ( WHV ) core protein ( WHc ) , which are identical to those in HBc [32 , 41] ( see Materials and Methods ) . The levels of intracellular capsids ( Fig 2A and 2C , 2nd panels ) , and the naked capsids released into culture medium ( Fig 2B , top right ) , as assessed by native agarose gel electrophoresis and western blot analysis , were also higher than those of HBc149 although still lower than those of the WT HBc . For HepG2 cells , the naked capsids released into the culture supernatant by HBc140 and HBc143 ( and even HBc149 ) were relatively abundant ( though still less than the WT HBc ) ( Fig 2B , top right ) although the levels of intracellular capsids from these mutants were very low ( Fig 2A , 2nd panel ) . Thus , the release of naked capsids into the culture supernatant might be enhanced by the linker ( and CTD ) deletions in HBc140 and HBc143 . An enhanced release of capsids plus a partial defect in capsid assembly could explain the relative abundance of HBc140 and HBc143 proteins detected by SDS-PAGE western blot analysis ( Fig 2A , 3rd panel ) but very low levels of intracellular capsids ( Fig 2A , 2nd panel ) . For Huh7 cells , a similar phenomenon could have occurred but the released naked capsids from HBc140 ( and to a lesser degree , HBc143 ) could have been rapidly disrupted/degraded in the supernatant ( see also Fig 3 below ) . This could explain the relative abundance of these mutant proteins detected by the SDS-PAGE western blot analysis ( Fig 2C , 3rd panel ) but very low levels of intracellular and extracellular capsids ( esp . for HBc140 ) ( Fig 2C , 2nd panel; Fig 2D , top right ) . To assess the potential role of the linker in the context of the full-length HBc ( i . e . , with both NTD and CTD ) , we constructed HBc/Δ141–149 ( with the entire linker deleted ) and HBc/Δ145–149 ( deleting the C-terminal portion of the linker ) , which share the similar linker deletions as HBc140 and HBc143 but retain the CTD ( Fig 1 ) . Both of these linker deletion constructs were expressed and assembled into capsids like the WT HBc in both HepG2 and Huh7 cells ( Fig 3A and 3C , middle and bottom panels ) . These results were rather surprising in light of the previous report showing that deletion of the linker , thus fusing the CTD directly to NTD , abolished capsid assembly in the bacterial expression system , which was taken as evidence to indicate a need for a flexible linker between the NTD and CTD to prevent the CTD from interfering with NTD assembly [24] . In light of this surprising result , we constructed another partial linker deletion construct , in the context of the full-length HBc , by deleting HBc residues 141–144 ( i . e . , the N-terminal portion of the linker ) to make HBc/Δ141–144 ( Fig 1 ) . In addition , we made the same partial N-terminal deletion of the linker , in the absence of CTD , to construct HBc149/Δ141–144 ( Fig 1 ) . HBc/Δ141–144 was expressed and assembled just like the WT HBc in both HepG2 and Huh7 cells ( Fig 4A and 4C , lane 5 , middle and bottom panels ) . On the other hand , the same partial linker deletion , in the absence of the CTD , in HBc149/Δ141–144 did not rescue NTD expression or assembly ( Fig 4A and 4C , lane 6 , middle and bottom panels ) , unlike the deletion of the entire linker ( in HBc140 ) or its C-terminal portion ( in HBc143 ) described above . It was previously shown that the sequence of the linker between the NTD and CTD could affect capsid assembly in the bacterial expression system [24] . We thus tested two different linker substitution mutations that were shown to be either compatible or not with assembly ( Fig 1 ) . The substitution that disrupted assembly was a randomized WT linker sequence ( STETVPVLT , dubbed LR for “linker random” here ) , whereas the substitution that retained assembly was the replacement of the last seven residues of the linker by the first seven residues from the N-terminal end of HBc with the first two residues unchanged ( STMDIDPYK , dubbed LN for “linker N-terminal” here ) . Interestingly , we found both of these linker substitutions were similar to the WT HBc in expression and assembly in human hepatoma cells ( Fig 4A and 4C , lanes 2–3 , middle and bottom panels ) , in contrast to their severe defect in assembly in bacteria [24] , further attesting to the drastic effects of the expression host on the assembly behavior of the different HBc constructs . We also made a third linker substitution with a nine-residue segment ( TTLPETTII ) from a cellular protein ( dubbed LC for “linker cellular” here ) that is very similar to the WT HBc in sequence ( the middle six residues being the same as the WT linker and the other three residues representing conserved substitution: S141T , V148I , and V149I ) and in predicted secondary structure [24] . This substitution was also compatible with capsid assembly in hepatoma cells ( Fig 4A and 4C , lane 4 , middle and bottom panels ) . We next tested the potential effect of the linker deletions and substitutions on the HBc function in pgRNA packaging into NCs . Given the known critical role of CTD in mediating pgRNA packaging , it was no surprise that none of the CTD deletion mutants with or without linker deletions ( HBc140 , HBc143 , HBc149 , HBc149/Δ141–144 ) were able to support packaging of viral RNA ( Fig 2A and 2C , lanes 2–4 , top panels; Fig 4A and 4C , lane 6 , top panels ) . On the other hand , it was interesting that some of the linker mutations , in the presence of an intact CTD , also impaired pgRNA packaging . The complete linker deletion , HBc/Δ141–149 , showed a decrease in pgRNA packaging by ca . 5–10 fold after normalizing to the amount of capsids ( Fig 3A and 3C , lane 2 , top panels ) , whereas partial deletion of the C-terminal portion of the linker , HBc/Δ145–149 , decreased pgRNA packaging less severely , by ca . 3–4 fold ( Fig 3A and 3C , lane 3 , top panels ) . Partial deletion of the N-terminal portion of the linker , HBc/Δ141–144 had the weakest effect , decreasing pgRNA packaging by ca . 2 fold ( Fig 4A and 4C , lane 5 , top panels ) . In contrast to the linker deletions , none of the linker substitutions affected pgRNA packaging ( Fig 4A and 4C , lanes 2–4 , top panels ) , indicating that the specific sequence of the linker was not critical for this HBc function . As expected from the essential role of CTD in pgRNA packaging as well as in facilitating viral reverse transcription , none of the CTD deletion mutants ( HBc140 , HBc143 , HBc149 , HBc149/Δ141–144 ) showed any viral DNA in NCs ( Figs 1 , 2B and 2D , top left , lanes 2–4 ) . Intriguingly , even in the presence of the CTD , the complete linker deletion ( HBc/Δ141–149 ) and the C-terminal partial linker deletion ( HBc/Δ145–149 ) showed no viral DNA in NCs ( Fig 3B and 3D , lanes 5 , 6 , top panels ) , indicating a critical role of the linker , particularly its C-terminal portion ( 145–149 ) , in viral reverse transcription beyond its role in facilitating pgRNA packaging described above . On the other hand , the N-terminal partial linker deletion , HBc/Δ141–144 , contained some viral DNA in NCs , although at reduced levels compared to the WT HBc ( Figs 1 and Fig 4B , lane 5 , top panel ) . The three linker substitutions apparently contained viral DNA in their capsids at levels similar to the WT ( Fig 4B , lanes 2–4 , top panel; Fig 4D , lanes 3–5 , top panel ) . To assess the species of DNA synthesized in mutant capsids , we extracted viral DNA from the WT and mutant capsids and analyzed their DNA content by Southern blot analysis . Previous results from us and others indicated that certain capsid mutants allow viral DNA synthesis but are unable to protect their DNA content from exogenous nuclease digestion , which is routinely used to remove plasmid DNA during core DNA extraction [19 , 42 , 43] . To avoid this potential issue so as to obtain a more accurate assessment of viral DNA synthesized in the mutant capsids , we extracted capsid-associated DNA ( or core DNA ) without nuclease digestion but then degraded the contaminating plasmid DNA in the resulting core DNA preparation with DpnI , which digests plasmid DNA ( methylated in bacteria ) but not viral DNA synthesized in hepatoma cells [42] . All capsids that contained viral DNA ( the three linker substitutions and the partial N-terminal linker deletion , HBc/Δ141–144 ) based on the particle gel analysis ( Fig 4B and 4D ) had SS DNA ( i . e . , minus strand ) , although the SS DNA levels were reduced in HBc/Δ141–144 by ca . 2-fold compared to the WT HBc ( Fig 5 ) . As the SS DNA is reverse transcribed from pgRNA , this modest reduction of SS DNA in HBc/Δ141–144 was at least partly due to the moderately reduced levels of pgRNA packaging in this mutant described above . In contrast , HBc/Δ141–149 showed no DNA and HBc/Δ145–149 showed barely detectable levels SS DNA ( Fig 5 ) , consistent with the particle gel results ( Fig 3B and 3D ) . Again , this DNA synthesis defect could be partly the consequence of the defect in pgRNA packaging by these two mutants . These results thus indicated that the specific sequence of the linker was not critical for the first step of reverse transcription to generate the minus strand DNA , and a linker that was only five ( instead of the nine in WT ) residues long was sufficient for SS DNA synthesis . Intriguingly , the partial N-terminal linker deletion ( HBc/Δ141–144 ) , as well as two linker substitutions ( LR and LN ) , showed no RC DNA in their capsids in contrast to the WT HBc ( Fig 5 , lanes 5–7 ) . These three mutants did make immature DS DNA intermediates ( running as a smear between the SS DNA and RC DNA in Fig 5 ) , indicating they were able to initiate plus strand DNA synthesis and elongate the plus strand to a limited extent . However , only the conservative linker substitution ( LC ) was competent in RC DNA synthesis ( Fig 5 , lane 8 ) , thus implicating a critical role of the linker , in a sequence-specific manner , in the second step of reverse transcription ( extensive plus strand DNA synthesis to generate RC DNA ) . We next assessed the capacity of the linker mutants to be enveloped and secreted into the culture supernatant as virions . Viral particles ( including both virions and naked capsids ) released into the culture supernatant of transfected HepG2 or Huh7 cells were analyzed by native agarose gel electrophoresis , whereby naked ( non-enveloped ) capsids released into the culture supernatant were well separated from virions ( enveloped ) as the former migrated much faster than the latter on the gel ( Figs 2–4 , panels B and D ) . Complete virions were detected by Southern blot analysis of HBV DNA . Empty virions were detected by western blot analysis of the HBc protein in virions , assuming that the vast majority of HBc signal ( 99% or more ) from virions was from empty virions , as shown in previous studies [18 , 31 , 32] . As expected , HBV DNA in ( complete ) virions ( or naked capsids ) , readily detectable in WT virions , was not detected from HBc140 , HBc143 or HBc149 in HepG2 cells ( Fig 2B , top left ) ( true also for Huh7 cells; see below Fig 2D , top left ) , due to their lack of CTD , which is known to be essential for pgRNA packaging or DNA synthesis . On the other hand , the HBc protein signal detected in the WT virions ( i . e . , empty virions ) was also undetectable from these mutants when tested in HepG2 cells ( Fig 2B , top right ) . These results thus indicated that the linker , and/or CTD ( see below also ) , was important for secretion of empty virions . This suggestion was then confirmed by results obtained using Huh7 cells , when decreasing amounts of culture supernatant from the WT HBc transfection were analyzed along with that from the HBc140 and HBc143 transfection ( Fig 2D ) . When the amount of supernatant from the WT HBc transfection was decreased by 10-fold , the levels of naked capsids released into the medium were similar to those from the HBc143 transfection ( Fig 2D , top right , lanes 3 and 5 ) ; virion capsids were clearly detectable from the WT HBc even with this reduced loading whereas no virion capsids from either HBc143 or HBc140 were detected ( Fig 2D , top right , lanes 3–5 ) . As expected , the HBs signals were only detected with virions but not naked capsids ( Fig 2B and 2D , bottom ) . As HBsAg particles ( with no capsids or genome ) are not separated from virions ( either empty or complete ) on the agarose gels under these conditions [31 , 32] , the abundant HBsAg signals , in the absence of HBc signals at the top of the gel in the case of HBc140 , HBc143 and HBc149 represented just HBsAg particles ( no virions ) ( Fig 2B , lanes 2–4 , bottom; Fig 2D , lanes 4 , 5 , bottom ) , as verified by the detection of HBsAg at the same position on the gel in the complete absence of HBc expression ( Fig 2D , lane 6 , bottom ) . These results thus indicated that capsids formed by NTD , in the absence of CTD and the linker , could not be enveloped for secretion as empty virions . It was noticeable that the complete linker deletion ( HBc/Δ141–149 ) showed little to no naked capsids in the culture supernatant either ( Fig 3B and 3D , lanes 2 and 5 ) , suggesting that the complete linker deletion might also have blocked the release of naked capsids into the culture medium , or if released , was rapidly degraded in the supernatant . On the other hand , we detected a smeary HBc signal migrating just below the virions and much slower than naked capsids , detectable only from this mutant ( in Huh7 but not HepG2 cells ) , in a manner that was independent of the viral envelope proteins ( Fig 3B and 3D , lanes 2 and 5 , bottom ) . This result suggested that some naked mutant capsid might be disrupted once released extracellularly under certain conditions . The exact nature of the slowly-migrating HBc smear ( in a non-capsid form ) from this mutant , and its apparent cell line dependence , remained unclear . If the HBc/Δ141–149 capsid was indeed blocked from release from the cell , the excess mutant capsid might be degraded intracellularly such that its level in the cell did not exceed that of the WT HBc ( Fig 3A and 3C ) . The role of the linker in virion secretion , both complete and empty , was assessed in the context of HBc linker mutants which retain an intact CTD . Both the complete linker deletion ( HBc/Δ141–149 ) and the two partial linker deletions ( HBc/Δ141–144 and HBc/Δ145–149 ) , despite being competent for capsid assembly intracellularly , did not show any virion secretion ( Fig 3B and 3D , lanes 5 , 6; Fig 4B , lane 5; Fig 4D , lane 2 ) . As two of these three mutants ( HBc/Δ141–149 and HBc/Δ145–149 ) failed to synthesize any viral DNA and the third linker deletion mutant ( HBc/Δ141–144 ) failed to make RC DNA ( which is a prerequisite for complete virion secretion ) ( Figs 3–5 ) , the specific effect of these mutants on secretion of complete virions could not be ascertained from these experiments . However , these results clearly indicated that both parts of the linker were required for secretion of empty virions . The critical role of the linker in virion secretion was further confirmed with the linker substitution mutants . All three linker substitution mutants were defective in secreting empty virions ( Fig 4B , lanes 2–4 , bottom; Fig 4D , lanes 3–5 , bottom ) , although the conservative substitution ( LC ) showed a low level of empty virions ( ca . 10% of WT ) ( Fig 4B , lane 4 , bottom; Fig 4D , lane 5 , bottom ) . Again , since the LR and LN substitution mutants failed to make RC DNA ( Fig 5 ) , the specific effects of these mutations on DNA virion secretion could not be determined from these experiments . Interestingly , the conservative substitution ( LC ) allowed secretion of complete virions ( virion DNA ) , despite severely blocking the secretion of empty virions ( virion HBc ) ( Fig 4B , lane 4; Fig 4D , lane 5 ) . The results presented above indicated that the linker was required for virion secretion ( Figs 3B and 3D and 4B and 4D ) , but a role for CTD could not be excluded , since when the CTD alone was deleted and the linker was retained ( as in HBc149 ) , there was little to no accumulation of intracellular capsids ( Fig 2A and 2C , 2nd panel ) , precluding an assessment of its virion secretion capacity in the absence of the CTD . To overcome this limitation , we appended four positive R residues ( 4R ) to HBc149 , reasoning that the supply of the positive charges might rescue assembly of HBc149 , in the absence of CTD , by either interacting with non-specific RNA or with NTD of HBc [35 , 44] . Indeed , HBc149-4R , in contrast to HBc149 , accumulated substantial , though still lower than WT , levels of intracellular capsids that were released in the culture medium ( Fig 6B and 6D ) . As expected , the HBc149-4R mutant capsids failed to package pgRNA or synthesize viral DNA due to the lack of a complete CTD ( Fig 6A and 6C ) . Since the capsid levels of HBc149-4R were still lower than those of the WT HBc , we titrated the amount of WT HBc and HBc149-4R plasmids used for transfection , relative to the HBc-defective genomic construct , and measured levels of capsids and virions across the titration to facilitate a direct comparison of virion secretion efficiency of HBc149-4R relative to the WT HBc . Importantly , HBc149-4R , was secreted as virions ( empty ) as efficiently as the WT , when normalized to the capsid levels ( Fig 6B and 6D ) . Thus , the linker was able to support efficient secretion of ( empty ) virions in the absence of a complete CTD . Since the state of CTD phosphorylation is known to play a critical role in capsid assembly , pgRNA packaging , and reverse transcription , which were affected by the linker mutants studied here , we decided to test if the various linker mutants could affect the CTD phosphorylation state . Since HBc assembles into capsid particles rapidly in hepatoma cells , which can affect CTD phosphorylation state indirectly by influencing the accessibility of the CTD phosphorylation sites to host kinases and phosphatases , and CTD also undergoes dynamic phosphorylation and dephosphorylation associated with pgRNA packaging and reverse transcription , we decided to use the RRL in vitro translation system for HBc expression and assembly that we developed recently [35] . In this cell-free system , HBc is phosphorylated during translation by endogenous cellular kinases , at ( at least ) some of the same CTD sites as in vivo , which is independent of capsid assembly , pgRNA packaging or DNA synthesis [35] , and HBc assembly does not occur until triggered by exogenous phosphatase treatment . We therefore examined the CTD state of phosphorylation of the WT HBc and various linker mutants immediately after translation , before triggering capsid assembly , to determine HBc phosphorylation state in the absence of capsid assembly . Following resolution of HBc by SDS-PAGE , we used an NTD-specific mAb ( T2221 ) to measure the total HBc levels , irrespective of CTD state phosphorylation ( Fig 7 , top panel ) and two CTD-specific mAbs , B701 that is selective for the phosphorylated CTD with an epitope between 155–164 ( Fig 7 , middle panel ) , and 25–7 that is selective for the non-phosphorylated CTD with an epitope between 164–182 ( Fig 7 , bottom panel ) [18 , 35] , for western blot analysis . The specificity of the mAbs was verified by using the non-phosphorylated HBc protein purified from E . coli ( Fig 7 , lane 1 ) . The complete linker deletion mutant ( HBc/Δ141–149 ) , as well as the two partial deletion mutants ( HBc/Δ141–144 and HBc/Δ145–149 ) , showed strongly increased ( by ca . 5- to 7- fold ) B701 signal relative to the WT HBc after normalization of the total HBc signal ( as detected by mAb T2221 ) ( Fig 7 , lanes 4–6 ) , indicative of enhanced CTD phosphorylation at the B701 epitope . The linker substitution mutant , HBc-LN , also showed a similar effect on CTD phosphorylation to the linker deletion mutants , albeit to a lesser degree ( by ca . 3-fold ) ( Fig 7 , lane 8 ) . On the other hand , the 25–7 signal for the complete linker deletion ( HBc/Δ141–149 ) , the C-terminal partial linker deletion ( HBc/Δ145–149 ) , and the LN substitution mutants was modestly ( by ca . 2-fold ) increased relative to the WT HBc ( Fig 7 , lanes 4 , 5 , 8 ) , suggesting the 25–7 epitope was less phosphorylated in these mutants as compared to the WT HBc .
We have demonstrated here that mutations of the HBc linker affected multiple steps in HBV replication , including modulation of capsid assembly , pgRNA packaging , DNA synthesis , and virion secretion , implicating a critical role for the linker in multiple stages of HBV replication ( Fig 1 ) . The mechanisms of action for these linker functions remain to be elucidated . As the nine-residue long linker peptide is not known to have any enzymatic function or biochemical activity ( such as nucleic acid binding ) , we consider it plausible that the effects of the linker on the HBc functions in capsid assembly , pgRNA packaging , and reverse transcription , are exerted through its effects on the NTD or CTD ( Fig 8 ) . This is supported by our findings that the linker affected NTD assembly and CTD state of phosphorylation . On the other hand , the linker may function in a more direct manner ( independent of its effects on the NTD or CTD ) to facilitate virion secretion by interacting with the viral envelope proteins ( Fig 8 ) . As introduced earlier , in the absence of CTD , the linker is not required for capsid assembly in bacteria or under in vitro assembly conditions with high HBc and/or salt concentration [24 , 25] . However , deletion of the linker , thus fusing the CTD to NTD , or substitution of the linker sequence , interfered with NTD assembly in bacteria ( Fig 1 ) [24] . In sharp contrast , we have shown here that in human hepatoma cells , the linker interfered with NTD assembly if the CTD was absent , but in the presence of the CTD , linker deletions or substitutions did not interfere with capsid assembly . Consistent with the inhibitory effect of the linker on assembly by NTD reported here , a recent study also found that HBc149 failed to accumulate in a mouse hepatocyte cell line but HBc144 did ( similar to HBc143 here ) [38] . It thus appears that the NTD alone is sufficient , at least to a limited extent , for capsid assembly in human cells , but the presence of the linker , in the absence of the CTD , interferes with NTD assembly specifically in human cells but not in bacteria . Furthermore , we have shown here that in the absence of CTD , deletion of the linker sequence 141–144 ( HBc149/Δ141–144 ) was less effective in restoring capsid assembly , compared to deletion of the entire linker ( in HBc140 ) or 144–149 ( in HBc143 ) . This suggests that N- and C-terminal sequences of the linker are not equivalent in modulating capsid assembly and the C-terminal part of the linker ( 144–149 ) may have a more detrimental effect on NTD assembly than the N-terminal part of the linker ( 141–144 ) when the CTD is absent . How the linker may influence capsid assembly , in a host cell- and CTD-dependent manner , is one of the intriguing questions brought up by our studies here that warrants further studies . The linker may interfere with NTD assembly in human cells , in the absence of CTD , by affecting the conformation of NTD , or by interacting with a host factor ( s ) to inhibit assembly ( Fig 8 ) . When expressed in bacteria , the high protein concentration achieved may somehow overcome the inhibitory effects of the linker on NTD assembly , or host cell-specific factors may alleviate the linker effect . As HBc149-4R , in contrast to HBc149 , assembled efficiently in human cells , a role for electrostatic interactions between the highly basic CTD and a negatively charged ligand ( e . g . , RNA , or acidic residues in the HBc NTD ) can be implicated in alleviating the inhibitory effect of the linker on NTD assembly in human cells by the CTD . Moreover , capsid stability , instead of or in addition to assembly , could be affected by the linker , as suggested by the apparent disruption of the HBc/Δ141–149 ( with the complete linker deletion , fusing the CTD directly to the NTD ) capsid once it was released extracellularly . The linker , and its specific sequences , are important for capsid assembly in bacteria when both the NTD and CTD are present ( i . e . , in the context of the full-length HBc ) [24] , but not in human cells as we have shown here . Other than the differences in HBc subunit concentration and salt/pH conditions , phosphorylation of the HBc CTD , which occurs in human cells but not in bacteria and is furthermore modulated by the linker as we have shown here , is known to modulate capsid assembly [35] . This host cell-dependent and linker-modulated CTD phosphorylation ( Fig 8 ) may be part of the reason why deleting the linker or substituting its sequences interferes with capsid assembly in bacteria but not in human cells . In addition , CTD is known to interact with host factors in mammalian cells , such as I2PP2A and B23 [45] , and SRPK [46] , which may also contribute to the host cell-dependent effects of the linker mutations . Indeed , we have shown previously that the binding of I2PP2A and B23 to the CTD is modulated by the linker in the case of the duck hepatitis B virus core protein [45] , which is thought to be much longer than the HBc linker and located between position 186–230 [47] . It remains possible that deletion of the CTD impaired the production and/or stability of the mutant protein in human cells ( but not in bacteria ) , accounting for the very low expression level of HBc149 in hepatoma cells . However , we believe that the lower expression level of this mutant was mostly due to its defect in efficient assembly in mammalian cells ( and consequently , more rapid degradation ) . First , we have shown recently that this same mutant is expressed at levels equal to or higher than the WT HBc in a mammalian cell extract , the rabbit reticulocyte lysate in vitro translation system; yet , it still fails to assemble , unlike the WT HBc in the same system that assembles efficiently [35] . Second , HBc149 expression and assembly in human cells can both be rescued by co-expression of the WT HBc [35] . Deletion of the entire linker severely impaired pgRNA packaging , and partial deletion of sequences from 145–149 had a more deleterious effect than that of 141–144 , suggesting that the linker sequences from 145–149 had a more important role than 141–144 in pgRNA packaging in the presence of the CTD , similar to the non-equivalent role of the two parts of the linker on capsid assembly in the absence of the CTD . On the other hand , none of the linker residues individually was absolutely required for pgRNA packaging as they could be substituted without affecting pgRNA packaging . One potential mechanism for the linker to modulate pgRNA packaging may be via its influence on CTD phosphorylation ( Fig 8 ) , which we could demonstrate here . As we proposed recently [35] , hyper- or hypo-phosphorylation of HBc CTD can both impair specific pgRNA packaging , by decreasing overall RNA ( including the specific pgRNA ) binding affinity or failing to block non-specific RNA binding , respectively . Details of the effects of the linker on CTD phosphorylation , in a phosphorylation site- and maturation stage-specific manner will require comprehensive studies in the future . How the linker may affect CTD phosphorylation state also remains to be elucidated . One possibility is that the linker modulates CTD conformation , which in turn affects the accessibility of the CTD phosphorylation sites to host kinases and/or phosphatases . Alternatively , the linker may affect the recruitment of these CTD-modifying host enzymes , either directly by serving as binding sites for these factors , or through an indirect means ( Fig 8 ) . Additional effects of the linker , beyond affecting CTD phosphorylation , including its influence on NTD assembly , may also play a role in modulating pgRNA packaging . Deletion of the entire linker , or partial deletion of the linker sequences from 145–149 abolished viral reverse transcription , whereas deletion of 141–144 had only a modest effect . This result again suggests that the linker sequences from 145–149 had a more important role than 141–144 in reverse transcription , as in pgRNA packaging . However , since the linker is nine-residues long , it was impossible to construct a deletion mutant removing precisely half of the linker ( i . e . , 4 . 5 residues ) . So , it remains possible that HBc/Δ141–144 was more effective than HBc/Δ145–149 in making immature DNA ( and packaging pgRNA ) simply because it is one residue longer than HBc/Δ145–149 . On the other hand , as with pgRNA packaging , it is clear that none of the linker residues individually was required for SS DNA synthesis as they could be substituted with little effect on SS DNA levels . Furthermore , our results here have shown that a linker that is five ( instead of nine as in the case of the WT ) -residues long is still capable of supporting pgRNA packaging and SS DNA synthesis , at least partially . This is consistent with the observation that the linker is disordered in recombinant capsids assembled in bacteria from HBc149 ( i . e . , missing the entire CTD ) [9] and the notion that the linker may form a flexible , mobile array on the inner surface [24] of the maturing NC to facilitate this stage of viral DNA synthesis . On the other hand , RC DNA synthesis was clearly impaired by two of the three linker substitutions as well as the partial deletion from 141–144 , which had little effect on SS DNA synthesis . The remaining linker substitution that was competent for RC DNA synthesis is a very conservative one with almost identical sequence and predicted structure to the WT linker . Thus , for RC DNA production , the linker did not merely function as a flexible spacer but played a specific role . How the linker might facilitate RC DNA synthesis in a sequence-dependent manner is not yet known . As the CTD state of phosphorylation is known to be important for RC DNA synthesis [17 , 19 , 20] , and the linker sequences could affect CTD phosphorylation , the specific linker sequences could modulate RC DNA synthesis through their effect on CTD phosphorylation ( Fig 8 ) , as proposed above for their effects on pgRNA packaging . In addition , the linker may be involved in the conformational changes of the maturing NC that accompany , and may be required for , RC DNA synthesis [48] . In addition , the linker itself may undergo conformational changes , in a sequence-dependent manner , during the viral replication cycle that are modulated by the NTD or CTD . We note also that although no exogenous nuclease digestion was used during viral DNA extraction , our results here can’t exclude the possibility that in those mutants where no RC DNA was detectable , some RC DNA might actually have been made but degraded as soon as it was made in the cell . Perhaps the most intriguing result we have obtained here regarding the linker functions is its essential role in the secretion of empty virions . Those linker deletion and substitution mutants that impaired RC DNA synthesis were also defective in the secretion of complete ( RC DNA-containing ) virions . The conservative linker substitution ( LC ) that remained competent for RC DNA synthesis was also capable of secreting complete virions . This is expected as RC DNA synthesis is required for complete virion formation . A specific effect of these mutants on the secretion of complete virions , however , could not be ascertained from these results ( Fig 8 ) . On the other hand , it is clear from our results here that the specific linker sequence is critical for empty virion secretion . All linker mutations , either complete or partial deletions or substitutions , impaired secretion of empty virions . Even the conservative linker substitution ( LC ) , which was fully competent in all other aspects of the viral life cycle tested here including the secretion of complete virions , showed a severe defect ( though not as severe as the other linker substitution or deletion mutants ) in the secretion of empty virions . The LC linker substitution increased the ratio of complete to empty virions , by ca . 10-fold from ca . 1% to 10% [32] , by decreasing the secretion of empty virions without affecting that of complete virions . Thus , we have not only uncovered an essential role of the linker in the secretion of empty virions , but also revealed that the requirements for the secretion of complete vs . empty virions can be separated genetically . The efficient secretion of HBc149-4R capsids in empty virions further suggests that the linker is not only necessary but may be sufficient to support empty virion formation , although it remains formally possible that both the linker and several R residues from the CTD are required for the secretion of empty virions . As the beginning of the HBc CTD has the sequence 150RRRGR154… , it may be argued that HBc149-4R actually retains a severely truncated “CTD , ” i . e . , the first three ( or five without G153 ) residues of the CTD . Thus , further studies will be needed to clarify the contribution of the CTD , if any , in the secretion of empty HBV virions . It was recently reported that HBc147 capsids ( missing the entire CTD and two C-terminal residues of the linker ) failed to be secreted in empty virions [36] . Whereas the authors hypothesized that their result implicated a critical role for the CTD in empty virion secretion , our findings here suggest an alternative interpretation of the same result , i . e . , the last two residues of the linker ( 148 and 149 ) plays a critical role in supporting empty virions secretion . The CTD state of phosphorylation , which appeared to be affected by the linker mutations , is unlikely to account for the effect of the linker mutations on virion secretion as CTD phosphorylation state , per se , does not play a critical role in virion formation [18] . These results , combined , suggest the intriguing possibility that linker residues interact , directly , with the envelope proteins during virion formation ( Fig 8 ) . Whereas the linker is generally thought to be located inside the capsid [24 , 49] and thus unlikely to interact with the envelope proteins on the capsid surface , it may nevertheless be exposed , at least transiently , on the capsid surface . Some evidence in support of an exterior localization of the linker has indeed been presented; for example , epitopes attached to the linker are accessible to antibody binding in empty HBV capsids [50 , 51] . Additionally or alternatively , the linker sequence may be involved , perhaps via interactions with host factors , in trafficking of the capsids to the site of budding for their envelopment . Future studies , including high-resolution structural analysis , will be required to further elucidate the mechanisms of action of the linker functions uncovered here and to determine any structural changes in the capsid , at the various stages of viral replication , that may be modulated by the linker . For example , whereas the linker is known to affect the dichotomy of T = 4 or T = 3 capsids in bacteria , whether this is also the case in human cells remains to be determined . Furthermore , it remains unknown if both size classes of capsids are competent in pgRNA packaging or reverse transcription . On the other hand , both T = 3 and T = 4 capsids are found in extracellular virions [52] , indicating that they are both competent for virion formation and so the capsid size is unlikely to be a determinant of virion formation . As uncovered here , the critical roles that the HBc linker plays at multiple stages of HBV replication , which have been thought to involve only the HBc NTD and/or CTD , emphasize the close and dynamic interactions among all three regions of HBc that together carry out the multiple essential functions of HBc in viral replication . As conformational changes are likely to be associated with NC maturation and envelopment [6 , 30 , 32 , 48 , 53] , further structural studies of the HBc linker mutants that affect various stages of viral replication should provide important insights into the effects of the linker on the conformations of the HBc NTD , CTD , and the NC as a whole and how the conformational effects translate to functional effects on NC maturation and envelopment . The multiple roles of the HBc linker in HBV replication that we uncovered here provide an explanation for the high degree of sequence conservation in this region of HBc . In addition , as the same DNA sequence coding for the HBc linker also codes for the very N-terminal part ( residues 5–14 ) of the viral RT protein , the need to preserve polymerase sequence and functions possibly also has contributed to the conservation of the DNA sequence in this region of the HBV genome . However , as we highlighted recently [54] , the N-terminal sequences of the polymerase are actually not highly conserved and mutagenesis work so far indicates that this region of the polymerase is not essential for any known functions of the polymerase although it does contribute , to some degree , to the polymerase functions in pgRNA packaging and protein-primed initiation of reverse transcription . Thus , it is likely that the preservation of the HBc linker sequence and functions has played a more important role in the DNA sequence conservation of this region of the viral genome . On the other hand , some variations of the linker sequence have been observed [23] . In light of our findings here , future studies to examine the functional effects of the naturally occurring linker variations are warranted . HBc has emerged recently as the primary target , after the HBV RT protein , for developing effective antiviral strategies to clear HBV infection . Almost all agents in development so far are targeted to the NTD [5 , 55 , 56] . Our results here indicate that sequences outside the NTD , including the CTD as well as the linker , could represent important targets for HBc-directed antiviral development . In fact , a small molecule compound has been reported recently that inhibits HBc assembly and functions in a manner that is dependent on sequences in the CTD [57] . Similarly , it may be possible to identify compounds that target the conserved HBc linker region to inhibit multiple steps of HBV replication . Our discovery here of the multiple critical functions of the HBc linker in HBV replication also has broad implications . Thus , linkers connecting protein domains are common occurrences including those in other viral capsid proteins [58] . For the human immunodeficiency virus type 1 ( HIV-1 ) , the linker in its capsid protein has been shown to regulate capsid stability and reverse transcription [59] .
pCI-HBc and -HBc149 expressing the full-length and CTD-deleted HBc have been described before [35] . pCI-HBc149-4R is identical to pCI-HBc149 , except four R residues are added after HBc position 149 ( Fig 1 ) . pCI-HBc140 , -HBc143 , -HBc149/Δ141–144 , -HBc/Δ141–149 , -HBc/Δ141–144 , -HBc/Δ145–149 were derived from pCI-HBc through PCR-mediated mutagenesis for the expression of CTD and/or linker deletion mutants ( Fig 1 ) . Three linker substitution mutants of HBc were also constructed via PCR mutagenesis . The C-terminal seven residues of the linker were randomized in sequence in the mutant LR , or replaced with the seven N-terminal residues of HBc in LN as described before [24] . In the third substitution mutant , LC , the entire linker was replaced with a nine-residue segment from a cellular protein ( cellobiose dehydrogenase ) similar in sequence and predicted structure to the linker [24] ( Fig 1 ) . pSV-HBV1 . 5/C- expresses a HBc-defective HBV genome [30] , which is capable of supporting viral replication upon complementation with HBc . pCMV-HBV expresses the HBV pgRNA from the heterologous cytomegalovirus ( CMV ) immediate early promoter and the HBV surface mRNAs from the endogenous HBV promoter , leading the production of all viral RNAs and proteins required for replication and virion secretion [60 , 61] . A mouse monoclonal antibody ( mAb ) , clone T2221 , against the HBc NTD [39] was purchased from Tokyo Future Style ( Cat no . 2AHC24 ) . The mAb 10E11 against HBc NTD ( residues 2–10 ) [40] was purchased from Abcam ( Cat no . ab8639 ) . The mAb , anti-WHc , specific for the WHc NTD ( likely the first 8 residues ) , is cross-reactive with HBc due to the identity of the very N-terminal HBc and WHc sequences , as reported before [32 , 41] . The HBc CTD-specific mAbs , 25–7 and B701 , have been described recently [18 , 35] . The rabbit polyclonal antibody against HBc were purchased from Dako . The rabbit anti-HBs polyclonal antibody was purchased from Virostat [18] . The anti-preS2 mAb ( Arigo Biolaboratories ) detect the preS2 region that is shared by both the L and M ( but absent from the S ) HBV envelope proteins . HBc expression constructs and/or HBV genomic constructs were transfected into the human hepatoma cell line HepG2 or Huh7 cells ( kindly provided by Christoph Seeger , Fox Chase Cancer Center ) as previously described [42 , 62 , 63] . Briefly , HepG2 cells in 60-mm dishes were transfected with 4 μg of plasmid using FuGENE6 ( Roche ) . Huh7 cells seeded in 60-mm dishes were transfected with 10 μg of plasmid using CalPhos Mammalian Transfection Kit ( Clontech ) . Cells and culture supernatant were harvested on day 7 post-transfection . Cells were lysed with NP40 and HBc proteins in the cytoplasmic lysate were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , transferred to polyvinylidene difluoride ( PVDF ) membrane , and detected by the indicated antibodies as described previously [19 , 32] . Core DNA from NCs was isolated from the cytoplasmic lysate without nuclease digestion and analyzed by Southern blot analysis as described previously [42] . A genome-length , 32P-labeled HBV DNA probe was used to detect the viral DNA replicative intermediates by Southern blot analysis . Native agarose gel electrophoresis of intact NCs from the cytoplasmic lysate , or extracellular viral particles obtained after DNase I digestion of polyethylene glycol ( PEG ) precipitated cell culture supernatant [32] were carried out by using previously reported procedures [18 , 19 , 31 , 32] . Briefly , following transfer to nitrocellulose membrane , viral DNA associated with the particles was detected using a full-length HBV DNA probe , or pgRNA packaged into NCs by using a minus-sense riboprobe . The same membrane was subsequently probed with the indicated HBc or surface specific antibody to detect HBc or surface proteins . The signals from the 32P-labeled RNA probe were quantified using a phosphor imaging system ( GE Healthcare ) . The chemiluminescent signals representing the capsid protein were quantified using the ChemiDoc MP system and BioLab software , as previously described [64] . Densitometry using appropriately exposed films was also used in some cases to quantify the RNA and protein signals . All quantifications were repeated with at least three separate transfection experiments . A TnT-coupled rabbit reticulocyte lysate ( RRL ) in vitro translation system ( Promega ) was used to express the WT HBc or linker deletion/substitution mutants , as described previously [35] . In vitro-translated proteins were analyzed by SDS-PAGE and western blot using the indicated anti-HBc antibodies . | The hepatitis B virus ( HBV ) is a major human pathogen that infects hundreds of millions of people worldwide and represents a major cause of viral hepatitis , liver cirrhosis , and liver cancer . The HBV capsid protein ( HBc ) plays multiple roles in the viral life cycle and has emerged recently as a major target for developing antiviral therapies against HBV infection . HBc is divided into three separate regions , an N-terminal domain ( NTD ) responsible for capsid assembly , a C-terminal domain ( CTD ) that plays critical roles in the specific packaging of the viral pregenomic RNA ( pgRNA ) into replication-competent nucleocapsids and the subsequent reverse transcription of the pgRNA into the viral genomic DNA , and a linker region between the NTD and CTD . In contrast to the prevailing assumption that the linker merely serves to connect the NTD and CTD , we have discovered here that it plays a critical role in almost every stage of HBV replication . The linker likely exerted its pleiotropic effects via affecting the NTD and CTD as well as via direct interactions with other viral factors independent of the NTD or CTD . Our results thus not only deepen understanding of HBc structure and functions but also implicate the linker as a potential novel target for antiviral development against HBV infection . | [
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"re... | 2018 | Multiple roles of core protein linker in hepatitis B virus replication |
Deletion of Severe Acute Respiratory Syndrome Coronavirus ( SARS-CoV ) envelope ( E ) gene attenuates the virus . E gene encodes a small multifunctional protein that possesses ion channel ( IC ) activity , an important function in virus-host interaction . To test the contribution of E protein IC activity in virus pathogenesis , two recombinant mouse-adapted SARS-CoVs , each containing one single amino acid mutation that suppressed ion conductivity , were engineered . After serial infections , mutant viruses , in general , incorporated compensatory mutations within E gene that rendered active ion channels . Furthermore , IC activity conferred better fitness in competition assays , suggesting that ion conductivity represents an advantage for the virus . Interestingly , mice infected with viruses displaying E protein IC activity , either with the wild-type E protein sequence or with the revertants that restored ion transport , rapidly lost weight and died . In contrast , mice infected with mutants lacking IC activity , which did not incorporate mutations within E gene during the experiment , recovered from disease and most survived . Knocking down E protein IC activity did not significantly affect virus growth in infected mice but decreased edema accumulation , the major determinant of acute respiratory distress syndrome ( ARDS ) leading to death . Reduced edema correlated with lung epithelia integrity and proper localization of Na+/K+ ATPase , which participates in edema resolution . Levels of inflammasome-activated IL-1β were reduced in the lung airways of the animals infected with viruses lacking E protein IC activity , indicating that E protein IC function is required for inflammasome activation . Reduction of IL-1β was accompanied by diminished amounts of TNF and IL-6 in the absence of E protein ion conductivity . All these key cytokines promote the progression of lung damage and ARDS pathology . In conclusion , E protein IC activity represents a new determinant for SARS-CoV virulence .
Coronaviruses ( CoVs ) are vertebrate pathogens that cause severe diseases in a wide range of animals and infections in humans that until recently were limited to common colds [1] . Nevertheless , by the end of 2002 , a novel coronavirus causing the severe acute respiratory syndrome ( SARS-CoV ) emerged in China and rapidly spread worldwide causing around 8000 infections leading to death in 10% of the cases [2] , [3] . Since then , CoVs surveillance programs were intensified , and two additional human coronaviruses , already circulating in the human population , were identified as the causative agents of several cases of pneumonia and bronchiolitis ( HCoV-HKU1 and HCoV-NL63 ) [4] . Furthermore , in 2012 a novel coronavirus infecting humans , the Middle East Respiratory Syndrome Coronavirus ( MERS-CoV ) appeared in Saudi Arabia and disseminated to nine additional countries [5] , [6] . To date , 182 cases of MERS-CoV have been reported , which has led to 79 fatalities ( http://www . who . int ) . Clinical presentation of infected individuals involves acute pneumonia , sometimes accompanied by renal disease [7] . CoVs similar to SARS-CoV and MERS-CoV have also been isolated from bats widely distributed throughout the world [8]–[13] , which represents a potential reservoir for outbreaks of novel zoonoses into humans . Therefore , understanding the virulence mechanisms of these pathogens , will allow the development of effective therapies in order to prevent and control future outbreaks . SARS-CoV is an enveloped virus containing a positive sense RNA genome of 29 . 7 kb , one of the largest viral RNA genomes known . The genome encodes a viral replicase involved in the synthesis of new genomes and in the generation of a nested set of subgenomic messenger RNAs , encoding both structural proteins present in all CoVs: Spike ( S ) , Envelope ( E ) , Membrane ( M ) and Nucleoprotein ( N ) , and a group of proteins specific for SARS-CoV: 3a , 3b , 6 , 7a , 7b , 8a , 8b , and 9b [14] . SARS-CoV E protein is a 76-amino acid transmembrane protein actively synthesized during viral infection , that mainly localizes at the ERGIC region of the cell , where virus budding and morphogenesis take place [15]–[18] . Different requirements of E protein during the virus cycle have been described among CoVs . Elimination of E gene in transmissible gastroenteritis coronavirus ( TGEV ) or MERS-CoV leads to a replication-competent propagation-deficient phenotype [19]–[21] . In contrast , deletion of E gene from mouse hepatitis virus ( MHV ) or SARS-CoV does not abolish virus production , although viral titers are significantly reduced by 1000 to 20-fold , respectively [16] , [22] . Interestingly , E gene deleted SARS-CoV ( SARS-CoV-ΔE ) was attenuated in three animal models , and confers protection against challenge with parental virus in immunized hamsters , and in young or aged mice , representing a promising vaccine candidate [16] , [23]–[27] . Cells infected with SARS-CoV-ΔE show increased stress and apoptotic markers compared to wild type virus , perhaps resulting in a decreased productivity of infection [28] . Additionally , elimination of the E gene diminishes inflammation induced by SARS-CoV through the NF-κB pathway [27] . Remarkably , SARS-CoV E protein was found to self-interact forming a pentameric structure that delimits an ion conductive pore , which may play a role in virus-host interaction [29]–[32] . E protein ion conductivity was also confirmed for a set of CoVs from different genera [33] . The ion channel ( IC ) activity of SARS-CoV E protein was mapped within the transmembrane domain of the protein by using synthetic peptides [31] , [34] , [35] . Recent studies determined that both ion conductance and selectivity of E protein ion channel were highly controlled by the charge of the lipid membranes in which the pores were assembled . This suggests that lipid head-groups are components of the channel structure facing the lumen of the pore , a novel concept for CoV E protein ion channel [34] , [36] . Chemically synthesized SARS-CoV E protein showed slight preference for cations over anions when reconstituted in lipids that mimicked both charge and composition of ERGIC membranes , and displayed no specific selectivity for a particular cation [34] , [36] . In addition , point mutations that suppressed SARS-CoV E protein IC activity ( N15A and V25F ) have been identified and confirmed [34] , [35] . Several reports have analyzed the relevance of CoV E protein transmembrane domain , which contains ion-conduction properties , in virus maturation and production . Insertion of alanine residues within the transmembrane domain of MHV E protein rendered crippled viruses that evolutionary reverted to restore a proper structure of the alpha helix within the transmembrane domain [37] . Interchanging the genus β CoV MHV E protein transmembrane domain by those of CoVs from different genera revealed that only domains belonging to genus β , and γ , but not α , functionally replaced MHV E transmembrane domain in terms of viral production . It was speculated that this effect was a consequence of the possible different ion selectivity of these domains [38] . Replacement of genus γ CoV infectious bronchitis virus ( IBV ) E protein transmembrane domain , which displays IC activity , for vesicular stomatitis virus ( VSV ) G protein transmembrane domain lacking this function , interfered with an efficient trafficking and release of the viral progeny in the infected cells [39] . In contrast , mutation of threonine at position 16 to alanine , which is the amino acid change predicted to inhibit IC activity in IBV E protein did not affect virus-like particles formation , suggesting a multifunctional role of E protein [40] . Besides the E protein , SARS-CoV encodes two other ion-conducting proteins , 3a and 8a [41] , [42] . In a related virus , human coronavirus 229E ( HCoV-229E ) , novel IC activity has been described within the 4a protein [43] . The abundance and conservation of IC activity suggests an importance of influencing ion homeostasis within cells during the CoV infection cycle . Modulation of the cellular ion balance seems to be a common issue for viruses , as a growing list of viroporins are being identified , especially within RNA viruses [44] . Highly pathogenic human viruses such as influenza A virus , human immunodeficiency virus ( HIV ) , hepatitis C virus ( HCV ) and several picornaviruses , among others , encode at least one viroporin [45]–[49] . Viroporins have been involved in virus entry , trafficking , morphogenesis , maturation and even virulence [50]–[53] . Influenza virus M2 is essential for viral RNA release from infections virions within the endosome into the cell cytoplasm [45] and also for raising the pH at the trans-Golgi network lumen , which prevents premature activation of hemaglutinin , which may render non-infectious virions [54] . Similarly , HCV p7 protein equilibrates the pH at the Golgi apparatus , protecting acid-sensitive intracellular virions [51] . Coxsackievirus 2B protein alters Golgi and endoplasmic reticulum ( ER ) Ca2+ and H+ concentrations , which in turn delay protein transport through the secretory pathway facilitating virus assembly and preventing major histocompatibility complex ( MHC ) molecules from reaching the cell surface [48] , [55] , [56] . A recent finding described that influenza M2 protein IC activity triggers NOD-like receptor family , pyrin domain containing 3 ( NLRP3 ) inflammasome activation [52] . Furthermore , mutant versions of M2 protein that conduct Na+ and K+ ions apart from H+ ions more strongly elicited the inflammasome response [52] . This novel mechanism of immune system activation has also been proven for other viroporins [53] , [57]–[59] . Viral proteins with IC activity impact different aspects of the virus life cycle , however , the involvement of their IC activity in pathogenesis remain to be further explored . Previous findings demonstrated that SARS-CoV E protein is a virulence determinant . In this manuscript we analyze the contribution of E protein IC activity in pathogenesis . Two recombinant viruses , each one containing a single point mutation suppressing IC activity , were generated by reverse genetics . Mutant viruses showed a tendency to evolve and restore E protein IC architecture and activity after serial infections , and viruses with deficient IC activity were outcompeted by those displaying this function after co-infections . This highlights the importance of IC activity in virus fitness . Interestingly , infection of mice with a set of viruses lacking or displaying E protein IC activity , revealed that the activation of inflammasome pathway , and the exacerbated inflammatory response induced by SARS-CoV was decreased in infections by on channel deficient viruses . In addition , less lung damage and proper localization of Na+/K+ ATPase within epithelia , which prevents edema accumulation , was detected for the mice infected with the viruses lacking E protein IC activity . As a consequence , increased survival of the infected animals was observed when E protein ion conductivity was absent . Therefore , E protein IC activity is required for inflammasome activation and a novel determinant for the virulence of highly pathogenic SARS-CoV .
Deletion of SARS-CoV E gene resulted in a virus that was attenuated in three animal models , as we have previously shown [16] , [23] , [24] , [26] , [27] . E gene codes for the small multifunctional E protein , which displays IC activity [31] , [34]–[36] . To specifically test the relevance of IC activity in virus virulence , residues involved in E protein ion conductance were firstly identified . To this end a set of synthetic peptides representing the transmembrane domain of E protein were evaluated for their IC activity . These peptides contained point mutations that affect different conserved residues , or residues predicted to face the lumen of the channel pore [34] . Mutations N15A and V25F within the transmembrane domain of E protein completely disrupted IC activity [34] , [35] . Accordingly , two recombinant viruses containing each of these two changes in the E gene , rSARS-CoV-E-N15A ( N15A ) and rSARS-CoV-E-V25F ( V25F ) , were engineered ( Fig . 1 ) . A SARS-CoV with a mouse adapted ( MA15 ) genetic background [27] , [60] was used to generate these viruses , as infection of mice with SARS-CoV MA15 accurately reproduces the symptoms of human disease [27] , [60] . The mutant viruses were efficiently rescued , cloned by three rounds of plaque purification , and their sequence was confirmed ( data not shown ) . To test whether the introduced mutations may alter E protein subcellular localization affecting other functions of the protein , Vero E6 cells were infected with the wt virus , the viruses lacking IC activity ( N15A and V25F ) or a virus missing E gene ( ΔE ) as a control . Immunofluorescence analysis showed similar colocalization patterns of E protein and ERGIC , the subcellular compartment where E protein mainly accumulates during infection , for both the wt and the mutant viruses ( Fig . 2A ) , indicating that other functions of E protein associated with its localization are most likely not affected . Deletions or mutations within the E gene of several CoVs sometimes led to crippled viruses or to lower virus yields [16] , [20]–[22] , [37] , [39] . To test whether inhibition of E protein IC activity affects virus production , growth kinetics were performed in the monkey Vero E6 and mouse DBT-mACE2 cells [61] . Minor differences in growth rates were observed between the parental virus ( wt ) , that contains E protein IC activity , and the mutant viruses that lack E protein IC activity ( Fig . 2B ) , indicating that this function was not essential for virus growth in cell culture . More striking differences in plaque phenotypes were observed . Mutant viruses lacking E protein IC activity , apparently formed smaller plaques than wt virus , and V25F virus plaques were smaller than N15A virus ( Fig . 2C ) . A possible explanation for all these data could be that infection foci productivity and area may be quite similar regardless of E protein IC activity , as determined by viral titration , but higher cytopathic effect may be induced when E protein IC is present , rendering bigger plaques . Elimination of full-length E protein induced more severe growth defects ( Fig . 2B and Fig . 2C ) , suggesting that other functions of the protein contributing to virus production , apart from IC activity , may be affected . Inhibition of E protein IC activity slightly reduced virus production in cell culture in a relatively short period of time , but these differences were not significant . To further explore whether ion conductivity could improve viral growth and fitness , a long-term competition assay was performed between the wt virus and the N15A mutant lacking IC activity , that was relative stable through passages as will be described below . Vero E6 cells were co-infected with N15A mutant and the wt virus in a proportion 7∶3 , and the supernatant was serially passaged for 20 times every 24 hours . The E gene was sequenced every 4 passages , revealing that the proportion of wt virus steadily increased over the passages , accompanied by a decrease in the abundance of the N15A mutant . From passage 8 on , the wt virus took and maintained majority over the N15A mutant ( Fig . 3 ) . These results suggested that E protein IC activity for SARS-CoV confers a selective advantage improving virus production . To specifically analyze the contribution of E protein IC activity to SARS-CoV virulence , BALB/c mice were intranasally inoculated with the wt virus displaying E protein IC activity , or three independently-isolated clones of the mutant viruses N15A and V25F lacking E protein IC activity , and mice were monitored daily for 10 days ( n = 5/virus clone ) . All infected animals showed disease symptoms at 2 days post infection ( dpi ) , reflected by slower movements and ruffled fur ( data not shown ) . Mice infected with the wt virus started to lose weight by day 2 , and by day 5 all of them died ( Fig . 4 ) . Interestingly , although mice infected with the three clones of N15A mutant started to lose weight in a similar fashion , at day 4 almost all of them started to regain weight , recover from the disease , and 80–100% survived ( Fig . 4 ) . In contrast to N15A , mice infected with V25F virus experimented similar weight losses and survival rates ( from 0 to 20% ) than the wt virus ( Fig . 4 ) . A possible explanation for this apparent discrepancy was the reversion of the introduced mutation or the incorporation of compensatory mutations restoring E protein IC activity . To test whether this was the case , total RNA was collected from the lungs of infected mice at 2 and 4 dpi or from the lungs of mice that died after infection . The virus genome region containing E gene was sequenced , as it was the target of the point mutations inhibiting IC activity , and therefore a likely place to incorporate compensatory mutations . E genes from wt virus and N15A mutant virus remained stable during the course of the experiment , since no changes were found in viral RNA extracted either from lungs of several mice at 2 and 4 dpi or from dead mice ( Fig . 5A ) . In contrast , V25F viruses incorporated mutations in the E gene that led to amino acid changes either in the same position of the mutation that abolished IC activity ( F25C ) or in relatively close positions within the E protein transmembrane domain: L19A , F20L , F26L , L27S , T30I and L37R ( Fig . 5A ) . These evolved variants of the V25F virus appeared as early as 2 days after mice infection and , in some cases ( T30I mutant ) , completely overgrew the original virus by day 2 . The tentative compensatory mutations were also present in the viral population at 4 dpi and in dead mice ( Fig . 5A ) . Overall , the data obtained with wt and N15A viruses , which were genetically stable throughout the experiment , suggest that E protein IC activity is required for a virulent phenotype . To further analyze the evolution of the mutant viruses lacking E protein IC activity , two clones of the mutants N15A and V25F were serially passaged in cell culture . Throughout the 24 serial passages , E gene was sequenced at passages 0 , 8 , 16 and 24 for the two mutant viruses and wt as control . As observed during in vivo infection , the wt virus remained stable during the passages ( Fig . 5B ) . V25F viruses rapidly incorporated additional mutations within E gene ( L19A , L27S and T30I ) , reproducing our in vivo observations . The viruses incorporating T30I mutation completely out-competed the original V25F mutant by passage 8 ( Fig . 5B ) . In contrast , N15A viruses either remained stable or incorporated a mutation in the E gene ( A15D ) that appeared late , at passage 24 , suggesting that this mutant was more stable , confirming our in vivo results ( Fig . 5B ) . The data obtained in cell culture or after mice infection indicate that SARS-CoVs lacking E protein IC activity incorporated mutations at the E gene that directly reverted the original mutation that suppressed IC activity ( A15D and F25C ) or modified residues mapping to a close position of the E protein transmembrane domain . These modified residues face the original mutation inhibiting IC activity , when the ion channel is assembled ( Fig . 6 ) . To analyze whether these mutations restored IC activity , synthetic peptides representing the E protein transmembrane domain containing the mutations obtained after viral evolution in vivo and in cell culture ( N15D , V25L , V25F L19A , V25F F26C , V25F L27S , V25F T30I , V25F L37R ) , were synthesized . The IC activity of these peptides was evaluated in artificial lipid membranes as previously described [34] . Whereas peptides containing the original mutations N15A and V25F did not show any conductance , all the peptides containing the mutations obtained after viral evolution displayed similar conductance values than a wild type peptide ( Fig . 7 ) , indicating that all these compensatory mutations restored E protein IC activity . A correlation between IC activity and virulence was found in vivo , where N15A viruses lacking IC activity were attenuated compared to wt virus competent in IC activity . Mutant virus V25F , originally lacking ion conductivity , rapidly incorporated compensatory mutations upon infection in vivo that restored IC activity and thus caused pathogenicity . To test whether the recovery of IC activity was the unique determinant of virulence , and to rule out effects of other mutations arising outside of the E gene , recombinant viruses containing a set of the compensatory mutations that restored IC activity ( rSARS-CoV-EICrev ) : rSARS-CoV-E-V25F L27S ( V25F L27S ) , rSARS-CoV-E-V25F T30I ( V25F T30I ) , rSARS-CoV-E-V25F L37R ( V25F L37R ) were engineered , rescued and tested in mice . These viruses were virulent in mice in terms of weight loss and survival rates , causing similar disease as that caused by the wt virus ( Fig . 8 ) . We sought to confirm this data on another genetic background , so a recombinant SARS-CoV containing the mutation that restored IC activity in N15A mutant after cell culture passage was engineered rSARS-CoV-E-N15D ( N15D ) and evaluated . In agreement with the V25F revertants , the mutant N15D induced similar morbidity and mortality as wt ( Fig . 8 ) , confirming that E protein IC activity is a determinant of virus pathogenesis . Although E protein IC activity is not essential for virus growth in cell culture ( Fig . 2B ) , it is possible that production of virus in vivo further depends on ion conductivity . To test if the attenuation observed in vivo with IC inactive viruses is due to lower virus production , 16 week-old BALB/c mice were intranasally inoculated with the wt virus , the genetically engineered revertant viruses N15D and V25F T30I displaying IC activity , or the N15A mutant lacking IC activity . Mice lungs were collected at 2 and 4 days post infection , homogenized , and viral titers were determined . Interestingly , the virus lacking IC activity ( N15A ) grew to the same extent or even better than the wt and the revertant viruses , respectively , reaching titers higher than 108 and 107 PFU/gr of lung tissue at 2 and 4 dpi , respectively ( Fig . 9 ) . These data indicate that E protein IC activity does not significantly affect virus production in vivo , under these experimental conditions . Therefore the attenuation of the virus lacking IC activity is likely due to a host-specific effect mediated by the ion channel in the mouse , and not to a reduction in virus yields . To analyze the mechanisms by which IC inactivity confers less virulence , lung sections of mock-infected mice , or of those infected with the wt virus , IC revertants and N15A mutant were collected at 2 and 4 dpi , stained with hematoxylin and eosin and examined for histopathological changes . Mock-infected animals showed wide free alveolar and bronchiolar airways and no evidence of leukocyte infiltrates ( Fig . 10A ) . Animals infected with the viruses displaying IC activity , presented swollen alveoli walls and leukocyte infiltrates in the infected areas at both time points ( Fig . 10A ) . The histopathology caused by IC proficient viruses was even more dramatic at 4 dpi , where cell infiltrates were more abundant , and air spaces were collapsed by a profuse lung edema , which is the ultimate cause of acute respiratory distress syndrome ( ARDS ) that leads to lung failure and death ( Fig . 10A ) . Edema accumulation at 4 dpi was also reflected by a marked increase ( >1 . 5 fold ) in the weight of lungs in animals infected with viruses competent in E protein ion conductivity ( Fig . 10B ) . In contrast , mice infected with the virus lacking IC activity ( N15A ) showed moderate swollen lung epithelia and lung infiltrates that reflected a productive viral infection . However , at 4dpi , lung airways remained free from pulmonary edema , reflected by both the lung sections and in the minimal change of lung weight ( Fig . 10A and 10B ) . Such moderate changes in the lung may retain efficient oxygen exchange . These data suggested that E protein IC activity contributes to SARS-CoV induced lung edema . ARDS caused by SARS-CoV infection originates from the accumulation of a protein rich edema , leading to severe hypoxemia and eventually to death . Lung epithelial cells create an osmotic gradient between airways and lung interstitium controlling water levels within air spaces . Damage to the epithelium is therefore a major cause of edema accumulation . To test the correlation between presence of E protein IC activity and an increase in epithelial damage leading to edema accumulation , lungs from mock-infected and from mice infected with the wt or the N15A virus were processed at 2 and 4 dpi for immunofluorescence . Epithelium integrity was evaluated using a specific antibody for Na+/K+ ATPase , a key factor in establishing the osmotic gradient necessary for edema clearance , and infection was tracked using an antibody specific for N protein . At 2 dpi many infected cells ( around 16% ) were observed in lungs of mice infected with either wt or N15A virus ( Fig . 11A and S1 ) , overlapping with the most productive time of viral infection . Both viruses presented similar cell tropisms within lungs , infecting bronchiolar epithelium ( between 60–70% of the cells ) and alveolar epithelium ( around 10% of the cells ) ( Fig . 11A and S1 ) . Viral infections caused cell death leading to desquamation , especially at the bronchiolar barrier ( Fig . 11A ) . At 4 dpi the number of infected cells was dramatically reduced ( close to 1% ) ( Fig . 11B and S1 ) , accompanying viral titer decrease . Interestingly , wt infected mice showed abundant epithelia disassembly at this time point , especially in the bronchioles . Na+/K+ ATPase was mislocated from its basolateral position within the plasma membrane of epithelial cells as a consequence of bronchiolar barrier destruction , and detected in desquamated cells or cell debris present at air spaces ( Fig . 11B ) , where edema accumulation was also observed ( Fig . 10 ) . The removal of Na+/K+ ATPase from its native position within the epithelial barrier most likely prevented its function in edema clearance . In contrast , animals infected with N15A mutant , presented less damaged epithelia and Na+/K+ ATPase location was not disturbed ( Fig . 11B ) , which may allow edema resolution , as no accumulation of protein rich edema was observed under these conditions ( Fig . 10 ) . Severe damage caused to the epithelial barrier is associated with an acute inflammatory response in the lung parenchyma along with edema accumulation . Elevated levels of inflammatory cytokines IL-1β , TNF and IL-6 are found in the lungs of ARDS patients and play a key role in the progression of the disease [62] . IL-1β is an early response highly inflammatory cytokine that is tightly regulated . During viral infection , recognition of pathogen molecular associated patterns ( PAMPs ) by the cells , such as double stranded viral RNA , induces IL-1β mRNA expression and translation to generate the inactive form of the protein pro-IL-1β . Upon certain stimuli , pro-IL-1β is then cleaved by caspase-1 through inflammasome activation , generating the active form IL-1β , which is subsequently secreted to exert its function [52] . Interestingly , viral proteins with IC activity have been recently found to activate the inflammasome , which finally leads to the secretion of active IL-1β to the extracellular media [52] , [59] . We thus sought to test whether E protein IC activity was implicated in the production of active IL-1β in the lungs of SARS-CoV infected mice . First , the expression of pro-IL-1β mRNA and the amounts of its derived protein , inactive pro-IL-1β , were measured in wt- and N15A-infected mice at 2dpi . Infections with both wt and N15A mutant viruses induced similar overexpression of pro-IL-1β mRNA as compared with the mock-infected animals ( Fig . 12A ) . The increased levels of pro-IL-1β mRNA found in infected mice , correlated with enhanced amounts of inactive pro-IL-1β , which reached similar values in wt and N15A infections ( Fig . 12B ) . To analyze the levels of active , secreted IL-1β , bronchoalveolar lavages were performed at 2 dpi . The amount of IL-1β in the airways was significantly higher in the mice infected with the wt virus displaying E protein IC activity , over those infected with the mutant N15A missing this function ( Fig . 12C ) . Collectively , these data indicated that E protein IC activity promotes the secretion of mature IL-1β , without increasing pro-IL-1β transcription or synthesis . IL-1β enhances the production of TNF , another key early response cytokine , and IL-6 , which follows a more sustained increase over time [62]–[64] . Therefore , it is not surprising that both TNF and IL-6 levels were more dramatically increased in wt-infected mice in comparison with the N15A-infected mice at 2 dpi ( Fig . 12D and 12E ) . Furthermore , analysis of IL-6 levels in the bronchoalveolar lavages of infected mice at 4 dpi , revealed that overwhelming amounts of this cytokine , exceeding 10000 pg/mL , accumulated in wt-infected mice , whereas IL-6 levels were at least 2 . 5-fold lower when E protein IC activity was absent during infection ( Fig . 12E ) . All these results indicate that the presence of E protein IC activity correlates with the activation of the inflammasome and an acute inflammatory response that is deleterious for lung tissue .
Several viruses that cause severe diseases in humans encode small transmembrane proteins containing IC activity [44] . The alteration of host cell ion balance by these proteins is usually necessary for virus production and maturation , but the effect of IC activity in pathogenesis is less well understood . Coronaviruses are the causative agent of recent and likely future serious diseases . We have focused this study on SARS-CoV E protein , a virulence determinant displaying IC activity . In this manuscript , we sought to elucidate the role of E protein IC activity in virus pathogenesis by combining our knowledge of residues essential for E protein ion conductivity with the manipulation of SARS-CoV genome . To this end we used a mouse adapted genetic background ( MA15 ) assembled in a bacterial artificial chromosome ( BAC ) . Two rSARS-CoVs , each one containing mutation N15A or V25F in the transmembrane domain of E protein were generated to knock down its IC activity . Upon competition during several passages , the viruses lacking E protein IC activity were clearly overgrown by the parental virus , which replicated better . Nevertheless , these differences in viral growth needed several replication cycles to be amplified and detected , as only slight no significant changes in virus production were observed after 72 hours growth kinetics . In agreement with this result , when T16A mutation was introduced within IBV E protein , which represents the equivalent mutation to SARS-CoV E protein N15A , no alterations in the production of virus like particles ( VLPs ) were detected after 48 hours [40] . The fact that deeper alterations of CoV E protein transmembrane domain cause much more dramatic effects in virus production [37] , [39] may be due to additional structural or functional changes in E protein , besides their effect on ion conductivity . In conclusion , E protein IC activity , although not essential for virus production , confers and advantage to the virus by enhancing its fitness and growth . Accordingly , a selective advantage of IC activity has also been shown for influenza virus . Mutants lacking M2 protein IC activity were overgrown by the parental virus in competition assays in an even faster manner than in SARS-CoV , probably because the influenza virus lacking IC activity has more profound replication defects [65] , [66] . SARS-CoV encodes other two proteins , 3a and 8a , which also contain IC properties [41] , [42] . Therefore , an essential contribution of IC activity to virus production cannot formally be excluded for SARS-CoV , as 3a and 8a derived ion channels could functionally compensate the absence of E protein IC activity . SARS-CoV mutant viruses lacking E protein IC activity showed a clear tendency to revert both in cell culture and in vivo after mice infection . N15A and V25F mutant viruses , devoid of E protein ion conductivity , incorporated additional mutations in the E gene to restore IC activity , suggesting that this function confers a selective advantage to the virus . This trend was more evident in the case of V25F virus , which evolved more quickly and frequently than the N15A mutant virus . No reversion of E protein IC activity was observed for N15A mutant in mice , at least during the first five days post infection . Attempts to sequence the viral progeny at 9 and 10 days after the inoculation were unsuccessful , probably because the virus was mostly cleared by those time points . Nevertheless , as N15A mutant restored its ion channel activity after long number ( >24 ) of passages in cell culture , it is possible that after serial passages in vivo this mutant could also revert , as ion conductivity confers better fitness for the virus . Although both N15A and V25F mutations equally disrupted IC activity , the mechanisms by which this is achieved could be different . Replacement of N at position 15 to A , an amino acid predicted to be located facing the channel lumen , is not likely to affect the channel architecture . In fact , the rotational orientation in lipid bilayers of a labeled synthetic transmembrane peptide bearing this mutation was entirely consistent with that of a pentameric model [29] . In contrast , mutation at V25 implies the introduction of a larger side chain ( replacement of V to F ) at the monomer-monomer interface , which is likely to affect the overall structure of the homo-oligomer and therefore inhibit ion conductivity by causing larger structural changes . This may also explain the higher number of compensatory mutations found in V25F with respect to N15A virus , as the ways to recover a stable oligomer are more varied than those needed to recover channel activity . The compensatory mutations incorporated by the V25F mutant mapped to the opposite face of the transmembrane helix , although they are adjacent when the E protein pentamer is formed . Therefore , the compensatory mutations most likely restored the interaction and assembly between the mutated monomers , reinforcing our hypothesis ( Fig . 6 ) . IC activity restoration through virus passage suggests that this function is important for the virus . Several of the mutations restoring ion channel activity appeared both in mice and in cell culture . Therefore , it seems that reverting E protein ion conductivity , and not adaptation to mice , was its main goal . Nevertheless , the possibility that these mutations could also improve mouse adaptation through an ion channel dependent or independent mechanism cannot be fully excluded . E protein IC activity was also involved in SARS-CoV pathogenesis as tested in the mouse model . Viruses lacking IC activity that were stable during multiple passages ( N15A mutants ) caused reduced mortality , whereas the wt and the mutant viruses restoring IC activity during mice infection ( V25F background-evolved variants ) caused high mortality rates . Furthermore , genetically engineered viruses containing the point mutations necessary to recover E protein IC activity induced similar mortality as wt virus , reinforcing that E protein IC activity contributes to SARS-CoV pathogenicity . The relevance of viroporins in virus virulence has also been shown in other viruses , such as respiratory syncytial virus SH protein , influenza A virus M2 protein and classical swine fever virus p7 protein [67]–[70] , by deleting a large fraction of or the entire protein . Viroporins may play other critical functions apart from ion conduction . Therefore , a direct correlation between IC activity and virulence could not be formally established . To our knowledge , this is the first time in which the IC activity of a viroporin is directly linked to the virulence of the virus . The infection with highly pathogenic respiratory viruses , including SARS-CoV , is one of the causative agents of acute lung injury ( ALI ) and its most severe form , ARDS [71] . Just in the United States , 200 , 000 ARDS cases are reported annually with a 40% mortality rate [72] . Late stages of ARDS are characterized by development of pulmonary edema that leads to an impaired gas exchange , hypoxemia and eventually death . Infection of mice with rSARS-CoV-MA15 resulted in an abundant edema accumulation both in alveolar and bronchiolar spaces at late times post infection ( 4 dpi ) , which correlated with mortality . This phenotype was reproduced upon mice infection with other highly-virulent SARS-CoVs displaying IC activity based on alternative E protein sequences ( revertant viruses ) . On the other side , infection of mice with the attenuated mutant lacking E protein IC activity ( N15A ) caused significantly reduced edema accumulation , likely contributing to a majority of the animals surviving . Collectively , these data indicate that E protein IC activity in vivo promotes lung pathology through edema accumulation . The pulmonary epithelia regulate water levels present within air spaces , a critical parameter for gas exchange , and play a critical role in edema clearance [72] , [73] . Epithelial cells create an osmotic gradient mainly through a coordinated Na+ transport first from the airways to the cell cytoplasm through epithelial sodium channels ( ENaC ) , located at the apical part of the plasma membrane , and then to the interstitium by Na+/K+ ATPase , present at the basolateral region of the plasma membrane . This vectorial transport of Na+ is accompanied by a water removal from the airspace and edema resolution [72] , [73] . The integrity of alveolar and bronchiolar epithelia was analyzed by labeling of Na+/K+ ATPase in the lungs of mice infected with the virus containing or lacking IC activity . Interestingly , animals infected with the wt virus presented a strong disassembly of bronchiolar epithelia and mislocalization of Na+/K+ ATPase from its basolateral distribution within cells at late times , coincident with edema accumulation . In contrast , epithelia integrity was clearly preserved in the lungs of animals infected with the virus missing E protein IC activity . Intact lung epithelia may be required for proper function of the main components involved in edema resolution ( Na+/K+ ATPase and ENaC ) , which may explain the lack of edema and therefore the attenuation observed for this virus . As previously described for SARS-CoV , differences in viral tropism within lung cells , without affecting viral production , can induce different pathologies [74] . Nevertheless , we have observed no significant differences in the infection patterns in the presence or absence of E protein ion channel activity , suggesting that the virulence conferred by E protein IC activity does not depend on alternative tropisms . Pulmonary epithelia damage leading to ALI and ARDS is a consequence of a cytokine burst initiated , in this case , by viral infection . One of the key early-response cytokines driving proinflammatory activity in bronchoalveolar spaces is IL-1β [75] . IL-1β is mainly produced by macrophages and dendritic cells through inflammasome activation . Ion imbalances within cells have been described as triggers of this pathway [52] . The levels of active IL-1β secreted to the airways were enhanced when E protein IC activity was conserved in SARS-CoV infection . Taking into account that the presence or absence of SARS-CoV E protein IC activity did not interfere with the production of IL-1β precursors ( mRNA and protein levels of pro-IL-1β ) , these results suggest that E protein ion conduction may induce inflammasome triggering resulting in secretion of mature IL-1β . In agreement with this hypothesis , release of active IL-1β has recently been reported for viroporins of other viruses [52] , [53] , [57] , [59] . IL-1β is implicated in the development of diverse pathologies , including obesity , atherosclerosis , diabetes and several pulmonary illnesses such as asthma , pulmonary obstructive chronic disease and ARDS progression through edema accumulation [75]–[78] . Here , we report the implications of this cytokine in SARS-CoV pathology and E protein ion channel activity as a trigger of its production . ARDS progression involves the production of TNF , another early response cytokine , and IL-6 , which exerts its function in a more sustained manner accumulating during the disease [63] . We found that after SARS-CoV infection , these patterns of cytokine expression were clearly reproduced . TNF and IL-6 accumulated to higher levels in the lungs of animals infected with the wt virus displaying IC activity compared to the mutant lacking ion conductivity . IL-1β enhances the production of TNF , and IL-6 is stimulated by both cytokines providing an integrated amplified inflammatory response , detrimental for pulmonary function [63] . Elevated amounts of these cytokines have been reported in bronchoalveolar lavages of SARS-CoV patients [79] . Therefore , the enhanced amounts of active IL-1β found in the animals infected with the wt virus may explain the increased levels of TNF and IL-6 , which leads to severe pathology . It is important to note that the increased damage found in pulmonary epithelia infected with the virus displaying E protein IC activity may not be explained by a higher virus production , as suppression of E protein IC activity rendered similar growth in mice lungs during the analyzed time points . SARS-CoV early replication may be a relevant issue in the induction of pathology . We cannot exclude early replicative defects for the N15A mutant in mice , delaying virus growth during the first hours post-infection . Nevertheless , alternative explanations are also possible , as it has been described that some mutations at SARS-CoV S gene conferred increased virulence without affecting growth within mice , even at early times . This increased pathogenesis was mainly dependent on an exacerbated host response to the viral infection [74] . Accordingly , the enhanced inflammatory response triggered by E protein ion channel proficient viruses may be a major pathology inducer . In this study , we have shown that SARS-CoV E protein IC activity is a virulence determinant , influencing inflammatory responses , including those inflammasome-derived , pulmonary damage and disease outcome . Although not essential for virus production , E protein IC activity confers a selective advantage , as the parental virus , competent for ion conductivity , was more fit . Nevertheless , the virulence associated to E protein ion conductivity could represent a non-selectable consequence . SARS-CoV crossed species barriers from zoonotic reservoirs such as bats , palm civets and raccoon dogs to humans , causing a severe disease [71] . Possibly , in SARS-CoV infection of its natural hosts , E protein ion channel activity may not have a relevant impact in SARS-CoV pathogenesis , and therefore it was positively selected before crossing species barrier . In conclusion , this work provides several findings that may have translational relevance for other coronaviruses , such as the highly pathogenic MERS-CoV , and even on other viruses encoding proteins with IC activity .
Animal experimental protocols were approved by the Ethical Committee of The Center for Animal Health Research ( CISA-INIA ) ( permit numbers: 2011-009 and 2011-09 ) in strict accordance with Spanish national Royal Decree ( RD 1201/2005 ) and international EU guidelines 2010/63/UE about protection of animals used for experimentation and other scientific purposes and Spanish national law 32/2007 about animal welfare in their exploitation , transport and sacrifice and also in accordance with the Royal Decree ( RD 1201/2005 ) . Infected mice were housed in a ventilated rack ( Allentown , NJ ) . The African green monkey kidney-derived Vero E6 cells were kindly provided by Eric Snijder ( Medical Center , University of Leiden , The Netherlands ) . The mouse delayed brain tumor cells stably expressing the murine receptor for SARS-CoV ( DBT-mACE2 ) were generated as previously described [61] . Baby hamster kidney cells ( BHK-21 ) were obtained from American Type Culture Collection ( ATCC; CCL-10 ) . Cells were grown at 37°C with an atmosphere of 98% humidity , in Dulbecco's modified Eagle medium ( DMEM , GIBCO ) supplemented with 25 mM HEPES , 2 mM L-glutamine ( SIGMA ) , 1% non essential amino acids ( SIGMA ) and 10% fetal bovine serum ( FBS , Biowhittaker ) . Specific-pathogen-free 8 week-old BALB/c OlaHsd female mice were purchased from Harlan . Mice were maintained for 8 additional weeks in the animal care facility at the National Center of Biotechnology ( Madrid ) . All protocols were approved by the Ethical Review Committee at the center for animal health research ( CISA-INIA ) . For infection experiments , 16-week-old mice females were anesthetized with isoflurane and intranasally inoculated with 100000 PFU of the indicated viruses . All work with infected animals was performed in a BSL3 laboratory ( CISA , INIA ) equipped with a ventilated rack ( Animal transport unit-Bio containment unit , Harvard ) to store the animals during the experiment . An infectious cDNA clone encoding a mouse adapted ( MA15 ) SARS-CoV assembled in a bacterial artificial chromosome ( BAC ) in our laboratory [26] was used as the background to introduce the mutations that inhibited or restored E protein IC activity . Briefly , DNA fragments representing the nucleotides 26044 to 26779 of SARS-CoV-MA15 genome , flanked by the restriction sites BamHI and MfeI , respectively , were chemically synthesized ( Bio Basic Inc ) . These fragments contained different point mutations within the E gene , that generated amino acid changes inhibiting IC activity: N15A ( AAT to GCC ) and V25F ( GTA to TTC ) or restoring this activity: N15D ( GCC to GAC ) , V25F L19A ( GTA to TTC and CTT to GCA ) , V25F L27S ( GTA to TTC and TTG to TCG ) , V25F T30I ( GTA to TTC and ACA to ATA ) , V25F L37R ( GTA to TTC and CTT to CGT ) . The fragments containing these mutations were digested and exchanged in the original BAC . The genetic integrity of the cloned DNA was verified by restriction analysis and sequencing . BHK cells were grown to 95% confluency in 12 . 5 cm2 flasks and transfected with 6 µg of the infectious cDNA clones and 18 µl of Lipofectamine 2000 ( Invitrogen ) , according to the manufacturer's specifications . At 6 hours post transfection , cells were trypsinized , added to Vero E6 cells confluent monolayers grown in 12 . 5 cm2 flasks and incubated at 37°C for 72 h . Cell supernatants were harvested , passaged once on fresh cells and the recovered viruses were cloned by three rounds of plaque purification following standard procedures . Vero E6 or DBT-mACE2 cells were grown to confluency on 12 . 5 cm2 flasks and infected at a multiplicity of infection ( MOI ) of 0 . 001 . Cells supernatants were collected at 0 , 6 , 24 , 48 and 72 hpi and titrated on Vero E6 cells . For virus titration and plaque detection , supernatants of infected cells were added to confluent monolayers of Vero E6 cells and incubated for 45 min at 37°C . Media was removed and cells were overlaid with DMEM containing 0 . 6% of low melting agarose and 2% of fetal calf serum ( FCS ) . At 72 hpi cells were fixed with 10% formaldehyde and stained with crystal violet . Vero E6 cells were grown to 90% confluency on glass coverslips and infected with rSARS-CoV-ΔE , rSARS-CoV wt , rSARS-CoV-E-N15A and rSARS-CoV-E-V25F at an MOI of 0 . 3 . At the indicated hpi , media were removed and cells were washed twice with PBS and fixed with 4% paraformaldehyde in PBS for 30 min at room temperature . Then , cells were washed twice with PBS and permeabilized for 10 min with 0 . 2% saponin and 10% FBS in PBS . Primary antibody incubations were performed in PBS containing 10% FBS and 0 . 2% saponin for 1 h 30 min at room temperature . Immunofluorescence was performed using a mouse mAb specific for ERGIC53 ( dilution 1∶200 , Alexis Biochemicals ) , and a rabbit pAb specific for E protein [15] at 1∶2000 dilution . Coverslips were washed four times with PBS between primary and secondary antibody incubations . Alexa 488- or Alexa 546-conjugated antibodies specific for the different species ( dilution 1∶500 , Invitrogen ) were incubated for 45 min at room temperature in PBS containing 10% FBS . Nuclei were stained using DAPI ( dilution 1∶200 , Sigma ) . Coverslips were mounted in ProLong Gold anti-fade reagent ( Invitrogen ) and examined on a Leica SP5 confocal microscope ( Leica Microsystems ) . Colocalization studies were performed using Leica LAS AF v2 . 6 . 0 software . The genomic region including nucleotides 26017 to 26447 that contains the E gene was sequenced after RT and PCR reactions . Briefly , total RNA from infected cells or homogenized mice lungs , was collected and purified using RNeasy kit ( Qiagen ) according to the manufacturer's specifications . For RT reaction , 1 µg of RNA was used as template , random oligonucleotides primers and Thermoscript reverse transcriptase ( Invitrogen ) . The product was subsequently subjected to a PCR reaction using the oligonucleotides E-VS ( CTCTTCAGGAGTTGCTAATCCAGCAATGG ) and E-RS ( TCCAGGAGTTGTTTAAGCTCCTCAACGGTA ) and the Vent polymerase ( New England Biolabs ) , following manufacturer's recommendations . Sequence assembly and comparison with the consensus sequence of SARS-CoV-MA15 strain were performed with the SeqMan program ( Lasergene , Madison , WI ) . Confluent monolayers of Vero E6 cells grown in 12 . 5 cm2 flasks were infected at an MOI of 0 . 5 with the viruses rSARS-CoV wt , rSARS-CoV-E-N15A and rSARS-CoV-E-V25F . At 24 hpi , supernatants were collected and passaged on fresh monolayers of Vero E6 cells , performed 24 times , serially . E gene sequence was analyzed at passages 0 , 8 , 16 and 24 as described . For in vivo experiments , mice were intranasally inoculates with 100000 PFU of the viruses described above . Lungs were collected at days 2 and 4 post infection and incubated in RNAlater ( Ambion ) at 4°C for 48 hpi prior to −80°C freezing . To extract total RNA , lungs were homogenized in 2 ml of RLT lysis buffer ( QIAGEN ) containing 1% β-mercaptoethanol using gentleMACS Dissociator ( Miltenyi Biotec ) . Samples were centrifuged at 3000 rpm during 10 min , and RNA was purified from supernatants using RNeasy kit ( QIAGEN ) as previously described . Synthetic peptides representing the transmembrane domain of SARS-CoV E protein ( amino acids 7 to 38 ) encoding the point mutations that appeared after serial infections of the mutant viruses were generated by standard phase synthesis , purified by HPLC and their IC activity was tested in artificial lipid membranes , as previously described [34] . Total RNA from co-infected cells was isolated and E gene was sequenced as described above . Relative abundance of the rSARS-CoV wt and rSARS-CoV-E-N15A viruses within viral population was determined by quantifying the relative amounts of their respective E gene genetic markers in the sequence obtained from the population . 16 week-old BALB/c mice females were intranasally inoculated with 100000 PFU of the viruses rSARS-CoV wt , rSARS-CoV-E-N15A , rSARS-CoV-E-V25F , rSARS-CoV-E-N15D , rSARS-CoV-E-V25F L27S , rSARS-CoV-E-V25F T30I and rSARS-CoV-E-V25F L37R in 50 µl of DMEM containing 2% FCS . Weight loss and survival of the infected mice were monitored for 10 days . Animals reaching weight losses higher than 25% of the initial body weight were sacrificed according to the established euthanasia protocols . Mice were inoculated with 100000 PFU of the virus rSARS-CoV wt , rSARS-CoV-E-N15A , rSARS-CoV-E-N15D and rSARS-CoV-E-V25F T30I , sacrificed at days 2 and 4 post infection , and lungs were collected . To analyze viral growth , right lungs were homogenized in 2 ml of Phosphate Buffered Saline ( PBS ) containing 100 UI/ml penicillin , 100 µg/ml streptomycin , 50 µg/ml gentamicin and 0 . 5 µg/ml fungizone using MACS homogenizer ( Miltenyi Biotec ) according to manufacturer's protocols , and titered as previously described . To examine lung histopathology , left lungs of infected mice were incubated with 10% zinc formalin for 24 h at 4°C , embedded in paraffin , sectioned , and stained with hematoxylin and eosin . Five micron sections of zinc formalin fixed lungs were deparaffined at 60°C and rehydrated by successive incubations in 100% xylol , 100% ethanol and 96% ethanol . Antigen unmask was performed by boiling the samples in citrate buffer ( 8 . 2 mM sodium citrate; 1 . 8 mM citric acid , pH 6 . 5 ) for 5 min at 110°C in a decloaking chamber ( Biocare medical ) . Samples were then permeabilized with 0 . 25% Triton X-100 in PBS for 15 min and blocked with 10% bovine serum albumin ( BSA ) and 0 . 25% Triton X-100 in PBS for 30 min . Samples were labeled with a mouse monoclonal antibody specific for SARS-CoV N protein ( kindly provided by Ying Fang , South Dakota State University ) diluted 1∶250 and a rabbit monoclonal antibody specific for Na+/K+ ATPase alpha subunit ( Abcam ) diluted 1∶100 in 0 . 25% Triton X-100 and 10% BSA in PBS for 1 h 30 min at room temperature . Goat anti-mouse and goat anti-rabbit secondary antibodies bound to Alexa 488 and Alexa 594 fluorophores were used respectively at a dilution 1∶250 in 0 . 25% Triton X-100 and 10% BSA in PBS for 45 min at room temperature . Cell nuclei were stained with DAPI ( 1∶200 ) . Tissues were mounted in ProLong antifade reagent ( Invitrogen ) and analyzed in a Leica TCS SP5 confocal microscope . RNA extracted from lungs of infected mice was prepared as described above , and subjected to retro transcriptase reactions using a High-Capacity cDNA transcription kit ( Applied Biosystems ) to generate cDNAs . PCR using Taqman assays specific for IL-1β ( Mm01336189-m1 ) and 18S ribosomal RNA as a control ( Mm03928990-g1 ) [80] , [81] ( Applied Biosystems ) were performed . Data were acquired with an ABI Prism 7000 sequence detection system ( Applied Biosystems ) and analyzed using ABI Prism 7000 SDS v1 . 0 software . Gene expression relative to mock-infected samples is shown . Lungs from infected mice were collected at 2 dpi and the right lung was homogenized in 1 . 2 mL of protein lysis buffer containing Tris/HCl 10 mM , EDTA 1 mM , NaCl 150 mM , IGEPAL 1% , and complete protease inhibitor ( Roche ) pH8 , using MACS homogenizer ( Miltenyi Biotec ) . Samples were centrifuged for 1 h at 4°C and 13000× g and supernatants were collected . Pro-IL-1β levels were analyzed by Western blotting using a goat anti mouse IL-1β/IL-1F2 antibody ( R&D systems ) . As a loading control , beta-actin was labeled using a mouse monoclonal antibody ( Abcam ) . Bound antibodies were detected using a rabbit anti goat and a rabbit anti mouse HRP conjugated antibodies and the Immobilon Western chemiluminiscence substrate ( Millipore ) , following manufacturer's specifications . Densitometric analysis was performed in non-saturated exposures of several experimental replicates using Quantity One , version 4 . 5 . 1 software ( BioRad ) . Levels of pro-IL-1β were normalized to the levels of beta-actin . Following euthanasia by cervical dislocation , the trachea was exposed and cannulated through the animal mouth with a 19 gauge tube . Lungs were lavaged three times with 400 µl of cold phosphate buffered saline ( PBS ) . Samples were centrifuged for 10 minutes at 3000× g at 4°C to separate cellular content , and supernatants were collected to analyze their cytokine levels . Bronchoalveolar lavages were treated with IGEPAL reaching a final concentration of 0 . 2% , to inactivate sample infectivity . The expression of IL-1β , TNF and IL-6 was measured using Luminex technology and a mouse cytokine antibody bead kit ( Milliplex map kit , Millipore ) according to the manufacturer's specifications . | Several highly pathogenic viruses encode small transmembrane proteins with ion-conduction properties named viroporins . Viroporins are generally involved in virus production and maturation processes , which many times are achieved by altering the ion homeostasis of cell organelles . Cells have evolved mechanisms to sense these imbalances in ion concentrations as a danger signal , and consequently trigger the innate immune system . Recently , it has been demonstrated that viroporins are inducers of cytosolic macromolecular complexes named inflammasomes that trigger the activation of key inflammatory cytokines such as IL-1β . The repercussions of this system in viral pathogenesis or disease outcome are currently being explored . SARS-CoV infection induces an uncontrolled inflammatory response leading to pulmonary damage , edema accumulation , severe hypoxemia and eventually death . In this study , we report that SARS-CoV E protein ion channel activity is a determinant of virulence , as the elimination of this function attenuated the virus , reducing the harmful inflammatory cytokine burst produced after infection , in which inflammasome activation plays a critical role . This led to less pulmonary damage and to disease resolution . These novel findings may be of relevance for other viral infections and can possibly be translated in order to find therapies for their associated diseases . | [
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"prote... | 2014 | Severe Acute Respiratory Syndrome Coronavirus Envelope Protein Ion Channel Activity Promotes Virus Fitness and Pathogenesis |
Family studies suggest a genetic component to the etiology of chronic kidney disease ( CKD ) and end stage renal disease ( ESRD ) . Previously , we identified 16 loci for eGFR in genome-wide association studies , but the associations of these single nucleotide polymorphisms ( SNPs ) for incident CKD or ESRD are unknown . We thus investigated the association of these loci with incident CKD in 26 , 308 individuals of European ancestry free of CKD at baseline drawn from eight population-based cohorts followed for a median of 7 . 2 years ( including 2 , 122 incident CKD cases defined as eGFR <60ml/min/1 . 73m2 at follow-up ) and with ESRD in four case-control studies in subjects of European ancestry ( 3 , 775 cases , 4 , 577 controls ) . SNPs at 11 of the 16 loci ( UMOD , PRKAG2 , ANXA9 , DAB2 , SHROOM3 , DACH1 , STC1 , SLC34A1 , ALMS1/NAT8 , UBE2Q2 , and GCKR ) were associated with incident CKD; p-values ranged from p = 4 . 1e-9 in UMOD to p = 0 . 03 in GCKR . After adjusting for baseline eGFR , six of these loci remained significantly associated with incident CKD ( UMOD , PRKAG2 , ANXA9 , DAB2 , DACH1 , and STC1 ) . SNPs in UMOD ( OR = 0 . 92 , p = 0 . 04 ) and GCKR ( OR = 0 . 93 , p = 0 . 03 ) were nominally associated with ESRD . In summary , the majority of eGFR-related loci are either associated or show a strong trend towards association with incident CKD , but have modest associations with ESRD in individuals of European descent . Additional work is required to characterize the association of genetic determinants of CKD and ESRD at different stages of disease progression .
Chronic kidney disease ( CKD ) and end stage renal disease ( ESRD ) are associated with significant cardiovascular morbidity and mortality , with substantial economic burden [1]–[4] . Diabetes and hypertension are the primary risk factors for CKD and ESRD [5]–[8] but do not fully account for CKD and ESRD risk [9]–[11] . Studies indicate familial aggregation of ESRD [12] . In African Americans , high risk common variants in the MYH9/APOL1 locus account for much of the excess genetic risk for non-diabetic ESRD compared to their counterparts of European descent . In contrast , comparable genetic risk loci of severe renal phenotypes have not been identified in individuals of European ancestry [13]–[15] . Recently , 16 genetic risk loci associated with estimated glomerular filtration rate ( eGFR ) and prevalent CKD were identified and replicated by genome wide association studies ( GWAS ) in about 70 , 000 individuals of European ancestry in the CKDGen consortium [16] , [17] . Two of these loci were also identified by an independent consortium [18] . However , these studies focused on eGFR and prevalent CKD ( defined as eGFR <60 ml/min/1 . 73m2 ) at one time point , which encompasses the entire spectrum of CKD , and does not does not address the question of whether these genetic factors are involved in the initiation of CKD or in the progression to ESRD , the most advanced stage of CKD . We thus sought to analyze the association of the previously identified 16 eGFR-associated loci with the development of CKD and with ESRD in a total of over 34 , 000 individuals of European descent .
Overall , 26 , 308 individuals of European descent , from eight population-based prospective studies , who were free of CKD at baseline were included in the incident CKD analysis ( Table 1 ) . At baseline , mean age ranged from 40 . 5 to 71 . 7 years . After a median follow-up of 7 . 2 years , 2122 participants developed incident CKD . Of the 16 SNPs analyzed , 11 were associated with incident CKD ( Table 2 ) : SNPs in UMOD , PRKAG2 , ANXA9 , DAB2 , SHROOM3 , DACH1 , STC1 , SLC34A1 , ALMS1/NAT8 , UBE2Q2 and GCKR showed p-values ranging from p = 4 . 1×10−9 in UMOD to p = 0 . 03 in GCKR . The odds ratios ( OR ) for incident CKD of the minor alleles at each of the 11 loci ranged from 0 . 76 per copy of the T allele ( allele frequency 18% ) at the UMOD locus to 1 . 19 per copy of the A allele ( allele frequency 22% ) at PRKAG2 . After additional adjustment for baseline eGFR , 6 SNPs ( at the UMOD , PRKAG2 , ANXA9 , DAB2 , DACH1 and STC1 loci ) remained significantly associated with incident CKD , with minimal attenuation of effect size ( Table 2 ) . At each of the significant loci , the direction and the magnitude of the association was similar to those from the discovery analyses of eGFR and prevalent CKD [17] . For example , at the UMOD locus , each copy of the minor T allele at rs12917707 was associated with a 24% reduced risk for incident CKD , while in the CKDGen consortium the same allele was associated with higher eGFR [17] . Though the associations between incident CKD and SNPs in SLC7A9 , ATXN2 , PIP5K1B and VEGFA were not significant , the direction and magnitude of associations were consistent with our previous findings for the phenotypes eGFR and prevalent CKD [16] , [17] . TFDP2 was the only locus where we did not observe association with incident CKD . Of the 16 SNPs tested , 15 had the same direction of association with incident CKD as their original associations with prevalent CKD . The probability of observing this many SNPs with consistency in direction of associations is 0 . 0002 . We did not observe evidence for heterogeneity between studies at any of the 16 loci ( test for heterogeneity p>0 . 05 for all SNPs ) . For the ESRD analysis , we included four case-control studies with a total of 3775 ESRD patients and 4577 controls of European descent without CKD ( Table 3 ) . Mean age ranged from 50 . 7 to 66 . 2 years in cases and from 47 . 7 to 62 . 1 years in controls . Although the direction and magnitude of association for 8 SNPs ( at the UMOD , GCKR , PIP5K1B , PRKAG2 , STC1 , VEGFA , SHROOM3 , and ALMS1/NAT8 loci ) were consistent with our previous findings for eGFR and prevalent CKD [16] , [17] , only two SNPs showed nominally significant associations with ESRD ( Table 2 ) : rs1260326 in GCKR ( OR = 0 . 93; p-value = 0 . 03 ) and rs12917707 in UMOD ( OR = 0 . 92; p-value = 0 . 04 ) . The lack of association was not likely due to heterogeneity of ESRD cases as only two SNPs showed moderate heterogeneity in their associations with ESRD ( Table 2 ) : rs4744712 at the PIP5K1B locus ( p = 0 . 04 for heterogeneity ) and rs626277 at the DACH1 locus ( p = 0 . 02 for heterogeneity ) .
Among individuals of European Ancestry , most genetic loci associated with the quantitative trait eGFR are also associated with risk for initiation of CKD , with more than half of these associations independent of eGFR at the baseline examination . In contrast , only two SNPs were nominally associated with ESRD . To date , the genetic loci showing significant and replicated associations with ESRD are limited [13]–[15] , [19]–[26] , and genetic studies for incident CKD or for renal function decline in established kidney disease are only recently emerging [27]–[29] . The loci we analyzed were identified in association with renal function cross-sectionally and with prevalent CKD by GWAS in the general population . Typical of many SNPs uncovered in GWAS , the majority of these SNPs reside in intronic regions with unknown functional consequences , although several are associated with cis expression levels in liver tissue or leukocytes ( Table S3 ) [16] , [17] . These newly identified loci are non-overlapping with those previously identified in individuals of European or Asian descent with advanced diabetic nephropathy [19]–[26] , or in African Americans with non-diabetic ESRD [13]–[15] . For the ESRD analysis , we had adequate power to detect effects that were similar to those for prevalent CKD in the discovery GWAS , where odds ratios ranged from 0 . 8 to 1 . 19 [16] , [17] . In the present study , where associations were observed , the odds ratios for ESRD tended to be smaller and ranged from 0 . 92 to 1 . 11 . There are several potential explanations for this effect dilution . First , the mechanisms involved in the initiation of CKD , the progression of CKD , and the incidence of ESRD may differ [30]–[33] . Experimental animal data and gene expression profiling in human kidney biopsies suggest differential biological pathways contributing to kidney disease initiation and progression [34]–[36] . Second , the majority of patients with CKD die of cardiovascular disease before developing ESRD [37]–[39] . Thus , the genetic findings for kidney function in the general population may not apply to the highly selected group of dialysis populations . Finally , the process of progression from CKD to ESRD often involves repeated insults including episodes of acute kidney injury by diagnostic and operative procedures and therapies [40]–[43] , cardiac function deterioration [44] , variation in access to adequate health care [45] , [46] and other non-genetic factors [47] . Jointly , these factors may further decrease the relative impact of the small effects of SNPs derived from GWAS of eGFR in the general population at the earliest stage of disease initiation . The observed small effect sizes for ESRD in our study are in contrast to the large effect sizes observed in relatively small cohorts of individuals of African descent for variants in the MYH9/APOL1 locus , where odds ratios for ESRD ranged from 7 . 3 for the G1–G2 haplotype at the APOL1 locus to 2 . 38 for the E1 haplotype in the MYH9 locus [13]–[15] . However , the strong effect at this locus is an exceptional case and may be a consequence of a pronounced positive selection against vulnerability for Trypanosoma brucei rhodesiense infection at the price of a higher susceptibility for non-diabetic ESRD in African Americans not observed in other ethnicities . The establishment of large cohorts is thus needed for performing GWAS of CKD initiation and progression as well as ESRD to overcome the challenge of identifying novel loci significantly associated with these phenotypes with small effect sizes . The strength of our work lies in the large number of individuals studied . Further , we exclusively analyzed candidate SNPs identified by the unbiased method of GWAS [16] , [17] . However , some limitations warrant mention . First , seven of the eight cohorts used for the incident CKD analysis were also part of the CKDGen discovery effort; thus the two samples are not entirely “independent” . However , the phenotype studied differs substantially: in Köttgen et al [17] , we used prevalent eGFR data including those with CKD , while follow-up data in those without CKD at the baseline examination was used for the present incident CKD analysis . In the present work , we demonstrate robustness of our findings independent of baseline GFR . Second , we relied on only two serum creatinine measurements to define incident CKD , which may have introduced misclassification and biased our findings towards the null . Third , we did not account for pharmacological treatment with inhibitors of the renin-angiotensin-aldosterone system . Since these drugs may affect kidney function independently of kidney damage , their use may have diluted observable genetic effects [48] . Fourth , our study was not designed to detect fluctuations in eGFR . Furthermore , the etiology of ESRD in the cases we examined may vary between studies , though we observed a low degree of heterogeneity . Finally , our sample consisted of individuals of European ancestry; findings may not be generalizable to other ethnicities . SNPs associated with eGFR in population-based studies are associated with incident CKD , whereas modest associations were observed with ESRD . Additional work is necessary to characterize the genetic underpinnings across the full range of kidney disease phenotypes , which could ultimately lead to novel diagnostic and therapeutic strategies .
In all studies , all participants gave informed consent . All studies were approved by their appropriate Research Ethics Committees . In population based cohorts , serum creatinine measurements were calibrated to the National Health and Nutrition Examination Study ( NHANES ) standards in all studies to account for between-laboratory variation across studies , as described previously [10] , [16] , [17] . Using calibrated serum creatinine , we calculated the estimated glomerular filtration rate ( eGFR ) with the 4-variable MDRD equation [49] . For incident CKD , we analyzed studies of incident CKD in eight population-based cohorts in the CKDGen consortium with follow-up available: ARIC , CHS , CoLaus , FHS , KORA S3/F3 , KORA S4/F4 , the Rotterdam Study and SHIP . Each study's design is shown in Text S1 . Incident CKD cases were defined as those free of CKD at baseline ( defined as eGFR≥60 ml/min/1 . 73m2 ) but with a follow-up eGFR<60 ml/min/1 . 73m2 . Controls were those free of CKD at baseline and at follow-up . For the ESRD analysis , we performed four case control studies of ESRD . Cases were ESRD patients from six cohorts of ESRD patients: CHOICE , ArMORR , GENDIAN , 4D , MMKD and FHKS . Controls were those free of CKD ( defined as eGFR≥60 ml/min/1 . 73m2 ) in three population-based cohorts ( KORA F3 , KORA F4 , SAPHIR ) and one type 2 diabetes cohort ( GENDIAN ) . Each study's design is shown in Text S1 . In each study , we performed age- and sex adjusted logistic regression of incident CKD , with and without additional adjusting for baseline eGFR , or ESRD status with each SNP . In multicenter studies further adjustment for study-center was performed to account for possible differences between recruiting centers . For family-based studies , we applied logistic regression via generalized estimating equations ( GEE ) to account for the familial relatedness . Study-specific results were then combined by meta-analysis using a fixed effects model , using METAL ( http://www . sph . umich . edu/csg/abecasis/Metal/index . html ) [50] . When significant heterogeneity between studies was observed ( p for heterogeneity between studies <0 . 05 ) we used the random effects model [51] . Statistical significance was defined as a one-sided p-value <0 . 05 for each SNP without adjustment for multiple testing since all SNPs examined had strong prior probabilities of being associated with the outcomes and the same alleles were hypothesized to be associated with lower eGFR , incident CKD , and ESRD . We used the QUANTO software for power estimation , assuming an additive genetic model ( http://hydra . usc . edu/GxE ) [52] . For the ESRD analysis and for SNPs with minor allele frequency ranging from 0 . 2 to 0 . 4 we had 80–100% power to detect an OR ≥ 1 . 10 , whereas power was borderline for an OR of 1 . 05 to 1 . 09 . For example , for the SNP rs12917707 at UMOD , we had 100% power to detect an association with ESRD in the 3775 ESRD cases and 4577 controls assuming that the effect in ESRD would be the same or larger than the effect observed for prevalent CKD previously [16] , [17] . For the incident CKD analysis , we used the allele dosage information of each of the 16 SNPs from each study's genome wide data set imputed to HAPMAP CEU samples described previously [17] , [18] . Imputation provides a common SNP panel across all studies to facilitate a meta-analysis across all contributing SNPs . Information on each study's genotyping and imputation platform and quality control procedures are shown in Table S1 . Table S2 summarizes each SNPs imputation quality . De novo genotyping of the 16 SNPs was performed in each of the ESRD case-control studies as described previously [17] . Briefly , genotyping was performed either on a MassARRAY system using Assay Design v . 3 . 1 . 2 and the iPLEX™ chemistry ( Sequenom , San Diego , USA ) at the Helmholtz Zentrum in Munich , Germany ( ArMORR , GENDIAN , 4D , MMKD , FHKS , KORA S3/F3-subset without GWAS data , KORA S4/F4-subset without GWAS data , SAPHIR ) ; by using 5′ nuclease allelic discrimination assays on 7900HT Fast Real-Time Taqman PCR genotyping systems ( Applied Biosystems , Foster City , CA , USA ) at the Innsbruck Medical University ( ArMORR , GENDIAN , 4D , MMKD , FHKS , KORA F3-subset without GWAS data , KORA F4-subset without GWAS data , SAPHIR ) ; or as part of a larger panel of 768 SNPs genotyped on the Illumina Bead Station ( CHOICE ) . The SNPs rs347685 , rs11959928 , rs4744712 and rs12460876 were not available for de novo genotyping on the Sequenom platform , thus the proxy SNPs rs6773343 , rs11951093 , rs1556751 and rs8101881 , with pairwise r2 of 1 . 0 , 0 . 87 , 0 . 87 and 1 . 0 respectively [53] , were included in the MassARRAY multiplex PCR . For the obtained duplicate genotypes ( 9–22% of the subjects in GENDIAN , 4D , MMKD , FHKS , KORA F3-subset without GWAS data , KORA F4-subset without GWAS data , and SAPHIR; no duplicate genotyping possible due to limited DNA-availability in CHOICE and ArMORR ) concordance was 96–100% ( median: 100% ) . SNPs with a per-study call rate <90% or with a per-study HWE p value <0 . 0001 were excluded from further analysis ( rs6773343 and rs653178 in GENDIAN cases; rs13538 , rs267734 , rs10109414 , rs1394125 in ArMORR , rs6773343 , rs10109414 , rs1556751 , rs653178 , rs8101881 in CHOICE ) . In addition , individual samples with <80% successfully genotyped SNPs were excluded from further analysis . After these exclusions , call rates ranged from 91–100% ( mean: 98% ) across all studies and all SNPs . | Chronic kidney disease ( CKD ) affects about 6%–11% of the general population , and progression to end stage renal disease ( ESRD ) has a significant public health impact . Family studies suggest that the risk for CKD and ESRD is heritable . Unraveling the genetic underpinning of risk for these diseases may lead to the identification of novel mechanisms and thus diagnostic and therapeutic tools . We have previously identified 16 genetic markers in association with kidney function and prevalent CKD in general population studies . However , little is known about the relevance of these SNPs to the initial development of CKD or to ESRD risk . Therefore , we have now analyzed the association of these markers with the initiation of CKD in more than 26 , 000 individuals from the general population using serial estimations of kidney function , and with ESRD in four case-control studies in subjects of European ancestry ( 3 , 775 cases , 4 , 577 controls ) . We show that many of the 16 markers are also associated or show a strong trend towards association with initiation of CKD , while only 2 markers are nominally associated with ESRD . Further work is required to characterize the association of genetic determinants of different stages of CKD progression . | [
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... | 2011 | Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD |
HIV-1 cell entry commonly uses , in addition to CD4 , one of the chemokine receptors CCR5 or CXCR4 as coreceptor . Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists . Predictive methods for inferring coreceptor usage based on the third hypervariable ( V3 ) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests . All simple heuristics ( such as the 11/25 rule ) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively . Here , we show , based on a recently resolved structure of gp120 with an untruncated V3 loop , that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage . In particular , we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance . For a fixed specificity of 0 . 95 , a sensitivity of 0 . 77 was achieved , improving further to 0 . 80 when combined with a sequence-based representation using amino acid indicators . This compares favorably with the sensitivities of 0 . 62 for the traditional 11/25 rule and 0 . 73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement . A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4 . Furthermore , an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor . The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning .
HIV virions enter human host cells through consecutive interaction with the CD4 cell surface receptor and one of the two major coreceptors CCR5 and CXCR4 . After binding to CD4 , a conformational switch in the surface protein gp120 of HIV reveals the coreceptor binding site , most notably the third hypervariable loop region V3 . The V3 loop is considered to be the major viral determinant for coreceptor specificity [1] . After successful attachment to the host cell , fusion of the viral and host cell membranes takes place [2 , 3] . The coreceptor selectivity of the viral population is of central pathological and clinical importance . Whereas in newly infected patients , CCR5-using ( R5 ) variants dominate , in about 50% of the patients CXCR4-using ( X4 ) variants appear during later stages of the disease characterized by progression towards AIDS . The cause of the observed coreceptor switch during progression is not fully understood; however , the close relation between the increase in the number of X4 variants and the decline of CD4+ cells and the disease progression towards AIDS is commonly agreed upon [4 , 5] . The categorization in R5 and X4 viral variants is highly correlated with but not identical to other categorization schemes into macrophage ( M ) -tropic and T cell line ( T ) -tropic or nonsyncytium-inducing versus syncytium-inducing variants [6] . Coreceptor antagonists are a new drug class , providing therapeutic options in addition to the established repertoire of protease and reverse transcriptase inhibitors [5 , 7] . Using a different mechanism and acting at a different stage of the viral life cycle , they provide new points of attack against multiresistant strains . The observation that individuals carrying a 32-basepair ( bp ) deletion in the CCR5 coreceptor are highly resistant against HIV infection [8] specifically motivates the development of CCR5 antagonists . Some CCR5 antagonists have proven safe and effective in phase II clinical trials [9] and are now being tested in phase III trials . While CCR5 inhibitors have already entered clinical testing , candidates for CXCR4 inhibitors are in earlier stages of development . A major concern regarding drug treatment with CCR5 inhibitors is that it can select for the emergence of pre-existing or newly produced CXCR4-using variants [10 , 11] . The close relation with disease progression necessitates tight monitoring of coreceptor usage and possible switches while administering inhibitors for CCR5 or CXCR4 . Although phenotypic assays for monitoring coreceptor usage are commercially available , they are time-consuming and costly . To become a routine part of clinical diagnosis , inferring the phenotype from cheaper and faster genotypic analysis is desired . This approach has already entered routine clinical usage in resistance testing for the classical anti-HIV drug targets protease and reverse transcriptase [12] . Various methods for predicting phenotype based on sequence information are available . The most commonly used 11/25 rule predicts a viral strain to be X4 in the presence of positively charged amino acids at positions 11 or 25 of the V3 loop [13] . More recently , methods based on statistical learning techniques have been developed , which show improved sensitivity in detecting X4 viral strains compared with the simple 11/25 rule [14] . Neural nets [14] , decision trees [15] , support vector machines ( SVMs ) [15] , and position-specific scoring matrices [1 , 16] have been applied , most of them significantly outperforming the simple 11/25 rule [17] . To date , information on the three-dimensional structure of the V3 loop has not been exploited for predicting the coreceptor type used by a viral population . Including structural information can improve predictive performance and , even more importantly , be a first step towards a deeper understanding of the structural aspects of coreceptor usage . Several studies analyzed conformational properties of the V3 loop . However , these investigations did not particularly consider the impact on coreceptor usage . As Lusso [18] points out , structural understanding of coreceptor specificity is limited at the moment . In recent work , Watabe et al . [19] suggested empirical potentials to assess the fit of sequence variants to loop candidates generated by Monte Carlo variation of NMR peptide structures . So far , structural studies have been based on peptide structures , as no completely resolved structure of gp120 was available . The situation has changed with a recently published crystal structure of the HIV-1 JR-FL gp120 protein including the V3 loop by Huang et al . [20] . See Figure 1 . Although some evidence for conformational changes in the loop structure exists , there is an ongoing debate about the relevance of V3 loop conformation to coreceptor selectivity [21–23] . Sharon et al . [21] suggest that alternative conformations of the V3 loop play a key role in determining the coreceptor specificity of HIV-1 . On the other hand , Scheib et al . [22] argue that there is a predominant conformation for both R5 and X4 variants and that varying sequence features are responsible for specificity towards the respective coreceptor . Here , we describe the first structure-based approach to predicting HIV-1 coreceptor usage . In particular , we propose and evaluate a novel structural descriptor for capturing the spatial distribution of five functionally defined atom types in the V3 loop ( see Figure 2 ) . In a practical scenario , only sequence data but no structures will be available for different viral variants . Thus , we chose to evaluate two approaches: ( 1 ) to use a simple descriptor ( V3SDCβ ) , which approximates the position of all functional side chain atoms by the fixed Cβ positions of the structure 2b4c [20]; and ( 2 ) a descriptor V3SDscwrl , which uses the crystal structure 2b4c [20] as a rigid backbone template for the V3 loop region and models side chains using SCWRL [24] . SCWRL is a reliable and fast program to predict side chains for large sets of sequences . By comparing the descriptors V3SDCβ and V3SDscwrl , which represent structures of viral variants at two different levels of approximation , the tradeoff between increased uncertainty and the improved information about side chain location and length can be assessed . To specifically address the structural uncertainty in the presence of insertions and deletions , we evaluate the performance separately for sequence variants with substitutions only , as opposed to variants also exhibiting insertions and deletions relative to the reference V3 loop of the structure 2b4c . To derive structural descriptors from the modelled variants , the side chains are represented by functional atoms , labelled as hydrogen-bond donor , acceptor , ambivalent donor/acceptor , aliphatic , or aromatic ring , according to Schmitt et al . [25] . For the subsequent prediction based on an SVM , the spatial arrangements are encoded by 15 distance distributions , one for each pair of functional atom types . Thus , for each atom-type combination ( e . g . , donor–donor , donor–acceptor , . . . ) all Euclidean distances between the respective atoms are computed and condensed into a distribution function , similar to a smoothed histogram . The set of 15 distance distributions is used as vectorial input to the SVM . See Figure 3 for a schematic overview and the section Structural Descriptors for methodological details . The proposed structure representation is related to ideas from protein structure comparison and prediction . Distributions of atomic distances have been used successfully in structure comparison [26 , 27] . In protein structure prediction , distributions of distances have been applied as knowledge-based potentials to evaluate the fit of a sequence to a specific structure [28 , 29] . In the context of protein function , Stahl et al . [30] have used distance-based descriptions to cluster active sites of enzymes based on chemical and geometric properties . For the analysis of protein–protein interaction interfaces , Mintseris and Weng [31] have proposed atomic contact vectors which consist of contact counts derived from thresholded distance matrices . Aloy and Russell [32] have suggested empirical potentials to assess the compatibility of a pair of sequences to the contacts formed in a known complex of two respectively homologous sequences . In a similar setting , MULTIPROSPECTOR [33] uses a threading algorithm to align a pair of sequences to a structurally resolved protein–protein complex . In addition to the interface energy term as in [32] , this method also uses the threading score for the protomers themselves . Structural understanding in the present problem is seriously hampered by the fact that structural details on complexation with the coreceptor are unknown . This is why we refrain from an attempt to integrate structural information on the coreceptors . Another aggravating factor is that no crystal structures are available for viral variants . As it is unlikely that comprehensive structural data on the wealth of viral variants will become available , modelling of side chains , and potentially also changes in the backbone , is necessary .
To assess the predictive performance of the structure-based descriptors , we compared the two variants V3SDCβ and V3SDscwrl against purely sequence-based predictions by the 11/25 rule , which predicts X4 in the presence of positively charged residues at positions 11 or 25 , and Indicator . Indicator performs prediction based on an SVM using a binary sequence encoding , which uses a bit-vector to indicate the presence or absence of a specific amino acid at a specific V3 loop sequence position . We evaluated the two structural descriptors and the two sequence-based predictors on data compiled from the Los Alamos HIV Sequence Database and several publications [14 , 34–37] . The evaluation is performed on a dataset containing 514 mutually distinct V3 sequences ( SEQindels , 514 ) and a smaller subset , containing 432 sequences without indels ( SEQnoindels , 432 ) . Each of the sequences is annotated as either using CCR5 only or being capable of using CXCR4 . See Materials and Methods for methodological details and the Dataset and sequence alignment section for a description of the dataset . For measures of performance we used the sensitivity at the specificity of the 11/25 rule , the area under the ROC curve ( AUC ) , the accuracy at a cutoff of 0 . 5 ( for the posterior probability obtained by the SVM ) , and the positive predictive value ( PPV ) at the specificity of the 11/25 rule . Of all these measures , we consider the sensitivity at the specificity of the 11/25 rule as most important in practice , because it focuses on detecting X4 viral variants at an acceptable level of false positives ( R5 erroneously considered to be X4 ) . See the section Evaluation and definition of performance measures for definitions of the performance measures . Figure 4 contains ROC ( receiver operating characteristic ) curves for a performance comparison of the methods . ROC curves plot ( 1-Specificity ) against Sensitivity for varied decision cutoffs , ranging from predicting mainly R5 ( towards the lower left corner ) to predicting mainly X4 ( towards the upper right corner ) . On our dataset ( see the section Dataset and sequence alignment for details ) , the 11/25 rule has a sensitivity of 0 . 6186 while exhibiting a specificity of 0 . 9463 . Considering the routine clinical application of this simple rule , the benefit of improving the sensitivity towards X4 viral variants is obvious . For the fixed specificity of 0 . 9463 ( i . e . , maintaining a fixed number of false positives ) , the sequence-based indicator prediction using a linear SVM improves sensitivity to 0 . 7340 . A similar improvement has been reported previously [15 , 17] when applying statistical learning methods in comparison to the traditional 11/25 rule . For the simpler form of structural descriptor V3SDCβ , the performance is below the Indicator prediction at a sensitivity of 0 . 6959 . Still , this constitutes a considerable improvement over the 11/25 rule . Thus , as features different from pure sequence information are encoded in this structural descriptor , its analysis can provide important insights regarding structural features . Using structural models for the sequence variants with side chains placed by SCWRL [24] , predictive performance improves considerably over the simple structural descriptor V3SDCβ and even compared with the Indicator encoding . The structural descriptor V3SDscwrl improves sensitivity to 0 . 7742 . SCWRL faces a hard task in optimizing side chain conformations as no direct contacts between the side chains within the loop with side chains of binding partners are present . However , the improved predictive performance indicates that the additional information over the V3SDCβ descriptor helps in discriminating coreceptor usage . One important aspect might be the information about side chain length and volume , which is completely lost in the V3SDCβ descriptor . An overview of predictive performance for further measures can be found in Table 1 . The observed ordering of methods regarding performance are similar to the trend observed for the sensitivities . The absolute performance increases regarding AUC and accuracy are smaller . This is because AUC and accuracy are less responsive to improvements in detection of X4 variants due to the class imbalance towards R5 samples . In Table 2 the statistical significance of relative sensitivity improvements between methods is tabulated . The improvement from the 11/25 rule to the Indicator is significant at a p-value of 0 . 0059 ( paired Wilcoxon test ) , as is the improvement of V3SDscwrl over Indicator ( 0 . 0137 ) . The error bars in Figure 4 are nonoverlapping for the sensitivities at the specificity of the 11/25 rule ( dashed line ) . This also indicates significant differences in predictive performances . Considering the different type of information in the sequence-based and the structural descriptors , we combined the respective features to assess whether further predictive improvements are feasible . The sequence-based and structural features were combined by concatenating the corresponding feature vectors . As seen in Figure 4 , combination of the sequence-based Indicator encoding and the structural descriptor V3SDscwrl further improves sensitivity to 0 . 8041 at the specificity of the 11/25 rule ( 0 . 9463 ) . This indicates that sequence and structure convey complementary information , to some extent . See Table 1 for further performance measures and Table 2 for a significance assessment of the relative improvement . For a fixed specificity of 0 . 9 , a similar increase from sequence-only to structure-based descriptors can be observed: 0 . 7946 ( Indicator ) , 0 . 8474 ( V3SDscwrl ) , 0 . 8603 ( V3SDscwrl + Indicator ) . The previous performance assessment was done only on viral variants without insertions or deletions relative to the V3 region of 2b4c . However , for broad applicability it is desired to cover sequences with indels as well . Investigating the positions of observed insertions and deletions shows that they are not uniformly distributed along the V3 region . Instead , there are preferences for certain positions . Figure 5 illustrates the positional distribution of insertions and deletions . Around position 7 , insertions and a few deletions can be observed . After position 12 there is a rare three-residue deletion , occurring in two sequences in our dataset . Between positions 14 and 15 there is a rather common two-residue insertion . The effect of this insertion on the β pairing within the hairpin is unclear; it might disrupt the pairing . A rather common deletion is observed at position 22 . Higher rates of insertions and deletions can be found around position 24 , the bulgy middle region . In this neighborhood it appears to be easier to structurally adapt to insertions and deletions by slight conformational changes . For sequence variants containing insertions relative to the V3 region of 2b4c , the inserted residues were ignored in the descriptor . For variants with deletions , only the remaining residues contributed to the descriptor . For insertions as well as deletions , no remodelling of the backbone or loop closure was performed . We compare the sensitivity at the specificity of the 11/25 rule for the full dataset including indels with the performance reported above in the section Predictive Performance of Sequence-Based and Structural Descriptors . Whereas the sensitivity of the 11/25 rule drops to 0 . 5782 , the performances for Indicator ( 0 . 7182 ) , V3SDscwrl ( 0 . 7712 ) , and for the combination of Indicator and V3SDscwrl ( 0 . 8052 ) change only slightly . This shows that the proposed structural descriptor is sufficiently robust to handle sequence variants containing indels . See Protocol S1 for additional material on viral variants with indels . To assess the importance of features in the structural descriptors , we used three approaches for scoring how characteristic the respective features are for each coreceptor class . First , we analyzed the separation of the two coreceptor classes by each feature using the Wilcoxon test-statistic ( Wilcoxon ) . Second , the ratios of feature variability between and within the two coreceptor classes were assessed ( variation ratio ) . Third , a random forest classifier was used to estimate the feature importance of each feature ( RF importance ) . Random forests are predictive classifiers and were applied as substitutes for the SVMs above , because their construction as an ensemble of decision trees allows the extraction of feature importance measures . See Figure 6 for pairwise scatter plots of the three importance measures and Figure 7 for an illustration of RF importance . Finally , we investigate the relevant residue pairs contributing to the characteristic features . The proposed descriptor yields a considerable performance increase over the established 11/25 rule and even compares favorably with newer methods based on statistical learning ( Indicator ) . In contrast to purely sequence-based coreceptor usage predictions , the proposed structural representation captures the relative three-dimensional arrangement of chemical groups . From a biophysical perspective , this relative placement of chemical groups is determining which coreceptor the viral variant will bind . Due to its robustness with respect to sequence variants containing indels , it can be applied in realistic scenarios and on large-scale datasets . The most interesting aspect of the proposed descriptor is its integration of structural data , providing the first application of structural data in the context of coreceptor usage prediction . The combination of methods from structural bioinformatics with statistical learning methods allows for competitive performance as well as interpretation of coreceptor usage at the structural level . Despite its good performance , there are several limitations and possible directions for improvement , either by methodological enhancements or by integration of further experimental data . As almost no side chain interactions take place within the V3 loop and the binding partner is not available in the structural model , SCWRL faces a difficult task in optimizing side chains . One possible way of relaxing this difficulty is by considering ensembles of alternative side chain conformations in the structural descriptor . From a methodological point of view , alternative conformations are easy to integrate into the distance distributions in a weighted manner . A further possible bottleneck is the assumption of a fixed backbone structure . Further understanding of the structure–function relationship of coreceptor usage or new insights in the debate mentioned above [21–23] could be incorporated into the descriptor . Instead of the fixed backbone structure , several alternatives are possible . Experimentally resolved peptide structures could be used to model sequence variants or molecular dynamics simulations could be used to generate ensembles of backbone variants . With all these alternatives , the proposed descriptor provides a generic way of incorporating new structural information on V3 loop conformation; especially interesting would be crystal structures of X4 viral variants . Another interesting perspective is to correlate the discriminative spatial features of the V3 region to spatial arrangements in the coreceptor . Published chemokine receptor models [38 , 39] could be used to generate such spatial descriptions and to search for complementary arrangements of physicochemical properties . Finally , the proposed method to describe the spatial arrangement of physicochemical properties is not limited to the demonstrated application , in principle . By providing a vectorial representation of a binding site , it can be used as a generic way of describing and comparing any set of binding sites regarding geometric and physicochemical features involved in different protein–protein interactions .
From the HIV Sequence Database at Los Alamos National Laboratory and several publications [14 , 34–37] , we obtained 1 , 100 clonal samples with annotated coreceptor phenotype from 332 patients . To reduce the risk of positively biased results , we removed all duplicate V3 sequences ( i . e . , sequences with 100% sequence identity to another sequence in the dataset ) , resulting in 514 mutually distinct sequences . For each of the samples , the coreceptor phenotype is denoted as R5 , X4 , or R5/X4 . R5/X4 are viral strains being capable of using either of the two coreceptors . R5/X4 and X4 variants were pooled into a single class ( called X4 in the sense of X4-capable ) , as opposed to variants that are limited to using CCR5 ( called R5 in the sense of R5-only ) . The dataset after duplicate removal contains 363 R5 and 151 X4 samples . We aligned these sequences using the multiple alignment package MUSCLE [40] with default parameters . Visual inspection showed no obvious degeneracies or problems in the alignment . The alignment of this sequence dataset ( called SEQindels , 514 ) shows that 82 sequences contain insertions and deletions relative to 2b4c . By restricting the set SEQindels , 514 to V3 variants without indels relative to the V3 region of 2b4c , we obtained 432 mutually distinct V3 loop sequences ( called SEQnoindels , 432 ) . Of those sequences , 97 are X4 variants , 335 are R5 strains . The traditional 11/25 rule is an empirically derived procedure routinely used in clinical practice to predict coreceptor usage . It predicts a viral variant to be X4 if there is a positively charged amino acid at V3 position 11 or 25 [13] . Among simple sequence rules ( i . e . , not based on statistical learning ) , Resch et al . consider the 11/25 rule to be the best predictor of coreceptor usage [14] . Various statistical learning methods were used to improve predictive performance [1 , 14 , 15] . Here we use linear SVM prediction based on an indicator encoding of the sequences ( Indicator ) [17] . A viral variant is encoded by an indicator vector ( consisting of only zeros and ones ) . Each component in this vector indicates the presence or absence of a specific amino acid at a specific V3 position . The protein structure of the HIV-1 JR-FL gp120 protein including the V3 loop ( Protein Data Bank ( PDB ) structure 2b4c [20] , based on a CCR5-using JR-FL variant ) was retrieved from the RCSB PDB ( http://www . pdb . org ) . The V3 loop in chain G ranging from residues 296 to 331 was extracted . Based on this loop backbone , we model the side chain positions for each sequence variant using SCWRL [24] . As no structure information for the sequence variants is directly available , we chose to evaluate two approaches: ( 1 ) to use a simple descriptor ( V3SDCβ ) , which approximates the position of all functional side chain atoms by the fixed Cβ positions of 2b4c; and ( 2 ) a descriptor V3SDscwrl , which is based on modelled side chains . This way the tradeoff between increased uncertainty and the improved information about side chain location and length can be assessed . We then represent the side chains by five functional atom types , labelled as hydrogen-bond donor , acceptor , ambivalent donor/acceptor , aliphatic , or aromatic ring . Amino acids R , N , Q , K , and W are classified as donors . Acceptors are N , D , Q , and E . Ambivalent donor/acceptors comprise H , S , T , and Y . As aliphatic amino acids , we consider A , R , C , I , L , K , M , P , T , and V . Pi-stacking centers are H , F , W , and Y . This definition follows [25] , but does not assign backbone centers as pi-stacking . For aliphatic and aromatic interaction centers , all involved atom positions were averaged per residue to compute a pseudo-atom . In contrast to [25] , who weight atoms by their solvent access for computing the pseudo-atom of aliphatic side chains , we used the unweighted average of the respective carbons as the solvent exposure of the V3 loop , which can be seen as rather uniform . For the subsequent statistical analysis , the spatial arrangement of these functional properties is encoded by distance distributions . For each of the 15 combinations of functional atom types ( i . e . , donor–donor , donor–acceptor , etc . ) , pairwise Euclidean distances between the respective pseudo-atoms in the V3 loop are calculated . Note that the number of these distances depends on the number of pseudo-atoms in the two groups . From these distance matrices , we derive distance distributions using a kernel density estimate with Gaussian kernel and bandwidth of 1 Å . The density estimates are then discretized by uniform sampling at intervals of 0 . 5 Å , resulting in a 15 ( distance distributions for atom type combinations ) times 100 ( sample points ) dimensional vector . The resulting vector is used as a structural descriptor for a given sample , as an alternative to the purely sequence-based indicator representation , and used as input to the statistical learning method . The bandwidth as well as the sampling intervals for the distance-based descriptors have been set to reasonable values based on empirical observations . To keep computation times feasible , they were not optimized systematically . For the sequence indicator encoding ( Indicator ) , a linear kernel is used , as previous studies showed that nonlinear kernels do not help for simple sequence encodings [17] . For prediction based on the structural descriptors , a radial basis function kernel [41] is applied , as it provides better performance than a linear kernel in this case . In both cases probabilistic predictions are obtained from the SVM by the method of Platt [42] to get estimates of prediction confidence and a scoring classifier for the ROC analysis . To optimize SVM parameters , we conducted parameter grid searches . For the linear kernel ( Indicator ) , we varied the cost parameter log2 C in [-7 , 2] . For the radial kernel ( V3SDCβ , V3SDscwrl ) , we varied the cost parameter log2 C in [-6 , 5] and the gamma parameter log2 γ in [-15 , −5] . Optimal parameter values were obtained from ten bootstrap samples of the dataset and kept fixed for the subsequent analysis and evaluation . Each bootstrap sample contained 9/10 of the number of samples in the original dataset ( drawn with replacement ) , using the default in the R package e1071 [43] . To assess predictive performance for the structural descriptors , we performed ten replicates of 10-fold cross-validation . Evaluation of predictive performance was done using ROCR [44] . The measures used for evaluation of predictive performance are sensitivity at the specificity of the 11/25 rule , AUC , accuracy , and PPV . In the following , Ŷ denotes the predicted coreceptor class and Y is the coreceptor actually used for a sample . P and N denote the number of positives ( X4 ) and negatives ( R5 ) . TP and TN denote the number of correctly predicted positives and negatives , and FP and FN denote the number of samples incorrectly predicted as positive or negative , respectively . Sensitivity is defined as P ( Ŷ = X4|Y = X4 ) and is estimated as TP/P . The specificity P ( Ŷ = R5|Y = R5 ) is estimated as TN/N . The AUC is calculated by adding the area of trapezoid strips under the ROC curve . This is equal to the value of the Wilcoxon-Mann-Whitney test statistic and also to the probability that the classifier will score a randomly drawn positive sample higher than a randomly drawn negative sample [45] . The accuracy is defined as P ( Ŷ = Y ) and estimated by ( TP + TN ) / ( P + N ) at the cutoff 0 . 5 for the posterior class probability . The PPV P ( Y = X4| Ŷ = X4 ) is estimated as TP/ ( TP + FP ) . For evaluation of the importance of the features in the structure-based descriptor , we used three scoring schemes . First we used the -log10 ( p-value ) of the Wilcoxon rank-sum statistic ( Wilcoxon ) [46] . As a second measure we utilized the ratio of feature variation between and within groups ( variation ratio ) , which is frequently used in gene ranking for microarray analysis [47] . For a feature i , this ratio is where and denote the average of feature i across all samples and across samples belonging to class k only . Third , we used the random forest feature importance scores ( RF importance ) based on the mean decrease of the Gini index [48] . Computations on the sequence variants and structural models were performed using Python and Biopython [49] . For computationally intensive parts , Grid Engine was employed for running the analysis on a compute cluster . Analysis of the results and prediction was performed using the statistical language R [50] with the packages e1071 [43] , randomForest [51] , and ROCR [44] . Protein structure visualizations in Figures 1 , 2 , 3 , and 8 were created using PyMOL [52] . The source code for prediction and analysis is available upon request .
The PDB ( http://www . pdb . org ) accession number used in this paper is for gp120 ( 2b4c , chain G ) . | HIV-1 cell entry requires a chemokine coreceptor in addition to the CD4 cell surface receptor . The two most common types of HIV coreceptors are called CCR5 and CXCR4 . Whereas CCR5-using viral variants dominate directly after infection and during early stages of the disease , in about 50% of the patients , CXCR4-using variants appear in later stages of the disease , suggesting the coreceptor switch to be a determinant of disease progression . HIV coreceptors received substantial attention as antiviral drug targets , with CCR5 antagonists being currently tested in phase III clinical studies . Treatment with coreceptor antagonists requires continuous monitoring of coreceptor usage . The prominent role of coreceptors in disease progression and their potential as antiviral drug targets provides incentives for methodological improvements in coreceptor prediction and better understanding of the underlying determining factors regarding sequence and structural aspects . Our proposed method is the first approach to predict coreceptor usage based on structural information as opposed to established sequence-based methods . Including structural information improves predictive performance and is a first step towards a deeper understanding of the structural aspects of coreceptor usage . | [
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Neisseria meningitidis ( Nme ) asymptomatically colonizes the human nasopharynx , yet can initiate rapidly-progressing sepsis and meningitis in rare instances . Understanding the meningococcal lifestyle within the nasopharyngeal mucosa , a phase of infection that is prerequisite for disease , has been hampered by the lack of animal models . Herein , we compare mice expressing the four different human carcinoembryonic antigen-related cell adhesion molecules ( CEACAMs ) that can bind the neisserial Opa protein adhesins , and find that expression of human CEACAM1 is necessary and sufficient to establish intranasal colonization . During infection , in vivo selection for phase variants expressing CEACAM1-specific Opa proteins occurs , allowing mucosal attachment and entry into the subepithelial space . Consistent with an essential role for Opa proteins in this process , Opa-deficient meningococci were unable to colonize the CEACAM1-humanized mice . While simple Opa-mediated attachment triggered an innate response regardless of meningococcal viability within the inoculum , persistence of viable Opa-expressing bacteria within the CEACAM1-humanized mice was required for a protective memory response to be achieved . Parenteral immunization with a capsule-based conjugate vaccine led to the accumulation of protective levels of Nme-specific IgG within the nasal mucus , yet the sterilizing immunity afforded by natural colonization was instead conferred by Nme-specific IgA without detectable IgG . Considered together , this study establishes that the availability of CEACAM1 helps define the exquisite host specificity of this human-restricted pathogen , displays a striking example of in vivo selection for the expression of desirable Opa variants , and provides a novel model in which to consider meningococcal infection and immunity within the nasopharyngeal mucosa .
Invasive meningococcal disease is a suddenly striking , life-threatening condition with high mortality rates , even under intensive medical care [1] . Yet , despite its deadly potential , Neisseria meningitidis ( Nme ) is a normal resident of the healthy human throat [2] . The events that precipitate a transition from commensalism to invasive meningococcemia or meningitis remain a matter of contention that remain difficult to address in the absence of an experimental model . While several studies have described strategies to study N . meningitidis in the mouse [3] , [4] , [5] , [6] , [7] , [8] , the strict specificity of neisserial virulence factors precludes them from reproducing the intimate association of Nme with human mucosal tissues . Reflecting this host restriction , the neisserial colony opacity-associated protein adhesins ( Opa ) are integral outer membrane proteins that bind to select human carcinoembryonic antigen-related cell adhesion molecules ( CEACAMs ) [9] , [10] . Numerous in vitro studies have observed Opa binding to CEACAM1 , CEACAM3 , CEACAM5 and/or CEACAM6 , one or the other of which are expressed by most cells encountered by the meningococci [11] , [12] , [13] , [14] . Opa binding to any one of these CEACAMs is sufficient to allow bacterial adhesion and , in the case of polarized epithelial cells , transcytosis across epithelial monolayers [15] . The cellular response upon Opa binding does , however , depend upon the receptor bound because each CEACAM has the potential to elicit a distinct signaling response [16] , [17] , [18] , [19] . Here , we show that expression of a full-length human CEACAM1 transgene is necessary and sufficient to allow the meningococci to establish intimate attachment to the nasopharyngeal mucosa and prolonged colonization following intranasal challenge of mice . Mice expressing other human CEACAMs were not colonized , highlighting the central importance of CEACAM1 for meningococcal infection . In addition to providing the first evidence to support the critical contribution of Opa-CEACAM1 binding in the nasopharynx , this model reveals the in vivo phenotypic selection of expressed Opa variants that bind CEACAM1 , and allowed us to detail the relative contribution of innate and adaptive immune processes that provide a barrier to meningococcal colonization and disease .
CEACAM1 protein expression at the initial site of contact between Nme and the host nasopharyngeal mucosa is a prerequisite for this receptor to support meningococcal colonization . Consistent with its availability for Opa protein binding , the CEACAM1-humanized mice displayed human CEACAM1 protein on the apical side of the mucosa along the olfactory epithelium , the respiratory epithelium lining the maxillary sinuses and , in a spotted pattern , above the palate and the nasopharyngeal duct ( Fig . 1A ) . The transgene is expressed under the control of the human CEACAM1 promoter region , and careful histological analysis has established that its overall expression pattern matches well with that in humans [20] , [21] . Reinforcing this important point , we confirmed that CEACAM1 and CEACAM5 are also expressed on primary human nasal epithelial cells ( HNEPC ) ( Fig . 1B ) . In order to define the binding potential of the prototypical serogroup B strain MC58 to the relevant human CEACAM family members , all four MC58 opa genes were expressed in E . coli and used for gentamycin protection assays . Each MC58 Opa protein was observed to mediate host cell binding and bacterial engulfment in a CEACAM1-dependent manner ( Fig . 1C ) . Next , we intranasally inoculated three cohorts of mice with Nme: CEACAM1-humanized mice , CEABAC mice ( which express human CEACAM3 , -5 , -6 , -7 ) , and a control group of wild-type littermates . While meningococci could not be recovered from wild-type or CEABAC mice after the first day of infection , viable bacteria were recovered from CEACAM1-humanized mice for as long as seven days , with one transgenic animal still colonized at day ten ( Fig . 1D ) . Carriage was not found to be age-dependent , since mice were still readily colonized at 5–7 months of age ( Fig . 1E ) . Human CEACAM1 is , therefore , required for meningococcal colonization of the nasal tissues . To further characterize this model , we determined the minimal dose required to successfully induce meningococcal carriage by infecting mouse cohorts with varying numbers of CFUs . As little as one thousand CFUs of Nme MC58 was sufficient to colonize one-quarter of the CEACAM1-humanized mice . With increasing CFUs in the inoculum , the carriage frequency increased such that 75% of the cohort was infected by 105 CFUs ( Fig . 1F ) . The intranasally infected mice were routinely monitored for bacteremia , however none yielded a positive blood culture . To test whether human CEACAM1 impacts systemic infection by Nme , we used an intraperitoneal challenge model . Upon infection with 106 CFU of Nme MC58 , comparable survival curves were obtained with both genotypes ( Fig . 1G ) . Therefore , human CEACAM1 drives nasal carriage , but does not seem to facilitate bacterial replication during disseminated infection in mice . Neisserial Opa protein expression is phase-variable , turning on and off randomly during bacterial cell division [22] , [23] . When mice were inoculated with an Nme MC58 clone that had turned off Opa expression , CEACAM1-humanized mice were unexpectedly colonized at a rate comparable to mice infected with an Opa-expressing clone . However , strikingly , every recovered colony now expressed an Opa protein ( Fig . 2A , upper panels; compare inoculum to recovered colonies ) , indicating that nasal colonization selected for an Opa+ phenotype . There was no obvious selection for any particular Opa variant ( s ) in vivo , presumably because all variants in this strain could bind CEACAM1 ( Fig . 1C ) , making it likely that the frequency of expressing each allele would be related to the length of pentanucleotide sequences response for phase variable switching at each locus [24] , [25] . This selection for Opa-expressing bacteria was replicated using an Opa− clone selected from another prototypical serogroup B strain , H44/76 ( Fig . 2A , lower panels ) , confirming that this was not a strain-specific response . To confidently attribute the in vivo colonization to Opa-CEACAM1 binding , we disrupted all four opa alleles . Disruption of the Opa proteins did not cause gross defects in the strain , since their growth curve and protein expression pattern were indistinguishable from the parental strain ( Fig . S1D and E ) . However , while the parental strain efficiently colonized the CEACAM1-humanized mice , the Opa-deficient bacteria almost entirely lost their ability to persist after intranasal infection ( Fig . 2B ) . When combined , this represents the first direct evidence that the interaction between Nme Opa proteins and human CEACAM1 facilitates Nme colonization of the nasopharynx . Nme frequently live within the nasopharynx of healthy individuals , and are routinely considered normal flora when they remain at this site [26] . Considering that our model reflects this commensal state , we sought to assess whether asymptomatic colonization triggered an innate response . Upon infection with Nme , a robust inflammatory response marked by elevated chemokines KC , MIP-1α and MIP-2 as well as cytokines TNF-α and IL-1β was observed in the nasal tissue of CEACAM1-humanized mice and also , to a significantly lower extent , in wild-type mice ( Fig . 3 ) . While we are unaware of similar studies being performed in humans , these elevated cytokine responses reflect that seen in the nasal washes of children with Haemophilus influenzae [27] , which is presumed to colonize a similar niche within the human nasopharynx . In the CEACAM1-humanized mice , heat-inactivated Nme caused an inflammatory response similar to that of the viable wild-type strain , whereas the cytokine response to viable but Opa-deficient meningococci was significantly lower; in the case of TNFα and IL-1β , the response to Opa-deficient meningococci was indistinguishable from the uninfected mice . Consistent with Opa-CEACAM1 binding being required for the augmented response , presumably due to more intimate mucosal association and/or prolonged persistence of the Opa-expressing bacteria , the wild-type mice responded to wild-type , Δopa and heat-inactivated Nme in a manner reflecting that seen with Δopa bacteria in CEACAM1-humanized mice . Myeloperoxidase , a marker for neutrophil infiltration , was found to be elevated in all inoculated mice ( bottom right panel , Fig . 3 ) , indicating involvement of PMNs in this model . Correspondingly , significant infiltrates of PMNs were found in the nasal mucosa and lumen of both CEACAM1-humanized and WT mice upon challenge ( Fig . 4A ) . PMNs of both genotypes were positive for mouse Ceacam1 , and in CEACAM1-humanized mice , they also expressed human CEACAM1 ( Fig . S2A ) . To address the role of PMNs , we used an in vivo depletion strategy using the anti-Gr-1 antibody clone RB6-8C5 ( see Fig . S2C and S2D ) . In PMN-depleted mice , leukocytic infiltrates were virtually absent in nasal tissue ( Fig . 4A ) . Interestingly , Nme infection was accompanied by increased damage of the mucosal tissues in CEACAM1-humanized mice depleted of PMNs , as lesions in the otherwise intact mucosal epithelium were visible ( Fig . S2B ) ; such damage was not apparent in animals where PMNs were not depleted . In accordance with a protective role for PMNs in phagocytic clearance of meningococci , the majority of meningococci in infected nasal tissues were associated with PMNs ( Fig . 4B ) . Notably , in vitro infection experiments revealed that PMNs derived from bone marrow of CEACAM1-humanized mice bound meningococci more effectively than did those taken from WT mice ( Fig . S3 ) . When PMNs were depleted from the transgenic mice , Nme were more abundantly associated with the apical surface of the epithelium , corresponding with sites of human CEACAM1 expression , and in some instances even infiltrated into the tissue ( Fig . 4B ) . Tellingly , the bacteria were restricted to the lumen and were not tissue-associated in the PMN-depleted wild-type mice . Neutrophil depletion had a dramatic effect on Nme nasal colonization ( Fig . 4C ) . In CEACAM1-humanized mice , the number of viable meningococci recovered from nasal tissues was much higher than in untreated mice ( compared to Fig . 1C ) at same time points . Moreover , viable bacteria could still be recovered from transgenic mice after 14 days under these conditions . Even in WT animals , PMN depletion lead to a transient susceptibility to meningococcal carriage . However , while viable Nme were not recovered from wild-type mice at day seven , 64% of the transgenic animals were still colonized at this point . Therefore , PMNs play a crucial role for the rapid clearance of meningococci within the mucosa , which partially masks the positive effect that human CEACAM1 has on Nme persistence within the tissues . In addition to binding CEACAMs , Opa proteins can also facilitate inter-bacterial aggregation , which contributes to the colony opacity phenotype [28] . The transient susceptibility of PMN-depleted wild-type mice allowed us to assess whether in vivo selection for Opa protein expression depends upon the presence of human CEACAM1 ( Fig . S4 ) . While selection was not apparent in the wild type mice after one day infection ( Fig . S4Bii , compare upper and lower panels ) most colonies recovered after 3 days did express Opa . Together , these results suggest that mucosal CEACAM1 allows selective colonization by Opa-expressing Nme phase variants in the inoculum , but that Opa-dependent bacterial aggregation and/or other CEACAM1-independent benefits of Opa expression must also benefit Nme residing within the mucosa . As expected , the increase in myeloperoxidase activity apparent during the response to infection was reversed when neutrophils were depleted ( bottom right panel Fig . 4D ) . The nasal inflammatory cytokine response to infection was clearly altered upon PMN depletion . When neutrophils were absent , significantly higher levels of the chemokines KC , MIP-1α and MIP-2 were present when compared to neutrophil-sufficient CEACAM1-humanized mice , which , in turn , displayed strongly enhanced chemokine levels compared to non-infected mice ( top panels Fig . 4D ) . In WT mice , a similar yet less pronounced trend was observed for the CXC chemokines KC and MIP-2 but not for the CC chemokine MIP-1α . Interestingly , the pro-inflammatory cytokines TNF- α and IL-1β showed a fundamentally different pattern than the chemokines ( bottom left and middle panel Fig . 4D ) . TNF-α and IL-1β were both upregulated during infection of normal CEACAM1-humanized but not significantly in WT mice . Yet , upon PMN-depletion , TNF-α remained unchanged whereas IL-1β was actually decreased . Therefore , PMNs seemingly do not influence TNF-α but either directly or indirectly influence the release of IL-1β; whereas , in the absence of neutrophils , the increased bacterial burden and tissue damage leads to an enhanced chemokine response . The polysaccharide capsule of Nme is the major virulence factor protecting these bacteria against complement-mediated lysis and phagocytosis . Despite its importance during invasive disease , the capsule is thought to be dispensable for their ability to colonize the human nasopharynx [29] . Furthermore , in vitro studies suggest that the capsule sterically hinders adhesion to epithelial cells [30] , [31] . In considering these points , we tested the impact of the capsule on colonization by comparing wild-type meningococcal strains MC58 and H44/76 with their corresponding isogenic capsule-deficient mutants , MC58ΔsiaD and H44/76ΔsiaD , respectively . To our surprise , MC58ΔsiaD showed a decreased , yet not fully abrogated , ability to colonize the CEACAM1-humanized mice ( Fig . 5Ai ) , whereas H44/76ΔsiaD colonized the mice as well as the parental H44/76 strain ( Fig . 5Aii ) , indicating strain-specific differences in the effect of capsule on colonization . Neutrophil-depletion allowed significantly increased colonization by MC58ΔsiaD , suggesting that capsule confers resistance against phagocytosis or other neutrophil-mediated bactericidal mechanisms within the mucosa ( Fig . 5Ai ) . While most meningococcal disease is caused by encapsulated strains , invasive capsule-deficient Nme have recently emerged [32] , [33] , leading to suggestion that they have acquired novel virulence capacity . Nme strain 2275 is an invasive isolate that is naturally non-encapsulated and cannot produce CMP-NANA since it possesses the capsule null locus 2 ( cnl-2 ) in place of the capsule operon . Despite this defect , strain 2275 was virulent in a mouse invasive challenge [33] . Perhaps related to this point , these strains can use exogenous cytidine-5′-monophospho-N-acetylneuraminic acid ( CMP-NANA ) as a substrate to decorate their lipo-oligosaccharide ( LOS ) with sialic acid so as to reduce complement deposition [34] and , in some strains , also impede nonopsonic phagocytosis [35] . Notably , strain 2275 colonized the CEACAM1-humanized mice to an extent comparable to the unencapsulated MC58ΔsiaD ( Fig . 5Aiii ) , suggesting that its increased virulence is not due to correspondingly increased fitness within the mucosal tissues . Next , we sought to further explore the role of complement in controlling meningococcal nasal colonization . By injecting mice with cobra venom factor ( CVF ) , an agent that elicits an unbridled complement cascade , we triggered massive complement activation shortly before intranasal administration of wild-type ( encapsulated ) Nme MC58 . This treatment resulted in a significant reduction in colonization , indicating that activated complement components actually interfere with mucosal-associated meningococci ( Fig . 5B ) . Ultimately , CVF treatment leads to in vivo decomplementation lasting for several days , during which complement components are completely consumed ( Fig . S5A and S5B ) . When mice were infected 30 h after CVF injection , by which time they were completely decomplemented , we surprisingly saw no effect on colonization with MC58 ( Fig . 5Ci ) or H44/76 ( Fig . 5Cii ) . Therefore , while activated complement can combat infection within the mucosa ( Fig . 5B ) , complement components do not normally play a critical role in controlling nasal colonization . However , when both complement and PMNs were depleted , a strong increase in bacterial burden was observed that was significantly greater than either of the treatments alone ( Fig . 5C ) . Complement and neutrophils thus work synergistically as an innate barrier to counteract early-phase colonization . Exposure to pathogenic bacteria should prompt adaptive immune responses that protect against recurrent infections with the same infectious agent . To assess the adaptive response following colonization , we performed a recurrent infection experiment , in which mice were intranasally infected with 105 CFU of Nme H44/76wt , H44/76Δopa , or heat-inactivated H44/76 at the beginning of the experiment ( day 0 ) and again at day 21 ( infection schedule depicted in Fig . 6A ) . Intranasal challenge infections with a high-dose ( 108 CFU ) H44/76 were then performed either at day 21 , at which point mice had been previously exposed to a single bacterial inoculation , or at the end of the experiment at day 52 , by which point mice had been exposed to the meningococci twice before , and then colonization of the mice was assessed three days post challenge . Unexpected when considering that the mice had been colonized for up to 10 days prior , the CEACAM1-humanized mice could be re-infected with the same strain when challenged again on day 21 . However , when they were exposed twice before challenge at day 52 , meningococcal colonization was almost completely abrogated , indicating a sterilizing immune response ( Fig . 6B , left panel ) . Interestingly , when the mice were exposed twice to 105 viable H44/76Δopa or 105 heat-inactivated H44/76 , the former of which can replicate but not adhere to CEACAM1 while the latter can adhere but not replicate , no protection was observed . To address whether this is dose-dependent , we intranasally inoculated mice with 108 H44/76Δopa or heat-inactivated H44/76 before challenging with viable H44/76 to assess colonization ( Fig . 6B , right panel ) . Indeed , in this setting , the mice became protected against colonization with H44/76wt after two exposures , indicating the heightened immune response upon viable wild type Nme infection of the transgenic mice results from the increased delivery of antigens as the bacteria replicate in association with the CEACAM1-expressing tissues . Adaptive mucosal protection against bacteria typically involves IgA that inhibits adhesion and promotes bacterial aggregation , thereby facilitating their removal by cilia movement . Indeed , we found significant levels of Nme-specific IgA , but not IgG , in nasal lavage fluids of CEACAM1-humanized mice after twice being inoculated with 105 H44/76wt , but not in the other infection groups ( Fig . 6C ) . Protection against colonization correlated significantly with nasal anti-meningococcal IgA levels of individual CEACAM1-humanized animals ( Fig . 6D , left panel ) . In CEACAM1-humanized mice pre-infected with 105 H44/76Δopa or heat-inactivated H44/76 , nasal IgA titers remained low , and did not correlate with colonization ( Fig . 6D , middle and right panel ) . Nasal IgA levels poorly correlated with serum IgA levels and serum IgG levels , suggesting that mucosal IgA is locally produced rather than serum derived ( Fig . S6 ) . Serum Nme-specific IgG was found to rise quickly in CEACAM1-humanized mice infected with 105 H44/76wt , even before the second intranasal infection ( Fig . 6E ) . In WT mice , a significantly weaker response to H44/76wt emerged only after the second challenge . Exposure to 105 H44/76Δopa or heat-inactivated H44/76 did not elicit any detectable Nme-specific IgG . Serum anti-meningococcal IgA levels mirrored the findings for IgG , whereas meningococcal-specific IgM showed only a weak and transient elevation in both , WT and CEACAM1-humanized mice ( Fig . S7 ) . High concentrations of serum meningococcal-specific IgG were found in mice of both genotypes upon exposure to 108 H44/76Δopa or heat-inactivated H44/76 , demonstrating the dose-dependency of this response ( Fig . 6F ) . The occurrence of Nme-specific complement fixing antibodies defines a clinically relevant correlate of protection against invasive disease . The serum bactericidal antibody assay ( SBA ) was , therefore , used to monitor serum protection of the repeatedly infected mice . Strikingly , about 40% of CEACAM1-humanized mice developed positive SBA titres ( i . e . ≥8 , considering the use of rabbit complement [36] ) in response to the first exposure to H44/76 and almost 80% were protected after the second exposure , whereas less than 20% of WT mice showed any SBA titres after the second meningococcal challenge ( Fig . 6G , upper panel ) . Consistent with their low Nme-specific Ig titres , mice exposed to 105 H44/76Δopa or heat-inactivated H44/76 did not develop positive SBA titres . However , after high intranasal doses , robust SBA titres did emerge in both these groups ( Fig . 6G , lower panel ) . Plotting SBA titres versus Nme-specific serum IgG of individual animals revealed that persistent growth of Opa-expressing Nme in the tissues of CEACAM1-humanized mice led to protective SBA titres already at comparatively low titres of Nme-specific Ig . The threshold above which all analyzed serum samples were SBA positive ( i . e . ≥8 ) was ∼5 , 000 ng/ml in CEACAM1-humanized mice infected with 105 live H44/76wt ( Fig . 6H , upper left panel ) , compared to ∼15 , 000 ng/ml in WT mice exposed to the same dose ( Fig . 6H , upper right panel ) . The thresholds of both mouse strains exposed to 108 H44/76Δopa or heat-inactivated H44/76 were in a similar range ( 13 , 000–30 , 000 ng/ml ) . This suggests that meningococcal growth within the tissues elicits protective immunity more effectively than does a higher dose or repeated exposure to the same antigen . Interestingly , in CEACAM1-humanized mice repeatedly inoculated with Opa-expressing live or heat-inactivated bacteria , there was a very strong and highly significant correlation between serum IgG and SBA titres ( Fig . 6H ) . Therefore , Opa-CEACAM interaction might influence the antibody repertoire or functionality generated in response to intranasal exposure . A key , yet unexpected , feature governing the success of meningococcal serogroup C polysaccharide vaccines relies on their ability to induce sterilizing immunity within the mucosa [37] . We sought to determine whether the CEACAM1-humanized mouse model could serve as platform in which to assess the potential of novel vaccine candidates to protect against nasopharyngeal colonization . Since it represents a ‘gold standard’ as far as efficacy , we sought to test the effect of the serogroup C-conjugated polysaccharide vaccine . First , we established that serogroup C Nme could colonize the CEACAM1-humanized mice , providing evidence that the dependence on human CEACAM1 was not restricted to serogroups B strains . Next , we immunized the mice with the conjugate vaccine , alum alone , or no vaccine ( Fig . 7A and B ) . The vaccine elicited serogroup C-specific protection , conferring complete protection against the serogroup C strain without affecting infection by serogroup B strain . In both WT and CEACAM1-humanized mice , vaccination mounted robust serum Nme-specific IgG titers , whereas the rise in Nme-specific IgM was transient and only a weak Nme-specific IgA response was achieved ( Fig . 7C ) . In contrast to the IgA-dominated mucosal response to meningococcal infection of the CEACAM1-humanized mice , immunity conferred by the vaccine correlated with Nme-specific IgG without any IgA response being apparent in either mouse genotype ( Fig . 7D ) . Since nasal IgG and serum IgG concentration showed a significant correlation , the nasal IgG appears to be mainly serum derived ( Fig . S8 ) .
The intimate relationship between Nme and the human host begins with their attachment to the nasopharyngeal mucosa . From here , the bacteria may penetrate into the local submucosa [38] or , in very rare instances , disseminate to cause rapidly progressing invasive disease [39] . Herein , we have used mice expressing human CEACAM1 to establish a colonization model that reveals a central role for human CEACAM1 binding for meningococcal colonization and persistence within the nasopharynx . This development allowed us to consider both bacterial phenotype selection and the relative contribution of immune processes in vivo . Consistent with meningococcal infection relying on an ongoing selection for phase variants expressing the phenotype that allows persistence within its niche , bacteria persisting within the nasal passage were uniformly Opa-expressing , even when the mice were inoculated with Opa-negative isolates . This phase variant selection for Opa+ variants in vivo mirrors previous in vitro findings of Opa+-selection in primary nasopharyngeal cells [23] . Perhaps more surprisingly , the innate inflammatory response to meningococcal infection relied on bacterial Opa expression and human CEACAM1 expression in the tissues ( Fig . 2 ) . In fact , the mucosal inflammatory response in CEACAM1-humanized mouse was the same regardless of whether the Opa-expressing bacteria were alive or dead , while the relatively low cytokine response of these same mice to viable Opa-deficient Nme reflected that of Opa-expressing bacteria in wild-type mice ( Fig . 4 ) . The efficient CEACAM1-dependent engulfment ( Fig . 1B ) and/or delivery of Opa-expressing bacteria to the submucosal tissues ( Fig . 4B ) presumably explains this effect . Since this work focused on the functional interplay between meningococcal Opa proteins with human CEACAM1 , we cannot exclude any contribution of other factors , such as the pilus . While the pilus cannot interact with murine cells , it promotes interbacterial tethering that culminates in the formation of microcolonies [40] . However , we observed successful colonization with the serogroup C strain 90/18311 ( Fig . 7B ) which is not piliated ( Fig . S1A ) and - unlike that seen with Opa proteins ( Fig . 2A ) - there was no selection during colonization for pilus-expressing phenotypes ( data not shown ) . While disease caused by Nme occurs when these bacteria proliferate systemically , the absence of a bacteremic outcome in our model reflects normal colonization in humans . In fact , considering that less than 1 in 25 , 000 natural infections in humans lead to invasive meningococcal disease during endemic periods [41] , it seems likely that some as yet unidentified genetic and/or environmental cofactor ( s ) contribute to disease . Various groups have used neonatal and/or iron supplementation of mice to study invasive disease [5] , [6] , however these do not consider mucosal colonization . One study did establish persistent infection after intranasal inoculation of Swiss-Webster mice , however there is no basis to compare our studies since they did not localize the bacteria within the tissues [7] and we did not see any persistence of three different strains in the wild type FvB littermates of our CEACAM1 transgenic animals . The introduction of human alleles encoding other proteins targeted by other meningococcal virulence factors has proven fruitful for understanding invasive disease . Transgenic mice expressing the human complement regulator membrane protein , CD46 , reported to bind the neisserial pilus , are more susceptible to disseminated meningococcal infection [4] , [35] . One particularly important advance has been the demonstration that transgenic mice expressing the human serum iron transport protein transferrin are susceptible to invasive meningococcal disease because Neisseria sp . can readily access this iron pool [3] , [8] . Combining the CEACAM1 colonization model with these transgenes and/or environmental insults such as viral co-infection [3] , [42] , smoking [43] or extremes in humidity [41] may ultimately prove informative to understanding the transition from asymptomatic infection to disseminated disease . Individuals with complement deficiency have heightened susceptibility to invasive meningococcal disease [44] , prompting us to explore whether this key innate defense affected meningococcal colonization of the nasopharynx . It has been speculated that capsule is not necessary for nasopharyngeal colonization , since about 16% of meningococci isolated from healthy carriers are devoid of the capsule operon [29] . Three different capsule-deficient Nme strains used in this study colonized CEACAM1-humanized mice successfully at different frequencies ( 23% , 30% and 100% , respectively; Fig . 5A ) , reflecting the situation in humans well . The finding that loss of capsular expression had a more marked effect on MC58 than it did on H44/76 was unexpected . Considering that these strains are both typed as B:15:P1 . 7 , 16 and ST-32 , and that genome alignments reveal a high degree of sequence similarity between them [38] , [39] , it seems most plausible that the phase variation and/or antigenic variability of some as yet uncharacterized virulence factor may account for this difference . Still , most meningococci in human carriers are encapsulated , and our model suggests that the capsule can facilitate their survival within the mucosa . Global activation of the complement cascade using cobra venom factor conferred protection against subsequent meningococcal challenge , suggesting that the meningococci's ability to bind complement-regulatory factors [45] , [46] , [47] , [48] , [49] may contribute to its fitness within the mucosa rather than just during invasive disease . Perhaps surprisingly , however , mice that were depleted of late complement components were not more susceptible to meningococcal colonization unless neutrophils were simultaneously depleted ( Fig . 5C ) , implying a buttressed defense with both factors contributing to protection . The absence of a relevant experimental model in which to assess colonization has led to serum bactericidal antibody becoming the primary correlate of protection for any meningococcal vaccine candidate . Our observation that the SBA titres did not strictly correlate with sterilizing immunity ( Fig . 6B and 6G ) is , therefore , both unexpected and enlightening . This presumably results , at least in part , from the requirement for Nme-specific Ig at the mucosal surface to confer protection ( Fig . 6C ) . Notably , while heat-inactivated Nme elicited a cytokine response indistinguishable from that towards viable Opa-expressing meningococci , Nme-specific IgA did not arise unless viable bacteria persisted in the tissues . Since herd immunity relies on the eradication of Nme carriage , this has obvious implications for the advent of any nasal-targeted vaccine . In contrast to naturally-acquired immunity , the serogroup C capsule-conjugate vaccine generated Nme-specific IgG in the mucosa without any IgA response ( Fig . 7 ) . It is important to consider that the conjugate vaccine-induced response targets the serogroup C capsule , whereas the sialic acid-based serogroup B capsule is non-immunogenic , implying that the differences in immune response may reflect this difference in antigen composition . However , our findings mirror observations made in human vaccinees that received serogroup C meningococcal vaccines , in which memory responses yielded nasal IgG but not IgA [37] , [50] . While the human studies could not challenge these individuals with Nme , our studies confirm that the systemic IgG response arising from parenteral immunization penetrates the mucosa so as to confer sterilizing immunity . Parenteral immunization thus tends to elicit a systemic response that promotes SBA and mucosal immunity , whereas nasal infection can produce localized protection with little systemic Ig . When considering these differences , they must make us pause regarding the strict reliance on SBAs when considering the potential susceptibility of individuals either prior to or post-vaccination with meningococcal vaccines .
N . meningitidis strains were grown on GC agar ( Becton Dickinson , Sparks , USA ) supplemented with IsoVitalex ( Becton Dickinson , Sparks , USA ) at 37°C with 5% CO2 and a water saturated atmosphere . Details about strains and their mutants are reviewed in table S1 . Opa and pilin expression status , confirmation of capsule type ( and absence in knockout mutants ) and comparative growth curves of mutants and parental strains of all meningococci used in this study were determined by western blotting as shown in Fig . S1 . For expression in E . coli , all four different opa genes from N . meningitidis MC58 were subcloned into pTrc99A vector . E . coli were grown on LB agar at 37°C . To induce expression in transformants of the opa genes , IPTG was added to the growth media at 40 µg/ml . All cells were incubated in 37°C incubators equipped with 5% CO2 and a water-saturated atmosphere . Hela cells stably expressing CEACAMs or the empty vector were maintained in RPMI+10%FBS . Primary normal nasal epithelial cells HNEPc were purchased from Promocell ( C-126200 ) and cultured in Keratinocyte serum-free media supplemented with 0 . 05 mg/ml bovine pituitary extract and 5 ng/ml epidermal growth factor ( Gibco 17005-057 ) . Binding of E . coli expressing N . meningitidis MC58 Opa proteins and their invasion into human CEACAM1 , CEACAM3 , CEACAM5 or CEACAM6 expressing HeLa cells was performed as described elsewhere [9] . Bacterial suspensions of about 1010/ml were admixed with an equal volume of twofold concentrated SDS loading buffer containing 10% beta-mercaptoethanol and were boiled for 10 min prior to analysis on 12% ( for detection of Opa protein ) or 15% ( for detection of pilin ) SDS-polyacrylamide gel electrophoresis and subsequent transferred to nitrocellulose membrane ( Hybond C-extra , Amersham Biosciences , Little Chalfont , UK ) . Opa proteins were detected using mouse monoclonal antibody 4B12C11 , which detects most gonococcal and meningococcal Opa proteins [51] . Pilin was detected using mouse monoclonal antibody 10H5 . 1 . 1 , a kind gift of Dr . Maggie So ( University of Arizona , Tucson , USA ) . Generation of CEACAM1-humanized mouse line was described in Gu et al . [20] and CEABAC mice expressing human CEACAM3 , 5 , 6 , 7 were described by Chan and Stanners [52] . Both mouse lines were on FvB background . In each case , transgenic animals were bred with wild-type animals to provide wild type littermates as controls . All animal experiment procedures approved by the Animal Ethics Review Committee of the University of Toronto ( Permit Numbers: 20008007 and 20008657 ) , which is subject to the ethical and legal requirements under the province of Ontario's Animals for Research Act and the federal Council on Animal Care ( CCAC ) . All efforts were made to minimize suffering . The infection protocol used in this study is similar as described in Yi et al . [7] . For intranasal infection , six week old mice were anesthetized with Isofluran ( Baxter , Missisauga , Canada ) inhalation and a total of 10 µl inoculum containing the indicated amount of meningococci were applied to both nares . An overnight lawn of growth of meningococci was harvested into 1 ml of PBS containing 1 mM of MgCl2 ( PBS/Mg ) and OD600 was measured to adjust the number of bacteria . For inoculum preparation , one volume of bacterial suspension was mixed with one volume of sterile filtered 32 mg/ml human holo-transferrin in PBS/Mg ( Sigma Aldrich , Oakville , Canada ) . To ensure bacterial dosage in every experiment , serial dilutions were plated onto GC agar supplemented with IsoVitalex . At indicated time points , animals were sacrifized by CO2 inhalation and blood was drawn by cardiac puncture . CFU counts were assessed by retrograde lavage of the upper airways through the trachea with 0 . 5 ml of PBS/Mg and swabbing of the exposed nasal cavities using aluminum shaft applicators ( Puritan Medical Products , Guilford , USA ) resuspended into 500 µl of PBS/Mg . Serial dilutions of samples were plated onto GC agar plates supplemented with IsoVitalex and VCNT inhibitor ( Becton Dickinson , Sparks , USA ) to suppress outgrowth of nasal flora . After overnight incubation of inoculated plates , meningococcal colonies were enumerated and expressed as the sum of recovered colony forming units ( CFU ) from each mouse . For intraperitoneal injection , the overnight growth of meningococci from a GC agar plate supplemented with IsoVitalex was resuspended in 10 ml of Brain-Heart- Infusion ( BHI ) ( Becton Dickinson , Sparks , USA ) supplemented with 60 µg/ml Deferoxamine mesylate ( Sigma Aldrich , Oakville , Canada ) as iron chelator and incubated at 37°C under constant agitation for 4 h . Then , the OD600 of the suspension was assessed and the inoculum adjusted in BHI . Serial dilutions of the inoculum were plated onto GC agar plates supplemented with IsoVitalex to ensure correct bacterial concentration . Six to eight week old mice were injected 200 µl of inoculum and , at a different site , 200 µl sterile saline containing iron dextran ( Sigma-Aldrich , St . Louis , USA ) commensurate with 2 mg Fe3+ as a source of iron . For immunization , the meningococcal serogroup C polysaccharide conjugate vaccine NeisVac-C ( Baxter , Mississauga , Canada ) was used . Mice received 1 µg of polysaccharide , conjugated to 1–2 µg of tetanus toxoid and adsorbed to 50 µg alum , corresponding to 1/10 of a single dose as purchased from manufacturer , subcutaneously on day 0 , 21 and 42 . Control groups received an equal amount of alum ( alhydrogel , Invivogen , San Diego , USA ) alone instead . Where indicated , mice were injected intraperitoneally with 200 µl sterile saline containing 20 µg with Cobra Venom Factor ( CVF ) ( Quidel , San Diego , USA ) at either 4 h or 30 h prior to infection to either over-activate the complement system or completely deplete the mice of serum complement , respectively . Neutrophil depletion for up to three days was achieved by a single i . p . injection of 200 µl of sterile saline containing 250 µg of RB6-8C5 ( hybridoma line courtesy of Prof . Paul Allen , Department of Pathology and Immunology , Washington University School of Medicine , St . Louis ) at 24 h prior to infection . If neutrophil depletion was needed for a longer period of time , the RB6-8C5 injection was repeated every 48 h . Five µl of tail vein blood were obtained from mice and immediately diluted in 45 µl of ice-cold PBS containing 10 mM EDTA to inhibit complement degradation . Cellular components were removed by brief centrifugation at 1000× g for 1 min and supernatant was diluted 1∶10 in PBS , 10 mM EDTA . An equal volume of 2× SDS buffer without reducing agent was added and the sample was not boiled prior to loading onto 6% SDS-PAGE to avoid dissociation of C3 into its alpha and beta chain . After transfer to nitrocellulose membrane ( Hybond C-extra , Amersham Biosciences , Little Chalfont , UK ) , mouse C3 was detected using goat-anti-mouse C3 antiserum ( Genetex , Irvine , CA , USA ) . Paraffin-embedded skull sections were stained for human CEACAM1 using CEA-Dako or a matched negative control ( Dako , Burlington , Canada ) . Mouse Ceacam1 was detected using rabbit-anti-mouse Ceacam1 ( generous gift from Prof . Nicole Beauchemin , McGill University , Montreal ) or normal rabbit serum as negative control . For detection of neutrophils , NIMP-R14 or a rat IgG isotype control ( Abcam , Cambridge , USA ) was used and the signal amplified with goat-peroxidase-anti-peroxidase conjugate ( Jackson Immunoresearch , West Grove , USA ) . HRP-conjugated secondary antibodies were obtained from Jackson Immunoresearch , West Grove , USA . Visualization was achieved by incubation with 3 , 3′-Diaminobenzidine ( Sigma-Aldrich , Oakville , Canada ) according to manufacturer's recommendations . After immunostaining , nuclei were counterstained using Harris' Hematoxylin ( VWR , West Chester , USA ) and samples were dehydrated in an ascending ethanol/xylene series and mounted with SHUR/Mount ( Triangle Biomedical Sciences , Durham , USA ) . Paraffin-embedded sections of mouse skulls were stained for human CEACAM using mouse monoclonal D14HD11 ( Abcam , Cambridge , USA ) and meningococci were detected by rabbit polyclonal anti-meningococcal antiserum . Secondary antibodies were goat-anti-mouse-IgG-Alexa647 and goat-anti-rabbit-IgG-Alexa594 ( Life Technologies , Burlington , Canada ) . Autofluorescence was quenched by 10 min incubation in 0 . 3% Sudan black ( Sigma-Aldrich , Oakville , Canada ) in 70% ethanol , samples washed with PBS and mounted with Prolong Gold antifade with DAPI ( Life Technologies , Burlington , Canada ) . Images were taken on a Leica DM IBRE epifluorescence microscope ( Leica , Wetzlar , Germany ) . Primary murine neutrophils were isolated from bone marrow of femur and tibia of CO2 euthanized mice and purified on a discontinuous Percoll gradient ( 80%/65%/55% ) . PMNs were recovered at the interface between the 80% and 65% Percoll solution and washed in PBS . Purity of neutrophils using this technique is usually greater than 90% . PMNs were seeded onto mouse-serum coated coverslips at a density of 5×105 in 500 µl in DMEM supplemented with 5% FBS , spun down for 10 min at 63× g and let rest for 3 h at 37°C , 5% CO2 . Then , cells were infected at a multiplicity of infection of approximately 25 of freshly harvested meningococci grown overnight on GC agar plates supplemented with IsoVitalex . The bacteria were spun onto the PMNs by centrifugation at 63× g for 5 min and incubated at 37°C , 5% CO2 for 30 min . Then , samples were washed with HBSS and fixed with 3 . 7% paraformaldehyde . Nme were stained using rabbit polyclonal anti-meningococcal antiserum , actin as stained using alexa 488-labeled phalloidin and nuclei were stained using DAPI . Twenty-five to fifty cells per condition were randomly imaged and the number of adherent/internalized bacteria manually quantified . Nasal wash fluid was collected from mice as described in the intranasal infection procedure , but 0 . 7 ml PBS containing 5 mM EDTA , 2 µg/ml Aprotinin , 2 . 5 µg/ml Leupeptin and 1 µg/ml pepstatin ( all obtained from Sigma-Aldrich , Oakville , Canada ) were used for lavage . When nasal swab sample were collected , they were resuspended into the nasal wash fluid obtained from the same mouse ( i . e . the nasal tissue sample for each mouse contained nasal wash and mucosal tissue and debris collected with the swab ) . The samples were kept on ice until further processing . The samples were spun on a tabletop centrifuge at 13 , 000 rpm for 10 min at 4°C and the supernatant recovered and sterile filtered through 0 . 22 µm cellulose acetate SpinX columns ( Corning Inc . , Corning , USA ) . Samples were used neat or in 1∶10 dilution for chemokine ELISAs using DuoSet ELISA Development System kits for mouse CXCL-1/KC , CCL3/MIP-1α or mouse CXCL-2/MIP-2 ( R&D Systems , Minneapolis , USA ) , and for cytokine ELISAs using BD OptEIA mouse TNF ELISA Set II and mouse IL-1β ELISA set ( BD Biosciences Pharmingen , San Diego , USA ) , according to the manufacturer's instructions . Pellet of cells and tissue debris from mouse nasal samples after centrifugation described above for cytokine ELISAs was used to determine myeloperoxidase activity as marker for neutrophil infiltration . Pellets were resuspended in 300 µl of 50 mM potassium phosphate buffer pH 6 , 0 containing 50 mM Hexadecyltrimethylammonium bromide ( HTAB ) and homogenized for 10 s . Then , 700 µl of 50 mM potassium phosphate buffer pH 6 , 0 were added and sonicated for 30 s . The samples were snap-frozen and thawed three times and centrifuged 10 min at 13 , 000 rpm on a tabletop centrifuge and supernatants were transferred to Spinx columns for sterile filtration as above . Peroxidase activity was measured relative to a standard curve prepared from human myeloperoxidase ( Sigma-Aldrich , Oakville , Canada ) by incubation with SureBlue peroxidase substrate ( KPL , Gaithersburg , USA ) , following manufacturer's instructions . Maxisorp 96 well flat-bottom immuno plates ( Nunc , Rochester , USA ) were coated with 50 µl per well of a suspension at OD600 of 0 . 2 of heat-inactivated ( 56°C for 30 min ) N . meningitidis H44/76 in PBS and allowed to dry . Wells were washed four times with wash buffer ( PBS containing 0 , 05% Tween-20 ) and blocked 1 h with PBS containing 5% BSA before addition of 50 µl per well of diluted sample . After incubation for 2 h at room temperature , wells were washed three times and 50 µl per well of 1∶10 , 000 dilution of AP-goat-anti-mouse IgG Fc ( γ ) or AP-goat-anti-mouse IgM ( Jackson Immunoresearch , West Grove , USA ) , or AP-goat-anti-mouse IgA ( Abcam , Cambridge , USA ) , were added . After 1 h incubation , wells were washed thrice and 100 µl per well of BLUEPHOS AP detection substrate ( KPL , Gaithersburg , USA ) were added and plates incubated at 37°C . Then , the reaction was stopped by adding 100 µl/well of AP-Stop solution ( KPL , Gaithersburg , USA ) and OD620 was measured . Complement-mediated serum bactericidal antibody activity was measured using washed , exponential growth-phase bacteria grown to OD600 of 0 . 6 in BHI supplemented with 0 . 25% glucose and 0 . 02 mM CMP-NANA . For measuring bactericidal activity , the mouse sera were heat-inactivated ( 56°C for 30 min ) to remove endogenous complement activity and were added to the bacteria in serial dilutions . Baby rabbit complement ( Pel-Freez , Rogers , USA ) was added as exogenous source of complement at a final concentration of 20% . An aliquot of each reaction was plated onto GC agar plates supplemented with IsoVitalex directly upon addition of the complement ( t = 0 ) and after 1 h incubation of the mixture at 37°C . Viable CFU were enumerated and serum bactericidal antibody assay for each mouse serum dilution considered positive when CFU counts after 1 h incubation were <50% of those at t = 0 . | Neisseria meningitidis ( Nme ) , a common cause of bacterial meningitis , are carried asymptomatically in the nasopharynx by a substantial proportion of healthy individuals . Their strict adaptation to the human as host has so far impeded the development of animal models to study the meningococcal lifestyle in vivo . While several human CEACAMs are recognized by the neisserial Opa protein adhesins , we show here that the expression of human CEACAM1 in transgenic mice is necessary and sufficient to allow nasal colonization by Nme . The dependence on human CEACAM1 is attributable to the Opa proteins , since intranasal infection with Opa-negative colonies of Nme selects for bacteria expressing Opa proteins , and genetically Opa-deficient meningococci are unable to colonize these animals . We use this new mouse model to examine how innate immune factors such as neutrophils and complement limit colonization . Furthermore , we compare how adaptive responses elicited by colonization and those generated by parenteral vaccination differentially confer sterilizing immunity . Together , this work provides the first evidence of the critical nature of Opa-CEACAM1 binding in vivo , demonstrates that this is a major determinant of the host restriction by Nme , and reveals a clear disparity between immune correlates of sterilizing immunity conferred by natural colonization versus parenteral immunization . | [
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] | 2013 | In Vivo Adaptation and Persistence of Neisseria meningitidis within the Nasopharyngeal Mucosa |
Trypanosoma cruzi , the causative agent of Chagas disease , presents wide genetic diversity . Currently , six discrete typing units ( DTUs ) , named TcI to TcVI , and a seventh one called TcBat are used for strain typing . Beyond the debate concerning this classification , this systematic review has attempted to provide an inventory by compiling the results of 137 articles that have used it . A total of 6 , 343 DTU identifications were analyzed according to the geographical and host origins . Ninety-one percent of the data available is linked to South America . This sample , although not free of potential bias , nevertheless provides today’s picture of T . cruzi genetic diversity that is closest to reality . DTUs were genotyped from 158 species , including 42 vector species . Remarkably , TcI predominated in the overall sample ( around 60% ) , in both sylvatic and domestic cycles . This DTU known to present a high genetic diversity , is very widely distributed geographically , compatible with a long-term evolution . The marsupial is thought to be its most ancestral host and the Gran Chaco region the place of its putative origin . TcII was rarely sampled ( 9 . 6% ) , absent , or extremely rare in North and Central America , and more frequently identified in domestic cycles than in sylvatic cycles . It has a low genetic diversity and has probably found refuge in some mammal species . It is thought to originate in the south-Amazon area . TcIII and TcIV were also rarely sampled . They showed substantial genetic diversity and are thought to be composed of possible polyphyletic subgroups . Even if they are mostly associated with sylvatic transmission cycles , a total of 150 human infections with these DTUs have been reported . TcV and TcVI are clearly associated with domestic transmission cycles . Less than 10% of these DTUs were identified together in sylvatic hosts . They are thought to originate in the Gran Chaco region , where they are predominant and where putative parents exist ( TcII and TcIII ) . Trends in host-DTU specificities exist , but generally it seems that the complexity of the cycles and the participation of numerous vectors and mammal hosts in a shared area , maintains DTU diversity .
Trypanosoma cruzi is a pathogenic microorganism , the causative agent of Chagas disease , characterized by high genetic and phenotypic intraspecific diversity . Population genetics suggests that clonality is an important mode of propagation of the natural populations of T . cruzi [1] , although , likely sexual reproduction [2 , 3] and recombination events occur to some extent and are important mechanisms that generate genetic diversity within the taxon , as discussed in a recent review [4] . The consensual nomenclature recognizes six discrete typing units ( DTUs ) named TcI to TcVI and a recently proposed seventh , Tcbat [5–7] . This classification is widely used as a reference in epidemiological studies . However , there is not consensus on the best method to identify the different DTUs . Similarly , the evolutionary relationships between the DTUs and therefore the evolutionary history of T . cruzi continue to be researched [8] . Several mechanisms of evolution have been recognized such as clonality , hybridization , and conventional and nonconventional genetic exchanges . In addition , several studies have demonstrated the extraordinary plasticity of the T . cruzi genome . The evolutive relationships among these DTUs has not been fully elucidated , but two of them ( TcV and TcVI ) clearly have a hybrid origin with TcII and TcIII as putative parents [9] according to the authors , TcIII and TcIV could also originate from a hybrid between TcI and TcII [10 , 11] but some claim that is not the case [12 , 13] . TcI and TcII remain two pure lines that are evolving separately from a common ancestor dating from approximately 1–3 million years ago [11 , 13] . The main properties of the different DTUs have been reported previously [3 , 5 , 14 , 15] . Briefly , ( i ) TcI has a wide distribution , from the southern United States to northern Argentina and Chile; this DTU is the most frequently sampled in sylvatic cycles , but it is also frequent in domestic cycles and it is the dominant DTU responsible for the transmission of Chagas disease in endemic countries located north of the Amazon basin; ( ii ) studies show that TcII , V and VI are more likely to be associated with domestic cycles and patients with chronic Chagas disease in the Southern Cone countries and Bolivia; ( iii ) TcIII and IV are mainly sampled in rainforest sylvatic cycles; ( iv ) Tcbat previously identified in bats , has recently been found in humans [7 , 16–18] . It is well known that various DTUs can coexist in the same vector and in a single host [19–21] . The different DTUs present substantial genetic diversity . Various reports have shown that the parasite’s genetic diversity has a profound impact on its epidemiological , biological and medical characteristics [22] . Consequently , it is indispensable to characterize the genotypes that are circulating in space and in hosts . Moreover , the tracking of the different genotypes is of great interest in eco-epidemiology , providing a better understanding of epidemiological systems . After the biogeographic overview of T . cruzi DTUs by Miles and his colleagues [23] , no other exhaustive review has been done , while very numerous new genotyping studies using new genetic markers and additional parasite strains have been conducted . Although we are conducting studies on the limits of DTUs classifications of T . cruzi strains and their actual existence as genetically separated units , it seemed important to take all existing data that refer to the current classification and to examine the geographic properties and host specificities of the different DTUs .
Data were obtained from a total of 137 articles ( including our own published results ) selected after searching PubMed ( http://www . ncbi . nlm . nih . gov/pubmed ) with “DTU” , “genetic characterization” , “lineage” , “genotype” , “isozyme” , “isoenzyme” , and “Trypanosoma cruzi” as key words . This research , as exhaustive as possible , was updated to April 27 , 2016 . Research has also been conducted by authors having worked on the genetic characterization of T . cruzi strains . For our published data , additional data , not present in the publications , was included in the current inventory because this information was available from our own records . For example , the names and data concerning the strain origins analyzed in Barnabé et al . [24] were added here . The publications included in the inventory used genetic markers that allowed DTU typing according to the consensual nomenclature in 6–7 DTUs [5 , 6] . Moreover , in some cases correspondences between typing methods with different markers were used for the data interpretation [6 , 25] . The data are shown in an Excel spreadsheet ( S1 Table ) where each line corresponds to a single determination from an isolate , a strain , a laboratory clone , mammal blood or tissue samples , and different vector digestive tract samples ( “sample type” column in S1 Table ) . Several lines were recorded when different DTUs were detected in a strain and its laboratory clones . When more than 1 DTU was detected in one vector or mammal host ( mixed infection ) , several lines corresponding to each DTU were recorded in the file . A total of 6 , 343 determinations were compiled . Each of them has a code corresponding to the strain/sample name reported in the publications , except for the records not identified with a name but only counted in publications , which we have labeled “anonymous” . In a few publications , undistinguished DTUs were reported for part of the identifications; consequently , additional categories were created for them: TcI/TcII ( three cases ) , TcII/TcV/TcVI ( 26 cases ) , TcII/TcVI ( two cases ) , TcIII/TcIV ( 31 cases ) , and TcV/VI ( 47 cases ) . These undistinguished DTUs accounted for 1 . 7% of the total inventory . The geographical origin was informed by the country name ( no missing data ) , the upper continental subdivision of North , Central , and South America , the upper administrative divisions such as state , province , department or region , and the lower administrative divisions such as municipality , province , or community according to the information existing in the publications . The collection dates of the strain or biological samples were not always documented ( 52 . 5% of missing year data ) . Host origin was generally informed by the species ( 31 missing data ) , and columns were added indicating the order , genus and tribe for the triatomines . Also , the cycles to which the different hosts belonged were classified as “domestic” when the hosts were living and/or were captured in the intra- and peridomicile areas , and “sylvatic” when the hosts were captured in the field outside domestic areas . When the location of the capture site was missing , the wild mammals where classified in the sylvatic cycle except for the synanthropic species such as opossums and rodents for which the information was considered as unknown ( uk ) . The information on the methods used for the characterization of the DTUs is also included in S1 Table . The first column indicates if the DTU was characterized at nuclear or mitochondrial level or both , the second one indicates the method ( s ) used , and the third one on the markers , the names of the genes , or the number of loci for MLMT ( multilocus microsatellite typing ) and MLEE ( multilocus enzyme electrophoresis ) .
The 6 , 343 samples of T . cruzi DTUs compiled in this review were identified in vectors and mammalian hosts from 19 different countries , covering an area from the southern United States to Argentina ( S2 Table ) . No data is available from Belize in Central America , and Uruguay and Guyana in South America . The vast majority of data relate to South America ( 90 . 7% ) . The DTUs were identified in 86 genera ( 32 missing cases ) , 158 different species of which 42 are vectors belonging to 7 genera ( Dipetalogaster , Eratyrus , Meccus , Mepraia , Panstrongylus , Rhodnius , and Triatoma ) . Approximately of the identifications in South America 49 . 3% were from vector species; however , in North and Central America most of the identifications were from vectors ( 69 . 3% and 65 . 8% respectively ) . The mammal species belong to nine orders of which the most represented is the Primate order ( 61 . 5% ) , because 59 . 4% of the identifications in mammals were made in samples from humans ( n = 1902 ) . One-third of the DTU identifications ( 31 . 0% ) corresponded to parasites from hosts ( vectors and mammals ) captured in sylvatic ecotopes , 57 . 6% from intra- and peridomestic hosts , and the others were undetermined ( n = 719 , 11 . 3% ) because in several studies the origin of the vectors was not specified . In 1 . 7% of the samples , the DTU ( n = 109 ) was reported as a group of DTUs: ( i ) in one dog , 15 coati from Brazil , and ten triatomines from Argentina , TcII , TcV , and TcVI were not distinguished; ( ii ) TcII or TcVI was reported in two T . infestans from Paraguay; ( iii ) 47 infections with TcV or TcVI in dogs , humans , T . infestans from Chile and Bolivia and P . megistus in Brazil were reported; ( iv ) in 31 vectors and mammal hosts from Brazil and Mexico TcIII/TcIV were not discriminated; and ( v ) in three cases TcI and TcII were not discriminated in T . pallidipennis . In the 6234 other records , TcI was found in approximatively 60 . 0% of the overall identifications; TcII , TcV and TcVI were identified in around 10% each; and TcIII , TcIV and Tcbat were rarer with percentages ≤ 3 . 6% . Fig 1 presents the proportions of DTUs observed , excluding from the calculation the ambiguous DTU determinations over the entire endemic area , and in North , Central and South America ( see below ) . According to the current available records , the DTU distribution was different between North , Central , and South America ( Fig 1 ) . In Central America only two DTUs ( TcI and TcIV ) were identified while all DTUs were detected in South America . In North America the latest studies have identified TcII , TcV and TcIII in addition to TcI and TcIV , which remain the major strains , in Central America . In South America the DTU distribution was highly variable depending on the country , and the current trend is a predominance of TcI north of the Amazon and the presence of all DTUs south of the Amazon with abundance of TcV and TcVI ( Fig 2 ) . Tcbat is a recently proposed DTU that is genetically more closely related to TcI than to any other DTU . Therefore this DTU is probably underestimated because it is not recognized by the markers used in many publications , and consequently it may have been erroneously equated with TcI . This DTU was identified in 59 bats belonging to 12 different species in Brazil , Colombia , and Ecuador [16 , 17 , 26 , 27] , in one specimen of T . sordida from the State of Mato Grosso do Sul State in Brazil [28] , and in a Colombian patient infected with a mixture of TcI and TcBat [18] . As mentioned above , TcI was the most frequently identified DTU in the overall sample , with a lower percentage in South America ( 58 . 2% ) than in North America ( 79 . 5% ) and Central America ( 93 . 3% ) . It was identified in all the countries included in the study . In South America , the low frequencies of TcI in Argentina ( 19 . 9% of 589 determinations ) and Paraguay ( 2 . 8% of 181 ) contrasted with the proportions of this DTU in the other South American countries ( at least > 47 . 0% ) ( Fig 2 ) . TcII was much more rarely identified ( 9 . 6% of overall DTUs identified ) . It was not identified in Central America out of 120 identifications , and only 13 identifications were reported from North America out of 459 ( 2 . 8% ) . Eight of these 13 TcII were found in Mexico , four in T . dimidiata captured in domestic cycles in the state of Veracruz [29] and four in Meccus pallidipennis collected in Michoacan [30] . The five other identifications were from mice and rats captured in the immediate surroundings of the dwelling of the first described autochthonous case of T . cruzi transmission in Louisiana , near New Orleans [31 , 32] . In South America , TcII presents a higher proportion , reaching 10 . 4% and was reported in Colombia , Surinam , Peru , Bolivia , Brazil , Argentina , Paraguay and Chile . TcIII and TcIV , which are thought to result from ancestral hybridization between TcI and TcII , reached 3 . 4% and 3 . 6% of the identifications , respectively . In North America , both of these DTUs were reported in Mexico in several publications [29 , 30 , 33 , 34] , but for the moment only TcIV has been identified in the US [24 , 31 , 35 , 36] . In Central America , only TcIV has been identified in Guatemala in humans and vectors [37 , 38] . In other Central American countries , neither TcIII nor TcIV has been reported . In South America , TcIII could be more cosmopolitan ( Argentina , Bolivia , Brazil , Chile , Colombia , Paraguay , Peru and Venezuela ) than TcIV , which has not yet been identified yet in Argentina , Chile and Paraguay . The last two DTUs , TcV and TcVI , were the recent hybrids , derived from hybridizations between TcII and TcIII . These DTUs showed the most differential geographical distribution . Indeed , TcV was identified in North America in exceptional cases in Mexico ( Veracruz ) in T . dimidiata as well as above-mentioned TcII [29] . TcV and TcVI have never been identified in US in 148 determinations , nor in Central America in 120 cases . In contrast , in South America , these DTUs together have frequently been identified in several countries , Argentina ( 76 . 9% ) , Bolivia ( 44 . 6% ) , Chile ( 28 . 6% ) and Paraguay ( 55 . 2% ) —but very rarely in others such as in Colombia ( 1 . 1% ) [24 , 39–41] , Ecuador ( 3 . 3% ) [42] , and Brazil ( 1 . 5% ) [24] . In Peru they were identified in 13 . 0% [24 , 43 , 44] . Moreover , when the two DTUs coexist , different proportions can be observed in the different countries . The most remarkable case was the identification of TcV and TcVI in Bolivia with 43 . 1% and 1 . 0% respectively , while in Argentina TcVI was more common ( 50 . 0% ) and TcV less frequently detected ( 26 . 5% ) .
Based on the available typing data , the first outstanding result is the predominance of TcI strains . This DTU , genetically diversified , is found throughout the geographic distribution of T . cruzi and in all cycles where it is always dominant . There are probably no ecological systems ( i . e . geographical areas where the parasite evolves between mammalian hosts and vectors specific species ) where TcI is absent . However , it appears that TcI strains do not develop well in some mammal species such as those within the order Cingulata since this order is rarely infected with TcI ( Table 3 ) . The ecological systems are usually complex networks of relationships involving many species of mammals and vectors , and strain diversity may be maintained because of differential interactions between the parasite’s hosts and genotypes . TcI is an old DTU that has evolved since 3–16 MYA as previously proposed [71] , and its very high genetic diversity is consistent with a long-term evolution . Moreover , recombination between TcI strains appears to be more frequent than previously thought [2 , 3 , 72] . The recombination events ( i . e . sex ) generally increase the variability of the organisms and thus increase their resilience , allowing new areas to be conquered and especially new hosts that have probably played a key role in the large dispersion and adaptation of TcI . Another question is the geographical origin of TcI . A North-South clustering was recognized , even if some incongruence remains to be explained [73–75] . In an analysis of TcI , the Gran Chaco region was proposed as an origin , while human TcI may have a North/Central American origin [75–77] . It should be noted that if the current trend is to propose sub groups within TcI , the presence of subunits , evolving separately , must be previously evidenced which is not yet the case . Also , it has been proposed that marsupial species of the family Didelphidae family are the ancestral hosts of TcI [78] given that , among others , TcI predominates in these animals . Based on our recent analysis of COII and CytB gene sequences previously deposited in GenBank [8] , we evaluated the haplotype and nucleotide diversities of TcI within the order Didelphimorphia , and we observed that these indices were comparable to those obtained for all the other orders of wild mammals combined . This assesses the larger genetic diversity in marsupials than in other animals , supporting a longer association . The remarkable expansion of TcI , which invaded most of environments , does not allow its origin to be determined from the picture of its geographical distribution alone . TcII is a DTU as old as TcI , but it has been sampled much more rarely . The strains belonging to this DTU carry mitochondrial genes ( mtTcII mitochondrial cluster ) whose sequences show substantial genetic divergence from TcI . Moreover , this DTU presents a much lower genetic diversity than TcI . For example , the haplotype diversity of COII and CytB genes are 0 . 39 and 0 . 48 , while for mtTcI they are 0 . 81 and 0 . 58 respectively [8] . A similar level of differences is also observed for nucleotide diversity . The available data on the geographical distribution of TcII suggest that it is absent or extremely rare in some ecosystems ( Central and North America ) . It seems that TcII strains would not have had the same expansion capacity as TcI among the wild cycles , and they probably found refuge mostly in certain wild mammals . TcII is already reported in different wild mammals of the Chiroptera , Cingulate , Didelphimorphia and Primate orders . However , its strong association with primates in the Atlantic Coastal Rainforest in Brazil should be noted [79] . In humans , it is relatively abundant , accounting for 20% of human strains , but it is highly abundant in Brazil ( 66% of human strains identifications ) and rare in most other countries except Colombia ( 15% ) and Chile ( 30% ) . For now , its geographical distribution is more consistent with a South American origin , and further south than north of the Amazon basin where this DTU is more abundant . TcIII and TcIV are DTUs that do not seem to be present throughout the entire endemic area . First , it is important to note that the genetic data do not clearly define these two groups separately . The genetic diversity of TcIII-TcIV is very large and the monophyly of each DTU is not really highlighted . Several studies showed that these strains are the result of ancient hybridization ( s ) between TcI and TcII strains , which suffer over time from genetic rearrangements , decreasing their level of heterozygosity at the expense of mosaic mitochondrial and nuclear genes [80] . Recombination events have probably occurred several times and this would have given a mtTcIII group composed of polyphyletic subgroups of strains . Therefore , the wild strains from the US , attributed to TcIV , seem to be a monophyletic subgroup differing from the others long ago [81] , but whose closest ancestors have probably disappeared . There is little doubt that TcIII and TcIV DTUs have a sylvan origin , but these strains infect humans more than occasionally: the current database shows that TcIV is reported in 84 human cases in six countries ( Brazil , Colombia , Ecuador , Guatemala , Peru and Venezuela ) , and 11 canine cases . Similarly , TcIII is reported in 26 human cases in Brazil and Paraguay . The two TcV and TcVI DTUs include strains derived from the hybridization of TcII and TcIII strains [9] . They are usually considered hybrids and they are heterozygous at several loci and SNPs ( single nucleotide polymorphisms ) . In our database , a total of 21 . 3% of the determinations belong to these DTUs . Some of these strains have spread across large geographic areas through the clonal propagation mode [82] . Both DTUs are clearly associated with domestic cycles since only 10 . 5% of them are identified in hosts from wild cycles . They are identified in some Didelphimorphia and in different species of rodents but only in the Southern Cone countries and Bolivia . Previously , the Gran Chaco region was proposed as the original location of these DTUs , where they are very abundant and where the putative parents are also present [15] , and this hypothesis fits well with the current observed distribution of these DTUs . The universe of Hemiptera vectors of T . cruzi or potential vectors is huge since currently over 141–147 triatomine species are recognized , about 130 occur in the Americas , and it appears that all of these are able to transmit the parasite . Most of these species are involved in wild cycles with at least 100 species of mammals playing a role of host and/or reservoir . In the current data , only 37 species of vectors are included and for the majority of them , very few DTU determinations were made , even though these vectors are generally widely distributed . Similarly , the knowledge of the parasite genetic variants that infect mammals , except for humans , and to a lesser extent for Didelphidae , is very limited . In various regions , in a context of high anthropization and climate changes , it is urgent to study the impact of these environmental modifications on potential vectors and their hosts . Several studies of experimental infections of vectors with different strains of T . cruzi showed differences in susceptibility [83] and even suggested that the strains are pathogenic and induce more or less deleterious effects in bugs [84] . Few studies relate comparisons of DTUs in experimental infections in a single triatomine species . For T . infestans in which this was done , significant developmental differences in the vector were observed depending on the DTU it was infected with , and after experimental double infections: in 50% of cases , only one of the two DTUS was detected after a few days of infection [85 , 86] . As a field observation , we can report the case of Triatoma sordida , a primary vector in the northeast of the city of Santa Cruz , in Bolivia , in which TcI was predominantly detected while in mammals of the same area , TcV was a major strain [59] . In wild mammal hosts , experimental infections of two important reservoirs in the US ( placental and marsupial ) showed DTU-mammal association [87] . Examples could be multiplied but we can already conclude that the vectors and even the wild mammal hosts can influence the distribution of DTUs . Whatever the host , there is a balance between parasite genotypes and hosts which probably depends on environmental conditions such as outside temperature for vectors or immune and nutritional status for mammals . The diversity of hosts , and environmental conditions certainly explain the maintenance of parasitic diversity and the emergence of new variants by natural selection . Therefore the distribution of DTUs reported here , although very informative , is only a temporary picture that will inevitably evolve over time , above all if drastic environmental changes occur such as deforestation , intensive farming , urbanization , and unexpected climatic upheavals . | Trypanosoma cruzi , the causative agent of Chagas disease , has been classified into six genetic groups ( discrete typing units , DTUs ) named TcI-TcVI and a seventh one called TcBat . Currently , several genetic molecular markers are used to classify the strains after their isolation in culture or directly from biological samples . The current inventory compiling the published works aiming to identify the DTUs of T . cruzi strains accumulated a total of 6 , 343 identifications . Although this inventory is not free of sampling bias , like all samples , it is the largest sampling to date and hence likely represents the closest picture of the current diversity of T . cruzi strains ( i ) circulating throughout the endemic area from the southern United States to Argentina and ( ii ) circulating in vectors as well as in wild and domestic mammals , and humans . Data analysis helps identify trends and provides a basis for further comparisons of new data , in a context where human factors ( migration , vector control , urbanization , deforestation , agricultural expansion , resource exploitation ) influence the epidemiological patterns of Chagas disease . | [
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"par... | 2016 | Over Six Thousand Trypanosoma cruzi Strains Classified into Discrete Typing Units (DTUs): Attempt at an Inventory |
The Caenorhabditis elegans one-cell embryo polarizes in response to a cue from the paternally donated centrosome and asymmetrically segregates cell fate determinants that direct the developmental program of the worm . We have found that genes encoding putative deubiquitylating enzymes ( DUBs ) are required for polarization of one-cell embryos . Maternal loss of the proteins MATH-33 and USP-47 leads to variable inability to correctly establish and maintain asymmetry as defined by posterior and anterior polarity proteins PAR-2 and PAR-3 . The first observable defect is variable positioning of the centrosome with respect to the cell cortex and the male pronucleus . The severity of the polarity defects correlates with distance of the centrosome from the cortex . Furthermore , polarity defects can be bypassed by mutations that bring the centrosome in close proximity to the cortex . In addition we find that polarity and centrosome positioning defects can be suppressed by compromising protein turnover . We propose that the DUB activity of MATH-33 and USP-47 stabilizes one or more proteins required for association of the centrosome with the cortex . Because these DUBs are homologous to two members of a group of DUBs that act in fission yeast polarity , we tested additional members of that family and found that another C . elegans DUB gene , usp-46 , also contributes to polarity . Our finding that deubiquitylating enzymes required for polarity in Schizosaccharomyces pombe are also required in C . elegans raises the possibility that these DUBs act through an evolutionarily conserved mechanism to control cell polarity .
Asymmetric cell division is a key mechanism for generating cellular diversity during development . The embryo of the nematode Caenorhabditis elegans is an excellent model for studying this mechanism . Much of the cellular machinery that controls the process of asymmetry in vertebrate organisms is conserved in the nematode . In particular , the par genes , which were discovered in C . elegans , are part of a conserved cassette of key polarity regulators [1] , [2] . In C . elegans one-cell embryos , a group of physically interacting proteins , PAR-3 , PAR-6 , and PKC-3 , initially distribute uniformly around the cell cortex but then polarize into what is defined as the anterior domain [3] . At the same time , PAR-1 , PAR-2 , and LGL-1 become enriched at the posterior cortex [4]–[7] . These two PAR domains in the anterior and posterior remain mutually exclusive in an inter-dependent fashion throughout the cell cycle , and are partitioned asymmetrically into the two daughter cells , AB and P1 , which are also asymmetric in their size and cell fate determinant composition [3] , [8] . The establishment of distinct PAR domains occurs through an interaction of the centrosome , nucleated by centrioles provided by the sperm , with the cortex [9] . This interaction breaks the symmetry of the embryo and specifies the posterior pole [10]–[13] . This break in symmetry is mediated primarily by a local down-regulation of the small GTPase RHO-1 , leading to local inactivation of the actomyosin cytoskeleton [12] , [14]–[16] , and through a redundant microtubule-mediated recruitment of PAR-2 to the cortex [17] , [18] . The local inactivation of cytoskeletal contractility causes an actomyosin cortical flow towards the anterior , and the proteins PAR-3 , PAR-6 , and PKC-3 move with the cortical flow , leaving behind an unoccupied cortex to which the posterior PARs are recruited [12] , [19] . Mutations that block centrosome maturation also block the cortical flow and polarity establishment [20]–[22] . In addition , mutation of the gene pam-1 reduces the association time of centrosomes at the cortex and also blocks polarity establishment [23] , [24] . Establishment is completed by prophase of the first mitosis . The anterior-posterior PAR domains are maintained through the rest of the first cell cycle by mechanisms distinct from those used to establish them . PKC-3 phosphorylates PAR-2 and likely LGL-1 to prevent them from localizing to the anterior cortex [6] , [7] , [25] and PAR-2 prevents the accumulation of anterior PAR proteins in the posterior by an unknown mechanism possibly involving PAR-1 and PAR-5 [18] , [25] , [26] . Similarly , LGL-1 contributes to exclusion of the anterior PARs , but the mechanism is unknown [6] , [7] . C . elegans regulates protein turnover through conserved ubiquitin-mediated degradation mechanisms [27] . Ubiquitin conjugation requires the activity of E1 , E2 , and E3 enzymes , and results in the covalent attachment of mono-ubiquitin or poly-ubiquitin to a target protein [28] . Modification by poly-ubiquitin often leads to protein degradation . Mono-ubiquitylation tends to regulate protein activity or acts as a signal for packaging into multivesicular endosomes [29] , [30] . As with other post-translational protein modifications , ubiquitylation is reversible . Several classes of proteins have the ability to remove ubiquitin from specific substrates after it has been added , or to break up and recycle poly-ubiquitin chains [28] , [30] . These enzymes are collectively called deubiquitylating enzymes , or DUBs . Deubiquitylation activity presents biochemical regulatory networks with the opportunity to temporarily modify proteins by removal of ubiquitin to recycle proteins otherwise destined for degradation , or to modulate their function . In this study , we explored the role of three putative deubiquitylating enzymes ( DUBs ) in embryo polarization . These three conserved DUBs , MATH-33 , USP-46 , and USP-47 , function redundantly at the genetic level with regard to their functions in polarization of C . elegans one-cell embryos . Based on our analysis we propose that these DUBs function during polarity establishment by contributing to positioning the centrosome , and thereby promoting the cytoskeletal changes that initiate polarity establishment .
math-33 was identified in an RNA interference ( RNAi ) -based screen for enhancers of embryonic lethality of weak par-1 and par-4 mutants [31] . math-33 encodes a protein with a meprin and TRAF homology ( MATH ) domain , and a ubiquitin carboxy-terminal hydrolase ( UCH ) domain . Math-33 depletion increases the lethality of par-1 ( zu310ts ) , par-4 ( it57ts ) , and strongly increases lethality in three weak par-2 mutant alleles , but not lgl-1 ( tm2616 ) or a partially suppressed par-3 ( e2074 ) nonsense mutation ( Table 1 ) . To determine whether enhancement of par-1 , par-2 and par-4 conditional mutants simply reflected a general requirement for MATH-33 in embryos compromised by any conditional mutation , we tested eight other genotypes: emb-9 ( g23ts ) , emb-9 ( b189ts ) , zyg-9 ( b288ts ) ; unc-4 ( e120 ) , mom-2 ( ne874ts ) ; unc-5 ( e53 ) , mom-4 ( ne1539ts ) , wrm-1 ( ne1982ts ) , mig-5 ( rh147ts ) , lit-1 ( ne1991ts ) ( Table S1 ) [32]–[36] . We found that MATH-33 depletion could only enhance two of the tested mutations , indicating that math-33 is not a non-specific enhancer . Two strains , emb-9 ( g23ts ) and zyg-9 ( b288ts ) ;unc-4 ( e120 ) showed significantly increased embryonic lethality . However a different allele of emb-9 , b189ts , failed to show significant changes in embryonic lethality , indicating that different mutations or genetic backgrounds are more sensitive to comprising deubiquitylation . Because all three alleles of par-2 ( it5ts , it87 and e2030 ) are enhanced strongly by depletion of MATH-33 , we investigated the role of MATH-33 in polarity . To determine whether MATH-33 has a specific role in polarity we examined the phenotypes of homozygous math-33 mutants and asked whether the increased lethality of math-33 when combined with par-1 , par-2 and par-4 correlated with polarity defects . We obtained the probable null allele math-33 ( tm3561 ) , and found that homozygotes for the outcrossed mutation showed maternal effect , cold-sensitive and weakly penetrant embryonic lethality , along with weakly penetrant larval lethal and sterile phenotypes ( Table 1 , Figure S1 ) . We found that RNAi of math-33 in par-2 ( it5ts ) at permissive temperature resulted in polarity phenotypes at frequencies typical of strong par-2 mutants ( Table 2 ) , and that depletion of PAR-1 and PAR-4 by RNAi in math-33 ( tm3561 ) did not simply enhance the par-1 and par-4 polarity phenotypes but instead resulted in synthetic phenotypes that resembled par-2 mutants ( Table 2 ) . For example , whereas neither math-33 ( tm3561 ) nor par-1 ( RNAi ) alone exhibited the characteristic par-2 phenotype of transverse spindle orientation in P1 , in combination they resulted in 71% transverse P1 spindles . Together these data indicate that MATH-33 has a role in polarity and argue that the effect is most closely related to the function of PAR proteins that act in the posterior . To address whether the genetic interaction between par-2 ( it5ts ) and math-33 ( RNAi ) is specific or whether other ubiquitin hydrolases also interact with par-2 , we used RNAi to deplete 22 of 25 genes containing a Ubiquitin Carboxy-terminal Hydrolase ( UCH ) domain defining the class of DUBs to which MATH-33 belongs . We found that T05H10 . 1 RNAi depletion causes significant increases of par-2 ( it5ts ) embryonic lethality ( 15% to 28%; Figure 1A ) . Furthermore , depletion of these 22 DUBs in math-33 ( tm3561 ) mutants revealed that depletion of T05H10 . 1 , hereafter referred to as USP-47 , caused synthetic lethality in the math-33 ( tm3561 ) mutant by increasing embryonic lethality from 35% to 95% ( Figure 1A ) , and by increasing the lethality of a second probable null math-33 allele , ok2974 , from 45% to 93% . USP-47 is closely related to MATH-33 ( Figure 1B ) and appears to be a paralog of MATH-33 that is conserved among bilatera ( TreeFam ) , whereas MATH-33 is a more widely conserved protein found in most eukaryotes . This conservation led us to hypothesize that these two genes have overlapping functions in embryonic polarity , which we tested by examining the phenotype of math-33 ( - ) ; usp-47 ( RNAi ) early embryos . We found that math-33 ( tm3561 ) ; usp-47 ( RNAi ) and math-33 ( ok2974 ) ; usp-47 ( RNAi ) two-cell embryos displayed variably penetrant polarity phenotypes similar to those of par-2 mutants , including synchronous cell divisions , symmetry of cell size , and transverse spindles in both AB and P1 , and occasional P1 cytokinesis defects ( Table 2 and Figure 1C ) . Because math-33 ( ok2974 ) was indistinguishable from math-33 ( tm3561 ) in these assays , for simplicity we limited subsequent analysis to the tm3561 allele . We hypothesized that the simultaneous loss of MATH-33 and USP-47 would affect the distribution of PAR-2 and PAR-3 at the cell cortex . One-cell embryos immunostained for PAR-2 and PAR-3 and scored between onset of pronuclear migration and centration indicate that math-33 ( tm3561 ) and math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos exhibit a variable decrease in the size of the posterior cortical domain , and a reciprocal increase in the size of the anterior cortical domain ( Figure 2A , 2B ) . This result illustrates that the loss of MATH-33 causes a very mild defect in PAR protein distributions at the one-cell stage and that the loss of both DUBs causes stronger defects . To determine the basis for the smaller posterior domain marked by PAR-2 , we examined math-33 ( tm3561 ) ; par-2::gfp; usp-47 ( RNAi ) embryos by time-lapse video microscopy , and observed two phenotypic classes ( Figure 2C ) . In class I , PAR-2::GFP was recruited weakly to the cortex in a domain whose size was comparable to wild type and was maintained through the first cleavage . These embryos went on to divide normally . In class II , PAR-2::GFP was initially recruited to a smaller domain , which failed to expand and did not persist through cell division . These class II embryos exhibited polarity defects at first and second cleavages ( Figure 2C ) . To determine whether there was a reciprocal effect on the anterior PAR domain , we attempted to construct a math-33 ( tm3561 ) strain expressing PAR-6::mCherry . Unfortunately , perhaps due to the over-expression of PAR-6 , we were unable to maintain math-33 ( tm3561 ) ; par-6::mCherry worms in stock . However , lgl-1::GFP; par-6::mCherry; math-33 ( tm3561 ) worms are relatively healthy , perhaps because LGL-1::GFP expression counteracts the PAR-6::mCherry . Depletion of USP-47 in this strain resulted in a clearing of PAR-6::mCherry during establishment that was less robust than in wild type ( Figure S2 ) . The result that both PAR-6 clearing and PAR-2 localization in the posterior are impaired suggests that the establishment of a posterior domain is compromised in the absence of MATH-33 and USP-47 . The sequence similarities between MATH-33 and USP-47 ( Figure 1B ) and their shared role in polarity raise the possibility that these two proteins act to deubiquitylate common substrates . PAR-2 is a putative E3 ubiquitin ligase that may self-regulate through auto-ubiquitylation [25] . Since math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos resemble par-2 mutants , we hypothesized that ubiquitylated-PAR-2 could serve as a regulatory target of the DUBs . To test this , we attempted to examine PAR-2 protein levels in embryo protein extracts , but were not able to obtain interpretable results . Instead , we tested whether the effects of the DUBs would be suppressed in a genetic background that bypassed the requirement for the PAR-2 protein: par-2 ( lw32 ) ; lgl-1::gfp . The lw32 allele is a likely null allele; it contains a nonsense mutation that truncates the PAR-2 protein at amino acid 233 [37] resulting in absence of a domain required for cortical localization [25] and RNAi treatment of this allele does not increase the severity of the phenotype [6] . Overexpression of LGL-1 can bypass the absence of functional PAR-2 [6] , [7] . We reasoned that if the DUBs acted by stabilizing PAR-2 the requirement for the DUBs would be suppressed by overexpression of LGL-1 . We depleted MATH-33 and USP-47 simultaneously in the par-2 ( lw32 ) ; lgl-1::gfp strain and observed 86% embryonic lethality compared to 4% lethality in par-2 ( lw32 ) ; lgl-1::gfp control RNAi ( Figure 3A ) . Embryos displayed a smaller posterior domain as revealed by LGL-1::GFP distribution , failed to maintain the posterior domain , and had transverse mitotic spindles in 6/6 P1 blastomeres ( Figure 3B ) . We interpret these results to mean that the two DUBs do not act exclusively through PAR-2 to control polarity , and that their effects on the establishment of a posterior domain are likely to be parallel to the requirement for PAR-2 . Careful examination of math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos revealed that in addition to par-2-like phenotypes , cortical contractility is decreased , embryos frequently lack pseudocleavage ( Figure 1C ) , exhibit low penetrance cytokinesis defects ( fewer than 2 in 10 embryos in most experiments ) , and occasional polar body extrusion defects ( not quantified ) . The lack of pseudocleavage in this subset of embryos led us to hypothesize that absence of the DUBs may lead to defects in actomyosin function , which could lead to the failure to establish a posterior domain . To test this hypothesis , we observed cortical NMY-2 ( type II non-muscle myosin heavy chain ) [38] in 20 math-33 ( tm3561 ) ; nmy-2::gfp; usp-47 ( RNAi ) embryos by confocal live imaging ( Figure 4A , Videos S1 , S2 ) and found that contractile myosin foci are present , but that the extent of myosin clearing , which we defined as the absence of foci from the posterior as a portion of the embryo length , was variably defective . Embryos showed a range in the extent of myosin clearing; 4/20 embryos showed normal clearing , 6/20 embryos no clearing and 10/20 embryos intermediate levels of clearing ( Figure 4B ) . We confirmed this result by observing endogenous NMY-2 by immunostaining in embryos after pronuclear condensation but before pronuclear meeting , a stage when myosin clearing is readily detectable in wild type [14] . Three out of seven math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos at this stage showed no evident clearing of myosin foci compared to none of six controls ( Figure 4C ) . Furthermore , when we examined the velocity of myosin foci movement from posterior to anterior we found that wild type foci moved at a rate of 2 . 0±0 . 6 µm/minute ( n = 13 foci from 6 embryos ) , whereas foci in math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos moved at a rate of 1 . 5±0 . 7 µm/minute ( n = 23 foci from 11 embryos p = 0 . 016 in a Student's t-test ) . The wild type rate is slower than previously reported rates [12] , perhaps due to the lower temperature at which our observations were made . We also compared flow rates from embryos that we judged to have no clearing to those embryos with clearing and saw no significant rate differences ( 1 . 3±0 . 05 µm/minute n = 8 foci from 3 embryos with no clearing and 1 . 6±0 . 06 µm/minute n = 15 foci from 8 embryos with myosin clearing ) . This is consistent with previous observations that clearing is a consequence of flow away from the posterior as well as prevention of formation of new posterior foci [12] and implies that the activity of the DUBs is required for both . Because cortical flow is dependent upon a signal from the centrosome [12] , we examined the position of the centrosome in fixed embryos immunostained for NMY-2 ( Figure 4C ) . In this small data set we noted that the centrosome appeared to be significantly more distant from the cortex in math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos ( Figure S3A ) and that this correlated with the extent of myosin clearing . However , because fixed embryos do not provide sufficient temporal resolution , we examined centrosome behavior in live embryos using beta tubulin::GFP to follow centrosomes . We assayed initial position of the centrosome relative to the male pronucleus and the cortex , and determined the time that the centrosome spent in close proximity to the cortex . In math-33 ( tm3561 ) ; tbb-2::gfp; usp-47 ( RNAi ) embryos the position of the centrosome with respect to both the pronucleus and the cortex was variably abnormal ( Figure 5A; Videos S3 , S4 , S5 ) . We observed two phenotypic classes with respect to pronuclear attachment . In some embryos , at first detection , the centrosome was attached to the male pronucleus ( n = 18/31 from multiple experiments , Figure 5B , Video S4 ) . In the other embryos the centrosome was detached from the pronucleus ( n = 13/31 from multiple experiments , Figure 5B , Video S5 ) . In all 13 cases in which centrosomes were initially detached from the male pronucleus , the centrosome and pronucleus eventually migrated toward one another and re-associated ( Video S5 ) . The centrosome was also detached from the pronucleus in 5/14 math-33 ( tm3561 ) ; tbb-2::gfp embryos . There is no statistical difference in the number of detached centrosomes between math-33 ( tm3561 ) and math-33 ( tm3561 ) ; usp-47 ( RNAi ) . Because the frequency and severity of polarity defects is much lower in math-33 ( tm3561 ) embryos , this argues that centrosome attachment to the male pronucleus is not essential for polarity establishment . Measurements of centrosome-to-cortex distances in wild-type , math-33 ( tm3561 ) and math-33 ( tm3561 ) ; usp-47 ( RNAi ) are presented in Figure 5B . In wild type embryos the centrosome at first appearance is within 2 µm of the cortex with an average distance of 0 . 25 µm ( n = 9 ) . In math-33 ( tm3561 ) and in usp-47 ( RNAi ) the average distance from the cortex ( 1 . 4 µm n = 5 and 1 . 1 µm n = 11 respectively ) . math-33 ( tm3561 ) centrosomes are significantly more distant than wild type , but have an average centrosome distance that is within wild type range . In contrast , centrosomes in math-33 ( tm3561 ) ;usp-47 ( RNAi ) showed a wide range of distances . When we separated the two classes of embryos with respect to centrosome attachment to the pronucleus ( last two columns in Figure 5B ) we noted that when the centrosome was attached to the male pronucleus , it was more likely to be far from the cortex . Indeed , the average centrosome-cortical distance of 1 . 7 µm ( n = 13 ) in those math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos with detached centrosomes , and in those embryos with attached centrosomes , the average distance was significantly further , 6 . 4 µm ( n = 18 , p<0 . 001 ) . To determine whether the initial distance of the centrosome from the cortex correlated with polarity defects , we examined pseudocleavage , an indirect indicator of polarity establishment , in these same embryos . We defined absence of pseudocleavage as the absence of detectable persistent medial cortical invaginations in mitotic prophase . Embryos lacking pseudocleavage are indicated by red dots in Figure 5B . In the 17 math-33 ( tm3561 ) ;usp-47 ( RNAi ) embryos in which pseudocleavage was detectable , the distance of the centrosome from the cortex was on average 2 . 1 µm , whereas in the 14 embryos lacking detectable pseudocleavage , the average distance was significantly larger , 7 . 0 µm ( Figure 5C ) . We saw no correlation between detached centrosomes and defects in polarity as assayed by absence of pseudocleavage . Most detached centrosomes from math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos first become visible within 4 µm of the cortex , and amongst these; 25% ( n = 12 ) failed to undergo pseudocleavage , For embryos with attached centrosomes within 4 µm , 28% ( n = 7 ) embryos failed to undergo pseudocleavage . Therefore , the detachment status of the centrosome does not appear to influence whether polarity establishment will occur . Because the centrosome's dwell time at the cortex can also affect polarity [23] , [24] , we compared the time that centrosomes spend in close proximity to the cortex in wild type , math-33 ( tm3561 ) and math-33 ( tm3561 ) ;usp-47 ( RNAi ) embryos . In the wild type embryos centrosomes stayed within 4 µm of the cortex for an average of 8 . 8 minutes ( n = 3 ) . In contrast , math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos exhibited dwell times of 2 . 5 minutes ( n = 6 ) and math-33 ( tm3561 ) embryos spent an average of 5 . 8 minutes ( n = 3 ) . Thus , dwell time at the cortex is also shortened by loss of deubiquitylation activity . In summary , we find that math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos have abnormal centrosome behavior patterns that are consistent with the centrosome having a reduced affinity for the cortex and for the pronucleus . Because distance from the cortex correlated with failure of pseudocleavage and previous work showed that dwell time at the cortex correlated with polarity defects , we hypothesized that defects in centrosome-cortex interaction are responsible for the observed defects in polarity establishment . To test the hypothesis that weakened centrosome-cortex interaction caused the polarity defects in math-33 ( tm3561 ) ; usp-47 ( RNAi ) we used dhc-1 ( RNAi ) to deplete dynein , which causes centrosomes to strongly localize at the cortex [24] , and scored polarity phenotypes and centrosome behavior . We reasoned that if the defect in cortical association was causing the polarity defect , then forcing a tight association of the centrosome and cortex would suppress the polarity defects . Depletion of DHC-1 in both math-33 ( tm3561 ) and math-33 ( tm3561 ) ; usp-47 ( RNAi ) resulted in a closer initial association of the centrosome to the cortex ( Figure 5D ) . We also found that 10/10 math-33 ( tm3561 ) ; usp-47 ( RNAi ) ; dhc-1 ( RNAi ) embryos displayed robust pseudocleavage , whereas only 4/7 math-33 ( tm3561 ) embryos simultaneously treated with usp-47 ( RNAi ) and DH11 . 5 ( RNAi ) , a control for the non-specific effects of double RNAi , had pseudocleavage ( Figure 5E ) . Thus , influencing the centrosome position to bring it closer to the cortex suppresses math-33; usp-47 ( RNAi ) polarity phenotypes , consistent with the hypothesis that the primary effect of the DUBs on polarity is to promote association of the centrosome with the cortex . The gene pam-1 is known to affect centrosome dynamics in the early embryo [23] , [24] in a way that is similar , but not identical to , the loss of the DUBs . Loss of PAM-1 does not appear to affect the initial proximity of the centrosome to the cortex , but like the loss of the DUBs , results in premature departure of the centrosome from the cortex [23] . To test for possible genetic interaction between pam-1 and math-33 , we examined centrosome behavior and pseudocleavage in math-33 ( tm3561 ) ; pam-1 ( RNAi ) embryos expressing tubulin::GFP . We found that pam-1 ( RNAi ) enhanced centrosome defects in math-33 in a way similar to usp-47 ( RNAi ) . Relative to math-33 ( tm3561 ) or pam-1 ( RNAi ) alone , we noted an increase in the fraction of centrosomes that were located further from the cortex ( Figure 5D ) , an increase in the frequency of centrosomes detached from the nucleus ( Figure S3B ) , as well as more frequent lack of pseudocleavage ( red dots in Figure 5D ) . As a control we also depleted PAR-2 , a protein not known to affect the centrosome position , in math-33 ( tm3561 ) . PAR-2 depletion had no effect on the initial distance of the centrosome from the cortex , and did not cause any synthetic effects that led to the loss of pseudocleavage ( Figure S3C ) . Thus loss of pam-1 enhances math-33 centrosome and polarity phenotypes . In an effort to gain insight into possible targets of MATH-33 and USP-47 , we examined expression patterns and localization of MATH-33 and USP-47 in embryos and adult worms . We found that anti-MATH-33 and anti-USP-47 antibodies and pie-1 promoter driven MATH-33::GFP and USP-47::GFP transgenic worm strains gave consistent results in early embryos: MATH-33 is present at high levels in both cytoplasm and nucleus , whereas USP-47 is only detected at high levels in the cytoplasm ( Figure 6 ) indicating that the cytoplasm is the most likely site of action of the two DUBs . Furthermore , antibody staining showed that MATH-33 is present in most or all cells in the worm ( not shown ) and is enriched in the germline ( Figure S4A ) , whereas USP-47 is present primarily in the germline ( Figure S4B ) . Expression of Ppie-1::math-33::gfp in the germline of math-33 ( tm3561 ) mutants was able to suppress lethality and sterility phenotypes ( Figure S1B ) , indicating that the maternal contribution of MATH-33 to embryos is sufficient to compensate for most essential MATH-33 functions . The presence of a UCH domain in MATH-33 and USP-47 suggest that these proteins act as deubiquitylation enzymes . Removal of ubiquitin could have different consequences: a ) prevention of degradation , b ) modulation of protein activity c ) effects on endocytosis or recycling of membrane proteins [29] , [30] . We tested whether the major action of MATH-33 and USP-47 was to antagonize poly-ubiquitylation by asking whether compromising protein turnover could suppress the lethality and polarity defects of math-33 ( tm3561 ) ; usp-47 ( RNAi ) worms . We compromised protein degradation by crossing rpn-10 ( tm1349 ) or rpn-10 ( tm1180 ) into math-33 ( tm3561 ) worms . RPN-10 , like its baker's yeast homolog [39] , [40] , is a non-essential ubiquitin recognition protein; although its relationship to proteasome function is unclear , mutants in rpn-10 result in abnormal accumulation of poly-ubiquitylated protein [41] , [42] . Mutants in rpn-10 were also found to be able to suppress phenotypes of par-2 ( it5ts ) [42] . Our results show that rpn-10 mutations strongly suppress math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryonic lethality and polarity phenotypes ( Figure 7A , 7B ) . Because compromising protein degradation could affect polarity in a number of ways , we asked whether rpn-10 mutations also suppressed the centrosome positioning defects in math-33; usp-47 ( RNAi ) embryos . We found that centrosomes in rpn-10 ( tm1349 ) ; math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos were indeed always found very close to the cortex , similar to wild type ( Figure 7C , Video S6 ) . Therefore , we conclude that in their role in early embryonic polarity , the DUBs are likely to regulate protein turnover . A recent paper from Kouranti and colleagues [43] identified a novel and redundant role for a group of five DUBs , including homologs of math-33 and usp-47 , in polarity of Schizosaccharomyces pombe . Their results suggested to us that the role of this class of deubiquitylases in polarity could be evolutionarily conserved . The yeast proteins Ubp15p and Ubp5p are homologs of MATH-33 and USP-47 ( Figure S5B ) . Because of the link between the yeast DUBs and polarity , we hypothesized that homologs of ( ubp4 , ubp9 ) , the two other S . pombe UCH-containing DUBs that affect polarity might also function in C . elegans polarity . C . elegans USP-46 and E01B7 . 1 are homologs of the yeast proteins UBP9 and UBP4 , respectively ( Figure S5C ) . RNAi depletion of neither gene increases embryo lethality in wild type or in math-33 ( tm3561 ) ( data not shown ) . However , a deletion mutant allele usp-46 ( ok2232 ) in combination with simultaneous math-33 ( RNAi ) and usp-47 ( RNAi ) resulted in 75% embryonic lethality ( n = 1330 ) , compared to 30% lethality ( n = 1408 ) in math-33 ( RNAi ) ; usp-47 ( RNAi ) in wild type . Of 20 usp-46 ( ok2232 ) embryos in which MATH-33 and USP-47 were simultaneously depleted , 11 displayed transverse P1 spindle orientations ( Figure 8A ) compared to 1/30 math-33 ( RNAi ) ; usp-47 ( RNAi ) in wild type . However , math-33 ( tm3561 ) ; usp-46 ( ok2232 ) double mutants are largely sterile , producing only a few oocytes and no fertilized eggs , raising the possibility that the two genes act redundantly in gametogenesis . After mating to wild-type males , the math-33 ( tm3561 ) ; usp-46 ( ok2232 ) mutants can produce a few fertilized embryos which fail to hatch . Five of the six one-cell embryos we were able to obtain in this way displayed transverse P1 spindle orientations at the two-cell stage ( Figure 8A ) indicating defects in polarity . More recently a new deletion mutation , usp-47 ( tm4950 ) , became available; we found that double mutants of usp-47 ( tm4950 ) ; usp-46 ( ok2232 ) are unhealthy and display mild embryonic lethality and sterility . At 16° , these double mutants have no evident polarity defects , but at 25° some embryos display transverse spindles in P1 ( 4/18 , Table 3 ) , while the single mutants do not . usp-46; usp-47 mutant embryos depleted for math-33 display a high penetrance of transverse spindle orientations ( 11/15; Table 3 ) , and embryos appear to be similar in most respects to math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos ( see Figure 1B ) . Taken together , the increasing penetrance of polarity phenotypes as a function of loss of activity of the three deubiquitylases ( Table 3 ) suggests that the three enzymes function redundantly in early polarity , but that they vary in their contributions to the polarization process .
Ubiquitin regulation appears to have an important but not well-understood role in C . elegans embryonic polarization . One of the earliest discoveries was that PAR-2 has homology to RING domain E3 ubiquitin ligases , suggesting that ubiquitin ligase activity may be important for excluding anterior PARs from the posterior [5] , [37] . Hao and colleagues showed that the PAR-2 RING domain is required for robust transgene rescue of embryos lacking endogenous PAR-2 , indicating that it is likely to be an active ubiquitin ligase in vivo , although this activity is not absolutely essential for function [25] . Biochemical targets of PAR-2 , however , are unknown . Other results that relate ubiquitin-based regulation to polarization include the finding that PAR-6 levels are affected by activity of the ubiquitin ligase CUL-2 and its adapter protein FEM-3 [44] , and that mutations of C . elegans homologs of the BRAT family of ubiquitin ligases have been shown to be able to suppress weak par-2 phenotypes [45] . Weak impairment of protein turnover through mutation of rpn-10 has also been shown to suppress par-2 phenotypes [42] , and depletion of the proteasome regulatory subunit , rpn-2 results in abnormal spindle orientation in AB at the two-cell stage [46] . We report here additional evidence for an important role of ubiquitylation in embryonic polarity . We show that a group of three putative deubiquitylating enzymes , MATH-33 , USP-46 and USP-47 , contribute to polarity establishment in C . elegans , likely in a redundant fashion . Analysis of math-33 ( tm3561 ) ; usp-47 ( RNAi ) embryos revealed that when the products of these two genes are missing or reduced , PAR protein domains are abnormally sized , cortical actomyosin flow is weak or fails , and centrosomes are variably positioned with respect to the cell cortex . Because distance of centrosomes from the cortex correlates with the severity of the polarity phenotypes , and because blocking movement of the centrosome away from the cortex by depleting dynein heavy chain restores polarity establishment in math-33 ( tm3561 ) ; usp-47 ( RNAi ) , we propose that the primary role of the DUBs in polarity is to promote the association of the centrosome and the cortex . Prior to polarization in the wild-type C . elegans embryo , the centrosome at first detection using the centrosome marker SPD-2 can be observed within a 0–9 um range ( 5 um average ) from the cortex [11] , [47] . It then moves to within 0–4 um at the time of polarity initiation [11] , [47] . In embryos lacking functional MATH-33 and USP-47 the position of the centrosome when first detected is often distant from the cortex and these unassociated centrosomes remain so . Furthermore , centrosomes in embryos lacking these two DUBS that are initially close to the cortex leave the cortex sooner than in wild type embryos . Therefore , we suggest that forces that maintain close association of the centrosome and cortex are weakened or absent and as a result , polarity establishment as assessed by myosin clearing and pseudocleavage is either weak or non-existent . However , we also see that centrosomes are often detached from the paternal pronucleus , indicating that the pronuclear-centrosomal interaction is defective . Centrosome-nuclear attachment is in many cases unnecessary for the proper localization of centrosomes in cells [48] , and indeed we see that detached centrosomes can associate closely with the cortex and see no correlation between nuclear detachment and polarity defects . However , centrosomes that are detached from the nucleus are more likely to be closely apposed to the cortex and centrosomes that are attached to the paternal pronucleus are more likely to be more distant from the cortex . This correlation suggests that weakened centrosome interactions with cortex and pronucleus creates a competition between the two for binding to the centrosome . Because centrioles enter the embryo in association with the sperm pronucleus , detachment from the pronucleus must occur between the time that the embryo is fertilized and when TBB-2::GFP first allows the centrosome to become visible . Furthermore , all detached centrosomes are capable of re-attaching to the paternal pronucleus in a manner similar to maternal pronuclear capture by the growing astral microtubules . This suggests that a microtubule-mediated tracking mechanism [48] in which pronuclei migrate towards the centrosome on astral microtubules functions in a relatively normal way . Because of this , we speculate that the early centrosomal attachment to the pronucleus uses a different mechanism than the microtubule-based mechanism that promotes attachment during prophase . We also propose that the action of the tracking mechanism contributes to the early departure of the centrosomes from the cortex in the embryos lacking DUBs and having initially detached centrosomes . We observed a few cases in which embryos with centrosomes closely apposed to the cortex had polarity defects . This could be due to shortened time of cortical association , or could indicate that factors other than centrosome-cortex association are affected by the loss of the DUB activity . Our results are consistent with previous reports correlating centrosome proximity to the cortex with robust polarity establishment [23] , [24] However , they are in apparent contradiction to a recent study concluding that centrosomes can initiate polarity establishment at a distance from the cortex [47] . This apparent contradiction can be reconciled by distinguishing between the initial signaling event , ( referred to as “symmetry breaking” [41] ) and an ongoing process of polarity establishment . Our results are consistent with a model in which the initial signaling event can happen at a distance from the cortex . Indeed we did not observe any math-33; usp-47 double loss of function embryos in which a PAR-2 domain failed to be established . Although this could be explained by incomplete USP-47 RNAi knockdown , or by genetic redundancy in regulation of the targets of the DUBs , an equally likely interpretation is that the DUBs do not affect the early signaling event , but rather affect events downstream of that signal . The relationship we note between centrosome proximity and robust polarity establishment supports the notion that these downstream events involve continued interaction of the cortex with the centrosome or its associated astral microtubules or both and that this interaction is most efficient when the centrosome and cortex are more closely apposed . The establishment phase of polarity is certainly affected by the loss of MATH-33 and USP-47 . However we also observed distinct defects in polarity maintenance such as loss of PAR-2::GFP and LGL-1::GFP domains at the posterior cortex after prophase . The loss of maintenance is probably caused by re-entry of the anterior PARs into the posterior domain and could occur for two reasons . One is that math-33 ( tm3561 ) ; usp-47 ( RNAi ) causes the initial clearing of myosin to be impaired , and thus the initial size of the posterior domain to be smaller . Afterwards , maintenance could fail due to the inability of the reduced amount of the posterior PARs to exclude anterior PARs . A second possibility is that the DUBs actively participate in maintaining the posterior domain . However there is less evidence of a role for MATH-33 in maintenance . LGL-1 has been proposed to primarily have an active role in maintenance only [6] , and we found that although MATH-33 depletion increases polarity defects in par-2 ( it5ts ) there are no effects on lgl-1 ( tm2616 ) mutants . We observed variable penetrance and expressivity of the mutant phenotypes in the math-33; usp-47 ( RNAi ) embryos . Three explanations for this variability are possible . First RNAi depletion of USP-47 could be incomplete . Second , the DUBs could play a modulatory role rather than an essential role , such that even a complete loss of function would not result in a fully penetrant phenotype . Third , the targets of the two DUBs are under redundant regulatory control; indeed our finding that USP-46 functions redundantly with MATH-33 and USP-47 supports this notion . We found that one of the strongest combinations of DUBs was the usp-46; usp-47 double mutants depleted for MATH-33 . Although a triple mutant combination could resolve the issue , obtaining triple mutants is not feasible because math-33 ( tm3561 ) ; usp-46 ( ok2232 ) worms are sterile . Other proteins are also known to be required for proper interaction of the centrosome with the cortex in one-cell C . elegans embryos: dynein components and regulators , such as dynein heavy chain [49] , and a puromycin sensitive aminopeptidase ( PAM-1 ) [23] , [24] . Reduction or loss of function of neither the dynein group nor PAM-1 precisely mimics the phenotypes resulting from the loss of DUBs . In embryos severely depleted of dynein heavy chain , 15% of centrosomes can become detached from the pronucleus , and in most embryos the centrosomes become tightly and persistently associated with the cortex [24] . Thus a key function of dynein is to positively promote dissociation of the centrosome from the cortex , but also to promote association with the pronucleus . In contrast MATH-33 and USP-47 promote cortical association and pronuclear attachment . In pam-1 mutant embryos the centrosome-pronuclear complex is correctly positioned at the posterior cortex , but the complex spends less time at the cortex , leaving early , leading to polarity establishment defects [23] , [24] . Because both the centrosome association and pronuclear attachment are mildly enhanced in math-33 ( tm3561 ) ;pam-1 ( RNAi ) embryos it seems likely that the two proteins affect both processes; our experiments , however , do not allow us to distinguish whether the DUBs and PAM-1 act through a common pathway or affect centrosome dynamics through two separate pathways . A proposed function of PAM-1 is that it removes N terminal peptides from proteins to allow them to be ubiquitylated [23] . If this is the case , it is not clear how the DUBs would work with PAM-1 in a common pathway . The DUBs appear to act by protecting proteins from degradation rather than by modifying protein activities or localizations . Evidence for this is that rpn-10 mutations are effective suppressors of math-33 ( tm3561 ) ; usp-47 ( RNAi ) . Mutations in rpn-10 lead to an increase in the amounts of poly-ubiquitylated proteins and result in higher steady state levels of the protein TRA-2 [41] . By analogy with its homologs in other organisms , RPN-10 could act to recognize a subset of ubiquitylated substrates at the proteasome , and therefore mutating rpn-10 may compromise efficacy of degradation of a subset of proteins at the proteasome [40] , [50] . rpn-10 mutation corrects the centrosome association with the cortex as well as attachment to the pronuclear envelope suggesting that it fully suppresses all centrosome position phenotypes associated with loss of the DUBs . In contrast , depletion of dynein heavy chain suppresses the defects in centrosome-cortex association and polarity , but does not suppress pronuclear envelope detachment . This indicates that phenotypic suppression by depleting dynein likely bypasses the normal regulatory target of the DUBs . A unifying hypothesis would be that the DUBs are required to maintain an appropriate level of one or more key proteins required for centrosomes to interact with the pronucleus and the cortex; alternatively the cortical association defects could be independent of the pronuclear attachment defects and the two be mediated by different proteins . There are many types of proteins that could be necessary for the centrosome position . The simplest hypothesis is that these proteins are centrosome components , but cytoskeletal motors , regulatory proteins such as kinases , and proteins that mediate interactions between microtubules and nuclei or cortical proteins are also candidates . Comparison of our results with results from studies of homologous proteins in the fission yeast Schizosaccharomyces pombe raises the possibility that these DUBs have a conserved role in eukaryotic cell polarity . A screen in S . pombe of DUB function showed that ubp4 , ubp5 , ubp9 , ubp15 , and sst2 , although non-essential individually , act redundantly to affect asymmetric endocytosis [43] . Because this group contained two proteins homologous to MATH-33 and USP-47 , Ubp5p and Ubp15p , we speculated that if the functions were evolutionarily conserved , homologs of the other members of the group might have redundant roles in C . elegans , and led us to the discovery of a role for USP-46 , the homolog of UBP9p , in C . elegans embryos . In S . pombe , microtubules are required to control proper polar growth [51] , [52] , so there may be an underlying common mechanism that involves the centrosome . Alternatively , since Kouranti et al . [43] noted strong phenotypes related to endocytosis , it may be possible that membrane proteins in C . elegans that contribute to cytoskeletal regulation are regulated by the DUBs . The DUBs we examined appear to have both overlapping and distinct functions that may extend to diverse biological processes . math-33 ( tm3561 ) mutants display mild polarity phenotypes , whereas usp-46 ( ok2232 ) and usp-47 ( RNAi ) do not . This indicates math-33 individually plays a more crucial role in regulating C . elegans polarity than the other two DUBs . It is unlikely , however , that math-33's role is limited to early embryo polarity establishment . After nine outcrosses , math-33 ( tm3561 ) mutants remain pleiotropic , displaying embryonic lethality , larval lethality , and sterility . Furthermore , math-33 has been reported to interact genetically with vab-10 , ksr-1 , and skn-1 in C . elegans [53]–[55] , and as we report , emb-9 , and either zyg-9 or unc-4 . This indicates that math-33 is likely to act in several biological pathways that may not relate directly to polarity . MATH-33 homologs in other organisms provide little insight on an exact mechanism of action in polarity , but suggest that the homologous DUBs have diverse functions . In Saccharomyces cerevisiae , the math-33 homolog UBP15 causes mislocalization of the cell membrane protein Gap1 to cytoplasmic membranes , and results in lower permease activity [56] . In mammals , the math-33 homolog USP7/HAUSP has been studied extensively for its ability to bind and deubiquitylate p53 and the ubiquitin ligase MDM2 , and it has also been implicated as a negative regulator of PTEN localization to the nucleus [57]–[59] . USP7 was also found to coimmunoprecipitate with the PAR-1 homolog MARK4 [60] but does not appear to be able to deubiquitylate it [61] . USP7 is also considered to be a therapeutic target for cancer therapy due to its broad role in genomic stability [62] . In contrast , there is less known about the roles of USP-46 and USP-47 in other organisms . In our study we found that the individual loss of either usp-47 or usp-46 have no discernible phenotypes in early C . elegans embryos . USP-47 has not previously been studied , but USP-46 has been shown to have deubiquitylation activity , and to regulate the levels of GLR-1 abundance in a fashion that suggests USP-46 deubiquitylation of GLR-1 on endosomes prevents degradation in the multivesicular body/lysosome pathway [63] . It is possible then that USP-46 and USP-47 may have biological roles that are redundant with other DUBs , and as a result have fewer obvious mutant phenotypes than math-33 . In summary , we have identified three deubiquitylase genes , math-33 , usp-47 and usp-46 that are required for proper polarity establishment in C . elegans . The enzymes encoded by these genes appear to act by stabilizing proteins that promote the interaction of the centrosome with the cell cortex . A future challenge will be to determine the targets of these deubiquitylases with respect to their role in early embryonic polarity and also whether those targets are widely conserved in animals .
Nematode strains were maintained under standard conditions [64] . Genetic strains used in this work are listed in Table S2 . The math-33 ( tm3561 ) mutation is a frameshift that occurs at Q508 and creates a stop codon at position 510 , which is within but near the end of the UCH domain , and we confirmed this by sequencing ( Figure S5A ) . The math-33 ( ok2974 ) mutation also is expected to cause a frameshift within the isopeptidase domain at or after T348 based on deletion data at Wormbase . org , sequence ID WBVar0094061 . We predict that both math-33 alleles would be null or strong loss of function alleles because the conserved enzymatic isopeptidase domain is disrupted in both mutations . We confirmed by PCR that deletions are present in strains carrying math-33 ( ok2974 ) , usp-46 ( ok2232 ) , and usp-47 ( tm4954 ) at locations reported by the C . elegans knockout consortium and by the S . Mitani Lab . The reported endpoints are available at wormbase . org . Temperature sensitive strains were incubated at 16° for experiments in which embryonic lethality was measured . The cold sensitive math-33 ( tm3561 ) worms were maintained at 20° and shifted to 16° for experiments . C . elegans embryos were released from the uterus of gravid hermaphrodites with a 10 gage needle point in distilled water or in phosphate buffered saline , pH 7 . Embryos were then moved onto a 2% agarose pad on a glass slide and covered with a glass cover-slip sealed with petroleum jelly for observation . During microscopy experiments in which phenotypes were assessed , the microscope environment was cooled to 16° for the duration of the experiment , except where noted in the figure or table . For live confocal imaging we used a Zeiss LSM 710 confocal microscope with a 63× Plan-Apochromat oil immersion lens , a stage cooled to 15°C , and Zen imaging software ( Zeiss ) . For confocal immunostaining we used either the Zeiss LSM 710 a Leica TCS SP2 DMRE-7 microscope with a 63× oil immersion lens , and Leica imaging software . For standard wide field microscopy we used a Leica DMRA2 microscope with a 63× Leica HCX PL APO oil immersion lens , an ORCA-ER camera , and Openlab software . Measurements were determined by measuring pixel distances using Openlab software and were converted to micrometers . Tracing of embryo borders to measure cortical domain size was performed using Openlab . To measure domain size in this way , the circumference of the embryo cortex was traced by a “walking mouse cursor” method around the embryo . PAR-2 and PAR-3 domains were measured independently; domain boundaries were defined as the point when the cortical signal equaled the background fluorescence . If one pole of an embryo was damaged during sample processing , that domain was not measured , but the opposite domain was measured and added to the average . The extent of myosin clearing was measured as a function of the entire embryo anterior-posterior length . Time-lapse confocal stacks were examined and the embryo length was measured using Image J . Then , we measured a distance through the longitudinal midline of the embryo between furthest posterior point and the edge of myosin foci clusters . Clearing was then expressed as the myosin clearing distance divided by the entire embryo length . The kymographs shown examining myosin clearing were generated with a 1 pixel-wide line through the center of the anterior-posterior axis and embryos were imaged roughly every 30 seconds . From separate 8 pixel-wide kymographs we measured flow rates as in [12] for up to three foci per embryo that remained in the posterior of the embryo during the establishment phase and were visible for at least four frames . We considered embryos to be undergoing polarity establishment after a small absence of myosin foci could be observed at the posterior . However , since some samples did not have myosin clearing , we measured flow rates for foci present during the 8 minutes prior to the transition of myosin from large foci to small puncta . We calculated flow rates by the following formula , as in [12] velocity = [Δ µm]/frames*frame rate ( seconds/frame ) , and then converted into minutes . We observed wild type foci speeds in a range from 1 . 7 to 3 . 0 µm per minute which is different from the reported maximum of 7 . 9 µm per minute [12]; this difference could be due to the lower temperature ( 16° ) at which we imaged the embryos . Centrosome distance from the cortex was determined using Openlab software to measure the space between the edge of the centrosome to the closest edge of the cortex visible in the x-y plane of focus . Embryos were monitored and refocused at a minimum of every fifty seconds at and after the end of meiosis until the appearance of centrosomes could be detected via TBB-2::GFP . Afterwards , embryos were monitored over time until pseudocleavage was evident , or , in case of the absence of pseudocleavage , until after pronuclear meeting . Those centrosomes that appeared in the z plane to be very close to the bottom or the top of the embryo cortex were discarded from analysis . To measure the centrosome dwell time at the cortex we monitored the position of the centrosome relative to the closest part of the cortex over time; we defined departure from the cortex as the time that the edge of the TBB-2::GFP signal closest to the cortex was permanently more than 2 µm from the cortex . We measured the relative area of AB compared to the area of AB plus P1 ( Table 2 column 9 ) . For this analysis we measured the circumference of blastomeres , and estimated their area from the circumference assuming a circular shaped cross-section as in [6] . We defined synchronous second cleavages of AB and P1 as the completion of P1 cytokinesis within 10 seconds of AB , and defined transverse spindles as those with spindles oriented 70° or more relative to a 0° longitudinal axis , and as those embryos in which P1 divided out of the Z plane indicating a transversely oriented mitotic spindle was present . Absence of pseudocleavage was defined as the absence of detectable persistent medial cortical invaginations in mitotic prophase . We used Openlab software ( Improvision ) to quantify the depth of pseudocleavage furrows . We measured the shortest distance from the deepest part of the furrow to a line drawn across the outside edges of the furrow ( see Figure S3D ) . GST-fusion constructs fused to a fragment of MATH-33 comprised of amino acids Q517-K706 or to USP-47 amino acids E822-M1005 in pGex-6P1 vector ( GE Healthcare ) were used to express protein in E . coli for antigen production . Proteins were eluted from glutathione-agarose beads by mixing with purified GST fusion precision 3C protease ( GE Healthcare ) and used to produce antibodies in guinea pigs ( MATH-33 ) , ( Cocalico Biologicals ) or in both a guinea pig and a rabbit ( USP-47 ) , ( Pocono Mountain Rabbit Farm ) . Affinity purification of the desired antibodies was accomplished by adsorbing sera to antigen-agarose Hi-Trap columns ( GE Healthcare ) , and eluting the purified antibody with 1 M glycine , pH 3 . Immunoflourescence signals from anti-MATH-33 and anti-USP-47 antibodies were strongly reduced in math-33 ( tm3561 ) and usp-47 ( RNAi ) embryos respectively , demonstrating specificity . Embryos were processed by methanol fixation followed by incubations with primary and secondary antibodies [4] . Primary antibodies used were rabbit anti-PAR-2 [5] , anti-PAR-3 mouse monoclonal [38] , rabbit anti-NMY-2 [65] , and mouse anti-Tubulin mouse monoclonal ( a gift from Margaret Fuller ) guinea pig anti-MATH-33 , and rabbit anti-USP-47 ( this study ) . Secondary antibodies used were Alexa Fluor 488 anti-rabbit , Alexa Fluor 488 anti-guinea pig ( Invitrogen ) , FITC anti-rabbit , Cy3 anti-mouse , and Cy3 anti-guinea pig ( Jackson Laboratories ) . Slides were mounted in Vectashield containing DAPI ( 4′ , 6-diamidino-2-phenylindole ) . RNAi of math-33 , usp-47/T05H10 . 1 , par-4 , DH11 . 5 , pam-1 , usp-46 , and dhc-1 was performed with clones from the Ahringer library ( MRC Geneservice ) [66] . DH11 . 5 was chosen as a control in double RNAi experiments because it is expressed embryonically according to nextDB [67] but has no detectable RNAi depletion phenotype . Depletion of 22 C . elegans DUBs was done with RNAi clones from both the Ahringer and Vidal RNAi libraries [66] , [68] . We depleted PAR-1 by expressing the full length mRNA ( GenBank Accession No . U22183 ) in vector pPd129 . 36 as in Hurd & Kemphues [69] . RNA interference of target genes was achieved by expressing dsRNA in E . coli followed by feeding of whole bacteria to worms [70] . dsRNA expression was induced with 1 mM IPTG at room temperature for 3 hours , and cultures were concentrated 10× before seeding non-nutrient agar plates containing 12 . 5 µg/ml tetracycline and 50 µg/ml carbenicillin . RNAi was performed at 16°C for 36–48 hours . math-33 or usp-47 full length cDNAs were isolated from a Gibco Proquest Library by Iproof PCR amplification ( BioRad ) and cloned into pIC26 ( unc-119; Ppie-1 driving a GFP-TEV-S tagged open reading frame ) [71] . The Ppie-1::gfp-tev-s::math-33 and Ppie-1::gfp-tev-s::usp-47 transgenic worm strains were generated by microparticle bombardment of unc-119 ( ed4 ) as in Praitis et al . 2001 [72] . We identified 25 UCH domain-containing proteins in C . elegans by using the ScanProsite tool from the ExPASy website of the Swiss Institute of Bioinformatics [73] . A phylogenetic tree showing sequence relationship of UCH domains in C . elegans as well as humans and fission yeast was generated by aligning the relevant UCH domains in Megalign ( Lasergene ) , and by plotting node distances with Phylip software [74] . We predicted closest homologs between species by using BLAST in the species of interest ( NCBI ) [75] . We based our assessment of USP-47's relative conservation amongst bilatera on Treefam-generated information present on the Wormbase usp-47 gene information webpage ( wormbase . org ) . | In eukaryotes , modification of proteins by the covalent ligation of a protein called ubiquitin is an important regulatory mechanism . In this study we found that deubiquitylation enzymes , which are known to cleave ubiquitin off of target proteins , are required for asymmetry in one-cell embryos of the nematode C . elegans . In one-cell embryos the establishment of asymmetry depends on a signal from the centrosome , a microtubule-organizing center . This signal breaks homogeneity in the contractile cytoskeleton located at the cortex of the embryo . We have identified three deubiquitylation enzymes that are necessary for the centrosome to properly localize adjacent to the cortex to perform its symmetry-breaking role . Furthermore , a homologous group of enzymes in fission yeast also regulates cell polarity . Our results suggest that a novel mechanism of centrosome localization regulated by ubiquitylation exists in C . elegans; this mechanism is masked by genetic redundancy and may be an evolutionarily conserved mechanism for cell asymmetry . | [
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"biochemis... | 2012 | Deubiquitylation Machinery Is Required for Embryonic Polarity in Caenorhabditis elegans |
SslE , the Secreted and surface-associated lipoprotein from Escherichia coli , has recently been associated to the M60-like extracellular zinc-metalloprotease sub-family which is implicated in glycan recognition and processing . SslE can be divided into two main variants and we recently proposed it as a potential vaccine candidate . By applying a number of in vitro bioassays and comparing wild type , knockout mutant and complemented strains , we have now demonstrated that SslE specifically contributes to degradation of mucin substrates , typically present in the intestine and bladder . Mutation of the zinc metallopeptidase motif of SslE dramatically impaired E . coli mucinase activity , confirming the specificity of the phenotype observed . Moreover , antibodies raised against variant I SslE , cloned from strain IHE3034 ( SslEIHE3034 ) , are able to inhibit translocation of E . coli strains expressing different variants through a mucin-based matrix , suggesting that SslE induces cross-reactive functional antibodies that affect the metallopeptidase activity . To test this hypothesis , we used well-established animal models and demonstrated that immunization with SslEIHE3034 significantly reduced gut , kidney and spleen colonization by strains producing variant II SslE and belonging to different pathotypes . Taken together , these data strongly support the importance of SslE in E . coli colonization of mucosal surfaces and reinforce the use of this antigen as a component of a broadly protective vaccine against pathogenic E . coli species .
Pathogenic E . coli can be broadly classified as either extraintestinal pathogenic E . coli ( ExPEC ) , the main cause of urinary tract infection ( UTI ) , newborn meningitis and sepsis , or as intestinal pathogenic E . coli ( InPEC ) causing diarrhoeagenic infections . Among the intestinal pathogens there are at least six well-described groups: enteropathogenic E . coli ( EPEC ) , enterohaemorrhagic E . coli ( EHEC ) , enterotoxigenic E . coli ( ETEC ) , enteroaggregative E . coli ( EAEC ) , enteroinvasive E . coli ( EIEC ) and diffusely adherent E . coli ( DAEC ) [1] . The plasticity of the E . coli genomes , due to the ability to gain or lose virulence attributes by horizontal gene transfer , allows these organisms to colonize different sites . Indeed , E . coli possesses an array of virulence factors which include various adhesins , capsule , iron-transporters , toxins and proteases ( reviewed in [1] ) . However , recent studies have suggested that the pathogenesis of E . coli is considerably more complex than previously appreciated involving additional virulence factors [2] , [3] . The absence of a broadly protective vaccine against pathogenic E . coli is a major problem for modern society since diseases caused by these bacteria are associated with significant human suffering and high healthcare costs . The overall problem is exacerbated by the rising rates of multi-drug resistant strains and by the emergence of new sequence types and hypervirulent strains [4]–[9] . We have recently proposed ECOK1_3385 as a promising vaccine candidate able to confer protection in a murine model of sepsis [10] , [11] . This protein , described as SslE ( for secreted and surface-associated lipoprotein from E . coli ) and formerly known as YghJ [12] , [13] , appears to be required for biofilm formation and for virulence of EPEC strains [14] , although more recent evidence indicates that SslE has no effect on adherence and biofilm formation in atypical EPEC strains [15] . Thus , the function of SslE remains to be fully elucidated . However , it is known that SslE is secreted through a type II secretion system ( T2SS ) , an exporting apparatus typically used by Gram-negative bacteria to secrete virulence determinants [16] . Two T2SSs exist in E . coli , designated as alpha ( T2SSα ) and beta ( T2SSβ ) [17] . The T2SSβ operon is composed of three genes ( yghJ , pppA , and yghG ) upstream of gspCβ . The first gene , yghJ , encodes for the SslE protein . A functional T2SSβ secreting a cognate SslE protein was recently studied in the non-pathogenic E . coli W strain [18] . Recently , it was reported that SslE belongs to a new sub-family of extracellular zinc-metallopeptidases , characterized by a M60-like zinc-metalloprotease domain HEXXHX ( 8 , 24 ) E [19] , that is distantly related to known viral enhancin zinc-metallopeptidases . The baculovirus enhancin protein Vef is able to digest intestinal mucins , facilitating the attachment and entry of the virus into epithelial cells [20] . Using biochemical and functional assays , we demonstrated that SslE is involved in E . coli degradation of mucin substrates . In addition , antibodies raised against SslE variant I from ExPEC strain IHE3034 were able to inhibit translocation of different E . coli pathotypes through a mucin-based matrix , suggesting a possible mechanism for in vivo protection . This hypothesis was corroborated by the fact that in mouse models of intestinal and urinary tract colonization , SslE variant I induced protective immunity also against E . coli strains expressing variant II . The widespread distribution and conservation of SslE , together with the ability to elicit functional antibodies , assessed both in vitro and in vivo , strongly support the potential of the SslE antigen to provide coverage against both intestinal and extraintestinal pathogenic E . coli strains .
It has been recently reported that although SslE is secreted by a T2SS , it is also found on the bacterial cell surface [10] , [14] . Confocal analysis of Z-stack images of an ExPEC strain IHE3034 stained for SslE and deconvoluted using Volocity Software , revealed that the antigen is translocated on the bacterial surface at specific foci ( Fig . 1A ) . Of interest , we observed that only a small proportion of bacteria ( 3% of total number ) expressed the antigen on the surface ( Fig . 1A ) . We determined that this phenotype is attributable to the polysialic acid capsule ( K1 antigen ) that it responsible for masking SslE on the bacterial surface ( Fig . S1 ) . The sslE deletion mutant strain ( IHE3034ΔsslE ) did not show any surface labeling ( Fig . 1B ) , confirming the specificity of the signal . Complementation of the mutant strain with a pET24b+ plasmid carrying the sslE gene ( including the promoter region ) restored antigen surface localization ( Fig . 1C ) . To exclude the possibility that the SslE signal at the bacterial surface could be partially attributed to the re-association of the secreted form of the protein to the membrane , we co-cultured the IHE3034 wild-type ( WT ) strain with the IHE3034ΔsslE strain engineered to express the GFP fluorescent protein . Staining of bacteria using SslE antibodies conjugated to FITC , revealed that the antigen was exclusively detected on the surface of the WT strain and not on the fluorescent bacteria , indicating that no SslE re-association occurred ( data not shown ) . As recently reported by Nakjang and collaborators [19] , HEXXHX ( 8 , 24 ) E is the full putative metalloprotease core motif of SslE ( residues: 1304–1322; SslE accession number: YP_006102500 ) , exclusively present in a recently characterized zinc metallopeptidase sub-family possessing mucinase activity [19] . The pattern “HEXXHX ( 8 , 24 ) E” consists of a conserved glutamate residue localized 8 to 24 amino acids from the “HEXXH” motif . To investigate the putative mucinolytic activity of SslE , we have applied a number of in vitro assays previously reported to specifically detect mucinase activity in bacteria [21]–[24] . The first approach is based on the use of bacteria grown on agar plates containing 0 . 5% bovine submaxillary mucin followed by amido black-staining [25] , [26] . Plates containing the IHE3034 WT strain incubated for 24 h revealed clear zones of mucin lysis ( Fig . 2A ) . However , no cleared areas were detected when the IHE3034ΔsslE knockout ( KO ) strain was added to the plates , indicating the specific contribution of SslE to the mucinase activity . Incubation of mucin-based plates with the complemented strain IHE3034ΔsslE::sslE_WT carrying the WT sslE gene fully restored the wild-type phenotype as assessed by the lack of amido black staining . To investigate the role of the M60-like core motif in mucin lysis , we transformed the IHE3034ΔsslE strain with the pET24b+ plasmid carrying a triple mutation in the putative metallopeptidase motif of SslE ( YVVGY vs . HEVGH ) . In particular , we introduced hydrophobic elements in the HEXXH motif ( Y and V ) , which by reducing the charge of the enzymatic task are likely to impair the mucinase activity . Testing of this mutant by the amido black assay revealed a phenotype comparable to the sslE KO strain ( Fig . 2A ) . These data were further confirmed by the In Vivo Imaging System ( IVIS-Perkin Elmer ) technology which allowed the visualization of bacterial migration through the agar-mucin matrix at different time points , using IHE3034 strains engineered for constitutive expression of a luciferase operon ( plux ) [27] ( Fig . 2B ) . Briefly , a mid-log bacterial culture of the bioluminescent strains was loaded in a well created at the center of a mucin-agar plate and bacterial distribution was detected after 24 h of incubation . IHE3034 ( plux ) and IHE3034 ( plux ) ΔsslE::sslE_WT strains , but not IHE3034 ( plux ) ΔsslE and IHE3034 ( plux ) ΔsslE::sslE_mut , were able to spread beyond the site of the initial inoculum ( Fig . 2B ) , confirming that SslE-dependent mucin degradation enables diffusion of E . coli through the agar . To test the hypothesis that anti-SslE antibodies may also inhibit mucinase activity in vitro , we developed an in vitro system to quantify the ability of strain IHE3034 WT to transverse a mucin-based gel matrix . An agar-based matrix gel containing 10% bovine submaxillary gland mucins was reconstituted in a 1 mL syringe and bacterial aliquots ( 108 CFU ) were layered on top of the gel and statically incubated for 3 h at 37°C in a vertical position to allow bacterial translocation . At the end of the incubation period , gel fractions were eluted from the bottom of the syringe , diluted and plated for CFU determination . After confirming the impaired phenotype of the sslE KO strain in traversing the mucin matrix compared to the isogenic WT strain ( ∼2 . 5 Log reduction ) ( Fig . 3A ) , we tested the ability of polyclonal antibodies generated by sub-cutaneous immunization of rabbit with the full length recombinant SslE from ExPEC strain IHE3034 ( anti-SslEIHE3034 ) to reduce bacterial translocation through the mucin agar-gel syringe . Anti-SslE IgGs and IgAs in the rabbit serum were measured by ELISA ( Fig . S2A and B ) . A significant dose-dependent inhibition of bacterial translocation was observed when the mucin-gel matrix was polymerized together with anti-SslE antibodies ( dose range 1∶50 to 1∶1350 ) ( Fig . 3B ) . At a dilution of 1∶50 the inhibitory effect of SslE antibodies was evident in all fractions collected , while higher dilutions principally affected bacterial translocation in the first two fractions . A higher dilution of 1∶4050 did not show an inhibitory effect in any of the collected fractions ( data not shown ) . The specificity of the inhibition was confirmed by the absence of an effect when using an antiserum against the unrelated ExPEC antigen c1275 [10] , at the lowest dilution ( Fig . 3B ) . On the other hand , since antibodies against a fragment of SslE , C-SslEIHE3034 , lacking the M60-like motif ( Fig . S3A ) , were still capable of impairing IHE3034 translocation through the mucin layer ( Fig . S3B ) , it is not possible to establish whether polyclonal antibodies have a direct or an indirect effect on SslE activity . As previously reported , SslE can be divided into two main variants [10] . Three hundred and eighteen E . coli sslE sequences were added to the 96 previously analyzed by Moriel et al . [10] ( Table S1 ) and global amino acid sequence alignment revealed that sequence variability was present and distributed along the entire protein sequence . Overall , amino acid sequence identity ranged from 86–100% , with the HEXXHX ( 8 , 24 ) E core motif fully conserved in all sequences analysed . A total of 155 E . coli unique protein sequences were identified and used to construct a phylogenetic tree ( Fig . 4 and Table S2 ) . The two main branches denoted the presence of two SslE clades ( encoding for two variants: I and II ) . To understand whether antibodies raised against variant I can cross-inhibit the mucinolytic activity of other SslE sub-variants , we selected a number of strains producing SslE variant II and belonging to different pathotypes . We tested the ability of an antiserum against SslE from strain IHE3034 ( SslEIHE3034 , belonging to variant I ) to prevent the translocation of intestinal and extraintestinal strains expressing SslE belonging to variant II . In particular , we selected an EPEC strain ( IC50 ) , a SEPEC ( septicemic-associated E . coli belonging to ExPEC ) strain ( IN1S ) , an ETEC strain ( GL53 ) and the EAHEC strain ( LB226692 ) recently identified to be responsible for the 2011 German E . coli outbreak . SslEIHE3034 antiserum inhibited the ability of all E . coli pathotypes tested ( expressing SslE variant II ) to traverse the mucin-based matrix ( Fig . 5 ) . The evidence that antibodies against SslEIHE3034 are functional and recognize different variants allows us to postulate that a vaccine containing this antigen may have the potential to protect against most pathogenic E . coli species . In order to test the protective efficacy of SslEIHE3034 ( variant I ) , we set up a mouse model of intestinal colonization using the ETEC GL53 strain . Mice were intragastrically infected with the bioluminescent GL53-Pem7-luxCDABE strain [27] and consistent bioluminescent signals were detected in the abdominal region until to 72 hours post-infection by the In Vivo Imaging System ( IVIS ) ( Fig . 6A ) . As observed for other intestinal E . coli pathotypes [28] , [29] , bacterial infection mainly occurs in the caecum tract ( Fig . 6B ) . This is consistent with data obtained by both CFU counts from infected intestinal ileum and caecum tracts ( Fig . 6C ) and confocal imaging of tissues ( Fig . 6D and E ) . After setting up the GL53 intestinal colonization , we evaluated the functionality of the sslE promoter in vivo . 2D bioluminescent signal in the abdominal region could be observed when the luciferase expression was driven by the sslE promoter ( Fig . 7A ) , compared to the positive control GL53-Pem7-luxCDABE . As expected , GL53 transformed with the luciferase promoterless plasmid gave no signal ( Fig . 7A ) [27] . In addition , 3D analysis confirmed that the signal was predominantly associated with the intestine ( Fig . 7B ) . SslE transcription in GL53 colonizing bacteria was further evaluated by reverse transcriptase-polymerase chain reaction ( RT-PCR ) , confirming that the sslE promoter is active in vivo ( Fig . 7C ) . Cross-protective efficacy was evaluated by immunizing 30 mice intranasally with the recombinant variant I SslE and challenging them with the ETEC strain GL53 ( expressing variant II SslE ) . Following immunizations with 30 µg of recombinant SslEIHE3034 at days 1 , 21 and 35 , mice were infected by oral gavage with 5×107 CFU of GL53 at day 49 . Intestinal caecum tracts were collected at day 51 , serial dilutions of the homogenized tissues were plated and the CFU numbers were enumerated . A statistically significant reduction ( 2 . 5 Log ) in the mean value of GL53 bacterial counts in the caecum was observed in mice immunized with the SslEIHE3034 antigen versus those treated with saline ( Fig . 8A ) . Anti-SslE responses in protected mice consisted of antibodies belonging to both IgG and IgA isotypes ( Fig . S2C and D ) . To further support the observation that SslEIHE3034 ( variant I ) induces heterologous protection , we considered two alternative models: a murine model of ascending UTI and a murine sepsis model . In the UTI model , 30 mice were intranasally inoculated with either cholera toxin ( CT ) alone ( as an adjuvant ) or an SslEIHE3034-CT mixture . Following three immunizations ( days 0 , 7 , 14 ) , animals were transurethrally challenged on day 21 with the UPEC strain 536 ( expressing SslE variant II ) and protection was assessed at 48 h post infection by determining the CFUs in the urine , bladder , kidneys and spleen . SslEIHE3034 immunization led to a significant reduction in median CFU/g ( P = 0 . 0394 ) in the kidneys and a more evident protection in the spleen with a 2 . 0 Log reduction in median CFU/g ( P = 0 . 0006 ) ( Fig . 8B ) . In the sepsis model , systemic E . coli infection was performed as recently reported [10] . Active immunization with SslEIHE3034 followed by challenge with the SEPEC strain IN1S ( expressing SslE variant II ) provided significant protection from mortality ( 60% survival , P<0 . 0001 ) ( Fig . 8C ) .
E . coli is a well-adapted human pathogen which uses the gut as a preferential niche and , as for other intestinal microorganisms , it persists in this region due to its ability to exploit a number of metabolic substrates and to stay in the outer mucus layer where commensal bacteria normally reside . Recent studies [30]–[33] , including those reported by our group [34] , [35] , have postulated that this microorganism has adapted to the human body by developing a sophisticated network of virulence and colonization factors . Among these adhesins , iron-uptake systems and IgA binding proteins may allow E . coli to out-compete the many species occupying an overcrowded environment such as the intestine . In this scenario , our finding that SslE contributes to E . coli mucinase activity suggests the involvement of this antigen in landscaping the E . coli territory allowing the establishment of a long lasting colonization . Indeed , shaping of the intestinal microbial community by the mucosa does not only depend on goblet cells secreting antimicrobial proteins , but also on a number of metabolic substrates vital to mucus-degrading bacteria [36]–[38] . In our study , the diminished capacity of the sslE mutant strain to translocate through a mucin-rich matrix in vitro suggests that SslE activity may facilitate bacterial penetration of the mucosal surface , including the inner mucus layer , to reach the underlying host epithelium . Although these data do not exclude that the catabolism of such glycoproteins may also contribute to an increased fitness of E . coli in the outer mucus layer , the pathogenic strains that are armed with immune evasion virulence factors may use SslE as a spearhead to penetrate the sterile inner mucus layer so as to intimately adhere to the epithelial cells of the host . The core motif , HEXXH , present in SslE is conserved in all families of the Clan of peptidase named MA ( M for metallo ) although it might also be present by chance in proteins with no peptidase activity [39] , [40] . Using the full putative metalloprotease domain of the ExPEC variant of SslE ( residues: 1082–1382 ) to search the Pfam-A protein families database , we confirmed that the entire top 100 hits ( E-value<8e-35 ) were M60-like domains ( Pfam ID: PF13402 ) . This domain is exclusively present in a recently characterized zinc metallopeptidase sub-family that possesses mucinase activity [19] . The multiple sequence alignment of the best hits showed the extended motif of the M60-like domain ( Supporting information Fig . S4 ) . These hits were mainly bacterial proteins from Gamma proteobacteria , and they have comparable sequence lengths to ExPEC SslE ( ∼1460–1520 a . a . ) . Interestingly , the majority of these proteins were predicted to be outer membrane lipoproteins that are N-terminally anchored to the outer membrane , which implies that these mucinases are dedicated to digestion of extracellular host glycoproteins . However , although our data support the hypothesis for the contribution of SslE to E . coli colonization by a mechanism likely to involve mucin degradation , we were not able to obtain direct evidence for such an enzymatic activity . Indeed , we observed that recombinant SslE binds to Zinc , but is unable to cleave a number of putative metalloprotease-target molecules including gelatin , casein , fibrinogen , and different collagens ( data not shown ) . However , since bacterial metalloprotease activities are known to depend on different parameters ( such as pH , temperature , salt concentration , etc . ) [41] , [42] , further screenings for appropriate in vitro conditions will be required . The large antigenic and genetic variability of pathogenic E . coli species has been a major obstacle to the development of a broadly protective vaccine . Indeed , the difficulty in predicting vaccine coverage and the lack of a correlate of protection , has led to numerous promising pre-clinical data not being confirmed by human studies [43]–[47] . By comparing the genome of an ExPEC strain causing neonatal meningitis to those of other ExPEC and nonpathogenic strains , we have recently proposed a number of well conserved protective antigens . Among them the most promising candidate was SslE , which due to its conservation in both intestinal and extraintestinal strains was proposed as a universal vaccine candidate . The anti-mucinase activity exerted by anti-SslE polyclonal antibodies in vitro , corroborated by a reduced colonization of caecum in mice immunized with recombinant SslE , further support the hypothesis that the impairment of mucin cleavage may account for the mechanisms of protection from E . coli infections in both the mucosal tissues of the gut and the urinary tract [48] , [49] . In addition , antibodies generated against SslE variant I showed cross-functional properties versus strains expressing variant II . Since polyclonal antibodies raised against full-length SslE are able to cross-inhibit antigen functional activity , we hypothesized that they may target conserved domains of SslE potentially involved in the metalloprotease activity . However , only a few strains were tested and further studies using a larger panel of clinically relevant strains would be needed to confirm such an assumption . In conclusion , the contribution of SslE to E . coli mucinolytic activity in vitro , and SslE mediated protection against intestinal and urinary tract colonization in vivo , indicate the importance of SslE as a novel colonization factor and a valid target for intervention strategies against disease caused by this important human pathogen .
Animal studies regarding intestinal colonization and sepsis models were carried out in compliance with current Italian legislation on the care and use of animals in experimentation ( Legislative Decree 116/92 ) and with the Novartis Animal Welfare Policy and Standards . Protocols were approved by the Italian Ministry of Health ( authorization 236/2010-B ) and by the local Novartis Vaccines and Diagnostics Animal Welfare Body ( authorization AEC 201010 ) . Animal studies for urinary tract infection experiments were conducted according to protocol #08999 approved by the University Committee on the Care and Use of Animals at the University of Michigan Medical School . The approved procedures are in compliance with University guidelines , State and Federal regulations , and the standards of the “Guide for the Care and Use of Laboratory Animals” . Genomic DNA was isolated using the GenElute Bacterial Genomic DNA Kit ( Sigma ) according to the manufacturer's instructions . ExPEC strain IHE3034 ( serotype O18:K1:H7 ) was isolated in Finland in 1976 from a case of human neonatal meningitis [50] . Strains were cultured in Luria-Bertani ( LB ) broth at 37°C with agitation and aeration . E . coli DH5α-T1R ( Invitrogen ) was used for cloning purposes and E . coli BL21 ( DE3 ) ( Invitrogen ) was used for expression of His-tagged fusion proteins . The clones carrying a specific antibiotic resistance cassette were grown in the presence of kanamycin ( 50 µg/ml ) or ampicillin ( 100 µg/ml ) . The isogenic sslE knockout mutant strain was constructed by replacement of the entire gene by an antibiotic resistance cassette . The upstream and the downstream regions of the sslE gene were amplified by PCR with the primers 1–2 and 3–4 ( Table S3 ) , using IHE3034 chromosomal DNA as template , and cloned into the pBluescriptKS ( Stratagene ) . The kanamycin resistance cassette was inserted between the two flanking regions in the plasmid . The resulting plasmid was used to electroporate the target strain . Single transformants were confirmed by PCR and Western blotting . Complemented strains were obtained by transformation of the sslE mutant with sslE_WT and sslE_mut recombinant plasmids , carrying the sslE wild-type gene or the gene mutated in the putative metallopeptidase motif . For amplification of the sslE gene , E . coli IHE3034 genomic DNA was used with the primers 5 and 6 ( Table S3 ) . The triple mutation ( mut ) ( H1274Y+E1275V+H1278Y ) was obtained by two overlapping PCRs performed with primers 7 , 8 and 9 ( Table S3 ) . Finally , the psslE_WT and psslE_mut constructs were generated carrying the sslE predicted promoter region upstream of the sslE gene . The two clones harboring these plasmids were produced by a PIPE method [51] that is based on the transformation of HK100 E . coli cells with a mix of a vector/insert PCR . The vector PCR was performed using the sslE_WT and sslE_mut templates with primers 10 and 11 ( Table S3 ) , while the insert PCR was obtained with E . coli IHE3034 genomic DNA template and primers 12 and 13 ( Table S3 ) . E . coli strains were grown to exponential phase in LB medium and fixed in PFA 1% for 20 min on a poly-L-lysine-coated slide ( Thermo scientific ) . After a blocking step in PBS+1% BSA , slides were incubated with anti-SslE rabbit serum and then with a donkey anti-rabbit IgG Rhodamine RedX-conjugated antibody ( Jackson Immuno-Research Laboratories ) . IHE3034 bacteria were localized using mouse polyclonal antibodies raised against whole cell IHE3034 , and the green fluorescent Alexa Fluor488 goat anti-mouse IgG . The samples were mounted using the Pro-Long Gold antifade reagent containing the blue-fluorescent nuclear counterstain DAPI ( Invitrogen ) . Images were acquired using a 100× oil objective ( 1 . 4 n . a . ) mounted on a Zeiss LSM710 confocal microscope . In the pictures the signal from SslE was pseudocoloured in green , while the signals from bacteria are shown in red . Z-stacks of images were deconvoluted using Volocity Software ( Improvision ) . Amplification and sequencing of the sslE gene was performed as previously described [10] . Assembly , alignment and comparison of the SslE deduced amino acid sequence was performed with GENEIOUS V6 software ( Biomatters . Available from http://www . geneious . com/ ) . In addition to the 96 sslE sequences used by Moriel et al . [10] , 318 E . coli sslE sequences were included . The final dataset comprised 414 isolates which comprised EXPEC , InPEC and faecal isolates . Further , sequences relative to unknown E . coli pathotypes were extracted from the NCBI database ( Table S1 ) . 155 unique SslE protein sequences were selected using GENEIOUS V6 software . The phylogenetic tree was inferred from the alignments by the neighbor-joining distance-based method implemented on MEGA4 [52] . The sslE gene was amplified by PCR from the IHE3034 genomic DNA template , cloned into the pET-21b vector ( Novagen ) and transformed into DH5α-T1R chemically competent cells for propagation . BL21 ( DE3 ) chemically competent cells were used for His-tagged protein expression . The protein was purified by nickel chelating affinity chromatography using a HisTrap HP column ( GE Healthcare ) followed by anionic exchange chromatography . The purified protein was finally dialyzed in phosphate-buffered saline ( PBS ) and stored at −20°C . The PsslE-luxCDABE plasmid was obtained by replacing the constitutive Pem7 promoter of the pGEN-luxCDABE with the sslE putative promoter region . To obtain the predicted sslE promoter region , a 484-bp fragment was amplified from IHE3034 genomic DNA by PCR using the primers 14 and 15 ( Table S3 ) . Chemically competent DH5α cells ( Life Technologies ) were used for transformation and ampicillin ( Amp100 ) was used as a marker of selection . The resulting PsslE-luxCDABE plasmid was confirmed by sequence analysis and used to transform the ETEC strain GL53 by electroporation , resulting in the GL53-PsslE-luxCDABE strain . Ten-week old CD1 female mice ( Charles River ) were infected intragastrically with 5×105 CFU of either the bioluminescent ETEC GL53-PsslE-luxCDABE strain or GL53-Pneg-luxCDABE ( promoterless control vector ) . Imaging of mice anesthetized with isofluorane ( 4% initially , 1 . 5% during image acquisition ) was performed with an IVIS Spectrum CT Imaging System ( Perkin Elmer ) . Detection of 2D bioluminescent signals was carried out without filters ( open ) , binning 8 and times of acquisition from 1 s to 1 min . 3D images were acquired with six filters ( 500 , 520 , 560 , 580 , 600 and 620 nm ) , using the same binning and acquisition times and reconstructed by the Living Image software ( version 4 . 3 . 1 ) . The GL53 infected caecum was homogenized using gentleMACS Dissociator ( MiltenyiBiotec ) in 10 ml PBS . After filtration and centrifugation , the pellet was incubated for 5 minutes at room temperature in 3 ml of RNA protect Bacteria Reagent ( Qiagen ) . After cell lysis , total RNA was purified using the RNeasy Mini kit ( Qiagen ) and an additional DNase treatment was done using the TURBO DNA-free kit ( Applied Biosystem ) , according to the manufacturer's protocols . Purity of RNA was assessed by electrophoresis on agarose gels . Reverse transcription and amplification of an sslE fragment with the primers 16 and 17 ( Table S3 ) from RNA were performed using the SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase kit ( Invitrogen ) . Five-week old CD1 mice were intranasally immunized with 30 µg of SslE antigen at days 1 , 21 and 35 . Saline was used as a negative control . Fourteen days after the last immunization mice were streptomycin-treated ( for 2 days ) to eradicate the resident flora and then they were infected by oral gavage with 5×107 CFU/400 µl of strain GL53/Ampr . Forty-eight hours after challenge , mice were euthanized and the intestinal caecum tract was recovered and homogenized . Serial dilutions of the suspension were plated on LB/Amp100 plates and the CFU were enumerated . Statistical significance of protection was determined using the Mann Whitney test . Female CBA/J mice , 6 to 8 weeks old , were transurethrally inoculated as previously described [53] . Purified antigen was mixed with cholera toxin ( CT ) ( Sigma ) at a ratio of 10∶1 . The vaccine was administered intranasally in a total volume of 20 µl/animal ( 10 µl/nostril ) . Animals received a primary dose on day 0 of 100 µg antigen ( containing 10 µg CT ) or 10 µg CT alone . Two boosts of 25 µg antigen ( mixed with 2 . 5 µg CT ) or 2 . 5 µg CT alone were given on days 7 and 14 , and mice were challenged on day 21 . E . coli 536 suspensions in phosphate-buffered saline ( PBS ) ( 50 µl/mouse ) were delivered transurethrally using a sterile 0 . 28-mm-inner-diameter polyethylene catheter connected to an infusion pump ( Harvard Apparatus ) , with a total inoculum of 108 CFU/mouse . For determination of CFU , organs were aseptically removed from euthanized animals at 48 h post inoculation and homogenized in PBS with a GLH homogenizer ( Omni International ) . Bacteria in tissue homogenates were enumerated by being plated on LB agar containing 0 . 5 g/liter NaCl using an Autoplate 4000 spiral plater ( Spiral Biotech ) , and CFU were determined using a QCount automated plate counter ( Spiral Biotech ) . Blood was collected as necessary from anesthetized mice by an infraorbital bleed using 1 . 1- to 1 . 2-mm Micro-Hematocrit capillary tubes ( Fisher ) , and serum was separated using Microtainer serum separator tubes ( Becton Dickinson ) . The animals were ≤15 weeks old at the conclusion of all experiments . CD1 outbred mice were immunized by subcutaneous injections at day 1 , 21 , and 35 with 20 µg of recombinant SslEIHE3034 formulated with alum or alum alone . Immunized animals were challenged at day 49 with a sublethal dose of a heterologous strain and survival was monitored for up to 4 days . The results are indicated as the percentage of survival from a total number of 40 mice . P values were determined using the nonparametric Mann-Whitney significance test . Mean values , standard deviation values , and the P values associated to two-tailed unpaired Student's t test were calculated using the Microsoft Excel application . A P value<0 . 05 was considered statistically significant . | Escherichia coli are the predominant facultative anaerobe of the human colonic flora . Although intestinal and extraintestinal pathogenic E . coli are phylogenetically and epidemiologically distinct , we recently proposed a number of protective antigens conserved in most E . coli pathotypes . In this study , we have elucidated the function of the most promising of these antigens , SslE , which is characterized by the presence of a M60-like domain representative of a new extracellular zinc-metalloprotease sub-family . In particular , in vitro analysis of the ability of an sslE knockout mutant strain to transverse an agar-based mucin matrix revealed that SslE is essential to E . coli mucinase activity . Evidence showing that SslE induces functional antibodies , preventing both in vitro mucin degradation but also in vivo gut , kidney and spleen colonization , further support the hypothesis that SslE may facilitate E . coli colonization by favoring the penetration of the sterile inner mucus layer leading to interaction with host cells . Finally , the ability of SslE to also induce protective immunity against sepsis , linked to its presence among different pathotypes , supports the use of such an antigen as a broadly protective E . coli vaccine candidate . | [
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"infectious",... | 2014 | SslE Elicits Functional Antibodies That Impair In Vitro Mucinase Activity and In Vivo Colonization by Both Intestinal and Extraintestinal Escherichia coli Strains |
Rheumatic heart disease ( RHD ) prevalence and mortality rates remain especially high in many parts of Africa . While effective prevention and treatment exist , coverage rates of the various interventions are low . Little is known about the comparative cost-effectiveness of different RHD interventions in limited resource settings . We developed an economic evaluation tool to assist ministries of health in allocating resources and planning RHD control programs . We constructed a Markov model of the natural history of acute rheumatic fever ( ARF ) and RHD , taking transition probabilities and intervention effectiveness data from previously published studies and expert opinion . Our model estimates the incremental cost-effectiveness of scaling up coverage of primary prevention ( PP ) , secondary prevention ( SP ) and heart valve surgery ( VS ) interventions for RHD . We take a healthcare system perspective on costs and measure outcomes as disability-adjusted life-years ( DALYs ) , discounting both at 3% . Univariate and probabilistic sensitivity analyses are also built into the modeling tool . We illustrate the use of this model in a hypothetical low-income African country , drawing on available disease burden and cost data . We found that , in our hypothetical country , PP would be cost saving and SP would be very cost-effective . International referral for VS ( e . g . , to a country like India that has existing surgical capacity ) would be cost-effective , but building in-country VS services would not be cost-effective at typical low-income country thresholds . Our cost-effectiveness analysis tool is designed to inform priorities for ARF/RHD control programs in Africa at the national or subnational level . In contrast to previous literature , our preliminary findings suggest PP could be the most efficient and cheapest approach in poor countries . We provide our model for public use in the form of a Supplementary File . Our research has immediate policy relevance and calls for renewed efforts to scale up RHD prevention .
Decision-makers in African countries face difficult tradeoffs when choosing among interventions that address acute rheumatic fever ( ARF ) and its sequel rheumatic heart disease ( RHD ) . There is evidence that ARF and RHD can be eradicated in both high-income and limited resource settings . [1–3] Yet these conditions remain neglected by the global health community . [4] Further , the prevalence of RHD appears to be increasing , and mortality rates in Africa are among the highest in the world . [4 , 5] ARF can usually be prevented by treating cases of streptococcal pharyngitis promptly with injectable benzathine penicillin G ( “primary prevention” ) . Among individuals with a history of ARF , regular prophylactic use of penicillin can reduce the risk of recurrent ARF recurrence and progression to RHD ( “secondary prevention” ) . [6] However , for many children and adults living with RHD in African countries , opportunities for prevention have been lost . Irreversible heart valve damage from RHD carries a high mortality rate that can only be mitigated by open heart surgery and valve replacement . [6 , 7] Tradeoffs between prevention and surgical treatment of RHD are especially stark in settings where there is currently no capacity to perform specialized cardiac surgery . At present , only a handful of African nations have independent , high-volume surgical programs . While some nations have semi-independent or low-volume surgical centers , most nations have no surgical capacity . [8] At the same time , coverage rates of ( relatively ) more affordable primary and secondary prevention measures are also unacceptably low , even in academic referral hospitals and in middle-income countries . [9] Hence the central question for RHD health policy in Africa is how to set priorities across prevention and treatment interventions in order to maximize the health of individuals at risk of , or affected by , ARF and RHD . At the same time , the global universal health coverage ( UHC ) movement has gained traction in light of the new Sustainable Development Goals ( SDGs ) , which explicitly call for countries to achieve UHC over the SDG period . [10] These goals add urgency to the need to set country-specific priorities around ARF and RHD by identifying which health care services could feasibly be included in a UHC “benefits package” at an acceptable cost . [11] A WHO consultation recently identified the most fair and equitable pathway to UHC as one that starts by providing full population coverage at zero patient cost for a focused set of services that preferentially improve health for the worst off . [12] Since RHD is a highly preventable disease of poverty , ARF/RHD interventions need to be considered as part of UHC benefits packages for African nations—especially for UHC schemes that strive to be “pro-poor , ” i . e . , that focus their initial efforts on improving health and economic outcomes among the poor . One goal of economic evaluation , then , is to identify which ARF/RHD services are most cost-effective and affordable and should thus receive first priority for a benefits package . There is scarce information on the cost-effectiveness of ARF and RHD interventions . Some have studied the most cost-effective method ( s ) of delivering primary prevention , [13] while others have studied whether echocardiography is a cost-effective tool for strengthening secondary prevention through “active” case-finding . [14] Only one analysis , which was undertaken for the original Disease Control Priorities in Developing Countries project ( “DCP1” ) in 1993 , explicitly studied the policy question of how to choose between prevention and surgical treatment . [15] Yet the literature on the epidemiology of RHD , cost of care , and effectiveness of treatment have evolved substantially since that time , necessitating an update of those findings . This study builds on the prior DCP1 analysis , incorporating up-to-date data and following contemporary modeling practices . The output of this work is a cost-effectiveness tool that decision-makers can use to allocate scarce resources for RHD efficiently at the local and national level as they consider the move towards UHC . In addition to presenting the methods and data sources for our model , we illustrate its use in a hypothetical low-income country setting .
The objective of our tool is to evaluate the incremental cost-effectiveness of achieving target coverage rates of one or more evidence-based interventions for ARF and RHD . We define coverage as the proportion of at-risk or affected individuals who are currently receiving ( “ante” ) or are intended to receive ( “post” ) the interventions . Our focus on incremental changes in coverage rates situates our analysis within the broader question of setting priorities for UHC in Africa . [17] We model the health gains and costs associated with a given increase in coverage for a reference individual , and in the base case , we present an incremental cost-effectiveness ratios ( ICER ) for increased coverage . Our modeling tool looks at three general intervention scenarios: The reference case for PP is the general population , whereas the reference case for SP is the individual with a history of ARF who remains at risk of ARF recurrence and progression to RHD . The reference case for VS is the individual with pre-existing RHD . The ICER in each scenario then reflects the value for money for achieving full coverage of the intervention described in that scenario . The anticipated end user of this tool is the ministry of health , so our analysis takes a health system perspective on costs rather than a societal perspective . The model is designed to estimate healthcare costs over a lifetime horizon ( 100 one-year cycles ) and discounted at the standard 3% per year . The three intervention scenario program costs are designed to be calculated on a separate worksheet and estimated over a time horizon that is appropriate to each intervention , again , discounted at 3% . The worksheet is designed so that the end user can , prior to running the model , estimate the program costs that would be required in order to achieve full coverage of PP , SP , and VS . In general , the cost of scaling PP includes community and provider education , surveillance , program administrative costs , and additional clinical expenses needed to manage all cases of streptococcal pharyngitis appropriately . The cost of scaling SP includes case finding efforts , maintenance of a patient registry , provider education , program administrative costs , and additional clinical expenses needed to deliver monthly penicillin injections to all cases . [24] Estimating the cost of scaling VS is somewhat more challenging and depends on the present availability of specialized surgery in the country . On the one hand , some ministries of health may wish to build local surgical capacity , so their costing exercise would focus on the capital and recurrent costs of a specialized health facility . On the other hand , other ministries of health may have good international relationships with centers that do a high volume of RHD surgeries ( e . g . , Sudan and India ) . These ministries may wish instead to scale VS through a program to find all potential surgical candidates and facilitating their surgery abroad while sharing costs with the host country . So their costing exercise would focus on case-finding efforts , patient travel , and any surgical costs borne by the local country government . We explore both of these choices in our example below . To illustrate how this model can be used to set local priorities , consider a hypothetical African country with low ( 10% ) baseline coverage of PP , SP , and VS . This country has approximately 4 . 9 million population aged 5–24 years who are at highest risk of RHD ( total population 20 . 9 million ) . The country’s life expectancy at birth is 68 years and its per capita gross domestic product is US$ 1300 . Its government currently spends $80 per capita on health . The crude prevalence of RHD in the pediatric population is 1% , and the cumulative incidence of ARF in this group is 1 . 7% ( back-calculated from an assumed 5 . 7% lifetime cumulative incidence of ARF ) . [25] The ministry of health in this country is considering the cost-effectiveness of scaling up PP , SP , and VS . For PP , the ministry’s target coverage rate is 70% . This low target coverage rate reflects a major challenge of PP , which is that a significant fraction of individuals with ARF do not seek care for sore throat even in the best of circumstances . [6] The objective of the PP program is to educate the community on ARF and strengthen primary care services , but the imperfect coverage reflects the realistic effectiveness of the intervention rather than its efficacy . For SP , the ministry’s target coverage rate is 92% . This coverage rate assumes that all individuals with a history of ARF are identified and enrolled in a registry and that these individuals are adherent to 11 of 12 of their monthly penicillin injections ( i . e . , they miss on average one dose each year ) . The SP program is relatively more expensive per patient than PP because of the human resources required to find cases and maintain a registry . We took PP and SP costs from the aforementioned publication of a combined PP and SP program in Cuba . [3] However , we estimated the program cost over 30 years instead of the 10 years originally published . We chose this longer time horizon for two reasons: first , to coincide with the useful life of a surgical center ( see below ) , and second , because experience from high-income countries suggests prevention efforts would need to be sustained over decades to achieve true “eradication . ”[1] The ministry has two options for VS . There is currently no surgical capacity in country , and the 10% of eligible individuals who have undergone surgery in recent years have all traveled to India and have been self-financed . The first option the ministry is considering is to build a local , high-volume , specialized surgical center . The second option is to invest heavily in getting all eligible individuals to surgery in India . In either case , the target is near-universal ( 95% ) coverage . The costs of these two VS options are estimated differently . Building a surgical center will require about US$ 20 million in capital investments and US$ 300 , 000 per year in recurrent operating costs ( including the direct cost of surgery as well as other activities such as case-finding and case management ) . [26] Assuming this center has a 30-year lifespan , the annualized capital cost would be about $970 , 000 . The sum of the annualized capital and recurrent costs is the cost of the program in our model , calculated on a per capita basis . For the alternative , i . e . , leveraging international surgical centers , there would be two components to the program cost . First , achieving universal coverage would involve case finding activities and referral of all possible surgical candidates; we assume this to be 50% of the per capita cost of the SP program . Second , the government would need to pay the Indian government for the operations themselves . Assuming the marginal cost of valve surgery to be $5000 , with 1000 surgeries performed a year ( as in the first option ) , and a 5% administrative cost , the cost per prevalent RHD case would be about $129 per year . Table 2 outlines the healthcare and program costs used in the illustration . In the base case scenario , we estimated ICERs for PP , SP , and VS ( both options discussed previously ) . We also conducted a univariate sensitivity analysis on all model inputs and present tornado diagrams of the ten inputs that were most influential on the ICERs . Finally , we conducted a probabilistic sensitivity analysis on all model inputs simultaneously over 2000 trials . Distributions used for the transition probabilities are given in Table 1 . All costs ( Table 2 ) were drawn from gamma distributions with high and low values that were 50% of the base case values . Both the univariate and probabilistic sensitivity analyses are preprogrammed into the model spreadsheets as Excel macros .
Table 3 presents in a league table the ICERs for the four potential interventions ( including two alterative approaches to VS ) in the hypothetical country described above . Scaling PP would be cost saving , and scaling SP would be cost-effective in the base case but with a very wide 95% credible interval . Scaling VS would not be cost-effective at usual thresholds of one to three times GDP per capita; however the international referral approach would be cost-effective at a threshold of less than three times GDP per capita . The three interventions would also result in differential gains in population health . Extrapolating to the entire population , scaling PP , SP , and VS would avert 501 , 1025 , and 218 total DALYs each year , respectively . These would translate into an increase in healthy life expectancy of 0 . 25 years for the general population , 19 . 5 years for individuals with a history of ARF , and 8 . 6 years for individuals with RHD , respectively . Relatively speaking , each scenario in our model was sensitive to different sets of inputs ( Fig 2 ) . For PP , the cost of secondary prevention , the discount rate , and the progression rate from mild to severe RHD were the most influential inputs . For SP , by and far the most influential input was the risk reduction from secondary prevention , which is reflected in the wide 95% credible interval for the ICER ( Table 3 ) . For VS , the discount rate and the progression rate from mild to severe RHD were the most influential inputs . When all input parameters were varied simultaneously , PP was the most acceptable intervention at the lowest levels of willingness to pay . SP became acceptable vs . PP above approximately $1000 per DALY . VS was not acceptable vs . SP at a maximum willingness to pay threshold of $50 , 000 per DALY; however , the probability of VS being acceptable was much higher if the international referral approach was taken as compared to the approach of building local surgical capacity . The large difference in program costs was the distinguishing factor in the VS cases as the health gains were the same for either approach . Fig 3 presents cost-effectiveness acceptability curves for the three scenarios .
We present a flexible economic evaluation tool that can be used to set priorities around RHD prevention and control in endemic , limited resource settings . This tool can allow ministries of health to allocate resources more efficiently by comparing beforehand the potential value for money of different RHD interventions . Recognizing that public health programs must now intersect with the Sustainable Development Goals , our analysis centers on the health and economic impact of achieving universal coverage for various RHD interventions . [27] Our hypothetical case study suggests that achieving universal PP coverage in particular can greatly improve population health and result in cost savings . These potential savings are an especially important consideration for governments looking for fiscal space to expand their benefits packages over time . Our findings contrast with the longstanding belief that SP is the most cost-effective approach to RHD control . This belief emerged from a limited number of economic evaluations conducted in the 1980s and early 1990s . [15 , 28] Our analysis , which uses more up-to-date data , corroborates the concern expressed by some experts that PP has been inappropriately neglected by ARF and RHD control programs . [29] At the same time , it should be noted that achieving full coverage of PP is infeasible in most settings , since many patients with RHD ( as many as 50% in one report from the USA ) do not recall a history of ARF . [30] Our model has been designed to account for inefficiencies in PP . For example , if local experts believe that only 50% of ARF cases can realistically be prevented , then the analyst could use a target coverage rate of 50% rather than 70% ( our assumption ) or higher . When this coverage rate is changed in our model and all other parameters are held constant , the ICER is slightly higher because of lower health gains for the same cost; however , the PP intervention is still cost saving . While our hypothetical country application is illustrative rather than prescriptive , a few general conclusions emerge from this exercise . First , programs to prevent RHD are probably more cost-effective than programs to treat RHD ( i . e . , by surgery ) . This should come as no surprise to clinicians and public health practitioners who deal with ARF and RHD; advocates have long pointed out that , amongst non-communicable diseases , RHD is uniquely preventable and even eradicable over time—by contrast , e . g . , to ischemic heart disease . [1] However once established , RHD carries high rates of morbidity and mortality among children and working-age individuals . [6 , 7] Second , our model identifies important data gaps that should be addressed in future RHD research . While the natural history of ARF and RHD is qualitatively well understood , few contemporary studies have estimated incidence and progression rates between disease states in a comprehensive manner . Hence the ICERs generated in our illustrative case are very sensitive to these inputs . High-quality data from longitudinal studies would greatly improve the precision of our model . Along these lines , RHD epidemiologists should engage health economists in their work to gather better data on the economic aspects of the disease . Lack of data is particularly challenging in the case of SP . We have not explicitly considered echocardiography-based screening ( “active case-finding” ) as an approach to SP . A recent cost-effectiveness analysis determined that active case-finding was cost-effective compared to passive case-finding . However , this model was predicated on the assumption that SP for cases identified through screening is as effective as SP for clinical cases of ARF and RHD . [31] In our view , there is insufficient evidence at present to suggest that active case-finding improves outcomes , and it is certainly not known how effective active case-finding is compared to passive case-finding . Experimental or quasi-experimental studies would be required to resolve this issue . Having said this , an active case-finding scenario could readily be incorporated into our model: a decision tree would be constructed that incorporated echocardiography test performance characteristics , and this tree would lead into separate Markov traces . Third , decisions on how to proceed with VS in very poor countries should be made carefully . Our results suggest that , for a hypothetical low-income country , building local VS capabilities may not be a good initial investment due to its high cost and limited impact on population health . If the government budget allows , however , a referral-based approach to VS may be cost-effective . By contrast , a lower- or upper-middle income country with a higher willingness-to-pay threshold ( e . g . , US$10 , 000 per DALY ) might reasonably consider building a local surgical program . Regardless , our analysis suggests that resources in countries similar to our hypothetical case should be invested in PP and SP until full coverage is achieved before moving onto VS . Aside from cost-effectiveness per se , an additional consideration for any public health intervention is affordability . While in this example PP would result in cost savings in the long run ( i . e . , a “negative” total incremental cost ) , these savings would only be realized after an up-front investment of about $874 , 000 per year that would rapidly reduce ARF and result in cost savings from cases of ARF and RHD averted . SP and VS would not be cost saving , however , and their annual incremental costs would be much higher–$771 , 000 for SP , and $831 , 000 to $5 . 2 million for VS ( depending on approach ) . Holding government health expenditure ( GHE ) constant over time , scaling SP would add 0 . 2% to GHE for the hypothetical country . The two VS approaches ( build surgical center or refer for surgery abroad ) would add 1 . 3% or 0 . 2% to GHE , respectively . While SP and VS would be expensive , the percentages listed above suggest they would not necessarily be financially unsustainable in a low-income country—particularly the less-expensive , referral-based VS approach . On the other hand , low-income countries have a large number of competing health priorities , and it may be that in any given country there are a number of interventions for conditions other than ARF and RHD that are more effective and less costly . These should , in principle , receive higher priority in the short run . [32] Still , for this particular hypothetical country , our analysis suggests that PP would be very effective and relatively inexpensive and could easily be included in any list of first-priority interventions . It should also be noted that , although cost-effectiveness analysis is an appropriate method for evaluating PP , SP , and some sorts of VS interventions , it might not adequately address the issue of whether to build local surgical capacity . Surgical centers have important implications outside the narrow field of RHD . [33] For instance , economies of scope would likely emerge from the ability to treat , e . g . , congenital heart disease or coronary artery disease at the same facility , using much the same capital and labor inputs . Such a center could also be an important hub for clinical training and scientific research , which have important non-health benefits to society . These broader economic considerations could be better accounted for in a benefit-cost analysis . [34] There are three important limitations to our analysis . First , we did not incorporate other significant sequelae of RHD , such as maternal mortality , infective endocarditis , and specific complications of surgery . [6] The complex interactions between the various RHD sequelae would best be handled using a microsimulation approach; however , good epidemiological data for RHD are at present scarce and not of sufficient quality or detail to inform such a model . Second , although we have framed our analysis in terms of UHC , we have not attempted to incorporate some of the non-health goals of UHC , such as financial risk protection , that would be better handled in a benefit-cost analysis or extended cost-effectiveness analysis . [17] Future studies could explore these complementary analytical approaches . Finally , our analysis is greatly limited by cost data . Our hypothetical country illustration relied heavily on “best guesses” or extrapolation of costs from other parts of the world . End users of our tool will need to collect their own primary cost data to get the most out of the analysis , since there are very few studies of ARF/RHD costs in Africa from which to draw . RHD continues to exact a high health and economic toll on African countries , but evidence-based prevention and treatment measures are currently underused . We have made available in the public domain a cost-effectiveness analysis tool that can be used at the local level to guide the scale-up of these interventions ( S1 File ) . In the future , we will seek to gather more empirical data on the natural history of RHD and the cost of care in African countries . These data will strengthen the precision of our model and its application in limited resource settings . | Rheumatic heart disease is a major cause of cardiovascular morbidity and mortality in Africa . Although there are effective medications and surgical procedures for rheumatic heart disease , they are under-used . What is more , these interventions can be expensive—even if they are feasible and effective . Unfortunately , there are currently very few economic studies on rheumatic heart disease , leaving ministries of health with little guidance on how to choose among various interventions and allocate resources to control programs . Our study describes the methods and data we used to develop a cost-effectiveness analysis tool that was intended specifically for decision-making in African countries . In our study , we also illustrate , in a hypothetical low-income African country , how the tool could be used . In our illustrative example , a prevention-oriented approach would save money in the long term , although other interventions could be cost-effective and feasible if enough financial resources were present . These findings contrast with previous studies and make a strong case that rheumatic heart disease prevention could be a high-priority intervention in Africa . We are making our tool publicly available and anticipate that ministries of health will use it as they develop or expand their rheumatic heart disease control programs . | [
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"hea... | 2016 | A Cost-Effectiveness Tool to Guide the Prioritization of Interventions for Rheumatic Fever and Rheumatic Heart Disease Control in African Nations |
Single microRNAs are usually associated with hundreds of putative target genes that can influence multiple phenotypic traits in Drosophila , ranging from development to behaviour . We investigated the function of Drosophila miR-210 in circadian behaviour by misexpressing it within circadian clock cells . Manipulation of miR-210 expression levels in the PDF ( pigment dispersing factor ) positive neurons affected the phase of locomotor activity , under both light-dark conditions and constant darkness . PER cyclical expression was not affected in clock neurons , however , when miR-210 was up-regulated , a dramatic alteration in the morphology of PDF ventral lateral neuron ( LNv ) arborisations was observed . The effect of miR-210 in shaping neuronal projections was confirmed in vitro , using a Drosophila neuronal cell line . A transcriptomic analysis revealed that miR-210 overexpression affects the expression of several genes belonging to pathways related to circadian processes , neuronal development , GTPases signal transduction and photoreception . Collectively , these data reveal the role of miR-210 in modulating circadian outputs in flies and guiding/remodelling PDF positive LNv arborisations and indicate that miR-210 may have pleiotropic effects on the clock , light perception and neuronal development .
Circadian oscillators consist of input pathways that receive external signals , such as light , temperature and food , a central pacemaker that generates rhythmicity , and output pathways that activate downstream rhythmic processes [1–3] . These internal timers allow organisms to adjust their physiology and behaviour to the most appropriate phases of the environmental 24 hour cycle imposed by the Earth’s rotation . The Drosophila core circadian clock is a network of approximately 70 clock neurons per hemisphere , grouped into three major clusters: Dorsal Neurons ( DN1 , DN2 and DN3 ) , ventral Lateral Neurons ( small ( s ) and large ( l ) LNvs ) and dorsal Lateral Neurons ( LNds ) . Four out of five s-LNvs and the l-LNvs express Pigment Dispersing Factor ( PDF ) , a neuropeptide which is involved in shaping locomotor activity in free-running conditions under constant darkness ( DD ) . Under light-dark ( LD ) conditions PDF sets the phase of evening activity and regulates sleep [4–6] . The PDF-positive LNvs send their projections to different parts of the brain , the s-LNvs reaching the dorsal area , while the l-LNvs send their projections to the contralateral part of the brain and arborise in the Optic Lobe ( OP ) , a structure that processes visual information from retinal or extraretinal photoreceptors [7 , 8] . Under LD 12:12 , flies display two bouts of locomotor activity , one in the morning , starting before Lights-ON , and the other in the evening , before Lights-OFF . Under DD conditions , flies remain rhythmic with a period of about 24h . Over the past fifteen years , several research groups have demonstrated how different neuronal clusters are responsible for specific behavioural activity , under LD , DD or LL ( constant light ) . In a simplified model , the s-LNvs are capable of driving dawn activity and sustaining rhythmicity under DD whereas a subset of LNds and the 5th s-LNv are responsible for dusk activity and for sustaining rhythmicity under LL [9–11] . At the molecular level , interlocked feedback loops generate the rhythmic expression of clock genes in the three neuronal clusters mentioned above . The circadian transcription factors CLOCK ( CLK ) and CYCLE ( CYC ) form a heterodimer ( CLK-CYC ) that activates the expression of the clock genes period ( per ) and timeless ( tim ) . Following complex phosphorylation and degradation dynamics , PER and TIM eventually accumulate , enter the nucleus , and repress the transcriptional activity of the CLK-CYC heterodimer , thus inhibiting their own transcription . CLK-CYC also drive the expression of vrille ( vri ) , Par Domain Protein 1 ε ( pdp1ε ) and clockwork orange ( cwo ) . VRI and PDP1ε are part of a second intersecting regulatory loop which controls Clk gene transcription and reinforces the circadian molecular oscillation , while CWO acts as a repressor of CLK-CYC-mediated transcription . Post-translational mechanisms , driven by kinases such as DOUBLETIME ( DBT ) , SHAGGY ( SGG ) , CASEIN KINASE2 ( CK2 ) , ubiquitin-ligases such as SUPERNUMERARY LIMBS and CULLIN-3 , and phosphatases such as PROTEIN PHOSPHATASE 1 and 2A , play important roles in regulating PER , TIM and CLK stability [12] . The result is the cyclical expression of several clock genes and hundreds of clock-controlled genes ( CCGs ) , which generate rhythmic physiological and behavioural outputs . Post-transcriptional post-translational regulation is therefore crucial for circadian clock functioning [13–17] . MicroRNAs ( miRNAs ) are a class of small ~22 nucleotide non-coding RNAs that act as important post-transcriptional regulators [18 , 19] . They negatively control gene expression by targeting mRNAs , mostly at the 3’ untranslated region ( 3’-UTR ) and triggering either translational repression or RNA degradation [20 , 21] . It has been estimated that approximately 30% of genes are regulated by at least one miRNA [22 , 23] so miRNAs are implicated in a wide variety of biological processes , including differentiation [24] , apoptosis [25] , lipid metabolism [26] , viral infections [27] , tumorigenesis [28–30] and neurodegeneration [31 , 32] and not surprisingly , complete deficiency of miRNAs is incompatible with life [33 , 34] . Several studies have demonstrated that miRNAs are involved in post-transcriptional regulation of fly clock or clock-controlled genes [35 , 36] . The over-expression of bantam miRNA in clock cells was shown to induce period lengthening by regulating the Clk 3’UTR [37] . miR-279 influences the behavioural output of flies by directly modulating the Unpaired gene , which is involved in the JACK/STAT pathway [38] . cwo is modulated by let-7 miRNA which , in turn , is regulated by the clock via the prothoracicotropic hormone signalling pathway , which stimulates the production of the molting hormone ecdysone [39] . Furthermore , it has been recently demonstrated that miR-124 is involved in the regulation of the phase of locomotor activity [40 , 41] and miR-276a regulates molecular and behavioural rhythms by inhibiting the expression of the clock gene timeless [42] . In spite of the role of several miRNAs in circadian phenotypes , most miRNAs from fly heads show little or no circadian oscillations [43] . Yet one of these ‘non-cycling’ miRNAs , miR-210 , which is up-regulated in cyc01 flies , does indeed show cyclical expression levels within the PDF pacemaker neurons under LD conditions , with a peak of expression at Zeitgeber Time 6 ( ZT6 ) [44] . Our study focuses on miR-210 and reveals how misexpression causes pleiotropic effects on circadian activity phase , on shaping projections of PDF-expressing LNvs , and on motion detection .
We analysed miR-210 knock-out mutants ( yw ( Ti-Gal4 ) miR-210KO ) and flies overexpressing the extended region ( 153 nt ) of the pre-miR-210 ( UAS-miR-210 ) in clock cells using the tim-gal4 ( UAS ) driver ( hereafter termed tim-Gl4 ( U ) ) . Over-expression of mature miRNA was evaluated by qRT-PCR under LD12:12 in adult fly brains dissected at ZT0 and on the 3rd day of DD at CT72 and was ~15 fold greater than wild-type ( S1A Fig ) . No miR-210 expression was detected in the yw ( Ti-Gal4 ) miR-210KO strain ( S1B Fig ) . Under DD , over-expression of miR-210 ( tim-Gl4 ( U ) /UAS-miR-210 ) led to disruption of locomotor activity cycles with 70% arrhythmicity . The remaining rhythmic individuals showed a period which was lengthened by approximately 1 h but with 5 h phase delay ( Fig 1A , Tables 1 and 2 ) . Knockout of miR-210 did not alter rhythmicity of flies ( Fig 1B , Table 1 ) but significantly advanced circadian phase by ~6 h ( Table 2 ) . Under LD12:12 , flies over-expressing miR-210 in clock cells lost their ability to anticipate the lights-ON transition ( as shown by Morning Index ( MI ) and Morning Anticipation ( MA ) values ) ( Tables 1 and 3 ) and delayed the evening activity onset ( Fig 2A , Table 3 ) . By contrast , yw ( Ti-Gal4 ) miR-210KO flies showed an advanced evening activity onset , while morning anticipation was not affected ( Fig 2B , Tables 1–3 ) . These results suggest that miR-210 levels can influence the rhythmicity and the circadian phase of activity under both LD and DD conditions . In the yw ( Ti-Gal4 ) miR-210KO strain , the sequence of miR-210 is replaced by the Gal4 cDNA [46] . These flies were crossed with flies carrying the UAS-cd8-GFP transgene , in order to detect the expression pattern of the endogenous miR-210 promoter . GFP signal was detectable in the optic lobe ( OL ) , Antenna Lobe ( AL ) , photoreceptors , Mushroom Bodies ( MB ) and in the Hofbauer-Buchner eyelet ( HB-eyelet ) ( Fig 3A ) . GFP expression was not detected in clock neurons ( Fig 3B and 3C ) , although miR-210 expression has been previously reported in these cells by qRT-PCR [44] . No differences in the expression pattern were observed between sexes . To identify the subset of neurons responsible for the modulation of locomotor activity by miR-210 , the Gal4 lines yw ( Ti-Gal4 ) miR-210KO , cry-Gal4 , Gal1118-Gal4 ( which essentially marks the PDF positive cells and , very weakly , some other clock neurons and non-clock cells [47] ) , pdf-Gal4 and C929-Gal4 ( which marks l-LNvs as the only clock cells as also some peptidergic non-clock cells [10 , 48] ) were used to set up crosses with UAS-miR-210 flies . In the progeny , the evening activity onset was delayed in all the genotypes compared to controls , with the exception of yw ( Ti-Gal4 ) miR-210KO and C929-Gal4 ( in the case of yw ( Ti-Gal4 ) miR-210KO only female progeny were analysed as the construct maps to the X chromosome ) ( S2 Fig , S1 Table ) . The cry-Gal4 driver was the only line that phenocopied the behavioural arrhythmicity in DD induced by over-expression of miR-210 with tim-Gl4 ( U ) ( S2 Table ) . Broad expression of miR-210 with tubulin-Gal4 , repo-Gal4 ( glial cells ) and elav-Gal4 ( pan-neuronal driver ) resulted in lethality . Expressing miR-210 in miR-210 KO males ( yw ( Ti-Gal4 ) miR-210KO;UAS-miR-210 ) affected the evening phase in LD and DD , restoring a wild-type phenotype ( Figs 1B and 2B , Tables 1–3 ) . Indeed , the advanced evening onset observed in miR-210 KO flies was delayed . These findings reveal that the expression of miR-210 in the OL , MB , AL , photoreceptors and HB eyelet , and also in clock neurons , although not in the l-LNvs , was sufficient to delay the phase of the evening onset of flies in LD . We depleted miR-210 expression in CRY positive and PDF positive clock neurons , combining the yw ( Ti-Gal4 ) miR-210 KO rescued flies with the cry-Gal80 or pdf-Gal80 repressors . In these flies , the phase of the evening onset was not affected ( yw ( Ti-Gal4 ) miR-210KO;UAS-miR-210/+; cry-Gal80/+ ) , or was weakly advanced ( yw ( Ti-Gal4 ) miR-210KO;UAS-miR-210/pdf-Gal80 ) compared to controls ( Fig 4A , Table 3 ) . On the other hand , under DD conditions flies showed a significantly advanced locomotor activity phase ( Fig 4B , Table 2 ) , a phenotype previously observed in miR-210 KO flies ( Table 2 ) . In addition , half of the flies without miR-210 expression in CRY positive clock neurons ( yw ( Ti-Gal4 ) miR-210KO;UAS-miR-210/+; cry-Gal80/+ ) became arrhythmic under DD , similar to the observation in tim-Gl4 ( U ) or cry-Gal4 miR-210 over-expressing flies ( Table 1 , S2 Table ) . Altogether , these data confirmed that miR-210 is indeed expressed in clock neurons , where it exerts a prominent role in setting locomotor phase in the absence of external cues , as well as supporting rhythmicity . To determine whether the advanced or delayed activity phase reflected molecular changes in clock protein oscillations in clock neurons , we performed PER staining in miR-210 over-expressing flies and miR-210 KO flies . Whole adult fly brains were dissected at five different ZT time points , under LD . Although manipulation of miR-210 levels did not affect PER cycling ( Fig 5A and 5B ) , a higher level of protein was detected in all canonical clock neurons of tim-Gl4 ( U ) miR-210 over-expressing flies compared to controls ( Fig 5A ) . Conversely , miR-210 KO led to a reduction in PER levels in clock neurons ( Fig 5B ) . Since miR-210 over-expression in clock cells reduced the rhythmicity of flies under DD , we also quantified PER in tim-Gl4 ( U ) /UAS-miR-210 flies under these conditions . The oscillations of PER persisted in clock neurons in tim-Gl4 ( U ) /UAS-miR-210 flies ( except for the l-LNvs , [49 , 50] ) , while the expression levels were similar to those of controls ( S3 Fig ) . The higher level of PER in LD was interpreted as being strictly related to the presence of light since it was missing or attenuated under DD . Taken together , these data indicate that miR-210 impacts on PER levels in clock neurons , but not on its cycling . In parallel with the detection of PER , brains were also stained with PDF antibody . Flies over-expressing miR-210 ( tim-Gl4 ( U ) /UAS-miR-210 ) exhibited aberrant PDF-positive arborisations of the l-LNvs in the optic lobe ( OL ) , as well as an altered star shaped morphology of the cell body ( resembling filopodia and lamellipodia protrusions ) ( Fig 6A ) . This phenotype was observed when the tim-Gl4 ( U ) , the C929-Gal4 or the gal1118-Gal4 drivers were employed ( Fig 6A and 6B and S4A Fig , respectively ) . As expected , given the lack of PDF projections in this area , the number of vesicles in the OL was reduced in the flies over-expressing the miRNA , compared to controls ( Fig 7A ) . A double PDF-GFP staining performed in brains over-expressing GFP and miR-210 under control of Gal1118-Gal4 ( S5 Fig ) revealed that miR-210 is involved in defining the pattern of l-LNv projections in the OL and shaping their cell bodies , rather than affecting the neuropeptide localization ( S5 Fig ) . No differences in l-LNvs arborisations were detected in yw ( Ti-Gal4 ) miR-210KO and pdf-Gal4/UAS-miR-210 flies ( Fig 6C , S4B and S4C Fig ) . In contrast to the l-LNvs , s-LNvs projections were able to reach the dorsal part of the brain , the area in which they usually send their axons . Since their termini experience daily circadian changes in morphology , shifting from the defasciculated ( ZT3 ) to the fasciculated ( ZT15 ) state of their termini [51] , the arborisations of these distal projections were investigated at ZT3 and ZT15 , quantifying the number of axon crosses [51] . As reported in Fig 8A and 8B , while miR-210 over-expression ( tim-Gl4 ( U ) /UAS-miR-210 ) reduced the number of axonal crosses ( blocking the termini in the fasciculated state ) , the complete absence of miR-210 ( yw ( Ti-Gal4 ) miR-210KO ) weakened the cycling of axonal crosses number ( Fig 8A and 8B ) . Taken together , these results implicate miR-210 in the remodelling of PDF positive neuron morphology . To ascertain if the arrhythmicity of flies over-expressing miR-210 was due to developmental defects , we took advantage of the Gal4-Gal80ts-UAS ternary system to target and manipulate the spatial and temporal expression of miR-210 in TIM-expressing cells . tim ( UAS ) Gal4 , tub-Gal80ts/UAS-miR-210 flies ( hereafter tim-Gl4 ( U ) , tub-Gl80ts/UAS-miR-210 ) were analysed at both restrictive ( 29°C ) and permissive ( 23°C ) temperatures . When miR-210 expression was activated only during development ( 29°C-23°C ) flies were mostly rhythmic under DD conditions ( Fig 9A , Table 4 ) , and displayed normal PER oscillations in LD ( S6A Fig ) , as well as normal circadian changes in the morphology of s-LNv dorsal termini ( Fig 8C ) . However , the number of PDF vesicles and the arborisation of the l-LNvs were affected ( Figs 7B and 9B ) and most of the flies lost their ability to anticipate the Lights-ON transition ( Table 4 ) . In contrast , when miR-210 expression was activated only during the adult stage ( 23°C-29°C ) , flies showed a high mortality possibly reflecting the increased level of miR-210 expression at this temperature . The few flies that remained alive were weakly rhythmic over the first days and then became arrhythmic and again did not anticipate Lights-ON ( Fig 9A , Table 4 ) . PER oscillations were normal compared to those of controls ( S6A Fig ) . The l-LNvs did not exhibit any abnormal arborisations or shape or reduced number of PDF vesicles nor was the s-LNvs termini structural plasticity compromised ( Figs 7B , 8C and 9B ) . To assess the functional performance of the large LNvs , sleep was quantified in flies with or without these neuronal defects . Loss of PDF arborisation significantly increased daytime sleep ( S6B Fig ) . These data , which are consistent with those of Sheeba et al . and Shang et al . [52 , 53] , indicated that miR-210 affects total sleep levels and its temporal distribution . To identify genes directly or indirectly regulated by miR-210 , gene expression signatures of adult fly brains were defined and sampled at ZT0 , by over-expressing miR-210 in the TIM-expressing cells ( tim-Gl4 ( U ) /UAS-miR-210 ) . The results were compared to their control ( tim-Gl4 ( U ) ) . Significance Analysis of Microarray ( SAM ) -two-class analysis identified 2 , 376 differentially expressed genes ( 941 up-regulated and 1 , 435 down-regulated ) , considering a 7 . 0% false discovery rate ( S3 Table ) . A total of 78 genes were identified by comparing these results with putative miR-210 targets obtained from two target prediction algorithms , mirSVR and TargetScan 6 . 2 [23 , 54–56] ( S4 Table ) . The probability of obtaining this enrichment by chance is P = 3 . 774758 e-15 , as calculated from the hypergeometric distribution . Two genes within the top-ranking miR-210 target candidates ( the most down regulated , echinus and minidisc , S4 Table ) , and one chosen by a literature search ( SoxNeuro , for its role in Central Nervous System development [57 , 58] ) were selected and qRT-PCR analysis was used to validate their expression ( S7 Fig ) . The functional annotation tool DAVID , [59] was used to analyse the 2 , 376 differentially expressed genes to identify the biological processes represented in the expression signatures . A consistent number of genes involved in “Neuron development” ( GO:0048666 , 54 genes , BH adjusted p-value: 0 . 096 , S5 Table ) , “Circadian rhythms” ( GO:0007623 , 22 genes , BH adjusted p-value: 0 . 095 , S6 Table ) and “small GTPase mediated signal transduction” ( GO:0007264 , 19 genes , BH adjusted p-value: 0 . 098 , S7 Table ) were identified . A panel of 4 putative targets , already detected to be associated in vivo to the AGO1 in fly heads and therefore miRNAs regulated [37] , SoxNeuro ( SoxN ) , minidiscs ( mnd ) , Basigin ( Bsg ) and scribbled ( scrib ) , belonging to the biological processes mentioned above , were selected for a luciferase assay to test for a direct interaction ( Fig 10A ) . mnd was selected based on the ranking value ( S4 Table ) . For all other genes , selection was based on a literature search , for their involvement in axon guidance ( SoxNeuro [58] ) , or cell morphogenesis ( scribbled [60–62] and Basigin [63] ) . Among these , miR-210 targets were identified by performing luciferase assays in miR-210 over-expressing or control S2R+ Drosophila cells , transfected with reporter vectors containing wild-type or mutated 3’-UTRs . A significant reduction in luciferase activity was observed in cells transfected with the vectors containing wild-type 3’-UTRs of the mnd , SoxN and scrib putative targets , with the exception of Bsg . Normal levels of luciferase activity were restored in cells transfected with the vectors containing mutated 3’-UTRs of SoxN and mnd , with the exception of scrib ( Fig 10A and 10B ) . Therefore , mnd , and SoxN , may be direct targets of miR-210 . An in vivo RNAi screening was also performed on these genes , in order to determine whether their down-regulation in the TIM-expressing cells was able to phenocopy the loss of rhythmicity observed in flies over-expressing miR-210 . We set out to analyse RNAi lines for the two genes mentioned above crossed with the tim-Gl4 ( U ) line . Males from the progeny were tested for 3 days under LD , and for 7 days under DD . A qRT-PCR was performed in adult fly heads in order to validate the RNAi lines ( S8 Fig ) . Down-regulation of the genes examined did not affect morning or evening anticipation of light transitions nor rhythmicity under DD ( Table 5 ) . A whole mount PDF staining of the brain was performed at ZT0 to examine the morphology of the PDF projections of l-LNvs in flies knocked down for mnd and SoxN . No aberrant projections were detected in the tim-Gal4/UAS-RNAi flies analysed ( S9 Fig ) . The progeny of each RNAi line ( w;;UAS-RNAi-mnd/+ and w;;UAS-RNAi-SoxN/+ ) crossed with the tim-Gal4 driver were analyzed for 3 days under LD and 7 days under DD . R: rhythmic flies . Period values ( τ ) were averaged over all rhythmic flies per genotype and compared to those of controls ( RNAi/+ lines ) . MA ( morning anticipation ) and EA ( evening anticipation ) were detected , fly-by-fly , examining the bout of activity prior to light transitions as in [45] . To further examine any miR-210 role in shaping neuronal projection patterns , a transient transfection of miR-210 was performed in Drosophila neuronal BG3-c2 cells , which , once plated , spread arborisations in ~55% of neurons . While treated cells were co-transfected with a pAct-GFP , pAct-Gal4 and pUAST-miR-210 plasmids , the controls were co-transfected with i ) pAct-GFP and pAct-Gal4 , or ii ) pAct-GFP , pAct-Gal4 and pUAST-miR-Scramble plasmids . GFP-positive cells were then analysed after 120 h . Approximately 70% of miR-210 treated cells were found to lose their arborizations , compared to 45% in controls ( Fig 11A and 11B ) . No differences were observed compared to controls when a miR-Scramble was used for transfection ( Fig 11A ) . A propidium iodide test and Annexin V Apoptosis Detection Test were performed by cytofluorimetry to exclude the possibility that the morphological features of the BG3-c2 Drosophila cells were a result of a necrosis due to environmental perturbation ( over-expression of miR-210 ) , or to a programmed cell death triggered by the miRNA itself ( Fig 11C and 11D ) . Less than 9% of cells transfected for miR-210 ( among the GFP positive ) were shown to be undergoing apoptosis ( Annexin-positive ) and no neuronal necrosis was detected with a transfection efficiency of 8 . 5% ( Fig 11E ) . These results support the view that miR-210 over-expression plays a role in preventing arborisations in vitro , reflecting the similar observation with l-LNvs in the OL in vivo . As demonstrated above , over-expression of miR-210 in TIM-expressing cells during development causes an irreversible disruption of the l-LNvs distal arborisation in the optic lobe , and an aberrant neuronal cell body shape . Gene expression analysis highlighted the presence of genes that were down-regulated by miR-210 over-expression . Among these , genes involved in the organization and development of photoreceptors were also identified ( Fig 12A , S8 Table ) . It was therefore decided to use the optomotor response to evaluate the visual ability of flies in which miR-210 was over-expressed only during pre-adulthood developmental stages . tim-Gl4 ( U ) , tub-Gl80ts/UAS-miR-210 flies were raised at a restrictive ( 29°C-23°C ) or permissive ( 23°C-23°C ) temperature to over-express or prevent the expression of miR-210 during development , respectively . Adult males were placed in an incubator at 23°C for 3 days and collected and analysed at ZT18 , when wild type flies usually perform best [64] . Flies over-expressing miR-210 during development gave significantly fewer correct responses compared to controls ( Fig 12C ) . We concluded that over-expression of miR-210 during development induces visual defects . This is consistent with our microarray analysis , indicating an enrichment of affected genes involved in the photoreception pathways . A cluster of genes involved in maintaining circadian rhythmicity was identified amongst those that were differentially expressed ( down regulated ) at ZT0 in tim-Gl4 ( U ) /UAS-miR-210 flies compared to controls: tim , pdf , cryptochrome ( cry ) , open rectifier k+ channel 1 ( ork1 ) , cyc , neuropeptide F ( npf ) , dco and pdp1 ( Fig 12B , S6 Table ) . An additional gene expression analysis was performed at ZT12 , in both the over-expressing flies and controls ( S9 Table ) . pdf , cry and cyc transcripts were down-regulated in miR-210 over-expressing flies at both ZT0 and ZT12 , while sgg , per , disc overgrown ( dco- also called dbt ) and pdp1 were up-regulated at ZT12 ( Fig 12B ) . Only dbt was identified in silico as a putative target for miR-210 ( S4 Table ) , but it has not been identified as a miRNA-regulated transcript in vivo [37] . Our findings suggest that miR-210 modulates the expression of circadian clock components mostly indirectly .
In recent years , it has emerged that miRNAs play important roles in modulating a variety of physiological process . This study focuses on miR-210 which in mammals is involved in processes such as angiogenesis , neurogenesis , mitochondrial metabolism , apoptosis , proliferation and hypoxia [65] . In Drosophila the functional role of miR-210 has not been fully characterized . Expression levels of miR-210 have been shown to increase in cyc01 mutant flies [43] , and are cycling under LD in PDF neurons [44] , suggesting a link between miR-210 and the regulation of the circadian clock machinery . We showed that miR-210 is a modulator of the circadian locomotor activity of flies , under both LD and DD conditions because overexpression and knock-out of miR-210 in clock cells significantly delayed or advanced , respectively , the phase of the evening onset of flies under light-dark cycles , without affecting PER cycling in canonical clock neurons . This suggests that miR-210 regulates genes involved in the control of the circadian clock output pathways . In addition , miR-210 up-regulation in clock cells rendered most of the flies arrhythmic in DD ( and phase delayed the remaining rhythmic individuals ) , while the knock-out significantly phase advanced locomotor activity . In a recent paper , Chen and Rosbash identified miR-210 as one of the miRNAs expressed in PDF positive neurons , cycling with a peak in the middle of the day ( ZT6 ) , as measured by qRT-PCR [44] . Here we report its expression pattern in photoreceptors , Optic Lobe , Antenna Lobe , Mushroom Bodies and H-B eyelet , but we failed to detect miR-210 expression in clock neurons , as measured by a GFP reporter . A potential explanation for this is that we were not able to detect GFP in clock neurons by confocal microscopy due to limitations of this technique compared to qRT-PCR . Our behavioural data however , supported the hypothesis that miR-210 is transcribed in PDF clock neurons . By manipulating miR-210 expression levels with Gal4-Gal80-UAS , we were able to unveil the anatomical contribution of different clusters of clock cells in defining the locomotor activity phase of flies . We found that miR-210 over-expression in PDF positive neurons is sufficient to delay the evening onset of flies under LD and , surprisingly , its expression in these neurons is necessary to ensure the correct phase under DD . Although these results corroborate the data previously published by Chen and Rosbash [44] , we could not completely exclude that miR-210 might be also released from the H-B eyelet , via synaptic transmission , targeting the small LNvs . In the presence of light , miR-210 could be released by the H-B eyelet , to accumulate in PDF expressing neurons , with a peak of expression in the middle of the day at ZT6 , as reported by Chen and Rosbash [44] . We formulate this hypothesis because flies delayed their activity under LD , when the amount of miR-210 was highly elevated in PDF positive neurons , due to its constitutive expression driven by the Gal4-UAS system . On the other hand , weak or no advance in evening phase was observed in LD when miR-210 transcription was repressed in PDF/CRY positive neurons , despite the dramatically advanced phase activity under LD observed in the miR-210 knock out . This suggests that other structures expressing miRNA-210 ( i . e . the HB-eyelet ) may contribute to modulating evening activity . Altogether , these data suggest that miR-210 expression is required in the OL , AL , MB , photoreceptor and H-B eyelet to define the normal evening activity onset of flies when in LD . By contrast , under DD , miR-210 seems to be required at least in the PDF positive neurons as both repression of miR-210 expression in PDF positive neurons or miR-210 knock out show a 4–6 h advanced activity phase . We also observed that miR-210 up-regulation in TIM- or CRY- expressing cells or miR-210 depletion in CRY expressing cells , impinge on the circadian rhythmicity of flies under DD . This also suggests that an unbalanced miR-210 transcription between clock neurons and photoreceptors , H-B eyelet , MB and AL , may affect the clock’s circadian output reinforcing the view that miR-210 levels are critical for the circadian activity output of flies . miR-210 also controls the morphogenesis and the structural plasticity of the PDF expressing neurons . In the present study , the up-regulation of miR-210 levels in clock cells affected the l-LNv body shape and resulted in aborted termini of their neurites in the distal part of the OL . Although this phenotype was not severe in all the brains analysed , all were affected to some extent . Day-time sleep was increased in flies with aberrant projections in the PDF positive l-LNvs compared to control , suggesting that the function of these cells is impaired [52 , 53] . miR-210 over-expression during development results in aberrant projections of the large LNvs which appear during the mid-stage of the fly metamorphosis [7] . In addition , the LD and DD behaviour of flies with aberrant large PDF projections suggests their morphogenetic defects do not interfere with circadian locomotor activity . By contrast , the s-LNv neuronal pattern of tim-Gal4/UAS-miR-210 over-expressing flies did not appear to be affected: their projections still reached the dorsal part of the brain . However , it is well established that s-LNvs termini undergo circadian plastic changes in their morphology , with a higher degree of arborisation in the morning ( defasciculated state ) and a lower degree in the evening ( fasciculated state ) [51] . In between , at ZT6 , miR-210 reaches its maximum expression in the PDF positive cells [44] . We observed that in miR-210 KO , the cycling between the defasciculated and fasciculated state of the small-LNvs dorsal projections was damped as when miR-210 is constitutively over-expressed with the tim-promoter . It is interesting to highlight that the restricted temporal over-expression of miR-210 ( only during developmental or adulthood ) did not affect the s-LNvs terminal plasticity , suggesting that the s-LNvs phenotype of tim-Gal4/UAS-miR-210 flies is likely to be due to developmental defects caused by miR-210 up-regulation , coupled with its higher levels in clock cells during adulthood . Similarly , only when over-expression was maintained through development and adulthood ( tim-Gal4/UAS-miR-210 flies ) PER and per transcript levels were significantly higher compared to controls in all clock neurons . This implicates miR-210 in the modulation of the expression of developmental genes , as well as genes that affect expression of components of the core molecular clock . Further analysis of Drosophila small RNA expression datasets revealed that miR-210 represents more than 1% of all miRNAs in heads [66] . This places miR-210 in the top 50 abundant miRNAs expressed in the brain [67] , highlighting its importance in regulating biological processes in this tissue . As miR-210 is one of the top ten miRNAs that are predicted to be major regulators of developmental genes [68] , a microarray analysis of adult fly brains was performed at ZT0 . This was to examine its biological function and to identify the most likely targets . Analysis of the down-regulated transcripts in miR-210 over-expressing flies revealed an enrichment of genes involved in neuronal development including SoxNeuro , longitudinal lacking ( lola ) and Notch together with putative targets of miR-210 . All these genes are involved in the axonal patterning processes and they participate in photoreceptor differentiation . lola , encoding a transcriptional factor , was found to be a target of SoxNeuro [58] and lola expression in the Drosophila eye disc is activated or repressed by Epidermal growth factor receptor and Notch , respectively , to determine R3 , R4 and R7 photoreceptor cell fate [69] . SoxNeuro was shown to be a direct target of miR-210 in vitro but the down-regulation of SoxN did not affect the development of PDF arborisations in the OL . None of the down-regulated genes that are also putative targets of miR-210 affected the PDF projection or perikaria of the l-LNvs . As l-LNvs control relevant physiological functions such as setting the activity of flies and mediating light-arousal and sleep [6 , 52 , 53 , 70] , their altered development due to miR-210 over-expression might depend on the simultaneous perturbation of the expression levels of several genes . We also expressed miR-210 in the BG3-c2 neuronal cell line derived from the Drosophila larval CNS . This particular cell line has been reported to express the pdf transcript [71] and is also characterized by neurons mostly developing long finger-like arborisations , a few days after they are plated . This makes them a suitable in vitro system to investigate the role of miR-210 in modulating neuronal cells morphology . Interestingly , a significant fraction of the BG3-c2 cells expressing miR-210 lost their arborisations compared to their controls , paralleling the effects on the arborisation we reported in vivo in the l-LNvs . miR-210 is highly conserved between humans and flies [72] . Up- or down-regulation of hsa-miR-210 expression levels has already been associated with varying human diseases [65] . Moreover , it has been reported that miR-210 is up-regulated in a murine model of oxygen-induced proliferative retinopathy ( OIR ) [73] . This is interesting as we have shown that miR-210 over-expression during development alters the flies’ ability to perceive motion . It is not currently known , however , if this impairment is due to aborted projections of the l-LNvs in the optic lobe , or to a developmental defect of photoreceptors ( i . e . the R3 and R4 , the fate of which depends upon SoxN , lola , Egfr and N interactions ) [69] . To conclude , our in vivo and in vitro data indicate that Drosophila miR-210 affects behavioural circadian rhythmicity and the morphology of the PDF positive LNvs . It may also affect light signalling from the visual system to clock neurons and , in turn , the circadian phase of locomotor activity .
Flies were raised on standard cornmeal-yeast agar food in LD 12:12 cycles at 23C° . Several independent UAS-miR-210 lines were generated by cloning the 153 bp of the genomic region that contained the pre-miR-210 in a pUAST plasmid [74] . The following primers were used: F: GTAGTGATTCACCGACCACGT , R: ACCACGATGATGGAACAATG . Two of these lines ( UAS-miRNA-210 . 5 and UAS-miRNA-210 . 9 ) were crossed to tim-Gal4 ( UAS ) and the characterization of the progeny for behavioural ( locomotor activity profiles and optomotor response , S10 Table and S10 Fig ) and molecular features ( PER and PDF expression pattern and miRNA-210 expression levels , S10 Fig ) gave similar preliminary results . The UAS-miRNA-210 . 9 was therefore selected for subsequent analyses and named UAS-miRNA-210 . The other strains used in this study were previously characterized: tim-Gal4 ( UAS ) [75] , pdf-Gal4 [4] , C929-Gal4 [48] , tub-Gal80ts [76] , cry-Gal4 [77] , gal1118-Gal4 [47] , pdf-Gal80 and cry-Gal80 [9] The RNAi lines were obtained from the Vienna Drosophila RNAi Center [78] . The RNAi-SoxNeuro , the yw ( Ti-Gal4 ) miR-2l0KO , and the yellow1 strains , were obtained from the Bloomington Drosophila Stock Center . Controls ( Gal4 and UAS strains ) and mutant flies ( yw ( Ti-Gal4 ) miR-210KO and yellow1 ) were crossed with w1118 males prior to analysis . The locomotor activity of 1 to 3 day-old males was recorded for 3 days in LD and 7 days in DD conditions at 23C° or 29C° , using the Drosophila Activity Monitor System ( Trikinetic ) . Data were collected every 5 min and then analysed in 30 min bins using spectral analysis and autocorrelation , as described elsewhere [79] . Morning and evening anticipations were detected fly-by-fly , by examining the mean activity over 3 days under LD conditions and in accordance with [45] . The Morning Index was calculated as in [80] . Three days of activity in LD were used to generate average activity bar graphs . The LD evening phase onset was calculated manually on these graphs , as described in [45] . In particular , the evening phase onset was considered to be present when a bout of activity occurred after a period of rest during the day but it had to be composed of continuous movement with no more than one zero activity bin interspersed within , and with a steady increase in activity levels defining the onset . The DD activity phase was calculated manually , as the highest bout of activity occurring during the fourth day of constant conditions on smoothed data [79] . Sleep amount was calculated from the locomotor activity data by using a Microsoft Excel script in which sleep was defined as 5 min of consecutive inactivity of the flies [81] . 1 to 3 day-old male flies were entrained for at least 3 complete days and then collected at the indicated time points . Total RNA was extracted from approximately 25 brains for each genotype using ZR RNA MicroPrep ( ZYMO RESEARCH ) , according to the manufacturer’s instructions , and then quantified using the ND-1000 spectrophotometer ( Nanodrop , Wilmington , DE ) . The quality of RNA was checked by capillary electrophoresis ( RNA 6000 Nano LabChip , Agilent Bioanalyzer 2100 , Agilent Technologies ) and only samples with RNA Integrity Number ( R . I . N . ) values > 6 were used for microarray analysis . Where appropriate , total RNA was isolated from 30 male fly heads using Trizol ( Life Technologies ) , according to the manufacturer’s instructions . Gene expression profiling was carried out on the controls ( w;tim-Gl4 ( U ) /+ ) and miR-210 over-expressing flies ( w;tim-Gl4 ( U ) /miR-210 ) , sampled at ZT0 and ZT12 , using the Drosophila 2 . 0 custom platform ( GPL18767 ) . Four biological replicates were analysed for the controls and miR-210 over-expressing flies , for a total of 8 microarray experiments . Fifty ng of total RNA was labelled with “Low Input Quick Amp Labeling Kit , one color” ( Agilent Technologies , CA ) , following the manufacturer’s instructions . The synthesized cDNA was transcribed into cRNA , labelled with Cy3-dCTP and purified with RNeasy Mini columns ( Qiagen , Valecia , CA ) . The quality of each cRNA sample was verified by the total yield and specificity calculated with NanoDrop ND-1000 spectrophotometer measurements ( Nanodrop , Wilmington , DE ) . Six hundred ng of labelled cRNA were used in each reaction and the hybridization was carried out at 65°C for 17 hours in a hybridization oven-rotator ( Agilent Technologies , Palo Alto , CA ) . The arrays were washed using “Agilent Gene expression washing buffers” and “Stabilization and Drying Solution , ” as recommended by the supplier . Slides were scanned on an Agilent microarray scanner ( model G2565CA ) , and the Agilent Feature Extraction software version 10 . 5 . 1 . 1 was used for image analysis . Gene expression data are available in the GEO database using the accession number GSE77245 . Inter-array normalization of the expression levels was performed using the quantile method to correct experimental distortions [82] . A normalization function was applied to the expression data of all the experiments and the values of within-array replicate spots were averaged . Feature Extraction Software , which provided spot quality measures , was used to evaluate the quality and reliability of the hybridization . In particular , the flag “glsPosAndSignif” ( set to 1 if the spot had an intensity value that was significantly different from the local background and to 0 in all other cases ) was used to filter out unreliable probes: a flag = 0 was marked as “not available ( NA ) ” . Probes with a high proportion of “NA” values were removed from the dataset to attain a more solid , unbiased statistical analysis . Fifty percent of “NA” was used as the threshold in the filtering process , and about 23 , 700 Drosophila transcripts were obtained . Cluster analysis and profile similarity searches were performed with Multi Experiment Viewer version 4 . 8 . 1 ( tMev ) of the TM4 Microarray Software Suite . The identification of differentially expressed genes was performed using two class Significance Analysis of Microarray ( SAM ) algorithm [83] with default settings . SAM uses a permutation-based multiple testing algorithm and identifies significant genes and miRNA with variable false discovery rates ( FDR ) . This can be manually adjusted to include a reasonable number of candidate genes with acceptable and well-defined error probabilities . The normalized expression values of the biological replicates for each genotype were log2-transformed and averaged . The list of differentially expressed genes was functionally classified using the DAVID Gene Functional Classification tool ( https://david . ncifcrf . gov/ , [59] ) to identify significantly enriched biological processes ( Modified Fisher Exact p-value < 0 . 05 ) . The TargetScan 6 . 2 ( http://www . targetscan , Release: June 2012 , [23] and mirSVR ( http://www . microrna . org , Release: August 2010 , [54] ) algorithms were used to predict dme-miR-210 targets . To identify the most likely targets , attention was focused on putative mRNAs differentially expressed in miR-210 overexpressing flies , sampled at ZT0 and ZT12 [55 , 56] . Twenty-five brains were dissected for miR-210-3p and 2S rRNA quantification assays at the indicated time points . Each RT reaction ( 15 μl ) contained 10 ng of total purified RNA , 5X stem-loops RT primer , 1X RT buffer , 0 . 25 mM each of dNTPs , 50U MultiScribe reverse transcriptase and 3 . 8 U RNAse inhibitor . The reactions were incubated in a thermocycler ( Applied Biosystems ) in 0 . 2 ml PCR tubes for 30 min at 16°C , 30 min at 42°C , followed by 5 min at 85°C , and then kept at 4°C . The resulting cDNA was quantitatively amplified in 40 cycles on an ABI 7500 Real-Time PCR System , using TaqMan 2XUniversal Master Mix no AmpErase UNG Mix and TaqMan MicroRNA Assays ( Assay ID for miR-210-3p: 005997 and Assay ID for 2S rRNA: 001766 Applied Biosystems ) . Three replicates of each sample and endogenous control were amplified for each real-time PCR reaction . Total RNA extracted from the fly brains and heads , as described above , was used to validate the expression values obtained from microarray experiments and to confirm the silencing of specific genes in the RNA-interference lines respectively . The sequence of primers are detailed in S11 Table ) . For each sample , 1 μg of total RNA was used for first-strand cDNA synthesis , employing 10 mM deoxynucleotides , 10 μM oligo-dT and SuperScript II ( Life Technologies ) . qRT-PCRs were performed in triplicate in a 7500 Real-Time PCR System using SYBER Green chemistry ( Promega ) . The 2-ΔΔCt ( RQ , relative quantification ) method implemented in the 7500 Real Time PCR System software was used to calculate the relative expression ratio [84] . Luciferase reporter vectors containing the partial 3’-UTR of the indicated miR-210 target genes ( soxN , mnd , Bsg , scrib ) were generated following PCR amplification of the 3’-UTR from Drosophila cDNA and cloned into the pmirGLO Dual-Luciferase miRNA Target Expression Vector ( Promega ) . Where appropriate , the 3’-UTR was mutagenized at the miR-210 recognition site/s using the QuickChange Multi Site- Directed Mutagenesis kit ( Stratagene-Agilent Technologies , Palo Alto , CA ) following the manufacturer’s instructions . miR-210-sensor was obtained by annealing , purifying and cloning short oligonucleotides that contained three perfect miR-210 binding sites into the pmirGLO vector . 0 . 8 x 10^6 S2R+ Drosophila cells were plated in 24-well plates and co-transfected with 333 ng of the pmirGLO Dual-Luciferase ( Promega ) construct . This contained the wild type or mutant/deleted 3’-UTRs of the indicated miR-210 potential target genes , 333 ng of the p-Act-Gal4 ( a gift from Liqun Luo , Addgene plasmid #24344 , [85] ) and 333 ng of the pUAST-miR-210 plasmid ( the same utilized to generate the flies ) , using Cellfectin II Reagent , following the manufacturer’s protocol ( Life Technologies ) . Lysates were collected 24 hours after transfection and Firefly and Renilla Luciferase activities were measured with a Dual-Luciferase Reporter System ( Promega ) . Luciferase activity was calculated by normalizing the ratio of Firefly/Renilla luciferase to negative control-transfected cells . Transfections were performed in triplicate and repeated 3 times . Flies were entrained for 3 complete days and then collected under LD or DD conditions at the indicated time points and conditions . Flies were fixed for 2 hours in PFA 4% . About 10–12 brains were dissected in PBS and then treated as previously described in [45] . The antibodies used for the immunocytochemistry experiments were anti-PDF ( Developmental Studies Hybridoma Bank , dilution of 1:5 , 000 ) and anti-PER ( from R . Stanewsky; 1:2 , 500 ) . Alexa Fluor 488 and Alexa Fluor 568 ( both from Invitrogen; 1:500 ) were used as secondary antibodies . The brains were observed under the ZEISS LSM 700 confocal microscope and z-series were obtained . PER intensity was quantified with ImageJ version 1 . 48e . The average pixel intensity for each neuron was measured together with the signal from its corresponding background area . The final amount of signal was calculated using the formula “intensity = 100× ( signal − background ) /background” . PDF quantification was performed on whole adult brains stained with PDF antibody . Individual images were taken of planes at different depths to create a z-series for each lobe analysed . The size of the sections forming a z-series was 0 . 80 ± 0 . 2 μm . The images were z-stacked and large LNvs were analysed using the ImageJ ITCN plugin tool for counting vesicles . The PDF signal of small-LNvs termini was quantified , as described in REF [51] . The optomotor test was performed at ZT18 following the protocol of SI setup 1 [64] . tim-Gl4 ( U ) , tub-Gl80ts/miR-210 flies were raised at 23°C or 29°C and then analysed at 23°C . BG3-c2 Drosophila neuronal cells were obtained from DGRC and maintained in Shields and Sang M3 insect medium ( Sigma ) with 10% FBS ( Hyclone ) and 10 μg/mL insulin . Cells were co-transfected with a total of 1 μg of the following plasmids: pUAST-miR-210 , p-Act-Gal4 and p-Act5-stable2-neo ( a gift from Rosa Barrio & James Sutherland , Addgene plasmid # 32426 , [61] ) ( miR-210 treated ) , with p-Act-Gal4 and p-Act5-stable2-neo ( control ) or only with pUAST-miR-Scramble , p-Act-Gal4 and p-Act5-stable2-neo ( Scramble ) by using Cellfectin II Reagent , following the manufacturer’s protocol ( Life Technologies ) . GFP-positive cells were counted from 3 different fields of 3 different replicates and classified on the basis of their shape ( round or neuronal ) . The experiment was repeated 4 times . MiR-Scramble was generated cloning 106 bp of the genomic region that contained the pre-miR-305 in a pUAST plasmid [74] . The following primers were used: F: GTGTATCAACTGTCTCCCATGTCT , R:CGTATGCAAATCGCCTCATA . Transiently transfected cells were collected and stained with Annexin V Apoptosis Detection Set PE-Cyanine7 ( eBioscience-ThermoFisher Scientific , Waltham , MA ) and propidium iodide ( Roche Biochemicals , Indianapolis , IN ) , again following the manufacturer’s protocol . Apoptosis cells were analyzed using Cytomics FC500 ( Beckman Coulter , Brea , CA ) . | In recent years , the role of microRNAs in regulating the endogenous circadian clock and its rhythmic outputs for behaviour/physiology has been recognized . We have observed that depletion or over-expression of miR-210 in Drosophila melanogaster modulates the phase of locomotor activity , without affecting the molecular oscillation of the pacemaker neurons . Moreover , miR-210 over-expression dramatically alters the pattern of projections from the PDF-positive Lateral Neurons ( LNvs ) . Differentially expressed genes detected in miR-210 over-expressing flies implicated circadian processes , neuronal development , and photoreception . Taken together , our findings indicate the involvement of miR-210 in modulating circadian output and remodelling the projections of PDF clock neurons , and suggest that miR-210 may have pleiotropic effects on clock , light perception and neuronal development . | [
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"chronobiology"... | 2018 | Modulation of miR-210 alters phasing of circadian locomotor activity and impairs projections of PDF clock neurons in Drosophila melanogaster |
RNA-binding proteins ( RBPs ) establish the cellular fate of a transcript , but an understanding of these processes has been limited by a lack of identified specific interactions between RNA and protein molecules . Using MS2 RNA tagging , we have purified proteins associated with individual mRNA species induced by osmotic stress , STL1 and GPD1 . We found members of the Lsm1-7/Pat1 RBP complex to preferentially bind these mRNAs , relative to the non-stress induced mRNAs , HYP2 and ASH1 . To assess the functional importance , we mutated components of the Lsm1-7/Pat1 RBP complex and analyzed the impact on expression of osmostress gene products . We observed a defect in global translation inhibition under osmotic stress in pat1 and lsm1 mutants , which correlated with an abnormally high association of both non-stress and stress-induced mRNAs to translationally active polysomes . Additionally , for stress-induced proteins normally triggered only by moderate or high osmostress , in the mutants the protein levels rose high already at weak hyperosmosis . Analysis of ribosome passage on mRNAs through co-translational decay from the 5’ end ( 5P-Seq ) showed increased ribosome accumulation in lsm1 and pat1 mutants upstream of the start codon . This effect was particularly strong for mRNAs induced under osmostress . Thus , our results indicate that , in addition to its role in degradation , the Lsm1-7/Pat1 complex acts as a selective translational repressor , having stronger effect over the translation initiation of heavily expressed mRNAs . Binding of the Lsm1-7/Pat1p complex to osmostress-induced mRNAs mitigates their translation , suppressing it in conditions of weak or no stress , and avoiding a hyperresponse when triggered .
The regulation of the gene expression is essential to all cells; therefore , the proper protein accumulation is controlled at multiple steps , including transcription and translation , as well as mRNA and protein transport and stability . During stable conditions , post-transcriptional control may explain some 20% of steady-state mRNA levels , however during rapidly changing conditions , post-transcriptional regulation is crucial for the initial responses [1] . As most investigations have focused on the general mechanisms acting on mRNA fate [2–4] , little is known about the specific mechanisms for differential control of subgroups of genes . Stress represents an ideal scenario for studies of post-transcriptional regulation . Stress conditions force the cell to redirect its gene expression program . Until a cell has adapted to a sudden environmental change , it faces an acute energy shortage . Immediate survival prior to signaling-induced changes in gene expression depends on rapid post-translational events [5] . For medium-term adaptation and resumption of growth , the overall translation rate has to respond rapidly to changes in the environment , as protein synthesis represents a large fraction of the cell’s total energy expenditure . For rapid adaptations to preserve energy , post-transcriptional regulation acting on pre-existing mRNAs plays a major role by virtue of being faster than changes on the level of transcription initiation , and by intervening before the costly step of protein synthesis [6] . RBPs are fundamental for regulating transcript stability , localization , and translational efficiency , and thus sorting which mRNAs are to be expressed into proteins under specific conditions , ultimately contributing to stress survival and recovery . Thus , the mammalian RBP RBM3 protects neurons from cold shock [7] , and glycine-rich RBPs in plants perform similar functions under cold and drought stress [8] . Under hypoxia in mammalian cells , an alternative translation initiation complex is formed at hypoxia-responsive mRNAs to enhance their translation , comprising eIF4E2 and RBM4 [9] . In yeast , the RBP Slf1 promotes translation of a few mRNAs during peroxide stress through interaction with the ribosome; slf1 mutants are also hypersensitive to oxidative stress [10] . A hallmark of the acute stress response is a rapid and distinct global suppression of translation resulting from downregulation of ribosomal biogenesis factors and ribosomal proteins on multiple levels including initiation of transcription , transcript decay , and translational efficiency [11–14] . Pre-mRNAs encoding ribosomal proteins are subjected to the nonsense-mediated degradation pathway during osmotic stress [15] . In addition , transcript-specific regulation redirects the expression program to produce stress-protective proteins and reduce expression of proteins required for growth and proliferation . Stress induces a shift from cap-dependent to internal ribosome entry site dependent translation [16] . Oxidative stress causes increased translation of upstream ORFs ( uORFs ) and frameshifting events in mRNAs , potentially changing the proteome profile [17] . Specific RBPs are involved in promoting translation and inhibiting decay of mRNAs promoting stress survival [10 , 18–20] . Under severe stress , cytoplasmic stress granules ( SGs ) form , containing mRNAs to be silenced , ribosomal components and other RBPs . The majority of mRNAs are sorted to the stress-induced SGs and constitutive processing bodies ( PBs ) , whereas the minority required for stress survival is kept out of these granules , to instead be selectively translated [21] . Combined , these observations raise the question how the fates of stress-activated mRNAs are determined under stress conditions . To reveal the mechanisms for this requires identification of the specific interactions taking place between individual RBPs and mRNA species . Such studies lag behind those of transcription factors and specific promoters , partly since RBP/RNA interactions are weaker than for DNA-binding proteins/DNA . Several global studies have identified RBPs associated with the total mRNA population in yeast and other organisms [2 , 3 , 22–27] . However , only very few studies have succeeded in isolating the proteins associated with a single mRNA species in vivo , e . g . [28–30] . In this study , we aim to answer this by isolating individual osmostress-activated mRNA species , quantitating the proteins associated in vivo with each of them , and analyzing how deletion of these proteins impact on the expression of stress-activated genes . By comparison with the proteome associated with individual , not stress-related , mRNA species , we identify specific proteins preferentially binding to osmostress-activated mRNAs , STL1 and GPD1; notably members of the cytoplasmic Lsm1–7/Pat1 complex . The specific association between these proteins and STL1 and GPD1 mRNA was independently confirmed by quantitating stress and non-stress mRNAs bound to tagged proteins . We evaluated the impact of the Lsm1–7/Pat1 complex on the stress response by analyzing expression of osmostress-activated genes in lsm1 and pat1 mutants . We found those mRNAs to be more associated with polysomes than in the wild-type ( wt ) . Moreover , the mutants fail to regulate the amount of the corresponding stress proteins under hyperosmotic shock . At low stress levels , the accumulation of those proteins is not triggered in the wt , but this occurs in the mutants . Finally , by mapping ribosome transit at single nucleotide resolution using 5P-Seq of mRNAs , we demonstrate increased ribosome accumulation in pat1 and lsm1 mutants in the near vicinity upstream of the start codon . This was particularly pronounced for specific subsets of mRNAs with functional enrichment in translation and mating components , and for mRNAs more highly transcribed and associated with ribosomes under osmostress . Together , these observations indicate that Pat1 and Lsm1 are important for dampening ribosome transit in the 5’-UTR , and thus translation initiation of osmostress mRNAs , in particular under low stress conditions . We conclude that our biochemical co-purification approach has successfully identified RBPs with a particular role in regulating post-transcriptional expression of stress-activated mRNAs .
Our goal was to identify RBPs involved in differential regulation of specific mRNAs under hyperosmotic stress . To isolate mRNA-specific ribonucleoprotein ( RNP ) complexes , we developed a method similar to those previously described [28 , 31] based on affinity purification of MS2 aptamer 3’-tagged mRNAs and subsequent protein composition analysis by LC-MS/MS , targeting directly the proteins interacting with individual osmo-mRNA species ( Fig 1A , see Material and Methods ) . The MS2 loops were integrated into the genomic copies of GPD1 and STL1 genes ( S1A Fig ) . STL1 and GPD1 are well-characterized for their high level of induction under moderate hyperosmotic stress in yeast ( “osmo-mRNAs” ) [32–34] , under which condition they are also stabilized and actively translated [13 , 14 , 35] . They encode glycerol-3-dehydrogenase and a membrane-bound H+/glycerol symporter , respectively . Both are involved in intracellular accumulation of glycerol , the principal compatible osmolyte in yeast and essential for its survival under osmotic stress . As contrasting examples of osmostress non-responsive mRNAs , the ASH1 and HYP2 mRNAs were also tagged with MS2 loops . Under osmotic stress , ASH1 and HYP2 are not transcriptionally induced; these transcripts are instead destabilized and translationally inactivated [13 , 14 , 35] . The induction patterns and polysome profiles upon osmostress of these aptamer-tagged mRNAs were similar to their untagged full-length counterparts ( S1 Fig ) . As an additional test to verify the functionality of the tagged mRNAs , we analyzed the intracellular localization of those mRNAs by visualization of the MS2CP-GFP fusion protein bound to them ( S2A Fig ) . In each case , a punctate pattern of the GFP fluorescence appeared when mRNA-MS2L and the fusion protein were expressed . It was previously shown that certain mRNAs are found in granules where they are actively translated [36] . To test the nature of the GFP-positive granules , cells were treated with cycloheximide ( CHX ) before incubation in media with or without 0 . 6 M KCl ( S2A and S2B Fig ) . Such treatment prevents the formation of PBs and SGs , most likely by trapping mRNA on polysomes [36 , 37] . An increase in the number of mRNA-MS2L granules was observed after CHX treatment , significantly pronounced for STL1-MSL2 , GPD1-MSL2 and HYP2-MSL2 mRNA granules when it was followed by osmotic stress . The observed increase in mRNA granules in CHX-treated cells indicates an accumulation of ribosome-associated mRNAs in granules . As expected if these granules represent actively translating mRNAs , analysis of co-localization of those mRNA-MS2L granules with the PB marker Dcp2-RFP ( S2C Fig ) showed a lower percentage of PB signal overlapping with the stress-induced mRNAs STL1-MS2L ( 11% ) and GPD1-MS2L ( 20% ) than with the repressed mRNA HYP2-MS2L ( 44% ) . To rule out any effect of tagged mRNAs on PB formation , we quantitated the number of PBs during osmostress in cells expressing MS2L-tagged mRNAs and MS2CP-GFP . None of the tagged mRNAs caused a PB increase over cells not expressing tagged mRNAs ( S2D Fig ) . Altogether , these observations corroborate that most STL1 and GPD1 mRNA granules were distinct from PBs , in agreement with previous work showing that active translation can be found associated to granules . Before proceeding with LC-MS/MS analysis , the RNP isolation was optimized using ASH1 mRNA and its well-established interaction with She2p [28 , 38 , 39] ( S3 Fig ) . Then , from three independent experiments with the four MS2L-tagged mRNA and the non-tagged reference strain under osmotic stress ( 0 . 6 M KCl , 30 min ) , RNPs were isolated , and the protein compositions were subsequently analyzed . A corresponding analysis of unstressed cells was not feasible as the levels of STL1 and GPD1 mRNAs under such conditions are orders of magnitude lower . The presence of the MS2-CP-GFP protein and the specific mRNA-MS2L transcript was verified in the input and pull-down samples ( Fig 1B and 1C ) . Also , the enrichment in mRNA-MS2L after pull-down relative to the input sample was monitored by RT-qPCR , showing that only the respective mRNA used to isolate the RNPs was enriched ( Table 1 ) . These observations together indicated that the captured samples were indeed enriched in the mRNA-MS2L and its interacting proteins . We identified 202 , 76 , 93 , and 24 proteins reproducibly enriched ( > 2-fold higher than the untagged strain ) binding the GPD1 , STL1 , ASH1 , and HYP2 mRNAs , respectively . To identify those proteins enriched preferentially with the osmo-mRNAs GPD1 and STL1 , we selected the proteins significantly detected in both the GPD1-MS2L and STL1-MS2L strains , and > 3-fold more abundant in these strains than in the untagged strain and the ASH1-MS2L and HYP2-MS2L strains . According to these criteria , 21 proteins were listed as enriched in both the STL1 and GPD1 mRNA preparations ( Table 2 ) . As was the purpose of the experiment , we identified several proteins previously shown to bind RNA , including 8 proteins interacting with mRNA and one binding tRNA . In addition , we found several proteins not previously identified as RBPs , but potentially part of the osmotic stress response , e . g . the enzyme Glo2 and the transcription factor Usv1 ( see Discussion ) and proteins involved in cell morphogenesis and plasma membrane processes ( Htb1 , Sac7 and Dig1 ) . Notably , six out of the nine RBPs identified as specifically enriched in STL1 and GPD1 RNPs under osmotic stress are components of the Lsm1-7/Pat1 complex . Two Pat1-Lsm complexes have been recently reported , Lsm1-7/Pat1 in the cytoplasm and Lsm2-8/Pat1 in the nucleus [40] . We detected the cytoplasm-specific component Lsm1 , whereas the nucleus-specific Lsm8 is absent from our data ( Table 2; S1 Table ) , indicating that the cytoplasmic Lsm1-7/Pat1 associates with STL1 and GPD1 mRNAs . It has been extensively described that the Lsm1-7/Pat1 complex plays a critical role in mRNA decay via the 5’-3’ pathway , interacting with the oligoA tail at the 3’-end of transcripts , and through a protein bridge also with the 5’-cap [41 , 42] . Through this association to mRNAs targeted for decay , the complex promotes decapping via unknown mechanisms [43 , 44] . The same complex has also been implicated in translational control [45–48] . Unexpectedly , our data showed enriched association of components of this complex to GPD1 and STL1 mRNAs under osmotic stress , when these transcripts are induced , stabilized , and actively translated . To independently corroborate this enrichment , we performed a RNP pull-down assay under the same experimental conditions , but using GFP-tagged versions of some of the Lsm/Pat1 complex components [49] and analyzing the specific mRNA content by qPCR ( Table 3 ) . Under osmotic stress conditions , both GPD1 and STL1 mRNAs showed enriched ( > 2-fold ) binding compared with ASH1 to the four Lsm proteins tested ( Lsm1p , Lsm3p , Lsm4p and Lsm7p ) , and Pat1p . Using the same criteria , Pat1p was also preferentially captured by GPD1 and STL1 mRNAs when compared with HYP2 mRNA . We hypothesized that this association of the Lsm1-7/Pat1 complex to induced mRNAs could be related with two scenarios: one where highly induced and translated mRNAs recruit this complex to dampen the high induction signal and compensate for the transcriptional induction; or a second where this complex has an enhancing role in translation , as recently described for the decapping component Dhh1 [50] . To test those hypotheses , we first examined the phenotypes of pat1 and lsm1 mutants , with respect to steady-state mRNA levels , mRNA stability and global translational repression under osmotic stress . For all these experiments , native full-length mRNAs without the MS2L tag were studied . A time-course experiment was done in exponential growth cultures to measure the levels of GPD1 and STL1 mRNAs under osmotic stress in pat1 and lsm1 single mutants ( Fig 2 ) . Two to three times higher levels of GPD1 and STL1 mRNAs were observed in the pat1 and lsm1 mutants during stress than in wt ( Fig 2A ) , although the accumulation kinetics of both mRNAs were similar between wt and mutants , reaching maximum levels 15 – 30 min of stress exposure . To evaluate if the observed mRNA accumulation in the pat1 and lsm1 mutants was consequence of their role in decapping , mRNA decay rates were analyzed after 30 min of osmotic stress and transcriptional shut-off by addition of 1 , 10-phenantroline ( Fig 2B ) . No differences in decay rates for STL1 and GPD1 mRNAs between wt and pat1 mutants were observed however . Next , we aimed to evaluate the impact of Pat1 and Lsm1 depletion on the regulation of translation under osmotic stress . For this , we first analyzed global translation profiles by separating polyribosomes ( polysomes ) by high-speed sedimentation centrifugation after 30 min of treatment with 0 . 6 M KCl ( Fig 3 , S4 and S5 Figs ) . Consistent with earlier results [20] , after 30 min the cells had already recovered from the transient inhibition of the global translation ( Fig 3A left panels ) . In pat1 and lsm1 mutants , the polysome proportion ( P/FM ratio ) was slightly higher than in wt cells . We verified that a proper global translation inhibition did occur in the wt strain at short times ( 6 and 15 min ) when osmotic stress was added ( S5A Fig ) ; and also , as previously described for starved cells [47] , that in the pat1 mutant the ability to undergo translational repression under osmotic stress was impaired ( S5A Fig ) . To further analyze the impact of pat1 and lsm1 mutations in translation during stress , we investigated the association of specific transcripts with ribosomes ( Fig 3B , left panels ) . In mRNAs associated with heavy polyribosomes in wt after 30 min of osmotic stress such as STL1 , GPD1 , GRE3 and ACT1 , we observed there was a reduction in the proportion of these mRNAs associated to non-polysomal fractions ( 1–2 ) in the pat1 and lsm1 mutants , turning out in a high P/FM ratio ( Fig 3B , bar graphs ) . Similarly , the translationally repressed mRNAs PWP1 and UTP13 , abundant in non-polysomal fractions in wt , and the moderately repressed HYP2 , were also more associated to polysomal fractions ( 3–8 ) in both mutants ( Fig 3B left panels , bar graphs , S4 Fig ) . To better understand how these mutations affect the flux of those mRNAs on or off polyribosomes under osmotic stress , we analyzed their distribution at several short times after osmotic shock ( S5B Fig ) . The stress-responsive GPD1 and STL1 mRNAs in pat1 mutants accumulated in non-polysomal fractions ( 1–2 ) at a short time ( 6 min ) after KCl addition similar to wt , and shifted to polyribosome fractions when the osmotic stress progressed . However , after 30 min was observed a markedly higher P/FM ratio in the pat1 mutant ( S5B Fig , bar graphs ) . By contrast , the stress-repressed PWP1 and UTP13 mRNAs were in high density fractions without stress and shifted to non-polysomal fractions under osmotic stress in wt , while remaining associated to polysomes in pat1 mutants at 15 and 30 min . The ACT1 mRNA , neither strongly upregulated nor repressed by osmostress , also showed higher accumulation to heavy fractions in the pat1 mutant , and the HYP2 mRNA , moderately repressed by osmostress , followed a similar pattern . For the non-induced mRNAs ( PWP1 , UTP13 , ACT1 , HYP2 ) , the P/FM ratio was only moderately increased at 30 min . These results show that deletion of the PAT1 gene resulted in increased progression of mRNAs to heavy fractions under osmotic stress for stress-induced transcripts , and in prolonged association to polysomes for stress-repressed transcripts . Two potential explanations fit with our observations of higher mRNA polysomal association in lsm1 and pat1 mutants: 1 ) ribosomes stall during translation elongation and this could be due to impaired ribosome disassembly and recycling from the mRNAs; 2 ) an increased frequency of active ribosomes accessing the mRNAs . To discriminate between these options , we analyzed the ribosome run-off from polyribosomes in the absence of CHX in cells treated with 0 . 6 M KCl for 30 min ( Fig 3A and 3B , right panels ) . To evaluate the effectiveness of run-off , we calculated the ratio P/FM [run-off] to P/FM [control] for each strain ( Fig 3A ) . The proportion of run-off polysomes in pat1 and lsm1 mutants was not significantly different from the wt , indicating that most ribosomes are not irreversibly stalled . Consistent with this global analysis , the run-off of specific mRNAs from the polysomes into the non-translating fractions also showed only slight differences between pat1 and lsm1 mutants and the wt ( Fig 3B , right panels and bar graphs ) . These observations are therefore most consistent with the second model , where the Lsm1-7/Pat1 complex may limit ribosome access to the mRNAs under osmotic stress . The above results documented that the components of the Lsm1-7/Pat1 complex , Lsm1 and Pat1 , regulate ribosome dynamics in vivo . To determine the output of this effect on protein levels and see if it was specific for subgroups of mRNAs under osmotic stress , we analyzed the accumulation of specific GFP-tagged proteins in a pat1 mutant background . We selected a representative set of proteins , either strongly stress-induced or not induced , and for which the levels could be reliably monitored by GFP fluorescence . We measured the accumulation of the GFP-tagged proteins in the presence of KCl through a panel of different concentrations from low to medium severe stress ( 0 . 3 , 0 . 6 , and 1 M ) ( Fig 4 ) . Three different expression patterns were observed in the pat1 mutant under osmotic stress . The first pattern , represented by Gpd1 and Gpp2 , showed a striking increment in protein levels in this mutant under low to medium osmostress ( 0 . 3 M KCl ) . By contrast , no or low accumulation of those proteins was triggered in wt under the same conditions . Moreover , in higher salt concentrations ( 0 . 6 and 1 M KCl ) , when accumulation of those proteins was triggered in the wt , pat1 mutants displayed 2–3 fold higher GFP signal . The second pattern was shown by Gre3 , where a similar or even smaller GFP signal increment was detected in pat1 mutants than in wt in presence of KCl . To estimate the contribution of the mRNA amount to the protein synthesis , we calculated the ratio between the areas under the curve of mRNA ( S6 Fig ) and protein ( Fig 4 ) after shock with 0 . 6 M KCl ( for protein , the area was calculated till the levels reached a plateau ) . The pat1 mutant showed an increased production of 1 . 7 and 5 . 0 times more Gpd1p and Gpp2p per mRNA amount than wt , respectively ( S2 Table ) . This indicates that the increment over wt levels of these proteins was too high to be explained by the increases of the corresponding mRNAs in the pat1 mutant . The third pattern was seen for proteins not induced by the assayed osmostress conditions; Eno1p , Qcr6p and Hac1p . Here , the protein levels declined further in pat1 mutants than in wt , and their changes under osmostress were uncorrelated with the changes in mRNA levels ( Fig 4 and S6 Fig ) . Altogether , these data indicate that a deregulation of translation can be a significant cause of abnormal protein levels in pat1 mutants . These results are compatible with Pat1 being a translational repressor for highly induced osmo-mRNAs; however , it does not act homogeneously on all transcripts . Specifically , under osmotic stress , it dampens the protein levels from GPD1 and GPP2 , and prevents excessive accumulation of their encoded proteins . Still , other prerequisites for this effect probably exist as shown by the GRE3 mRNA , which is also strongly induced at the transcript and protein levels , but where PAT1 deletion has only a minor effect . In order to further understand how the Lsm1-7/Pat1 complex affects the ribosome dynamics , we performed a genome-wide analysis of the ribosome protected regions in pat1 and lsm1 mutants using the recently developed 5P-Seq technique [51 , 52] . This approach offers an in vivo snapshot of ribosome footprints by sequencing 5’-phosphorylated mRNA co-translational degradation intermediates , and does not require the use of translation inhibitors or in vitro mRNA digestion . We performed three independent 5P-Seq experiments with each strain ( wt , pat1 and lsm1 mutants ) with or without osmotic stress ( 30 min , 0 . 6 M KCl ) . Alignment with respect to the start codon of the 5P-Seq reads , at the metagene level , showed a clearly increased ribosome accumulation around the translational start , almost identical for both pat1 and lsm1 mutants ( Fig 5A , left panels ) . This ribosome accumulation was detected on both sides of the start codon ( position -14 where ribosomes are paused at the P site during initiation [51 , 52] ) . A regular three-nucleotide pattern appeared at the start codon ( Fig 5A ) , indicating the onset of active translation . This shift in periodicity was also clear from the analysis of the counts of protected molecules in the three frames ( S7A Fig ) . As previously described , when the same analysis was repeated around the stop codon , the three-nucleotide pattern resulting from co-translational 5’ - 3’ mRNA degradation was observed , with a marked peak 17 nucleotides upstream of the stop . Here , however , no difference in ribosome protection was detected between wt and the pat1 and lsm1 mutants ( Fig 5A , right panels ) . This difference being highest before the start codon , and absent in the 3’ region of the genes , indicates that cells depleted for Pat1 or Lsm1 present a general increased ribosome protection in the 5’ regions of the mRNAs , including the 5’-UTR . These differences cannot be explained by a hypothetical limited decapping in the mutants , as 5P-Seq measures only RNA molecules with a 5’ phosphate ( i . e . after decapping ) . Therefore , independently of any potential capping differences , ribosomes accumulate in the 5’ regions of decapped mRNAs undergoing 5’-3’ decay . To objectively measure the extent of this ribosome accumulation in the 5’ region , we calculated a loading ratio to compare different regions of the genes . We arbitrarily defined three 45 nucleotides ( nt ) regions around the start codon: upstream of the start codon ( uS , between bases -60 and -15 ) , downstream of the start codon ( dS , -14 to 31 nt , including the ribosome paused at the start ) and downstream of dS region ( ddS , 32 to 77 nt within the ORF ) ( Fig 5A ) . To distinguish ribosome accumulation specifically in the 5’ region from overall differences in ribosome protection , we also defined two regions near the end region ( -62 to -17 nt upstream and -18 to 45 nt downstream of the stop codon , uE and dE respectively ) . Two loading ratios were calculated covering the 5’ region ( uSvsdS and uSvsddS ) and one covering the 3’ end ( uEvsdE ) for each strain and each condition . A positive ratio for a gene thus indicates that there are more ribosome footprints towards the 5’ side of the analyzed region ( i . e . ribosomes accumulate upstream ) . Consistent with the metagene analysis , the pat1 and lsm1 mutants present an increase in 5’-UTR ribosome protection also when analyzed at the single gene level ( Fig 5B and 5C and S7B Fig ) . Comparing the 5’ regions , the distribution of the 5’ loading ratios indicating a global accumulation of ribosome footprints in the 5’-UTR in both the pat1 and lsm1 mutants increased notably relative to wt , showing more genes above the diagonal ( Fig 5B left and mid panels ) ; also observed by the shift towards positive values when plotting the cumulative distribution of gene-specific loading ratios ( Fig 5C left and mid panels ) . The mutants showed more ribosome accumulation in uS than in dS , and these differences were more pronounced for the uS versus the ddS region ( a region within the ORF ) . Similar differences , but at a lower degree , were also observed during osmotic stress ( Fig 5A–5C , bottom panels ) . By contrast , no significant changes in 3’ loading ratios were observed confirming that the ribosome accumulation is specific of the 5’regions of the mRNAs ( Fig 5B and 5C , right panels ) . To determine if the 5’-UTR accumulation of ribosomes in pat1 and lsm1 mutants was general for the mRNA population , or if instead specific gene sets showed a different behavior , we performed a Gene Ontology ( GO ) analysis ( http://babelomics . bioinfo . cipf . es/ ) . To do so , we ranked the mRNAs according to a score based on their fold change of 5’-UTR ribosome accumulation in mutants relative to wt ( calculated as log2 [uSvsdSmutants/uSvsdSwt] ) . This showed that different subsets of genes are clearly enriched among the mRNAs at both ends of the score distribution , both prior to and after stress ( S5 Table ) . Prior to stress , the mRNAs with the highest score were enriched for genes involved in translation , nucleobase and amine metabolism , and transcription elongation . During stress , the highest scores were found with mRNAs related to signal transduction and response to stimulus and pheromone . The differences between the over-represented genes with and without stress were consistent with the two distinct biological environments: the enriched GO terms in actively growing cells were related to gene expression and protein synthesis , while under stress , to signal transduction and stimulus response . As we found the Pat1 and Lsm1 proteins to be preferentially associated with the osmo-mRNAs , GPD1 and STL1 , we were interested to see if osmotic stress-induced genes had a higher 5’-UTR ribosome protection in lsm1 and pat1 mutants . We defined an “osmotic-stress induced” gene subset ( Osmotic Stress Response; OSR ) containing 544 transcripts , for which accumulation is induced under osmotic stress , using a compendium of several published mRNA expression datasets ( details in S6 Table ) . The cumulative distribution of 5’-UTR ribosome protection for the OSR group , like the global graphs ( Fig 5C ) , showed a higher overaccumulation of ribosomes around the 5’UTR in lsm1 and pat1 mutants compared to wt . This difference decreased after 30 min of osmotic stress . To evaluate if there were differences between the OSR group and the total population of mRNAs , we calculated the increment of cumulative distribution median between wt and mutants ( Fig 5C ) . Interestingly , the OSR group showed higher increments that were statistically significant along the 5’UTR ( uSvsdS ) than the main group of analyzed transcripts under unstressed conditions , however not under stressed conditions ( S4 Table ) . These observations indicate that the effect of Pat1 and Lsm1 on ribosome distribution along the 5’-UTR is more pronounced for transcripts encoding osmotic stress-induced proteins . Together , the 5P-Seq results indicate that ribosomes may load more frequently onto the 5’-UTR of mRNAs in lsm1 and pat1 mutants , resulting in overprotection of this region; this effect is strongest in unstressed cells . In view of the enhanced production of Gpd1 and Gpp2 protein in the mutants under weak stress ( Fig 4 ) , this is indicative of increased recruitment of 40S subunits and translation initiation . Lsm1 and Pat1 may thus limit ribosome access and translation of mRNAs , in particular for stress-responsive mRNAs before stress is imposed .
The set of RBPs associated with the total transcriptome has been assessed several times using different methodologies and from different organisms and cells [3 , 24–27 , 53] . Importantly , those studies identified the major mRNA-binding proteins forming the core of most mRNPs , and shed light on global post-transcriptional regulation and the importance of RBP/mRNA interactions for that . There are only few reported attempts to isolate the protein set associated with a single mRNA species in vivo [28–30 , 54] . Therefore , the regulation of individual mRNAs and subsets of transcripts by RBPs is poorly understood . Here , we isolated specific RNP complexes binding to individual osmo-mRNAs in Saccharomyces cerevisiae to understand the composition of these complexes and how they can regulate the fate of the associated mRNA . We modified the MS2 aptamer-tagged mRNAs method [28 , 31] and used it to identify 21 proteins with preferential binding to the GPD1 and STL1 osmo-mRNAs under osmotic stress ( Table 2 ) . The use of MS2 aptamer-tagged mRNA methods has been questioned , reasoning that the MS2-CP-GFP protein binding to the MS2-tagged mRNA might block the 5’-3’ degradation and thereby stabilize 3’mRNA fragments and bias the results [55 , 56] . On the other hand , numerous publications where the MS2-MCP system was used have shown that the transcripts detected by MS2-MCP are intact , and their copy number and localization similar to untagged endogenous mRNAs ( reviewed in [57] ) . In this study , we verified that the mRNAs tagged with MS2 sequences displayed a behavior upon stress similar to the corresponding native mRNAs , both with respect to transcriptional induction and polysome profiles ( S1 Fig ) . Moreover , they did not preferentially co-localize with PBs ( S2 Fig ) , and the punctate pattern of STL1-MS2L mRNA was similar to the native mRNA using FISH [58] . Importantly , in our study all the ensuing functional assays , showing altered behavior of stress-activated mRNAs in lsm1 and pat1 mutants were performed with native , untagged , full-length mRNAs . In the last decade , several studies have reported novel RBPs . Most of them are well-characterized enzymes , now proposed to have a “moonlighting” role as post-transcriptional regulators [4 , 24 , 59 , 60] . In our study we identified 12 proteins not previously described as RBPs , out of 21 preferentially binding STL1 and GPD1 mRNAs . Several hypotheses about why these proteins bind RNA and/or form part of RNPs are on the table . There are examples of enzymes that post-transcriptionally regulate specific target mRNAs , such as cytosolic aconitase/IRP1 and GAPDH [59 , 61] . It has also been proposed that the RNAs could regulate those proteins , by competing with substrates for enzyme binding sites , or as assembly scaffolds for alignment of enzymes in a biochemical pathway [24] . Moreover , they can be part of the spatiotemporal regulation of signaling molecules , as described for the sequestration of TORC1 in SGs during heat stress [62] . Even though the role of those newly identified RBPs remains unknown , most of them are clearly related to the osmotic stress response . An interesting example is the methylglyoxal-catabolism enzyme Glo2 , shown here to bind GPD1 and STL1 mRNAs . Cross-regulation between the glycerol synthesis and methylglyoxal catabolism at multiple levels has already been demonstrated [63–65] . Our present findings suggest new levels of this regulation . Another candidate to be involved in posttranscriptional regulation would be the sodium stress-response transcription factor Usv1 [66] . Our results suggest that many proteins associate with mRNAs and possibly influence their fates , providing dense connections between different layers of cellular regulation . An unexpected finding was the enrichment of several components of Lsm1-7/Pat1 complex binding GPD1 and STL1 mRNAs under osmotic stress conditions when these transcripts are stabilized and actively translated [14 , 20] . The Lsm1-7/Pat1 complex is considered a conserved player in 5’ to 3’ mRNA decay , linking deadenylation to decapping [41–43 , 67 , 68] . In yeast , this complex preferentially binds U-rich tracts near the 3’ end of oligoadenylated rather than polyadenylated mRNA [3 , 69 , 70] . Lsm1-7 is composed of seven Sm-like proteins forming a ring , and it is the C-terminal extension of Lsm1 that approaches the RNA-binding pockets of Lsm1-7 enhancing the RNA binding properties of the core [70 , 71] . Pat1 is a multifunctional protein interacting with several proteins involved in decapping , mRNA decay and translational repression , and the participation of the Lsm1-7/Pat1 complex in those processes has been reported in several studies [41 , 45 , 47 , 72] . Here we corroborate that Lsm1 and Pat1 , and probably the entire Lsm1-7/Pat1 complex , are general translational repressors . Importantly , we show for the first time that this function is stronger for specific mRNA groups . This is particularly obvious in weak to intermediate osmotic stress ( Fig 4 ) , suggesting that the major role of Lsm1-7/Pat1 complex in translation regulation is to permit balanced responses to environmental changes . First , we show that a major role of Pat1 and Lsm1 binding GPD1 and STL1 mRNAs under osmotic stress is to inhibit their translation . We cannot dismiss additional effects on mRNA decay and transcription; but they may make minor contributions to the phenotype of lsm1 and pat1 mutants under the conditions investigated here . This is supported by several observations: ( a ) pat1 mutants overaccumulate Gpd1 protein in response to osmotic stress ( Fig 4 ) ; ( b ) we did not find other proteins related with mRNA decay interacting with the Lsm1-7/Pat1 complex significantly enriched together with STL1 and GPD1 mRNAs ( S1 Table ) , such as Dcp1 and Dcp2 decapping proteins , the CCR4-NOT deadenylation complex components , the Dhh1 DEAD-box helicase , or the Xrn1 5’-3’ exonuclease [41 , 42 , 72] , nor the RNA pol II components Rpb4 and Rpb7 [66]; ( c ) STL1 and GPD1 mRNA half-life did not change in pat1 mutants under osmotic stress ( Fig 2B ) ; ( d ) a higher proportion of GPD1 and STL1 mRNAs is associated to polysomes in pat1 and lsm1 mutants under osmotic stress ( Fig 3 and S5 Fig ) , and ( e ) 5P-seq analyses show increased presence of ribosomes on mRNAs around the translational start in lsm1 and pat1 mutants ( Fig 5 ) . Second , we show that the inhibition of translation mediated by Pat1 and Lsm1 is not homogeneous and affects more strongly specific mRNA subsets depending on cell condition . This is supported by the following key findings: ( a ) the 5’-UTR ribosome overprotection showed by pat1 and lsm1 mutants affects specific mRNA sets ( S5 Table ) , moreover the affected mRNA sets change between control and stress conditions; ( b ) under stress , pat1 and lsm1 mutants also showed higher levels of some of the osmo-induced proteins than wt , and also they are already induced at low hyperosmosis , when moderate or high osmostress are normally required for strong induction in a wild-type strain ( Fig 4 ) . This last finding is consistent with the high increment of 5’UTR ribosome overprotection in the pat1 and lsm1 mutants showed by the OSR set ( Fig 5C and S7B Fig ) in non-stress conditions , which diminished with the stress; suggesting that the most important effect of Lsm1 and Pat1 is to attenuate translation of these mRNAs under conditions of no or weak stress . It is noteworthy that other mRNAs such as QCR6 and HAC1 increase to higher levels during osmostress in pat1 mutants than in wt ( S6 Fig ) , but the corresponding proteins still do not accumulate more in the mutant ( Fig 4 ) . A scenario such as osmotic stress shows the relevance of coordination of the different layers of gene regulation , to be able to respond in the right way to the changing environment and produce the adequate levels of proteins needed for the adaptation fast but also on the proper time . The cellular response to hyperosmotic shock has been shown to be graded with respect to the severity of osmostress; the higher the osmolarity , the higher the amplitude and the longer the duration of the increased expression of stress proteins . Counterintuitively , mild osmoshock produces a quicker expression response than severe osmoshock [34 , 73] . Imbalanced stress responses have several negative consequences for the cell viability and fitness . For example , the overexpression of GPD1 with consequent high accumulation of glycerol does not increase cell osmotolerance , but rather impairs growth in some cases [74 , 75] . Here we show that Pat1 and Lsm1 proteins have a notable effect , preventing overaccumulation of the Gpd1 and Gpp2 proteins . These two enzymes form the short branch of glycolysis involved in glycerol synthesis and are under expression control by the HOG pathway under weak to moderate hyperosmosis . Moreover , the 5P-Seq data reveal that numerous mRNAs encoding components of the pheromone response pathway accumulate ribosomes in their 5’-UTR in pat1 and lsm1 mutant under osmotic stress . Among those identified mRNAs , we found the kinase Fus3 . The HOG and mating pathways share several components , yet exhibit remarkable signal fidelity; hyperosmotic stress does not promote mating , and mating pheromones do not activate Hog1 [76] . To coordinate the two signals , the osmotic stress pathway limits pheromone signaling in different ways: delaying the expression of pheromone-induced genes , and promoting phosphorylation of Rck2 and Ste50 [77] , to postpone its responsiveness . This suggests that the control of the translation of mRNAs related with pheromone response through Pat1 and Lsm1 might be another way of limiting cross-talk with this pathway . Altogether , our study shows that the Lsm1-7/Pat1 complex thus has a role in the translational induction regulation of stress proteins , and suggests that it regulates the accumulation of specific groups of proteins to coordinate and moderate the osmotic stress response . In this study , we corroborate a role for Pat1 and Lsm1 as translation repressors . As previously shown under other stress conditions such as glucose deprivation and amino acid starvation [47 , 78] , pat1 mutants also fail to inhibit global translation under moderate osmotic stress ( S5 Fig ) . Moreover pat1 and lsm1 mutants both show a high proportion of polysomes ( P/FM ) when the global translation is recovered after 30 min of osmotic stress ( Fig 3A ) . Additionally , we observe an abnormally high association of specific mRNAs to polysomes in pat1 and lsm1 mutants under osmotic stress ( Fig 3B ) . Importantly , those ribosomes are able to run off mRNAs in assays done without CHX , indicating that they are not stalled and probably are in active translation ( Fig 3A and 3B , second column ) . In addition , the pat1 and lsm1 mutants exhibit almost identical behaviors in our experiments where ribosome association is tested such as polysome profiles and 5P-Seq ( Figs 3 and 5 ) . Based on all these results , we propose that the translation repression mediated by Pat1 is dependent of the entire Lsm1-7/Pat1 complex , in contrast to a model where Lsm1-7 is recruited to the mRNA after Pat1 inhibits translation initiation [72] . This is supported by the fact that the Pat1 mid-domain and C-terminal domain are essential for translation repression [72] and it is through the C-terminal domain that Pat1 interacts with Lsm2-3 [44 , 71] . In addition , the integrity of the Lsm1-7/Pat1 complex is required for the recognition of oligoadenylated tails and total RNA binding activity , and it is in the context of a complex that Pat1 directly interacts with RNA [79] . An alternative but not mutually exclusive view is that the phenotypes in lsm1Δ mutants are due to depletion of Pat1 from the cytoplasm , as Pat1 is known to localize in the nucleus in such mutants [80] . Our 5P-Seq analysis of the 5’-phosphorylated mRNA co-translational degradation intermediates , where only the properly decapped mRNAs are analyzed , shows ribosome overprotection in the 5’-UTRs in pat1 and lsm1 mutants ( Fig 5 ) . That only this subset of mRNAs is analyzed is an inherent limitation of this technique [52] . As that protection is upstream of the translation start , this cannot reflect ongoing translation , but could mean that these mRNAs are more charged with preinitiation complexes in the absence of Lsm1 or Pat1 . Sequencing of 5’ degradation intermediates has been shown to be capable of identifying binding of protein complexes to the 5’-UTR of mRNAs [81 , 82] . Considering previous observations reporting about Pat1 , such as its association with ribosomes through its N-terminal domain , its sedimentation rate consistent with interactions with the 48S complexes , and its reduction of translation initiation by limiting the formation of a 48S preinitiation complex [72 , 83] rather than through a global decapping defect , the high rate of translation of pat1 and lsm1 mutants in our work may be interpreted as a consequence of maintaining a high level of 40S ribosomal subunit recruitment to mRNAs . However , increased binding of other proteins or complexes than 48S complexes cannot be excluded . It should be noted that in mutants defective in the Lsm1-7/Pat1 complex , poly ( A ) tails are shorter [42] . This could mean decreased Pab1 binding in the 3’-UTR , which could indirectly affect recruitment of other proteins to the 5’-end of an actively translating mRNA . Numerous factors determine the dynamics in translation between different mRNAs , but initiation is rate limiting for most of them [84–86] . Initiation rates are dependent on the accessibility of ribosomes to the start codon and their loading onto mRNA [87] , and this is in competition with the mRNA degradation machinery to be loaded . We thus propose that the Lsm1-7/Pat1 complex associates with stress response mRNAs , modulating their translation through limiting ribosomal access , which adds another level to the adaptability of gene regulation during rapidly changing conditions , to promote cell survival and fitness .
Unless indicated , cells were grown at 30°C till mid-log phase ( OD600 0 . 4–0 . 5 ) in synthetic medium supplemented with appropriate amino acids and 2% glucose as the carbon source . To induce osmotic stress , KCl was added to the culture as specified in the Figure legends . The genotypes of all yeast strains used in this study are listed in S7 Table . The pat1-hphNT deletion strains were generated as described [88] . The specific MS2 loop-tagged mRNAs were generated as described previously [31] . For each gene to be tagged , a cassette loxP::Sphis5+::loxP::MS2L was obtained by PCR using pLOSHIS5MS2L as a template ( kind gift from Jeffrey E . Gerst , Weizmann Institute of Science , Rehovot , Israel ) [31] and the oligonucleotides listed in S8 Table . PCR products were transformed into yeast and positive transformants were selected growing on SC plates lacking histidine . To confirm integration , genomic DNA extracted from single colonies were analyzed by both PCR and sequencing ( using oligos complementary to the coding region before the MS2 loops and 3’-UTR after the MS2 loops , see S8 Table ) . To remove the Sphis5+ marker , positive colonies were transformed with pSH47 ( URA3 marker ) , and Cre recombinase expression was induced by growing transformed cells in YP rich medium containing 2% galactose for 2 h . Yeast clones carrying loxP::MS2L integration were selected by the loss of ability to grow on SC plates lacking histidine , and verified by PCR . Finally , the loss of pSH47 was promoted by growing the final positive transformants in YPD medium for 3 days and selecting for the ability to grow in SC plates containing 100 mg/l 5-fluorouracil . To visualize the intracellular localization of the mRNA-MS2L , expression of GFP fusion protein was induced by methionine depletion ( 1 h ) under non-stressed or osmotic stress conditions ( 30 min , 0 . 6 M KCl ) . Where indicated , CHX was added to 100 μg/ml final concentration , 20 min prior to sampling in non-stressed conditions or prior to addition of KCl . After the incubation time , cells were fixed with 4% formaldehyde and stored at 4°C until analysis . The images were captured in a Zeiss Axio Observer fluorescence microscope and processed with ImageJ software ( NIH ) . Cells with granules were counted using at least 200 cells for each mRNA . The number of granules per cell was counted using at least 60 cells for each mRNA in duplicate . Additionally , for the co-localization of the mRNA-MS2L with the PBs , cells were transformed with Dcp2-RFP plasmid [37] . For the in vivo capture of specific RNPs [39] , strains expressing a specific mRNA-MS2L ( the background strain not expressing an mRNA-MS2L was included as a negative control ) were transformed with the plasmid pMS2CP-GFP [31] , which expresses the MS2 coat protein ( MS2-CP ) fused with GFP under the MET25 promoter ( provided by Jeffrey E Gerst ) . Yeast cells were grown in 600 ml of SC liquid medium lacking histidine with constant shaking at 30°C until OD600 1 . 0 was reached . Cells were collected by centrifugation , washed once with SC medium lacking methionine and , transferred to SC medium lacking methionine during 1 h at 30°C . To induce osmotic stress , KCl was added to 0 . 6 M KCl after 30 min of transferring the cells and incubated for another 30 min . Cells were collected by centrifugation and washed once with PBS buffer lacking Ca2+ and Mg2+ . Crosslinking was done by resuspending cells in PBS buffer containing 0 . 05% formaldehyde and incubating at room temperature for 15 min with slow agitation . The cross-linking reaction was terminated by adding glycine solution ( pH 8 . 0 ) to 0 . 125 M and incubating for 5 min with slow shaking . Fixed cells were pelleted , washed with ice-cold PBS , quick-frozen in dry ice PBS and kept at -80°C until used . Cell pellets were resuspended in 1 ml per 100 OD600 units of ice cold lysis buffer [20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1 . 8 mM MgCl2 , 1 mM DTT , 80 U/ml RNasin ( Promega ) , 1 × protease inhibitor ( Complete , Mini , EDTA-free , Roche ) , 0 . 2% Triton X-100] and aliquoted in 0 . 5 ml/tube . An equal volume of 0 . 5 ml of glass beads was added to each tube and cell were disrupted in a FastPrep device ( Bio101 ) , at intensity setting 0 . 5 for 3 rounds of 30 s at 4°C . Glass beads and cell debris were sedimented at RCF 1700 for 1 min at 4°C , and the supernatants were transferred to a new microcentrifuge tube . To clear the supernatant , a second centrifugation at RCF 15300 for 15 min at 4°C was done . Afterwards , the aliquots from the same sample were collected together and the protein concentration was determined using the Bicinchoninic Acid ( BCA ) assay ( Pierce ) . An aliquot was kept as reference input sample . For RNP capture , 30 mg of total protein was incubated with 100 μl of GFP-Trap beads ( ChromoTek ) , washed and prepared for RNA manipulation following the manufacturer’s specifications , and 100 μg/ml of yeast tRNA ( Sigma , R8508 ) , for 3 h at 4°C with gentle agitation . After the RNP capture incubation , samples were washed 5 times with 1 × lysis buffer , and finally RNP complexes were eluted from the beads by adding 120 μl of 1 × cross-link reversal buffer ( 50 mM Tris-HCl pH 7 . 0 , 5 mM EDTA , 10 mM DTT , 1% SDS ) and incubating for 1 h at 70°C with agitation . 20 μl of eluted sample was kept for RNA determination and the remaining volume was used for western blot or LC-MS/MS analysis . Three independent replicate experiments were performed , for each tagged and untagged strain . Pull-downs of RNPs using specific GFP tagged proteins [89] were done following a similar protocol described above but with the following modifications . For the RNP capture , 300 μg of total protein were incubated with 20 μl 50% GFP-Trap beads slurry . Elution from the beads was done in 100 μl of 1 × cross-link reversal buffer . 80 μl of eluted samples was used for RNA determination by qPCR . Three independent experiments were done for each strain . Protein samples were dried in a vacuum centrifuge after determination of protein concentration by the Qubit protein assay ( ThermoFisher Scientific ) . After resuspension in SDS/PAGE sample buffer , 9 μg of protein from each sample were fractionated in a 12% polyacrylamide/SDS gel , and the gel was stained with colloidal Coomassie Blue . Afterwards , the bait protein band was excised separately and the remainder of each gel lane divided into three pieces . Protein digestion was performed as described elsewhere [90] , and the resulting peptide mixture was dried in a vacuum centrifuge and resuspended in 0 . 1% trifluoroacetic acid ( TFA ) , 2% acetonitrile ( ACN ) ; 20 μl for the bait protein band and 9 μl for the other gel pieces . Mass spectrometry ( LC-MS/MS ) analysis was performed by loading 5 μl of tryptic peptides from each digestion mixture onto a trap column ( NanoLC Column , 3 μm C18-CL , 75 μm × 15 cm; Eksigen ) , desalted with 0 . 1% TFA for 10 min at a flow rate of 2 μl/min , and transferred onto an analytical column ( LC Column , 3 μm C18-CL , 75 μm × 12 cm , Nikkyo ) equilibrated in 0 . 1% formic acid ( FA ) in 5% ACN . Elution was carried out with a linear gradient of 5% to 40% ACN ( 0 . 1% FA in ACN ) at a flow rate of 300 nl/min . The gradient length was 45 min for the peptides generated from the bait protein band and 120 min for the peptides derived from the other gel pieces . Mass spectrometry analysis was performed with a nanoESI qQTOF ( 5600 TripleTOF , AB Sciex ) mass spectrometer . The instrument was operated in data-dependent acquisition mode , in which a 250 ms TOF MS scan from 350–1250 m/z , was followed by 50 ms product ion scans from 100–1500 m/z on the 50 most intense 2–5 charged ions . MS/MS data were processed with ProteinPilot v4 . 5 ( AB Sciex ) . Peak lists were generated from the instrument wiff files using ProteinPilot default parameters . The peak lists derived from the three gel pieces of each sample were combined to perform a single search whereas the peak list obtained from the bait protein band was used on a separate search . Protein identification was performed with the Paragon algorithm within ProteinPilot software using the UniProt database . The following parameters were used: trypsin specificity , cys-alkylation , no taxonomy restriction , and the search effort set to thorough . The number of peptides assigned to each protein with 95% confidence or higher as well as the chromatographic intensity of each peptide are part of the ProteinPilot output . Both parameters were used to estimate protein abundance . The first quantitative method was based on the top three most intense peptides [91] . For the second quantitative method , the number of peptides assigned to each protein normalized by its molecular weight was used , which provides similar results to the emPAI calculation [92] . Two search results for each sample were obtained , one for the bait protein band and another for the remainder of the SDS/PAGE gel lane . Thus , four sets of quantitative values for each sample were generated , log transformed and normalized by the median of each set . The two quantitative sets based on peptide count were combined , but those based on peak intensity were treated individually . Three biological replicates were analyzed for each condition , and only proteins with quantitative values in at least two replicates were considered . Statistical significance of the difference relative to the untagged strain was calculated with a Student’s t-test analysis . Since proteins considered candidates to be differentially abundant were to be validated by a different set of experiments , no FDR correction was used . Differentially abundant proteins were selected as follows: only the proteins with minimum 2 values in at least one mRNA-MS2L strain and significant p-value ( < 0 . 05 ) were included in the following sorting . For the subsequent filtering , the average of the normalized values from the 3 biological replicates , ratios of mRNA-MS2L/untagged strain and the ratio mRNA-MS2L ( strain A ) /mRNA-MS2L ( strain B ) were calculated . Total RNA extraction from cells was carried out using phenol-chloroform extraction and ethanol precipitation as previously described [15] . Samples from pull-downs were treated for 30 min at 37°C with 0 . 2 μg/μl Proteinase K . RNA was extracted once with phenol:chloroform:isoamyl alcohol ( 25:24:1 ) and once with chloroform:isoamyl alcohol ( 24:1 ) , and precipitated with 0 . 3 M sodium acetate , 2 volumes of 96% ethanol and 0 . 08 μg/μl glycogen . To extract the RNA from the polysomal fractions , 16 mM EDTA and 0 . 4% SDS were added , one extraction with phenol:chloroform:isoamyl alcohol ( 25:24:1 ) was performed , and RNA was precipitated twice , once with cold 96% ethanol and next with 2 . 5 M LiCl . Afterwards , RNA was treated with DNase I ( Thermo Fisher ) according to the manufacturer’s protocol . cDNA was synthesized in 20 μl reactions containing the DNase I treated RNA , 5 μM of oligo ( dT ) ( Thermo Fisher ) , 200 units/μl of M-MLV RT ( Thermo Fisher ) , 1 × First Strand Buffer , 10 mM DTT , and 0 . 8 mM dNTPs . qPCR was performed in a reaction final volume of 10 μl using Eva Green ( Solis ) for fluorescent labeling , 2 μl cDNA , and 0 . 2 μM of the corresponding oligonucleotides ( S8 Table ) . Real-time PCR reactions were performed under the following conditions: 95°C for 15 min to activate the polymerase , followed by 40 cycles of 10 s at 95°C , 20 s at 60°C , and 10 s at 72°C . At the end of the amplification cycles , a melting curve analysis was conducted to verify the specificity of the reaction . For each analyzed primer pair , a negative control was included and a standard curve was made with serial dilutions of cDNA samples pool ( 1/2 , 1/5 , 1/10 , 1/25 , 1/50 , 1/100 and 1/500 ) . For measurement of mRNA stability , transcription was stopped by addition of 1 , 10-phenantroline to 100 μg/ml , and samples were taken at different time points thereafter ( 0 , 5 , 10 , 15 , 25 , 35 and 45 min ) [35] . RNA was extracted and analyzed as described above . Polyribosome analysis was performed as previously described [20] . Cells grown to mid-log phase were treated with 0 . 1 mg/ml CHX and collected by centrifugation . Cells pellets were lysate in lysis buffer ( 20 mM Tris-HCl , pH 8 . 0 , 140 mM KCl , 5 mM MgCl2 , 0 . 5 mM DTT , 1% Triton X-100 , 0 . 1 mg/ml CHX , and 0 . 5 mg/ml heparin ) by bead bashing . Finally , glycerol was added to the supernatant to a final concentration of 5% , and extracts were stored at -80°C . Samples of 10–12 A260 units were loaded onto 10 – 50% sucrose gradients and were separated by ultracentrifugation for 2 h and 40 min at 35 , 000 rpm in a Beckman SW41Ti rotor at 4°C . Gradients were then fractionated and recorded using Density Gradient Fractionation System and Isco UA-6 ultraviolet detector ( Teledyne Isco , Lincoln , NE , USA ) . For runoff experiments , cells and samples were handled as described above but CHX was omitted in both the lysis buffer and the gradient solutions . Western blot was performed using standard protocols . Antibodies used were mouse anti-GFP ( 1:1000 , Roche ) , rat α-She2 ( 1:50 , kind gift from Ralf Jansen , University of Tübingen , Germany ) , α-mouse ( 1:5000 , Sigma ) and α-rat ( 1:500 , Amersham ) secondary antibodies conjugated to horseradish peroxidase . Accumulation of GFP-tagged proteins was detected by recording green fluorescence emission ( 520 nm ) in an Omega Polarstar fluorescence plate reader ( LabVision ) . Plates were incubated at 30°C and measurements were taken automatically every 4 min for a period of 140 min . To normalize the data , OD600 measured from the same well at the same time than green fluorescence and , was used to remove the effect of growth differences between strains . Libraries for 5’-phosphate sequencing ( 5P-Seq ) were prepared as specified [52] using 6 μg of DNA-free total RNA . To select 5’-P mRNA degradation intermediates , samples were directly subjected to selective ligation of a synthetic DNA/RNA oligo containing unique molecular identifiers ( UMIs ) ; samples were incubated overnight at 16°C with 20 units of T4 RNA ligase 1 ( New England Biolabs ) in the presence of 10 mM DNA/RNA rP5_RND oligo [52] . RNA integrity was checked by electrophoresis in 1 × TAE agarose gel and ribosomal RNA was depleted using Ribo-Zero Magnetic Gold Kit ( Epicentre ) . PolyA-enriched RNA was fragmented at 80°C for 5 min in the presence of RNA fragmentation buffer ( 40 mM Tris-acetate , pH 8 . 1 , 100 mM KOAc , 30 mM MgOAc ) , and reverse transcribed with Superscript II ( Life Technologies ) primed with random hexamers . The retrotranscription reaction was incubated for 10 min at 25°C , 50 min at 42°C and heat inactivated for 15 min at 72°C . Second strand cDNA synthesis was performed by a single PCR cycle ( 1 min at 98°C; 2 min at 50°C and 15 min at 72°C ) using Phusion High-Fidelity PCR Master Mix with HF Buffer ( New England Biolabs ) and priming with BioNotI-P5-PET [52] . From this point onwards , libraries were generated as described previously [93] . Briefly , double-stranded cDNA was first purified using HighPrep beads ( Magbio ) and then bound to Dynabeads M-280 Streptavidin beads ( Life Technologies ) according to the manufacturer’s instructions . Bound DNA molecules were successively washed and subjected to repair of ends , dA addition and adaptor ligation using Nebnext DNA Library Prep Master Mix ( NEB ) . 0 . 5 μl of common adapter ( 2 . 5 μM ) was ligated to each sample . The common adapter was prepared by annealing P7MPX_linker_for and P7MPX_linker_rev [52] . Beads were washed and subjected to PCR amplification [30 s 98°C; 19 cycles of ( 20 s 98°C , 30 s 65°C , 30 s 72°C ) ; 5 min 72°C] using Phusion High-Fidelity PCR Master Mix with HF Buffer ( NEB ) and 0 . 1 μM final concentration of PCR-PE 1 . 0 and the appropriate PE2_MTX [52] . Libraries were quantified by Qubit using the dsDNA HS assay kit and the size was checked using a RNA Bioanalyzer . A 5P-Seq pool was made mixing 8 ng of each amplified 5P-Seq library , and was size selected using 0 . 6 × - 0 . 9 × ( v/v ) HighPrep beads ( Magbio ) to 300 – 500 bp . The 5P-Seq pool was sequenced using an Illumina NextSeq 500 High Output Kit v2 . We trimmed the first 8 nt off each read ( containing the unique molecular identifier , UMI ) and aligned the rest to the S . cerevisiae genome ( R64-1-1 ) . For mapping , we used Hisat2 with default parameters ( except maximum intron size set to 2 kb ) . Reads with identical 5’mapping site and UMI were considered PCR duplicates , and collapsed . To compute gene-specific pausing we generated count tables for each gene for the 45 nt regions upstream . Only genes with at least 20 5P-Seq read in the defined regions were considered for the analysis . Raw and processed sequencing data are deposited at GEO with accession number GSE107250 . | When confronted with external physical or chemical stress , cells respond by increasing the mRNA output of a small number of genes required for stress survival , while shutting down the majority of other genes . Moreover , each mRNA is regulated under stress to either enhance or diminish its translation into proteins . The overall purpose is for the cell to optimize gene expression for survival and recovery during rapidly changing conditions . Much of this regulation is mediated by RNA-binding proteins . We have isolated proteins binding to specific mRNAs induced by stress , to investigate how they affect the stress response . We found members of one protein complex to be bound to stress-induced mRNAs . When mutants lacking these proteins were exposed to stress , ribosomes were more engaged with translating mRNAs than in the wild-type . In the mutants , it was also possible to trigger expression of stress proteins with only minimal stress levels . Tracing the passage of ribosomes over mRNAs , we saw that ribosomes accumulated around the start codon in the mutants . These findings indicate that the protein complex is required to moderate the stress response and prevent it from overreacting , which would be harmful for the cell . | [
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... | 2018 | The Lsm1-7/Pat1 complex binds to stress-activated mRNAs and modulates the response to hyperosmotic shock |
Amino acids are among the earliest identified inducers of yeast-to-hyphal transitions in Candida albicans , an opportunistic fungal pathogen of humans . Here , we show that the morphogenic amino acids arginine , ornithine and proline are internalized and metabolized in mitochondria via a PUT1- and PUT2-dependent pathway that results in enhanced ATP production . Elevated ATP levels correlate with Ras1/cAMP/PKA pathway activation and Efg1-induced gene expression . The magnitude of amino acid-induced filamentation is linked to glucose availability; high levels of glucose repress mitochondrial function thereby dampening filamentation . Furthermore , arginine-induced morphogenesis occurs more rapidly and independently of Dur1 , 2-catalyzed urea degradation , indicating that mitochondrial-generated ATP , not CO2 , is the primary morphogenic signal derived from arginine metabolism . The important role of the SPS-sensor of extracellular amino acids in morphogenesis is the consequence of induced amino acid permease gene expression , i . e . , SPS-sensor activation enhances the capacity of cells to take up morphogenic amino acids , a requisite for their catabolism . C . albicans cells engulfed by murine macrophages filament , resulting in macrophage lysis . Phagocytosed put1-/- and put2-/- cells do not filament and exhibit reduced viability , consistent with a critical role of mitochondrial proline metabolism in virulence .
Candida albicans is an opportunistic fungal pathogen that commonly exists as a benign member of the human microbiome . Immunosuppression , or microbial dysbiosis , can predispose an individual to infection , enabling this fungus to initiate and develop a spectrum of pathologies , including superficial mucocutaneous or even life-threatening invasive infections [1 , 2] . As a human commensal , C . albicans can asymptomatically colonize virtually all anatomical sites in the host , each with a characteristic and unique microenvironment , with differing nutrient and microbiome compositions , physical properties , and levels of innate immune defenses [3] . The ability to colonize and infect discrete microenvironments is attributed to an array of virulence characteristics , a major one being its morphological plasticity . As a pleomorphic organism , C . albicans can assume at least three distinct morphologies: yeast-like , pseudohyphae , and true hyphae , where the latter two are commonly referred to as filamentous morphologies ( for review see [4–7] ) . Strains that are genetically locked in either yeast or filamentous forms fail to mount infections in vitro and in vivo infection models , supporting the concept that morphological switching , rather than the specific morphology per se , is a requisite to virulence [4 , 6 , 8–10] . The environmental signals known to trigger morphogenesis in C . albicans reflect the conditions within the human host , such as temperature ( 37 °C ) , CO2 , alkaline pH , the presence of serum , N-acetylglucosamine , and a discrete set of amino acids . Early studies examining amino acid-induced morphogenesis implicated metabolism as being important for filamentation , and the inducing effects were shown to correlate to the specific point-of-entry in metabolism [11–13] . The most potent inducers of filamentation are amino acids that are catabolized to glutamate , such as arginine and proline , which enters the TCA cycle via α-ketoglutarate . Importantly , arginine and proline can supply nitrogen and carbon for intermediary metabolism and their catabolism provides energy to support diverse cellular functions . Studies examining proline uptake and distribution during filamentous growth suggested that proline catabolism results in an increase in the cellular reducing potential , i . e . , enhanced levels of reduced flavoproteins were noted [11] . Several of the conclusions from these earlier studies , in particular that filamentous growth of C . albicans is linked to repression of mitochondrial activity [11–13] , appear to conflict with more recent reports showing that filamentation is dependent on mitochondrial respiratory activity [14–18] . Clearly , the underlying mechanisms through which amino acids induce filamentation remain to be defined . In particular , the basis of arginine- and proline-induced morphogenesis needs to be placed in context to the current mechanistic understanding of the signaling cascades implicated in morphogenesis . Among the central metabolic signaling pathways in C . albicans linked to morphogenesis , the best characterized are the mitogen-activated protein kinase ( MAPK ) and the 3’-5’-cyclic adenosine monophosphate/Protein Kinase A ( cAMP/PKA ) signaling systems , which activate the transcription factors Cph1 and Efg1 , respectively [8 , 19 , 20] , reviewed in [4 , 7 , 21 , 22] . Ras1 is a small GTPase required for proper MAPK and cAMP/PKA signaling , and specifically for the induction of filamentation by amino acids and serum [23 , 24] , reviewed in [22 , 25] . Recently , Grahl et al . have proposed that intracellular ATP levels and increased mitochondrial activity control the activation of Ras1/cAMP/PKA pathway [14] . In this intriguing model , the adenyl cyclase ( Cyr1/Cdc35 ) works cooperatively in a positive feedback loop with ATP as key input . Accordingly , ATP promotes Cyr1 binding to the active GTP-bound form of Ras1 thereby reducing the ability of Ira2 to stimulate the intrinsic GTPase activity of Ras1 . As a consequence , enhanced Cyr1 activity leads to elevated levels of cAMP and amplification of PKA-dependent signaling , activating the effector transcription factor Efg1 and the expression of genes required for filamentous growth [26–28] , reviewed in [22 , 25 , 29 , 30] . Some morphogenic signals appear to bypass the requirement for Ras1 ( reviewed in [29 , 30] ) . By example , CO2 is a well-characterized stimulus for morphological switching in C . albicans; CO2 binds directly and activates Cyr1 [31] . Ghosh et al . have proposed that arginine-induced morphogenesis is the consequence of arginase ( CAR1 ) dependent metabolism to ornithine and urea , and subsequent urea amidolyase ( DUR1 , 2 ) -dependent generation of CO2 from urea [32] . Also , the G protein-coupled receptor Gpr1 , which has been implicated in amino acid-induced morphogenesis , does not appear to require Ras1 . Gpr1-initiated signals activate Cyr1 by stimulating GTP-GDP exchange on the Gα protein Gpa2; the active GTP-bound form of Gpa2 is thought to bind to the Gα-binding domain within the N-terminal of Cyr1 leading to enhanced cAMP production ( reviewed in [22 , 30] ) . It has been reported that Gpr1 senses the presence of extracellular methionine [33] and glucose [34] , however recently , lactate has been proposed to be the primary activating ligand [35] . The role of Gpr1 in amino acid-induced morphogenesis remains an open question . C . albicans cells respond to the presence of extracellular amino acids using the plasma membrane-localized SPS ( Ssy1-Ptr3-Ssy5 ) sensor complex [36–38] . In response to amino acids , the primary sensor Ssy1 ( Csy1 ) is stabilized in a signaling conformation leading to Ssy5-mediated proteolytic processing of two latently expressed transcription factors , Stp1 and Stp2 [36] . The processed factors efficiently target to the nucleus activating the expression of distinct sets of genes required for assimilation of external nitrogen . Stp1 regulates the expression of SAP2 , encoding the major secreted aspartyl proteinase , and oligopeptide transporters ( OPT1 and OPT3 ) ; whereas Stp2 derepresses the expression of a subset of amino acid permeases ( AAP ) that facilitate amino acid uptake . STP1 expression is controlled by nitrogen catabolite repression ( NCR ) , a supra-regulatory system that represses that utilization of non-preferred nitrogen sources when preferred ones are available [39] . The endoplasmic reticulum ( ER ) -localized chaperone Csh3 is required for the functional expression of both Ssy1 and AAPs , and thus acts as the most upstream and downstream component of the SPS sensing pathway [37] . Strains lacking either Ssy1 or Csh3 fail to efficiently respond to the presence of extracellular amino acids and serum and exhibit impaired morphological switching [37 , 38] . It has not previously been determined if the SPS-sensor induces morphogenesis directly in response to extracellular amino acids , or indirectly , the consequence of enhanced amino acid uptake and subsequent intracellular signaling events . In this report , we show how amino acid-induced and SPS-sensor-dependent signals are integrated into the central signaling pathways that control yeast-to-hyphal morphological transitions in C . albicans . Our results indicate that the augmented levels of intracellular ATP , resulting from catabolism of proline in the mitochondria , correlate with activated Ras1/cAMP/PKA and Efg1-dependent gene expression . The magnitude of the response is sensitive to the levels of glucose in a manner consistent with glucose repression of mitochondrial function . The SPS-sensor plays an indirect , but important , role in enhancing the uptake of the inducing amino acids . Finally , we show that C . albicans cells express proline catabolic enzymes when phagocytosed by murine macrophages , and that inactivation of proline catabolism diminishes the capacity of C . albicans cells to induce hyphal growth and escape engulfing macrophages .
We assessed the capacity of ornithine , citrulline , and the 20 amino acids commonly found in proteins to induce filamentous growth in C . albicans . Wildtype ( WT ) cells were grown as macrocolonies on MES-buffered ( pH of 6 . 0 ) synthetic dextrose ( 2% glucose ) medium containing 10 mM of each individual amino acid as sole nitrogen source . As shown in Fig 1A , proline and arginine strongly induced filamentous growth as evidenced by the formation of wrinkled macrocolonies . Microscopic evaluation of cells from wrinkled colonies confirmed filamentous growth ( mainly hyphal ) . Ornithine , a non-proteinogenic amino acid and a catabolic intermediate in the degradation of arginine , induced pronounced filamentous growth . Of the amino acids tested , aspartate consistently produced smooth macrocolonies comprised of round cells exclusively yeast-like in appearance . Consequently , aspartate was chosen as a negative reference control for subsequent studies . Using quantitative RT-PCR ( qRT-PCR ) , we analyzed the expression of known hyphae-specific genes ( HSG ) ECE1 , EED1 , HWP1 , UME6 , ALS3 , HGC1 , SAP4 , and SAP5 [4] in cells from colonies grown on media with arginine , proline , ornithine and aspartate . With the exception of EED1 , the expression of HSG were clearly induced in cells grown on media with morphogenic amino acids , ≥ 7-fold higher than in cells grown on aspartate ( S1 Fig ) . SAP4 , a known Efg1-regulated gene [40] , exhibited the highest level of induction , ≥ 80-fold higher levels than in aspartate grown cells . These experiments were repeated using liquid cultures , and the same trends were observed . These results confirm the appropriateness of using macrocolonies to score amino acid-induced morphogenesis . Next , we evaluated whether SPS-sensor activation was required for amino acid-induced morphogenesis . This was accomplished by assessing SPS-sensor dependent Stp2 processing [36] . A strain carrying a functional C-terminal HA tagged Stp2 ( Stp2-HA; PMRCA44 ) was grown in minimal ammonium-based synthetic dextrose ( SD ) medium , extracts were prepared 5 min after induction by the indicated amino acid . Arginine ( R ) , asparagine ( N ) , aspartate ( D ) , glutamine ( Q ) , histidine ( H ) , lysine ( K ) , serine ( S ) and ornithine ( Orn ) efficiently activated the SPS-sensor; extracts contained the shorter processed form of Stp2 ( Fig 1B , upper panel ) . Next , we assessed Stp2-dependent promoter activation using an integrated PCAN1-NanoLuc-PEST reporter construct; the expression of the luciferase signal is controlled by the CAN1 promoter , which is strictly dependent on the SPS-sensor and Stp2 ( S2 Fig ) . The inclusion of the 41-amino acid PEST sequence confers a shorter NanoLuc lifetime , which facilitates a tighter coupling of transcription and translation [41] . Enhanced luminescence was observed only in cells induced with the amino acids giving rise to Stp2 processing ( Fig 1B , lower panel ) . Notably , proline , which induces robust filamentation , did not activate the SPS-sensor as no Stp2 processing or luminescence was detected . Conversely , aspartate , which does not induce filamentous growth , robustly activated the SPS-sensor as determined by Stp2 processing and enhanced luciferase activity . These results indicate that amino acid-induced morphogenesis is not obligatorily coupled to SPS-sensor signaling . The contribution of signals derived from the SPS sensing pathway on filamentation induced by arginine , ornithine and proline was examined . Arginine , a potent inducer of the SPS-sensor ( Fig 1B ) , induced filamentation in an SPS-sensor independent manner; filamentation was observed in mutants lacking components of the SPS sensing pathway ( Fig 1C ) . By contrast , ornithine , also a potent inducer of the SPS-sensor , induced filamentous growth in a strictly SPS-sensor- and Stp2-dependent manner ( Fig 1C ) . Notably , Stp1 , a transcription factor that induces genes required for extracellular protein utilization , is not required for ornithine-induced filamentation . Proline , which does not induce SPS-sensor signaling , promoted filamentous growth in an SPS-sensor independent manner ( Fig 1C ) . Importantly , the filamentation was greatly reduced in cells lacking CSH3 ( csh3Δ/Δ ) , encoding a membrane-localized chaperone required for the functional expression of Ssy1 and most amino acid permeases [37 , 38] ( Fig 1C and S3B Fig ) , suggesting that amino acid uptake is required for amino acid-induced morphogenesis . The clear requirement of the SPS-sensor in facilitating ornithine-induced filamentation provided the opportunity to rigorously test the notion that uptake is essential . Based on the knowledge that amino acid permease-dependent uptake is dependent on Stp2 and not Stp1 , we used the CRISPR/Cas9 system to introduce ssy1 null mutations in strains expressing constitutively active Stp2 ( STP2* ) or Stp1 ( STP1* ) [36] ( S3A Fig ) . The results clearly show that STP2* , but not STP1* ( Fig 1D ) , bypasses the ssy1 null mutation , indicating that the permeases responsible for ornithine uptake are encoded by a SPS-sensor and Stp2 controlled genes . Similarly , SPS-sensor dependence was observed for the inducing amino acids alanine , glutamine , and serine . Together , these results indicate that amino acid-induced filamentous growth is dependent on the uptake of the inducing amino acid . Two core signaling pathways , i . e . , MAPK and cAMP/PKA , are known to transduce metabolic signals that affect filamentous growth ( Fig 2A ) . We evaluated the capacity of amino acids to induce filamentation in cells carrying null alleles of RAS1 and the effector transcription factors , CPH1 and EFG1 diagnostic for MAPK and cAMP/PKA signaling , respectively [8 , 20 , 42] ( Fig 2B ) . Similar to wildtype , cph1Δ/Δ cells were wrinkled in appearance , indicating that amino acid induced filamentation was largely independent of MAPK signaling . By contrast , the colonies derived from ras1Δ/Δ and efg1Δ/Δ cells were smooth . As expected , the efg1Δ/Δ cph1Δ/Δ double mutant strain also formed smooth colonies . These results indicate that the inducing signals are primarily transduced by the cAMP/PKA pathway . A clear dependence on Ras1/cAMP/PKA signaling was also observed for other inducing amino acids , i . e . , alanine , glutamine , and serine ( S7A Fig ) . Our results demonstrating that amino acid-induced morphogenesis is strictly dependent on Ras1 is contrary to current models that postulate that amino acids-initiated signals are transduced by Gpr1/Gpa2 ( reviewed in [22 , 30] ) . According to these models , amino acid signaling should be Ras1 independent . The finding that amino acid-induced morphogenesis is Ras1 dependent suggested that amino acid-initiated signals promote GTP-GDP exchange . To test this notion , we assessed the levels of Ras1-GTP in cells after induction by amino acids ( Fig 2C ) . Our results clearly show that in contrast to cells induced with aspartate , cells induced with arginine , proline and ornithine possess increased levels of activated Ras1 in its GTP bound form . Consistent with this observation , inactivation of IRA2 led to constitutive hyphal growth in YPD at 30 °C , conditions that normally do not induce hyphal growth ( S3C Fig ) . In an attempt to directly assess the requirement of adenylyl cyclase we obtained the characterized cdc35Δ/Δ ( cyr1 ) strain [28] . Contrary to expectations , this strain did not grow in the synthetic media used here , even when the media was supplemented with 100 μg/ml uridine and/or 10 mM dibutyryl cAMP . Unfortunately , the lack of growth precluded a direct assessment of the role of Cyr1 . We tested whether amino acid catabolism was required to activate PKA-signaling by examining the morphology of colonies from cells grown on medium containing arginine or proline as sole nitrogen source and enzyme specific inhibitors . In C . albicans , arginine is primarily catabolized via the arginase ( CAR1 ) pathway to ornithine and urea . Nω-hydroxy-nor-arginine ( Nor-NOHA ) , a potent competitive inhibitor of arginase [43] , inhibited arginine-induced filamentation in a dose-dependent manner ( Fig 2D ) . Similarly , L-tetrahydrofuroic acid ( L-THFA ) , a specific competitive inhibitor of proline dehydrogenase ( Put1 ) [44 , 45] , greatly impaired filamentation in a dose-dependent manner ( Fig 2E ) . The data demonstrate that the arginine- and proline-inducing signals are derived from their catabolism . Intracellular levels of ATP are thought to provide a key input for Ras1/cAMP/PKA signaling [14] . Consistent with this notion , in comparison to cells grown in the presence of non-inducing nitrogen sources , such as aspartate and ammonium sulfate , cells grown in the presence of the morphogenic amino acids arginine , ornithine or proline contained similar , and significantly higher levels of ATP ( Fig 3A ) . Although urea is a good inducer of filamentous growth , urea grown cells contained low levels of ATP . This finding is consistent with previous reports that morphogenic induction by urea is dependent on DUR1 , 2-dependent metabolism that generates CO2 [31 , 32] . Arginine and ornithine are catabolized to proline in the cytoplasm , and proline is subsequently metabolized to glutamate and then α-ketoglutarate in the mitochondria [12] . These metabolic events generate the reduced electron donors , FADH2 and NADH , which are oxidized by the mitochondrial electron transfer chain leading to ATP synthesis ( Fig 3A ) . We posited that the increased levels of ATP resulting from the catabolism of arginine , ornithine and proline is the consequence of their shared metabolic pathway . To test this , we used methylene blue ( MB ) , which effectively uncouples electron transport from the generation of a proton motive force generated by mitochondrial respiratory complexes I-III [46 , 47] . The inclusion of MB in media containing ornithine or proline completely inhibited filamentous growth ( Fig 3B ) . The inhibitory effect of MB in cells growing on arginine was not complete; a higher concentration of MB was required to noticeably inhibit filamentation , consistent with the generation of CO2 by the DUR1 , 2-dependent metabolism of urea [32] . We sought independent means to assess levels of reduced electron donors generated by the metabolism of the morphogenic amino acids . The membrane-permeable redox indicator TTC ( 2 , 3 , 5 triphenyltetrazolium chloride , colorless ) is converted to TTF ( 1 , 3 , 5-triphenylformazan , red ) in the presence of NADH and has been be used to monitor mitochondrial respiratory activity of colonies [15 , 48] . Colonies growing on proline , arginine , and ornithine exhibited a more intense , deep red pigment than colonies growing on aspartate ( Fig 3C , top panel ) . The redox-sensitive dye resazurin can be used in liquid culture to monitor the reducing capacity of the intracellular environment [49]; resazurin is non-fluorescent , but is readily reduced by NADH or to a lesser extent by NADPH to highly red fluorescent resorufin ( excitation 560 nm , emission 590 nm ) . Consistent with the results obtained using TTC , cells growing with proline , arginine , or ornithine as sole nitrogen source exhibited 6–8-fold more resorufin fluorescence than aspartate grown cells ( Fig 3C , bottom panel ) . These results indicate that cells grown in the presence of proline as the sole nitrogen source have a reducing intracellular environment , a finding aligned with the previous report by Land et al . [11] . Arginine is degraded in a pathway that bifurcates after the initial reaction catalyzed by Car1 , which forms ornithine and urea ( Fig 4A ) . Ornithine is subsequently metabolized by ornithine aminotransferase ( CAR2 ) to form glutamate γ-semialdehyde , which spontaneously converts to Δ1-pyrroline-5-carboxylate ( P5C ) . P5C is converted to proline by the PRO3 gene product . Cytoplasmic proline is transported into the mitochondria where it is converted back to P5C by proline oxidase ( PUT1 ) . Finally , the mitochondrial P5C is converted to glutamate by the PUT2 gene product [50 , 51] , which is then converted to α-ketoglutarate via Gdh2 . Urea is further catabolized in the cytosol by urea amidolyase ( DUR1 , 2 ) forming NH3 and CO2 . Based on our results demonstrating that the filament-inducing effect of ornithine and proline requires mitochondrial respiration , we investigated if both branches of the bifurcated arginine degradative pathway could independently trigger filamentous growth . To accomplish this , we used a CRISPR/Cas9 strategy to construct PMRCA18-derived strains individually lacking CAR1 ( S3D Fig ) , DUR1 , 2 ( S3E Fig ) , or PUT1 , PUT2 and PUT3 ( S3F Fig ) , or both PUT1 and DUR1 , 2 ( S3G Fig ) . Growth-based assays , on solid and in liquid media , confirmed that the car1-/- strain exhibited impaired growth on synthetic glucose medium ( SXD ) containing arginine as a sole nitrogen source , whereas the strain grew like wildtype ( WT ) on SXD medium containing either 10 mM ornithine , proline or urea ( Fig 4B and S4 Fig ) . As expected , and similar to previous reports [32] , the dur1 , 2-/- strain exhibited severely impaired growth on medium containing urea as sole nitrogen source , but grew well in media containing arginine , ornithine or proline as sole nitrogen sources ( Fig 4B and S4 Fig ) . Cells lacking the proline oxidase ( put1-/- ) were able to grow in media containing arginine , but unable to grow when ornithine or proline were the sole source of nitrogen ( Fig 4B and S4 Fig ) , indicating that ornithine utilization is strictly dependent on the mitochondrial proline catabolic pathway ( Fig 4B and S4 Fig ) . Quite surprisingly , in media with 2% glucose the put1-/- dur1 , 2-/- double mutant strain retained the ability to grow using arginine as sole nitrogen source , albeit slower , clearly suggesting that an arginase-independent pathway exists in C . albicans ( Fig 4B and S4 Fig ) . Consistently , the car1-/- strain also exhibited residual growth using arginine as sole N-source in media with 2% glucose as primary carbon source . However , the car1-/- strain is unable to use arginine as the sole carbon source and did not grow in media lacking glucose ( S3D and S5 Figs ) . Thus , the arginase-independent pathway enables the use of arginine merely as a nitrogen source . Next , we analyzed the expression of genes involved in arginine catabolism in cells after shifting them to minimal medium containing 10 mM arginine ( YNB+Arg ) as sole nitrogen and carbon source ( Fig 4C ) . One hour after the shift , the proline catabolic genes PUT1 and PUT2 were significantly upregulated . The levels of PUT3 , the proline activated transcription factor that is constitutively bound to the promoter of PUT1 and PUT2 , did not change [52] . Strikingly , DUR1 , 2 gene expression remained constant . Contrary to the assumption that Dur1 , 2 is responsible for alkalization of the medium , the consequence of the deamination of arginine-derived urea [53] , we observed that the dur1 , 2-/- mutant still alkalinized the medium ( Fig 4D and S5 Fig ) . Notably , both put1-/- and put2-/- strains failed to grow in this medium ( Fig 4D ) , indicating that the proline catabolic pathway branch of arginine utilization is essential for growth when arginine is both carbon and nitrogen source . Accordingly , an increased flux through the proline branch of the pathway and subsequent deamination of glutamate provides the likely explanation for the alkalization of the medium ( Fig 4A ) . Consistent with their ability to support growth , arginine , ornithine and proline induced the expression of HA epitope-tagged Put2 ( Put2-HA ) ( Fig 4E ) . The induction was rapid , 1 h following the shift from YPD to SXD ( X = 10 mM Asp , Arg , Orn or Pro ) ; in the presence of arginine and ornithine , the Put2 levels were elevated and almost as high as in proline-induced cells . Aspartate did not induce Put2 expression . Together these results indicate that arginine and ornithine are efficiently metabolized to proline , and metabolism associated with proline branch is required for the use of these amino acids as energy sources for growth . To test whether proline catabolism is required for arginine-induced morphogenesis , we tested whether filamentation would be reduced by inhibiting Put1 with L-THFA ( Zhu et al . , 2002; Zhang et al . , 2015 ) . As expected , L-THFA inhibited arginine-induced morphogenesis ( Fig 4F ) . We then carried out a genetic analysis to dissect the pathway triggering filamentous growth in the presence of arginine . Consistent with the existing model for arginine-induced morphogenesis [32] , the car1-/- strain formed extensively wrinkled colonies comprised mainly of filamentous cells in the presence of 10 mM urea ( Fig 4G ) . However , in comparison to wildtype colonies growing on arginine media , wrinkling was delayed and was first noticeable after 48 h of incubation . On media with an equimolar amount of arginine and urea ( Arg + Urea ) the car1-/- strain developed wrinkled colonies clearly visible after only 24 h . These findings suggest that arginine metabolism via the proline branch induces filamentation more rapidly than the CO2 ( HCO3- ) generated by the Dur1 , 2-dependent degradation of urea . Consistent with this notion , colonies formed by the put1-/- mutant remained relatively smooth even after 48 h of growth ( Fig 4G ) . In summary , our results indicate that the metabolism associated with proline branch of the arginine degradation pathway generates the primary and most rapid signal of arginine-induced morphogenesis . The capacity of proline to stimulate filamentous growth is significantly affected by glucose availability ( Fig 5A ) . In comparison to colonies formed on synthetic media with 10 mM proline containing 2% glucose ( SPD ) , colonies on media containing 0 . 2% glucose ( SPD0 . 2% ) exhibited larger feathery zones of hyphal cells emanating around their periphery . These findings are reminiscent of reports that C . albicans cells grown on media with methionine as nitrogen source and low glucose exhibit robust filamentation [33] . Next , we considered the possibility that glucose repression of mitochondrial function , known to occur in Saccharomyces cerevisiae [54 , 55] , may underlie the difference . At low glucose concentrations , i . e . , non-repressing conditions , we expected that cells would use proline as an energy source to generate ATP . Proline utilization was assayed directly by measuring the amount of residual proline in culture supernatants after a 2 h incubation period . In media containing 2% glucose , cells took up < 2% of the proline . By contrast , cells growing in low glucose ( 0 . 2% ) or 1% glycerol used 12–15% of the available proline ( Fig 5B , blue bars ) . The levels of Put2 were similar indicating that its expression is independent of glucose ( Fig 5B , insert ) . These results indicate that proline is taken up and metabolized more efficiently in cells under non-repressing conditions . Consistently , cells grown in the presence of 0 . 2% glucose had the highest levels of ATP , roughly 2-fold more ATP than cells grown in either 2% glucose or 1% glycerol ( Fig 5B , red bars ) . These results are consistent with mitochondrial activity in C . albicans being subject to glucose repression . To critically test this , we assessed the effect of varying the glucose concentration from 0 . 05–4% . Cells were grown for 16 h in media containing the pH indicator bromcresol purple . At high glucose concentrations ( 0 . 5–4% ) the media remained acidic , indicating cells were growing fermentatively using proline merely as a nitrogen source ( Fig 5C ) . By contrast , at glucose concentrations ≤ 0 . 2% , the media became alkaline , indicating that cells were respiring and using proline as the primary energy source . The increased flux through the proline pathway is expected to yield elevated NH3 generated by the mitochondrial glutamate dehydrogenase ( GDH2 ) catalyzed deamination of glutamate . To directly assess mitochondrial activity under these conditions , we carried out extracellular oxygen consumption analysis in a high-throughput microplate format ( S6A and S6B Fig ) . Cells grown in repressing SPD2% exhibited the lowest oxygen consumption whereas those grown at SPD0 . 2% had the highest consumption , higher than cells grown in SPG . As previously pointed out , Put2 levels were similar across all conditions ( Fig 5B ) . Together , these results indicate that proline is taken up and then metabolized more efficiently in cells growing under low glucose concentrations . Consistently , Put2 levels were elevated in rich media containing yeast extract and peptone when non-repressing , non-fermentative carbon sources replaced glucose; i . e . , glycerol or lactate ( Fig 5D ) . Similarly , cells express elevated levels of Put2 when grown in hyphal inducing Spider medium , a medium rich in amino acids and mannitol as a primary carbon source . Nitrogen regulation of transcription in fungi is a supra-pathway response that is commonly referred to as nitrogen catabolite repression ( NCR ) , which functions to ensure that cells selectively use preferred nitrogen sources when available . Briefly , NCR regulates the activity of GATA transcription factors Gln3 and Gat1; in the presence of preferred nitrogen sources , these factors do not gain access to the promoters of NCR-regulated genes ( reviewed in [56] ) . Previous studies have shown that certain amino acids , traditionally classified as poor ( e . g . , proline ) in S . cerevisiae , were readily utilized by C . albicans mutants lacking Gln3 and Gat1 [57]; the introduction of null alleles of both GLN3 and GAT1 in C . albicans did not impair growth using proline as sole nitrogen source , whereas growth on urea was severely affected . Consistent with these findings , we found that Put2-HA was constitutively expressed in gln3Δ/Δ/ gat1Δ/Δ mutant grown in medium containing high levels of the preferred nitrogen source ammonium sulfate ( Fig 5E ) . Our data indicate that in C . albicans proline utilization is not subject to NCR , a conclusion aligned with recently published transcriptome analyses [52] . In total , the data are consistent with mitochondrial metabolism and respiration being required to generate the signals responsible for amino acid-induced morphogenesis . The ability to optimally catabolize amino acids as sole nitrogen and carbon sources account for the concomitant increase in ATP . To critically test this notion , we examined whether mitochondria-derived ATP acts epistatic to Ras1/cAMP/PKA signaling . Consistent with Grahl et al . [14] , colonies derived from a strain carrying the RAS1G13V allele , encoding a hyperactive mutant form of Ras1 , exhibited filamentous growth in the presence of MB ( S7B Fig ) , clearly indicating that Ras1 acts downstream of the mitochondria-derived metabolic signal . We sought to place our novel insights regarding the critical role of proline metabolism in the induction of hyphal growth in a broader biological context and tested whether proline catabolism affects the capacity of C . albicans cells to form hyphae within macrophages and escape killing . First , using indirect immunofluorescence microscopy we examined whether Put2-HA is expressed in C . albicans cells engulfed by murine RAW264 . 7 macrophages ( Fig 6A ) . C . albicans CFG185 ( PUT2/PUT2-HA ) cells were co-cultured with macrophages ( MOI of 5:1; C:M ) for 90 min . Strain CFG185 exhibits activation of proline catabolism in the presence of arginine , ornithine , and proline ( Fig 4E ) . The macrophages were imaged using antibodies against the HA tag ( 1° , rat anti-HA; 2° , goat anti-rat antibody conjugated to Alexa Fluor 555 ) and LAMP-1 , a lysosomal marker that is enriched in phagosomes . Confocal images clearly showed that C . albicans cells engulfed by macrophages express Put2 , and that the Put2 expressing fungal cells are localized to Lamp1 compartments ( see the orthogonal view of merged channels , lower left panel ) . The results indicate that C . albicans cells within macrophage phagosomes express Put2 . Next , we assessed the importance of the proline catabolic pathway components to escape macrophages . To facilitate comparisons with results obtained in other laboratories , we repeated the construction of the proline catabolic pathway mutations in the SC5314 strain background; strains lacking PUT1 , PUT2 , PUT3 or both PUT1 and PUT2 were constructed using CRISPR/Cas9 . The full genome of each mutant strain was sequenced; the sequence coverage varied from 42—65X and after assembly the contig coverage accounted for ≥98 of the reference SC5314 genome ( Assembly 22 , version s06-m01-r01; [58] ) . Each strain was found to carry the intended null mutation in the correct chromosomal locus and no large dissimilarities to the reference genome or off-target mutations were evident . Furthermore , no phenotypic differences were detected in comparison to the PMRCA18-derived strains . As expected , SC5314 ( WT ) and CRISPR/Cas9 control strains ( pV1093 and pV1524 ) , lacking guide sequences to target Cas9 , exhibited robust hyphal growth when co-cultured with RAW264 . 7 macrophages ( Fig 6B ) . By contrast , and similar to heat killed SC5314 , the strains carrying put1-/- , put2-/- , put3-/- and put1-/- put2-/- mutations were unable to efficiently form filaments from within engulfing macrophages ( Fig 6B ) . As hyphal formation enables C . albicans cells to escape macrophages and thereby facilitates survival , we analyzed the candidacidal activity of macrophages by assessing fungal cell viability by assessing colony forming units ( CFU ) . Consistent with our microscopic analysis , in comparison to wildtype cells , the proline mutants were killed more efficiently ( Fig 6C ) . Together , these results indicate that C . albicans cells rely on proline catabolism to induce hyphal growth in phagosomes , a response that facilitates escape from killing by macrophages . We carried out time lapse microscopy ( TLM ) to visualize growth of C . albicans cells within phagosomes of engulfing macrophages . Wildtype and put1-/- stains , constitutively expressing GFP ( ADH1/PADH1-GFP ) and RFP ( ADH1/PADH1-RFP ) , respectively , were co-cultured together with bone marrow-derived macrophage ( BMDM ) for 30 min before washing off non-phagocytosed external fungal cells . As expected , the GFP expressing wildtype cells robustly formed hyphae in the phagosome . By contrast , RFP expressing put1-/- mutant did not form hypha or grow . The result confirms that proline catabolism is required for growth and escape of C . albicans cells from macrophage phagosomes .
In this study we have found that ATP generating mitochondrial proline catabolism is required to induce hyphal development of C . albicans cells in phagosomes of engulfing macrophages . The finding that proline catabolism , also required for the utilization of arginine and ornithine , is required to sustain the energy demands of hyphal growth provides the basis to understand the central role of mitochondria in fungal virulence . The energy status of the fungal cell is clearly a key signal that engages the genetic programs underlying yeast-to-hyphal transitions . The dependence on the energy producing proline catabolic pathway to induce C . albicans cells to switch morphologies is instrumental in their ability to escape from macrophages . Our results are consistent with a recent model postulating that elevated cellular levels of ATP induces hyphal morphogenesis [14] and with early reports that amino acid catabolism promotes filamentous growth [12 , 13 , 59] . Our experimental findings are schematically summarized in Fig 7 . Our work provides a framework to integrate several fragmentary observations regarding amino acid-induced morphogenesis . For example , Land et al . [11 , 12] observed that the most potent morphogenic amino acids arginine and proline are those metabolized to glutamate . Our results show that this occurs strictly via the mitochondrial localized proline utilization pathway essentially as described in S . cerevisiae [60–62] with the exception that proline metabolism is not under nitrogen regulation ( Fig 5E , [52] ) . Consistently , ornithine , an intermediate in arginine catabolism , also acts as a potent inducer of morphogenesis ( Fig 1A; [12 , 59] ) . Glutamate is further converted to α-ketoglutarate , an intermediate in the TCA cycle . These metabolic reactions are coupled to the generation of reduced electron carriers FADH2 and NADH , which are oxidized in the mitochondria powering ATP synthesis . Amino acid induction of hyphal growth exhibits a strict requirement for Ras1 ( Fig 2B ) and cells grown in the presence of these inducing amino acids have high levels of active Ras1 ( Fig 2C ) and elevated levels of intracellular ATP ( Fig 3A ) . The metabolic inhibitors nor-NOHA ( Car1 ) and L-THFA ( Put1 ) and methylene blue ( MB , an uncoupler of mitochondrial respiration , block the induction of filamentation ( Figs 2D , 2E and 3B ) . Our analysis demonstrates that arginine and proline induce morphogenesis by virtue of a shared metabolic pathway ( Fig 4C–4F ) . Together , our findings are well aligned to the recent model proposed by Grahl et al . [14] , where mitochondrial ATP synthesis facilitates Ras1 activation in cooperation with the adenylate cyclase ( Cyr1 ) leading to increased cAMP production and to activation of the Efg1 transcription factor . The finding that arginine-induced hyphal growth occurs rapidly ( Fig 4G ) , suggests that a brief exposure to arginine may suffice to trigger filamentous growth . According to Grahl et al . ( 2015 ) , Ras1 activation by ATP appears to be independent of the AMP kinase , a key regulator of cellular energy homeostasis . The ATP-binding pocket within the active site of mammalian adenylyl cyclase has been shown to act as an ATP sensor [63] . Although it has been proposed that Cyr1 may function similarly as an ATP sensor this has yet to be confirmed in C . albicans . Regardless of the mechanism , exceeding a critical threshold of ATP is likely required to induce cAMP synthesis . It is known that the cAMP produced by Cyr1 does not necessarily correlate to the strength of the inducer and that transient short-lived spikes in cAMP are sufficient to trigger phosphorylation and eventually activation of Efg1 [29] . Consequently , spikes of ATP transiently generated by proline catabolism may efficiently induce hyphal specific genes ( HSG ) . We have clearly shown that arginine- , ornithine- and proline-induced hyphal growth is dependent on Ras1 , which is not accounted for by other models of amino acid-induced morphogenesis ( reviewed in [4 , 7] , despite the fact that Ras1 is known to be important in induction of filamentous growth in the presence of amino acid-rich serum [24] . Both the presumed amino acid sensing Gpr1-Gpa2 pathway [64 , 65] and the Dur1 , 2-dependent CO2 model for arginine-induced morphogenesis [32] are thought to bypass Ras1 and involve direct interactions with adenylyl cyclase ( Cyr1 ) . Also , contrary to the previous report [32] , CO2 generated by the Dur1 , 2-dependent catabolism of urea is not the primary morphogenic signal . Specifically , induction of filamentous growth in the presence of arginine or proline as sole nitrogen source proceeds more quickly than that observed by the metabolism of urea ( Fig 4G ) . In addition , DUR1 , 2 expression is tightly regulated by NCR , i . e . , in the presence of ammonia , urea metabolism is repressed [57] . By contrast , the conversion of arginine to proline is not under NCR control ( Fig 5E , [52] ) . Finally , when cells were shifted from YPD to medium containing arginine as sole carbon and nitrogen source , proline catabolic genes ( PUT1 and PUT2 ) were derepressed much faster than DUR1 , 2 ( Fig 4C ) , indicating that arginine is rapidly converted to proline . We have noted that the constitutive expression of arginase represents a common and undesired technical problem in proteomic analyses using SILAC ( Stable Isotope Labeling by/with Amino acids in Cell culture ) due to the rapid conversion of arginine to proline in eukaryotes [66–69] . In Schizosaccharomyces pombe , the deletion of two arginase genes ( one a CAR1 homologue ) and the single ornithine transaminase ( CAR2 homologue ) rectified this problem [66] . We predict , that similar deletions would be helpful in the quantitative analysis of the C . albicans proteome . Earlier reports by Nickerson and Edwards [70] and Land et al . [11] suggested that mitochondrial activity is repressed during filamentous growth . By contrast , other more recent work has shown that hyphal formation occurs predominantly under aerobic conditions [17] and is associated with increased respiratory activity [14 , 15] . Based on our findings ( Fig 5C ) , the seemingly conflicting observations could be explained if , as in S . cerevisiae , the synthesis of mitochondrial respiratory enzymes are subject to glucose repression [71 , 72] . There is surprisingly little information available regarding glucose repression of mitochondrial function in C . albicans , and whether the regulatory circuits are wired similar to those in S . cerevisiae . However , we note that Land et al . [11] used growth conditions with high glucose ( 1 . 8%; 100 mM ) , whereas studies by [14 , 15] were carried out using low glucose ( 10 mM , i . e . , ≈ 0 . 2% ) . In striking contrast to the current view that C . albicans mitochondrial function is insensitive to glucose repression [73–75] , our results clearly demonstrate that glucose represses respiration in the presence of proline ( S6 Fig ) . Cells grown aerobically in high glucose exhibit fermentative metabolism ( Fig 5C ) , i . e . , the well-characterized Crabtree effect [76] . In glycolysis , conversion of glucose to pyruvate is coupled to reduction of NAD+ and to the generation of ATP . Only small amounts of the cofactor is available in the cytosol . Consequently , when mitochondrial functions are glucose repressed , cells use fermentation to oxidize NADH and regenerate NAD+ , thereby enabling cytoplasmic ATP synthesis to continue . Under conditions when proline is the sole nitrogen source and high glucose is present , cells use glucose for energy and as carbon-source , whereas proline catabolism merely supplies cells with nitrogen , i . e . , proline utilization is low ( Fig 5B ) . However , when glucose becomes limiting ( <0 . 2% ) , the respiratory capacity of mitochondria increases ( S6 Fig ) , enabling cells to efficiently oxidize NADH and generate ATP by oxidative phosphorylation; under these conditions , cells use proline for energy and as the carbon- and nitrogen-source , i . e . , proline utilization is high ( Fig 5B ) . Together our results show that proline metabolism is a sensitive indicator of mitochondrial function in C . albicans . Our observation that high glucose represses mitochondrial function , provides a mechanistic understanding of how high glucose inhibits hyphal morphogenesis [13 , 33] . Cells grown on 2% glucose have elevated levels of reduced cofactors , such as NADH ( Fig 5A ) , suggesting that the capacity of mitochondria to oxidize NADH is suboptimal , i . e . , the cellular capacity to regenerate NAD+ is rate limiting , a phenomenon termed over-flow metabolism [54] . It is important to note that , based on the S . cerevisiae paradigm , the pyruvate formed in glycolysis needs to be converted to acetyl-CoA to prime the TCA cycle . The mitochondrial-localized pyruvate dehydrogenase complex is predominantly responsible for the conversion of pyruvate to acetyl-CoA during glucose-limited , respiratory growth [71 , 72] . Indeed , pharmacological inhibition of glycolysis has been shown to arrest filamentous growth of C . albicans even in the presence of proline [11] . Alternatively , β-oxidation of lipids may contribute the necessary acetyl-CoA [9] . We have placed the SPS sensing pathway , the primary sensing system of extracellular amino acids , in context to the major intracellular signaling pathways governing in nutrient regulated morphogenesis . SPS-sensor initiated signals do not directly induce hyphal growth , but rather facilitate morphogenesis by up-regulating the capacity of cells to take up inducing amino acids ( Fig 7 ) . Experimental support for this conclusion includes the following observations . First , amino acid-induced activation of SPS-sensor signaling does not strictly correlate with the induction of filamentous growth ( Fig 1A ) . Second , the inability of a ssy1 null mutant to undergo morphogenesis can be rescued by expressing a constitutively active form of Stp2 ( STP2* ) but not Stp1 ( STP1* ) . Stp2 is the effector transcription factor that controls amino acid permease gene expression , whereas Stp1 activates the expression of secreted aspartyl proteases and oligopeptide transporters [36] . Consistently , and similar to Kraidlova et al . [77] , we found that the expression of six C . albicans orthologues ( GAP1-GAP6 ) of the S . cerevisiae general amino acid permease ( GAP1 ) are regulated by the SPS sensing system , perhaps with the exception of GAP4 expression , which is comparatively expressed at very low levels . Third , filamentous growth is dependent on amino acid catabolism . The weak filamentation observed in the csh3Δ/Δ mutant grown in 10 mM proline can be attributed to the residual uptake of proline as previously described [37]; apparently , the residual systems are expressed and function at high extracellular concentrations of proline [59 , 78] . Thus , the filamentous growth defect observed in cells lacking a functional SPS sensing pathway , i . e . , SSY1 or CSH3 null mutants , is due to the inability to efficiently take up inducing amino acids from the extracellular environment , a requisite for their metabolism [37 , 38] . Together our findings have important implications on understanding how C . albicans cells interact with host immune cells . Transcriptomic studies examining macrophage-C . albicans interactions by Lorenz et al . [9] showed that arginine biosynthesis genes are peculiarly upregulated in phagocytosed cells . Furthermore , the results suggest that the phagosome is likely a glucose-poor environment as an increased expression of genes that favor gluconeogenesis and mitochondrial function was also noted [9] . Interestingly , arginine utilization appears to proceed concomitant with arginine biosynthesis as deduced from the increased arginase transcripts in phagocytosed cells [9 , 32] . In a follow-up study , the apparent upregulation of arginine biosynthesis was suggested to be a response to the macrophage oxidative burst [79] . Interestingly , the expression of DUR1 , 2 in phagocytosed cells was not significantly altered . Our finding that the enzymes responsible for proline utilization are upregulated indicates that proline is either made available by the host or is the result of arginine catabolism . In the light of these results , the challenging question is where the hyphae inducing amino acids come from , from the macrophage or from nutrients stored within C . albicans cells prior to their being phagocytosed . In S . cerevisiae , > 90% of free arginine is sequestered in the vacuole and the non-compartmentalized and cytosolic arginine is catabolized by arginase [80] . Given that arginine is catabolized to proline via the arginase pathway with ornithine acting as a transitory intermediate , it is possible that vacuolar stores of arginine are activated in the phagosome to support the demand for cellular energy . When glucose becomes limiting , C . albicans may rely on the catabolism of amino acids , particularly proline , as primary energy . This is reminiscent of the requirement of proline catabolism for Trypanosome survival in the Tsetse fly vector [81–84] . Proline-induced morphogenesis is repressed under acidic conditions [13 , 59] , presumably a condition confronting newly phagocytized C . albicans cells . This raises the interesting conundrum as to how C . albicans cells deal with this environmental challenge and filament . It is possible that Stp2-mediated alkalization of the phagosome reported by Vylkova and Lorentz [85] is a key predisposing event that facilitates proline-induced morphogenesis . We found that alkalization is not Dur1 , 2-dependent ( Fig 4D ) , indicating that an alternative mechanism triggers alkalization . Accordingly , the Stp2-dependent induction of arginine uptake and its subsequent Put1- and Put2-dependent metabolism generates glutamate , which is deaminated to α-ketoglutarate by glutamate dehydrogenase ( Gdh2 ) ( Fig 7 ) . The resulting NH3 may provide the explanation for the observed alkalization . As already pointed out , the source of amino acids in the macrophage phagosome remains a very interesting question . Numerous metabolic signatures appear to reflect a microenvironment with a poor nitrogen content . For example , based on the transcriptional analysis of the C . albicans-macrophage interaction , OPT1 , encoding an oligopeptide transporter , is upregulated in phagocytosed cells [9] . OPT1 expression is controlled by the SPS-sensor signaling and the downstream transcription factor Stp1 [36 , 39] . STP1 expression is itself under tight NCR control [39] . Thus , the upregulated expression of OPT1 strongly suggests that NCR is relieved in phagocytosed cells and that sufficient levels of amino acids are present to induce the SPS-sensor . As to the origin of amino acids in the phagosome , C . albicans may excrete amino acids liberated from storage compartments , loaded during growth in rich media . In S . cerevisiae , under defined conditions , amino acids are known to be excreted at detectable levels [86] and under certain circumstances activate SPS-sensor signaling [87] . Thus , amino acids may provide an autocrine function to induce filamentous growth of phagocytosed C . albicans cells . The results presented here provide a clear example of how C . albicans cells sense and respond to nutrients present in the host to ensure proper nutrient uptake and continued survival . The molecular components underlying nutrient uptake are often referred to as virulence factors . When afforded the opportunity , C . albicans will alter developmental programs to optimize nutrient uptake systems that enable the better exploit host environments and to evade the primary immune response [3 , 88 , 89] . The identification and understanding of fungal virulence factors is necessary to therapeutically disturb their function upon infectious growth and thereby facilitate the ability of host immune systems to re-establish and maintain the integrity of the host . We are excited by the prospect of exploiting mitochondrial proline metabolism to probe the nutrient environment of the macrophage phagosome , a currently poorly characterized environment .
C . albicans strains and primers used are listed in Supporting Information , S1 Text ( S1 Table ) and S2 Text ( S2 Table ) , respectively . All strains were cultivated in YPD medium ( 1% yeast extract , 2% peptone , 2% glucose ) at 30 °C . Minimal synthetic dextrose ( SD ) medium containing 0 . 17% YNB ( Yeast Nitrogen Base without amino acids and without ammonium sulfate; Difco ) , 2% glucose , and 5 g/l ammonium sulfate ( ≈ 38 mM ) was used as indicated . Media were made solid by 2% ( w/v ) Bacto agar . Where appropriate , 200 , 100 or 25 μg/ml nourseothricin ( Nou; Jena Biosciences , Jena , Germany ) was added to the medium . The ability of amino acids to induce filamentous growth was determined on buffered solid synthetic ( SXD ) media containing 0 . 17% YNB , 2% glucose , and 10 mM of the indicated amino acid ( X ) as sole nitrogen source , or at concentrations as described in the figure legends . Fifty mM 2- ( N-morpholino ) ethanesulfonic acid ( MES ) was included in media and the pH was adjusted to 6 . 0 using NaOH . To minimize residual nitrogen , the SXD media were made solid using 2% ( w/v ) highly purified agar ( Biolife , Milano , Italy ) . Where indicated 0 . 2% glucose , 1% lactate or 1% glycerol replaced 2% glucose as carbon source . The following media were used to screen CRISPR/Cas9-derived knockout phenotypes: YPD-MM; SUD; SPD; and YNB+Arg+BCP . YPD-MM is standard YPD supplemented with 1 . 5 mg/ml MM ( 2- ( [ ( ( [ ( 4-methoxy-6-methyl ) -1 , 3 , 5-triazin-2-yl]-amino ) carbonyl ) amino]-sulfonyl ) -benzoic acid; Dupont Ally ) ; SUD and SPD were prepared as SXD containing urea ( U ) , or proline ( P ) as sole nitrogen source; YNB+Arg+BCP contains 0 . 17% YNB , 10 mM arginine ( Arg ) as sole nitrogen and carbon source , and 0 . 03 μg/mL bromocresol purple ( BCP; Sigma ) as indicator , with the pH adjusted to 4 . 0 using 1 M HCl . Growth in the presence of specific metabolic inhibitors was assessed on media containing nor-NOHA ( N-hydroxy-nor-L-arginine; BioNordika AB , Sweden ) prepared in 100% dimethyl sulfoxide ( DMSO ) as 56 mM concentrated stock; a 26 mM working stock was prepared freshly diluting in ddH2O . L-tetrahydro-2-furoic acid ( L-THFA; Sigma ) and methylene blue ( MB; Sigma ) , were freshly prepared in ddH2O as 1 M and 3 mM stocks , respectively . Escherichia coli strain DH10B was used for the construction of plasmids; LB medium supplemented where required with carbenicillin ( Cb , 50 μg/ml ) , Nou ( 50 μg/ml ) , and/or chloramphenicol ( Cm , 30 μg/ml ) . LB was made solid by 1 . 5% Bacto agar . Liquid cultures were grown with agitation at 150–200 rpm . The density of yeast suspensions was determined and adjusted ( 1 OD600 = 3 x 107 CFU/ml ) [90] . Sterile Milli-Q ddH2O was used in all experiments . The CRISPR/Cas9 gene editing was used to inactivate both alleles of SSY1 ( C2_04060C ) , CSH3 ( C4_03390W ) , CAR1 ( C5_04490C ) , PUT1 ( C5_02600W ) , DUR1 , 2 ( C1_04660W ) , IRA2 ( C1_12450C ) , PUT1 ( C5_02600W ) , PUT2 ( C5_04880C ) or PUT3 ( C1_07020C ) . Sequences of synthetic guide RNAs ( sgRNAs ) , repair templates , and verification primers are listed in S2 Text ( S2 Table ) . The solo system plasmids pV1093 or pV1524 were used [91 , 92] . These plasmids contain a cassette comprised of the Candida/Saccharomyces codon-optimized CAS9 endonuclease gene , NAT gene ( recyclable in pV1524 ) , sgRNA cloning site , and flanking sequences for genomic integration . For pV1093 and its derivative plasmids , the cassettes were integrated in an ENO1 locus , whereas pV1524 and its derivatives were integrated in the NEUT5 locus . The sgRNAs were designed as described [93] and were inserted in pV1093 or pV1524 by linker ligation . To summarize , oligo pairs p43/p44 ( SSY1 ) , p49/p50 ( CSH3 ) , p55/p56 ( CAR1 ) , p61/p62 ( DUR1 , 2 ) , p67/p68 ( IRA2 ) , p73/p74 ( PUT1 ) , p79/p80 ( PUT2 ) , and p85/p86 ( PUT3 ) , were separately phosphorylated and annealed prior to ligating them to dephosphorylated Esp3I ( BsmBI ) -digested pV1093 or pV1524 . Ligation reactions were purified and introduced into E . coli by electroporation . Transformants were selected on solid LB+Cb ( or +Nou for pV1524 cloning ) incubated overnight at 37 °C . Plasmids were sequenced using primer p91 ( FS95 ) . Plasmids ( 3 to 6 μg ) containing the 20-bp sgRNA for SSY1 ( pFS013 ) , CSH3 ( pFS017 ) , CAR1 ( pFS024 ) , DUR1 , 2 ( pFS039 ) , IRA2 ( pFS028 ) , PUT1 ( pFS080 , pV1093 derivative ) , PUT1 ( pFS088 , pV1524 derivative ) , PUT2 ( pFS083 ) and PUT3 ( pFS084 ) were digested with KpnI and SacI to release the cassette . Repair templates ( RT ) containing stop codon and specific restriction site were produced by template-less PCR using oligo pairs p45/p46 ( SSY1 ) , p51/p52 ( CSH3 ) , p57/p58 ( CAR1 ) , p63/p64 ( DUR1 , 2 ) , p69/p70 ( IRA2 ) , p75/p76 ( PUT1 ) , p81/p82 ( PUT2 ) , and p87/p88 ( PUT3 ) . For the creation of ras1-/- and RAS1G13V mutants , plasmid pV1121 ( recreated using primers p98/p99 sgRNAs ( RAS1 ) ) and RT ( p94/p95 ( RAS1 ) and p92/p93 ( RAS1G13V ) ) from Vyas et al . ( 2015 ) were used . PCR-purified digested plasmid and repair templates were co-transformed into C . albicans cells ( PMRCA18 or SC5314 background ) at a 1:3 ratio ( w/w , plasmid:repair template ) . The hybrid lithium acetate/DTT-electroporation method , with minor modifications , was used for transforming C . albicans [94] . After applying 1 . 5 kV of electric pulse , cells were recovered in YPD medium supplemented with 1 M sorbitol for at least 4 h and then plated on YPD+Nou plates; NouR colonies were selected 2 days after plating . NouR transformants were pre-screened according to the expected phenotype prior to PCR and restriction analysis using primers and restriction enzymes indicated in S3 Fig and S2 Text ( S2 Table ) . Strain CFG240 ( put1-/- ) expressing PADH1-RFP was constructed as follows . Strain CFG139 ( put1-/- ) is Nou resistant ( NouR ) due to a CRISPR/Cas9 cassette integrated in NEUT5 . CFG139 was grown in YP + 2% maltose , the Nou sensitive ( NouS ) strain CFG155 was isolated as a colony exhibiting reduced growth on YPD supplemented with 25 μg/ml Nou . CFG155 was transformed with KpnI- and SacI-digested pJA21 ( PADH1-RFP ) [95] . RFP-positive clones were verified by PCR ( p112/p113 ) . SC5314 derived strains expressing C-terminal HA tagged Put2 were constructed by transforming a 4 . 8 kb epitope-tagging cassette amplified using primers p108/p109 and plasmid pFA6a-3HA-SAT1 as template ( generous gift from Karl Kuchler ) . NouR transformants were screened by colony PCR using primers p110/p111 and verified by immunoblot analysis . Genomic DNA was isolated from put1-/- ( CFG139 ) , put2-/- ( CFG207 ) , put3-/- ( CFG146 ) , put1-/- put2-/- ( CFG159 ) and CRISPR/Cas9 control strains CFG181 ( pV1093 ) and CFG182 ( pV1524 ) and sequenced . Prior to library construction , extracted DNA was purified with Agencourt AMPure XP beads ( Beckman Coulter , USA ) in order to remove short sequences ( <100 bp ) . Aliquots ( 25 μl ) of DNA were mixed with 45 μl of AMPure beads with a ratio of 1:1 . 8 and incubated 15 min . Initial DNA concentrations following purification were evaluated using Quant-iT PicoGreen dsDNA Assay kit ( ThermoFisher , USA ) . Absorbance was measured at 530 nm , using a Tecan Ultra 384 SpectroFluorometer ( PerkinElmer , USA ) . Library construction was carried out with the QIAGEN-FX kit ( Qiagen , Germany ) with a DNA input of 100 ng DNA per sample and a digestion time of 13 min without enhancer . Following fragmentation , adapter sequences were ligated , and ligated DNA fragments were amplified by 9 cycles of PCR and DNA was purified with AMPure XP beads . The quality of the library samples were evaluated with an Agilent Bioanalyzer using DNA1000 cartridges . The average length of the fragments excluding adapter sequences was 455 bp . Prior to sequencing , the samples were denatured with 0 . 2 N NaOH . A final volume of 570 μl of pooled library was mixed with denatured Phix control ( 30 μl ) and loaded on an Illumina Mi-Seq 2x300 flow-cell and reagent cartridge . De-multiplexing and removal of indexes and primers were done with the Illumina software v . 2 . 6 . 2 . 1 on the instrument according to the standard Illumina protocol . Initial de novo assembly of quality-controlled reads was done with SPADES v . 3 . 11 . 1 and standard settings [96] . Mapping of assembled contigs was done with Ragout v 2 . 0 [97] using Sibelia for synteny detection [98] . Visualization of results and generation of reports on the assembly quality and other factors were done with QUAST v . 4 . 6 . 1 [99] . The NanoLuc-PEST ( Nlucp ) construct was used to create the reporter of SPS-sensor dependent transcription ( S2 Fig ) . The presence of PEST sequences ensures rapid degradation of NanoLuc luciferase , thereby enhancing sensitivity [41] . Up- and downstream regions of the CAN1 ORF were amplified using genomic DNA from PMRCA18 as template and primers p100/p101 ( 0 . 9 kB upstream ) and p104/p105 ( 0 . 98 kB downstream ) ( S2 Text ) . An approximately 0 . 7 kB Nlucp gene sequence was amplified from plasmid pCA873 [100] using primers p102/p103 . These amplicons were digested with appropriate FastDigest enzymes ( Thermo Scientific ) and purified; i . e . , the CAN1 upstream amplicon was digested with KpnI/XhoI , the CAN1 downstream with XbaI/NotI , and Nlucp DNA fragment with XhoI/BamHI . Using T4 DNA Ligase ( Thermo Scientific ) , the upstream fragment was first ligated to KpnI/XhoI-digested pSFS2a vector [94] creating pFS006 . The purified Nlucp DNA was then ligated into XhoI/BamHI restricted pFS006 creating pFS007 . Finally , the downstream fragment was ligated into XbaI/NotI restricted pFS007 creating pFS010 . The plasmids were introduced into E . coli and transformants selected on LB+Cm+Nou plates incubated at 30 °C . The desired reporter construct , purified from KpnI/NotI restricted pFS010 , was introduced into C . albicans wildtype ( PMRCA18 ) and SPS-deficient mutant strains ( ssy1Δ/Δ , ssy5Δ/Δ , and stp2Δ/Δ ) by electroporation . Selection was carried out on YPD+Nou and NouR clones carrying the integrated Nlucp construct were identified by PCR using primers p107/p106 . For analysis of amino acid-induced SPS-sensor activation , Nano-Glo Luciferase Assay System ( Promega GmbH , Germany ) was used following the manufacturer’s protocol . Briefly , log phase SD cultures were first standardized to OD ≈ 0 . 8 before adding 50 μl of the cell suspension into each well of Nunc 96 well microplate ( white ) . Then , cells were induced with 50 μM of the indicated amino acids for 2 h at 30 °C . Fifty microliters ( 50 μl ) of Nano-Glo substrate diluted 1:50 in the supplied lysis buffer was added into each well of the microplate . After 3 min , bioluminescence was captured using microplate luminometer ( Orion II , Berthold Technologies GmbH & Co . KG , Germany ) . Luminescence reading from treated wells were deducted from wells spiked with ddH2O serving as uninduced control . Solid filamentation assay was performed as described [14] . Briefly , cells from overnight YPD liquid cultures were harvested , washed once , and resuspended in sterile ddH2O . The cell density of cell suspensions was adjusted to OD600 ≈ 8 before spotting 10 μl onto solid media . Plates were allowed to dry at room temperature before incubating at 37 °C as indicated to allow macrocolonies to form . Filamentation assays in the presence of metabolic inhibitors , nor-NOHA or L-THFA , were performed in a 6-well microplate format ( ~5 ml/well ) ; otherwise , all assays were carried out using standard Petri plates ( ~35 ml/plate ) . For filamentation assays in liquid cultures , cells were washed and then adjusted to OD600 ≈ 25 . Cells were diluted in pre-warmed liquid medium at OD600 ≈ 0 . 5 and then incubated at 37 °C with vigorous agitation for the specified time . Cell morphologies were assessed under epifluorescence microscopy using calcofluor white stain ( CFW , Fluorescent Brightener 28 , 1 mg/ml; Sigma ) . Hyphal specific gene ( HSG ) expression in 24 h old macrocolonies was analyzed as follows: using a sterile glass slide , three to four macrocolonies of wildtype strain ( PMRCA18 ) were collected by scraping and suspended in 1 ml of ice-cold PBS . Cells were harvested by centrifugation at 10 , 000 x g for 3 min ( 4 °C ) , snap frozen in liquid nitrogen and then stored at -80 °C until processed for RNA extraction . Gene expression in liquid grown cells was analyzed as follows: cells from overnight YPD cultures were harvested by centrifugation , washed and resuspended at an OD600 ≈ 25 in pre-warmed liquid medium and incubated at 37 °C for 2- and 4- h before harvesting the cells by centrifugation; the cell pellets were snap frozen in liquid nitrogen . For arginine catabolic gene expression analysis , SC5314 was used as wildtype strain . Briefly , cells from log phase YPD culture growing at 30 °C were harvested , washed 3X with PBS , diluted in pre-warmed YNB+Arg medium ( pH = 6 . 0 , without BCP ) at an OD600 ≈ 0 . 5 , and then incubated for 1 h at 37 °C under aeration . A portion of the washed cells were snap-frozen in liquid nitrogen to serve as reference ( t = 0 ) . Following 1 h incubation , cells in YNB+Arg were immediately harvested and then snap-frozen in liquid nitrogen for RNA extraction . To analyze the dependence of GAP genes expression to SPS pathway ( i . e . , Ssy1 ) , wildtype ( PMRCA18 ) and ssy1Δ/Δ ( YJA64 ) cells were grown to log phase in SD medium at 30 °C before spiking with 1 mM of glutamine or ddH2O for 30 min . Cells were collected from induced ( glutamine ) and non-induced ( ddH2O ) cultures and snap-frozen in liquid nitrogen . Total RNA was extracted from frozen cell pellets using RiboPure-Yeast Kit ( Ambion , Life Technologies ) essentially following the instructions of the supplier with the exception that cells were subjected to extra bead-beating step ( Bio-Spec; 1 × 60 sec , 4 M/s ) . DNase-treated RNA extracts were reverse-transcribed using SuperScript III and Random Primers ( Invitrogen , Life Technologies ) . cDNA preparations were diluted 1/40 in ddH2O and 5 μl were used as template for qPCR using KAPA SYBR Green ( Kapa Biosystems ) . Gene specific primers ( 500 nM ) were added and reactions were performed in a Rotor-Gene 6000 ( software version 1 . 7 ) . The ΔΔCt method ( 2-ΔΔCt ) was used to quantitate the relative levels of gene expression . Levels of gene expression were normalized to ACT1 or RIP1 [101] as indicated . A bioluminescence-based ATP detection kit ( Molecular Probes , Invitrogen ) was used to quantify ATP levels in macrocolonies grown on SXD medium as indicated . ATP was extracted from eight , 24 h-old macrocolonies harvested using a sterile glass slide and then suspended in 1 ml sterile ice-cold Tris Buffered Saline ( TBS; 50 mM Tris-Cl , pH 7 . 5 , 150 mM NaCl ) . Cells were harvested at 10 , 000 x g for 3 min ( 4 °C ) before re-suspending the entire pellet in TCA buffer containing 100 mM Tris-HCl ( pH = 8 . 0 ) , 10% trichloroacetic acid ( TCA ) , 25 mM ammonium acetate , and 4 mM EDTA . Cell suspension was transferred to pre-chilled tubes containing glass beads and then subjected to bead beating ( Bio-Spec; 5 × 1 min , 4 M/s with 2 min on ice between pulses ) . Cell lysates were collected and a portion of the supernatant was analyzed for ATP following the instruction of the manufacturer . Luminescence was analyzed using microplate reader ( Berthhold ) using 1 sec integration time . A portion of the same lysate was used to determine total protein concentration using the bicinchoninic acid ( BCA; Sigma ) assay . Results presented are average of ATP normalized to total protein concentration analyzed from 3 biological replicates; each replicate is an average of 2–3 technical replicates . For Stp2 cleavage analysis , cells expressing Stp2-HA ( PMRCA48 ) were grown to saturation in SD liquid medium overnight at 30 °C and then refreshed the following morning in 25 ml of fresh SD medium at a starting OD600 ≈ 0 . 3 . Cells were grown in a 30 °C-shaker to an OD600 of 1 . 5–2 . 2 . For induction experiments , a 500-μl aliquot of log phase culture were separately added to tubes containing the indicated amount and type of amino acids or an equal volume of water for control , and then incubated for 5 min at 30 °C in a thermoblock shaking at 700 rpm . For Put2-HA expression analysis , cells from overnight YPD cultures were harvested , washed and then grown as indicated . Whole cell lysates were prepared using NaOH/TCA method as described previously with minor modifications [102] . Cells were lysed on ice with 280 μl of ice-cold 1 . 85 M NaOH with 7% ß-mercaptoethanol for 15 min; proteins were precipitated ON at 4 °C by adding the same volume of cold 50% TCA . Protein pellets were quickly washed with ice-cold 1 M Tris base ( pH = 11 ) and then resuspended in 2X SDS sample buffer . In some instances , as indicated , due to highly variability in protein recovered from certain types of cells ( i . e . , yeast and filamentous forms ) sample loading was normalized based on protein content . Samples were denatured in sample buffer at 95–100 °C for 5 min , the proteins were resolved in sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) using 4–12% pre-cast gels ( Invitrogen ) and analyzed by immunoblotting on nitrocellulose membrane according to standard procedure . For Stp2-HA and Put-HA detection , HRP-conjugated anti-HA antibody ( Pierce ) was used at 1:2 , 500 dilution . For loading control , HRP-conjugated rat monoclonal α-tubulin antibody [YOL1/34] ( Abcam ) was used at 1:10 , 000 dilution . Membranes were blocked using TBST ( TBS + 0 . 1% Tween ) containing 10% skimmed milk; antibodies were diluted in TBST containing 5% skimmed milk . Immunoreactive bands were visualized by enhanced chemiluminescent detection system ( SuperSignal Dura West Extended Duration Substrate; Pierce ) using ChemiDoc MP system ( Bio-Rad ) . Active Ras1 ( Ras1-GTP ) was analyzed in macrocolonies using Pierce Active Ras Pull-Down Kit ( Thermo Scientific ) following the manufacturer’s instructions , but with an extra bead-beating step to ensure optimal disruption of cells . Five 24 h-old macrocolonies were scraped , pooled , and suspended in 1 ml ice-cold TBS in 2-ml microcentrifuge tubes ( with caps ) . Cells were collected by centrifugation at 10 , 000 x g for 3 min ( 4 °C ) and then resuspended in 400 μl of Lysis/Binding/Washing buffer ( 1X , Pierce kit ) supplemented with protease cocktail ( cØmplete mini , EDTA-free; Roche ) and 1 mM PMSF . Pre-chilled glass beads were added , cell suspensions were subjected to multiple cycles of bead beating ( 6 × 40 sec , 4 M/s , 2 min on ice between pulses ) . After an initial clarification step at 1 , 000 rpm for 5 min , supernatants were collected and total protein was determined using the BCA assay . The concentration of protein in lysates was adjusted to 2 mg/ml using the lysis buffer as diluent and then 500 μg of protein was used for the immunoprecipitation . We used 12 . 5 μg protein for input and eluted bound protein in 25 μl . Proteins were resolved by SDS-PAGE and analyzed by immunoblotting . Total Ras and active Ras-GTP were probed with primary monoclonal anti-Ras clone X ( 1:300 ) included in the kit , and secondary goat anti-mouse antibody ( 1:10 , 000; Pierce ) . For loading control , α-tubulin conjugated to HRP ( 1:10 , 000 ) was used . Membranes were blocked and the primary antibody diluted in TBST containing 3% BSA; the secondary and loading control antibodies were diluted in TBST containing 5% skimmed milk . Results presented are representative of at least 3 independent experiments . For drop plates , cells from log phase YPD cultures grown at 30 °C were harvested , washed , and then adjusted to OD600 ≈ 1 . Five microliters of 10-fold serially diluted cell suspension were spotted onto the surface of the indicated SXD media and incubated at 30 °C for 2–3 days and photographed . For liquid assays , washed cells from log phase YPD cultures were diluted in the indicated SXD liquid medium to a starting OD600 ≈ 0 . 05 , and 300 μl were transferred into each well of a 10 x 10-well microplate and grown continuously for > 20 h at 30 °C with constant agitation . OD600 readouts were captured using BioScreen C MBR analyzer ( Oy Growth Curves Ab Ltd , Helsinki , Finland ) . The membrane permeant , non-destructive redox indicator , Resazurin ( Sigma ) , was used to measure the metabolic activity of intact cells growing in SXD . Briefly , reduction of the nonfluorescent resazurin dye by NADH or NADPH oxidoreductases in metabolically active cells generates the highly fluorescent resorufin which emits fluorescence at 590 nm [49] . Cells from overnight YPD cultures were harvested , washed once with sterile ddH2O , and adjusted to OD600 ≈ 0 . 01 ( ~3 x 105 CFU/ml ) using the YNB-glucose base medium ( ~1 . 05x strength , pH = 6 . 0 ) . Using a multi-channel pipette , 95 μl of this cell suspension were added to the well of a 96-well microplate followed by addition of 5 μl of 200 mM amino acid stock ( 10 mM final concentration ) . Plates were incubated at 37 °C for 2 h with agitation protected from light . After 2 h , 20 μl of filtered Resazurin dye ( 0 . 15 mg/ml ) was added to each well and incubated for 2 h at 37 °C before measuring the fluorescence ( 560 nm excitation/590 nm emission ) using EnSpire microplate reader ( PerkinElmer ) . Macrocolonies grown on the indicated plates for 24 h were overlaid with 2 ml of molten TTC-agar solution ( 50–55 °C ) containing 0 . 1% TTC ( 2 , 3 , 5 triphenyltetrazolium chloride; Sigma ) dissolved in 67 mM potassium phosphate buffer ( PPB , pH = 7 . 0 ) with 1% agar [15] . Plates were photographed 30 min after the overlaid solution became solid . The colorless TTC dye is cell permeant which can be reduced to the reddish 1 , 3 , 5-triphenylformazan ( TTF ) by NADH in the mitochondria [15 , 48] . Oxygen consumption assay was performed in C . albicans grown in synthetic proline medium containing the indicated carbon source ( i . e . , 2% glucose ( SPD ) , 0 . 2% ( SPD0 . 2% ) , or 1% glycerol ( SPG ) using the Extracellular Oxygen Consumption Assay ( Abcam , ab197243 ) following manufacturer’s protocol . Briefly , cells from log phase YPD culture were harvested , washed 3X with PBS , and then diluted in the indicated media at OD600 ≈ 0 . 3 . A 150 μl cell suspension was added into each well of a 96-well microplate with black walls and clear bottom . Ten microliters of Extracellular Oxygen Consumption Reagent , or medium ( control ) , were added and the samples were mixed gently by moving the plate on a circular motion . FCCP ( final conc . 10 μM ) and antimycin ( final conc . 10 μg/ml ) were used as positive and negative controls , respectively . Plates were analyzed using Enspire microplate reader using Time Resolved Fluorescence ( TRF ) . Signals were read every 90 sec for 120 repeats with optimal delay time of 30 μs and gate ( integration ) time of 100 μs . Signal from wells without cells were used as background signal . The concentration of proline in media and in cell extracts was analyzed using the quantitative ninhydrin method [103] . Proline utilization was assessed as follows: cells grown overnight in YPD were washed and resuspended to an OD600 ≈ 0 . 5 in pre-warmed synthetic proline media containing 10 mM of proline and the indicated carbon source . The cultures were incubated under constant aeration for 2 h at 37 °C , and the amount of proline in culture supernatants was analyzed . Proline utilization was defined by comparison to non-inoculated media . The murine macrophage cell line RAW264 . 7 ( ATCC ) was cultured and passaged in Dulbecco’s modified Eagle’s medium/high glucose ( HyClone , GE Healthcare Life Sciences , Amersham , UK ) supplemented with 10% fetal bovine serum , 100 U/ml penicillin and 100 μg/ml streptomycin ( hereafter referred as D10 ) at 37°C with 5% CO2 . Prior to co-culture with C . albicans , RAW264 . 7 cells ( 1 x 106 ) in D10 medium were seeded on a 24-well microplate containing sterile cover slips and were allowed to adhere overnight in a humidified chamber at 37°C and 5% CO2 . Fungal cells ( 3 x 108 ) were harvested from overnight YPD cultures and stained with 1 mg/ml FITC solution in 0 . 1 M NaHCO3 buffer ( pH = 9 . 0 ) in the dark for 15 min at 30 °C . Cells were washed 3X with PBS before resuspending in equal volume of PBS . Fungal cells were added to macrophage at MOI of 3:1 ( Candida:Macrophage , C:M ) and were then allowed to interact for 30 min . Non-phagocytosed cells were removed by washing the cells at least 5X with pre-warmed Hank’s Balanced Salt Solution ( HBSS ) and 1X with D10 medium . Cells were allowed to interact for an additional 4 h in fresh D10 medium before fixing with 3 . 7% formaldehyde-PBS for 15 min in the dark at room temperature . Fixed cells were then washed 3X with PBS before staining with calcofluor white ( 10 μg/ml ) for 1 min . After 2X PBS washing , coverslips were mounted on glass slides using ProLong Gold antifade reagent ( Invitrogen ) . Images were obtained using LSM 800 , 63x/1 . 2 oil . The survival of C . albicans co-cultured with macrophages was assessed by colony forming units ( CFU ) essentially as described [104] . Briefly , RAW264 . 7 cells in D10 were seeded into a 96-well microplate at a density of 1 x 105 per 200 μl and allowed to adhere overnight . C . albicans cells from overnight YPD cultures were processed without staining and added at a MOI of 3:1 ( C:M ) . The co-cultures were incubated for 3 h prior to assessing fungal cell viability by CFU; each well was treated to final concentration of 0 . 1% Triton X-100 for 2 min to lyse macrophage and serial dilutions were prepared and plated onto YPD . CFUs were counted 2 days after incubation at 30 °C . The ability of macrophages to kill C . albicans ( % killing ) was determined by comparison of fungal CFU recovered in the absence of macrophages . RAW264 . 7 cells were co-cultured with C . albicans cells , CFG185 ( PUT2/PUT2-HA ) , for 90 min on glass coverslips at a MOI of 5:1 ( C:M ) . Cells were fixed in 3 . 7% formaldehyde-PBS for 15 min , and permeabilized in 0 . 25% Tween-20 for 15 min , both incubations were at room temperature . The fixed and permeabilized cells were incubated in zymolyase buffer ( 2U zymolyase 100T ( Zymo Research , Irvine , CA , USA ) , 10 mM DTT in PBS ) for 1 h at 30 °C . After washing , cells were incubated at room temperature in 0 . 25% Tween-20 for 10 min and blocked in 5% FBS for 30 min . Cells were incubated overnight at 4 °C with rat anti-HA ( Roche , Germany , #1867423 ) and rabbit anti-Lamp1 ( Abcam , UK , #ab24170 ) primary antibodies diluted 1:500 in 0 . 25% Tween-20 . Cells were washed with PBS and incubated 2 h with Alexa flour 488 goat anti-rabbit ( Invitrogen , Eugene , OR , USA #A11034 ) and Alexa flour 555 goat anti-rat ( Invitrogen , Eugene , OR , USA #A11034 ) secondary antibodies diluted 1:500 in 0 . 25% Tween-20 . Images were captured on a Zeiss 510 Meta confocal microscope , 63x/1 . 4 oil . Orthogonal views were constructed in FIJI imaging software . Primary bone marrow-derived macrophages ( BMDM ) were prepared from C57BL/6 mice ( 7–9 weeks old ) [105] . Briefly , bone marrow collected from mouse femurs were mechanically homogenized and treated with red blood lysis buffer ( 8 . 29 g/l NH4Cl , 1 g/l KHCO3 , 0 . 0372 g/l EDTA , pH = 7 . 4 ) before resuspending the washed bone marrow in differentiation medium ( complete RPMI medium ( R10 ) supplemented with 20% L929 conditioned media ( LCM ) ) . After 3 days , the cultures received an additional dose of 20% LCM . Between 16–24 hours prior to co-culture with fungal cells , the differentiated BMDM ( ~80–90% confluence ) were collected by scraping , suspended in R10 medium , counted , and seeded at 1 x 106 cells/dish in a 35-mm imaging dish ( ibidi , Martinsried , Germany ) . Wildtype SCADH1G4A ( ADH1/PADH1-GFP ) and CFG240 ( put1-/- ADH1/PADH1-RFP ) C . albicans cells from overnight YPD liquid cultures were collected by centrifugation , washed 3X with sterile PBS , and diluted to OD600 ≈ 0 . 5 in HBSS . Fungal cells were mixed 1:1 ( v/v ) in a sterile Eppendorf tube and vortexed . The BMDM cells were pre-washed 2 times with HBSS and an aliquot of mixed fungal cells in HBSS was added at a MOI of 3:1 ( C:M ) . The co-cultures were incubated for approximately 30 min in the humidified chamber , after which they were washed 5X with HBSS and 1X with CO2-independent medium to remove non-phagocytosed fungal cells . CO2-independent medium was added to the dish and TLM was carried out using Zeiss Cell Observer system ( 63x/1 . 4 oil ) equipped with appropriate filters to detect GFP and RFP . Images were acquired every 2 min for 5 h and then saved as movie at 10 fps . | Candida albicans is an opportunistic fungal pathogen that exists as a benign member of the human microbiome . Immunosuppression , or microbial dysbiosis , can predispose an individual to infection , enabling this fungus to evade innate immune cells and initiate a spectrum of pathologies , including superficial mucocutaneous or even life-threatening invasive infections . Infectious growth is attributed to an array of virulence characteristics , a major one being the ability to switch morphologies from round yeast-like to elongated hyphal cells . Here we report that mitochondrial proline catabolism is required to induce hyphal growth of C . albicans cells in phagosomes of engulfing macrophages , which is key to evade killing by macrophages . The finding that proline catabolism , also required for the utilization of arginine and ornithine , is required to sustain the energy demands of hyphal growth underscores the central role of mitochondria in fungal virulence . In contrast to existing dogma , we show that in C . albicans , mitochondrial function is subject to glucose repression , amino acid-induced signals are strictly dependent on Ras1 and the SPS-sensor is the primary sensor of extracellular amino acids . The results provide a clear example of how C . albicans cells sense and respond to host nutrients to ensure proper nutrient uptake and survival . | [
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... | 2019 | Mitochondrial proline catabolism activates Ras1/cAMP/PKA-induced filamentation in Candida albicans |
Drug resistance represents one of the main problems for the use of chemotherapy to treat leishmaniasis . Additionally , it could provide some advantages to Leishmania parasites , such as a higher capacity to survive in stress conditions . In this work , in mixed populations of Leishmania donovani parasites , we have analyzed whether experimentally resistant lines to one or two combined anti-leishmanial drugs better support the stress conditions than a susceptible line expressing luciferase ( Luc line ) . In the absence of stress , none of the Leishmania lines showed growth advantage relative to the other when mixed at a 1:1 parasite ratio . However , when promastigotes from resistant lines and the Luc line were mixed and exposed to different stresses , we observed that the resistant lines are more tolerant of different stress conditions: nutrient starvation and heat shock-pH stress . Further to this , we observed that intracellular amastigotes from resistant lines present a higher capacity to survive inside the macrophages than those of the control line . These results suggest that resistant parasites acquire an overall fitness increase and that resistance to drug combinations presents significant differences in their fitness capacity versus single-drug resistant parasites , particularly in intracellular amastigotes . These results contribute to the assessment of the possible impact of drug resistance on leishmaniasis control programs .
Leishmaniasis , a neglected tropical parasitic disease that is prevalent in 98 countries spread across three continents , is caused by protozoan parasites belonging to the genus Leishmania [1]; visceral leishmaniasis ( VL ) , caused by species of the Leishmania donovani complex , is a lethal disease if left untreated . The recommended first-line therapies for VL include: i ) pentavalent antimonials ( meglumine antimoniate and sodium stibogluconate ) , except in some regions in the Indian subcontinent where there are significant areas with drug resistance [2]; ii ) the polyene antibiotic amphotericin B or the liposomal amphotericin B formulation AmBisome; iii ) the aminoglycoside paromomycin; and iv ) the oral drug miltefosine . Although WHO [1 , 3] recommended the use of either a single dose of AmBisome or combinations of anti-leishmanial drugs in order to reduce the duration and toxicity of treatment , to prolong the therapeutic life-span of existing drugs and to delay the emergence of resistance , recent experimental findings have demonstrated the ability of Leishmania to develop experimental resistance to different drug combinations [4] . The emergence and spread of Leishmania antimonial-resistant parasites have led to a high rate of antimonial failure in India [5] and have raised questions about the selection and propagation risk of drug resistant parasites [6 , 7] . The spread of drug-resistant parasites in the field probably depends on their transmission potential , which is influenced by , among other factors , the relative fitness of drug-resistant versus drug-susceptible parasites . Previous results have demonstrated that the acquisition of drug resistance could have an impact on parasite fitness , which could in turn influence other important biological properties involved in the regulation of proliferation and differentiation of parasites [6 , 8] . As defined previously [6 , 9] , the fitness of Leishmania parasites can be measured by: i ) capacity to survive , grow and generate infective metacyclic forms in the vector , ii ) capacity to survive and grow in the mammalian host , and iii ) capacity of transmission between the host and the vector . Throughout its natural life cycle , Leishmania encounters adverse conditions that include: i ) nutrient starvation and acidification of the medium , conditions that induce metacyclogenesis [10] , ii ) heat shock , when the parasite moves from growth at 28°C in the sandfly to 37°C inside the mammalian host macrophage , and iii ) reactive oxygen species ( ROS ) and reactive nitrogen species ( RNS ) , when it is phagocytized by macrophages of the host [11 , 12] . Leishmania has evolved a broad spectrum of mechanisms to protect itself against these host defenses , including: i ) enzymes that detoxify ROS and RNS [13] , ii ) use of thiols as antioxidant defenses [14] , and iii ) inhibition of the host's oxidative defense mechanisms [15] . However , it is not strictly true that all of the above increased the fitness capacity of all L . donovani resistant strains , as some strains showed little or no difference in their in vivo survival capacity compared to antimony-sensitive strains [16] . As suggested , other factors such as the genetic background of parasites could be important in the in vivo fitness capacity of L . donovani isolates that are clinically resistant to antimonials [16] . In this work , we evaluate whether L . donovani lines that are experimentally resistant to single anti-leishmanial drugs [amphotericin B ( AmB ) , miltefosine ( MIL ) , paromomycin ( PMM ) and trivalent antimony ( SbIII ) ] and to drug combinations ( AmB-MIL , AmB-PMM , AmB-SbIII , MIL-PMM and SbIII-PMM ) , present any advantages in their ability to bear the different stress conditions with respect to susceptible cells expressing the luciferase gene ( Luc gene ) . For this purpose , we have studied the susceptibility of mixed promastigote populations under different stress conditions and their ability to infect and survive in mouse peritoneal macrophages . The results of this study using parasites that are experimentally resistant to single and multi-drug combinations are discussed in relation with their potential impact on future leishmaniasis control programs .
L . donovani promastigotes ( MHOM/ET/67/HU3 ) and the previously described derivative resistant lines A , M , P , S , AM , AP , AS , MP , and SP ( resistant to AmB , MIL , PMM , SbIII , AmB+MIL , AmB+PMM , AmB+SbIII , MIL+PMM and SbIII+PMM , respectively ) [4] were grown at 28°C in an RPMI 1640-modified medium ( Invitrogen ) supplemented with 20% heat-inactivated fetal bovine serum ( hiFBS , Invitrogen ) . L . donovani with the luciferase gene integrated into the parasite genome ( Luc line ) was grown in the same conditions . Phothinus pyralis luciferase gene ( luc ) was amplified from vector pX63NEO-3Luc [17] by PCR using the primers LucNcoIF 5’-GACGCCCATGGATGGAAGACGCCAAAAACAT-3’ and LucNotIR 5’-GACGTAGCGGCCGCTTACAATTTGGACTTTCCGC-3’ including ( in bold ) NcoI and NotI restriction sites , respectively . The luc gene was then cloned into the NcoI-NotI sites of vector pLEXSY-hyg2 ( Jena bioscience , Jena , Germany ) which harbor a marker gene for selection with hygromycin-B ( hyg gene ) . The vector generated was denominated pLEXSYHyg-Luc . In this construct , sequences of the 18S rRNA gene flanked the luc and hyg genes . Following linearization with SwaI , stationary promastigotes were transfected with 3 μg of linearized pLEXSYHyg-Luc plasmid to integrate the luc and hyg genes into the 18S rRNA ( ssu ) locus by homologous recombination , using a previously described protocol [18] . Twenty-four hours after transfection , the culture medium was supplemented with 25 μg/mL of hygromycin-B . Hygromycin-resistant parasites were usually selected after 7 days . After establishing the transgenic parasites , they were plated onto 1 . 5% agar plates containing culture medium plus 100 μg/mL hygromycin-B . After 10 days incubating at 28°C , clones were selected on agar plates and further propagated in liquid RPMI-modified medium supplemented with 100 μg/mL hygromycin-B . Integration of the expression cassette into the ssu locus was confirmed by PCR using genomic DNA from the wild-type ( WT ) and transgenic strains of L . donovani ( Luc line ) as a template . For this purpose , we used primer pairs ssu forward primer F3001 5’-GATCTGGTTGATTCTGCCAGTAG-3’ and 5’ utr ( aprt ) reverse primer A1715 5’-TATTCGTTGTCAGATGGCGCAC-3’ , and primer pairs hyg forward primer A3804 5’-CCGATGGCTGTGTAGAAGTACTCG-3’ and ssu reverse primers 3002 5’-CTGCAGGTTCACCTACAGCTAC-3’ . Promastigotes and amastigotes isolated as described previously [19] , were resuspended in HBS buffer ( 21 mM HEPES , 0 . 7 mM Na2HPO4 , 137 mM NaCl , 5 mM KCl , and 6 mM D-glucose , pH 7 . 1 ) supplemented with 25 μM cell-permeable DMNPE-luciferin . After 15 minutes at room temperature , aliquots of this suspension ( 100 μL/well ) were distributed into 96-well white polystyrene microplates . Luminescence was recorded with an Infinite F200 microplate reader ( Tecan Austria GmbH , Austria ) . To measure the luciferase activity of intracellular amastigotes contained within infected cells , the Luciferase Assay System ( Promega , Madison , Wis ) was used according to the instructions of the manufacture . Luminescence was measured in the Infinite F200 microplate reader immediately after mixing . Log-phase promastigotes from the control ( WT ) and A , M , P , S , AM , AP , AS , MP and SP resistant lines were mixed in a 1:1 ratio with log-phase promastigotes from the Luc line ( 1x106 mixed parasites/mL ) . Parasite density was microscopically determined every 24 h for a total of 144 h using Neubauer count chambers in order to monitor the growth . Parasite density in the WT line plus Luc line mixture was used as a control . Also , we evaluated the growth of each line in these mixed populations after 144 h of incubation , determining the luminescence as a measure of the cellular density of the Luc line . All growth experiments with the different parasite lines were performed in triplicate . In parallel , the mixed populations at a 1:1 ratio ( 4x106 mixed parasites/mL ) were sub-cultured every 48 h ( logarithmic phase ) and luminescence measured at the end of the first and second sub-culture . Promastigotes from the WT and resistant lines were mixed with the Luc line in a 1:1 ratio ( 4x106 mixed parasites/mL ) , and the proliferation of resistant parasites exposed to different stress conditions ( late stationary growth phase , starvation and heat shock plus pH modification ) was compared to the Luc line by measuring the luminescence intensity . The mixture of the WT and Luc lines was used as a control for all experiments . For experiments studying intracellular amastigotes , mouse peritoneal macrophages were obtained as described previously [20] and plated at a density of 3 x 104 or 3 x 105 macrophages/well in 96-well white polystyrene microplates or 24-well tissue culture chamber slides , respectively , in an RPMI 1640 medium supplemented with 10% hiFBS , 2 mM glutamate , penicillin ( 100 U/mL ) and streptomycin ( 100 μg/mL ) . Promastigotes from resistant or WT lines were mixed with the Luc line 1:1 ratio and maintained in culture for 6 days . Afterwards , the mixed populations of stationary phase cultures were used to infect macrophages at a macrophage/parasite ratio of 1:10 . Six hours after infection at 35°C and 5% CO2 , extracellular parasites were removed by washing with serum-free medium . Infected macrophages were maintained in culture medium at 37°C with 5% CO2 for 24 h and 96 h . To determine the infection index ( % infection x amastigotes/macrophages ) , infected macrophages maintained in 24-well plates were fixed for 30 min at 4°C with 2 . 5% paraformaldehyde in PBS buffer ( 1 . 2 mM KH2PO4 , 8 . 1 mM Na2HPO4 , 130 mM NaCl , and 2 . 6 mM KCl , adjusted to pH 7 ) , and permeabilized with 0 . 1% Triton X-100 in PBS for 30 min . Intracellular parasites and macrophages were detected by nuclear staining with ProLong Gold antifade reagent plus DAPI . To determine the intracellular proliferation profile of each line , infected macrophages maintained in 96-well plates were lysed and then the luminescence measured using the Luciferase Assay System ( Promega ) . Eight-week-old male BALB/c mice were purchased from Charles River Breeding Laboratories and maintained in our Animal Facility Service under pathogen-free conditions . They were fed a typical rodent diet and given drinking water ad libitum . These mice were used to collect primary peritoneal macrophages . All experiments were performed according to National/EU guidelines regarding the care and use of laboratory animals in research . Approval for these studies was obtained from the Ethics Committee of the Spanish National Research Council ( CSIC , file CEA-213-1-11 ) . Statistical comparisons between groups were performed using Student’s t-test . Differences were considered significant at a level of p<0 . 05 .
The firefly luciferase ( Luc ) [21] has proved to be a useful reporter gene for monitoring gene expression [22] and quantifying Leishmania infections in macrophages and animal models , with the overall aim of probing host-microbe interactions [23 , 24] . To assess the feasibility of using bioluminescence as a quantitative indicator of parasite proliferation , studies were performed to correlate bioluminescence with parasite number . For this purpose , the Luc gene was amplified by PCR and cloned into pLEXSY-hyg2 . The LUC-expressing vector was electroporated into L . donovani parasites which were then selected in the presence of hygromycin-B . To test whether luciferase activity correlated well with parasite number , 4-fold serial dilutions were prepared and their luciferase activity measured . An excellent linear correlation was observed between the number of transgenic promastigotes and the luminescence intensity ( S1 Fig ) . Transfectant parasites that overexpress luciferase ( from now on , Luc line ) were also tested for their ability to infect macrophages . Stationary-phase recombinant promastigotes were used to infect mouse peritoneal macrophages . Intracellular Leishmania infection was observed microscopically after DAPI staining and no significant differences were noted in the infectivity of the Luc line versus the WT line ( S2A Fig ) . Furthermore , an excellent correlation was observed between amastigote numbers and luminescence intensity ( S2B Fig ) . Collectively , these results strongly suggest that the Luc line constitutes a valuable tool for assessing the viability and dynamics of mixed populations . The promastigote number was evaluated every day for 6 days so that the growth features of each resistant line , or the WT line mixed in a 1:1 parasite ratio with the Luc line , could be studied and compared . We found that all mixed populations showed a similar growth profile as the control ( WT+Luc ) ( Fig 1A ) . Moreover , the luminescence values were similar for each of these mixed populations after the 6th day of culture ( Fig 1B ) . These results clearly indicate that the Luc line was present in the same ratio in all mixed populations and , therefore , there was no predominance of one line over another line under these conditions . To evaluate whether there was predominance of any resistant lines over the susceptible Luc line , promastigotes from resistant lines and the Luc line were mixed in a 1:1 parasite ratio and grown without stress or exposure to different stresses . To assess the growth recovery , the luminescence intensity of mixed populations was determined in all cases after 48 h of culture in standard conditions . The WT plus Luc lines ( WT+Luc ) mixture was used as a control . The total number of mixed parasite cultures shows no significant differences between WT+Luc and the resistant lines+Luc in the different stress conditions , ranging indistinctly between 22-32x107 mixed parasites/mL . Within the mammalian host , Leishmania promastigotes differentiate into amastigotes and multiply predominantly inside macrophages , where they are exposed to stress , including starvation , acidic pH , high temperatures ( heat shock ) and ROS and RNS production [11 , 26] . To determine whether intracellular amastigotes from resistant lines were able to displace in vitro intracellular amastigotes from the susceptible Luc line , macrophages were infected with mixed populations of promastigotes taken from 6 day-old cultures where , as shown in Fig 1 , no differences were observed in proliferation and the Luc line ratio . The infection index and luminescence of intracellular amastigotes were determined at 24 and 96 h post-infection to assess their infectivity and survival rates in mouse peritoneal macrophages . The infection indexes were similar in all cases , with values ranging for 24 h between 183±22 and 234±28 , and for 96 h between 182±27 and 250±34 . The results showed that in the early stage of macrophage infection ( 24 h ) all the resistant lines , except the M line , had a significant predominance over the Luc line compared to the control ( Fig 5A ) . The different lines showed a range of luminescence between 31 and 63% from SP+Luc and S+Luc populations , respectively , compared to the control ( Fig 5A ) . These results could be due to: i ) a higher percentage of metacyclic parasites , ii ) tolerance to oxidative stress , and/or iii ) tolerance to acidic pH and high temperatures of the resistant lines compared to the susceptibility of the Luc line . Also , in the late stage of infection ( 96 h ) , the resistant lines , again with the exception of the M line , were able to fully benefit from their initial advantage ( Fig 5B ) . They showed a range of luminescence between 27 and 68% from SP+Luc and A+Luc populations , respectively , compared to the luminescence produced by the control ( Fig 5B ) . Additionally , there were no significant differences on predominance of resistant lines between 24 and 96 h , with the exception of the S line ( p<0 . 05 ) . These results suggest that the predominance of some resistant lines over the Luc line is the result of a higher infection rate during the initial stage which reflects as a higher survival rate of intracellular amastigotes within macrophages . Leishmania have successfully adapted to different environments for thousands of years and developed a highly flexible nature . Their survival capacity mainly relies on their ability to suppress oxidative outbursts of the host defense mechanism [15] and on a unique oxidant-protective redox metabolism , where thiols play a key role in antioxidant defenses [14] . In this regard , we have previously described that the resistant lines , except the M and S lines , had higher non-protein thiol levels than the WT line [4] , which could contribute to a greater parasite survival rate within the host macrophages . It has been demonstrated that some L . donovani strains resistant to antimonials have a more variable and markedly higher capacity of in vivo infection compared to antimony susceptible Leishmania strains [16] . Strains resistant to antimony also have a higher metacyclogenic capacity [27] and have specifically evolved extra mechanisms to manipulate their host cells in order to avoid antimony-induced stress [28] . Such adaptations would not only improve the parasites survival capacity when stressed by antimony , but would also favor their survival in drug-free conditions . Since SbIII , AmB and MIL kill Leishmania through a common cell death pathway to achieve apoptosis , strains resistant to one or more of these drugs could develop tolerance to apoptosis , which would grant them a higher survival rate in macrophages , as we have observed with our Leishmania resistant lines ( Fig 5B ) . In conclusion , the experiments using our transgenic Leishmania luc line have clearly demonstrated and validated the fact that Leishmania lines experimentally resistant to individual and combinatorial anti-leishmanial drugs have an increased fitness compared to Leishmania susceptible lines , probably as a consequence of their metabolic adaptations which all converge on the higher tolerance to stress conditions , as recently described [25] . Subsequently , they also have a better chance of survival . However , although this approach using promastigotes for assessing the viability and dynamics of mixed populations have important advantages , their use on intracellular amastigotes has some methodological limitations . Therefore , the emergence and spread of drug-resistant parasites in the field will probably result in a greater competitive fitness cost with respect to susceptible parasites , plus negative effects on the chemotherapy strategies used to control leishmaniasis . | Chemotherapy is currently the only treatment option for leishmaniasis , a neglected tropical disease produced by the protozoan parasite Leishmania . However , first-line drugs have different types of limitations including toxicity , price , efficacy and mainly emerging resistance . The WHO has recently recommended a combined therapy in order to extend the life expectancy of these compounds . The emergence and spread of Leishmania antimonial-resistant parasites have led to a high rate of antimonial failure in India and have raised questions about the selection and propagation risk of drug resistant parasites . The spread of drug-resistant parasites in the field probably depends on their transmission potential , which is influenced by , among other factors , the relative fitness of drug-resistant versus drug-susceptible parasites . In light of this , we have designed experimental studies to determine whether Leishmania donovani parasites resistant to single and combinations of anti-leishmanial drugs present any advantages in their ability to bear the different stress conditions versus a susceptible L . donovani line . Our results suggest that resistant parasites acquire an overall fitness increase and that resistance to drug combinations presents significant differences in their fitness capacity , particularly in intracellular amastigotes . | [
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] | [] | 2015 | Fitness of Leishmania donovani Parasites Resistant to Drug Combinations |
Genome instability is a hallmark of cancer cells . One class of genome aberrations prevalent in tumor cells is termed gross chromosomal rearrangements ( GCRs ) . GCRs comprise chromosome translocations , amplifications , inversions , deletion of whole chromosome arms , and interstitial deletions . Here , we report the results of a genome-wide screen in Saccharomyces cerevisiae aimed at identifying novel suppressors of GCR formation . The most potent novel GCR suppressor identified is BUD16 , the gene coding for yeast pyridoxal kinase ( Pdxk ) , a key enzyme in the metabolism of pyridoxal 5′ phosphate ( PLP ) , the biologically active form of vitamin B6 . We show that Pdxk potently suppresses GCR events by curtailing the appearance of DNA lesions during the cell cycle . We also show that pharmacological inhibition of Pdxk in human cells leads to the production of DSBs and activation of the DNA damage checkpoint . Finally , our evidence suggests that PLP deficiency threatens genome integrity , most likely via its role in dTMP biosynthesis , as Pdxk-deficient cells accumulate uracil in their nuclear DNA and are sensitive to inhibition of ribonucleotide reductase . Since Pdxk links diet to genome stability , our work supports the hypothesis that dietary micronutrients reduce cancer risk by curtailing the accumulation of DNA damage and suggests that micronutrient depletion could be part of a defense mechanism against hyperproliferation .
The faithful replication of the genome is necessary for maintenance of genome integrity . Disrupting processes that ensure faithful DNA replication results in chromosome breakage , hyper-recombination , or gross chromosomal rearrangements ( GCRs ) [1–3] . This relationship has been particularly highlighted in the budding yeast S . cerevisiae , where GCRs arise at high rates in cells with defects in the S-phase checkpoint [4] , DNA replication licensing [5 , 6] , DNA replication elongation [7–9] , chromatin assembly [10] , and homologous recombination ( HR ) repair [8] . Altogether , these studies not only suggest a common origin ( i . e . , DNA replication ) , but also a common mechanism by which genome rearrangements are formed [2] . Defects that occur during DNA replication lead to elevated levels of DNA damage , including DNA double-strand breaks ( DSBs ) . In turn , these lesions may serve as substrates for the illegitimate repair processes resulting in GCRs . Therefore , identification of genes that prevent GCRs can potentially uncover novel genome caretakers that guard cells against the accumulation of mutations . In addition , unbiased identification of GCR suppressors could be a useful route for discovering novel genes and pathways that participate in DNA replication . Most of the current knowledge regarding GCR formation originates from candidate gene studies examining rearrangements at a single locus in budding yeast , the left arm of Chromosome V ( ChrV-L ) . Although this locus has been instrumental in the deciphering of many basic mechanisms governing genome stability in eukaryotes , examination of GCR formation at other loci provides a complementary view . For example , the use of yeast artificial chromosomes to study GCRs led to the discovery that defective chromosome condensation ( in a ycs4 mutant ) results in GCR events [7] . In addition , studies employing a Chromosome VII disome found that defects in DNA replication and checkpoint control elevate rates of chromosome loss and rearrangements following replication fork stalling [11] . In another study , Hackett et al . employed the telomeric region of ChrXV-L to study GCR events triggered by telomerase dysfunction [12] . This latter locus is particularly useful since GCRs at ChrXV-L involve break-induced replication ( BIR ) , a type of homologous recombination repair predicted to be a major source of genome rearrangements [2 , 13–15] . In contrast , GCRs formed at ChrV-L are primarily the consequence of de novo telomere addition [8] . This difference can be explained by the architecture of the telomere-proximal region on ChrXV-L , which contains two regions of homology ( HRI centered on the PAU20 gene , and HRII centered on the HXT11 gene; Figure 1A ) located 12 kb and 25 kb from the telomere [12] . These regions share a high degree of sequence identity with other regions in the genome [12] . As a consequence , DNA lesions formed at loci telomeric to HRI or HRII are predominantly repaired by BIR , producing nonreciprocal translocations in haploid cells . Notably , increased repair by BIR can also lead to loss of heterozygosity in diploid genomes , which may accelerate the process of tumorigenesis by inactivation of tumor suppressor genes . In this study , we screened the yeast genome for mutants that increase the level of chromosome rearrangements; specifically , those that increase the frequency of BIR-mediated nonreciprocal translocations . We report the construction of a strain containing a GCR reporter on ChrXV-L that is amenable to genome-wide screening and compatible with synthetic genetic array technology [16] . We employed this strain to systematically screen the gene deletion collection [17] leading to the identification of nine new GCR suppressors . Here , we focus on the characterization of one of the most potent GCR suppressors identified , BUD16 , which encodes yeast pyridoxal kinase ( Pdxk ) , a critical enzyme in vitamin B6 metabolism . We show that Pdxk is critical for the maintenance of genome integrity via its role in maintaining adequate levels of pyridoxal 5′ phosphate ( PLP ) , the biologically active form of vitamin B6 . Our results are consistent with a model whereby dTMP biosynthesis is the pathway affected by a decrease in PLP , thus providing an important link between dietary micronutrients , DNA replication and genome stability . Furthermore , since many epidemiological studies have linked defective vitamin B6 levels to an increased cancer incidence [18–23] , our study supports the hypothesis that micronutrients such as vitamin B6 curtails carcinogenesis by preventing genomic instability .
To generate a GCR reporter strain that is amenable to genome-wide screening , we adapted a system previously described by Hackett et al . [12] . We inserted the CAN1 and URA3 genes , two counter-selectable markers , ∼10 kb from the telomere of ChrXV-L ( Figure 1A ) . The simultaneous loss of CAN1 and URA3 ( detected on media containing canavinine [can] and 5-fluoro-orotic acid [5-FOA] ) at this locus occurs at a rate of 8 . 9 × 10−8 ( Table 1 ) , approximately 250-fold higher than the rate observed at ChrV-L ( 3 . 5 × 10−10; Table 2 ) . This elevated GCR rate may be due to the higher efficiency of BIR over de novo telomere addition in repairing DSBs . Moreover , the HRI and HRII regions on ChrXV-L display between 85%–97% homology with a total of 21 chromosome arms [12] . This large number of potential seeds for BIR may also explain the relatively high GCR rate at ChrXV-L . To ensure that the GCR events recovered from the simultaneous loss of CAN1 and URA3 are due to BIR , we analyzed GCR events in wild-type cells by pulsed-field gel electrophoresis ( PFGE ) using a scheme described by Hackett et al . [12] . Briefly , we isolated genomic DNA from parental canS 5-FOAS cells and cells that have undergone GCR events at ChrXV-L ( canR 5-FOAR cells ) . This DNA is then digested with PmeI to liberate a terminal restriction fragment , separated by PFGE , and finally transferred onto nitrocellulose by Southern blotting to be probed with a ChrXV-L-specific fragment ( NOP8; Figure 1B ) . If BIR occurs by employing any of the 21 homologous chromosome arms as a template , the resulting terminal restriction fragments liberated by PmeI are all predicted to be of lower molecular weight than the parent fragment ( ∼97 kb ) . As predicted , the analysis of nine canR 5-FOAR mutants derived from the wild-type strain indicate that nine out of nine have undergone a GCR event at ChrXV-L that is consistent with BIR , since their terminal restriction fragments all migrate faster than that of the parental strain ( Figure 1B ) . Furthermore , the analysis of four canR 5-FOAR strains by comparative genome hybridization using tiling microarrays identified breakpoints either in HRI , in the vicinity of the PAU20 gene ( in two of four strains analyzed ) , or in HRII ( i . e . , in the vicinity of the HXT11 genes , two of four strains; see Figure 1C for two representative examples ) . Lastly , we determined the GCR rate at ChrXV-L of a rad52Δ strain , since RAD52 is required for BIR . The rad52Δ strain does not produce any detectable GCR events under the standard conditions of our assay ( i . e . , the rate must be ≪8 . 4 × 10−9; Table 2 ) . This result suggests that most of GCR events observed at ChrXV-L are indeed dependent on RAD52 , a gene required for BIR . Collectively , the above results indicate that the ChrXV-L GCR reporter monitors BIR-type events . We crossed the resulting ChrXV-L GCR assay strain with the 4 , 812 viable open reading frame deletion strains [16 , 17] and employed a semi-quantitative papillation assay to monitor GCR formation ( Figure S1 ) . An initial set of 48 strains that scored positive were reconstructed in the ChrXV-L assay strain to determine their GCR rate by fluctuation analysis [24] ( Table 1 ) . This group included deletions in several known GCR suppressors such as the genes encoding Mre11 , a component of the MRX complex , the RecQ helicase Sgs1 , and the budding yeast FEN-1 homolog , Rad27 . Using this scheme , we identified nine gene deletions that display at least a 10-fold increase in GCR rate compared to wild type ( Table 1 and Figure 2A ) . Of these nine novel GCR suppressors , mutations in RMI1 , RAD5 , SLX8 , and HEX3 were independently reported during the course of this study to promote GCRs at ChrV [25–27] . The remaining five novel GCR suppressors include BUD16 , WSS1 , ESC2 , RML2 , and ZIP1 . Intriguingly , ZIP1 encodes a component of the synaptonemal complex that is active during meiosis [28] and is also expressed in mitotic cells [29] , suggesting a potential role for Zip1 in mitotic genome stability . RML2 encodes the mitochondrial L2 ribosomal protein [30] . Surprisingly , a Rml2-GFP fusion protein localizes to the nucleus [31] , suggesting a putative nuclear function for Rml2 . WSS1 encodes a weak suppressor of an smt3 mutation [32] , and ESC2 encodes a protein harboring a SUMO domain that has been linked to heterochromatic silencing [33 , 34] and the function of the Smc5/6 complex [35] . BLAST searches and alignments reveal that BUD16 encodes a putative Pdxk . With a GCR rate of 1 . 1 × 10−5 ( 124-fold above wild type ) , bud16Δ is within the range of very potent GCR mutator deletions that include rad27Δ ( 1 . 3 × 10−5; 148-fold over wild-type rate ) , mre11Δ ( 1 . 3 × 10−5; 140-fold ) , and sgs1Δ ( 1 . 2 × 10−5; 129-fold ) ( Figure 2A and Table 1 ) . Reintroduction of a plasmid encoding wild-type BUD16 complemented the genome instability of bud16Δ cells , eliminating the possibility that a second site mutation contributes to its elevated GCR rate ( Figure 2B ) . We also examined the type of GCR events promoted by the bud16Δ mutation by PFGE , which indicated that BUD16 prevents BIR-type rearrangements at ChrXV-L ( Figure 2C ) . Given that bud16Δ had the most profound effect on genome stability among the uncharacterized suppressors , we focused on deciphering its role in preventing chromosome rearrangements . In all organisms , Pdxk is an essential component of a vitamin B6 salvage pathway that ultimately produces PLP [36] . To ascertain whether BUD16 functions as the budding yeast Pdxk , we measured PLP levels in wild-type and bud16Δ strains . We found that the PLP levels of bud16Δ cells are only 1 . 8% of wild-type levels ( Table 3 ) . This result is somewhat surprising , since bacteria , yeast , and plants also possess a de novo vitamin B6 pathway that produces PLP in a Pdxk-independent manner . In yeast , this pathway is under the control of the SNO1 and SNZ1 genes [37] . However , these genes are not normally expressed during logarithmic growth but rather are expressed during stationary phase or under poor nutrient conditions . We found that the simultaneous deletion of the SNO1 and SNZ1 locus did not significantly reduce PLP levels ( 95 . 2% of wild-type levels; Table 3 ) or increase GCR rates ( Table 2 ) . Although SNO1 and SNZ1 deletion did not significantly impact genome stability ( or PLP levels ) in BUD16 cells , the de novo vitamin B6 synthetic pathway is nevertheless essential for viability in the absence of BUD16 . Indeed , we are unable to recover viable triple mutants from a cross between bud16Δ and sno1snz1Δ or double mutants from a cross between bud16Δ and snz1Δ ( Figure 2D ) . Overall , the decrease in intracellular PLP levels in bud16Δ along with its synthetic lethality with sno1snz1Δ are consistent with the idea that BUD16 functions in parallel with the de novo B6 pathway as a yeast pyridoxal kinase . Additional characterization of the bud16Δ strain in terms of growth and cell cycle kinetics is described in Text S1 and in Figure S2 and Tables S1 and S2 . To determine whether the bud16Δ mutation increases genome instability across the genome , we calculated the GCR rate of a bud16Δ strain at the ChrV-L locus [8] . We found that the bud16Δ mutation elevates the GCR rate at this locus 19-fold over the wild-type rate ( Figure 3A; Table 2 ) . Analysis of the GCR events involving ChrV by whole-chromosome PFGE reveals a mixture of events consistent with de novo telomere additions ( six out of eight events analyzed ) and nonreciprocal translocations ( two out of eight events ) ( Figure 3B ) . This ratio of telomere additions to translocations ( 4:1 ) is similar to the ratio of GCRs typically recovered from a wild-type strain [4] . Together , these results indicate that BUD16 suppresses different types of genome rearrangements at a minimum of two different loci in the genome , suggesting that BUD16 may act to prevent the occurrence of DNA lesions rather than by promoting a specific type of illegitimate repair . To further characterize the mechanism that underlies the high GCR rate of bud16Δ cells in the ChrXV-L assay , we crossed the bud16Δ GCR reporter strain to a strain containing a deletion of RAD52 , a gene necessary for all types of homologous recombination , including BIR [15] . The GCR rate at ChrXV-L of the bud16Δ rad52Δ double mutant was reduced to wild-type levels ( 2 . 4 × 10−8; Table 2 ) . However , this rate is far greater than the GCR rate of a rad52Δ mutation alone ( <<8 . 4 × 10−9; Table 2 ) . Furthermore , analysis of the terminal restriction fragment of the rearranged chromosomes in the bud16Δ rad52Δ double mutant shows terminal deletions in seven out of eight cases that are strikingly larger than those observed in either wild-type or bud16Δ strains ( 23–37 kb shorter in rad52Δ strains versus ∼7–17 kb in the RAD52+ strains; Figure 4A and 4B ) . This difference in the size of the ChrXV-L terminal restriction fragment suggests that bud16Δ rad52Δ do not undergo BIR-mediated GCR events that employ the HRI/II regions as seeds . Instead , in the absence of functional HR , these mutants are likely repaired by de novo telomere additions , leading to large terminal deletions . Together , the observations that BUD16 suppresses GCRs at multiple loci in a BIR-dependent and independent manner suggest that bud16Δ cells experience higher-than-normal levels of DNA lesions during vegetative growth . We find support for this possibility when tetrads from a cross between bud16Δ and rad52Δ are examined ( Figure 5A ) . We observe that the bud16Δ rad52Δ double mutant displays synthetic sickness and poor viability when compared to their congenic single mutants . This result suggests that bud16Δ cells may experience high levels of genotoxic stress that require the HR pathway for optimum viability . Consistent with this explanation , the bud16Δ mutation also displays synthetic sickness when crossed with an MRE11 gene deletion and to a lesser extent with deletion of RAD51 , two additional genes acting in the homologous repair of DSBs ( Figure 5A ) . We also observe a strong genetic interaction between the bud16Δ and rad6Δ mutations ( Figure 5A ) , suggesting that DNA lesions caused by the reduction of PLP levels may require post-replicative repair or lesion bypass . Based on this spectrum of genetic interactions , bud16Δ cells likely accumulate DNA lesions during DNA replication , possibly leading to replication fork stalling or collapse . To gain more direct evidence for the presence of active RAD52-dependent recombination in bud16Δ cells , we monitored the formation of Rad52 DNA repair centers [38] . Upon formation of lesions that engage HR , Rad52 relocalizes from a diffuse nuclear pattern into discernable punctate foci that coincide with DNA lesions . We thus expressed a Rad52 protein fused to the yellow fluorescent protein ( YFP ) in wild-type and bud16Δ cells and examined the presence of Rad52 repair centers by fluorescent microscopy ( Figure 5B ) . In bud16Δ cultures , 37%–75% of the cells display Rad52-YFP foci compared to 5%–21% of wild-type cells ( Figure 5B ) . In bud16Δ cultures , Rad52-YFP foci are surprisingly found in G1 ( unbudded ) cells but are most prevalent in S/G2/M ( budded ) cells ( 57%–75% in budded versus 37%–59% in unbudded cells ) . Intriguingly , the presence of Rad52 foci in G1 nuclei suggests the presence of persistent or unrepairable DNA lesions in cells that have undergone checkpoint adaptation [39] . Furthermore , we observe that budded bud16Δ cells display greater than one repair centre in 12–18% of the cells whereas this situation occurs in less than 2% of wild-type cells ( Figure 5C ) . Since up to ten DSBs may localize to one repair centre [38] , these results suggest that bud16Δ cells experience high levels of DNA lesions during DNA replication . Alternatively , we cannot exclude the possibility that bud16Δ cells have a dramatically reduced rate of DNA repair . However , bud16Δ cells are not sensitive to the radiomimetic alkylating agent methyl methanesulfonate ( MMS; Figure 5D ) , indicating that the Rad52 pathway is functional in these cells . Therefore , the increased presence of Rad52 foci in bud16Δ cells is most likely explained by an increased number of DNA lesions . Next , we examined whether the spontaneous DNA damage present in bud16Δ cells is sufficient to activate the DNA damage checkpoint pathway by assaying Rad53 , the yeast homolog of the tumor suppressor Chk2 . Rad53 kinase activation is observed by a detectable auto-kinase activity [40] concomitant with a reduced mobility on SDS-PAGE due to autophosphorylation . As shown in Figure 5E , Rad53 is hyperactivated in bud16Δ cells when compared to wild type , indicating that sufficient DNA damage is present in the bud16Δ mutant to activate the DNA damage checkpoint . Phosphorylation of Rad53 in cycling populations is often seen in strains that experience high levels of spontaneous DNA lesions , such as dia2Δ , rrm3Δ , and rmi1Δ , among others [25 , 41 , 42] . Altogether , our results are consistent with a model whereby bud16Δ cells experience high levels of DNA lesions , including DSBs . These DNA lesions most likely serve as substrates for illegitimate repair , resulting in elevated levels of genome rearrangements . The genome instability observed in the bud16Δ mutant correlates with low levels of PLP . However , this observation does not eliminate the possibility that its GCR mutator phenotype could be due to a PLP-independent function of Bud16/Pdxk . To address this possibility , we aimed to reduce PLP levels by alternative means to probe the relationship between PLP and genome integrity . As a first means , we inactivated other components of the vitamin B6 salvage pathway . In particular , when yeast are grown to log phase under laboratory conditions , the PLP precursor , pyridoxine , is actively transported into cells mainly , but not solely , by the Tpn1 transporter [43] . Therefore , we asked whether TPN1 deletion impacts total PLP levels and genome stability . Cells harboring a tpn1Δ mutation have low levels of intracellular PLP ( 8% of wild-type; Table 3 ) that are nevertheless higher than those of bud16Δ . At a genetic level , we find that the tpn1Δ mutation is not synthetic lethal with the sno1snz1Δ double mutant ( Figure 6A ) . The continued viability of the tpn1Δ sno1snz1Δ mutant supports the observation that although Tpn1 is a component of the B6 salvage pathway , it is not absolutely essential for pyridoxine transport [43] . Accordingly , the GCR rate of the tpn1Δ strain at ChrXV-L is increased 47-fold over the wild-type rate , which is less that of the bud16Δ GCR rate ( 124-fold over wild type; Figure 6B ) . However , both genes act in the same pathway to suppress genome rearrangements , as the double bud16Δ tpn1Δ mutant display the same GCR rate as bud16Δ ( Figure 6B ) . Lastly , we find multiple Rad52 recombination centers present in 4%–7% of tpn1Δ budded cells , suggesting the presence of catastrophic DNA damage similar to that seen in bud16Δ , albeit at a lower level ( Figure 6C ) . Altogether , these results further suggest that PLP levels correlate with genome integrity . In addition to manipulating PLP levels via genetic means , we also manipulated pyridoxine intake to further explore the link between PLP levels and genome integrity . To carry out these experiments , we disabled de novo vitamin B6 synthesis ( via SNO1 SNZ1 inactivation ) to exclude the contribution of this pyridoxine-independent pathway . We also impaired pyridoxine transport by deleting TPN1 . When grown in rich media , the resulting tpn1Δ sno1snz1Δ triple mutant has an elevated GCR rate ( 191-fold over wild type ) , which is greater than either the tpn1Δ or sno1snz1Δ mutants ( Table 2 ) . Accordingly , when we measure the PLP levels of this strain , we find that they are 5 . 8% of wild-type levels ( Table 3 ) . Importantly , we then supplemented the growth media of tpn1Δ sno1snz1Δ with 2 μg/ml pyridoxine as a means to stimulate its transport across the membrane . As shown in Figure 6D and Table 2 , addition of pyridoxine to the media of tpn1Δ sno1snz1Δ potently suppresses its GCR rate to wild-type levels . Critically , under the same conditions , the PLP levels of the tpn1Δ sno1snz1Δ strain are dramatically increased to 81 . 5% of wild type ( Figure 6E; Table 3 ) . Together , these data conclusively demonstrate a relationship between PLP levels and maintenance of genome integrity . We next examined the role of Pdxk on the genome integrity of human cells by employing the well-characterized vitamin B6 analog 4-deoxypyridoxine ( 4-DP ) [44] . First , we determined whether inhibition of human Pdxk leads to DNA damage , particularly DSBs . To detect DSBs in human cells , we examined the localization of 53BP1 , a DNA repair and signaling protein , by immunofluorescence microscopy . 53BP1 forms nuclear foci that colocalize with DSBs in mammalian cells and is thus a useful surrogate marker of this type of DNA damage [45] . As shown in Figure 7A and 7B , addition of 4-DP to the media of HeLa cells causes an accumulation of 53BP1 foci . Second , we found that 4-DP treatment also triggers activation of the checkpoint kinase Chk2 , as evidenced by phosphorylation of its Thr68 residue ( Figure 7C ) . Third , we analyzed the phosphorylation status of H2AX on its C-terminal Ser139 residue ( known as γ-H2AX ) , one of the earliest events in the response to DSBs . The presence of γ-H2AX was assessed via immunoblotting ( Figure 7C ) and flow cytometry ( Figure 7D ) [46] . In cells treated with the Pdxk inhibitor 4-DP , γ-H2AX clearly accumulates during S-phase between the 2N and 4N DNA content , similar to what is observed in yeast cells ( with Rad52-YFP ) . Importantly , to ensure that the described effects were not due to apoptotic effects caused by Pdxk inhibition at the concentrations of 4-DP employed above , we measured levels of apoptosis in HeLa cells by annexin V staining ( Figure S3 ) . From this data , we can rule out the possibility that 4-DP triggers DSB formation via the activation of an apoptotic program . Instead , our results indicate that , as in yeast cells , Pdxk inhibition induces DNA lesions and activation of the DNA damage response . We finally sought to narrow down the biological pathway in which PLP acts to promote genome stability . This is a difficult task , since PLP is a critical cofactor for numerous essential enzymes acting in amino acid and dTMP biosynthesis . However , our observations in yeast and human cells indicate a role for PLP in preventing DNA lesions during DNA replication , pointing to dTMP synthesis as the likeliest candidate pathway linking PLP to genome stability ( Figure 8A ) . This possible association is strengthened by the multitude of observations that link dTMP biosynthesis to genome integrity in both prokaryotes and eukaryotes ( reviewed in [47] ) . In this context , PLP deficiency may either cause aberrant uracil incorporation into DNA , generate a nucleotide imbalance that impairs DNA replication fork stability , or both . We therefore sought to assess the involvement of PLP in dTMP biosynthesis by testing whether bud16Δ cells accumulate uracil nucleotides in their DNA . To do so , we employed a recently described modified aldehydic slot blot assay that detects abasic sites produced when isolated DNA is treated with a uracil glycosylase enzyme [48] . As shown in Figure 8B , strains lacking Pdxk ( bud16Δ ) accumulate uracil in their genome significantly more than their wild-type counterparts . This accumulation is likely to be biologically important , as it is in the same range as the uracil accumulation observed in cells deficient in uracil glycosylase ( ung1Δ cells ) , the main enzyme dedicated to the removal of uracil in DNA ( Figure 8C ) . Furthermore , the double ung1Δ bud16Δ mutant accumulates more uracil in its genome than either of the single mutants , suggesting that UNG1 and BUD16 function in separate pathways to prevent uracil incorporation into DNA . These results are therefore consistent with a model in which the bud16Δ mutation increases dUMP pools , thereby increasing the frequency of dUTP incorporation into DNA . Accumulation of uracil in genomic DNA may lead to DSB accumulation and attendant genome instability via excision of uracil and production of excessive abasic sites . However , deletion of UNG1 does not suppress the bud16Δ genome instability rate and in fact results in a GCR rate increase ( Table 2 ) . This result indicates that either uracil excision is not a major cause of DNA damage in cells with low PLP levels , that an alternative excision pathway is involved , or that it is the accumulation of uracil that poses a threat to replication fork progression . Alternatively , it is also possible that a nucleotide pool imbalance caused by dUTP accumulation is a source of replication stress in bud16Δ cells . If bud16Δ cells have a defect in maintaining nucleotide pools , they may display some form of sensitivity to hydroxyurea ( HU ) a ribonucleotide reductase inhibitor . As shown in Figure 8D , HU dramatically affects the growth of bud16Δ cells at all concentrations tested and also leads to inviability of bud16Δ at 0 . 2 M HU , as measured by a colony-forming assay ( Table S3 ) . In contrast and as discussed above , bud16Δ cells are resistant to MMS , a DNA alkylating agent that causes DNA replication stress by impeding replication fork progression [49] ( Figure 5D ) . Therefore , bud16Δ cells are sensitive to the depletion of deoxyribonucleotides rather than to replication stress . From these results , we suggest that PLP deficiency triggers DNA lesions due to a nucleotide imbalance resulting from defects in dTMP biosynthesis .
In this report , we describe a screen for suppressors of rearrangements at ChrXV-L , a locus producing chromosome aberrations primarily via BIR . Since this locus is different from the commonly used ChrV-L locus , we expected overlapping and distinct sets of genes with those already known to prevent rearrangements of ChrV . Indeed , comparison of the results of our screen with a similar screen undertaken using the ChrV-L reporter chromosome [50] identifies only two overlapping GCR suppressor genes , RAD5 and MRE11 . This lack of overlap between both screens indicates that neither screen was saturating or that both loci can identify distinct classes of genome stability regulators . Indeed , we observed that disruption of some genes ( such as SGS1 ) potently affects the GCR rate at the ChrXV-L locus while having a much more modest effect at ChrV [51] , indicating that some genes may specifically suppress BIR . In addition to Pdxk ( Bud16 ) , most of the other genes identified in our screen are likely to prevent DNA replication stress , suggesting that an unbiased screen for GCR suppressors is a potentially fruitful means of discovering novel activities influencing DNA replication . In particular , deletion of WSS1 , which encodes a potential protease acting in the SUMO pathway [52] , is synthetic lethal with deletion of SGS1 , the yeast RecQ homolog [53] . This result , coupled with the high GCR rate of the wss1Δ strain , suggests that Wss1 , perhaps via its proteolytic activity , acts in the management of DNA replication forks to prevent their demise . Likewise , Esc2 likely participates in maintaining replication fork integrity and tolerance to replication stress , given the ascribed role of its fission yeast homolog Rad60 in these processes [35 , 54] . Perhaps more puzzling is our identification of ZIP1 , a meiotic gene , and RML2 , encoding a mitochrondrial ribosome component , as GCR suppressors . Therefore , this study revealed several novel GCR regulators , which may be part of several less well-understood GCR suppression pathways . It will be important to decipher whether the products of these genes do indeed participate in the maintenance of mitotic genome integrity . The link between decreased intracellular PLP levels and genome stability is important , since vitamin B6 deficiency correlates with heightened cancer risk [18–23] . This work therefore provides support for a model whereby subnormal levels of vitamin B6 may promote cancer development by engendering DNA lesions and attendant genome rearrangements . Given the poorly understood link between diet and cancer incidence , the ChrXV-L GCR assay provides a simple genetic system to probe the consequences of micronutrient deficiency on genome stability . Although the potential link between vitamin B6 and chromosome breakage had been suggested previously [20] , the lack of a genetically tractable system to study the role of micronutrients in genome integrity has prevented a definitive mechanistic explanation of the vitamin B6–cancer epidemiological link . This situation has led to a multitude of alternative explanations . For example , other groups have contended that PLP decreases cellular proliferation or protects cells from oxidative stress [55 , 56] . We directly tested the possibility that reactive oxygen species affect the GCR rate of bud16Δ cells by growing them in the presence of the reactive oxygen species scavenger N-acetylcysteine ( NAC ) . To our surprise , treatment with this compound increased rather than decreased the bud16Δ GCR rate ( Table 2 ) , indicating that reactive oxygen species may not play a major role in the formation of genome rearrangements when PLP levels are low . Replication stress is thought to be a major deleterious event , as it is a source of DNA lesions and genome rearrangements . Paradoxically , recent observations point to a beneficial role for replication stress as an innate defense mechanism against tumorigenesis . Indeed , replication stress appears to be a hallmark of precancerous and hyperproliferating cells [57 , 58] . In this context , replication stress leads to activation of a DNA damage response that initiates senescence , thereby stopping the growth of a potential tumor [57–61] . These observations suggest that cells are wired to produce DNA lesions when their proliferation is aberrantly stimulated . One key and unresolved question that emerges from this body of work pertains to the nature of the cellular processes that trigger replication stress in response to uncontrolled cell growth . We speculate that our work , which links depletion of PLP to replication-associated DNA lesions , provides a simple mechanism that could link hyperproliferation to the activation of the DNA damage response . Indeed , we hypothesize that the exhaustion of metabolites through unscheduled anabolic processes may be primarily sensed as DNA replication stress . It will therefore be interesting to see whether intracellular PLP levels are decreased in precancerous lesions or whether Pdxk inhibition can sensitize cells to oncogene-induced cellular senescence .
The CAN1 gene from strain BY4741 was amplified from genomic DNA and cloned next to the URA3MX gene marker in the BglII site of pAG60 [62] to yield DDp418 . To construct pBUD16 ( DDp626 ) , the BUD16 locus encompassing the BUD16 open reading frame was amplified by PCR from yeast genomic DNA and cloned in pCR2 . 1-TOPO ( Invitrogen , http://www . invitrogen . com ) and sequenced . The BUD16 locus was then excised with SpeI and NotI and cloned into the SpeI and NotI sites of pRS415 . To construct the GCR assay strain ( DDY643 ) the CAN1 gene of BY4741 strain was replaced with the MFA1pr-HIS3 marker [16] . This strain was then transformed with a PCR fragment containing the cycloheximide-resistance cyh2 marker to yield strain DDY642 . The CAN1-URA3 cassette was amplified from DDp418 with primers CAN1-URA3 F1: 5′-GAA TCT GCC GTT TCG ATT TAC TTC GAT AAA GTT TGC GTT GTG AGT CAT ACG GCT TTT TTG-3′ and CAN1-URA3 JM R1: 5′-GGA AAA TTC TGG TCT ATT CAC AAT GAC AAG CGG TGA GCG TGT ATA GCG ACC AGC ATT CAC-3′ ( underlined regions anneal to DDp418 and flanking regions are homologous to ChrXV-L ) . A second round of PCR with the following primers extended homology to the ChrXV-L region: CAN1-URA3 F2: 5′-TAT TGT GAA TTG AAA TTT AAA GTT ATC TCA AAT TCA AAT GAA TCT GCC GTT TCG ATT TAC-3′ and CAN1-URA3 R2: 5′-AGA TGG CTT TTC CAT CAG AGC CAT TGT GAA GAA ATC GGA GGA AAA TTG TGG TCT ATT CAC-3′ ( underlined regions anneal to the PCR product from the first round ) . This amplified fragment was introduced in DDY642 to yield DDY643 and was verified by PCR analysis . The MATα strain ( DDY644 ) used in the screen was derived from DDY643 by mating type switching . All other strains were generated using genetic crosses , via one-step disruptions or via transformation of the indicated plasmids ( see Table 4 for genotypes ) . To generate gene deletions in the ChrXV-L GCR assay strain , we employed synthetic genetic array technology , essentially as described by Tong et al . [16] . Briefly , DDY644 was mated to the MATa deletion strains from EUROSCARF ( http://web . uni-frankfurt . de/fb15/mikro/euroscarf/ ) on YPD and incubated at 30 °C overnight . Diploids were selected on SD-URA + 200 mg/L G418 and incubated at 30 °C for 2 d . Sporulation proceeded on YE + 0 . 05% glucose for 7 d at 25 °C . Once sporulated , haploids were selected by a four-step pinning procedure: two selections on SD-URA-HIS + cycloheximide ( 10 mg/L ) followed by two selections on SD-URA-HIS + cycloheximide ( 10 mg/L ) + G418 ( 200 mg/L ) . Following the fourth selection step , each deletion mutant was hand patched onto nonselective rich XY media ( 2% peptone , 1% yeast extract , 0 . 01% adenine , and 0 . 02% tryptophan ) . Fourteen mutants in duplicate were patched onto a single 10-cm plate . They were allowed to grow for 2 d at 30 °C and were then replica plated onto agar plates to remove excess cells prior to replica plating onto FC ( 5-FOA and can ) media . FC plates were incubated for 3 d at 30 °C following replica plating and analyzed for colony formation . Wild-type strains produced between 0–3 colonies on average . Therefore , we scored a patch as a positive hit if the threshold number of colonies per patch were equal to or greater than ten . Deletion mutants ( 1 , 160 ) were then placed in a “1 hit” category ( for those with one patch that displayed greater than ten colonies ) or were in a “2 hit” category ( for those with both patches that showed greater than ten colonies ) . The “2 hit” category list , which consisted of 273 mutants , was narrowed down by focusing on genes that are expressed in the nucleus [31] . However , we did not discard any gene deletion that had unknown localization data . These filters reduced the number of positive hits to 125 . Of these 125 , we reconstructed 48 deletions in the DDY643 background . Once constructed and confirmed , these mutants were frozen at −80 °C immediately in SC-URA to ensure retention of the CAN1-URA3 markers . Strains were grown in SC-URA media to select for the URA3-CAN1 ChrXV-L arm prior to streaking cells onto nonselective rich media ( XY ) . Single colonies were isolated and grown in 5 ml of XY medium until saturation . For wild-type strains , 1 ml of culture was spun down and plated onto a 10-cm FC plate and the number of cells/ml was calculated . A fluctutation test and the method of the median [24] was used to assess GCR rate . Similarily , for the ChrV-L GCR assay , a single colony was inoculated into 15 ml of XY medium until saturation . These cells were spun down and plated onto a 15-cm FC plate and the number of cells/ml was calculated . Again , a fluctuation test and the method of the median were used to measure GCR rates . To assess the effects of pyridoxine supplementation on genome stability , cells were grown in the absence or presence of 2 μg/ml pyridoxine hydrochloride ( Supelco; http://www . sigmaaldrich . com/ ) . Wild-type , bud16Δ , tpn1Δ , sno1snz1Δ , and tpn1Δ sno1snz1Δ strains were grown in 100-ml cultures of XY + 2% glucose ( with the exception of tpn1Δ sno1snz1Δ ) to early log phase ( OD600 1 . 0 ) . The cells were spun down and washed thrice with 50 ml of cold double-distilled water to remove any external PLP . Cells were then pelleted and lysed by glass beads in 5% TCA . Lysates were clarified by centrifugation at high speed to remove cell debris . PLP measurements were done blindly at the diagnostic division of Anticancer ( http://www . anticancer . com/ ) . We assessed Rad52-YFP focus formation assay essentially as described by Lisby et al . [38] with the following modifications . Three independent isolates of each strain containing the pRAD52-YFP plasmid ( a gift of Grant Brown ) were grown in SC-LEU . Cells were imaged on an Nikon Eclipse E600 FN microscope ( http://www . nikonusa . com ) equipped with an ORCA ER2 camera ( http://www . hamamatsu . com ) and Chroma filters ( http://www . chroma . com/ ) . Micrographs were taken in 21 z-stacks with 0 . 007-μm increments . For each independent isolate , a minimum of 180 cells were examined . HeLa cells were grown in DMEM supplemented with 10% fetal calf serum . SiHa cervical carcinoma cells were obtained from the American Type Culture Collectin ( http://www . atcc . org ) and maintained in exponential growth by twice-weekly subcultivation in minimal essential medium containing 10% fetal bovine serum ( Gibco , http://www . invitrogen . com/ ) . A stock solution of 4-DP ( 200 mM ) was prepared in 0 . 9% saline , diluted in growth medium , and adjusted to pH 7 . 2 . Human whole-cell extracts ( 25 μg ) were prepared by boiling the cellular pellet in 1× SDS sample buffer for 5 min . Extracts were loaded onto an SDS-PAGE gel and after electrophoresis , proteins were transferred to a PVDF membrane ( Millipore , http://www . millipore . com/ ) and immunoblotted with either the phospho-Chk2 ( Thr68 ) or Chk2 primary antibodies ( Cell Signaling Technology , http://www . cellsignal . com/ ) followed by horseradish peroxidase-coupled secondary antibody ( Jackson ImmunoResearch Laboratories , http://www . jacksonimmuno . com/ ) . Rad53 immunoblotting and autokinase assays were carried out on denatured cell extracts exactly as described previously [9 , 63] . HeLa cells grown on coverslips were washed twice in PBS and fixed with 2% PFA for 1 h at room temperature , washed , and permeabilized with 1% Triton-X in PBS ( 1h , room temperature ) and subsequently blocked for an additional 1 h in 10% antibody dilution buffer ( 10% normal goat serum , 3% BSA , and 0 . 05% Triton X in PBS ) . Monoclonal 53BP1 antibody ( Transduction Laboratories ) was diluted in blocking buffer and incubated with cells overnight at room temperature followed by two 10 min washes in 0 . 05% Triton X-100 in PBS . The appropriate Alexa-555 conjugated secondary antibody ( Molecular Probes , http://probes . invitrogen . com/ ) was diluted 1:500 in 10% antibody dilution buffer and incubated with the coverslips for 2 h at room temperature . After several washes with PBS , the cells were stained with DAPI ( 10 μg/ml ) for 20 min and mounted with ProLong Gold anti-fade agent ( Molecular Probes ) . Briefly , ∼1 × 108 cells grown from a saturated culture were spun down , washed , and then resuspended in TE ( pH 7 . 5 ) and Zymolyase ( Zymo Research , http://www . zymoresearch . com . Plugs were formed by mixing liquefied low melt agarose ( SeaKem; http://www . lonza . com ) with the resuspended cells and solidified in plug molds . Plugs were transferred into LET solution ( 0 . 5M EDTA pH 8 . 0 , 0 . 01M Tris-HCl ( pH 7 . 5 ) , 40mM DTT , and 0 . 4mg/ml Zymolyase ) overnight at 37 °C . Plugs were then transferred into fresh tubes containing NDS solution ( 0 . 5 M EDTA pH 9 . 5 , 0 . 01 M Tris-HCl pH 7 . 5 , 1% N-lauroyl sarcosine sodium salt , and 2 mg/ml proteinase K ) and incubated at 50 °C overnight . Plugs were washed several times in TE ( pH 7 . 5 ) and incubated for 1 h . Plugs containing whole chromosomes were immediately run on a CHEF-DR III system ( Bio-Rad , http://www . bio-rad . com/ ) using a 1% agarose gel and 0 . 5× TBE at 14 °C , switch time 6–120 s , angle 120° for 24 h with a voltage gradient of 6V/cm . To examine the size of the terminal restriction fragment on ChrXV-L , whole chromosomes were prepared as described above in agarose plugs and were then digested with the restriction enzyme PmeI . Plugs of digested chromosomes were run on a 1% agarose gel in 0 . 5× TBE at 14 °C , switch time 1–15 s , angle 120° , 19 h with a voltage gradient of 6V/cm . SiHa cells ( 5 × 105 ) were fixed in 1 . 4 ml of 70% ethanol and kept at −20° C for up to two weeks before analysis . All fixed samples were prepared for antibody staining and analyzed on the same day . One milliliter of cold Tris-buffered saline , pH 7 . 4 ( TBS ) was added to each tube , then the cells were spun down and resuspended in 1 ml of cold 4% FBS and 0 . 1% Triton X-100 ( TST ) and placed on ice . Cells were allowed to rehydrate for 10 min , then spun down and resuspended in 200 μl of mouse monoclonal anti-phospho-histone H2A . X antibody ( Upstate Biotechnology http://www . millipore . com/ ) , which was diluted 1:500 in TST . Tubes were incubated on a shaker for 2 h at room temperature , rinsed with cold TST , and resuspended in 200 ul of secondary antibody ( Alexa 488 goat antimouse IgG [H + L]F[ab′]2 fragment conjugate [Molecular Probes] diluted 1:200 in TST ) for 1 h at room temperature . Cells were rinsed in 2% FBS in TBS and resuspended in 400 μl of cold TBS containing 1 μg/ml DAPI . Samples were analyzed using a Coulter Elite dual laser flow cytometer ( http://www . beckmancoulter . com ) . List mode files were analyzed using WinList software ( Verity Software House , http://www . vsh . com/ ) . DNA was isolated from yeast strains using Qiagen ( http://www . qiagen . com ) gravity tip columns as per the manufacturer's protocol and assayed for uracil incorporation as described in Cabelof et al . [64] . Briefly , 4 μg of DNA was blocked for 2 h at 37 °C in a 2× tris/methoxyamine buffer ( final concentration: 100 mM methoxyamine [Sigma-Aldrich , http://www . sigmaaldrich . com/] and 50 mM Tris-HCl , pH 7 . 4 ) . DNA was precipitated with 7 . 5% volumes of 4 M NaCl and 4 volumes of ice-cold 100% ethanol and resuspended in TE buffer , pH 7 . 6 . DNA was then treated with 0 . 4 units of Uracil DNA Glycosylase ( New England Biolabs , http://www . neb . com ) for 15 min at 37 °C , immediately precipitated , and resuspended in TE buffer , pH 7 . 6 . DNA was then probed with 2 mM aldehydic reactive probe ( Dojindo Molecular Technology , http://dojindo . com/ ) for 15 min at 37 °C followed by ethanol precipitation and resuspension in TE buffer , pH 7 . 6 . DNA was then heat denatured , immobilized onto a nitrocellulose membrane ( Schleicher and Schuell , http://www . whatman . com/ ) , and baked under vacuum as originally described by Nakamura et al . [48] . The dried membrane was washed in 5× SSC for 15 min at 37 °C , then incubated in a prehybridization buffer ( 20 mM Tris , pH 7 . 5; 0 . 1 M NaCl; 1 mM EDTA; 0 . 5% casein w/v; 0 . 25%BSA w/v; and 0 . 1% Tween-20 v/v ) for 30 min at room temperature . Streptavidin-POD conjugate ( Roche , http://www . roche . com/ ) was added at a 1:2 , 000 dilution for 45 min at room temperature . Membrane was washed in TBS/Tween-20 three times at 37 °C , incubated in ECL solution ( Pierce , http://www . piercenet . com/ ) for 5 min at room temperature , then visualized and quantified using a ChemiImagerTM system ( Alpha Innotech , http://www . alphainnotech . com/ ) . Data are expressed as the integrated density value of the band per microgram of DNA loaded on the membrane . Yeast genomic DNA was prepared from saturated 10-ml cultures essentially by the method of [65] . Genomic DNA ( 2 μg ) was digested for 2 h with 10 U of HaeIII and purified by phenol-chloroform extraction and ethanol precipitation . HaeIII-digested genomic DNA ( 50 μg ) was labeled and hybridized by the method of [66] . Briefly , after blunting the DNA ends with T4 polymerase , the fragments were ligated to unidirectional linkers and amplified by ligation-mediated PCR in the presence of aminoallyl-modified dUTP . Indirect labeling was performed using monoreactive Cy5 ( for the parental strain ) or Cy3 ( strains that had undergone GCR ) NHS esters that react specifically with the aminoallyl-dUTP . Control and experimental samples were combined , and the labeled DNA was hybridized to a yeast whole-genome ChIP-on-chip microarray ( 4 × 44K; Agilent Technologies , http://www . home . agilent . com/ ) and scanned at the University Health Network Microarray Centre ( http://www . microarrays . ca/ ) . Microarray images were processed with GenePix Pro 6 . 0 ( Molecular Devices , http://www . moleculardevices . com ) . Data were analysed as described previously [67] . Hybridization data were preprocessed with ArrayPipe 1 . 7 [68] , the background was subtracted using the “foreground-background” correction method , the data were normalized using the “linear model for microarray analysis ( limma ) loess ( subgrid ) method , ” and the results were mapped using the University of California Santa Cruz genome browser ( http://genome . ucsc . edu/cgi-bin/hgGateway ) . Text S2 contains supplementary materials and methods for apoptosis analysis , yeast DNA content , and cell size distributions . | Cells must ensure the integrity of genetic information before cellular division . Loss of genome integrity is particularly germane to tumorigenesis , where it is thought to contribute to the rapid evolution of the malignant cell towards the fully cancerous phenotype . It is therefore imperative that we understand fully how cells maintain the integrity of the genome and how it is lost during tumorigenesis . In this study , we developed an assay that allowed us to systematically interrogate each gene of the budding yeast S . cerevisiae for its respective contribution to genome integrity . We report the identification of nine novel genes that increase the rate of genome instability in yeast when deleted . To our surprise , one of the genes we identified encodes the enzyme pyridoxal kinase , which acts in the metabolism of vitamin B6 . We show that pyridoxal kinase influences genome stability by promoting the conversion of dietary vitamin B6 into its biologically active form , pyridoxal 5′ phosphate . Our work indicates that vitamin B6 metabolites are critical to maintain genome stability and supports a long-standing model , which hypothesizes that vitamin B6 reduces cancer risk by curtailing genome rearrangements . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"nutrition",
"homo",
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] | 2007 | A Screen for Suppressors of Gross Chromosomal Rearrangements Identifies a Conserved Role for PLP in Preventing DNA Lesions |
The formation of single-stranded DNA ( ssDNA ) at double-strand break ( DSB ) ends is essential in repair by homologous recombination and is mediated by DNA helicases and nucleases . Here we estimated the length of ssDNA generated during DSB repair and analyzed the consequences of elimination of processive resection pathways mediated by Sgs1 helicase and Exo1 nuclease on DSB repair fidelity . In wild-type cells during allelic gene conversion , an average of 2–4 kb of ssDNA accumulates at each side of the break . Longer ssDNA is formed during ectopic recombination or break-induced replication ( BIR ) , reflecting much slower repair kinetics . This relatively extensive resection may help determine sequences involved in homology search and prevent recombination within short DNA repeats next to the break . In sgs1Δ exo1Δ mutants that form only very short ssDNA , allelic gene conversion decreases 5-fold and DSBs are repaired by BIR or de novo telomere formation resulting in loss of heterozygosity . The absence of the telomerase inhibitor , PIF1 , increases de novo telomere pathway usage to about 50% . Accumulation of Cdc13 , a protein recruiting telomerase , at the break site increases in sgs1Δ exo1Δ , and the requirement of the Ku complex for new telomere formation is partially bypassed . In contrast to this decreased and alternative DSB repair , the efficiency and accuracy of gene targeting increases dramatically in sgs1Δ exo1Δ cells , suggesting that transformed DNA is very stable in these mutants . Altogether these data establish a new role for processive resection in the fidelity of DSB repair .
Homologous recombination is a major mechanism of repair of both DNA double-strand breaks ( DSBs ) and gaps that occur spontaneously or are induced by endonucleases , radiation or radiomimetic agents . Most of the enzymes involved in recombination are conserved among bacteria , yeast and human [reviewed in [1] , [2]] . A key protein in recombination called Rad51 mediates DNA strand exchange between a damaged DNA molecule and a homologous intact DNA template . Rad51 forms a nucleoprotein filament on 3′ single-stranded DNA ( ssDNA ) that is capable of a genome-wide search for a homologous template sequence and subsequent strand invasion . Upon strand invasion , 3′ ends initiate new DNA synthesis that allows recovery of lost information at the site of the DSB . Subsequent resolution of the recombining molecules forms a final product that in mitotic cells typically is a noncrossover . A necessary prerequisite of Rad51 filaments is the formation of 3′ ssDNA tails at DNA breaks . In budding yeast , the Mre11/Rad50/Xrs2 ( MRX ) complex together with Sae2 is responsible for the initiation of resection while two nucleases , Exo1 and Dna2 together with the Sgs1/Top3/Rmi1 ( STR ) complex form long ssDNA at DSBs [3] , [4] , [5] . Similar pathways of resection operate at yeast telomeres [6] . A two step mechanism in which mre11 and rad50 homologues play an initial role has also been described in the archeal organism Pyrococcus furiosus [7] . Human orthologs of these proteins , Mre11-Rad50-Nbs1 ( MRN ) with CtIP , Exo1 and BLM , play similar roles in 5′ strand resection [3] , [8] , [9] . In Xenopus laevis , Dna2 also processes DSB ends [10] . Human Dna2 has an important role in the maintenance of mitochondrial DNA [11] , yet its nuclear role remains to be determined [12] . Resection of the 5′ strand is highly regulated by DNA damage checkpoint proteins , chromatin remodeling factors and cyclin-dependent kinase . DNA damage checkpoint proteins exhibit complex interactions with enzymes involved in resection because they both stimulate and later limit resection [[13] and reviewed in [14] , [15]] . Cell cycle control of resection influences the choice between DSB repair pathways: nonhomologous end joining ( NHEJ ) or homologous recombination ( HR ) [16] , [17] , [18] . The extent of resection at DSBs is clearly regulated . However , how resection proceeds during DSB repair and the consequences of excess or limited resection on the fidelity of repair are not known . Most estimations of end resection in yeast were made using DSBs generated by either the HO or I-SceI endonucleases that cannot be repaired by gene conversion because homologous sequences are deleted [e . g . [19] , [20]] . Resection of such breaks is unlimited and proceeds at about 4 kb per hour . An alternative approach used to study resection utilized single strand annealing ( SSA ) , a repair process relying on extensive resection and annealing between distant direct DNA repeats [e . g . [21]] . While these assays were very useful in identifying proteins involved in resection it remains unknown how resection proceeds when the break is being repaired by the most natural pathway , gene conversion . It is important to examine the resection at DSBs that are repaired normally because resection determines sequences that are used for homology search and repair . Here for the first time , we estimated the length of ssDNA generated during DSB repair using several assays with different kinetics of repair - allelic and ectopic gene conversion and break-induced replication ( BIR ) . We demonstrate that about 2 to over 10 kb of ssDNA is generated depending on the kinetics of repair . Secondly , we determined the role of resection in the fidelity of DSB repair and gene targeting . Using mutants exhibiting decreased rates of resection we show that the length of 3′ tails defines the sequences involved in homology search and recombination donor choice . We demonstrate that normal resection at a DSB prevents deleterious repair via de novo telomere addition . Finally , we show that decreased resection was accompanied by dramatic increases in both the accuracy and efficiency of gene targeting . Together these results uncover a new role for resection in the regulation of DSB repair pathways .
Formation of ssDNA at DSB ends was extensively studied in multiple assays where the DSB was unrepairable . These assays were very useful in identifying enzymes involved in resection , however DSBs are normally repaired quickly by homologous recombination . Here for the first time we examined the kinetics and size of ssDNA generated at DSBs repaired by gene conversion and investigated the fidelity of DSB repair in mutants with limited resection . First , we measured ssDNA at a DSB repaired by an ectopic recombination assay in haploid cells where HO endonuclease induces a break within a MATa sequence inserted at the ARG5 , 6 locus on chromosome V ( Figure 1A ) [22] . This MATa sequence shares 1 . 9 kb ( 1 . 4 kb proximal and 0 . 5 kb distal from the HO break ) homology with MATa-inc on chromosome III . MATa-inc carries a point mutation in the HO recognition site that prevents HO cleavage . The DSB induced at MATa locus is processed to form 3′ ssDNA tails that invade a homologous template MATa-inc sequence located on chromosome III and initiate new DNA synthesis . Within one hour of HO nuclease induction a DSB occurred in all cells and the subsequent repair of the DSB takes about 3 to 7 hours in wild-type cells ( Figure 1B ) [22] . In this assay as well as in all other assays presented in this work , HO induction is continuous , therefore when the break is repaired by nonhomologous end joining , HO endonuclease cuts the MAT sequence again . We determined the amount of ssDNA generated during repair by following the temporary loss of several EcoRI restriction enzyme cleavage sites in the vicinity of the DSB as previously described ( Zhu et al . 2008 ) . Our goal was to find the distance from the HO cut site where at least half of the cell population removes the 5′ strand during repair . We used 4 probes that detect resection beyond 0 . 9 kb , 3 . 3 kb , 6 . 5 kb , and 17 . 3 kb from the break . As shown in Figure 1B , at least 70% of cells degraded DNA beyond 0 . 9 kb from the DSB ends and about half of the cell population degraded the 5′ strand beyond 3 . 3 kb from the DSB ends . Only 85% of cells repaired the break , therefore degradation of the 5′ strand beyond 17 . 3 kb from the HO cut site likely results from resection in cells that did not repair the break . Unrepaired breaks continue to be resected for at least 36 hours [[5] and data not shown] . The maximum amount of ssDNA at the DSB is observed at the time when the first recombination product starts to accumulate at 3 hours after HO endonuclease induction . To confirm that resection rate at the DSB induced on chromosome V is comparable to our previous estimation on chromosome III , we measured the resection in a strain lacking an essential protein for gene conversion , Rad51 . As in wild-type cells , an HO break was induced in all cells within one hour ( Figure 1C ) . Resection was measured 3 . 3 and 17 . 3 kb distal to the DSB in rad51Δ mutant cells ( Figure 1C ) . The average rate of resection was 3 . 6 kb/hr , which is very similar to the rates determined in donorless wild-type and rad51Δ strains at the MAT locus on chromosome III [5] . A similar rate of resection was previously estimated at several loci on different chromosomes and on a plasmid substrate [20] , [21] , [23] . We concluded that the locus we used to measure resection during repair is representative and that half of the cell population resected at least 3–6 kb of the 5′ strand on one DSB end during repair . Given that 3′ ends are stable for several hours following break induction [20] , [21] this is presumably the average amount of ssDNA that is active in the search for homologous sequences in this ectopic recombination assay . This amount of ssDNA is several times more than the homologous sequence available ( 0 . 5 kb ) . It is therefore probable that more ssDNA is formed than is required for repair . However this extensive resection may help to maintain DNA damage checkpoint arrest and give cells the necessary time for repair . Normally DSB repair occurs with a fully homologous template molecule such as a sister chromatid or homologous chromosome . We therefore also estimated the amount of ssDNA generated during allelic recombination where the break is induced at the MATa locus on chromosome III and is repaired by homologous recombination with a MATα-inc sequence on a homologous chromosome III ( Figure 2A ) . Allelic recombination is faster than ectopic as it is completed within 2 to 4 hours ( Figure 2B and Figure S1 ) . Faster kinetics of allelic recombination in diploid cells when compared to ectopic recombination in haploid cells is not due to MATa/α heterozygosity . We previously demonstrated that ectopic recombination in MATa/α heterozygous haploid cells and in MATa haploid cells is equally slow [22] . Also resection rate in haploid and diploid cells is the same ( Figure S2 ) . The maximum amount of ssDNA during allelic recombination accumulates at 2 hours after break induction ( Figure 2B ) . We measured resection beyond 1 . 2 kb , 2 . 6 kb , 3 . 8 kb , 5 . 0 and 10 . 2 kb from the DSB ends ( Figure 2B ) . Southern blots for each probe used in this assay are shown in Figure S1 . At least half of the cells resected DSB ends beyond 1 . 2 kb and 2 . 6 kb from the DSB and only 20% beyond 5 . 0 kb . These are significantly shorter sizes of ssDNA than in ectopic recombination . Interestingly , a small fraction of cells ( 10% ) resected beyond 10 . 2 kb of ssDNA , suggesting that sometimes sequences far away from DSB ends can be active in the homology search even during fast allelic recombination . We think that during gene conversion most of the detected ssDNA is formed prior to strand invasion and is likely active in search for homologous sequence because new DNA synthesis and final product occurs 1 . 5–3 hours after DSB formation and only 20 minutes after strand invasion ( Figure 1 and Figure 2; [24] and J . Haber , personal communication ) . The third assay we used to measure resection was break-induced replication ( BIR ) where only one DSB end is homologous to the template DNA and , after strand invasion , the 3′ end is extended to copy the chromosome to its very end ( Figure 2C ) [25] . In this assay the break is induced at the MATa locus , identical to the allelic recombination assay investigated above . It is a slow repair pathway as it takes about 6 to 10 hours to see the recombination product ( Figure S3 ) [25] . The slow kinetics of initial DNA synthesis in BIR is not caused by terminal nonhomology , because BIR is equally slow even when the DSB end is perfectly homologous with its template [25] . We measured 5′ strand resection in the BIR assay at 2 . 6 kb , 15 . 5 kb and 27 . 5 kb from the DSB end ( Figure 2C ) . Southern blots for all probes used for this assay are shown in Figure S3 . As shown in Figure 2D , most cells resected the 5′ strand beyond 2 . 6 kb and about half of the cell population resected DSB ends beyond 15 . 5 kb from the break . No significant resection was observed beyond 27 . 5 kb from the cut site . BIR is distinct from gene conversion because strand invasion in BIR monitored by Rad51 recruitment ( ChIP ) to template DNA , occurs with kinetics similar to gene conversion ( Jain et . all , 2009 ) while new DNA synthesis is detectable only 2–3 hours after strand invasion . Therefore the very long ssDNA created during BIR suggests that resection continues after strand invasion but before new DNA synthesis . Altogether we conclude that the amount of ssDNA created during repair depends on the kinetics of repair and secondly that resection , at least in BIR assay , continues after strand invasion and probably helps to maintain DNA damage checkpoint arrest until new DNA synthesis and final repair product forms . An implicit idea in current models of homologous recombination is that ssDNA determines sequences used for the homologous template search [26] . Now that we have verified the amount of ssDNA at DSBs , and the enzymes involved in resection are known , allowing us to manipulate the rate of ssDNA formation , we decided to verify this general concept . As shown above , an average of at least several kb of ssDNA is generated on one side of the break , which is much greater than the minimum homology required for efficient DSB repair . In DSB-induced recombination there is no difference in the efficiency of repair when homology is increased above 250 bp on each side of the break in a chromosomal context [27] and above 80 bp in a plasmid context [28] . Moreover , in yeast mating-type switching where an HO-induced break at the MAT locus is very efficiently repaired by recombination with the HMR and HML templates , the homology is limited to a few hundred base pairs [29] . Therefore , the length of ssDNA generated during resection at a DSB is greater than the minimum amount of sequence needed for efficient homology search and repair . It is possible that the formation of long ssDNA tails at DSBs increases the fidelity of repair by activating longer sequences in the homology search . Indeed , previously it was demonstrated that sequences located further away from the break are used for homology search preferentially over sequences within the first 0 . 5 kb from the break [30] . To verify whether resection determines the sequences involved in homology search and impacts template sequence choice we used a competition assay designed by Inbar and Kupiec [30] . In this assay , an HO break is induced on chromosome II at a ura3 sequence that was inserted in the middle of the LYS2 gene . A DSB at this site can be repaired by recombination with one of two homologous template sequences , URA3 located on chromosome V or LYS2 located on chromosome XV ( Figure 3A ) . The total homology between ura3 sequences is 1 . 1 kb and between lys2 sequences is 4 . 9 kb . In agreement with the original report [30] we observed that lys2 sequences that are further from the break are used preferentially as a recombination template . Only about 20±3% of wild-type cells repair the DSB by recombination with the URA3 donor sequence . Given that during ectopic recombination an average of 3–6 kb of ssDNA accumulates at both sides of a DSB ( Figure 1B ) , it is likely that LYS2 sequences are used 4 times more often than URA3 simply because they are 4 times longer . With the average rate of resection of 4 kb/hr , ura3 sequences are resected within 7–8 minutes and lys2 sequences are completely resected within an additional 30–40 minutes after DSB formation . When URA3 sequences were increased to match the LYS2 length , both templates were used with almost equal frequency [30] . Here we tested whether additional copies of URA3 on centromeric or multicopy plasmids could influence the donor choice . As previously demonstrated , plasmid DNA can be efficiently used for repair of chromosomal DSBs [28] . When an additional URA3 sequence was provided on a centromeric plasmid we did not observe any change in donor choice ( data not shown ) . However , the presence of a multicopy plasmid ( 2μ ) carrying URA3 sequences increased URA3 sequence usage for DSB repair to 55% ( Figure 3B ) . This result suggests that sequences further away from the break are used often as a template for repair even if short sequences close to the break have multiple copies of intact templates . Another conclusion is that long Rad51 nucleofilaments have a higher chance of identifying single homologous sequence than short nucleofilaments even when multiple homologous sequences are available . This feature of homology search may inhibit recombination between short repeats . Finally , when both processive resection pathways dependent on Sgs1 and Exo1 were eliminated , URA3 was almost the only donor used for repair ( >95% ) demonstrating that the ssDNA exposed during resection determines the sequences involved in homology search ( Figure 3B and 3C ) . The viability of wild-type cells and of sgs1Δ or exo1Δ single mutants was comparable , while in the double mutant sgs1Δ exo1Δ , repair by gene conversion and viability are decreased to ∼11% . Altogether our data suggest that resection activates long stretches of ssDNA in the homology search and plays an important role in the choice of donor sequences . We previously found that decreased resection in sgs1Δ exo1Δ cells leads to a partial deficiency in homologous recombination . In haploid cells it is not possible to follow the fate of broken unrepaired chromosomes because these cells die due to the loss of essential genes upon resection and missegregation of the acentric part of the chromosome . To verify the fate of broken chromosomes where the breaks are very poorly resected , we used a disomic strain that carries a second truncated copy of chromosome III with the HO break site close to its end ( Figure 4A ) . In this assay most wild-type cells repair the break by BIR ( 82% ) , a small fraction of cells can repair the break by gene conversion ( 13% ) using very short homology ( 46 bp ) distal to the DSB , and the frequency of chromosome loss is only 4% ( Figure 4B ) . These cells survive normally even when the DSB is not repaired because there is second copy of chromosome III that is not cut . The efficiency of repair was estimated by plating wild-type , sgs1Δ , exo1Δ and sgs1Δ exo1Δ cells on YEPGal plates and replica-plating the grown colonies onto plates lacking adenine or leucine . In exo1Δ and sgs1Δ single mutants , there was a slight increase of chromosome loss and gene conversion at the expense of BIR consistent with a previous report ( Figure 4B ) [31] . In sgs1Δ exo1Δ cells , DSB repair efficiency unexpectedly is comparable to wild-type cells and most cells maintain the chromosome that was cut by HO endonuclease . Importantly , Ade+ colonies are not sectored , indicating that the chromosome that was cut by HO endonuclease is maintained well for generations . Previous estimations of DSB repair by gene conversion in sgs1Δ exo1Δ mutant haploid cells showed a decrease by half or more [4] , [5] . To make sure that the Ade+ Leu− colonies correspond to repair by BIR , we verified the size of 27 individual repair products from Ade+ Leu− colonies using pulsed-field gel electrophoresis ( PFGE ) and subsequent probing with an ADE1 probe . We observed that all cells have products corresponding to the expected BIR repair product size , however 10 out of 27 have an additional product or several products similar in size to the initial chromosome III or slightly shorter . Examples of these products are shown in Figure 4C . Short chromosome products correspond to 9% of total products from 27 Ade+ Leu− colonies as measured by the relative band intensities of all the repair products . The presence of two or more products , often observed as bands with different intensities , suggested that the repair process occurred after cells divided or that the two sister chromatids were repaired by different pathways . As previously demonstrated , sgs1Δ exo1Δ cells are deficient in the damage checkpoint response [3] , [5] , and because DSB ends are not processed normally in these cells , a broken chromosome is not lost but instead cells divide with the damaged chromosome that eventually is repaired . To check whether these repair products are within the same cell or in independent cells we streaked several Ade+ Leu− colonies for single cells and further analyzed five such newly grown individual colonies for their repair products . In this case only either a BIR product or a single short product was observed ( Figure 4C ) . The alternative products are very similar in size to the cut chromosome III , suggesting that they arose by de novo telomere formation . To verify this , we sequenced short chromosome ends and confirmed new telomere formation mostly within the first 5 kb from the DSB ( Figure 5A and 5B ) . Rarely telomeres were added at a distance of over 10 kb away from the break , suggesting that even in sgs1Δ exo1Δ cells the broken chromosome is eventually degraded and telomeres are added far from the HO break site . The 3′ ends are stable only for several hours and later are degraded [20] . Therefore , it is likely that both strands of unprotected ends are eventually degraded in sgs1Δ exo1Δ cells and telomeres can be added further away from the break . Examples of sequences where de novo telomeres were formed are shown in Figure 5A and 5B . Most telomeres were added at short 1–5 GT-rich sequences , a characteristic similar to spontaneous telomere formation [32] . Therefore , slow resection greatly stimulates de novo telomere formation , but does not change the sequence preference where telomeres are added . Importantly , we verified 30 Ade+ Leu− colonies in sgs1Δ exo1Δ single mutant and in wild-type cells and did not observe any short de novo telomere products ( data not shown ) suggesting that these enzymes have a redundant function in inhibiting de novo telomere formation at DSBs likely related to their role in resection . In the BIR assay we described above , the HO break is close to a chromosome end that could stimulate telomere formation in cells with poor resection . To test whether proximity to telomeres affects new telomere formation in resection deficient cells , we used a diploid sgs1Δ/sgs1Δ exo1Δ/exo1Δ strain to carry out an allelic recombination assay between two chromosomes III identical to the one used for resection verification ( Figure 2A ) . Here the break is induced at the MATa locus in the middle of the chromosome . The proximal and distal parts of the chromosome that is cut by HO endonuclease has ADE1 and THR4 markers , respectively , that allowed us to follow product formation on minimal media plates ( Figure S4 ) . 19% of cells repair the break by gene conversion ( Ade+ Thr+ ) and only 2% show chromosome loss ( Ade− Thr− ) . The lower level of gene conversion in this assay compared to the ectopic recombination assay between MATa and MATa-inc [5] likely results from the necessity for resection of a 0 . 7 kb long Ya sequence during allelic recombination between MATa and MATα-inc . The rest of the cells maintained the ADE1-marked centromeric part of the chromosome that could correspond to either BIR or de novo telomere addition . To distinguish between these repair pathways , we analyzed products from 29 Ade+ Thr− colonies by PFGE and again observed a dramatic increase in new telomere formation at the DSB sites . All tested Ade+ Thr− colonies contained BIR products and 6 out of these ( ∼20% ) also contained de novo telomere products ( Figure S4 ) . Therefore , slow resection stimulates de novo telomere formation even when the break is in the middle of the chromosome . In a similar study at the same locus in rad52Δ mutant cells that simply cannot repair a DSB but resect normally , de novo telomeres are not formed unless long telomere seeding sequences are provided [33] . Altogether these data suggest that slowly processed DSB ends gain features that predispose them to form de novo telomeres . Pif1 is a major telomerase inhibitor in budding yeast [34] . In pif1Δ mutants , telomeres are longer and new telomeres are formed more frequently at spontaneous and induced chromosomal breaks [[35] , [36] , [37]; reviewed in [38]] . Interestingly , Pif1 was shown recently to be phosphorylated upon DNA damage in a Mec1-Rad53-Dun1-dependent manner and this phosphorylation is needed for Pif1 to inhibit de novo telomere formation [39] . Previously , we and others demonstrated that during G2/M DNA damage checkpoint arrest , Mec1/Ddc2 recruitment and Rad53 phosphorylation are partially defective in sgs1Δ exo1Δ cells [3] , [5] . Therefore , we thought that increased de novo telomere formation in sgs1Δ exo1Δ mutant cells could be due to poor damage checkpoint activation and lack of phosphorylated Pif1 [39] . To explore this possibility , we tested whether the frequent de novo telomere formation phenotypes observed in both sgs1Δ exo1Δ and pif1Δ mutants are epistatic . We examined DSB repair products in pif1-m2 and pif1-m2 sgs1Δ exo1Δ mutant cells . The pif1-m2 mutation eliminates the nuclear form of Pif1 while mitochondrial Pif1 is still present [37] . Surprisingly , we found that pif1-m2 single mutants are defective in BIR , as only half of the colonies retain growth on ade- plates ( Figure 4B ) . The detailed analysis of Pif1's role in BIR will be presented elsewhere ( W . H . C . and G . I . , data not shown ) . Here we analyzed repair products from 30 individual Ade+ Leu− colonies of pif1-m2 sgs1Δ exo1Δ triple mutant cells and 60 individual Ade+ Leu− colonies of pif1-m2 mutant cells ( Figure 4D and 4F , and data not shown ) . Only 1 out of 60 Ade+ Leu− products from pif1-m2 cells showed repair by de novo telomere formation ( Figure 4F ) . However , in pif1-m2 sgs1Δ exo1Δ triple mutants almost all ( 29/30 ) products contained either BIR and de novo telomere or just de novo telomere products ( Figure 4D ) . De novo telomere products constituted 53% of all products from Ade+ Leu− colonies as measured by the intensity of the bands corresponding to the BIR and de novo telomere products . In pif1-m2 sgs1Δ exo1Δ triple mutants , we observed much better maintenance of the broken chromosome when compared to pif1-m2 cells ( Figure 4B ) , however this increase in Ade+ colonies turned out to correspond to repair via new telomere addition . Similar results we obtained in pif1-4A sgs1Δ exo1Δ mutant where pif1 phosphorylation mutant protein was defective in inhibiting new telomere formation at DNA breaks [39] . In pif1-4A sgs1Δ exo1Δ mutant 19 out of 20 products from Ade+ Leu− colonies formed new telomeres ( data not shown ) . In conclusion these results demonstrate that Pif1 actively suppresses new telomere formation even in cells that are partially defective in the DNA damage checkpoint response . We sequenced the ends of de novo telomeres in pif1-m2 sgs1Δ exo1Δ mutant cells and showed that most of them are formed within the first 1 kb from the DSB , which are even closer than those observed in sgs1Δ exo1Δ double mutant cells ( Figure 5A and 5B ) . In triple pif1-m2 sgs1Δ exo1Δ mutants , there is no further decrease in resection when compared to sgs1Δ exo1Δ cells ( Figure S5 ) , indicating that Sgs1/Exo1 and Pif1 independently and in different way suppress de novo telomere formation at DSB ends . In sgs1Δ exo1Δ mutant cells , checkpoint response in response to a single DSB is decreased [3] , [5] . Therefore one possible reason for the very high rate of new telomere formation observed in sgs1Δ exo1Δ cells is the decreased damage checkpoint response . However we clearly demonstrated in the section above that Pif1 , which needs to be phosphorylated by Mec1 in order to inhibit new telomere formation , is still active in sgs1Δ exo1Δ cells . To examine further whether decreased checkpoint response in sgs1Δ exo1Δ cells is exclusively responsible for the very high level of new telomere formation , we tested DSB repair ( BIR assay ) in rad24Δ and rad9Δ mutants that are damage checkpoint deficient . Again we analyzed 20 products from Ade+ Leu− colonies from each mutant by PFGE . We found only 1 out 40 products in rad24Δ corresponds to new telomere formation ( data not shown ) . Further we constructed a triple mutant mec1Δ sml1Δ pif1-m2 and again analyzed the frequency of new telomere formation . We found only 5 out of 30 products correspond to new telomere formation ( Figure 4E ) , which is significantly more than in pif1-m2 but also much less than in sgs1Δ exo1Δ pif1-m2 where almost all Ade+ Leu− colonies contained new telomere products . Together these data suggest that checkpoint deficiency in sgs1Δ exo1Δ cells alone does not explain the very high rate of new telomere formation , and points towards resection as an important factor . Telomerase is recruited to single-stranded overhangs at the ends of chromosomes via Cdc13 and the Ku complex [reviewed in [40]] . Both proteins are needed for de novo telomere formation at DSBs [36] , [41] . Deletion of YKU70/80 almost completely suppresses the increased de novo telomere formation observed in pif1Δ cells [36] . Another function of Ku at telomeres is protection from nucleases [42] , [43] , [44] . In sgs1Δ exo1Δ cells , DSB ends are processed minimally and so the function of the Ku complex is presumably limited to telomerase recruitment . To test whether slow resection at DSBs bypasses the need for Ku in de novo telomere formation , we constructed the quadruple mutant pif1-m2 sgs1Δ exo1Δ yku70Δ and measured the frequency of de novo telomere formation at DSB ends . As shown in Figure 4G , most of the DSBs still lead to de novo telomere formation ( 22/29 ) , however the total intensity of new telomere products dropped to 15% compared to 53% observed in pif1-m2 sgs1Δ exo1Δ cells . Similarly in sgs1Δ exo1Δ yku70Δ cells we observed a relatively high number of new telomere formation ( 10 out of 40 products ) comparable to sgs1Δ exo1Δ . However , the intensity of new telomere product was again reduced ( data not shown ) . Therefore , slow resection exhibited by sgs1Δ exo1Δ mutants partially suppresses the need for the Ku complex in de novo telomere addition at DSBs . The Ku complex plays the opposite role of Sgs1 and Exo1 as it protects DNA ends from degradation . Because in the absence of resecting enzymes Ku becomes partially dispensable for new telomere formation , we propose that the role of Sgs1 and Exo1 specifically in resection , rather than other functions of these enzymes , is important in preventing new telomere formation at DSBs . Cdc13 was shown to be recruited to a DSB in the middle of a chromosome even in wild-type cells where telomeres are not formed [45] . To verify whether Cdc13 recruitment is stimulated at poorly resected DSB ends we performed chromatin immunoprecipitation ( ChIP ) with Myc-tagged Cdc13 in wild-type , pif1-m2 , sgs1Δ exo1Δ and pif1-m2 sgs1Δ exo1Δ mutant cells . We measured Cdc13 recruitment before HO break induction and 8 hours after break induction when significant BIR products start to accumulate [25] . As shown in Figure 5C , recruitment of Cdc13 increased at 8 hr after break induction in sgs1Δ exo1Δ and pif1-m2 sgs1Δ exo1Δ cells about 3- to 6-fold relative to wild-type cells . These results suggest that slow resection results in higher or longer lasting recruitment of Cdc13 that may then stimulate de novo telomere formation . While constructing additional gene deletions in sgs1Δ exo1Δ strain we observed that gene targeting in sgs1Δ exo1Δ mutants was surprisingly very efficient . This is in contrast to a general decrease in DSB-induced recombination which we tested in the different assays described above . We attempted to delete several genes in sgs1Δ exo1Δ cells and in each case gene targeting was highly efficient . For example , deletion of ygr042w ORF with ygr042w::KanMX cassette was 140-fold higher in sgs1Δ exo1Δ than in wild-type cells . To examine more carefully the efficiency of gene targeting we transformed a thr4::URA3 cassette containing 1 . 1 SmaI-HindIII URA3 fragment into ura3–52 wild-type cells , and derivative sgs1Δ , exo1Δ , dna2Δ pif1-m2 and sgs1Δ exo1Δ mutant cells and determined the amount of Ura+ and Ura+ Thr− colonies . We further normalized the level of Ura+ colonies to the plating efficiency of each strain and compared the efficiency of gene targeting in wild-type and mutant cells . As shown in Figure 6A , the targeting efficiency increased in all single mutants , by 3- to 4-fold in the absence of Sgs1 or Dna2 and over 30-fold in the absence of Exo1 , relative to wild-type cells . Strikingly , when both Sgs1 and Exo1 are absent the efficiency of targeting increases over 600-fold . It is likely that in the absence of enzymes which process DSB ends the stability of transformed DNA is increased and therefore gene targeting is more efficient . We also measured the accuracy of targeting among over 200 Ura+ colonies and observed that in sgs1Δ exo1Δ mutant cells almost all targeting events are accurate ( 99% ) , which is significantly higher than in wild-type cells ( 72 . 2%; p<0 . 05 ) . It is possible that in wild-type cells quick degradation of the 5′ strands exposes the URA3 marker sequence of the targeting cassette and activates it in a homology search that leads to integration at a locus other than THR4 . The strain we transformed was ura3–52 , therefore one possibility was that the thr4::URA3 cassette was integrated at ura3–52 locus . To test this possibility we transformed the same thr4::URA3 cassette into the strain where the entire URA3 ORF was deleted [46] . In this strain , the URA3 marker within the cassette shared no homology with the genome . However , again we observed that only 78% of gene targeting was accurate . Therefore inaccurate integration is not caused by resection reaching URA3 sequences within cassette that stimulates integration at the ura3–52 locus . However it is still possible that marker sequence within the cassette can , when resected , inhibit accurate integration . The accuracy of gene targeting rose from 72% to 87% or 95% when we increased the flanking homology length within thr4::URA3 cassette from 1 . 1 to 2 . 5 or 6 . 5 kb . This suggests again that cells use longer stretch of ssDNA in homology search and it is beneficial for the repair accuracy . With longer homology we also observed 6–7 fold increase in targeting efficiency in wild-type cells , which is still 2 orders of magnitude less than the increase observed in sgs1Δ exo1Δ cells . Only a slight increase in efficiency of targeting with longer homology but normal resection is not surprising because the resection rate ( 4 kb/hr ) is high enough to degrade the cassette very quickly even when flanking homology is relatively long .
We determined for the first time the length of ssDNA generated during DSB repair and showed that resection determines sequences used for homology search . Three different assays with different kinetics were used to measure resection: allelic recombination , ectopic recombination and BIR . Allelic recombination lasts about 2–3 hours which is similar to recombination between sister chromatids [47] and the average length of ssDNA accumulated during such repair is only 2–4 kb on each side of the break . Longer ssDNA ( 3–6 kb ) was formed during the slower ectopic recombination assay . In these two assays , most of ssDNA accumulated before strand invasion , as the final repair product is formed 1 . 5 to 3 hours after DSB induction and only 20 minutes after strand invasion ( Figure 1 and Figure 2; [24] and J . Haber , personal communication ) . We suggest that most ssDNA we detected in gene conversion assays participates in homology search . The 2–6 kb of ssDNA greatly exceed the amount of homology needed for efficient repair but may be beneficial for the fidelity of repair . If only very short sequences next to the break are involved in the homology search it may lead to recombination with short repeats . Involvement of longer ssDNA tails in the homology search would limit such recombination events . Indeed we demonstrated that in a mutant that generates only very short ssDNA at DSB ends , the homology search is limited to the vicinity of the break and repair involves only a short repeated sequence . In genomes with high numbers of short repeats , such as the 1 million copies of the 0 . 3 kb-long Alu repeats found in the human genome , involvement of longer sequences in the homology search can constitute an additional barrier for nonallelic recombination besides mismatch repair or the cohesin-mediated bias toward intersister chromatid recombination . However , resection is a double-edged sword as activation of longer ssDNA in the homology search also brings the risk of involvement of repetitive sequences located further away from the break [48] . Equal involvement of both DSB ends probably decreases such events [49] . Also , cells are able to downregulate resection to prevent too extensive chromosome degradation at DSBs [13] . Another reason why cells resect long ssDNA during repair probably relates to DNA damage checkpoint activation and maintenance . This could be particularly important during very slow repair by BIR where we found 10–15 kb of ssDNA . In BIR strand invasion occurs with kinetics comparable to gene conversion but 3′ ends prime new DNA synthesis only 2–3 hours later [25] , [49] . The much longer ssDNA formed during BIR than during gene conversion suggests that resection continues after strand invasion . It is likely that this extensive resection after heteroduplex formation stimulates maintenance of DNA damage checkpoint arrest until cells repair the break . In conclusion short ssDNA might be sufficient for strand exchange processes but not for the checkpoint arrest that is needed to complete repair . Telomere addition at spontaneous or induced DSBs was previously described in yeast , mouse and human cells [reviewed in [38]] . It is an extremely rare chromosome aberration in wild-type cells but frequent in tumor cells . Here we observed that cells in which resection is limited to a few hundred base pairs frequently use the alternative pathway of repair via de novo telomere addition . When the yeast inhibitor of telomerase , Pif1 , was eliminated in poorly resecting mutants , most cells repaired the DSB using de novo telomere addition . Why do sgs1Δ exo1Δ mutant cells very frequently use the alternative pathway of repair via de novo telomere addition ? We think that a combination of repair , checkpoint and resection defect in this mutant is a key for the high level of new telomere addition at DSBs . Separately repair or checkpoint defective mutants do not affect new telomere formation so dramatically . In mutants such as rad52Δ that do not repair the breaks , new telomeres are not added [33] or added infrequently [35] . Also in checkpoint deficient cells like rad24Δ or rad9Δ there is only a slight increase in new telomere formation at DSBs . However in a sgs1Δ exo1Δ mutant , besides repair and checkpoint deficiency , decreased degradation at a DSB site prevents chromosome loss that gives cells more chance for alternative repair . Indeed all de novo telomeres in sgs1Δ exo1Δ and more than half in pif1-m2 sgs1Δ exo1Δ mutants formed only when cells divided once or more times after DSB induction . Another factor stimulating de novo telomere formation is that in sgs1Δ exo1Δ cells binding of Cdc13 , a protein essential for telomerase recruitment , is increased . It is possible that at poorly resected DSBs like at telomeres that have very short ssDNA overhangs , Cdc13 can successfully compete with RPA for ssDNA . We also demonstrated that decreased resection partially bypassed the need for Ku70/80 for de novo telomere formation . The Ku complex is required for spontaneous and MMS-induced de novo telomere addition in cells with normal resection [36] , [41] . Ku contributes to this process by direct recruitment of telomerase and/or by protecting DSB ends from degradation . Here we demonstrate , in a strain where ends are relatively stable with minimal ssDNA formation , a dramatic increase in de novo telomere formation even in the absence of Ku . The fact that deletion of both Sgs1 and Exo1 partially bypasses the need for the Ku complex that protects DNA ends from degradation indicates that the role of Sgs1 and Exo1 in resection , rather than another function of these enzymes , is important in preventing new telomere formation at DSBs . Gene targeting is a major technique in molecular biology that allows the precise modification of the genome and is envisaged as being a major future therapeutic approach for genetic disorders . One of the major problems of gene targeting in higher eukaryotes is the low efficiency and particularly the very low accuracy of this process . Here we observed a dramatic increase in gene targeting efficiency and accuracy in the absence of enzymes which resect 5′ strands , Sgs1 and Exo1 ( Figure 6A ) . In the absence of both Sgs1 and Exo1 , DSB ends are processed by the MRX complex and Sae2 to generate 100–1 , 000 bp of ssDNA [4] , [5] . This is similar in size to the homologous sequence frequently provided as transformed DNA for gene targeting . We suggest that transformed DNA is similarly processed initially by the MRX complex and later by Exo1 and Sgs1 . In the absence of both Sgs1 and Exo1 , this transformed targeting cassette is probably stable for much longer than in wild-type cells , having only short ssDNA at the ends active in the homology search ( Figure 6B ) . Together this gives a higher chance for correct integration into the genome . Longer homology within the transformed cassette increases gene targeting only slightly ( 6–7 fold ) , and is not comparable to the increase observed in the absence of processive resection enzymes . This is not surprising given the high 4 kb/hr resection rate . Sgs1 has an additional role in DSB repair as it suppresses the crossover pathway , presumably by double Holliday junction dissolution [22] , [50] . Gene targeting presumably relies on crossover events between the transformed DNA cassette and the chromosome , therefore it is possible that besides decreased resection the absence of Sgs1 can stimulate targeting by increasing the crossover pathway . In the absence of both Sgs1 and Exo1 the accuracy of gene targeting is also increased . One possibility is that exposure of marker sequences within the transformed cassette inhibits correct integration even when the marker has no homology within the genome . Future experiments with cassettes that do not have a marker will help to investigate this possibility . An alternative explanation could be that quick exposure of the 3′ ends may lead to their degradation [20] leaving less homology on the ends of the transformed cassette and therefore more inaccurate targeting . It is likely that in human cells where Exo1 and BLM play comparable roles in resection to their yeast counterparts [3] , gene targeting efficiency or accuracy could be increased by temporary depletion of both enzymes . Indeed , in human cells depletion of BLM helicase stimulates gene targeting [51] . However , in human cells there are additional nucleases that are not present in yeast such as cytoplasmic/nuclear TREX1 3′ exonuclease that is expressed in all cells . TREX1 was shown to degrade DNA arising from DNA replication , DNA damage , endogenous retroviruses or viral infection and TREX1-null mice accumulate DNA leading to chronic damage checkpoint activation [52] , [53] [54] . These phenotypes strongly suggest that transformed DNA cassettes are a substrate for TREX1 as well . We think that one reason for the relatively less efficient and much less accurate gene targeting observed in human and other organisms when compared to yeast is due to the presence of additional nucleases that degrade transformed DNA . Future experiments will examine whether TREX1 interferes with gene targeting . A tempting speculation is that Sgs1 and Exo1 degrade any linear DNA in cells like the cDNA of retrotransposons or viral DNA thus protecting the genome from integration of foreign DNA . Accordingly in the absence of Sgs1 , the frequency of retrotransposition increases in yeast [55] . Taking these observations together we have demonstrated that the extent of resection from a DSB has a fundamental role in determining the outcome of DSB repair and in the maintenance of genome stability . It will be important to understand how resection is regulated at a molecular level by checkpoint proteins and by chromatin remodeling to diminish the deleterious pathways of repair .
All strains used here are derivatives of four strains: ( i ) tGI354 to study ectopic recombination ( hml::ADE1 MATa-inc hmr::ADE1 ade1 leu2–3 , 112 lys5 trp1::hisG ura3–52 ade3::GAL::HO arg5 , 6::HPH::MATa ) ; ( ii ) MK181 , a gift from Martin Kupiec , to study recombination template choice with respect to resection distance ( MATa-inc ura3-HOcs-inc ade3::GAL-HO ade2-1 leu2–3 , 112 his3–11 , 15 trp1–1 can1–100 ) ; ( iii ) yGI234 to study allelic recombination ( hml::ADE1/HML MATa/MATα-inc hmr::ADE1/HMR ade1/ade1 leu2–3 , 112/leu2–3 , 112 lys5/LYS5 trp1/trp1 ura3–52/ura3–52 THR4/thr4::URA3 ade3::GAL::HO/ADE3 ) ; and ( iv ) disomic AM1003 to study BIR ( MATa-LEU2-tel/MATα-inc ade1 met13 ura3 leu2–3 , 112/leu2 thr4 hml::ADE1/hml::ADE3 hmr::HPH ade3::GAL-HO FS2::NAT/FS2 ) . A list of all strains is presented as supplemental material ( Table S1 ) . Measurement of resection during DSB repair was done by following the transient loss of restriction enzyme cutting sites at different distances from the break . DNA isolated by glass bead disruption using a standard phenol extraction method was digested with restriction enzymes and separated on 0 . 8% agarose gels . Southern blotting and hybridization with radiolabeled DNA probes was carried out as described previously [56] . Multiple DNA probes used for hybridization to detect 5′ strand resection beyond the restriction site , as well as the sequences of DNA primers used to prepare the probes by PCR , are listed in Table S2 . Intensities of bands on Southern blots corresponding to probed DNA fragments were analyzed with ImageQuant TL ( Amersham Biosciences ) . Quantities of DNA loaded on gels for each time point were normalized using a TRA1 gene DNA probe . DSB end resection beyond each restriction site for each time point in the ectopic recombination assay was estimated as a percentage of the signal intensity loss corresponding to the fragment of interest before break induction . In allelic recombination and in BIR assays both the HO cut chromosome and the homologous template chromosome have the same sequences and only the cut chromosome is being resected . In these two assays , therefore , we calculated DSB end resection beyond each restriction site as a percentage of the signal intensity corresponding to half of the signal of the fragment of interest before break induction . The donor choice assay was performed as previously described [30] . The probe used for detecting products of DSB repair was almost the entire LYS2 ORF ( XbaI – HindIII ) . The kinetics of product formation ( repair ) at each time point was determined by dividing the normalized intensity of the band corresponding to the product by the normalized intensity of the initial uncut band at time 0 . To analyze DSB repair products in AM1003 derivative mutant strains , chromosomal DNA plugs were prepared and separated on a 1% agarose gel at 6 V/cm for 30 hrs ( initial time = 10 s , final time = 35 s ) by using the CHEF DRII apparatus ( Bio-Rad ) , followed by Southern blotting and hybridization using a DNA fragment containing ADE1 sequence as a probe . To determine where de novo telomeres were added , PCR was carried out using one primer complementary to telomeric repeat sequence ( CA16; 5′-CACCACACCCACACAC-3′ ) [57] and another primer at a different distance from the HO break site ( Telo-F10 [∼5 kb upstream of HO]; 5′-GTCGTGCAGGTACGACTTTA-3′ , Telo-F8 [∼4 kb upstream of HO]; 5′-TCTTTCCCTCGCTACTCACA-3′ , Telo-F6 [∼3 kb upstream of HO]; 5′-GTGAGCGTACAAGAAGCAAA-3′ , Telo-F2 [∼2 kb upstream of HO]; 5′-GTTAAGTAGTAAGTTTGCGGAG-3′ , Telo-F4 [∼1 kb upstream of HO]; 5′-CCAACTTTCTAGTATTCGGACA-3′ ) . Amplified PCR products were isolated from agarose gels and sequenced . ChIP analysis of Cdc13 binding was performed and quantification was done as described previously [5] . α-Myc antibody was purchased from Sigma ( 9E10 ) . After immunoprecipitation and reverse crosslinking , SyBrGreen-based real-time PCR was carried out on an ABI 7900 machine using a pair of primers which anneal 1 kb upstream of the HO break site ( MATX-F2 , 5′-GGTAGGCGAGGACATTATCTATCA-3′; MATX-R3 , 5′-GAAGAATACCAGTTTATCTCGCATTCAAATC-3′ ) as well as primers specific for the PRE1 gene located on chromosome V as a control . An approximately 2 . 1 kb thr4::URA3 cassette was PCR amplified from genomic DNA of the AM476 strain and 1 µg was used as a gene replacement construct for transformation of 1×108 JKM139 cells and its derivative mutant cells . To calculate gene targeting efficiency , the number of Ura+ colonies was normalized by plating efficiency and transformation efficiency with uncut centromeric YCp50 plasmid for each strain . The accuracy of gene replacement was calculated as the number of Ura+ Thr− transformants . | Chromosomal breaks occur spontaneously or are induced by ionizing radiation and many chemotherapeutic drugs . DNA double-strand breaks are processed by nucleases and helicases in yeast and human to generate single-stranded DNA that is then used for repair by recombination with homologous chromosome . Single-stranded DNA at chromosomal breaks also constitutes a signal for cells to arrest cell cycle progression until the DNA damage is repaired . This study examines the consequences of elimination of enzymes that process chromosomal breaks to single-stranded DNA on the fidelity of repair and genome stability in the model organism yeast . Mutants deficient in these enzymes often fail to repair the breaks by homologous recombination and instead add new telomeres at the breaks . Formation of new telomeres is associated with partial loss of the chromosome arm distal from the break . Such chromosomal aberrations were frequently observed in tumor cells and are responsible for about 10% of human genomic disorders resulting from chromosomal abnormalities . We also observed that elimination of enzymes that process chromosomal breaks into single-stranded DNA greatly stimulates genome manipulation by gene targeting , suggesting that transformed DNA is also a substrate for degradation by these enzymes . We discuss the possibility of using a similar approach in mammalian cells where gene targeting is inaccurate and less efficient when compared to yeast . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"biology/recombination",
"genetics",
"and",
"genomics/gene",
"therapy",
"molecular",
"biology/dna",
"repair"
] | 2010 | Defective Resection at DNA Double-Strand Breaks Leads to De Novo Telomere Formation and Enhances Gene Targeting |
A fundamental goal in cellular signaling is to understand allosteric communication , the process by which signals originated at one site in a protein propagate dependably to affect remote functional sites . Here , we describe the allosteric regulation of the receptor tyrosine kinase KIT . Our analysis evidenced that communication routes established between the activation loop ( A-loop ) and the distant juxtamembrane region ( JMR ) in the native protein were disrupted by the oncogenic mutation D816V positioned in the A-loop . In silico mutagenesis provided a plausible way of restoring the protein communication detected in the native KIT by introducing a counter-balancing second mutation D792E . The communication patterns observed in the native and mutated KIT correlate perfectly with the structural and dynamical features of these proteins . Particularly , a long-distance effect of the D816V mutation manifested as an important structural re-organization of the JMR in the oncogenic mutant was completely vanished in the double mutant D816V/D792E . This detailed characterization of the allosteric communication in the different forms of KIT , native and mutants , was performed by using a modular network representation composed of communication pathways and independent dynamic segments . Such representation permits to enrich a purely mechanistic interaction-based model of protein communication by the introduction of concerted local atomic fluctuations . This method , validated on KIT receptor , may guide a rational modulation of the physiopathological activities of other receptor tyrosine kinases .
Signal transduction in cells is regulated through complex networks of dynamical interactions between macromolecules . The proteins controlling these communication networks respond to changes in the cellular environment by switching between different conformational states [1] . A great number of proteins acting as ligand/substrate-dependent activators contribute to cell signaling pathways . Among such proteins , receptor tyrosine kinases ( RTKs ) play a leading role in the regulation of physiological processes crucial for cell survival , growth , proliferation and differentiation [2] . In general , the binding of a ligand to the extra-cellular region of RTKs induces receptor dimerization which in turn leads to the activation of the intracellular tyrosine kinase domain . All RTKs are ATP-binding proteins and catalyze the same reaction , i . e . the transfer of the to specific tyrosine sites . They thereby trigger multiple signaling cascades via the recruitment of enzymes and adaptor proteins [3]–[5] . Kinase domains are essentially molecular switches that can adopt at least two extreme conformations – the “on” and “off” states , corresponding to maximum and minimum protein activity . The catalytically efficient “on” conformation is very similar ( conserved ) among all kinases . The “off” state ( inactive ) is not subject to the biological and structural constrains that the active state must supply , and consequently different types of kinases have developed distinct ( divergent ) conformations . In the native state , RTKs preexist in several conformations ranging between these two extreme ones . The equilibrium between the various conformational populations can be displaced by the binding of an extra-cellular ligand or an inhibitor , phosphorylation events or point mutations [6] . In particular mutations are mainly responsible for the deregulation of RTK activity , provoking various forms of cancer , inflammatory diseases ( e . g . arthritis ) and neuronal disorders ( e . g . Alzheimer's pathology ) [7] . The binding of a ligand/substrate/inhibitor , a point mutation , the modification of the amino acids ionization state or environmental changes ( pH , temperature , concentration , ionic forces ) can be considered as perturbations of a biological object and described in terms of signal propagation theory and molecular dynamics [8]–[12] . The propagation of a perturbation signal across a protein three-dimensional structure relates to the concept of allosteric coupling or communication . The increasing amount of high-quality experimental data provides evidence that allosteric communication is a general phenomenon observed in the majority of proteins [13] . Allosteric regulation is at play when a given perturbation at a specific site of a protein alters the conformational and/or thermodynamic state of a spatially distinct site in the same molecule . According to the classical view [14] , [15] , allosteric coupling occurs as an outcome of a network of interactions that physically link the coupled sites – two-state structural-based signal transmission through a unique pathway . However , multiple experimental evidences have shown that allosteric coupling can be mediated solely by transmitted changes in the protein dynamics/motions [6] as a consequence of a re-distribution of the protein conformational populations [16] . This statement suggests the existence of multiple potential communication tracks with a preferred pathway under a given condition . Attempts were recently made to classify protein allosteric effects and illustrate those using typical examples [17] . Such examples reflect the dual nature of allosteric coupling , which can be manifested in the form of a global conformational change or the modification of local atomic fluctuations . In either case , information transmission may take place through well-structured connectivity pathways or multiple dynamic micro-pathways in the protein residue network [18] , [19] . A number of in silico techniques aiming at predicting connectivity pathways that mechanically transmit allosteric interactions have been developed , based on evolutionary conservation information [20]–[22] , native contacts within the protein residue network [23] , [24] or dynamical correlations from molecular dynamics ( MD ) simulations [25]–[29] . Some efforts have also been made to identify specific sites in proteins that are able to accumulate energy in response to perturbations , even occurring at distant locations [11] , [30] , [31] . These approaches were developed with the aim of providing a rational understanding of various allosteric effects in divergent proteins and their complexes . Mutation-driven allosteric regulation bears a particular interest due to the importance of mutations in clinical and pharmacological research . Clarkson and co-authors have observed two types of propagation responses to point mutations in serine proteinase eglin c , a small globular protein [32] . One type of propagation response was discovered in the form of contiguous pathways of dynamic changes and the other as dispersed changes associated with subtle protein backbone deformations . Following these observations , Schrank and co-authors were able to design adenylate kinase mutations that impact distant functional sites only through the modulation of the protein atomic fluctuations [33] . MD simulations of the native and mutated forms of the cytoplasmic protein kinase ABL and the EGF receptor probed the global allosteric changes induced by oncogenic mutations [34] . In these cases , the allosteric communication was described in terms of a dynamic coupling between structurally rigid and conformationally adaptive segments [25] , [35] . It was supposed that these structural elements form a dynamic network of interacting functional regions that control the long-range inter-domain communication and allosteric activation in protein kinases . We are studying the type III RTK family , which includes the stem cell factor ( SCF ) receptor KIT , along with PDGFR , Flt3 and FMS . Upon the binding of the physiological ligand SCF , KIT ectodomain undergoes a conformational change that leads to dimerization and in turn activation of the intra-cellular protein kinase domain ( PTK ) . The activation of KIT cytoplasmic region is characterized by a large conformational transition , that involves the detachment of the autoinhibitory juxtamembrane region ( JMR , residues 544–581 ) from PTK , the opening of the activation loop ( A-loop , residues 810–835 ) away from the ATP binding site and the structural rearrangement of regions such as the C-helix ( residues 631–647 ) and the glycine-rich loop ( residues 596–601 ) ( Figure 1 ) . Point mutations localized in the JMR or the PTK of KIT were identified in various forms of cancer ( gastro-intestinal stromal tumors GISTs , acute myeloid leukemia AML , mast cell leukemia MCL , germ cell tumors… ) and mastocytosis [36]–[39] . In particular , the substitution of the amino acid in position 816 located in the A-loop ( indicated as a black sphere on Figure 1 ) from aspartate ( D ) to valine ( V ) is an activating mutation associated with mastocytosis and cancer and is archetypal of the resistance to anti-cancer drugs such as Imatinib ( ® ) [40] , [41] . As was evidenced by X-ray data , the overall fold of KIT kinase domain is not significantly altered by point mutations in position 816 [42] . However , we have previously demonstrated the impact of the D816V mutation on the structure and dynamics of two regulatory segments of the KIT cytoplasmic region [43] . First , we have shown that D816V induces the partial unfolding of the small 817–819 helix in the A-loop , a local effect prompted by the loss of the negative capping of the helix by the aspartate in position 816 . The second and noteworthy event consisted in the stabilization of the anti-parallel in the JM-Switch fragment of the JMR located more than 15 Å away from the mutation site in the inactive state ( Figure 1 A ) . This long-range effect led to a notable secondary structure reorganization of the JMR accompanied by a tertiary restructuring that resulted in an axial repositioning of the JM-Switch respectively to PTK [43] . Such long distance structural effect is indubitably a manifestation of allosteric intra-molecular communication in KIT . The analysis of the interaction network between the JMR and PTK regions of KIT gave preliminary insights into the non covalent contacts alterations induced by the 816V mutation [43] . In the present work , we described the allosteric communication in the native and D816V-mutated KIT in terms of information transmission . We evidenced ( i ) a well-established communication between the activation loop ( A-loop ) and the distant juxtamembrane region ( JMR ) in the native protein , ( ii ) the disruption of such communication in the mutant produced by the oncogenic mutation D816V and ( iii ) the restoring of the communication by in silico mutagenesis through a counter-balancing mutation D792E . The communication patterns observed in the native and mutated KIT correlated with their structural and dynamical properties . Particularly , the long-range effect of the D816V mutation on the JM-Switch fragment of the JMR was completely disappeared in the double mutant D816V/D792E . The JMR structure in the double mutant was found very similar to that observed in the native KIT . The accurate characterization of the allosteric communication in the different forms of KIT , native and mutants , was performed by using a modular network representation composed of communication pathways and independent dynamic segments ( Figure 2 ) . Such representation enriches a purely mechanistic model of protein communication based on well-defined interactions by the introduction of concerted local atomic fluctuations . The approaches were implemented in the form of a MOdular NETwork Analysis ( MONETA ) package coupled with the R software [44] and the python scripting interface of Pymol [45] . This package permits to efficiently build , analyze and visualize modular network representations of protein three-dimensional structures .
Although molecular insight has been gained for the precise assessment of the D816V mutation induced conformational changes in KIT kinase , the description of the allosteric communication taking place between the two functionally distinct and spatially distant receptor regulatory regions – the activation loop ( A-loop ) and the juxtamembrane region ( JMR ) – remains a challenge . In the present study , a two-component molecular model of intra-protein signal propagation was used to: ( a ) determine key residues or regions in KIT kinase involved in allosteric coupling , ( b ) perform a comparative analysis of wild type KIT and D816V mutant allosteric communication profiles , ( c ) propose a protein-based design to neutralize the long-range effect of D816V mutation on KIT structure and dynamics . The allosteric communication across KIT tyrosine kinase was analyzed by using the conformational MD ensembles generated previously for the native ( WT ) and D816V-mutated ( MU ) KIT cytoplasmic region in the auto-inhibited inactive state [43] . Intra-molecular interactions and inter-residue dynamical correlations were computed from the MD trajectories and were used to identify chains of connected residues or communication pathways and clusters of locally coupled residues or independent dynamic segments ( Figure 2 ) . The ability of KIT protein residues to communicate efficiently was evaluated by using the measure of communication propensity [9] . The communication between two residues is estimated as fast when their commute time , expressed as the variance of their inter-residue distance over long MD trajectories [25] , [46] , is small . We supposed that chains of residues interacting by pair and displaying high communication propensities between them would represent pathways of well-defined interactions through which information would be transmitted efficiently . We denote such chains of residues as communication pathways ( CPs ) . Any two adjacent residues in the CP are connected by non-bonded interaction ( s ) and the commute time for transmitting information between any two members of a CP is below a given threshold ( see Materials and Methods for a detailed description ) . To identify regions of KIT that represented the most striking features of the protein's local dynamics , we used a statistical technique , local feature analysis ( LFA ) [47] . This formalism projects the correlation matrix computed from an MD trajectory in such a way that it reduces off-block diagonal correlations and identifies n seed degrees of freedom corresponding to atoms or residues . Further , clusters of neighboring residues centered on the n selected seed residues and displaying concerted local atomic fluctuations were defined ( see Materials and Methods for details on the implementation ) . Assuming that they represent regions of the protein that are independent in terms of their dynamical behavior , we denote such clusters of residues as independent dynamic segments ( IDSs ) . IDSs are believed to play a crucial role in binding and/or in allosteric propagation by shifting or exchanging their energy content [11] , [30] , [31] , [48] . communication pathways and independent dynamic segments can be considered as two different media through which a given perturbation is likely to be propagated either mechanically via well-defined interactions ( CPs ) or via concerted local atomic fluctuations ( IDSs ) . The combination of these two types of analyzes provided a useful two-component modeling framework for the characterization of the allosteric communication in KIT tyrosine kinase in the wild type and mutated forms . Importantly , such framework permits to go beyond a purely mechanistic model of signal propagation in proteins and to account for the duality inherent to the manifestations of allosteric coupling . communication pathways were systematically generated from each residue of both wild type ( WT ) and D816V-mutated ( MU ) KIT proteins . The per-residue percentage of fast commute times , CP maximum length and number of CPs are reported on Figure 3 A . Highly connected residues or hubs , located at the crossroad of many CPs , display about 20% of fast commute times . Structural mapping of CPs characteristic features brings out the structural fragments of KIT that present such hubs ( Figure 3 B ) : the loop following the C-helix ( C-loop-2 , residues 649–655 ) , the E-helix ( residues 764–785 ) , the catalytic loop ( residues 790–797 ) , the ( residues 804–808 ) , the loop immediately following the A-loop ( P+1 loop , residues 835–843 ) and the F-helix ( residues 850–865 ) . Moreover , a dense network of CPs links three of these structural fragments , namely C-loop-2 , the E-helix and ( Figure S1 ) . In addition , residues L678 , I798 , L799 , L800 , F858 and L862 that compose KIT catalytic spine ( see below ) also display high communication capabilities ( protein partial surface shown on Figure 3 B , right panel ) . The biological significance of these structural fragments was previously reported in the literature . Namely , C-loop-2 was identified as part of a “molecular brake” crucial for RTKs inactive state stabilization [49] . It was also suggested that C-loop-2 may control the long-range inter-monomer coupling in the activation complexes of the receptor tyrosine kinase EGFR [25] . The E-helix was found to stabilize the ATP binding pocket in a large number of kinases through its interactions with the and [50] . The catalytic loop is highly conserved in terms of structure and sequence among kinases and acts as a cornerstone of the active site [51] . The P+1 loop contains a conserved APE motif ( residues 837–839 ) [51] . The F-helix was shown to play an important role in the dynamic assembly of protein kinase active structure , serving as an anchor for two hydrophobic spines that traverse both lobes of kinases , the C ( catalytic ) -spine and the R ( regulatory ) -spine [52] . Furthermore the integrating role of the F-helix for allosteric communication in the cytoplasmic kinase ABL and in the RTK EGFR was recently pointed out [25] . Consequently , the structural fragments of KIT identified by our CP analysis as presenting communication hubs were previously reported as playing crucial functional roles in the activation/deactivation mechanisms of other receptor tyrosine kinases or cytoplasmic kinases . Although the communication profiles of WT and MU KIT share a similar global shape ( Figure 3 A ) , a number of dissimilarities are observed . For example , certain residues display shorter and less probable CPs in MU compared to WT . This deterioration of communication efficiency is particularly evident for residue V559 of the JMR , residue N652 of the C-loop-2 , several residues in the E-helix ( residues 764–785 ) , the catalytic loop residues ( residues 790–797 ) , the two residues 808–809 between the and the A-loop and the residues of the P+1 loop that follows the A-loop . In terms of communication , residues N652 of the C-loop-2 and residues A794 , A795 and N797 of the catalytic loop are involved in dense networks of CPs that span across both lobes of the protein in WT , whereas they communicate efficiently with residues located only within one lobe in MU , the N-lobe for N652 and the C-lobe for the catalytic loop residues . This very reduced number of CPs may indicate a break in the communication between the N- and C-lobes of KIT cytoplasmic region induced by the mutation D816V . By contrast several residues in the N-lobe display slightly increased communication efficiency in MU compared to WT ( Figure 3 A ) . They are located after the JMR ( 583–584 ) , in the loop preceding the C-helix ( residues 625–630 ) and in the C-helix ( residues 631–647 ) . These residues display very poor communication capability in WT while they participate in CPs that extend along the C-helix or across the of the N-lobe in MU . This increased communication of specific regions of the N-lobe is accompanied by a significant reduction of their atomic fluctuations upon D816V mutation . Consequently , the comparative analysis of WT and MU KIT communication profiles reveals that the mutation D816V alters the allosteric communication between the N- and C-lobes of KIT protein , while it favors the emergence of dense and rather localized communication networks within the N-lobe . Independent dynamic segments were identified in both WT and MU proteins . First , a principal component analysis ( PCA ) was performed on the motions of KIT cytoplasmic region . The first 18 and 19 PCA modes were found sufficient to describe the essential dynamics of WT and MU , respectively . They were consequently retained to apply the subsequent local feature analysis ( LFA ) [47] . The LFA formalism selected 18 and 19 seed residues representative of the most striking features of WT and MU local dynamics . These residues were mostly located in KIT flexible regions: the N- and C-terminal extremities , the JMR , the truncated kinase insert domain ( pseudo-KID ) , some solvent exposed loops in the N- and C-lobes and the A-loop ( Figure 4 ) . A similar localization of seed residues was also observed in previously reported studies [47] , [53] . In each form , WT and MU , 10 independent dynamic segments , which we refer to as Si , , were then defined around these seed residues ( Table S1 ) . IDSs represent about one third of the total number of KIT residues and their by-residue atomic fluctuations account for 56% and 60% of the total atomic fluctuations for WT and MU , respectively . IDSs are essentially composed of residues displaying fast communications only with neighboring residues along the sequence in the [i−4; i+4] range: no or very few communication pathways ( CPs ) were generated from residues contained in the IDSs ( traced in orange on Figure 3 A ) . This observation is consistent with IDSs displaying dynamics that are minimally coupled to the other regions of the protein . Noticeably , the protein segments known to participate in KIT activation as key regulatory elements contain IDSs ( Figure 4 ) , namely the auto-inhibiting juxta-membrane region ( JMR , residues 547–581 ) , the C-helix ( residues 631–647 ) , the activation loop ( A-loop , residue 810–835 ) and the G-helix ( residues 877–885 ) that serves as a platform for the peptide substrate binding in KIT active structure [54] . The functional roles of these regions were previously characterized in different contexts of kinase-mediated physiological processes . The juxtamembrane region whose autoinhibitory function is specific to type III RTKs ( KIT , PDGFR , C-FMS and FLT3 ) has also been shown to play crucial roles in the dimerization and activation mechanisms of other RTKs ( EGFR , HER4 ) [55] , [56] . The C-helix is involved in the R ( regulatory ) -spine [52] and has been emphasized as mediating conformational changes in the inactive-to-active transition of several kinases [55]–[59] . The importance of the A-loop conformation and its dynamics for kinases activity in general has been largely documented [4] . Although the G-helix function has not been characterized in details , recent experimental studies have demonstrated the essential roles of its mobility for several kinases [60] , [61] . Consequently , the regulatory segments of KIT identified by our LFA analysis as containing or representing independent dynamics segments were previously reported as the elements mediating conformational changes associated with the activation/deactivation of other receptor tyrosine kinases or cytoplasmic kinases . IDSs are overall located in the same regions of KIT in WT and MU ( Figure 4 ) . However the boundary lines of the IDSs vary between the two proteins . The principal differences are observed for the IDSs S2 and S3 in the JMR ( Figure 4 A , top panels ) , S5 in the N-lobe ( Figure 4 A , middle panels ) and S8 in the A-loop ( Figure 4 A , bottom panels ) . Particularly , in the A-loop of WT , S8 comprises residues 824 to 831 whereas in MU it expands toward the position of the mutated residue V816 and includes a large part of the A-loop ( residues 816–832 ) ( Figure 4 B ) . This expansion of the IDSs S8 is a consequence of increased LFA correlations between the neighboring 816–823 and 824–831 fragments ( Figure 4 A , bottom panels ) . Furthermore , the residues in this sequence ( from 816 to 832 ) display an overall increase of atomic fluctuations in MU ( 2 . 57 Å0 . 90 Å ) compared to WT ( 1 . 73 Å0 . 72 Å ) . In the JMR , the position of S3 in KIT sequence is shifted from residues 574–581 in WT to residues 571–577 in MU ( Figure 4 B ) . This shift corresponds to a change in the atomic fluctuations profile of the JMR residues . As a consequence S2 and S3 immediately follow each other in MU ( Figure 4 A , top panels ) . These two IDSs correspond well to the structural definitions and morphological roles of the JM-Switch and JM-Zipper fragments respectively . In the N-lobe of WT , the IDS S5 matches the highly flexible loop preceding the C-helix ( residues 626–633 ) while in MU it is slightly expanded ( residues 625–635 ) and also includes residues from the glycine-rich loop ( residues 598–601 ) ( Figure 4 B ) . This expansion is documented by off-diagonal correlations between the glycine-rich loop and the loop preceding the C-helix in MU ( Figure 4 A , middle panels ) and by a considerable decrease of the atomic fluctuations of residues 626–633 in MU ( 1 . 41 Å0 . 41 Å ) compared to WT ( 2 . 34 Å0 . 48 Å ) . This comparative analysis of WT and MU local dynamics reveals that the D816V mutation influences the local atomic fluctuations of several structural fragments of KIT protein . In particular the JM-Switch , JM-Zipper , C-helix , P-loop and A-loop residues display more concerted atomic fluctuations in MU compared to WT . The A-loop dynamics changes indicate that the perturbation taking place in position 816 propagates to the downstream 817–819 helix , 820–823 and 824–832 anti-parallel . The apparition of concerted dynamics between the glycine-rich loop and the C-helix of mutated KIT may influence the protein activation as the glycine-rich loop normally helps positioning ATP [54] . The independent analyzes performed on KIT cytoplasmic region , one based on communication propensity and the other based on local dynamic features , provide a comprehensive two-component modeling framework for the rational description of information transmission throughout KIT structure . First , communication pathways enabled to identify residues or structural elements of KIT that serve as communication hubs in the protein residue network . Second , independent dynamic segments matched regulatory segments whose plasticity and structural rearrangements are essential for the inactive-to-active transition of KIT cytoplasmic region , as it has been reported in the literature . Moreover the classification proposed here of the different regions of KIT according to their role highly connected hubs or locally correlated clusters in the protein allosteric communication can be related to recently published work identifying structurally rigid ( minimally frustrated ) and plastic ( locally frustrated ) clusters of residues in kinases [35] . Therefore , our analysis together with the reported data contribute to bring out general trends governing allosteric communication and structure assembly in this protein family . In this section , we combined the definition of the communication pathways , CPs , and the characterization of independent dynamic segments , IDSs , to build an integrated modular network representation of KIT cytoplasmic region . We used such representation to localize and visualize the key factors governing the allosteric communication in KIT protein . In WT , the IDS S8 localized in the A-loop was defined from residue 824 to 831 . The analysis of non-covalent interactions between the A-loop and the other regions of the protein shows that an H ( hydrogen ) -bond between the hydroxyl oxygen atom of Y823 ( A-loop ) , immediately preceding S8 , and one of the carboxylate side-chain oxygen atoms ( ) of D792 ( catalytic loop ) is formed during more than 95% of the simulation time ( Figure 5 A ) . Moreover , residues D792 , H790 and N797 of the catalytic loop are involved in a cyclic H-bond pattern to form a local interaction network that is very stable along the MD trajectories: the occupancies for the and H-bonds are of 95% and 93% of the simulation time . These residues are also located at the crossroad of numerous CPs . In particular , one CP reaches residue V559 that immediately precedes the IDS S2 in the JMR , as illustrated on Figure 5 D . Consequently in WT , information from the A-loop is transmitted to the JMR through the catalytic loop , with Y823 as a pivotal residue in this communication . Upon D816V mutation , as we previously put in evidence [43] , the small 817–819 helix shows partial unfolding . This structural change is associated with the expansion of the IDS S8 from residues 816 to 832 and results in a shifted position of the residue Y823 unfavorable to the formation of the H-bond , whose occupancy consequently decreases by two folds down to less than 45% in MU ( Figure 5 B ) . On the contrary , D792 rather interacts with N797 for more than 85% of the simulation time . Its H-bond contact with H790 side chain is perfectly preserved as in the WT ( the occupancy factors are 98 and 95% respectively ) . This alteration of the catalytic loop local interaction network is accompanied by a drastic reduction of the catalytic loop residues capability to communicate efficiently with the distant KIT regulatory elements . As we mentioned above , several catalytic loop residues , including N797 , are involved in dense networks of CPs than span both lobes of the protein in WT whereas the number of CPs is much reduced in MU and the remaining CPs are confined within the C-lobe . No established communication was observed between the residues D792 ( catalytic loop ) and V559 ( JMR ) , which manifests a decoupling between the catalytic loop and the JMR in MU ( Figure 5 E ) . These observations suggest that the well-established allosteric communication in WT between the activation loop , A-loop , and the distant juxtamembrane region , JMR , channeled through the catalytic loop was disrupted by the oncogenic mutation D816V positioned in the A-loop . Consequently , we interpret the propagation of D816V effects between the A-loop and the JMR in terms of a communication break between the two remote sites . The structural reorganization of the JMR that we observed in MU [43] likely reflects this communication interruption . This interpretation is consistent with the results obtained by principal component analysis revealing a decoupling of A-loop and JMR motions in the mutant [43] . To further relate the observed changes in KIT allosteric communication with the thermodynamic properties of the protein , relative residue stability constants were calculated using the COREX/BEST algorithm [62] , [63] for the WT and D816V-mutated forms . Consistent with the observed atomic fluctuations , a positive variation of 0 . 58 kcal/mol for the residues 816–832 ( A-loop ) and a negative change of −0 . 68 kcal/mol for the residues 560–570 ( JM-Switch ) stability constants were observed upon D816V mutation . These values may be indicative of the modulation of the energy landscape of KIT protein by the D816V mutation , an effect associated with the alternative communication in WT and D816V mutant unraveled here . Our modular network analysis of KIT cytoplasmic region has revealed the crucial role of residues Y823 and D792 in the allosteric coupling/decoupling between the A-loop and the JMR . Considering the correlation observed between the occupancy value of the H-bond and the presence/absence of CPs between D792 ( catalytic loop ) and V559 ( JMR ) ( Figure 5 A–B , D–E ) , we hypothesized that a way to re-establish the communication between the A-loop , the catalytic loop and the JMR in the D816V mutant would be to restore the H-bond . To this aim , we substituted the aspartate ( D ) in position 792 by a glutamate ( E ) , bearing the same charge and displaying a longer side chain . We anticipated that such replacement would facilitate the formation and stabilization of an H-bond with Y823 that adopted in MU an orientation unfavorable to such interaction . The in silico-designed double mutant D816V/D792E will be referred to as dbMU . To characterize the structural and dynamical properties of the double mutant ( dbMU ) , two 50-ns MD trajectories were produced using a protocol similar to that applied for WT and MU . The RMS deviations computed on the backbone atoms of dbMU ( mean value , m . v . , of 2 . 21 Å0 . 36 Å and max . at 3 . 48 Å ) are comparable to those of WT and MU ( m . v . of 2 . 64 Å0 . 54 Å and max . at 4 . 04 Å , m . v . of 2 . 43 Å0 . 35 Å and max . at 3 . 36 Å , respectively ) ( Figure 6 B ) . Snapshots taken at 14 , 26 , 38 and 50 ns display the partial unfolding of the small 817–819 helix ( Figure 6 A ) , the local effect of the D816V mutation also observed in MU ( see Figure 4 in [43] ) . However the structure of the JM-Switch fragment of the JMR ( residues 560–570 ) remains only partially ordered during the simulations ( Figure 6 A ) , adopting in dbMU a conformation and a position similar to those observed in WT ( Figure 6 D ) and quite different from the well-structured axially positioned JMR of MU ( see Figure 4 in [43] ) . The significant fold gain induced by the D816V mutation resulting in the stabilization of the extended anti-parallel ( residues 558–572 ) is not observed in the double mutant D816V/D792E . To analyze the allosteric communication in the double mutant , a modular network representation was built . The characterization of independent dynamic segments in dbMU showed two distinct IDSs in the A-loop ranging from residue 814 to 821 and from residue 824 to 831 respectively , while one extended and homogeneous IDS S8 from residues 816 to 832 was identified in MU . Moreover , the atomic fluctuations of residues 824–831 are considerably reduced in dbMU ( max . of 2 . 11 Å at G827 ) compared to MU , while the flexibility of the residues 816–823 is higher in dbMU ( max . of 2 . 62 Å at I817 ) than in MU ( max . of 2 . 35 Å at I817 ) and in WT ( max . of 1 . 81 Å at N819 in WT ) ( Figure 6 C ) . Furthermore , the analysis of non-covalent contacts in dbMU indicates an alternative local H-bond pattern that differs from those observed in WT and MU ( Figure 5 C ) . First , the replacement of aspartate ( D ) to glutamate ( E ) at position 792 restored the H-bond equivalent to that observed in WT ( ) . Second , E792 interacts stably ( during 94% of the simulation time ) with H790 . Nevertheless the H-bond acceptor partner is changed from the nitrogen atom of histidine in WT and MU to the backbone carbonyl oxygen atom in dbMU . N797 residue in dbMU forms alternatively an H-bond either with H790 like in WT or with E792 like in MU with an approximately equivalent probability ( 45 and 51% respectively ) . The observed H-bond pattern in dbMU shows a hybrid nature coming from the superimposition of two networks : the cyclic H-bond local interaction network observed in WT and the H-bond evidenced in MU . This hybrid H-bond interaction network in dbMU produces first the re-establishment of the allosteric communication between the A-loop and the JMR , through the catalytic loop as a channel ( Figure 5 F ) and second contributes directly or not to the conservation of the local structural changes induced by the D816V mutation ( 817–819 helix unfolding ) . To elucidate the JMR thermodynamic properties in the double mutant , binding free energies of the JMR and its fragments to PTK were calculated . As we reported previously the JMR was more tightly attached to PTK in the WT KIT than in the D816V mutant [43] . Single-point MM-GBSA calculations ( see thermodynamic cycle on Figure 3c in [43] ) allowed to estimate the relative attachment of JMR to PTK in dbMU ( Table 1 ) . In spite of its tendency to overestimate the absolute values of free energy differences , the MM-GBSA method was often shown to reproduce well the correct free energy trends [64] . The global binding free energy change ( ) between dbMU and MU is of −50 . 62 kcal/mol , indicating that the additional mutation D792E restores the JMR attachment to PTK as observed in the wild type KIT . The entropic contribution , which reflects the conformational variability of the JMR , is more favorable in both WT and dbMU than in MU . In addition , the enthalpic contribution is also more favorable in dbMU compared to MU , suggesting that interactions between JMR and PTK are formed upon D792E mutation . Binding free energy changes computed for the different fragments of the JMR also show tighter attachment to PTK in dbMU compared to MU , except for the highly solvent-exposed JM-P . The computed values ( Table 1 ) are very similar to those reported for the wild type and D816V-mutated form ( Figure 3 in [43] ) . Consequently , these calculations reveal that the similar structural properties and dynamical behaviors displayed by the JMR in the wild type and D816V/D792E-mutated KIT forms correspond to nearly equivalent thermodynamic landscapes . We interpreted the long-range structural and dynamic effects induced by the D816V mutation in terms of a communication disruption between two regulatory segments of KIT cytoplasmic region . Our results emphasize the importance of both Y823 and D792 residues in the allosteric communication between the A-loop and the JMR and consequently in the stabilization of the KIT native inactive state . In this section , we confront our results with experimental data available in the literature describing the biological importance of these residues . Generally , Y823 stands as the phosphorylation site of KIT A-loop . However , recent in vitro characterization of Y823F-mutated KIT cytoplasmic region has shown that this mutant is auto-activated much faster than the wild type , while it remains very sensitive to inhibitors that target KIT A-loop inactive conformation [65] . These experimental observations support the hypothesis that the H-bond is a key factor in the control of the conformational switch between KIT inactive and active states . Indeed , any event the attachment of a phosphate group on Y823 side chain or the substitution of Y823 by a phenylalanine that shall weaken or completely abolish the interaction with D792 promotes KIT activation . Our molecular model of KIT allosteric communication provides additional useful insights on the mechanisms by which H-bond disruption may provoke the structural reorganization and repositioning of the remote JMR , facilitating its detachment from PTK – the triggering first step of the enzyme inactive-to-active state transition [43] . It should be noted that when Y823 is substituted by a phenylalanine , an interaction can be established between F823 and D792 [66] that may still control the protein inter-conversion between inactive and active states . Residue D792 located in KIT catalytic loop is highly conserved among protein kinases and was believed to act as a general catalytic base [67] . However , recent studies have shown non-consistency with such suggestion and it was proposed that this aspartate may rather assist substrate positioning or dissociation [68] . Mutation of this residue in several kinases significantly reduces the reaction rate but does not abolish activity [68] . Taking these data into consideration , we performed an in silico substitution of D792 by a glutamate in the structure of KIT active state ( PDB id: 1PKG [54] ) , containing both ADP and peptide O-phosphotyrosine bound to the protein . The obtained active conformation of D792E mutant was found very similar to that of the wild type , suggesting that the protein active form can accommodate the longer side-chain of the glutamate with minimal structural rearrangement ( Figure S2 ) . Based on this observation , we supposed the D792E mutation would impact KIT conformational dynamics by inducing a partial counter-balance to compensate the destructive effect of the D816V mutation on the allosteric communication between two regulatory regions while preserving KIT enzymatic activities . This believable design would supply a framework for the in vitro comparison of the activation rates of the double mutant D816V/D792E , single mutant D816V and the native protein . A number of other mutational hot spots were identified in KIT kinase [40] , [42] , [69] , namely V560 , V654 , T670 , D820 , N822 and A829 . V654 and T670 have been documented in samples from GIST patients exhibiting resistance against Imatinib [69] . V654 , located in Cloop-2 , generates 12 CPs spanning across both lobes of the protein . T670 , often designated as the “gatekeeper” residue as it is located at the edge of the ATP binding site , participates in the network of CPs linking the C-loop-2 , the E-helix and the . Therefore , our analysis suggests that these two mutational hot spots may be important for KIT allosteric communication as they likely contribute to mechanical information transmission . These suggestions agree with recent studies showing an important role for the “gatekeeper” residue T790 in the allosteric communication of the RTK EGFR [25] . As we showed , V560 of the JMR and A829 of the A-loop were identified as residues participating in independent dynamic segments , namely the IDSs S2 and S8 , respectively . D820 and N822 of the A-loop , together with D816 , were found part of the IDSs S8 only in the D816V-mutated KIT form . This finding suggests a common activating mechanism for the mutations of these residues . By a structural analysis of KIT inactive and active structures , we previously observed that D820 and N822 side chains form an H-bond that stabilizes a motif in the protein inactive state [43] . The clinically observed mutations D820A/E/G/Y and N822H/K are likely to disrupt the H-bond resulting in the unfolding of the 820–823 motif , which would produce a shifted position of Y823 unfavorable to the formation of the H-bond and hence to the establishment of the communication between the activation and catalytic loops . These findings depict a consistent description of the deregulation of KIT activity by oncogenic mutations which is in agreement with the available experimental data . We have studied the molecular determinants of the allosteric regulation of KIT receptor tyrosine kinase in the native form and two mutated forms , the oncogenic mutant D816V and the in silico-designed double mutant D816V/D792E . We were able to describe and modulate the communication between two remote principal regulatory segments , the activation loop ( A-loop ) and the juxtamembrane region ( JMR ) . A strong correlation between such communication and the structural and dynamical features of the protein was established . The analysis was realized by a made-in-house computational tool MONETA based on a two-component modeling framework . This method , validated on receptor KIT , may guide a rational description and modulation of the physiopathological activities of other receptor tyrosine kinases altered by various perturbations such as phosphorylation events , substrate , ligand or inhibitor binding . The description of networks that represent only a local area ( intra-molecular component ) of vast communication pathways established between proteins , constitutes a first step toward an integrated description of signaling across different spatio-temporal scales , from intra-protein to cellular levels .
Pre-processed data for wild type ( WT ) and D816V-mutated ( MU ) KIT were collected as described in [43] . The initial coordinates were taken from the crystallographic structure of the auto-inhibited inactive form of KIT ( 1T45 , 1 . 90 Å resolution ) [70] . In silico substitution of D816 into a valine ( V ) was performed using MODELLER 9v7 [71] , [72] . Two 50-ns MD simulations were performed for each of the WT and MU proteins , with different starting velocities . Snapshots were written every 1 ps . A similar data generation and collection procedure was applied for the double mutant D816V/D792E ( dbMU ) . According to the RMSD profiles ( see Figure 2 in [43] and Figure 6 B ) , the three systems WT , MU and dbMU took less than 20 ns to relax . Consequently , we retained the last 30 ns of each trajectory as the productive simulation time . This provided conformational ensembles of equal size ( 60 000 snapshots ) for further statistical analysis . To assess the convergence of the data , the average time auto-correlation functions were computed the inter-residue distances of WT , MU and dbMU: ( 1 ) where is the distance between the atoms of residue and residue , is the number of atoms and the normalizing pre-factor is the inverse of the total number of possible pairs of atoms from an ensemble of atoms . The obtained curves were fitted to exponential model functions [73] using XmGrace [74] . Coefficients and are given in Table 2 . Standard analyzes of the MD trajectories were performed with the ptraj module of AMBER 11 [75] . The DSSP method [76] was employed for secondary structure assignment within ptraj . Hydrogen bonds were detected between donors and acceptors ( oxygen or nitrogen atoms ) with a distance cutoff of 3 . 5 Å and no angle cutoff using the hydrogen bonding facility of ptraj . Binding free energies of the JMR were evaluated using the MMGBSA method [77]–[79] implemented in AMBER 9 [80] . The binding free energies were evaluated on the equilibrated conformations of WT , MU and dbMU ( single-point calculations ) . Details for the calculations were given elsewhere [43] . The concept of communication propensity [9] was used to identify communication pathways ( CPs ) . The communication propensity of a pair of residues is inversely related to their commute time , expressed as a function of the variance of the inter-residue distance [9]: ( 2 ) where is the distance between the atoms of residue and residue , respectively . The smaller the variance the more efficient the communication between the two residues . commute times can be calculated from the Kirchhoff matrix of an Elastic Network Model ( ENM ) [9] or from all-atom MD trajectories [25] , [46] . In this work , commute times were computed between all protein residues from the last 30 nanoseconds of WT , MU and dbMU MD trajectories . We describe CPs as chains of neighboring residues whose communication propensities between each other are high . Hence information is likely to be transmitted along CPs in short commute times . We generate CPs iteratively according to the following algorithm: A residue will be considered as a neighbor of residue if residues and : ( a ) are not adjacent in the sequence , ( b ) contact each other via non-bonded interactions and ( c ) communicate efficiently ( commute time below ) . The way communication pathways are grown ensures that any two adjacent residues are connected by non-covalent interactions and that every residue in the CP is reachable from any other point in an equivalent short commute time . Non-bonded interactions were recorded along the MD simulations using LIGPLOT [81] . Two residues were considered as interacting when they formed at least one non-bonded interaction for at least 50% of the simulation time . To discriminate between high and low communication propensities , ie between fast and slow commute times , a threshold was chosen so that highly connected residues communicate efficiently with about 20% of the total number of residues in the protein , consistently with [46] . The threshold values were 0 . 09 , 0 . 09 and 0 . 10 for WT , MU and dbMU respectively . We used a statistical technique called Local Feature Analysis ( LFA ) [47] , [53] to identify independent dynamic segments ( IDSs ) . Principal Component Analysis ( PCA ) was performed on the covariance matrices calculated from the 30 last ns of the MD trajectories of WT , MU and dbMU using ptraj . Among the eigenvectors associated with eigenvalues , , the first 19 , 18 and 20 ones were sufficient to describe 80–85% of the total atomic fluctuations of WT , MU and dbMU respectively . These vectors were consequently used to apply the LFA formalism [47] . In brief , this formalism projects the correlation matrix in such a way that it reduces off-block diagonal correlations and identifies seed degrees of freedom corresponding to atoms or residues . From the global PCA modes of WT , MU , dbMU , one can define local LFA output functions with minimum correlation [47]: ( 3 ) where is the projection of the atomic fluctuations onto eigenvector : . The residual correlations between LFA outputs are given by [47]: ( 4 ) so that in the limit the LFA outputs are completely decorrelated by space: . Rather than computing all outputs , the sparsification algorithm described by Zhang and Wriggers [47] was applied using the statistics program R [44] to approximate the entire outputs with only a small subset of outputs . is the ensemble of seed degrees of freedom that enable to reconstruct the outputs with minimal error . At each iteration of the sparsification algorithm , the outputs were reconstructed given the current set of degrees of freedom and the reconstruction mean square errors were evaluated . Out of the available degrees of freedom , the seed index displaying the maximum reconstruction error and not being already picked up was chosen as the index into . Seed indexes were added to until indexes were chosen , standing for seed atoms or residues of WT , MU and dbMU respectively . Consequently , the obtained seed residues are representative of the most striking features of WT , MU and dbMU local dynamics . The residual correlation between residue and residue was evaluated as [47]: ( 5 ) where is the ( x , y , z ) coordinate index and is the number of retained PCA modes . We define IDSs as clusters of neighboring correlated residues whose centers are the seed atoms or residues identified by the LFA . Given a seed residue , the corresponding IDS is extended progressively by adding neighboring residues in the protein structure three dimensional space as long as: ( 6 ) where is the sub-matrix corresponding to the residual correlation pattern and is the correlation threshold value . Distance matrices consisting of the average smallest distances between all residue pairs were computed using the g_mdmat program of GROMACS 4 . 5 . 3 [82] . Two residues i and j were considered close if the average smallest distance between them was lower than a given threshold . To discriminate between correlated and uncorrelated residues , a threshold value was arbitrary chosen as . About 1 . 0–1 . 5% of all LFA cross-correlations lie above this threshold for WT , MU and dbMU . | The majority of functionally important biological processes are regulated by allosteric communication within individual proteins and across protein complexes . Receptor tyrosine kinases ( RTKs ) control signal transduction pathways and consequently represent a typical paradigm . The mutation-induced deregulation of RTK activity impairs crucial cellular physiological functions and causes serious human diseases . The present study focuses on the allosteric communication across the three-dimensional structure of the RTK KIT cytoplasmic region . Combining a mechanistic model of information transmission with the analysis of concerted local atomic fluctuations we examined and compared the communication profiles in the native and D816V-mutated proteins . This approach permitted to localize and visualize communication routes in the native KIT and revealed that these routes were disrupted in the mutant D816V . We proposed in silico mutagenesis as a mean to restore the communication detected in the native KIT . Our work sheds light on the allosteric communication in RTKs , a phenomenon playing an essential role in signaling pathways albeit experiments do not provide the atomic details of the path followed in going from one structural element to the other . A rational understanding of the molecular determinants underlying the effects of disease-related kinase mutations may contribute to the improvement of targeted therapies . | [
"Abstract",
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"Results/Discussion",
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] | [
"oncology",
"medicine",
"biology",
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"biology"
] | 2012 | Allosteric Communication across the Native and Mutated KIT Receptor Tyrosine Kinase |
Adult schistosomes live in the host's bloodstream where they import nutrients such as glucose across their body surface ( the tegument ) . The parasite tegument is an unusual structure since it is enclosed not by the typical one but by two closely apposed lipid bilayers . Within the tegument two glucose importing proteins have been identified; these are schistosome glucose transporter ( SGTP ) 1 and 4 . SGTP4 is present in the host interactive , apical tegumental membranes , while SGTP1 is found in the tegumental basal membrane ( as well as in internal tissues ) . The SGTPs act by facilitated diffusion . To examine the importance of these proteins for the parasites , RNAi was employed to knock down expression of both SGTP genes in the schistosomula and adult worm life stages . Both qRT-PCR and western blotting analysis confirmed successful gene suppression . It was found that SGTP1 or SGTP4-suppressed parasites exhibit an impaired ability to import glucose compared to control worms . In addition , parasites with both SGTP1 and SGTP4 simultaneously suppressed showed a further reduction in capacity to import glucose compared to parasites with a single suppressed SGTP gene . Despite this debility , all suppressed parasites exhibited no phenotypic distinction compared to controls when cultured in rich medium . Following prolonged incubation in glucose-depleted medium however , significantly fewer SGTP-suppressed parasites survived . Finally , SGTP-suppressed parasites showed decreased viability in vivo following infection of experimental animals . These findings provide direct evidence for the importance of SGTP1 and SGTP4 for schistosomes in importing exogenous glucose and show that these proteins are important for normal parasite development in the mammalian host .
Schistosoma mansoni is a parasitic platyhelminth that causes the chronic , often debilitating disease , schistosomiasis affecting several hundred million people globally . Infection is initiated following skin penetration by larval parasites called cercariae which rapidly adapt to the intra-mammalian environment in a process called cercarial transformation . These transformed juvenile parasites are now called schistosomula and they move from the epidermal tissues into the blood stream where they mature . Adult worms reside in the mesenteric veins of their mammalian hosts , where they are generally found as male-female pairs . The entire worm is surrounded by a continuous cytoplasmic unit , or syncytium , called the tegument . The host interactive surface of the tegument is unusual in that it consists of two tightly apposed , lipid bilayer membranes that are highly invaginated . The internal , basal membrane of the tegument consists of a normal ( trilaminate ) lipid bilayer containing many invaginations . This bilayer extends periodically beneath the underlying muscle to enclose areas called “cell bodies” ( or cytons ) which contain nuclei and protein synthetic machinery [1] . Adult worms use large quantities of host glucose; they are reported to consume the equivalent of their dry weight in glucose every 5 hours [2] . While the adults possess a functional gut , they have been shown to take up glucose directly across their external body surface by facilitated diffusion [3] , [4] . Three glucose transporter mRNAs were originally identified from Schistosoma mansoni and these were designated schistosome glucose transporter protein ( SGTP ) 1 , 2 and 4 [5] . Only SGTP1 and SGTP4 displayed glucose transport activity when expressed in Xenopus laevis oocytes . In the Xenopus uptake assay , both proteins functioned as typical facilitated diffusion glucose transporters , exhibiting glucose stereospecificity , relaxed specificity for other hexoses , sodium independence and marked inhibition by cytochalasin B [5] . Immunolocalization of SGTP1 and SGTP4 revealed that both of these proteins are localized in the tegument of schistosomula and adult worms [6] . SGTP4 appears to be localized uniquely to the tegument , while SGTP1 can also be detected within the body of the worm , particularly in muscle [6] . The presence of facilitated diffusion transporters in the tegument implies that schistosomes have the capacity to take up glucose by passive diffusion . Localization of the SGTPs by immuno-electron microscopy reveals that SGTP4 is present predominantly or exclusively within the apical membranes , while tegumental SGTP1 is found only within the basal membrane [7] , [8] . This asymmetrical localization of the two SGTPs in the tegument suggests that the host interactive protein ( SGTP4 ) , acts to import sugar from the bloodstream into the tegument and that SGTP1 acts to transport some portion of this sugar to underlying tissues . The Km for glucose transport by the apical tegumental membrane transporter SGTP4 is greater than that of the basal transporter SGTP1 ( in Xenopus oocytes ) [5] . This should give an advantage to the basal membrane transporter to associate with any free glucose that is not utilized in the tegumental syncytium so that it can be moved more deeply into the body of the worm . We have long hypothesized that the SGTPs function to transport exogenous glucose across the tegumental membranes and into body of the worms [9] . However , until the advent of RNAi methods for use with schistosomes , we could not effectively test this fundamental notion . In this work , we show that suppressing SGTP gene expression using RNAi does impair schistosome glucose uptake capabilities and can debilitate the parasites in vitro and in vivo .
The availability of a nearly complete draft of the S . mansoni genome [10] permits a careful bioinformatic analysis for facilitated glucose transport protein genes and this identifies a total of four SGTP genes . In addition to the three genes previously identified , another facilitated glucose transporter homolog can now be identified . The gene , which we designate SGTP3 , is currently identified as hypothetical protein Smp_127200 . Searches of dbEST reveal that ESTs exist for all four SGTP genes demonstrating that these genes are expressed in mammalian stage schistosomes . Because SGTP1 and SGTP4 are clearly demonstrated to be expressed in the adult tegument and appear to be the predominant facilitated glucose transporters in adult S . mansoni , we focused our RNAi studies on these two genes [6] . To determine whether SGTP1 and SGTP4 are amenable to gene silencing in schistosomula via the RNAi pathway , parasites were treated with two siRNAs spanning distinct positions for each target . All siRNAs were effective and showed comparable knockdown for each target ( not shown ) . One of each target-specific siRNA was then selected for all subsequent experiments: SGTP1siRNA1 for SGTP1 and SGTP4siRNA1 for SGTP4 . Parasites were electroporated with SGTP1siRNA1 , SGTP4siRNA1 , or a mix or both siRNAs . Control parasites were treated with an irrelevant siRNA or were not exposed to siRNA at all . Parasites were then cultured for 14 days in Basch medium before being harvested for gene expression analysis . Figure 1 shows that the transcript levels of both targets were substantially reduced when parasites were treated with each siRNA separately or in combination , compared to controls ( Figure 1A ) . Gene knockdown is specific; siRNAs targeting SGTP1 have no effect on SGTP4 expression levels and vice versa . The reduction in transcript levels was more striking for SGTP4 ( ∼85% ) than for SGTP1 ( ∼55% ) . Schistosomula treated with an siRNA targeting SGTP1 alone or with a mix of siRNAs targeting both SGTP1 and SGTP4 exhibited a similar decrease in SGTP1 gene expression . Likewise , parasites treated with an siRNA targeting SGTP4 alone or with a mix of siRNAs targeting both SGTPs exhibited a similar decrease in SGTP4 gene expression , as depicted in Figure 1 . To monitor SGTP gene suppression in adults , six week old worms were recovered from infected mice and electroporated with SGTP1 plus SGTP4 siRNAs . Transcript levels were measured 14 days after siRNA treatment by qRT-PCR and the results are shown in Figure 1B . About 70% suppression of SGTP1 and 90% of SGTP4 was observed in adult parasites following treatment , compared to control parasites electroporated with an irrelevant siRNA or parasites electroporated in the absence of siRNA . Western blotting analysis was undertaken in order to assess the impact of gene suppression on target protein levels . Figure 1C shows that the suppression of both targets resulted in a substantial diminution of SGTP1 ( top panel ) and SGTP4 ( middle panel ) protein levels in these parasites . In contrast , both proteins were easily detected in extracts of control parasites . In the bottom panel , the amino acid permease control protein SPRM1hc was detected in all extracts , demonstrating that comparable levels of protein were present in each lane . The glucose uptake capacity of SGTP-suppressed schistosomula versus controls was compared . As shown in Figure 2 , parasites treated with SGTP1 or SGTP4 siRNAs had a significant ( P = 0 . 0005 ) and similar ( ∼50% ) reduction in glucose uptake capacity compared to the control group . Parasites treated with a mix of both SGTP siRNAs showed an even more pronounced reduction in glucose uptake ( to ∼70% ) and this decrease was significantly different from the values obtained using single SGTP-suppressed parasites ( P = 0 . 003 ) . In the presence of cytochalasin B , a glucose transporter inhibitor , the intake of glucose by control parasites was decreased further ( to ∼80% , P = 0 . 008 ) ( Figure 2 ) . To determine whether SGTP suppression and the resulting decrease in glucose uptake capacity affected the phenotype of cultured schistosomula , parasite viability in culture was measured by Hoechst staining 14 days after siRNA treatment . Schistosomula were maintained either in complete RPMI ( containing 10 mM glucose ) or in glucose-depleted RPMI ( containing 0 . 05 mM glucose ) . Figure 3 shows that suppressing the SGTP1 and SGTP4 genes did not significantly affect the viability of parasites kept in medium containing relatively high levels of glucose . However SGTP-suppressed parasites cultured in RPMI containing low glucose were significantly less viable ( by >40% ) than their control counterparts ( P = 0 . 02 ) . Parasites cultured in RPMI with no glucose do not survive beyond 48 hours . It is noteworthy that control parasites experience stress under low glucose conditions such that 62 . 4% ±5 . 6 of them remain viable after 14 days in culture ( compared with 93 . 3% ±14 . 2 of control parasites cultured under high glucose conditions ) . To investigate whether RNAi-mediated gene silencing of SGTP1 and SGTP4 affects parasite viability in vivo , we infected groups of 7–8 mice with 1 day old control or SGTP1+ 4-suppressed schistosomula . Figure 4 shows the number of worms recovered from these mice 28 days after infection . There was a significant reduction in worm burden in the SGTP-suppressed group compared to either control group . SGTP gene expression analysis was undertaken on the worms recovered from the infected mice and the data were compared to the gene expression pattern of suppressed and control schistosomula that had been maintained in culture . SGTP-suppressed parasites cultured for 7 days ( white bars , Figure 5 ) exhibited ∼65% suppression of SGTP1 ( Figure 5A ) and close to 100% suppression of SGTP4 ( figure 5B ) . After 28 days cultured ex vivo ( grey bars , Figure 5 ) , mRNA levels in the SGTP dsRNA-treated worms were rising but remained substantially lower than control levels ( ∼50% for SGTP1 ( Figure 5A ) and ∼70% for SGTP4 ( Figure 5B ) ) . In contrast , after 28 days in vivo ( black bars , Figure 5 ) , SGTP-suppressed parasites recovered from infected mice were no longer suppressed; SGTP transcript levels had returned to normal , or above normal , levels . Essentially the same results were observed when schistosomula were treated with long dsRNAs specific for SGTP1 and SGTP4 by soaking . The level of gene suppression using this metholology was comparable to that reported above for parasites exposed to SGTP-specific siRNA by electroporation . Parasites treated with SGTP-specific , and control , long dsRNA were used to infect mice and were recovered by perfusion 28 days later . As for the siRNA work , significantly fewer worms were recovered from the SGTP-suppressed group versus controls in this experiment using long dsRNA ( not shown ) . Finally , as seen with siRNA-treated parasites , recovered parasites were no longer suppressed ( data not shown ) .
In this work we show that two Schistosoma mansoni glucose transporter ( SGTP ) genes , SGTP1 and SGTP4 , are susceptible to suppression via RNAi . Of the two SGTPs targeted we find that SGTP4 is consistently better suppressed than SGTP1 using different siRNAs , long dsRNA and at two different life stages tested . This is consistent with the notion that genes expressed in schistosome tissues that are in direct contact with the environment ( e . g . the tegument or the gut ) are more efficiently suppressed by RNA interference compared to genes expressed in other tissues . SGTP4 is predominantly and perhaps exclusively expressed in the tegument [6] , [7] whereas SGTP1 is additionally expressed in the internal tissues of the parasite , notably in the muscle [6] , [8] . In the past we have noted that genes expressed predominantly or exclusively in the tegument ( e . g . SmAQP ) can be potently suppressed while those expressed both in the tegument and in internal tissues ( e . g . SPRM1hc ) are more poorly suppressed using the same protocols [11] , [12] , [13] . This may reflect differences in the ability of dsRNAs to enter internal tissues or to the differential expression of RNAi pathway components in different organs . The level of SGTP4 gene suppression in schistosomula is ∼80% . This is the case when parasites are treated with dsRNA targeting SGTP4 alone or when treated with two siRNAs targeting SGTP4 and SGTP1 . In a similar manner , the level of suppression of SGTP1 remains essentially the same when SGTP1 alone is targeted for suppression or when SGTP1 and SGTP4 are both targeted . These results support previous work [14] showing that more than one gene can be suppressed at one time in schistosomes . Our quantitative data show that the RNAi machinery is not saturated by multiple siRNAs targeting different mRNAs . The level of inhibition of glucose uptake into SGTP1-suppressed parasites is comparable to that seen for SGTP4-suppressed parasites . When SGTP1 alone is suppressed , glucose should still be able to enter the parasite tegument freely via the outer tegumental membrane transporter SGTP4 . However , the movement of imported glucose further into the body of the SGTP1-suppressed parasites would then be impaired since this transporter is present on the tegumental basal membranes and on the membranes of other internal tissues . The inability of imported glucose to be efficiently transported out of the tegument and into the deeper tissues using SGTP1 would increase tegumental glucose concentrations and likely impede the further import of glucose by facilitated diffusion from the external environment . This is reflected in lower radiolabeled glucose being taken in to the SGTP1-suppressed parasites compared to controls . When SGTP4 alone is suppressed , less glucose should enter the worms across the tegument compared to controls but any glucose that does enter and that is not utilized within the tegument should be efficiently transported inward via SGTP1 . This would promote further glucose diffusion into the parasites via residual tegumental SGTP4 transporters . Parasites with both SGTP1 and SGTP4 genes suppressed exhibit a significantly greater impairment of radiolabeled glucose uptake compared with parasites that have had just one of the transporter genes suppressed . This likely reflects both a lower level of glucose uptake into the tegument via SGTP4 and an impaired ability to move that glucose into the internal tissues via SGTP1 . Note that the level of glucose uptake in the doubly suppressed parasites is higher than that seen in parasites treated with a chemical inhibiter of facilitated glucose transporter protein function - cytochalasin B . This compound has been shown to block SGTP1 and SGTP4 function since it inhibits radiolabeled glucose uptake into Xenopus oocytes that are expressing SGTP1 or SGTP4 [5] . The double SGTP knockdown parasites exhibited a higher glucose uptake ( of ∼30% versus untreated controls ) compared to parasites treated with cytochalasin B ( whose uptake was ∼20% of untreated control parasites ) . Likely this reflects the high potency of cytochalasin B in almost completely shutting down all SGTP function . In contrast , RNAi leads to SGTP gene knockdown ( but not gene knockout ) and the presence of residual functional SGTP protein in the siRNA-treated groups does permit some label uptake . Residual protein includes any protein generated before siRNA administration as well as new protein derived from transcripts that survive the RNAi treatment . The diminished ability of SGTP-suppressed schistosomes to import glucose unequivocally demonstrates that these parasites do use both SGTP1 and SGTP4 to efficiently take in sugar . In a similar vein , earlier work reported that glucose uptake is impaired in schistosomes following exposure to SGTP antisense oligonucleotides [15] . However , this work noted non-specific effects with some oligonucleotides and considerable variability between treatments , making the data equivocal [15] . Previous work has demonstrated that SGTP1 is important for glucose uptake from the environment in the sporocyst life stage [16] . In order to establish whether the inability to import glucose by the SGTP-suppressed parasites had a detrimental impact on the worms , their viability was compared with that of control parasites in vitro and in vivo . Parasites in culture whose glucose transporter genes are suppressed show no significant phenotypic differences compared with controls , when they are maintained in medium with a high glucose concentration ( 10 mM ) for up to 14 days . However , when these parasites are instead cultured in low glucose medium ( 0 . 05 mM ) for 14 days , significantly fewer suppressed parasites survive compared with controls . This suggests that , in the sugar-poor environment , an impaired ability to import glucose upsets parasite metabolism and decreases viability . When SGTP-suppressed parasites infect mice , fewer of them survive to adulthood relative to controls . This is the case despite the fact that glucose concentrations in blood are high ( ∼5 mM ) . These data suggest that the parasites' glucose demands in vivo are higher than in culture and this likely reflects the need for parasites in vivo to generate more energy ( through glucose catabolism ) to allow them migrate through tissues , invade the vasculature and combat host immune effectors . The level of RNAi-mediated target gene suppression diminishes with time in culture . After 4 weeks in vitro the level of suppression of SGTP1 is ∼50% compared with ∼65% at day 7 post treatment . For SGTP4 the suppression level at week 4 in culture is 70% compared with >95% at day 7 . These data demonstrate that the RNAi effect remains substantial even after a month in culture . In contrast , equivalent parasites recovered from infected mice 4 weeks after RNAi treatment exhibit no remaining SGTP gene suppression . Those parasites that have survived in vivo have SGTP mRNA levels at or even above control levels . Similar variable outcomes of RNAi in schistosomes ex vivo compared to in vivo have been reported in other studies [17] , [18] . One hypothesis is that RNAi is variably effective in different parasites and/or that different individuals in the treated parasite population received different amounts of siRNA . Those in which SGTP knockdown is least effective , or that received less dsRNA , survive because the expression of their SGTP genes is minimally impaired . Another hypothesis is that worms in vivo are more metabolically robust and this leads to a shorter half life of the dsRNA and/or its downstream effectors . In mammalian cells the longevity of the RNAi effect can depend on cell type: in non-dividing cells suppression can persist for several weeks whereas in rapidly dividing cells the effect may last only from 3 to 7 days . [19] . Schistosomes in culture appear quiescent; they do not develop as quickly and fully as do parasites in infected animals and this may contribute to the persistence of gene suppression observed in the cultured worms . In summary , this work shows that by demonstrably suppressing glucose transporter gene expression in schistosomes using RNAi , parasite feeding is hindered and this can significantly lower parasite viability . These findings provide direct evidence for the importance of SGTP1 and SGTP4 for schistosomes in importing exogenous glucose and show that the proteins are important for normal parasite development within the mammalian host .
Infection of mice with schistosome parasites was carried out following review and approval by the Institutional Animal Care and Use Committee of Tufts University or Instituto René Rachou - FIOCRUZ . The Tufts animal management program is accredited by the American Association for the Accreditation of Laboratory Animal Care , meets the National Institutes of Health standards as set forth in the “Guide for the Care and Use of Laboratory Animals” ( National Academy Press , Washington DC , 1996 ) , and accepts as mandatory the PHS “Policy on Humane Care and Use of Laboratory Animals by Awardee Institutions” and NIH “Principals for the Utilization and Care of Laboratory Animals Used in Testing , Research and Training” . Biomphalaria glabrata snails infected with S . mansoni were obtained from Dr . Fred Lewis ( Biomedical Research Institute , Rockville , MD ) . In some experiments parasites were obtained from snails infected at Instituto René Rachou - FIOCRUZ , Belo Horizonte , MG , Brazil . Schistosomula were prepared from cercariae released from infected snails and were cultured in Basch medium at 37°C , in an atmosphere of 5% CO2 as described [20] . Parasite viability was ≥90% at the beginning of each experiment as assessed by Hoechst staining [11] . In some experiments schistosomula were kept in complete RPMI medium which is RPMI supplemented with 10 mM Hepes , 2 mM glutamine , 5% fetal calf serum and antibiotics ( 100 U/ml penicillin and 100 µg/ml streptomycin ) . Adult worms were recovered by vascular perfusion from Balb/c mice that were infected with 125 cercariae , 6 weeks previously . Adult parasites were maintained in Basch medium for RNAi experiments . Schistosomula and adult worms were treated either with synthetic siRNAs ( IDT , Coralville , IA ) or with long dsRNAs specific for SGTP1 or SGTP4 ( GenBank accession numbers L25065 and L25067 , respectively ) . The siRNAs were designed with the help of the RNAi Design Tool at http://www . idtdna . com/Scitools/Applications/RNAi/RNAi . aspx . The siRNAs targeting SGTP1 are SGTP1siRNA1: 5′-GGAGCATTCAGTTGTGGTTGGGTTG-3′spanning the coding sequence at positions 229–254 and SGTP1siRNA2: 5′-ACATAAAGAAGCTGAGGCACGTAAA-3′ spanning the coding sequence at positions 647–672 . The siRNAs targeting SGTP4 are SGTP4siRNA1: 5′-GAAATAGCTCCCTTATCTCTTCGTG -3′ , which cover positions 447-472 of the coding sequence and SGTP4siRNA2: 5′-GTGACACCAAGTTTCTTATATGCTC-3′ which cover positions 186-211 of the coding sequence . The negative control siRNA ( 5′-CTTCCTCTCTTTCTCTCCCTTGTGA-3′ ) is the “DS Scrambled Neg” obtained from IDT , Inc . This sequence does not match any in the S . mansoni genome . Target-specific siRNA delivery to the parasites was performed by electroporation as described previously , using 2 . 5 µg/50 µl ( 2 . 8 µM ) of each siRNA for schistosomula and 5 µg/50 µl ( 5 . 6 µM ) for adults [21] , [22] . Long dsRNA was prepared as described previously [21] . The primer sequences for preparing long dsRNA targeting SGTP1 are SGTP1-T7 , 5′-ggtaatacgactcactatagggCTAATCGGATACAATCT-3′ and SGTP1-T3 , 5′-ggaattaaccctcactaaagggAATGAAATACGAGAAA-3′ which spans the coding sequence at positions 79–514 . The lower case sequences represent T7 or T3 RNA polymerase promoter sequences . SGTP4 long dsRNA was prepared as described [17] . A non-schistosome derived long dsRNA used as an irrelevant control was generated from the yeast expression plasmid pPIC9K , as described earlier [17] . Long dsRNA was delivered to the parasites by soaking cultured schistosomula overnight with 50 µg/ml of irrelevant or SGTP-specific long dsRNA [21] , [22] . Gene suppression was assessed post-treatment by comparing mRNA and protein levels in target versus control groups . The levels of expression of SGTP1 and SGTP4 genes in schistosomula and adult worm pairs treated with gene-specific dsRNA were measured by quantitative real time PCR ( qRT-PCR ) , using custom TaqMan gene expression systems from Applied Biosystems ( Foster City , CA ) . The procedure , involving total RNA extraction and quantitative real time PCR , has been described [21] . The following primers and probe were selected to detect SGTP1: SGTP1 forward , 5′-CTGCAGCTTATTCACTGAGTCAATC- 3′; SGTP1 reverse , 5′-CCACCGATGTTTTTCTGTATAACAGGAT-3′ and SGTP1 probe , 5′-FAM- TCAATGGTTATCCAATCTAATTGT- 3′ . To detect SGTP4 expression , the following primers and probe were used: SGTP4 forward 5′-AGCCAAGGAGTTAACTTATTATGCAATTTATTG 3′-; SGTP4 reverse , 5′- TCCAACAGATAATAACGATAACTAAAAATGGTAAGAA-3′ and SGTP4 probe , 5′-FAM- CAATGGCATCATTAATGC- 3′ . Alpha tubulin was used as the endogenous control gene for relative quantification employing the ΔΔCt method [23] . Results were graphed as gene expression level relative to the group treated with control irrelevant dsRNA . Parasite lysates were prepared by adding 50 µl of ice cold cell disruption buffer ( PARIS Kit , Ambion , TX ) followed by incubation for 30 minutes on ice . The protein content in each extract was estimated using the BCA Protein Assay Kit ( Pierce , IL ) according to the manufacturer's instructions . Soluble protein ( 5 µg in 20 µl SDS-PAGE sample buffer ) was subjected to SDS-PAGE under reducing conditions , blotted onto PVDF membrane and blocked using detector block solution ( KPL , Inc . ) for 1 h at room temperature . The membrane was then probed overnight at 4°C with affinity purified rabbit anti-SGTP1 or anti-SGTP4 serum at 1∶500 [5] or antibody directed against a control schistosome protein ( SPRM1hc ) [13] . Bound primary antibody was detected using goat anti-rabbit IgG conjugated to horseradish peroxidase ( Invitrogen , Inc . ) , diluted 1∶5000 , followed by incubation with the chemiluminescent substrate LumiGLO ( KPL , Inc . ) and the membrane was exposed to X-ray film . The same membrane was probed three times to detect SGTP1 , SGTP4 and the loading control protein , SPRM1hc . For each re-use , the membrane was first incubated for 30 min at room temperature with 2% SDS and 0 . 7% β-mercaptoethanol to strip bound antibody and was then washed in phosphate buffered saline twice for 30 min each . To evaluate if SGTP1 and SGTP4 gene knockdown affected parasite survival in culture , viability was assessed by Hoechst staining [11] . Three day old schistosomula electroporated in the presence of a mix of SGTP1 and SGTP4 siRNAs at 2 . 5 µg each , were cultured in RPMI medium containing either 10 mM or 0 . 05 mM D-glucose for 14 days . Parasite mortality was determined in samples containing ∼100 parasites each , by adding 1 µg/ml Hoechst 33258 to the cultures at room temperature . After 10 min dead parasites were counted using a 460 nm reading filter . Viability in each group was calculated as the average value ( +/− standard deviation ) from triplicate experiments relative to controls treated with irrelevant siRNA . Schistosomula treated with siRNA specific for SGTP1 , SGTP4 , or a mix of equal amounts of both siRNAs , were compared for their ability to take up glucose relative to control parasites treated with an irrelevant siRNA . Schistosomula , 14 days after siRNA treatment , were washed four times in wash medium ( RPMI without glucose and supplemented with 10 mM Hepes , 2 mM glutamine , and antibiotics ( 100 U/ml penicillin and 100 µg/ml streptomycin ) ) and resuspended in 30 µl of wash medium supplemented with 0 . 1 M D-glucose . Each sample received 1 µl of [1 , 2-3H]2-deoxyglucose at 1 µCi/ml ( Amersham , Piscataway , NJ ) followed by a 30 min incubation at room temperature . Parasites were subsequently washed four times in wash medium before being disrupted in 30 µl of 2% SDS . The parasite lysate was added to 1 ml Scintiverse scintillation fluid ( Fischer ) and subjected to liquid scintillation counting . Radiolabel uptake was calculated per 1000 schistosomula . Assays were performed in triplicate for each group and averaged for analysis . Glucose uptake in control parasites was also measured in the presence of cytochalasin B ( 40 µM ) which was added to the parasite culture for 30 min prior to the start of the uptake experiment . One day old cultured schistosomula were electroporated with SGTP , or control , or no , siRNA and the groups were divided into three samples each . The first sample was immediately used to infect female BALB/c mice ( ∼1 , 000 parasites/mouse ) and the other two samples were kept in culture for 7 days or for 28 days to determine the efficiency of gene knockdown ( at day 7 ) and to monitor long-term suppression in vitro ( at day 28 ) . Mice were infected by injecting schistosomula in 100 µl of RPMI without phenol red into the thigh muscle of the animals using a 1 ml tuberculin syringe and a 25G-1 needle . Twenty eight days later , the mice were euthanized and adult worms recovered by portal vein perfusion . Recovered worms were counted , examined under a light microscope and subsequently their SGTP gene expression levels were determined , as described above . The same procedure was followed in experiments in which schistosomula were exposed to long dsRNA by soaking , except that mice were infected 24 h after parasite exposure to long dsRNA . All data were analyzed using GraphPad Prism 4 software . One Way ANOVA was used to compare median values among three or more groups . Student's t-tests were used to compare the means between a target group and a control group and p values close to or less than 0 . 05 were considered significant . The following GenBank accession numbers apply to the DNAs targeted in this work: SGTP1: L25065 and SGTP4: L25067 . | Schistosomes are parasitic worms that live in the blood streams of ∼200 million people globally . They import glucose from host blood directly across their skin ( the tegument ) . In the tegument the parasites possess glucose transporter proteins designated SGTP1 and SGTP4 . SGTP4 sits on the outermost tegumental membranes while SGTP1 sits in the tegumental basal membrane ( and on internal tissues ) . We have long hypothesized that SGTPs are involved in taking in glucose from the host but until the advent of advanced molecular technologies for use with schistosomes ( notably RNA interference ) , we could not test this fundamental notion . In this work we employed RNAi to suppress expression of both SGTP genes in schistosomes . In support of our hypothesis , we find that SGTP1 or SGTP4-suppressed schistosomes do exhibit an impaired ability to import glucose compared to control worms and that this effect is compounded by suppression of both genes simultaneously . When suppressed parasites are cultured in glucose-depleted medium fewer of them survive . In addition , suppressed parasites showed decreased viability in experimental animals . These findings provide direct evidence of the importance of these tegumental transporters for schistosome feeding and show that these SGTPs are important for normal parasite development in the mammalian host . | [
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... | 2010 | Suppressing Glucose Transporter Gene Expression in Schistosomes Impairs Parasite Feeding and Decreases Survival in the Mammalian Host |
This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever ( DF ) incidence rates at street level in Guangzhou city , China . Spatiotemporal scan technique was applied to identify the high risk region of DF . Multiple regression model was used to identify the socio-environmental factors associated with DF infection . A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF . Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city . Age group ( 65+ years ) ( Odd Ratio ( OR ) = 1 . 49 , 95% Confidence Interval ( CI ) = 1 . 13 to 2 . 03 ) , floating population ( OR = 1 . 09 , 95% CI = 1 . 05 to 1 . 15 ) , low-education ( OR = 1 . 08 , 95% CI = 1 . 01 to 1 . 16 ) and non-agriculture ( OR = 1 . 07 , 95% CI = 1 . 03 to 1 . 11 ) were associated with DF transmission . Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude ( β = -5 . 08 , P<0 . 01 ) and latitude ( β = -1 . 99 , P<0 . 01 ) . The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou . As geographic range of notified DF has significantly expanded over recent years , an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention .
Dengue fever ( DF ) is a widespread vector-borne viral infectious disease which has a rapidly increase in infections , geographic distribution , and the severity cases[1] . The rapidly expanding global footprint of DF has evolved to a major public health problem due to increased geographical extension , climate changes , population growth and global travel in the last 50 years [2] . DF is endemic and has been reported in more than 100 countries including the southeast Asia , the Americas , the western Pacific , Africa [3] . 3 . 9 billion people are at the potential risk of DF in these endemic regions [4] . The high economic burden brought could not been neglected [5] . Historically , DF has re-emerged in China in 1978 , from its first appearance in Foshan city of Guangdong province and then subsequently it has been reported in other areas such as Guangdong , Guangxi province and Hainan island after 32 years [6] . Since then , DF outbreak and epidemics were reported every year affecting several thousands of people , predominantly in the southeast coastal regions including Hainan , Guangxi , Fujian , Zhejiang and Yunnan provinces [7] . It was assumed that the large-scale epidemics occurred before 1990s was due to the imported dengue virus [8] . During the period 1978–2008 , a total of 655 , 324 cases including 610 deaths were recorded by Guangdong province Health Department . Vector-borne scientists have predicted that DF could potentially become an endemic disease in China [9] . For example , in 2014 , a large outbreak with more than 37 , 000 cases has occurred in Guangzhou city [10] . Due to the lack of effective vaccine and antiviral treatment , vector control is considered as a useful measure towards prevention of dengue disease [11] . DF epidemics in the different districts appeared not homogenous , due to the change of the transmission pattern of spatial and time [11] . However , the spatial clusters , socio-environmental factors at the new and smallest administrative unit ( street level ) and the temporal cluster at daily level in Guangzhou have not been explored in this epidemic regions . To help decision-makers or policy-makers in targeting the prevention and control areas and reduce the economic burden , vector control techniques could be selectively applied at high-risk areas or clusters of DF . Hence , this study aimed to examine the spatiotemporal pattern of DF using spatiotemporal scan technique at street-level[10 , 12 , 13] , to identify the socio-environmental risk factors of DF and to explore the spread of DF over the study period for improving prevention and control of DF and guiding to future study .
Ethical approval for this project was approved by Sun Yat-Sen University Ethical Review Committee ( Approval No: 2015024 ) and all of the data analyzed were anonymized . Guangzhou , as the third-largest city in China and the world-famous trade port , located at the Pearl River Delta Region of Guangdong province and spanned from 112° 57' to 114° 03' E longitude and 22° 26' to 23° 56' N latitude [14] ( Fig 1 ) . The total area under the city's administration is 7 , 434 . 4 square kilometers and the permanent resident population is 12 , 700 , 800 ( 2010 ) [15] . Guangzhou city has 12 districts and 166 streets . The permanent resident population of each street ranged from 3397 to 391287 ( 2010 ) [16] . Monthly averages range from 13 . 6°C in January to 28 . 6°C in July , while the annual mean is 22 . 6°C [14] , the relative humidity is approximately 68% , whereas annual rainfall in the metropolitan area is over 1 , 700 mm [14] . Daily data on indigenous DF cases were collected from China Notifiable Disease Surveillance System and Guangzhou Center for Disease Control and Prevention ( CDC ) for the years 2006 to 2014 . There were 240 cases with unknown street-level address in 2014 . These cases were excluded in this study . DF cases were diagnosed according to the national diagnostic criteria of DF , including the epidemiological exposure history , clinical manifestations and laboratory confirmation [10] . The street-level geographic vector polygon map of Guangzhou city was obtained from Guangzhou CDC and the latitude and longitude of the centroid of each street were calculated directly in the ArcGIS 10 . 0 software . The counts number of the indigenous DF cases were aggregated to counts at the street-level . Street-wise socio-demographic data was retrieved from the demographic bulletin of the 6th National Population Census [17] . Data on the urban-rural structure of communities was collected from the National Bureau of Statistics of People's Republic of China [18] . The location of all cases were matched to the street-level vector map based on their home addresses . The annual occurrence of street-wise first indigenous DF cases were mapped along with the date of onset . A retrospective spatiotemporal scan test was implemented using SaTScan ( Version 9 . 4 . 1 ) software . Firstly , the spatiotemporal cluster analysis of DF in Guangzhou from 2006 to 2014 was conducted annually . In brief , DF case , population and coordinates data were used as inputs in SaTScan . Scanning window for the spatiotemporal scanning method is the spatial scan combining with temporal scan . The scan window is a cylinder . The base of the cylinder is circle which represents the spatial dimension , and the height of the cylinder represents the temporal dimension . The radius of the circle varied from zero to the maximum spatial cluster size of 50% of the population at risk which could avoid pre-selection bias . In this study , the heights of the cylinder were varied daily from zero to 1 year . The results with the statistical significance of p-value were reported by Monte Carlo simulation replication at 9999 . The maximum log likelihood ratio ( LLR ) calculated in Poisson distribution is considered as the most likely cluster . The secondary clusters are defined as the second maximum LLR estimated by poisson model [19] . In this study , a holistic purely spatial cluster analysis from 2006 to 2014 was implemented with the same upper limits in the spatial window . ArcGIS ( Version 10 . 3 . 1 ) were used to convert the outputs of scan analysis into maps and visualize the spatial and temporal clusters . Univariate logistic regression and a stepwise logistic regression model were conducted to explore the relationship between the socio-environmental risk factors and the street with DF cases at high risk and low risk . Dichotomous dependent variable was set based on relative risks ( RRs ) of each street from the purely spatial cluster analysis result . The streets with RRs ≥1 were assigned “1” and those with RRs <1 were assigned “0” . The potential socio-environmental risk factors included at street-level were as following: percentage of people in each age-group; floating population; non-agriculture population; percentage of people with lower education ( lower than undergraduate ) ; percentage of different type communities ( urban communities , urban-rural communities and rural communities ) in all of the communities in each street . The floating population is defined as the people living in the street currently whose census registers were recorded in other street of the district in Guangdong province . There are two type of the census registers including agriculture and non-agriculture in China . The non-agriculture population was defined as the people whose census registers were recorded in the urban , not in the rural . The variations in the distribution of DF along the latitude and longitude of streets centroids were detected using Poisson model during the study period [20] . To explore the difference of DF distribution in the last three years and the first six years , we divided the study period into two periods: period 1 is from 2006 to 2011 and period 2 was from 2012 to 2014 . The dependent variable in this modeling was the differences of DF annual mean incidence rates of the all the streets which occurred DF epidemic between the period 1 and period 2 in Guangzhou .
The epidemic pattern of daily indigenous DF cases fluctuated during 2006 to 2014 with three major outbreaks in 2006 , 2013 and 2014 ( Fig 2 ) . The number of DF cases ranged from 0 to 1 , 627 cases daily ( mean = 52 . 9 , SD = 182 . 48 ) . Interestingly , outbreaks showed an increasing trend after 2010 . Fig 2 also displayed the daily variability of the number of streets with infected cases from 2006 to 2014 . The peaks in DF cases generally coincided with streets of high DF cases . The spread of indigenous DF incidence rates in each high-risk street was displayed in Fig 3 . All streets in Yuexiu , Liwan and Haizhu district , several streets in Baiyun , Panyu and Tianhe districts and streets in Huangpu , Luogang and Nansha district had relatively high DF spread . The streets with highest increase in DF were located in Baiyun , Panyu and Huangpu district . Baiyun districts included the streets with highest spread . Fig 4A showed the spatial distribution of high-risk areas or clusters of DF at street-wise . There were 75 high risk streets ( RRs ≥ 1 ) in the southwest of Guangzhou city . These streets were located mostly in Yuexiu , Liwan and Haizhu district , the southern part of Baiyun , the northern part of Panyu , Tianhe and Huangpu district . Fig 4B depicts the sum of daily indigenous DF cases of the streets with RRs <1 and RRs ≥ 1 during the study period . Spatial and temporal clusters of indigenous DF cases were showed in Fig 5A and 5B , respectively . The most likely clusters ( n = 9 ) were detected each year during 2006 to 2014 ( P<0 . 01 ) and the secondary clusters ( n = 2 ) were identified in 2006 and 2013 ( P<0 . 01 ) ( Table 1 ) . The most likely clusters were concentrated in streets of Yuexiu , Liwan and Haizhu districts . In 2006 , the most likely cluster included the southern Panyu district and part of the southern Nansha district whereas the secondary cluster included the northern Conghua district . In 2014 , the most likely clusters included the farther northern Baiyun district with the secondary clusters in the northern Zengcheng district ( Table 1 ) . The significant temporal clusters were found in autumn season , i . e . , late August to early November during 2006 to 2014 , except in 2008 and 2009 . Fig 5C shows the streets with the occurrence of first indigenous DF cases each year . The first indigenous DF cases occurred within or close to the spatial cluster circles yearly , except in 2014 , where it occurred in the distant Nansha district . The results of univariate and step-wise logistic regression model analyses were presented in Table 2 . In the univariate analysis , the age-groups , the percentage of non-agricultural population and the urban-rural population per street had significant association with DF risk: 0–14 years ( OR = 0 . 84 , 95% CI = 0 . 75 to 0 . 94 ) , 15–64 years ( OR = 0 . 94 , 95% CI = 0 . 88 to 0 . 99 ) , urban-rural communities ( OR = 0 . 97 , 95%CI = 0 . 95 to 0 . 98 ) and rural communities ( OR = 0 . 95 , 95% CI = 0 . 93 to 0 . 97 ) had negative association with DF risk whereas 65+ years ( OR = 1 . 26 , 95% CI = 1 . 15 to 1 . 39 ) , nonagricultural population ( OR = 1 . 05 , 95%CI = 1 . 04 to 1 . 07 ) and urban communities ( OR = 1 . 03 , 95% CI = 1 . 02 to 1 . 05 ) had positive association with DF risk . After the stepwise variable selection , four variables were entered into the multivariate logistic regression model . The results demonstrated that DF was statistically significantly associated with population belonging to 65+ years ( OR = 1 . 49 , 95% CI = 1 . 13 to 2 . 03 ) , floating population ( OR = 1 . 09 , 95% CI = 1 . 05 to 1 . 15 ) , non-agricultural population ( OR = 1 . 07 , 95% CI = 1 . 03 to 1 . 11 ) and low-education population ( OR = 1 . 08 , 95% CI = 1 . 01 to 1 . 16 ) . A statistically significant and negative association was obtained between the spread of DF incidence rates and longitudes ( β = -5 . 08 , P < 0 . 01 ) and latitudes of the streets ( β = -1 . 99 , P < 0 . 01 ) ( Table 3 ) . The results indicated that DF incidence rates increased with the areas geographically variation which may provide with the information of target streets for DF prevention and control in the future .
The results of this study suggested that DF incidence rates in the different districts appeared to be heterogeneous which was due to the changes in the transmission pattern of DF spatially and temporarily . A previous study has indicated that the prevention and control strategies towards DF will depend on high-risk and low-risk clusters [21] . Understanding and identifying the potential spatial and temporal clusters of DF transmission is the fundamental measure for surveillance and control [22] . A couple of studies have conducted cluster analysis of DF in Guangdong [23 , 24] . Previous research identified six risk factors for DF infection in Pearl River Delta [25] based on 2013 dengue surveillance data , which may improve our comprehension of the differences and socio-environmental factors on DF incidence rates . But in addition , few other studies have demonstrated that socio-demographic factors , such as population growth , levels of education , demographic structure and urbanization could influence the DF spread [26–30] . However , our research used a dynamic spatial and temporal analysis based on long term data ( ie . , January 2006 and December 2014 ) to detect the spatial clusters of DF and identify associated socio-environmental factors at a street level in Guangzhou . Moreover , Guangzhou was struck by an exceptionally severe outbreak in 2014 , resulting in almost 40 , 000 laboratory-confirmed DF cases . This outbreak is the largest and most severe epidemic of dengue fever ever documented in China , with incidence rates exceeded the combined total of all previous years [31 , 32] . This study detected spatial clusters of DF high risk regions in Guangzhou city and suggested the geographic range of notified dengue cases has significantly expanded over recent years . Relative importance of risk factors may vary across space and time . This finding will provide useful information for developing dynamic early warning system for DF transmission . We have performed stepwise logistic regression model as this technique was applied in the vector-borne diseases research . Our results demonstrated that old aged population ( 65+ years ) , floating population , low-education people and non-agriculture people were the potential determinants for the spread of DF . DF transmission has been reported in both rural and urban areas , and the dengue viruses have fully adapted to a human-Aedes aegypti-human transmission cycle , previous studies showed that the urbanization was linked to the DF incidence rates [33 , 34] . Guangzhou , as a large urban center of the tropics , where crowded human populations , especially nonagricultural population , live in intimate association with equally large mosquito populations . This setting provides the ideal home for maintenance of the viruses and the periodic generation of epidemic strains . In this longitudinal study , the result indicated nonagricultural population was positively related with DF risk , the central urban area and the old city area were the high-risk areas , where most aged ( 65+ years ) Guangzhou residents lived . The streets with high nonagricultural population in Guangzhou normally have higher population density and poor housing conditions and less environmental management . Previous studies have suggested that the accumulation of a susceptible population was essential to trigger DF epidemics [35] . In this study , a large number of floating population may be more susceptible for DF transmission . Residents , especially the aged , have the habit of planting flowers or hydrophyte in flowerpots or in household courtyards in Guangzhou . Several studies have identified the vegetation and breeding mosquitoes to DF that “vegetation can provide resting or feeding sites for mosquitoes or can serve as a proxy for the presence of breeding sites . " Water storage , containers with an abundance of organic matter ( e . g . those used for striking plant cuttings ) or those amongst foliage or under trees ( e . g . discarded plastic ) . As such progeny have been linked to a greater risk [36] . These containers with water provide a suitable breeding condition for mosquitoes . The water landscape and afforest landscape around the houses were also a perfect breeding habitat for mosquitoes . In addition , the movement of aged population may be limited to house surroundings and nearby areas , thus , increasing the chances of exposing themselves to mosquitoes . People with low-education generally have lack of knowledge and practices on the prevention measures of DF . These people usually work as laborers and spend most of their time outside , this in turn , may have given the possibility of being bitten by the mosquitoes . Another possible reason could be that these people live in rented apartments where the sanitary conditions are sub-optimal , thus this may have increased the chances of mosquitoes breeding and exposure . The results from temporal cluster analysis indicated that the DF clusters occurred mainly in autumn , particularly , in late August to early November . Indigenous DF cases peaked seasonally despite limited intra-annual climatic variability and seasonal fluctuations . In addition , the availability of immature densities of Aedes albopictus ( primary vector in Guangzhou ) was consistent with the dengue seasonality [37 , 38] as the vector biology and viral replication are temperature and moisture dependent [39 , 40] . These results could be used in planning future prevention and control measures towards DF , particularly , during the high-risk season . The consistent occurrence of first indigenous DF case within or close to the spatiotemporal clusters during the study period , except in 2014 requires further investigation . Over all , in the high risk streets , there were more indigenous DF cases than in the low risk streets: The cases in high risk streets occurred earlier and accelerated faster than those in the low risk streets as well . Without considering the number of cases , similar waves and crests were found in 2 sorts of streets . This could be due to the daily movements of working people from their living areas to working areas , i . e . , the high-risk areas . We observed an interesting result in the epidemic patterns of DF incidence rates during the study period . If the first case occurred in early summer , i . e . , June or July , large outbreaks often occurred . For example , large epidemics in 2006 , 2012 , 2013 and 2014 were initiated with the occurrence of first case in June , July , July and June respectively . Although there were not many DF cases in 2012 , the longest cluster period of DF was observed . On the contrary , if the first case occurred too early and too late , the large outbreaks often could not be triggered . In 2007 and 2010 epidemics , the first case occurred in April whereas in 2008 , 2009 and 2011 epidemics , the first case occurred in November , August and September . If the first case occurs too early , the local department of health may plan to provide early warnings of DF outbreaks and implement prevention and control measures , whereas if it occurred too late , the reduced density of mosquito and the capacity of virus loading could help to decrease the risk of a large DF outbreak . Although imported cases was considered as an important trigger for the DF outbreak in Guangzhou , scientists could not confirm whether or not the dengue outbreaks in Guangzhou were initially triggered by the imported cases [39] . So other uncertainties of DF outbreak are still unknown and needs further studies . In recent years , the impact of climate change on the transmission of mosquito-borne diseases has been studied in China [40] . Our results showed significant variation in the spatial distribution of DF in Guangzhou and that the geographic range of notified cases has expanded in this city ( from south towards north and concentrate on the southwestern Guangzhou city ) over the study period . Previous study reveal the movement tracks of the centre of mass for annual incidence rate of DF at municipality level in China , showing that the geographic expansion of dengue epidemics , such as gradually shifting from southern China ( Guangdong , Guangxi , and Hainan ) to northeastern China ( Fujian and Zhejiang ) and southwestern China ( Yunnan ) [41] . The associations between the spread of DF incidence rates and longitude and latitude were observed in this work , also demonstrated that DF has spread towards the southwestern Guangzhou city during the study period . Dengue is a complex disease and the spatiotemporal distribution involves socio-environmental factors , such as climate change , population movement , mosquito density and urbanization . Hence , future studies should include the impact of climatic and entomological factors on the transmission of DF in Guangzhou city . To our knowledge , this is the first study to investigate the spatiotemporal clusters of DF and assess the socio-environmental factors in Guangzhou city using the spatial techniques at street-level . The study provides readily accessible information on DF spread and GIS maps on high-risk areas which can be used by the local Department of Health towards prevention and control of DF in Guangzhou . There are two limitations in this study: 1 ) Model included few variables on socio-environmental factors , as it was difficult to obtain all other street-level data . 2 ) As this study is an ecological study , measurement and information biases are possible . For example , the data on the socio-demographic factors were only obtained from the 6th Nation Population Census ( collected in 2010 ) as the national demographic census in China was only conducted once 10 years . The socio-demographic data varied by time in Guangzhou and may have little impact on our results . However , we believe that the relative changes by different street level is unlikely to change dramatically in Guangzhou . We obtained the floating population in Guangzhou between January 1st 2006 and December 31st 2014 by accessing the registers at the online Guangzhou Statistics Bureau website ( http://www . gzstats . gov . cn/ ) . In addition , under-reporting is most likely possible as people with sub-clinical symptoms usually do not seek medical attention . The biases and drawbacks of stepwise multiple regression are well established within the statistical literature , including bias in parameter estimation , inconsistencies among model selection algorithms , etc . Whittingham et , al . discussed these issue and showed that stepwise regression allows models containing significant predictors to be obtained from each year's data [42] . In this study , we conducted stepwise logistic regression model as this technique was applied in the vector-borne diseases research , so as to select the main risk factors and develop predictive model . The spatial-temporal analysis presented in this paper differs from the one by explaining the observed distribution and perhaps ultimately permitting prediction . In conclusion , this study has detected spatiotemporal clusters and variation of DF epidemics , and assessed socio-environmental risk factors for DF in Guangzhou city . These results could be implemented towards prevention and control measures of DF in high-risk areas in Guangzhou . | Dengue fever ( DF ) as a mosquito-borne viral disease remains a challenge for the prevention and control caused by the increased population , global development , human movement , and urbanization in the last five decades . The largest DF outbreak occurred with more than 40 , 000 cases in Guangdong in 2014 since DF re-emerged in China . The accurately spatiotemporal identification of DF transmission and the related socio-environmental factors are considered to be important for the strategy decision-making of the official government . This study first identified the spatiotemporal pattern and socio-environmental factors associated with DF occurrence at street and daily level in Guangzhou , China from 2006 to 2014 , using spatiotemporal scan statistical methods . The results suggested that DF control should be targeted in the southwest of Guangzhou during autumn , particularly 75 high risk streets . We found that the aged population , floating population , low-education population and the non-agricultural population significantly contributed to the DF clustering risk at street level . Finally , a spread trend of DF toward southwest part of Guangzhou was noticed . These results could be implemented towards prevention and control measures of DF in high-risk areas in Guangzhou . | [
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"cluster... | 2018 | Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China |
Recent evidence suggests that the presence of more than one pathogenic mutation in a single patient is more common than previously anticipated . One of the challenges hereby is to dissect the contribution of each gene mutation , for which animal models such as Drosophila can provide a valuable aid . Here , we identified three families with mutations in ADD3 , encoding for adducin-γ , with intellectual disability , microcephaly , cataracts and skeletal defects . In one of the families with additional cardiomyopathy and steroid-resistant nephrotic syndrome ( SRNS ) , we found a homozygous variant in KAT2B , encoding the lysine acetyltransferase 2B , with impact on KAT2B protein levels in patient fibroblasts , suggesting that this second mutation might contribute to the increased disease spectrum . In order to define the contribution of ADD3 and KAT2B mutations for the patient phenotype , we performed functional experiments in the Drosophila model . We found that both mutations were unable to fully rescue the viability of the respective null mutants of the Drosophila homologs , hts and Gcn5 , suggesting that they are indeed pathogenic in flies . While the KAT2B/Gcn5 mutation additionally showed a significantly reduced ability to rescue morphological and functional defects of cardiomyocytes and nephrocytes ( podocyte-like cells ) , this was not the case for the ADD3 mutant rescue . Yet , the simultaneous knockdown of KAT2B and ADD3 synergistically impaired kidney and heart function in flies as well as the adhesion and migration capacity of cultured human podocytes , indicating that mutations in both genes may be required for the full clinical manifestation . Altogether , our studies describe the expansion of the phenotypic spectrum in ADD3 deficiency associated with a homozygous likely pathogenic KAT2B variant and thereby identify KAT2B as a susceptibility gene for kidney and heart disease in ADD3-associated disorders .
The interrogation of the entire genome via next generation sequencing ( NGS ) technology has revolutionized clinical diagnostics . For medical genetics that traditionally focuses on finding monogenetic causes for Mendelian diseases , NGS has not only introduced much higher mutation detection rates but also unprecedented complexities . A recent retrospective analysis of more than 7000 exomes revealed multiple molecular diagnoses in around five percent of cases with suspected monogenic disease [1] , suggesting that patients with multilocus diseases are underrecognized . The phenotypic complexity of multilocus diseases , of which digenic disease represents the simplest and most common form , can be challenging for the physician , both when it comes to finding a diagnosis and to genetic counseling and risk assessment . Two distinct disease phenotypes in a single patient may present with a completely new clinical phenotype . On the other hand , two overlapping disease phenotypes may be misinterpreted as a single disease with increased severity . The underlying genetic defects are equally difficult to predict . Both compound phenotypes caused by mutations in two completely unrelated genes [1] and overlapping disease phenotypes caused by mutations in genes within the same pathway are possible [2–4] . But as genes can be pleiotropic , there are most likely many exceptions to this . Also , while two loci may be equal in importance [2] , a second variant may simply enhance the general or organ-specific penetrance of a given mutation [4] . One important challenge is therefore to decompose the contributions of each gene mutation or variant to the clinical phenotypes in question . So far , most reports on digenic inheritance in Mendelian disease have focused on known disease genes [1 , 5 , 6] . However , the diagnosis is even more difficult when dealing with genes that have previously not been associated with any genetic diseases . In this study , we identify three families with mutations in ADD3 , encoding for adducin-γ , with intellectual disability , microcephaly , cataracts and skeletal defects , further supporting that ADD3 is a disease gene as previously reported for a single family [7] . We further use mutation validation in Drosophila and mammalian cell culture to demonstrate that in one of the families additional phenotypes in kidney and heart are associated with a homozygous missense variant in the lysine acetyltransferase KAT2B .
Six individuals in three families ( families A-C ) with intellectual disability and varying degrees of microcephaly ( Table 1 ) were identified for this study . Individuals from family A and B also shared bilateral cataracts , corpus callosum defects as well as specific skeletal defects such as shortening of the third and fourth metatarsals ( Fig 1A–1D and Table 1 ) , while the affected boy from family C suffered from epilepsy , severe speech delay and suspected cerebral palsy ( Table 1 ) . The affected sibs in the consanguineous family A additionally presented with steroid-resistant nephrotic syndrome ( SRNS ) , a progressive renal disease characterized by podocyte lesions and massive proteinuria [8] , and cardiomyopathy ( Table 1 ) . For individual II-1 and II-3 , proteinuria was first detected at 7 and 12 years of age , respectively , and end-stage renal disease was diagnosed a decade later . Individual II-6 was diagnosed with SRNS and end-stage renal disease at the age of 13 years . In kidney biopsies , individuals II-3 and II-6 ( Fig 1E ) both showed focal segmental glomerulosclerosis ( FSGS ) . In the biopsy of individual II-6 , whose renal disease was at a more advanced stage , hypertrophic and vacuolated podocytes ( Fig 1E ) as well as tubular atrophy , interstitial fibrosis and inflammatory cell infiltrates could also be found . In addition , all affected individuals from family A developed dilated cardiomyopathy with progressive heart failure and arrhythmia ( Table 1 ) . Cardiac failure was the cause of death for both individuals II-1 and II-3 . For the affected individuals in family A and C , the presence of mitochondrial disease was excluded by muscle biopsy . Moreover , high-resolution karyotypes were normal for all patients , and CGH arrays ( performed for family B and C ) did not show significant abnormalities . Consequently , whole exome sequencing ( WES ) was performed on two affected members of family A as well as on the affected individuals and the parents of family B and C , after obtaining written informed consent and study approval . WES led to the identification of recessive potentially damaging mutations in ADD3 , all segregating with the disease as confirmed by Sanger sequencing ( NM_016824 . 4: family A: homozygous c . 1975G>C , p . E659Q; family B: compound heterozygous c . 86A>G , p . N29S; c . 1588G>A , p . V530I ( both on the same allele in the mother ) , c . 995A>G , p . N332S ( heterozygous in the father ) ; family C: homozygous c . 995A>G , p . N332S ) ( Fig 2A and 2B and Table 2 ) . In 148 , 632 reference individuals from the gnomAD browser ( http://gnomad . broadinstitute . org/ ) , the ADD3 mutations were present at low frequencies and only in the heterozygous state ( Table 2 ) . The identified ADD3 mutations result in the substitution of amino acids located in the head and tail region of the protein product adducin-γ ( Fig 2B ) . In humans , adducins form heterotetramers that are composed of either adducin-α and -γ ( the most widely expressed ) or adducin-α and -β ( restricted mainly to erythrocytes and specific brain regions ) [9] . These heterotetramers regulate the actin cytoskeleton by capping the barbed ends of F-actin and by promoting the interaction between actin and spectrin [9 , 10] . Recently , a homozygous mutation in ADD3 was shown to cause cerebral palsy , epilepsy , borderline microcephaly , thin corpus callosum and intellectual disability in one family [7] . As the phenotype of this family shows overlap with all our families , particularly family C , our study supports the pathogenicity of the previously identified ADD3 mutation . Family A , which was characterized by additional cardiomyopathy and SRNS , exhibited another potentially damaging homozygous mutation in lysine acetyltransferase 2B ( KAT2B ) . No other pathogenic variant was identifying after applying a set of filters excluding common variants in the general population ( dbsnp>1% ) or in our in-house database as well as variants predicted not to be deleterious . The KAT2B variant ( NM_003884 . 4: c . 920T>C , p . F307S ) segregated with the disease and was not present in the reference individuals from the gnomAD browser ( Fig 2A and Table 2 ) . KAT2B is known to acetylate a variety of substrates , including histones ( preferentially H3 ) , and to function as a transcription coactivator together with CBP/p300 [11–13] . The identified KAT2B missense variant affects a highly conserved amino acid within the PCAF homology domain ( Fig 2C ) , which is required for the interaction with CBP/p300 [14] . By studying mRNA and protein expression in patient fibroblasts from affected members of family A using qPCR , western blotting and immunostainings , we found no significant decrease for adducin-γ at the mRNA or protein level ( Fig 2D and S1A and S1C Fig ) . However , KAT2B protein ( but not mRNA ) levels were significantly reduced ( Fig 2 and S1B and S1D Fig ) . Thus , we reasoned that the KAT2B variant could contribute to the extended phenotype observed in family A . To test this hypothesis , we decided to perform functional validation of both mutations in Drosophila melanogaster . Drosophila hu li tai shao ( hts ) corresponds to the sole homolog of all three adducin genes in humans . As previously described [15] , htsnull hemizygous animals died at the late larval stage , with only a few escapers progressing into adult stage . The escapers showed rough eyes , uncoordinated movements and inability to fly leading to death within 24h after eclosion ( S2A Fig ) . For mutation validation , we re-expressed in this htsnull background the human wild-type ( WT ) and mutant constructs using the ubiquitous driver tubulin ( tub ) -GAL4 ( see S1 Table for precise genotypes ) . As E659 , the amino acid mutated in adducin-γ , is located in a very poorly conserved region ( Fig 2B ) , we performed rescue experiments with WT and mutated human adducin-γ . While re-expressing each of the adducins alone failed to rescue the viability , the co-expression of adducin-α and -γ ( hereafter referred to as adducin-αγ WT ) led to around sixty percent of viable mutant adults ( Fig 3A ) . Importantly , when co-expressing adducin-γ E659Q together with adducin-α ( adducin-αγ E659Q ) , we observed a significantly reduced partial rescue of fly viability ( Fig 3A ) . The surviving animals did not present with any defects in eye and wing morphology ( S2A Fig ) but showed climbing impairment in a geotaxis assay ( Fig 3B ) [7] . To express the transgenes with endogenous expression levels , we also used an available GAL4 insertion in the hts locus . This insertion leads to a partial lethality over htsnull , which could be fully restored by adducin-αγ WT but not by E659Q ( S3A Fig ) . Altogether , these results suggest that adducin-γ E659Q is a hypomorphic mutation . Drosophila Gcn5 is homologous with KAT2B and its paralog KAT2A . Gcn5E333st hemizygous animals died at late larval stage/early pupal stage as previously reported for this null mutation [16] . The expression of Drosophila Gcn5 ( hereafter referred to as Gcn5 WT ) with tub-GAL4 or another ubiquitous driver ( daughterless ( da ) -GAL4 ) led to a full rescue ( S3B Fig and Fig 3C ) . By contrast , the expression of human KAT2A and KAT2B , either alone or in combination , did not restore the viability of the mutant ( Fig 3C ) , suggesting that the human orthologs have evolved in structure and function in comparison to Gcn5 . As the mutated amino acid in KAT2B , F307 , is conserved in Drosophila Gcn5 ( corresponding to Gcn5 F304 ) , we re-expressed Gcn5 F304S in the Gcn5E333st hemizygous background ( Gcn5 F304S ) . As a negative control , we re-expressed a predicted potentially damaging KAT2B variant ( S502F corresponding to Gcn5 S478F ) found in a homozygous state in a healthy individual from our in-house database . While Gcn5 S478F rescue animals were normal ( Fig 3C and S3B Fig ) , Gcn5 F304S had a dramatically decreased viability with death occurring either in pupal stages or a few days after eclosion ( Fig 3C ) . All adult escapers showed blistered wings , inability to fly and rough eyes and around 40 percent of the animals had defects in leg morphology ( Fig 3D and 3E ) . Interestingly , this phenotype corresponds to what has previously been described for the deletion of the entire PCAF homology domain , where the mutation is localized [16] . In agreement with the proposed function of Gcn5 in histone acetylation [16] , we further detected histone ( H3K9 ) acetylation defects for Gcn5 F304S but not for Gcn5 WT and control animals , as assessed by immunoblotting of larval nuclear extracts ( Fig 3F ) , suggesting that the mutation impairs the enzymatic activity of Gcn5 . Altogether , the results suggest that KAT2B F307S is a loss-of-function mutation in Drosophila . Since the presence of SRNS and heart defects in family A was the main phenotypic difference from the other families , we looked more specifically into the cardiac and renal system of the fly . The Drosophila heart is a tubular organ formed by contractile cardiomyocytes that pump the hemolymph ( analogous to the blood in vertebrates ) to the rest of the body . This organ system has proven to be an important tool for studying the genetics and pathophysiology of cardiac disease [17 , 18] . Therefore , we studied heart function in adult adducin and Gcn5 rescue flies . As illustrated in the M-mode traces obtained from high-speed movies , adducin-αγ E659Q did not show any significant differences in heart period , cardiac output , fractional shortening and arrhythmia index when compared to adducin-αγ WT ( Fig 4 ) . By contrast , Gcn5 F304S flies showed prolonged heart period and reduced cardiac output compared to Gcn5 WT and control flies ( Fig 5A–5E ) . Both Gcn5 WT and F304S rescue flies showed a reduction in the normal diastolic diameter compared to control flies ( Fig 5F ) , but only for Gcn5 F304S there was a reduction in contractility , measured as fractional shortening ( Fig 4G ) . Moreover , the Gcn5 F304S mutant showed a more irregular heartbeat compared to Gcn5 WT , reflected by an increase in the arrhythmia index ( Fig 5H ) . In further support of Gcn5’s requirement for normal heart function , the silencing of Gcn5 with a heart-specific driver ( tin>GAL4 ) led to a decreased cardiac output , an increased arrhythmia index and shortened diastolic diameter ( S4B–S4D Fig ) . Interestingly , while the knockdown of hts did not cause any significant heart phenotypes ( S4A–S4F Fig ) , the co-expression of htsRNAi and Gcn5RNAi significantly aggravated the heart period length and the arrhythmia index observed upon single knockdown of Gcn5 ( S4A and S4C Fig ) . The silencing efficiency for both RNAi lines were confirmed by qPCR and immunocytochemistry ( S5A–S5D Fig ) . Altogether , the results suggest that Gcn5 is important for heart function in Drosophila and that Hts deficiency can increase the phenotypic consequences of Gcn5 knockdown . The fly kidney is composed of garland and pericardial nephrocytes ( Fig 6A ) that perform the filtration of the hemolymph and Malpighian tubules that function as excretory tubes . The surface of nephrocytes is decorated with actin-anchored slit diaphragms showing high molecular similarity with those of mammalian podocytes [19–21] . Therefore , nephrocytes have successfully been used to functionally validate candidate genes for SRNS [22 , 23] . By immunostaining , we observed that endogenous Hts localizes below the slit diaphragms at the cell cortex of larval garland nephrocytes ( Fig 6B ) . A similar localization pattern was found when adducin-γ was overexpressed with its binding partner adducin-α . In this case , adducin-γ protein levels were significantly increased compared to expressing adducin-γ alone , suggesting that the stabilization by adducin-α is a prerequisite for proper function ( S6 Fig ) . For Gcn5 , we found a prominent expression in nephrocyte and podocyte nuclei ( Fig 6C ) . The endogenous localization patterns were specific as they were lost upon hts and Gcn5 knockdown , respectively ( S5A and S5B Fig ) . To study the requirements of Hts for the integrity of the slit diaphragm , we performed immunostainings for Kirre , the ortholog of the mammalian slit diaphragm protein Neph1 [24] . In line with the proposed role for adducin-γ in cortical actin regulation [25] , we found that htsnull larval garland nephrocytes showed a decrease of Kirre between adjacent nephrocytes ( Fig 7A and 7B ) . When rescued with adducin-αγ WT and E659Q transgenes , however , no major differences with respect to Kirre localization were seen in larval garland nephrocytes ( Fig 7A and 7B ) . Similarly , the morphology and number of adult pericardial nephrocytes were normal in both rescue animals compared to the control ( Fig 7C and 7D ) . Gcn5E333st hemizygous larvae presented with morphologically normal nephrocytes . Yet , we did detect a decreased H3K9 acetylation at this stage in the nephrocyte nuclei of the mutant , which could be rescued by Gcn5 WT but not by Gcn5 F304S ( Fig 8A ) . Moreover , the majority of adult Gcn5 F304S escapers showed mislocalized and/or abnormally shaped pericardial nephrocytes in the adult stage that were often reduced in number ( Fig 8B–8D ) , consistent with previously reported characterizations of important podocyte genes [26–28] . Together , the results demonstrate that , while Hts is important for nephrocyte function , the ADD3 missense mutation identified in family A is alone insufficient to cause a renal phenotype in flies . By contrast , Gcn5 F304S seems to impair Gcn5 function in nephrocytes . To address any functional synergism between Gcn5 and Hts in nephrocytes , we performed nephrocyte-specific silencing of Gcn5 and hts alone or in combination . In larval nephrocytes , the double knockdown of Gcn5 and hts caused an increase in Kirre mislocalization , compared to the single knockdowns ( S7A and S7B Fig ) . Moreover , while in 3-day-old adults hts knockdown did not affect pericardial nephrocyte number ( Fig 9A–9C ) , a significant decline of differentiated nephrocytes could be observed in 15-day-old adults ( Fig 9D ) . Similarly , the nephrocyte-specific expression of Gcn5RNAi caused a progressive decline of differentiated pericardial nephrocytes at 15 days , but not at 3 days post eclosion ( Fig 9A–9D ) . By contrast , the double knockdown of Gcn5 and hts caused a significant loss of differentiated nephrocytes already at 3 days post-eclosion ( Fig 9A–9C ) . Making use of the double knockdown phenotype , we also addressed the synergism between ADD3 and KAT2B mutations from family A . Considering that the KAT2B variant corresponds to an almost complete loss of function mutation , we performed a double knockdown of Gcn5 and hts rescued only with the adducin-αγ transgenes , thereby avoiding the complexity of bringing all the Gcn5 and hts alleles as well as the GAL4 driver and respective rescue constructs together in one fly . In this setting , the adducin-αγ WT combination partially rescued the loss of pericardial nephrocytes in 3-day old adult flies ( Fig 10A and 10B ) . By contrast the expression of the adducin-αγ E659Q combination showed the same degree of nephrocyte loss as the double knockdown at this stage . Together , these results suggest a functional interaction between KAT2B and ADD3 mutations in the nephrocyte , which may be of relevance for the renal phenotypes in family A . To validate our findings in human cells , we studied adducin-γ and KAT2B in cultured human podocytes . While KAT2B was as expected found in nuclei of podocytes , adducin-γ localized to the cell periphery similar as in nephrocytes ( S8A–S8D Fig ) . Both localization patterns were specific , as they were reduced upon lentiviral transduction of respective shRNAs and could be restored by re-expression of both wild-type and even mutant adducin-γ and KAT2B ( S8A–S8D Fig ) . To address the phenotypic effects of the ADD3 and KAT2B knockdowns , we analyzed adhesion and migration , which are processes typically affected in kidney diseases such as SRNS [29 , 30] . While podocyte adhesion was reduced in the single ADD3 and KAT2B knockdowns , the double knockdown showed additive effects ( Fig 10C ) . With regard to migration , the single knockdown showed a mildly increased migration . By contrast , the double knockdown led to a strongly impaired migration ( Fig 10D ) , providing further evidence for potential synergistic effects of mutations in both genes in the kidney .
Here , we identify ADD3 mutations in three different families with similar neurological , skeletal and ophthalmological phenotypes , thereby consolidating and expanding the mutational and phenotypic spectrum of ADD3 deficiency reported initially . Moreover , we use functional validation in Drosophila and human cells to characterize the contribution of an additional variant in KAT2B to the extended phenotype featuring one of the ADD3 families . While such additional variants are commonly excluded from WES datasets without even performing functional validation , particularly when the respective gene functions seem to be unrelated to the disease ( s ) in question , our results demonstrate that both the ADD3 and the KAT2B mutation could be pathogenic . Moreover , we show that ADD3-associated phenotypes can be unmasked by additional Gcn5/KAT2B deficiency in nephrocytes and human podocytes . Our study thus provides an example of a genetic disease where the tissue manifestation could be influenced by a second homozygous mutation on another chromosome . KAT2B has previously not been associated with any genetic disease . The severity of the KAT2B variation in the fly model compared with the relatively late-onset cardiac and renal defects in the patients indeed suggests a partial functional redundancy due to gene duplication in vertebrates . Accordingly , it has been shown that in mouse development loss of KAT2B can be compensated for by KAT2A [31] . Nevertheless , KAT2B is strongly expressed in mouse heart and kidney , particularly podocytes [32 , 33] , while KAT2A has a more widespread expression pattern [14] . Moreover , mouse and zebrafish studies have shown that KAT2B can perform functions that are non-redundant with KAT2A , even in the heart [34–39] , indicating that KAT2B deficiency alone could be sufficient for any clinical manifestations . What remains to be seen is whether the clinical impact of KAT2B deficiency needs to be uncovered by a sensitized genetic background , such as the ADD3 mutation , or whether it is the other way around . While our data do not exclude either possibility , it is interesting that rare but otherwise uncharacterized variants of KAT2B have been found to be enriched in a patient cohort with sporadic FSGS [32] , suggesting that KAT2B could be a susceptibility factor for FSGS forms with different primary causes . At the level of protein function and disease mechanism , the precise mode of interaction between adducin-γ and KAT2B is equally unclear . Apart from histones , KAT2B has recently been shown to acetylate a variety of proteins [40] . Among them are also cytoskeletal regulators , that when mutated cause FSGS or cardiomyopathy ( e . g . actinin-4 , TTC21B and myosin-7 ) . Thus , it is possible that adducin-γ could also be a target of KAT2B-dependent acetylation . Vice versa , any influence of adducin-γ defciciency on the activity of KAT2B cannot be excluded . Apart from more mechanistic functional studies , the identification of more patients with the same or other variants in only ADD3 or KAT2B combined with careful characterization of their phenotypes will be crucial to define the precise role of each gene and their potential functional interaction in humans .
Following informed written consent , we obtained clinical data , blood samples and skin biopsies from the affected individuals . This study was conducted with the approval of the Comité de Protection des Personnes pour la Recherche Biomédicale Ile de France II . Approval was obtained under the number DC 2014–2272 Whole-exome sequencing ( WES ) was performed for affected individuals II-3 and II-6 from family A and for the two parents and the affected sib from family B and family C . Whole-exome capture was performed with the Agilent SureSelect Human All Exon Kit , 51Mb , V4 ( family A ) , the Roche MedExome kit ( family B ) or a proprietary system from GeneDx ( family C ) . The enriched library was then sequenced on either Life Technologies SOLID ( paired end with 75+35 base pair ( bp ) reads; family A ) or Illumina systems ( family B: 2x150 bp reads; family C: 2x100bp read ) . Images were analyzed and the bases were determined according to Lifescope or bcl2fastq Conversion Software v2 . 17 . Variants were called as described [41] . Crosses were maintained on standard cornmeal-yeast food at 25°C except for RNAi crosses ( 29°C ) . The fly stocks were used in this study can be found in S1 Table . For Htsnull rescue constructs we used an N-terminal V5 tagged human ADD3 ( clone IMAGE: 6649991 ) , WT or carrying the E659Q mutation , and the N-terminal HA tagged human ADD1 ( gift from Vann Bennett , Duke University ) . For Gcn5null rescue constructs we used the C-terminal HA tagged human KAT2B ( clone IMAGE: 30333414 ) , the N-terminal Flag tagged human KAT2A ( gift from Laszlo Tora , Institut de Génétique et de Biologie Moléculaire et Cellulaire , Strasbourg ) and the C-terminal Flag tagged Drosophila Gcn5 , WT ( gift from Clement Carré , University Pierre et Marie Curie ) or carrying the mutations F304S or S478F ( corresponding to human mutation F307S and S502F ) . All mutations were inserted using the QuickChange site-directed mutagenesis kit ( Stratagene ) according to the manufacturer’s protocol . Subsequently , the rescue constructs were subcloned into a pUASTattB vector ( gift from Konrad Basler , University of Zurich ) and injected into flies at attP landing sites by Bestgene , USA . A conditionally immortalized human podocyte cell line developed by transfection with the temperature-sensitive mutant ( tsA58 ) of the SV40-T-antigen-encoding gene , was kindly provided by Dr . Saleem ( University of Bristol ) . In brief , the cells proliferated at the permissive temperature of 33°C , whereas growth arrest and differentiation were induced by incubation at the nonpermissive temperature of 37°C for 14 days . Cells were grown with 7% CO2 in RPMI 1640 medium supplemented with 10% fetal bovine serum , insulin-transferrin-selenium , glutamine , penicillin and streptomycin ( all from Life Technologies ) . Primary skin fibroblasts were obtained from individual II-3 and II-6 from family A and two different age matched controls . These cells were grown in OPTIMEM medium supplemented with 20% fetal bovine serum , glutamine , penicillin and streptomycin ( all from Life Technologies ) at 37°C with 7% CO2 . Small hairpin RNAs ( shRNAs ) Scramble ( Scb ) or targeting the 3’UTR of human ADD3 and KAT2B mRNA in the lentiviral vector pLKO . 1 were purchased from Sigma ( ADD3 clone: NM_019903 . 3-2280s1c1 TRCN0000123024; KAT2B clone: NM_003884 . 4-3192s21c1 , TRCN0000364135 ) . Lentiviral particles containing these constructs were produced in human embryonic kidney 293T cells as previously described [42] . ShScb , ADD3 or KAT2B depleted podocytes were obtained by transduction with the respective shRNAs lentiviral particles and subsequent puromycin selection . Human ADD3 and KAT2B , were subcloned from human full-length cDNA ( ADD3: clone IMAGE: 6649991; KAT2B clone IMAGE: 30333414 ) into the expression vectors pLentiGIII and PLEX-MCS , respectively . An HA tag was added in frame , before the stop codon , to the C terminus of ADD3 and KAT2B . The ADD3 E659Q and KAT2B F307S mutations found in affected individuals were introduced with the QuickChange site-directed mutagenesis kit ( Stratagene ) according to the manufacturer’s protocol . All constructs were verified by sequencing . ADD3 or KAT2B depleted podocytes were transduced with WT or mutant ADD3 or KAT2B lentiviral particles , respectively . Total RNA was isolated using a Qiagen RNA extraction kit ( Qiagen ) , following the manufacturer’s instructions . cDNA was prepared using reverse transcriptase Superscript II ( Invitrogen ) . PCR was performed using ReadyMix Taq PCR ( Sigma ) . After RNA extraction and cDNA preparation by RT-PCR , relative expression levels of genes of interest were determined by real-time PCR using the Absolute SYBR Green ROX Mix ( ABgene ) and specific primers as follows: ADD3 forward 5’-CTTGCTGGAATTGTTGTGGATAAG-3’ and reverse 5’-CTGGTGGGCCATGATCATC-3’; KAT2B forward 5’-ATCACACGGCTCGTCTTTGAC-3’ and reverse 5’-CACCAATAACACGGCCATCTT-3’; hts forward 5’-GCACTCCGGATCCCAAGAAG-3’ and reverse 5’-CAGGCACAAACTGGAGTGGA-3’ , Gcn5 forward 5’-CGATCGTCCAAGCAGTGAG-3’ and reverse 5’-TCCGCCTTGACGTTCTCATC-3’ . Experiments were repeated at least three times and gene expression levels were normalized to human HPRT or Drosophila melanogaster actin . Total cell or third instar larvae total protein extractions were performed and the resolved proteins were probed using the primary antibodies: anti-PCAF rabbit monoclonal ( 3378 , Cell Signaling , 1:1000 ) , anti-adducin-γ mouse monoclonal ( sc-74474 , Santa Cruz , 1:1000 ) and anti-αtubulin mouse monoclonal ( T5168 , Sigma Aldrich , 1:5000 ) . For immunoblotting of nuclear extracts [43] , the primary antibodies anti-acH3K9 rabbit polyclonal ( 39918 , Active motif , 1:1000 ) and anti-H3 mouse monoclonal ( 61475 , Active motif , 1:1000 ) were used as well as the corresponding HRP-conjugated secondary antibodies ( Amersham ECL , GE healthcare and Invitrogen ) . Bands were visualized using Amersham ECL Western Blotting Detection Reagent ( GE Healthcare ) and quantified by densitometry using Image J software . Fibroblasts or podocytes were plated on noncoated coverslips or coverslips coated with rat-tail collagen type I ( Corning ) , respectively . After 48h of culture cells were fixed with 100% ice-cold ethanol . Cells were incubated with a blocking solution ( PBS , 1% BSA , and 0 . 1% tween 20 ) and further permeabilized for ten minutes with PBS 0 . 1% Triton . Incubation with the following primary antibodies was done ON at 4°C: anti-PCAF mouse monoclonal ( sc-13124 , Santa Cruz , 1:100 ) , anti-adducinγ rabbit polyclonal ( sc-25733 , Santa Cruz , 1:100 ) and anti-HA ( 11 867 423 001 , Roche , 1:200 ) . For immunofluorescence in Drosophila , garland and pericardial nephrocytes were dissected from third instar larvae and adults , respectively , and fixed for 20 minutes in 4% paraformaldehyde at room temperature and stained according to standard procedures . For Kirre stainings an alternative fixation method ( “heat fixation” ) was used: nephrocytes were heat-fixed for 5 seconds at 90°C in 0 . 7% NaCl/0 . 05% TX-100 solution . The following primary antibodies were used: anti-Hts mouse monoclonal ( #1B1 deposited to the Developmental Studies Hybridoma Bank ( DSHB ) by Lipshitz , H . D . ) , anti-Gcn5 rabbit polyclonal ( gift from Jerry Workman , Stowers Institute for medical research , Kansas , 1:200 ) anti-Kirre rabbit polyclonal ( gift from Karl Fischbach , Institute for Biology , Freiburg , Germany , 1:200 ) , anti-Pyd2 mouse monoclonal ( deposited to the DSHB by Fanning , A . S , 1:100 ) , anti-acH3K9 rabbit polyclonal ( #06-942 , Upstate , 1:100 ) , AlexaFluor488-conjugated anti-horseradish peroxidase ( Jackson Immunoresearch , 1:400 ) , anti-HA ( 11 867 423 001 , Roche , 1:200 ) and anti-V5 rabbit polyclonal ( v8137 Sigma , 1:200 ) . The corresponding anti-isotype AlexaFluor antibodies ( ThermoFisher Scientific , 1:200 ) were used at room temperature for 2 hours . Nuclei were stained with Hoechst . Confocal images were obtained with a Leica TCS-SP8 confocal microscope , and post-treatment analysis was performed with Image J software . Results are presented as means ± standard error or standard deviation for the indicated number of experiments . Statistical analysis of continuous data was performed with two-tailed Student t test for pairwise comparisons or one-way analysis of variance for comparisons involving three or more groups , with Dunnet’s , Bonferroni or Dunn post hoc test , as appropriate . Pearson’s chi-squared test was used for analysis of categorical data . Linear relations between variables were analysed using linear regression analysis . P<0 . 05 was considered statistically significant . Analysis was carried out with GraphPad Prism software . ( *p<0 . 05; **p<0 . 01 , ***p<0 . 001 , ****p<0 . 0001 ) . All experiments were performed at least three times . | Genetic diseases with complex syndromic constellations may be caused by mutations in more than one gene . Most examples studied so far describe genetic interactions of known disease genes , suggesting that a large number of multilocus diseases remain unexplored . Assessment of mutation pathogenicity can be achieved using animal models . One main advantage of using Drosophila is that it allows easy in vivo gene manipulation in cell types that are relevant for the disease . Here , we report the pathogenicity of ADD3 mutations in three families with intellectual disability , microcephaly , cataracts and skeletal defects . Moreover , we provide evidence that the renal and cardiac phenotypes in one of the families could be unmasked by a homozygous variant in the lysine acetyltransferase encoding KAT2B gene . In Drosophila , this variant resulted not only in decreased viability , but also in functional defects in cardiomyocytes and nephrocytes , the latter being similar to mammalian podocytes . Our study implicates KAT2B as a susceptibility gene for steroid-resistant nephrotic syndrome ( SRNS ) and cardiomyopathy and emphasizes the importance of protein acetylation in kidney and heart function . | [
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... | 2018 | A homozygous KAT2B variant modulates the clinical phenotype of ADD3 deficiency in humans and flies |
Processes that repeat in time , such as the cell cycle , the circadian rhythm , and seasonal variations , are prevalent in biology . Mathematical models can represent our knowledge of the underlying mechanisms , and numerical methods can then facilitate analysis , which forms the foundation for a more integrated understanding as well as for design and intervention . Here , the intracellular molecular network responsible for the mammalian circadian clock system was studied . A new formulation of detailed sensitivity analysis is introduced and applied to elucidate the influence of individual rate processes , represented through their parameters , on network functional characteristics . One of four negative feedback loops in the model , the Per2 loop , was uniquely identified as most responsible for setting the period of oscillation; none of the other feedback loops were found to play as substantial a role . The analysis further suggested that the activity of the kinases CK1δ and CK1ɛ were well placed within the network such that they could be instrumental in implementing short-term adjustments to the period in the circadian clock system . The numerical results reported here are supported by previously published experimental data .
The circadian clock is a well-studied oscillatory biological system . It is nearly ubiquitous in eukaryotes and is found in similar versions in very different organisms , from unicellular cyanobacteria through filamentous fungi and plants to mammals [1] . It provides a mechanism for adaptation to the changing environment following a 24 h cycle , by , for example , readying the organism in advance for the next event of the day . In addition to establishing periods of wakefulness and rest , the mammalian circadian clock regulates many bodily functions , such as renal and liver activity and the release of appropriate hormones at different times [2] . The circadian clock is the pacemaker that in its normal function is responsible for the impact of shift work and jet lag on alertness , behavior , and health , and whose misregulation plays a role in such disorders as familial advanced sleep phase syndrome ( FASPS ) . In patients afflicted with FASPS , a shortened intrinsic period makes it difficult for affected individuals to have a normal work and social life . In addition to these more well-known effects , circadian rhythms also play a role in pathogenesis and can guide optimal treatment for diseases , including arthritis , asthma , cancer , cardiovascular disease , diabetes , duodenal ulcers , hypercholesterolemia , and seasonal affective disorder [3 , 4] . In many instances , circadian rhythms can be exploited to minimize dosage and side effects by timing appropriate therapies to the peak times of disease activity or symptoms , including pain [4] . A better understanding of the circadian clock and its workings might contribute to improved treatment of these disorders . Current models of circadian clocks show behaviors consistent with known biology and anticipated from engineering principles , such as a persistence of the free running period ( FRP ) in the absence of a daily stimulus and the ability to entrain to periodic external signals [2] . In addition , the circadian clock , particularly that of organisms lacking temperature regulation , exhibits temperature compensation—the period of oscillation is insensitive to changes in the external temperature [2] . Despite detailed studies on the molecular as well as the systems level [5–7] , open questions persist . Some can be addressed using mathematical analysis of the biological models , and examples from this class form the focus of the current work . Is there a difference in mechanism between phase advance and phase delay , as suggested by experimental observation that phase delay happens much more rapidly than phase advance [6] ? Which input pathways could potentially play a role in managing such phase responses ? Is the fact that the FRP of the human circadian clock is slightly larger than 24 h related to the difference in phase advance and delay ? As a first step toward answering such questions , which typically involve the simultaneous analysis of several network characteristics , we focus on the period-specific biochemical properties of the mammalian circadian network . We discuss which network structures are involved in setting the FRP , as revealed by detailed sensitivity analysis . The distribution of this responsibility within the network gives important clues toward a further understanding of the principles and concepts underlying network design . Intimately related to studying where in the network the FRP is regulated is the study of possible mechanisms present to modify the FRP temporarily or persistently , in order to accommodate external fluctuations . How flexibly can the system adjust to changing external situations , for example by undergoing phase shifts ? A potential point of intervention for the short-term management of the FRP is suggested here . The fundamental biochemical pathways involved in the clock systems of different eukaryotic species are well-known and have the same essential components . Negative feedback regulation of transcription is always present and often interlocked with positive feedback , thus increasing the complexity . Nuclear transport of transcriptional regulators is a central process in forming the feedback loops [2] . At the heart of the mammalian clock is the CLOCK protein ( CLK ) , which acts together with BMAL1 in a heterodimeric complex ( BCC ) . BCC is a transcriptional activator of the three per ( period ) homologues , two cry ( cryptochrome ) homologues , and the rev-erbα gene . REV-ERBα represses bmal1 expression , and regulates clock and cry1 expression [8] . Because cry1 expression is lowered by REV-ERBα , and CRY1 is an inhibitor of the BCC , which activates rev-erbα expression , a positive feedback loop is formed . The PER proteins are phosphorylated by several isoforms of casein kinase 1 ( CK1δ , CK1ɛ , and possibly others ) in a complex manner that regulates their degradation and nuclear trafficking [9] . PER1 and PER2 can form stable complexes with the kinases and either CRY protein [2 , 10] . The PER proteins are rate-limiting for this step and necessary for the nuclear import of the complex , making them the shuttle for nuclear CRY proteins [11] . Nuclear CRY and PER proteins all have an inhibitory effect on the activity of the BCC , probably following different mechanisms [10] , thereby closing four negative feedback loops affecting the expression of cry and per genes . PER2 plays a second role in the positive regulation of bmal1 expression [12] . Of the per homologues , per3 is the only one whose deletion hardly affects the rhythmicity of the system . While the roles of the two cry homologues appear similar and perhaps redundant , this is not so for the per homologues [13] . In humans , a single mutation in per2 causes FASPS [14] , and its loss causes arrhythmicity in mice [15 , 16] . The behavioral phenotypes of per1 null mutant mice were similar to those of per2 mutants; however , comparison of the molecular consequences of the mutations revealed significant differences between the two . Disruption of per2 expression was reported to result in reduced transcription levels of other clock genes , whereas PER1 appears to act predominantly at the posttranscriptional level [16] . An overview of the molecular mechanism of the circadian clock is found in [2] and [12] . Several mathematical models of circadian clock systems in different organisms have been formulated in recent years ( [7 , 17–21] , among others ) , providing different levels of detail . The most detailed model of the mammalian circadian clock to date was published by Forger and Peskin in 2003 [18] and was used for the current study . It describes the mechanistic action of 73 species ( proteins and mRNA , both in the cytosol and the nucleus ) and uses 38 parameters to model their interactions . The mathematical form of the model is a system of ordinary differential equations ( ODEs ) , and all reactions are modeled using mass action kinetics . As shown in Figure 1 , the model includes separate feedback loops for two homologues each of Cry and Per . The third Per homologue , Per3 , is not included , as its role is not well-understood and it is thought to participate mainly in the clock output [2] . Both CRY proteins inhibit the transcription of both crys and pers by binding to the BCC . A fifth feedback loop models the positive feedback mediated by the REV-ERBα protein , which modulates cry1 transcription . The effect of REV-ERBα on clk expression is omitted in the model . The complex pattern of phosphorylation of PER species by the CK1 family is simplified so as to occur in two stages , performed by one kinase C ( as named in the model ) at constant concentration , which plays the role of active kinase concentration . A primary phosphorylation allows for binding to CRY and nuclear transport . A secondary phosphorylation , which only occurs for Per1 , prohibits nuclear entry . The BCC concentration is modeled as having a constant value , which is a simplification , as bmal1 is rhythmically expressed , probably under positive feedback control of PER2 [12] and retinoic acid–related orphan receptor ( ROR ) [22] as well as negative feedback control of REV-ERBα [8] . The model's authors set values for the 38 parameters through a combination of experimental data available from mouse suprachiasmatic nucleus ( SCN ) and liver cells , and fitting to overall system behavior [18] . Under constant darkness conditions , the model is an autonomous oscillator of the limit cycle type and has a FRP of 24 . 3 h . The model encapsulates mathematically much of what is known about the mammalian circadian biochemistry , with a few omissions and simplifications . New discoveries will undoubtedly lead to improved versions of the model . As is , it is an excellent basis for theoretical investigation of this interesting and important network control system . The model agrees well with wild-type and mutant behavior . Circadian clock models are generally limit cycle oscillators , a characteristic of which is robustness of period and amplitude with respect to perturbations in their state variables ( corresponding to concentrations or activities here ) . Mathematically , such systems will asymptotically approach the limit cycle trajectory from any initial condition in the region of attraction of the limit cycle . The shape and situation of the limit cycle depends only on the parameters of the system . This property , however , can make it more difficult to study these systems . Without knowing the exact limit cycle trajectory , iterations over several periods of oscillation are needed to approximate it . It is not clear a priori how many periods are needed to reach the limit cycle to a given tolerance . At the same time , the exact limit cycle properties ( period , amplitudes , relative phases ) are of direct biological interest . In this article , we present a new method for the exact computation of sensitivity trajectories of limit cycle oscillators and sensitivities of derived quantities with respect to model parameters . Sensitivity analysis probes how a small variation of a parameter or initial condition away from a reference solution influences the trajectories of the state variables , and of derived quantities . Applied to biological systems , sensitivity analysis can help to analyze how changes in rate parameters or temporary perturbations in protein or mRNA concentrations can influence the behavior of a system . It is becoming a standard tool for systems biologists . The use of various sensitivity metrics has been explored in a variety of network biology studies [23 , 24] , including a number of simpler models of circadian rhythms [25–27] . However , the exact sensitivity analysis of oscillating systems is more challenging than for other dynamic systems [27 , 28] . It has been shown that the parametric sensitivities of periodic systems can be decomposed into a bounded and an unbounded part according to where Z ( t ) is the periodic matrix containing the parametric sensitivities at constant period . This part is sometimes referred to as the “cleaned out” sensitivities [29] , as opposed to the “raw” sensitivities S ( t ) , and is reported to contain information on shape and amplitude of the oscillation [30] . In order to achieve this decomposition , two conditions have to be met . First , the exact computation of the period sensitivities is required . Second , appropriate initial conditions for S ( 0 ) = Z ( 0 ) have to be found . Since one is interested in initial conditions of the state variables that lie on the periodic orbit , which in its shape and location depends on the system parameters , those initial conditions are not independent of the parameters . Consequently , the sensitivities cannot be initialized with the zero matrix as is usual in other dynamic systems . An incorrect initialization of the sensitivities leads to an unbounded error of unknown magnitude [28] . In the current work , we have derived a rigorous procedure for computing the sensitivity of the period of a limit cycle oscillator with respect to model parameters and applied this to the most detailed model of circadian rhythms available [18] . This has provided a particularly high-resolution view of the role different model elements play in setting the period of oscillation and for the first time to our knowledge highlighted that reactions involving Per2 have an especially strong effect on the period . Interestingly , the results point to a series of steps forming a reaction cycle , rather than to any particular step in that cycle . We describe and apply a strategy that identifies the exact limit cycle trajectory by solving a boundary value problem ( BVP ) and then using the solution of this BVP to calculate the exact sensitivities of state variables , amplitudes , and period of the oscillation without resorting to the iterative methods typically used for limit cycle systems . Here , we applied our sensitivity analysis methodology to study the limit cycle circadian clock model of Forger and Peskin [18] . Because the same parameters are used in multiple places throughout the model , the individual sensitivities were computed on a per-reaction basis , rather than a per-parameter basis . Due to parameter sharing , the 38 model parameters describe 231 reactions in the model . Sensitivity analysis was performed using both the original 38 lumped parameters or with an unlumped parameter set in which the effect of each of the 231 reaction rate parameters was probed individually . The unlumping as described in Materials and Methods does not change the physical model but rather provides a more detailed analysis of the roles of individual physical and chemical reactions . Meaningful results were only obtained in the unlumped calculation , rather than the lumped sensitivities obtained when analyzing the original model parameters directly , which simultaneously affected multiple reactions . This analysis revealed that the period setting is strongly dominated by processes within the Per2 feedback loop , but not the subtly different Per1 loop . The mechanism of this responsibility distribution is elucidated in several numerical experiments and supported by published experimental results . Moreover , a potential mechanism for short-term period adjustment is identified and discussed , namely the activity of CK1 isoforms .
The relative sensitivities of the FRP T with respect to each parameter pj on a per-reaction basis , , were calculated , and then rank-ordered by magnitude . Results for the top ten ranked sensitivities are graphically represented in Figures 1 and 2 . Per2-related reactions dominate by far in their influence on the period in the system . Eight of the ten highest-magnitude sensitivities are Per2 related ( including expression , transport , reaction , and degradation of Per2 ) , and only one is not directly related to the Per2 feedback loop ( nuclear export rate of Cry1 mRNA ) . The overwhelming dominance of Per2 processes in influencing clock period in the model is particularly interesting given that per2 mutations are linked to FASPS [14] . Figure 2 shows that the top-ranked sensitivities are significantly larger than the remainder , indicating a strongly localized distribution of sensitivity of the period within the network . At the same time , there is no single parameter ( and therefore process or reaction ) found to be the only control for increasing or decreasing the period of oscillation; rather , the period-setting responsibilities are shared among a number of processes within the Per2 negative feedback loop . Likewise , the localization of high sensitivity within the network cannot be attributed to a class of reaction or process ( such as phosphorylation , translation , or transcription ) . In order to test the influence of the exact network parameterization on the results shown here , additional parameter sets were created ( see Materials and Methods ) . The period sensitivity rankings of all modified parameter sets correlate highly with the nominal parameter set ( Spearman rank correlation factors between 0 . 890 and 0 . 966 ) , which is surprising given the large number of parameters with negligible period sensitivity . The majority of the top ten parameters shown in Figure 2 ( 6 . 3 of them on average ) are found in the top ten of the modified sets , an average of 4 . 3 of the original top five parameters are found in the top five of the modified sets , and the original top six parameters are represented in nine of the modified top ten . The casein kinase concentration ranks sixth or higher in all but two of them , and tenth or higher in all but one . An average of 5 . 8 parameters in the top ten is Per2-related . Thus , the results presented here do not depend very strongly on the particular parameter values in the Forger and Peskin model , but instead appear to be a property of this class of models in the neighborhood of the parameterization developed by Forger and Peskin [18] . Although Per1 and Per2 carry out similar reactions , only Per2 is singled out as highly significant in affecting the period of oscillation . Model dissection was used to analyze the source of this difference . There are four differences between Per1 and Per2 in the original model . One is a topological difference , in that PER1 can be phosphorylated a second time , which masks its nuclear localization sequence; doubly phosphorylated PER1 or any of its complexes cannot enter the nucleus . The remaining three differences are purely parametrical and of different relative magnitude—differences in transcription rates ( the per1 rate being 2 . 6-fold higher ) , rates of first phosphorylation ( the PER2 rate being 5-fold higher ) , and mRNA degradation rates ( the Per1 rate being 16-fold higher ) . These differences cause the PER2 concentration to be roughly 2 . 5 times that of PER1 , with minima and maxima occurring at almost the same times; the PER1 concentration is not negligible , however . The loss of Per1 alone does not abolish rhythmicity , but the loss of Per2 alone leads to a slowly decaying amplitude of the oscillation [18] . Each of the differences was studied in individual numerical experiments . In a first set of numerical “mutations , ” the rates that are different between Per1 and Per2 were made equal at either the value of the Per1-specific rate or the value of the Per2-specific rate . Then , the sensitivity analysis was repeated , and the ranking of the resulting sensitivities was compared with the ranking shown in Figure 2 ( unpublished data ) . In short , the findings pointed toward the mRNA degradation rate as well as the rate of primary phosphorylation being influential in making Per2 the period-setting feedback loop . In order to observe this effect more clearly , the rates of the same reactions were reversed in the next set of numerical experiments , before repeating the sensitivity analysis and ranking comparison as before . The results are shown in Figure 3 . Neither the only topological difference nor the different transcription rates for per1 and per2 are crucial for the differential behavior of the two homologues ( Figure 3A and 3B ) , as the sensitivity ranking remains largely unchanged . When the rates of primary phosphorylation were reversed , the maximum sensitivity in the network increased . While the highest magnitude sensitivity remained the Per2 mRNA degradation rate , Per1-specific rates now appeared almost alternating with the Per2-specific rates , as if in this scenario the two share the period-setting responsibility ( Figure 3C ) ; yet , the overall concentrations of Per1 and Per2 concentration remain largely unchanged . When the rates of mRNA degradation were reversed for both genes , a subset of Per1-specific rates moved up in the ranking , and at the same time , the maximum sensitivity found decreased in magnitude significantly ( Figure 3D ) . Per1 in this scenario dominated the period-setting , and its concentration was now a factor of ∼20 larger than the Per2 concentration . When both of the rates for mRNA degradation and primary phosphorylation were reversed simultaneously , a clear role reversal between Per1 and Per2 occurred ( Figure 4 ) . We can thus say that the combined action of mRNA degradation and primary phosphorylation of Per2 , in comparison to Per1 , are what cause the Per2 loop to dominate in setting the period of the circadian clock . It should be noted that the rates of mRNA degradation in the original model are more different in a relative sense than the rates of primary phosphorylation . The quantitative results obtained here may vary upon a more exact determination of the parameter values used in the model . However , the qualitative insights gained from the numerical experiments performed in this work appear to be robust to changes in parameter values . The analysis of period sensitivities identified the total active concentration of CK1 isoforms as well as the kinase-binding kinetics as being among the main determinants of the period . As the sensitivity analysis measures the effects of local ( infinitesimal ) parameter variations , a possible mechanism for modulating the period of the system through modulating the amount of active kinase in the system was verified by parametric studies . The results are shown in Figure 5 . The kinase concentration allows for modulation of the period over a wide range of parameter values . By varying the parameter 50% up or down , the period was changed by −5% or +9 . 7% ( −1 . 25 to 2 . 4 h ) , respectively . The sensitivity of the period with respect to kinase concentration remains negative , as the period becomes shorter with increasing kinase concentration and vice versa , over the entire range . It is assumed that a temporary change in the period will cause a permanent phase shift , a process called “parametric entrainment” in the circadian literature [2] . The results shown in Figure 5 suggest that by modulation of the kinase concentration , it is relatively easier to produce a phase delay ( a temporary decrease of kinase concentration , leading to a temporarily longer period ) than it is to produce a phase advance , as shown by the larger magnitude period variation achieved for a 50% decrease in kinase concentration compared with a 50% increase . A similar difference is noted in the maximum amplitude of the phase response curve ( PRC ) shown in Figure 6 , where the phase shift as the result of a short-term step change in active kinase concentration is shown as a function of time . This figure shows , again , that the same absolute change in kinase concentration at the right time results in a longer delay but shorter advance . The results also show that the magnitude of phase shift depends dramatically on when during the day the kinase modification is applied , both for delay and advance . It should be noted that changes in period shown in Figure 5 correspond to a stationary property of the system . In contrast , the response measured in Figure 6 describes a transient effect of a short-term disturbance of the system—the short-term parameter change does not allow for the system to approach its perturbed stationary state .
The results presented for the circadian clock system in mammals suggest a strongly localized distribution of functionality within the network . Through detailed analysis of the period sensitivities , it was shown that the period of oscillation is set by reactions distributed throughout the Per2 negative feedback loop . The ten parameters with the greatest period sensitivities are dominated by Per2-related species; likewise , Per2-related reactions have high sensitivities . Within the Per2 feedback loop there is a self-consistency as to the effects on the length of the period; changes that accelerate ( decelerate ) progress through the loop lead to a shorter ( longer ) period . For example , multiple processes that reduce the half-life of Per2 mRNA all produce a shortened period . These processes include faster mRNA degradation , faster mRNA export , and interestingly , slower transcription . Likewise , faster kinase binding or phosphorylation lead to faster migration of CRY1-bound species into the nucleus , which closes the negative feedback loop faster and results in a shorter period . Interestingly , PER2 and CRY1 interoperate to control the rate of nuclear transport of the phosphorylated complex . Changes that prolong the half-life for CRY1 in the nucleus ( faster dissociation of PER2-bound CRY1 species and slower CRY1 degradation ) result in a longer period , a fact that was recently confirmed in experimental studies [31] . Changes can also be understood through their effects in altering concentrations . Increases to the cytosolic concentrations of CRY1 ( faster nuclear export of CRY1 and bound species , faster transcription , or faster translation ) lead to shorter periods . Because unbound CRY cannot migrate from nucleus to cytosol , a delay of CRY1 in the nucleus slows the feedback from the Per2 loop and lengthens the period . Processes that increase the amount of Per2 produced ( faster Per2 transcription or translation , or slower mRNA degradation ) tend to lead to a longer period . This self-consistency makes intuitive sense of the network structure–function relationship computed here and lends additional support to the notion that the results are not overly dependent on the details of the particular model implementation here . Published experimental results indicate that the roles of Per1 and Per2 in the circadian clock mechanism are not redundant [13 , 16] . Our findings confirm these results on a network analysis level: the Per2 feedback loop carries responsibility that Per1 does not share . Due to the detailed and comprehensive model , the mechanistic detail behind this organization could be analyzed to show that the values of two reaction rates , the rates of mRNA degradation and of primary phosphorylation of Per2 , cause this feedback loop to dominate period-setting . In another computational study [32] , mRNA degradation rates were found to influence strongly the period of oscillation throughout a set of four different circadian clock models , not including the model studied here . The present study provides further insight in that it is mainly only one mRNA degradation rate , that of Per2 , that matters most . Even the second most sensitive mRNA degradation rate , that of Cry1 , is only 20% as significant with respect to the period . It should be noted that the authors of the model also report a sensitivity called “sensitivity of the badness of the fit” [18] . This quantity is only indirectly related to the period sensitivity , and is also only computed for the 38 “lumped” original parameters . Without dissecting the multiple roles of the original parameters , the Per2 loop cannot be identified as the period-setting feedback loop , nor does it become obvious how the period-setting responsibility is distributed throughout the loop . The sensitivities of the period with respect to all parameters associated with the Rev-Erbα loop were zero , suggesting that the Rev-Erbα loop may not participate in setting the period for this model . This hypothesis was confirmed by removal of the entire loop without consequence for the period ( unpublished data ) . Interestingly , this is an area where the model appears to disagree with experiment; experiments show that while Rev-Erbα is not essential for rhythmicity , period length and phase-shifting behavior are altered in null mutants [8] , although less so than in Per2 null mutants [15 , 16] . In this portion of the model , the action of REV-ERBα on Bmal1 expression is omitted , and the Rev-Erbα loop is parameterized in such a way that the resulting concentrations are essentially zero [33] . To test whether inaccuracies in the Rev-Erbα portion of the model could compromise conclusions regarding the role of the Per2 loop , simulations and sensitivities were computed for artificially manipulated versions of the model that substantially increased the activity of the Rev-Erbα loop . Even when the flux through the Rev-Erbα loop was increased by five orders of magnitude ( by increasing the transcription and translation rates and decreasing the degradation rates ) , and the corresponding sensitivities became significant and moved up the ranks ( ranging from 168th to 225th in the original parameterization versus 68th to 168th for the increased flux model ) , the top 50 ranking parameter sensitivities did not change significantly . Furthermore , in accordance with recent findings in the experimental literature [22 , 34] , we have constructed an alternate version of the model ( Dataset S3 ) with a sixth feedback loop involving ROR . This receptor was found to have an opposing role to Rev-Erbα in the control of Bmal1 expression . While Bmal1 was still not explicitly represented , we included the indirect effect of ROR as well as that of Rev-Erbα on the transcriptional activity of all circadian genes in the model . This change resulted in the use of four new state variables and ten new parameters . The preliminary parameterization of this sixth feedback loop was done using qualitative insights from the experimental data , and chosen so that the peak in ROR would follow the peak of REV-ERBα in the nucleus and so that transcriptional control of ROR would be similar to that of Per2 . The transcription rate of rev-erbα was increased 100-fold to make the corresponding concentrations more significant . Those modifications to the original model did not alter mutant behavior , period , or the results presented in this paper . The ranking of relative period sensitivities remains virtually unchanged . The ten new parameters ranked between 148 and 218 out of 241 . This suggests that while there are still discrepancies between the known biology and the model that will undoubtedly be resolved through future work , the results shown here are not sensitive to changes in this part of the model . It is of interest for the understanding of network design , as well as for potential therapeutic strategies , to identify possible points of intervention for period control . Furthermore , it is known that parametric entrainment ( which relies on period modulation to control the phase of oscillation ) plays a role in the mammalian circadian clock , although details are not understood [2] . The total CK1 concentration appeared fourth in the rank-ordered sensitivities , and is a quantity that deserves special attention . While it is modeled here as a constant quantity , it realistically represents a concentration of active kinase , which may not be constant throughout the cycle . Some of the other top ten parameters can be modified by genetic mutation on a long timescale ( such as the mRNA degradation rate ) , or a medium timescale ( such as transcriptional regulation ) . However , the ( active ) kinase concentration could potentially be regulated both on a very short timescale by post-translational modification through an input signaling pathway or a medium timescale through transcriptional regulation . In fact , CK1ɛ is known to inactivate itself by autophosphorylation , a process that is counteracted by cellular phosphatases [35] . It has been suggested that such phosphatases can be activated by signaling pathways such as the Wnt pathway [36] so as to activate CK1ɛ as a result of a signaling cascade . A comparison with experimental results for period-related abnormalities reveals that PER2 phosphorylation by CK1 has significant involvement in setting the period . It has been shown experimentally that in individuals with one type of FASPS , the human per2 gene is mutated at the site of its phosphorylation by CK1ɛ . This mutation causes hypophosphorylation and ultimately a phase advance , which is typically associated with a shortened FRP [14] . In individuals with another type of FASPS , the ck1δ gene is altered [37] . In hamsters , a mutation called tau in ck1ɛ causes a short circadian period . For the Forger and Peskin model , an increased rate of primary Per2 phosphorylation predicts a shortened period . This finding contradicted the prior observation that the tau mutation was a loss-of-function mutation of CK1ɛ in in vitro experiments [38] , thus leading to the discovery of the differential action of CK1ɛ on clock-related versus generic substrates . Recent findings have confirmed that the tau mutation is in fact a gain-of-function mutation with respect to the phosphorylation of PER2 [39] . It should be noted that the exact pattern and functional consequences of Per2 phosphorylation are simplified in the model [18] . More recently , its details have been investigated [9] , providing additional insight . In broad terms , there are two effects of PER2 phosphorylation . The phosphorylation site involved in FASPS ( Ser 659 ) was shown to increase nuclear retention and stabilization of PER2 . Phosphorylation at other sites of PER2 leads to increased degradation . Only the latter effect is represented in the model used for this . In order to substantiate the results in this study in the light of more recent experimental data , the phosphorylation pattern suggested in [9] was incorporated into the model . No new species were created , because the original model already includes a doubly phosphorylated PER2 species . Only three rate parameters were added to the model ( nuclear import rate of PER2pp species , and modified degradation rates for nuclear and cytosolic PER2pp species ) . The previously unused secondary phosphorylation rate of PER2 was reassigned . Parameter values were chosen to closely reflect the relative rates as published in [9] , based on the rate values in the original model; i . e . , the rate of secondary phosphorylation was set equal to that of primary phosphorylation , as suggested in [9] , at the value published in [18] . The modified model in MATLAB format can be found in Dataset S2 . This very preliminary parameterization resulted in a period of 23 . 82 h , and the mutant behavior with respect to knockouts as described in [18] was unchanged . Again , the parameters were unlumped , and the sensitivity analysis and ranking was repeated . The top six parameters are the same in sign and very similar in magnitude as those of the original model , confirming the dominant role of Per2 again . The twice-phosphorylated PER2 species appears twice in the top ten ( ranks seven and nine , unbinding from CRY1 and degradation , respectively ) , and the unbinding and binding kinetics between CRY1 and CLK:BMAL1 take ranks eight and ten , respectively . The period sensitivity with respect to the secondary phosphorylation rate is smaller than that of the primary ( rank 64 versus rank three ) , and positive , as expected . Thus , the inclusion of more recently discovered mechanistic detail regarding phosphorylation confirms the result that phosphorylation of PER2 and Per2-related nuclear trafficking are key period-setting reactions . It was previously shown in experiments that the circadian clock in humans , as well as in mice , takes longer to phase advance than phase delay , if exposed to jet lag conditions in the form of a 6 h time shift during daylight hours [6] . Jet lag is a transient phenomenon that in this case lasted several days , during which the organism is thrown off the steady-state periodic cycle and resets its new phase according to an entraining signal . While the PRC provides some insight in the phase-shifting behavior of the clock in response to an outside stimulus , relating this steady-state response to a transient , jet lag situation is generally difficult . During jet lag , the relative timing between “clock time” and “entrainment time” changes continuously , the system is not at its steady state , and the response to a stimulus in a ( nonlinear ) limit cycle system depends on the state of the system at which the stimulus is received . In fact , a recent computational study shows that designing an optimal input stimulus for rapid phase resetting is nontrivial even if the PRC is well known [40] . In the following discussion , the focus is therefore not on the exact mechanism of overcoming jet lag in the mammalian circadian clock model , but rather the apparent similarities in the asymmetry between phase delay and phase advance between observations in jet-lagged mice and the numerical experiments performed in this work . It is sometimes argued that this difference is caused by the FRP being longer than 24 h; however , the results presented here are in reference to the innate FRP of 24 . 3 h . New experimental results have furthermore identified the FRP in humans to be closer to 24 h than previously reported [41 , 42] . It is apparent that there could be a number of places in the network through which phase shifts can be introduced . For example , it has been suggested that Per1 is important during discrete entrainment , the phase response to transitions in the light stimuli , and is especially receptive to such signals during the night [43] . During the 6 h advance phase shift induced in the experiment by Reddy et al . , the light onset occurred in the middle of the former night , temporarily inducing Per1 mRNA [6]; however , the phase shift achieved in this experiment was markedly slower than the phase delay response . To produce the phase delay , the new light onset corresponds to the former noontime , which coincides with the time at which the kinase concentration is most influential as seen in Figure 6 . While the discussion here is solely circumstantial , and we have no formal proof that the kinase concentration is involved in the asymmetric phase shifting reported in [6] , our observations in Figures 5 and 6 show similarity in the sense that phase delay is accomplished relatively more easily than phase advance . In addition , the particular shape of the PRC in Figure 6 shows a large bias toward phase advance ( delay ) for increased ( decreased ) kinase activity; in other words , the PRC itself is asymmetric . If the kinase concentration is modulated throughout the cycle , the PRC suggests an effect only in the desired direction or else , if the modulation happens at a phase-shifted time , little effect at all . This could make the adaptation to a new phase during a transient situation such as jet lag easier to control . Thus , we hypothesize that the kinase concentration ( activity ) could be a particularly convenient control point . It may be used by the natural system , for example , as an additional way to process entrainment inputs during the day , especially those of long signal duration , thus acting as the control element for continuous entrainment discussed earlier . The kinase concentration may also be useful as a therapeutic point of intervention . Figure 5 shows that a 50% change in kinase concentration can lead to a 1 h to 2 h phase shift per day , and larger changes increase that shift further . While the exact molecular biology of the phase advance versus delay response is beyond the scope of a purely computational study , it is discussed next that a molecular basis for differences between phase advance and phase delay can be identified for this model system . The mechanistic reason for the differences between phase advance and delay in this model is that once PER2 is phosphorylated , two processes compete for it . Phosphorylated PER2 can be either degraded or bound by the CRY proteins , which protects them from degradation . Increased kinase activity results in more phosphorylated PER2 being formed , but it is also degraded at a higher rate . Therefore , the feedback loop is accelerated . In comparison , if the kinase activity is decreased , the PER2 concentration in the cytosol increases until the rate of phosphorylation , which is proportional to the product of PER2 concentration and kinase concentration , is equal to the maximum phosphorylation rate in the wild type . No process is competing with the slowed-down phosphorylation rate and the phase-delayed nuclear import of the PER–CRY complex . Taken together , these results suggest that control of the active kinase concentration is a possible way for the system to modify the period ( therapeutically or naturally ) , especially on short timescales and following entrainment signals received during daytime . This study illustrates computational approaches for probing structure–function relationships in network models—namely by showing how sensitivity analysis of a sufficiently detailed mechanistic model can relate theoretical results to experimental findings . The technique can be used both for refining the biological model and understanding the implications of network design for normal operation , disease , and therapeutic intervention .
An ODE model for a biological system is analyzed , where are usually concentrations of protein , mRNA , or other species . The parameters are typically reaction rate constants in mechanistic models , or lumped rates of processes such as transport between compartments . Given a fixed value for p , initial conditions on the limit cycle and the period of the oscillator are identified by solving a BVP for initial condition y0 ( p ) and period T ( p ) subject to a periodicity condition and a phase-locking condition for some arbitrary , with the limit cycle trajectory y ( t , p; y0 ( p ) ) given by the solution of and y ( 0 , p; y0 ( p ) ) = y0 ( p ) . From this , we obtain initial conditions for the state variables that lie on the limit cycle . The ( ny + 1 ) st condition in Equation 2 fixes the solution to a point on the limit cycle where the state variable yi is stationary . Any arbitrary state variable can be chosen for this constraint . This BVP was solved using NITSOL , an inexact Newton solver [44] , and CVODES , a stiff ODE solver with sensitivity analysis capabilities , for the integration of the dynamic system [45] . In most dynamic systems , the parametric sensitivities for a system such as Equation 3 are integrated from zero initial conditions according to where and . In the case of the solution of a BVP in a limit cycle system , Equation 4 still applies; however , setting the initial conditions to zero would not be correct . The initial conditions y0 ( p ) are now dependent on the parameters . Because this dependency is implicit through the solution of Equations 1 and 2 , it is not immediately clear how to set the initial conditions for the system in Equation 4 correctly . This problem is solved as follows . The set of Equations 1 and 2 can be differentiated with respect to the parameters p , and written in matrix form yielding the following expression where I is the ny × ny identity matrix , and S ( T , p;0 ) is the solution at time T ( p ) of sensitivity Equation 4 for zero as the initial condition . The matrix M is , the matrix of sensitivities of the state variables with respect to their initial conditions at T ( p ) . This matrix is also called the Monodromy matrix of the sensitivity system . For more detailed explanation , see [28] . The matrix Equation 5 can be solved for the matrix of unknowns . The calculation of the sensitivity trajectories can then easily and exactly be performed by integrating Equation 4 starting from , allowing the decomposition into a periodic part and an unbounded part as described previously . This method , in contrast to some previous publications [27 , 46] , enables the exact computation of the period sensitivities rather than approximating the result by truncation of a limit or by integration of the entire system for a sufficiently long time , resulting in significantly less computational effort and , in principle , exact results . All matrix manipulations were performed in MATLAB 7 . 4 . 0 ( R2007a ) . The circadian clock model was obtained as a MATHEMATICA file from the supplementary materials of [18] at http://www . pnas . org/cgi/content/full/2036281100/DC1/6 and was rewritten as MATLAB code ( available in Dataset S1 ) . The original model has 38 rate parameters , many of which are used in multiple roles within the reaction network; e . g . , the mRNA export rate is the same for all mRNA species . In a “lumped” sensitivity analysis , a parameter may be shown to have great impact on the period of oscillation . However , on a network analysis level , one is interested to see which of its multiple roles is the most important in the setting of the period . Therefore , the parameter was “unlumped , ” meaning new parameters were assigned to each species that is affected by it so that each new parameter corresponds to a unique chemical reaction or physical process . The parameter values of all those were the same as the value of the original , single parameter . In other words , the model itself did not change during this process , but it is now possible to distinguish the different roles a parameter might play . Doing so does not necessarily imply that the organism has the capability to independently control the unlumped parameters . The original set of 38 parameters was modified by first randomly choosing ten parameters , then by randomly modifying their value either by a factor two up or down . The resulting model was simulated over 40 nominal periods in order to approach the limit cycle . If the apparent period ( time difference between the last and second-to-last minimum in the CRY1 concentration ) was between 23 . 5 h and 25 h , the model was subjected to the BVP solver . This selection criterion was chosen because it is known that the mammalian clock oscillates roughly in this range of periods , and it is irrelevant to investigate parameterizations with known , unphysical periods . A total of 15 such models were generated , and 11 out of those were converged easily; the others were discarded . Possible reasons for nonconvergence include the presence of damped oscillations , which would not have been detected in the earlier test . By inspection , it was found that the modified parameters included both low- and high-sensitivity parameters in the nominal model . The parameter values of the 11 final models are found in Table S1 , along with the resulting period and ranking of the top 25 sensitivities . | Network models of biological systems are appearing at an increasing rate . By encapsulating mechanistic detail of chemical and physical processes , mathematical models can successfully simulate and predict emergent network properties . However , methods are needed for analyzing the role played by individual biochemical steps in producing context-dependent system behavior , thereby linking individual molecular knowledge with network properties . Here , we apply sensitivity analysis to analyze mammalian circadian rhythms and find that a contiguous series of reactions in one of the four negative feedback loops carries primary responsibility for determining the intrinsic length of day . The key reactions , all involving the gene per2 and its products , include Per2 mRNA export and degradation , and PER2 phosphorylation , transcription , and translation . Interestingly , mutations affecting PER2 phosphorylation have previously been linked to circadian disorders . The method may be generally applicable to probe structure–function relationships in biological networks . | [
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] | 2007 | The Per2 Negative Feedback Loop Sets the Period in the Mammalian Circadian Clock Mechanism |
The corpus callosum ( CC ) is the major commissure that bridges the cerebral hemispheres . Agenesis of the CC is associated with human ciliopathies , but the origin of this default is unclear . Regulatory Factor X3 ( RFX3 ) is a transcription factor involved in the control of ciliogenesis , and Rfx3–deficient mice show several hallmarks of ciliopathies including left–right asymmetry defects and hydrocephalus . Here we show that Rfx3–deficient mice suffer from CC agenesis associated with a marked disorganisation of guidepost neurons required for axon pathfinding across the midline . Using transplantation assays , we demonstrate that abnormalities of the mutant midline region are primarily responsible for the CC malformation . Conditional genetic inactivation shows that RFX3 is not required in guidepost cells for proper CC formation , but is required before E12 . 5 for proper patterning of the cortical septal boundary and hence accurate distribution of guidepost neurons at later stages . We observe focused but consistent ectopic expression of Fibroblast growth factor 8 ( Fgf8 ) at the rostro commissural plate associated with a reduced ratio of GLIoma-associated oncogene family zinc finger 3 ( GLI3 ) repressor to activator forms . We demonstrate on brain explant cultures that ectopic FGF8 reproduces the guidepost neuronal defects observed in Rfx3 mutants . This study unravels a crucial role of RFX3 during early brain development by indirectly regulating GLI3 activity , which leads to FGF8 upregulation and ultimately to disturbed distribution of guidepost neurons required for CC morphogenesis . Hence , the RFX3 mutant mouse model brings novel understandings of the mechanisms that underlie CC agenesis in ciliopathies .
The Corpus Callosum ( CC ) , the major commissure of the brain , is composed of millions of axons that connect the two brain hemispheres [1] , [2] . Malformation of the CC is one of the most frequent brain anomalies found at birth , and may occur in as much as 7/1000 of the total newborn population . The most severe form of CC malformation is its complete absence also called callosal agenesis . In mouse , callosal axons first start to cross the midline during late gestation at E16 . 5 [3] , [4] . Callosal axons are directed through the Cortical Septal Boundary ( CSB ) by several guidepost cell populations expressing guidance cues . Glial cell populations were first described to be involved in CC formation in this region [1] , . Guidepost glial cells are found at the glial wedge ( GW ) of the lateral ventricles ( initially described as the cortical septal plate [5] ) , in the induseum griseum ( IG ) of the medial pallium and in the so-called sling at the CSB [5]–[10] . More recently , GABAergic ( γ-aminobutyric acidergic ) neurons and glutamatergic neurons that populate transiently the CSB have also been shown to be involved in guiding callosal axons at the midline [11] . These glial and neuronal guidepost populations are also observed in the human foetal CC [2] , [12] . In humans , malformations of the CC have been found to be associated with a variety of syndromes [13] , [14] . In particular , a reduction or a complete absence of the CC has been found to be associated with several human syndromes recently recognized as ciliopathies [15] , [16] . However , it is not known where and at what stage of embryonic development cilia are required for proper CC formation . Several mouse models defective in cilia formation or function have been described in the literature , but only few have been shown to be associated with CC malformations and none of them has so far been used to explore the molecular mechanisms that underlie CC development . One reason is that most mouse mutants for ciliary genes die early during embryogenesis and that the surviving mutants present severe brain malformations that preclude the study of late defects such as CC formation . RFX transcription factors have been shown to play fundamental roles in the control of ciliogenesis by regulating many genes involved in cilia assembly or function [17] . Rfx3 deficient mouse mutants exhibit several hallmarks of ciliopathies and in particular left-right asymmetry defects and hydrocephalus [18] , [19] . We show here that Rfx3 deficient mice also harbour marked defects in CC development leading in most cases to agenesis of the CC . RFX3 is first expressed throughout the anterior neural tube and is then progressively restricted to particular cell populations , particularly at the midline CSB , before and while pioneer callosal axons cross the midline . Rfx3 loss of function leads to a distorted distribution of the neuronal but not of the glial guidepost cell populations that have both been shown to direct callosal axons through the midline . Reciprocal transplant experiments demonstrate that in Rfx3−/− brains , defects of the midline corticoseptal region are indeed responsible for improper crossing of the midline by callosal axons . However , conditional inactivation of Rfx3 at specific time points in corticoseptal cell populations does not lead to CC defects , demonstrating that RFX3 is required early during brain development to pattern the CSB . We show that E12 . 5 Rfx3 deficient brains present a mild expansion of Fgf8 expression in the rostromedial septum , similar to a Gli3 hypomorphic phenotype . We indeed show that GLI3 processing is altered in Rfx3 deficient brains . Last we show , using organotypic slice cultures , that ectopic FGF8 expression disrupts guidepost neuronal distribution similar to the in vivo defects observed in Rfx3 mutants . Altogether , our data show that loss of function of Rfx3 at early stages of embryonic development is responsible for disturbed GLI3 processing and to small alterations in Fgf8 expression , likely sufficient to induce dramatic aberrations in corticoseptal organization of guidepost neurons and consequently in CC formation . Rfx3 mouse mutants thus appear to be particularly informative for understanding the molecular mechanisms that govern early midline patterning and offers a rare insight into the causes of CC defects in ciliopathies .
The consequence of Rfx3 inactivation on CC development was analysed on sections stained with haematoxylin-eosin or immunostained for specific guidance markers of callosal axons: Neuropilin1 ( Npn-1 ) and L1CAM cell adhesion protein ( L1 ) ( Figure 1 and Figure S1 ) . At E18 . 5 , callosal axons have crossed the CC midline in wild-type ( WT ) mice ( Figure 1A , 1C , 1E and Figure S1A ) . In contrast , Rfx3−/− mice exhibited partial ( n = 4/11 ) to complete agenesis ( n = 4/11 ) of the CC , with few or no callosal axons crossing the midline ( Figure 1 and Figure S1 ) . Remaining Rfx3−/− mice did not exhibit any obvious callosal defects ( n = 3/11 ) . In Rfx3−/− brains , many callosal axons reached the midline but , instead of crossing it , accumulated on both sides of the midline and formed dense axonal bundles called Probst Bundles ( PB ) ( arrowheads , Figure 1 and Figure S1 ) . In some animals there was a relatively mild phenotype in which the two cerebral hemispheres fused correctly , and a few callosal axons still crossed the midline albeit with abnormal trajectories ( Figure 1B , 1F and Figure S1B ) . The most severe phenotype that we observed was a complete agenesis of the CC with no callosal axons crossing the midline . In these Rfx3−/− mice , the two cerebral hemispheres did not fuse correctly and displayed a large bulge along the inter-hemispheric fissure where callosal axons approach the midline ( Figure 1D and Figure S1C , symbol O ) . Additionally , we observed in these embryos strong defects in the formation of the hippocampal commissure but not of the anterior commissure ( Figure 1 , Figure S1 and not shown ) . Thus Rfx3 contributes to the formation of the CC and the hippocampal commissure . To understand how RFX3 is involved in CC formation , we analysed Rfx3 mRNA expression in coronal sections of the developing mouse telencephalon prior to and during CC formation . From E8 to E10 . 5 , Rfx3 was uniformly expressed in the entire neuroepithelium ( not shown ) . From E11 . 5 to E16 . 5 , Rfx3 expression became progressively restricted to specific rostro-caudal levels in the telencephalon ( Figure 2 and Figure S2 ) . Rfx3 hybridization signal was strong at the CSB ( * ) where the CC will form , and in the cingulate cortex ( CCi ) that contains pioneer callosal projection neurons [3] , [4] ( Figure 2B–2D ) . In addition , Rfx3 was expressed in the primordium of the IG and the ventricular zone of the GW at the border of the lateral ventricles ( Figure 2B1 , 2C1 , 2D1–D2 ) . Both regions surround the CSB and are known to be important for CC formation [1] , [7] , [8] . High Rfx3 expression was also observed at more rostral levels in the retrobulbar region and at caudal levels in the cortical hem ( CH ) , the choroid plexus , the ventral pallium ( VP ) laterally , as well as , in the preoptic area ( POA ) ( Figure 2B3 , 2C3 and 2D2–D3 , open arrows ) . From E16 . 5 to birth , Rfx3 expression in the rostral telencephalon was restricted to the IG , the GW and the cerebral cortex ( Figure S2E , S2G and Figure S3C , S3E to S3H ) . To clarify the nature of the embryonic midline cells expressing RFX3 , we performed co-labelling experiments with markers for different cell types . Glutamatergic guidepost neurons colonizing the forming CC express the calcium binding protein calretinin as well as several transcription factors known to promote the glutamatergic fate such as empty spiracles homolog 1 ( EMX1 ) ( Figure S3A ) and T-box brain transcription factor 1 ( TBR1 ) [11] ( Figure S2 and Figure S3A ) . In the embryonic IG , part of the neurons express calbindin and are also glutamatergic since they express EMX1 ( Figure S3B ) . In addition , GABAergic guidepost neurons can be identified using a GAD67-GFP mouse line in which the green fluorescent protein ( GFP ) is reliably expressed in GABAergic neurons . Finally , CC guidepost glia of the IG and of the GW can be distinguished by Nestin , Glutamate Aspartate Transporter ( GLAST ) and Glial Fibrillary Acidic Protein ( GFAP ) expression [5] , [6] . At the CSB , Rfx3 was expressed in glutamatergic guidepost neurons labelled for calretinin , reelin and TBR1 as early as E14 . 5 ( Figure 2D and Figure S2A , S2B and S2D , * ) . In the IG , Rfx3 mRNA was detected in glutamatergic neurons labelled for the calcium binding protein calbindin ( Figure S2F ) . After E16 . 5 , at the brain level where the cerebral hemispheres have already fused , Rfx3 was no longer expressed by glutamatergic guidepost neurons of the CC white matter ( Figure S2E1 , arrow ) . Rfx3 was still expressed by glutamatergic calretinin+ neurons of the marginal zone ( MZ ) and calbindin+ neurons of the IG ( Figure S2E2 and S2G ) . We observed no co-localization between GAD67-GFP and Rfx3 in neurons from E14 . 5 to E18 . 5 ( not shown and Figure S3C ) . Thus , Rfx3-expressing neurons of the corticoseptal region are strictly glutamatergic . Moreover , Rfx3-positive cells populating the ventricular zone in the GW region from E14 . 5 to E18 . 5 are radial glial cells , labelled for Nestin , GFAP and GLAST ( Figure S3D–S3F ) . However , no such overlap can be observed in the IG region , confirming that Rfx3-positive cells are not glial cells in the IG ( Figure S3F1 ) . In addition to Rfx3 expression in the midline , we also observed at E12 . 5 , that Rfx3 mRNA was detected in pioneer calretinin+ glutamatergic cortical neurons of the preplate ( Figure 2C ) . From E13 . 5 to E16 . 5 , Rfx3 mRNA was found in calretinin+ glutamatergic neurons in all layers of the developing cortex ( Figure 2D and Figure S2B–S2E , S3E ) and at E18 . 5 it was found in the projection neurons of the upper cortical layers labelled for Special-AT-rich sequence Binding protein 2 ( SATB2 ) and in those of the lower cortical layers labelled for COUP-TF Interacting Protein 2 ( CTIP2 ) ( Figure S3G , S3H ) . By contrast , we never detected any Rfx3 hybridization signal in the reelin+ Cajal Retzius cells or calbindin+ neurons of the cortical MZ ( Figure S2A , S2F ) . The presence of Rfx3 transcripts at the midline in the corticoseptal region and in the cerebral cortex is consistent with the importance of this gene in CC formation . Given the large distribution of RFX3 in the embryonic brain , it might contribute to the proper development of the cortex or of midline structures . We , thus , examined if these different regions are affected by Rfx3 inactivation . We first analysed the cerebral cortex of Rfx3 mutants . In Rfx3−/− cortex , the laminar distribution of SATB2+ and CUX1+ ( Cut-like homeobox 1 ) callosally projecting neurons [20]–[26] was normal ( Figure S4A–S4D ) . In addition , CTIP2+ cortical layer V and TBR1+ cortical layers V–VI which contain about 20% of the callosally projecting neurons , were similar in mutant and WT brains ( Figure S4E–S4H ) . To study if RFX3 expression in cortical neurons is necessary for axonal growth in the CC , we investigated whether the targeted inactivation of Rfx3 in pyramidal cortical neurons results in pathfinding defects . Using a Ngn2-CreER driver line , we induced recombination of a Rfx3 floxed allele in neurogenin 2 ( NGN2 ) -derived glutamatergic projection neurons of the cortex by tamoxifen application at E13 . 5 [27] , [28] . While Rfx3 was not any more expressed in the cerebral cortex of Rfx3f/f; Ngn2-CreERtm+/− mice , ( compare Figure S4I2 and S4J2 ) , the SATB2+ callosally projecting neurons and the CC still formed normally ( n = 7/7; Figure S4J1 and S4L ) . This result shows that the loss of Rfx3 in cortical pyramidal neurons is not responsible for callosal axon guidance defects . To determine if RFX3 was required for the development of the CSB we followed the organization of guidepost cells in mutant CSB compared to WT . We first followed the distribution of CC guidepost glutamatergic neurons in Rfx3−/− mice at E18 . 5 , after callosal axons have crossed the midline . Glutamatergic neurons of the CC , labelled for TBR1 and calretinin were shifted laterally , leaving a large portion of the CC devoid of neurons ( not shown and Figure 1A and 1B , open arrowheads ) . Calretinin+ and calbindin+ glutamatergic neurons were both severely disorganized through the IG ( Figure 1A–1D , open arrowheads ) . In addition , a progressive disorganization of CC glial cells was noticed in Rfx3−/− CC regions . The GFAP-positive astrocyte-like cells of the IG and of the midline were disorganized and the curvature of the radial glial processes was increased ( Figure 1E and 1F , open arrowheads ) . Because this disorganization could be a secondary effect of callosal misrouting , we also looked at the distribution of guidepost cells before callosal axons cross the midline . As early as E14 . 5 , glutamatergic guidepost neurons labelled for reelin , calretinin and TBR1 failed to form a well organized band of neurons at the CSB of Rfx3−/− mice and instead accumulated ectopically on both sides of the midline ( Figure 3A–3D; open arrowheads; Figure 4A–4D and not shown ) . In addition , Reelin+ Cajal Retzius and calretinin+ neurons lost dramatically their tangential distribution in the MZ layer and are more broadly distributed in the cortico-septal region ( Figure 3A–3D and Figure 4A–4D ) . In addition , they lost their fusiform/bipolar shape . For both neuronal populations , given the density of the cells , the number of neurons in the corticoseptal region and the MZ was difficult to quantify . Similarly , from E14 . 5 to E16 . 5 glutamatergic neurons labelled for calbindin were mislocalised in the cortical MZ and IG of Rfx3−/− brains ( Figure 3E–3F and Figure 4E and 4F ) . Moreover , some calbindin+ neurons were found to accumulate within the CC white matter ( Figure 4E and 4F ) . The midline neuronal defects were accompanied , at E16 . 5 , by pathfinding errors of pioneer callosal axons that failed to cross the midline and formed ectopic bundles ( Figure 4A–4F , white arrowheads ) . By contrast , GAD67-expressing GABAergic neurons were properly positioned through the lateral part of the CC at E16 . 5 in Rfx3 mutant ( Figure S5A and S5B ) . Finally , immunohistochemistry with several markers for astrocytes ( nestin , GLAST and GFAP ) , indicated that the position and organization of the guidepost glial cell populations of the GW and of the midline glial zipper was indistinguishable in WT and Rfx3−/− mice , suggesting that their development is not sensitive to the loss of Rfx3 ( Figure 4G–4H and Figure S5 ) . Altogether , these experiments indicate that RFX3 is necessary for the proper positioning of multiple corticoseptal neuronal populations but not of glial cell populations at the midline early in development . To verify if CC guidance defaults were caused by altered development in the midline region , we performed transplantation experiments as previously described [11] . Midline structures comprising the CC were transplanted into telencephalic slices at E16 . 5 , using different combinations of wild type and Rfx3−/− embryos ( Figure 5 ) . When midline explants from Rfx3+/+ mice were transplanted into Rfx3+/+ slices , DiI-labelled callosal axons crossed the midline ( Figure 5A; n = 7 slices with crossing axons out of 10 ) , thereby reproducing the in vivo behavior of callosal axons . By contrast , with Rfx3−/− midline explants transplanted in Rfx3−/− slices , DiI-labelled callosal axons failed to cross the midline ( Figure 5B; n = 0 slices with crossing axons out of 3 ) . Similarly , transplantation of midline from Rfx3−/− mice into Rfx3+/+ slices leads to impaired midline crossing of axons ( Figure 5C; n = 2 slices with crossing axons out of 7 ) . We then tested whether the transplantation of Rfx3+/+ midline into Rfx3−/− mutant slices could restore correct pathfinding of DiI-labelled Rfx3−/− callosal axons . Remarkably , Rfx3+/+ midline structure restored normal axonal guidance of the majority of Rfx3−/− callosal axons ( Figure 5D; n = 5 slices with crossing axons out of 7 ) . Therefore , the misrouting of callosal axons in Rfx3 mutant embryos is due to defects in corticoseptal midline associated structures . To study if RFX3 expression in guidepost glia or neurons is required for callosal axon growth , we investigated whether the targeted inactivation of Rfx3 in both cell type progenitors might result in cell differentiation defects that could have an impact on axonal guidance . We thus inactivated Rfx3 in GFAP-positive radial glia precursors by mating Rfx3f/f and hGfap-Cre+/− mice [29] . According to Zhuo et al . , 2001 , these mice start to express Cre recombinase in the forebrain at E13 . 5 [29] . We observed that while Rfx3 was already inactivated at E15 . 5 in CC guidepost glia and neurons of Rfx3f/−; hGfap-Cre+/− mice ( Figure 6A and 6B ) , the CC still formed normally ( Figure 6C–6F; n = 9/9 ) . This result shows that the loss of Rfx3 in midline neuronal and glial guidepost cells as early as E15 . 5 is not responsible for CC agenesis . However , we cannot exclude that RFX3 is needed in the glutamatergic guidepost neurons that invade the CSB region from E12 . 5 to E14 . 5 . To test this possibility , we induced recombination of Rfx3 floxed allele specifically in guidepost neurons , as early as E12 . 5 , by mating Rfx3f/f and Emx1-Cre+/− mice [30] . Inactivation of Rfx3 in Emx1+ precursors of Rfx3f/−; Emx1-Cre+/− mice led to loss of Rfx3 in all the CSB anlage at E12 . 5 and in midline postmitotic glutamatergic neurons at E14 . 5 ( Figure 6G and 6H ) . While Rfx3 was already inactivated at E12 . 5 in the CSB region of Rfx3f/−;Emx1-Cre+/− , we did not observe any callosal pathfinding defects ( Figure 6I–6L; n = 6/6 ) . These results also sustain the conclusion that Rfx3 is not required in CC neurons . Finally , Rfx3 was inactivated in GABAergic neurons originating from the ventral telencephalon by using the Nkx2 . 1-Cre+/− mice [31] . In accordance with the absence of Rfx3 expression in CC guidepost GABAergic neurons , Rfx3f/−;Nkx2 . 1-Cre+/− mice did not present any CC defects ( not shown ) . Taken together , these results demonstrate that callosal pathfinding defects observed in Rfx3−/− mice are not due to a cell autonomous function of RFX3 in CC guidepost cells suggesting a requirement for RFX3 for proper CC development , at early embryonic stages , during midline specification . To understand how RFX3 governs CC midline structure formation before E12 . 5 , we looked at early patterning defects that could affect Rfx3−/− brains . Telencephalic patterning relies on the interaction of well-described dorsal , rostral and ventral signalling centres in the forebrain that produce secreted signalling molecules [32] . We first analysed the expression of genes characteristic for the dorsal signalling centres in Rfx3−/− embryos by in situ hybridization . Genetic evidences show that Bone morphogenic 4 ( Bmp4 ) is essential for roof plate formation in the mouse forebrain [33] . BMP4 is expressed in the telencephalic midline at E10 . 5 and in the entire forebrain midline at E12 . 5 . Moreover , several WNT proteins are expressed in the cortical hem [34] which is crucial for dorsal midline development . However , our analyses did not reveal any differences in Bmp4 and Wnt2b expression between E12 . 5 WT and Rfx3−/− embryos ( n = 2/2 , Figure S6A–S6D ) . Thus no major defects could be observed in dorsal midline markers in Rfx3−/− brains . The rostral signaling center is specified at E8 . 5 as the anterior neural ridge ( ANR ) at the anterior border between the ectoderm and neuroectoderm and will give rise to the commissural plate at later stages . Both the ANR and the commissural plate express FGF8 that has been shown to be important to induce ventral and rostrodorsal cell fates [35] . We determined the telencephalic rostral expression profile of Fgf8 in wild type and Rfx3−/− embryos . As observed in Figure 7 , at E12 . 5 , Fgf8 expression was restricted to the commissural plate of wild type embryos . Remarkably , we observed an extension of Fgf8 expression into the rostromedial pallium in Rfx3−/− embryos on both coronal and sagittal sections ( n = 6/6 , Figure 7A , 7B and Figure S7A , S7B ) . These data suggest that RFX3 is necessary to restrict Fgf8 expression to the commissural plate . FGF8 has been shown to induce Sprouty2 gene expression which in turns negatively regulates FGF8 signalling [36] , [37] and we observed a small expansion of Sprouty2 in the rostromedial pallium consistent with an increase in FGF signalling in the midline ( n = 7/7 , Figure 7C , 7D and Figure S7C , S7D ) . Several key transcription factors have been associated with the specification of the commissural plate in mouse , including: SIX3 , nuclear factor I/A ( NFIa ) and EMX1 [38] . We precisely analyzed the expression of these markers but did not observe extensive variations in the expression of Emx1 ( n = 3/3 ) , Six3 ( n = 3/3 ) , and Nfia ( n = 4/4 ) between WT and Rfx3 mutant mice ( Figure S6E–S6H and not shown ) suggesting that ectopic FGF8 in Rfx3−/− rostral telencephalon does not induce dramatic changes in the expression of these key transcription factors . It has been shown that proper Sonic Hedgehog ( SHH ) signalling is required to maintain FGF8 signaling at the rostral midline . In addition , defects in ciliary proteins lead to defective SHH signaling in many tissues and organs ( for review see [39] ) and also in the telencephalon [40] , [41] . In comparison with Rfx3+/+ embryos , we did not observe any differences in Shh expression in the ventral telencephalon of Rfx3−/− mutants ( n = 3/3 , Figure S6I and S6J ) . However , we observed that SHH signalling is likely to be affected since the Shh target genes Patched1 ( Ptc1 ) ( n = 3/4 ) and Gli1 ( n = 2/3 ) , were both slightly down-regulated in the Rfx3−/− ventral telencephalon ( Figure S6K–S6N , arrows ) . Taken together , these findings suggest that the up-regulation of Fgf8 expression does not coincide with an up-regulation of SHH signalling in the ventral telencephalon . Interestingly , it has been shown that Gli3−/− embryos present an abnormal development of the prosencephalic midline with a similar ectopic expansion of Fgf8 expression into the dorsal midline [42]–[44] . Since GLI3 processing has been shown to require cilia [45] and that RFX3 regulates ciliogenesis in several mouse cell types , we hypothesized that the FGF8 expression defects could result from abnormal function of GLI3 in Rfx3−/− embryos . Gli3 mRNA expression did not appear to be affected in Rfx3−/− telencephalon , as observed by in situ hybridization on coronal sections ( Figure S6O and S6P ) . Thus , RFX3 does not seem to act on Gli3 transcription . Gli3 produces two antagonistic protein isoforms: the full-length activator form ( GLI3A ) and the proteolytic cleaved repressor form ( GLI3R ) [46]–[48] with the ratio between GLI3A and GLI3R being an important determinant of patterning for various tissues . We thus investigated GLI3 proteolytic processing by western blot in wild type or Rfx3 deficient brains and found that the GLI3R/GLI3A ratio is reduced in mutant brains ( Figure 7E and 7F ) . These results show that RFX3 is required for the proper specification of the CSB at early stages of embryonic development and that this RFX3 function is likely to be mediated by altered GLI3 processing . To study whether ectopic FGF8 signalling could be responsible for the disorganisation of guidepost neurons at the CSB , we performed ex-vivo cultures of brain slices at E12 . 5 in the presence or absence of ectopic FGF8 ( Figure 8 ) . Ectopic FGF8 sources were provided by bath application ( Figure 8B ) or by implanting FGF8-coated beads into the rostromedial pallium ( Figure 8D ) where the extension of Fgf8 expression was observed in Rfx3−/− embryos . We followed the distribution of guidepost neurons 2–3 days later by immunostaining for Calretinin . Remarkably , under both conditions , we observed drastic consequences of ectopic FGF8 on guidepost distribution at the midline in treated explants ( n = 10/10 after FGF8 bath application and n = 6/6 with FGF8-coated beads ) compared to control ( n = 11/11 without FGF8 bath application and n = 5/5 with control-coated beads ) . The observed phenotypes were similar to what is observed in Rfx3−/− brains . The tangential distribution of Calretinin+ neurons through the cortical MZ was severely reduced and neurons were broadly distributed at the CSB and in cortical layers . No MZ could be clearly distinguished . We also noticed a marked thinning of the commissural plate as it was observed in several Rfx3 mutant mice compared to WT mice ( Figure 7B and white arrowheads in Figure S6 ) . These results support the hypothesis that FGF8 controls guidepost neuronal distribution at the CSB . Hence , in Rfx3−/− brains , the observed increase in Fgf8 expression can be indirectly responsible at early stages for disturbed guidepost neuron distribution by acting on CSB patterning , but also directly responsible at later stages for the positioning of guidepost neurons at the CSB . Therefore , loss of CC in Rfx3−/− mice likely results from the perturbed processing of GLI3 which controls Fgf8 expression early during development .
RFX3 is strongly expressed in corticoseptal guidepost cells from E14 . 5 to E16 . 5 . However , we did not observe any consequences of Rfx3 loss of function on CC formation by Emx1-Cre or hGFAP-Cre induced Rfx3 inactivation in these cells , suggesting that RFX3 function in guidepost cells is not required for commissural axon formation or that RFX3 function in these cells is masked by a redundant function of other RFX transcription factors . Indeed , 7 RFX proteins are present in the mouse genome and at least one , RFX4 , has already been shown to play major functions in brain patterning and to be widely expressed in the telencephalon [49] , [50] . However , no precise description of RFX4 expression in guidepost neurons or glial cells have been described and CC callosum defects have not been precisely investigated in RFX4 mutants . Hence , the function of RFX proteins in guidepost cells still remains to be deciphered . Nevertheless , our work shows that RFX3 plays a crucial function in early patterning of the CSB . We show that RFX3 is required early to restrict FGF8 expression in the telencephalic rostral midline . Our results suggest that a small increase in Fgf8 signalling is sufficient to disorganize the CSB and hence guidepost neuron distribution at early stages of embryonic development . This is supported by in-vivo observations on Rfx3 mutant brains . On the other hand , our ex-vivo explant experiments support the hypothesis that FGF8 also has a direct action on guidepost neuron distribution at later stages . Altogether , these observations show that FGF8 plays a critical function for the distribution of guidepost neurons at the CSB . Previous data in the literature indicated that reducing FGF8 signalling in the telencephalon leads to severe brain phenotypes , and in particular to holoprosencephaly [35] , [51] , [52] and Fgf8 hypomorphic mutants show corpus callosum defects [51] . Moreover Fgf8 inactivation by Emx1-Cre induced recombination has been shown to induce CC defects at E18 . 5 , suggesting a key role for FGF8 in CC formation [38] . In all these studies , while FGF8 signalling defects were associated with severe alterations of the dorsal signalling centres and possible defects at the CSB , the distribution of guidepost neurons was not examined . In Rfx3−/− mice , unbalanced FGF8 expression in the rostral telencephalon is not associated with alterations of the dorsal signalling centre but is nevertheless sufficient to disturb the distribution of guidepost neurons , leading to the conclusion that FGF8 signalling is primarily responsible for guidepost neuron organization at the CSB and hence CC formation . Our work also indicates that RFX3 acts on FGF8 signalling by regulating GLI3 activity in the telencephalon . This in agreement with previous works showing that GLI3 acts upstream of FGF8 signalling in the telencephalon . Indeed , in Shh mutants Fgf8 expression is lost , whereas in Gli3 mutants Fgf8 expression is expanded [42]–[44] . Moreover , in Shh; Gli3 double mutants , Fgf8 expression is also expanded suggesting that this expansion occurs independently of Shh in a Gli3 mutant background [44] . Last , loss of Gli3 rescues ventral patterning defects in Shh mutants but not in Fgfr1;Fgfr2 mutants , placing FGF signalling downstream of Gli3 [53] . This also explains why the upregulation of Fgf8 expression likely occurs in Rfx3−/− brains despite an observed downregulation of Shh signalling . Our observations and these data are consistent with the conclusion that Rfx3 acts on GLI3 activity and consequently on FGF8 signalling at the rostral telencephalic midline . Interestingly , our work brings a mechanistic interpretation to the observation that Gli3 mutations have been found in human patients suffering from Acrocallosal syndrome [54] . In addition , hypomorphic Gli3Pdn mutant mice show CC defects [55] with a similar but more severe increase in FGF8 expression in the rostral midline [42] . Consistent with our observations , Gli3Pdn mutants also show strong alterations of guidepost neuronal organization ( D . Magnani and T . Theil , unpublished ) . In Rfx3−/− brains , we observed a reduced processing of GLI3 in its repressor form and a reduction in SHH signalling in agreement with the already described function of RFX3 in ciliogenesis in various cell types . Indeed , the function of cilia in regulating SHH signalling and GLI3 processing has been well documented in various cell types and organs ( for review see [39] ) . Interestingly , RFX proteins have been shown to control consistently the regulation of several IFT components , dynein retrograde motors and many basal body associated proteins such as MKS1 from C . elegans to mammals [17] , [56] , [57] . Many of these RFX target genes appear to be associated with overall reduced SHH signalling and reduced GLI3 processing when mutated in mouse . These observations suggest that RFX3 indirectly modulates GLI3 activity and SHH signaling in the anterior telencephalon by regulating the levels of several proteins involved in cilia associated transport and biogenesis . In humans , several syndromes resulting from mutations in genes encoding ciliary proteins are associated with corpus callosum malformation of various severity [15] . Recently , mutations in the Kif7 gene involved in ciliogenesis and GLI3 processing have been found in human patients suffering from acrocallosal syndrome , characterized by Corpus Callosum and digit malformations [58] . Our work provides a first insight into the cellular mechanisms that are responsible for Corpus Callosum defects following GLI3 processing alterations . Our work demonstrates that small alterations in GLI3 processing is correlated with altered patterning of the CSB and aberrant distribution of guidepost neurons in this region and that this is sufficient to induce midline crossing defects of callosal axons . In mouse , only a few ciliary mutants with altered patterning of the telencephalon have been described . The cobblestone hypomorphic mutation of Ift88 , and the ftm , and alien mutants all show severe morphological defects of the brain associated with dorsal ventral patterning defects of the telencephalon [40] , [41] , [59] . All three mutants are associated with an alteration in GLI3 processing , but with a more severe shift in the balance of GLI3 activator and repressor forms than in Rfx3 mutants . However , the CC was not precisely investigated in these mutants , probably because embryos die too early to allow for an analysis of CC development . Another interesting mutant is a Wnt1-Cre induced Kif3A-deleted mouse that shows craniofacial anomalies due to neural crest migration defects and is associated with agenesis of the CC [60] . Neural crest migration is required for the proper patterning of the telencephalon , in particular by acting on FGF8 rostral patterning centre [61] , [62] . The patterning of the telencephalon has not been described in Wnt1-cre; Kif3Aflox/flox mice but it is tempting to hypothesize that dysregulation of FGF8 signalling could be sufficient to mis-pattern the commissural plate in this mutant . Hence , the Rfx3−/− mutant represents the first mouse model establishing a link between proper GLI3 processing and the distribution of guidepost neurons at the CSB for CC formation . In conclusion , the analysis of Rfx3−/− mice provides strong evidence for the important contribution of corticoseptal neuronal populations in CC formation and for the critical function of Fgf8 signalling in CSB patterning at early stages of CC formation . It provides new understanding of the cellular origins of CC defects in human ciliopathies .
All animal research has been conducted according to relevant national and international guidelines . Rfx3-deficient and floxed mice were generated and genotyped as previously described [18] . GAD67-GFP knock-in mice , hGfap-Cre+/− , Ngn2-creERTM+/− , Emx1-cre+/− , Nkx2 . 1-cre+/− mice used in this work have been previously described [27] , [29]–[31] . Brains were fixed in 4% PFA/PBS at 4°C until experimentation and then dehydrated in graded series of ethanol ( 25–100% ) . Brains were transferred into absolute butanol and substituted for paraffin in graded series of butanol/paraffin solutions . Sections of 10 µm were deparaffinized in Methylcyclohexan , rehydrated and stained with Hematoxylin following standard procedures before mounting in Eukitt . Brain embryos were dissected and fixed overnight at 4°C in 4% paraformaldehyde ( PFA ) ( Sigma P6148 ) in 1×PBS ( Invitrogen ) . Brains were cryoprotected in 30% sucrose and cut in coronal 50 µm-thick frozen sections for staining . Mouse monoclonal antibodies were: Nestin ( 1/600 ) ( BD bioscience ) . Rat monoclonal antibodies were: L1 ( 1/200 ) ( Chemicon , Temecula , CA ) and CTIP2 ( 1/500 ) ( Abcam , Cambridge , UK ) . Rabbit polyclonal antibodies were: calbindin ( 1/2500 ) and calretinin ( 1/2000 ) ( Swant , Bellinzona , Switzerland ) ; CUX1 ( 1/200 ) ( Santacruz , Heidelberg , Germany ) ; EMX1 ( 1/250 ) ( gift form A . Trembleau ) ; GFAP ( 1/500 ) ( DAKO , Carpinteria , CA ) ; GFP ( Molecular Probes , Eugene , OR ) ; SATB2 ( 1/500 ) ( gift from V . Tarabykin ) ; RFX3 ( 1/100 ) [63] , TBR1 ( 1/500 ) ( Abcam , Cambridge , UK ) . Goat polyclonal antibody was Npn-1 ( 1/50 ) ( R&D System , Minneapolis , Mn ) . GLAST guinea-pig polyclonal antibody was ( 1/2000 ) ( Millipore , Billerica , MA ) . GFP chicken polyclonal antibody was ( 1/500 ) ( AVES , Oregon , USA ) . Fluorescence immunostaining: The primary antibodies were detected with secondary antibodies coupled with Cy or Alexa ( Jackson ImmunoResearch and Molecular Probes; respectively ) . For RFX3 detection , we used an amplification system with secondary anti-rabbit IgG coupled to biotin ( 1/250 ) ( Jackson Laboratory ) and subsequent revelation with Streptavidin Fluoprobes 547 ( Interchim ) ( 1/400 ) . Sections were counterstained with Hoechst 33258 ( Molecular Probes ) , mounted on glass slides and covered in Mowiol 4–88 ( Calbiochem , Bad Soden , Germany ) . Embryos were fixed overnight at 4°C in 4% PFA in PBS . Embryos were transferred in PBS/30%sucrose , cryoprotected in PBS/15% Sucrose ( Sigma S0389 ) /7 . 5% Gelatin ( Merck 4078 ) and frozen at −50°C . Cryosections of 20 µm were collected , thaw-mounted on polylysin coated slides ( Miom France ) . Hybridization was performed as previously described ( Niquille et al . , 2009 ) . Rfx3 mRNA probe was previously described [18] . Embryonic brains were treated as described for immunocytochemistry procedure and coronal 100 µm-thick sections were cut using a vibratome ( Leica Microsystems ) . 100 µm free-floating vibratome sections were hybridized with digoxigenin-labeled cRNA probe as described before [64] . To combine in situ hybridization with immunocytochemistry , fast Red ( Roche ) was used as an alkaline phosphatase fluorescent substrate instead of NBT/BCIP solution . Slides were incubated in Fast Red ( Roche ) until the appearance of staining in dark chamber at RT . Thereafter , sections were fixed for 15 min in 4% PAF and immunostaining was performed . Fluorescent-immunostained sections were imaged using confocal microscope ( Zeiss LSM 510 Meta , Leica SP5 or Zeiss Qasar 710 ) equipped with 10× , 20× , 40×oil Plan-NEOFLUAR and 63×oil , 100×oil Plan-Apochromat objectives . Fluorophore excitation and scanning were done with an Argon laser 458 , 488 , 514 nm ( blue excitation for GFP and Alexa488 ) , with a HeNe laser 543 nm ( green excitation for Alexa 594 , CY3 and DiI ) , with a HeNe laser 633 nm ( excitation for Alexa 647 and CY5 ) and a Diode laser 405 nm ( for Hoechst staining ) . Z-stacks of 10–15 plans were acquired for each CC coronal section in a multitrack mode avoiding crosstalk . Images processing: all 3D Z stack reconstructions and image processing were performed with Imaris 6 . 0 software . To create real 3D data sets we used the mode “Surpass” . The colocalization between two fluorochromes was calculated and visualized by creating a yellow channel . Figures were processed in Adobe Photoshop CS2 . Western blots were performed following standard procedures . Gli3 6F5 mouse monoclonal antibody was kindly provided by S . Scales [65] . Gli3XT and WT body samples were kindly provided by M . Willaredt and S . Schneider-Maunoury . We have used our previously developed in vitro model of CC organotypic slices [11] . Embryos were placed in ice cold dissecting medium ( MEM Gibco ref 11012-044 with 15 mM glucose and 10 mM Tris pH 7–9 ) . Brains were removed and embedded in 3% low-melting point agarose ( In vitrogen ) . 250 µm thick coronal sections were then cut using a vibrotome filled with cold dissecting medium and slices at the level of the CC were collected in the same medium . CC slices were cultured on Millicell membranes in tissue dishes containing 1 ml of BME/HBSS ( Invitrogen ) supplemented with glutamine , 5% horse serum , and Pen/Strep [54] . In our slice assay , as in vivo , the callosal axons from dorso-lateral neocortex develop later and their growth cones enter after E16 . 5 the CC region in successive streams over a period of several days . Our slice assay performed at E16 . 5 allowed us to study: ( 1 ) the function of CSB cells , ( 2 ) the outgrowth properties of the majority of callosal axons that are growing through the CC after E16 . 5 and ( 3 ) the effects of transplantations on callosal axon navigations . The transplantation assay was performed at E16 . 5 as previously described [11] to analyze the growth of either WT or Rfx3−/− DiI-labelled callosal axons within midline structures of WT or Rfx3−/− slices . Small explants of E16 . 5 WT or Rfx3−/− midline structure comprising were excised using tungsten needles and transplanted into the midline of WT or Rfx3−/− host slices . After incubation for 48 hours , the slices were fixed and axon trajectories through the various regions were analysed by confocal analysis . For FGF8 bath application: slices of E12 . 5 brains were cultured as above but FGF8 ( FGF-8b isoform , R&D Systems , ref 423-F8 ) or BSA ( control ) was added in the culture medium at 1 µg/ml in PBS 1× . After two days of incubation , slices were processed for immunohistochemistry as described above . For FGF8-coated beads experiments: slices of E12 . 5 brains were cultured as above but FGF8-coated beads or control-coated beads were implanted in the rotromedial pallium . After three days of incubation , slices were processed for immunohistochemistry as described above . To make FGF8-soaked beads , 45 µm polystyrene beads ( Polysciences ) were rinsed in PBS , the beads were pelleted by 5-min centrifugation at 13 , 000 rpm and the supernatant was removed . They were incubated in 5 mg/ml heparin for 1 hour at room temperature , rinsed and then incubated with 250 µg/ml mouse FGF8b ( R&D Systems ) in 0 . 5% bovine serum albumin ( BSA ) in PBS overnight at 4°C . Control beads were incubated in 0 . 5% BSA in PBS . Before implantation , beads were rinsed three times for 10 minutes in PBS . The nomenclature for callosal development is based on the “Atlas of the prenatal mouse brain” [66] . | The Corpus Callosum is the major brain commissure that links the two cerebral hemispheres in mammals . Absence or reduction of the corpus callosum is the most frequent brain malformation observed at birth in humans and leads to cognitive and behavioural deficits . Agenesis of the Corpus Callosum is frequently observed in ciliopathies , a group of human diseases due to defects in cilia assembly or function . However , the cellular origin of this brain malformation in these syndromes remains elusive . RFX3 transcription factor is a key regulator of ciliogenesis in mouse . Here , we show that the Rfx3 mutant mouse shows impaired Corpus Callosum formation . By transplantation experiments , we demonstrate that this is due to defective distribution between the two hemispheres of a transient neuronal population ( guidepost neurons ) required for routing callosal axons . We show that this abnormal distribution is due to altered FGF8 signalling at early stages of brain development . Our observations show that small but focused signalling defects resulting in specific alterations in the distribution of guidepost neurons are responsible for corpus callosum agenesis . | [
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] | 2012 | The Ciliogenic Transcription Factor RFX3 Regulates Early Midline Distribution of Guidepost Neurons Required for Corpus Callosum Development |
Many biological activities originate from interactions between small-molecule ligands and their protein targets . A detailed structural and physico-chemical characterization of these interactions could significantly deepen our understanding of protein function and facilitate drug design . Here , we present a large-scale study on a non-redundant set of about 20 , 000 known ligand-binding sites , or pockets , of proteins . We find that the structural space of protein pockets is crowded , likely complete , and may be represented by about 1 , 000 pocket shapes . Correspondingly , the growth rate of novel pockets deposited in the Protein Data Bank has been decreasing steadily over the recent years . Moreover , many protein pockets are promiscuous and interact with ligands of diverse scaffolds . Conversely , many ligands are promiscuous and interact with structurally different pockets . Through a physico-chemical and structural analysis , we provide insights into understanding both pocket promiscuity and ligand promiscuity . Finally , we discuss the implications of our study for the prediction of protein-ligand interactions based on pocket comparison .
At the molecular level , many functions of proteins in a living cell can be attributed to or regulated by their interactions with small-molecule ligands such as metabolites or drugs [1] , [2] . A high-resolution structural description of protein-ligand recognition is very important for understanding protein function and designing new compounds for therapeutic purposes . As revealed in many of the crystal structures of proteins in complex with their ligands , protein-ligand interactions usually take place at preferred sites on the protein surface known as “pockets” [3] , [4] , in contrast to relatively flat geometric shape of protein-protein interaction sites [5] . Traditionally , the study of a protein-ligand complex structures often focuses on the structural or physico-chemical characteristics that are thought to be specific to that individual pocket [6] . However , it is becoming more and more clear that proteins are generally promiscuous in that they interact with multiple distinct ligands [7] , [8] . One naturally seeks detailed structural insights into both the origin and generality of this intriguing observation . In this regard , a comprehensive , large-scale comparative study on all protein pockets in all protein structures that are solved to date may uncover principles that explain the promiscuity of protein-ligand interactions . In such a study , the first question is: How many representative pockets are there in the structural space of all pockets ? This echoes a similar question asked about the fold space of proteins [9] , [10] . A very recent study addressed this by comparing the pockets of 5 , 000 single-domain proteins [11] . It was found that a few hundred pocket structures are enough to represent all structure shapes in this set , and similar shaped pockets are also found in artificially generated proteins , which were built and selected based on thermodynamic stability but not biochemical function . In this sense , the structural space of protein pockets is degenerate and surprisingly small . Since the number of known bioactive ligands [12] , [13] is much larger than the available number of pocket shapes , the implication is that a given pocket shape can accommodate more than one type of ligand , thus generating the promiscuity responsible for the evolution of biochemical function [14] , [15] . The observation that pocket shapes are degenerate suggests that the same ligand could bind to pockets of similar shape but located in different proteins , thus leading to side-effects of drug molecules through unexpected “off-target” interactions [16] , [17] . However , the specific interplay of pocket geometry and chemical environment with the types of ligands that are bound was not addressed in that study [11] as it focused on the properties of pockets in proteins without a companion analysis of the bound ligands . In the current contribution , we address this issue . A second question is: To what extent can we infer a similar protein-ligand interaction by matching protein pockets ? The answer to this question has practical applications for protein function prediction [18] or small-molecule compound screening [19] . In order to match pockets , many computational approaches have been developed to compare pockets based on their structural and/or physico-chemical features ( for a review see [20] ) . These methods may be categorized into two classes: The first is based on the structural alignment of pocket-lining residues or atoms [21]–[24] , and the second is based on comparison of descriptors independent of the residue or atom alignment [25]–[27] . The former class is generally more accurate , albeit slower than an alignment-free method , due to the complexity of the alignment algorithm . In that regard , we recently proposed an efficient , robust method , APoc , for large-scale pocket comparison [28] . On the other hand , since a structural alignment is not required , alignment-free methods might have an advantage in dealing with flexible pockets . Their disadvantage is that they often lack a direct physical interpretation for why two pockets are similar as assessed by their fingerprints . Another interesting question is: How different are the ligands that bind to the same pocket ? Obviously , if the ligands are very similar , they are very likely to have similar interactions with the pocket , e . g . , that might contain a common anchor and variable region [29] . However , if the ligands possess different scaffolds and/or chemical properties , it might not be obvious as to what , if any , interactions are conserved . How does a pocket maintain favorable interactions with very different ligands ? Conversely , a ligand may be found in pockets of different protein structures . How different are those pockets that interact with the same ligand ? An early study of pockets from non-homologous proteins that bind the nine most common ligands suggests that there are shape variations in these pockets [30] . This further raises the question of how a ligand manages to interact with different pocket shapes . To address these questions , we performed a comprehensive comparative study on a large curated set of over 20 , 000 ligand-bound pocket structures from crystallized protein-ligand complexes . We first characterize the structural space of these pockets . This is followed by an analysis of the correlations between pocket similarity and ligand chemical similarity . Then , we investigate both pocket promiscuity ( one pocket accommodating different ligands separately ) and ligand promiscuity ( one ligand recognized by different proteins ) , respectively . Finally , the implications of our study are discussed .
To answer this question , we have collected all crystal structures of protein-ligand complexes deposited in the PDB till May 2012 and curated a non-redundant set of 20 , 414 ligand-bound pockets , which contains 9 , 485 unique ligands ( see Methods ) . A pocket is defined by ligand-binding sites , i . e . , the amino acids in physical contact with the ligand . We then performed all-against-all pocket comparisons using the pocket comparison method APoc [28] . Pocket similarity is evaluated by the pocket similarity score ( PS-score ) , which measures the geometry of backbone Cα atoms of aligned pocket-lining residues , as well as their side chain orientation and chemical properties . Identical pocket structures have a perfect PS-score of 1 . Significant similarity emerges starting from a PS-score higher than 0 . 36 ( see Table S1 ) . Fig . 1 shows the APoc alignments of six adenosine diphosphate ( ADP ) binding pockets from six different proteins against a common ADP-binding pocket from protein kinase Chk2 [31] . These examples illustrate pocket similarity at various significance levels of their PS-scores . In the first example ( Fig . 1A ) , another protein kinase [32] , a homolog of Chk2 , matches Chk2 both the pocket and global fold structures at a PS-score of 0 . 81 , an associated P-value of 1 . 0×10−12 , and a Template Modeling score ( TM-score ) of 0 . 77 . TM-score is a measure for protein global structural similarity , and a TM-score higher than 0 . 40 is significant [33] . In the other five cases , there is low or no global structural similarity , reflected by both visual inspection and low TM-scores of no more than 0 . 37 . However , APoc detects similarity in their ligand-binding pockets . An inositol phosphate kinase [34] exhibits a strong resemblance to Chk2 in their pockets at a PS-score of 0 . 66 , a P-value of 1 . 2×10−8 , and an RMSD of 1 . 6 Å in the aligned pocket-lining Cα atoms ( Fig . 1B ) . Two proteins in ATP-grasp folds , a glutathione synthetase [35] and a FAICAR synthase [36] , display highly significant similarity at PS-scores of 0 . 51 and 0 . 46 , together with P-values of 2 . 0×10−5 and 7 . 8×10−4 , respectively . The last two examples , a pyridoxal kinase [37] and a signaling protein GlnK [38] , show lower pocket similarity to that of Chk2 at PS-scores of 0 . 40 and 0 . 38 , and P-values of 7 . 2×10−3 and 4 . 6×10−2 , respectively . In these two cases , there are some adjustments by ligands in their docking poses in response to the structural variations of their pockets , yielding relatively low , but still significant PS-scores . We then seek to find the smallest set of pockets ( or templates ) that are sufficient to represent the full set of pockets at a given level of similarity . In terms of graph theory , pocket similarity relationships can be viewed as a directed graph G , wherein each node defines a pocket , and an edge from pocket A to pocket B indicates that A as a representative pocket has significant similarity to B above a specified PS-score threshold . Thus , the sought-after set of representative pockets is the smallest dominating set of the graph G ( see Methods ) . Fig . 2A shows the growth of representative protein pockets versus year . As background , the total number of pockets examined exhibits an exponential growth , especially from the years 1990 to 2000 . After this initial rapid increase , however , the annual growth rate has been gradually slowed down from 26% in 2001 to 15% in 2011 . The trend is similar in N , the number of selected representative pockets , but the pace of growth is even slower . For example , at a PS-score threshold of 0 . 40 , the annual growth of N decreased from 14% in 2001 to 4% in 2011; at the PS-score of 0 . 50 , the rate is 24% in 2001 and 9% in 2011 . These results suggest that many pockets are structurally redundant , e . g . , the highly similar ATP-binding pockets from a large family of protein kinase catalytic domains that happen to be the binding-sites of many designed inhibitors as well . The observation that the number of representative pockets is approaching a plateau at a significant PS-score of 0 . 40 supports the notion that the structural space of ligand-bound pockets is close to complete , and a set of 1 , 315 pockets may represent the current pocket library at this similarity level . Pairwise comparisons between matched target pockets and these representatives give a mean alignment RMSD of 1 . 74 Å , a mean alignment coverage of 84%; half of these comparisons have a highly significant P<1×10−4 ( Fig . S1 ) . Note that this number of representative pockets is higher than that reported in a previous study [11] , which found 339 representatives in 5 , 000 proteins of less than 250 residues . If we use the same protein length criterion , the total number of pockets is reduced 65% , and a total of 332 representative pockets were obtained at a PS-score of 0 . 40 . These numbers are therefore consistent . At a high PS-score of 0 . 50 , a set of 3 , 158 representative pockets are selected , and about 96% of matching pocket comparisons have a RMSD of 2 . 5 Å or less , 90% have an alignment coverage better than 70% , and 94% with a P<1×10−4 . From a network prospective , the structural space of pockets is highly connected , meaning that virtually all pocket nodes can reach other pocket nodes through a path of significantly related pockets; that is , the Largest Strongly Connected Component ( LSCC ) dominates G . About 97% of all pockets belong to the LSCC at a PS-score of 0 . 40 , and the percentage is 75% at 0 . 45 ( Fig . 2B ) . Notably , a phase transition occurs at a PS-score threshold of 0 . 50 , when the space becomes disconnected with 1 , 834 strongly connected components ( or clusters ) , and the corresponding LSCC consists of only 7 . 7% of all pockets . At this level , the pocket space becomes discrete and members in the same cluster could be evolutionarily related . For instance , the LSCC at PS-score of 0 . 50 is composed of 1 , 571 ATP- and ADP-binding pockets , about 90% of them are from protein kinases , and the remaining from likely related proteins whose function is also dependent on ATP , such as glutathione synthases , SAICAR synthases , and some other types of kinases . Some examples are shown in Fig . 1 . A common assumption for inferring protein-ligand interaction is that similar pockets bind similar ligands . The relationship between ligand similarity and pocket similarity , however , needs a thorough examination . Here , we use a 1024-bit fingerprint to compare the chemical similarity of ligands in terms of their pairwise Tanimoto coefficient ( Tc , see Methods ) . As shown in Fig . 3 , the distribution of all-against-all ( excluding self comparison ) Tc values of 9 , 485 ligands in our data gives a mean Tc value of 0 . 162 and a standard deviation of 0 . 088 . The distribution has a long tail , suggesting that there exist many similar ligands in our set . A Tc score higher than 0 . 4 appears in less than 2% of all cases . In our analysis below , Tc scores above 0 . 4 are deemed significant . Five ligands whose structures are related to ADP are demonstrated as examples in Fig . 3 . These ligands have Tc values ranging from 0 . 4 to above 0 . 9 . Fig . 4A shows the distribution of ligands at different pocket similarity levels , defined by the P-values of their PS-scores . For a 0 . 01≤P-value<0 . 05 , about 13% of ligand pairs share significant chemical similarity at a Tanimoto coefficient ( Tc ) >0 . 4 . This percentage increases to 31% and 37% as one increases the pocket similarity level to a P-value of 1×10−3 and 1×10−5 , respectively . The percentage drops to 18% at pocket P<1×10−5 . This unexpected observation is due to many pockets are promiscuous and interact with chemically different ligands . The PDB is biased towards these types of pockets , because they are often from putative drug targets , e . g . , protein kinases , proteases , etc . In some cases , it is possible to identify local pocket similarity when overall global structural similarity is likely absent . Fig . 4B displays only those cases where pockets are from proteins with different global structures at a TM-score <0 . 4 [33] . About 24% and 52% of pockets recognize similar ligands at a Tc>0 . 4 , at a corresponding P-values of 1×10−3 and 1×10−5 , respectively . The percentage increases to 84% for highly similar pockets at P<1×10−5 . However , we note that the number of cases considered here is much smaller than Fig . 2A and might therefore underestimate pocket promiscuity . The regime where P<1×10−5 comprises of only 0 . 7% of pockets . The subset is dominated by GDP-binding pockets that appear in multi-domain proteins with low global similarity but high pocket similarity . Nevertheless , the analysis shows that it is possible to detect pockets that share both similar ligands and pockets , even though they may be from two proteins with very different global structures . Next , we ask the question of how many protein-ligand interactions observed in the PDB can be matched to a template that has both a similar pocket and a similar ligand ? To answer this question , for each target pocket , we search for the best structural hit that satisfies two conditions: ( i ) global sequence identity <30%; and ( ii ) chemical similarity Tc of ligands larger than a specified value . The result is shown in Fig . 5 . At a significant Tc>0 . 4 , about 86% of pockets can find a template hit with significant pocket similarity at the PS-score P<0 . 05 that binds similar ligands . The numbers are 72% , 60% , and 54% at P<0 . 01 , 0 . 001 , and 0 . 0001 , respectively . At a highly significant Tc>0 . 7 , most ( 60% and 50% of ) pockets hit a template at P<0 . 05 and <0 . 01 , respectively . The result shows that structural comparison of pockets could be useful for inferring ligand-binding . In particular , many of these top structural hits come from proteins with low global structural similarity or even different structural folds . At a Tc>0 . 4 and P<0 . 05 , about 35% of the top template hits are from proteins with global TM-score <0 . 4 . The percentage is 19% at a Tc>0 . 7 . These are challenging cases for sequence-based methods , but could in principle be dealt with by adopting a structure-based approach . However , we also note that for all Tc values , there remain a significant fraction of pockets that are structurally unrelated and yet they bind similar ligands . The above results indicate that pockets of similar shapes can attract a diverse set of ligands with different chemical properties . One obvious explanation that accounts for this observed chemical diversity is that a similarly shaped pocket may have a different amino acid composition , thus generating different physico-chemical environments favored by chemically different ligands , e . g . , homologs with modified substrate specificities . A second reason is that , for large pockets , some small-molecule ligands may be bound to at least partially different regions of the pockets , and these ligands may not necessarily have similar chemical properties . Of special interest are promiscuous pockets , i . e . , the same pockets recognized by ligands with different chemical structures . To examine pocket promiscuity , we selected a set of 59 , 157 pairs of pockets of comparable size , each pair having a highly significant PS-score >0 . 6 , sequence identity = 100% , and bound ligands at low Tc<0 . 3 . These pockets are essentially from the same proteins crystallized with different ligands . The set is composed of 6 , 913 unique pockets , or 34% of all pockets in our set , and they are from 421 different clusters determined at a PS-score of 0 . 50 . At this level , about 25% , 31% , and 36% of all pocket clusters with more than 2 , 10 , and 20 members contain at least one promiscuous pocket , respectively . Thus , it is clear that promiscuous pockets are not rare at all . Fig . 6 shows four examples . In each case , the same pocket is shown to interact with two ligands of different structures . Perhaps , the most well-known examples are ATP-binding pockets of protein kinases , for which many novel inhibitors have been designed . Two such examples are shown in Fig . 6A , where a protein kinase p38α accommodates two drugs , Imatinib [39] and Sorafenib [40] , thereby inhibiting ATP from binding at the same pocket . Although these two inhibitors were originally designed to target different protein kinases and cancer types , they have been shown to interact with other protein kinases such as MAP kinase p38α . In the second example ( Fig . 6B ) , two anti-inflammatory drugs , Indomethacin [41] and Celecoxib [42] , are demonstrated to interact with a common protein target , cyclooxygenase-2 ( COX-2 ) . Both drug molecules bind to the active site of the enzyme . The third example involves MurD ligase , which catalyzes the formation of peptidoglycan ubiquitously in bacteria but is absent in human; thus , it is an attractive target for the design of novel anti-bacterials . Fig . 6C depicts two experimental compounds intended for this target . They are both N-substituted derivatives of d-Glutamic acids , and are recognized by the same set of active site residues of the enzyme [43] , [44] . Last , we present a well-known promiscuous protein , pregnane X captor ( PXR ) , which is a nuclear receptor protein responding to a variety of endogenous and exogenous chemicals . Fig . 6D displays the interaction of PXR with two compounds [45] , [46] , which have a very different chemical structure at a very low Tc of 0 . 065 , yet they are found in the same , largely hydrophobic pocket . How does a promiscuous pocket carry on interactions with different ligands ? We decomposed atomic contacts at the protein-ligand interfaces of the above 59 , 157 complex structures . On average , 28% , 22% , and 4% of interactions are hydrophilic , hydrophobic , or aromatic , respectively; the remaining are either neutral ( or slightly favorable interactions , 35% ) or energetic unfavorable ( 11% ) interactions . As shown in Fig . 7 , a comparative analysis revealed that most ( 58% ) physical interactions of similar type are conserved between pairs of complexes . Individually , 64% of hydrophilic or hydrogen-bond interactions , 53% of aromatic interactions , and 66% of hydrophobic interactions are conserved on average . Since aromatic contacts are rare in some structures , they are not required to be conserved to maintain stable protein-ligand interactions , yielding the peak at zero conserved interactions observed in Fig . 4B . Overall , even though ligands may have a very different scaffold , they may achieve the same physical interactions with the same pocket residues . Second , specific contacts ( i . e . , hydrophilic or hydrogen-bonding interactions ) contribute only 28% of all contacts on average . As a result , favorable interactions are more flexible than might be expected on average . Finally , the plasticity of protein pockets may allow different types of interactions [11] . The mean PS-score of these pockets is 0 . 86 , and most cases have a P-value<1×10−12 . These are highly similar but clearly not identical pockets . The flexibility of side chains permits different types of contacts with different ligands to form . Finally , we perform an analysis on pockets that accommodate chemically similar or identical ligands . Fig . 8A shows the structural similarity of pockets that recognize similar ligands at various but significant Tc values >0 . 4 . For Tc≤0 . 8 , it is clear that most pocket pairs are structurally dissimilar , with only about 5–6% of pocket pairs having a significant PS-score , even though they recognize similar ( but not identical ) ligands . The fraction of similar pockets pairs at P<0 . 05 increases to 14% for 0 . 8<Tc≤0 . 99 . Thus , even here , on average very similar ligands interact with structurally distinct pockets . Furthermore , 66% of pockets in our set interact with virtually the same ligand ( Tc>0 . 99 ) that binds to at least one other pocket . This set includes 1 , 475 unique ligands , and about 25% and 13% of pocket pairs binding the same ligand share a similarity at P<0 . 05 and 0 . 0001 , respectively . Thus , many ligands are promiscuous and interact with structurally different pockets . We further focus on a set of 5 , 991 pockets bound to 51 of the most frequently observed ligands ( see Table S2 ) , each with more than 30 distinct pockets . For each ligand , we gathered all its pockets , which forms pocket subspaces of ligands , converted pocket similarity relationship into graphs , and subsequently performed graph analyses . As shown in Fig . 8B , for 78% of these ligands , more than half of their pockets are clustered together to form the LSCC at a P-value<0 . 05 in their respective subspaces . The percentage is 51% at a P-value<0 . 01 . This result implies that at least a fraction of structural features are conserved between some of these pockets within the LSCC cluster , though the substructure conservation is not necessarily always transitive . Thus , most pockets are structurally related , albeit some at low level of similarity . Nevertheless , for each type of ligand , one may represent the entire relevant pocket space using a few representative pockets , dependent on desired level of similarity , as shown in Table S2 . For example , one needs 31 , 19 , 23 , and 15 pockets to cover 456 , 431 , 371 , and 329 observed pockets at P<0 . 05 for ADP , HEM , NAD , FAD , the top four mostly common ligands in the set , respectively . The result that the same ligand may interact with different pockets suggests that there exists multiple interaction poses between the ligand and its pockets . One major contributing factor to the multiple interacting poses is the conformational change of the ligands . Fig . 8C shows the cumulative distribution of the atomic RMSD for the same ligands observed in the similar pockets ( P<0 . 05 ) versus the dissimilar pockets ( P≥0 . 05 ) . In about 70% and 82% of similar pockets , the corresponding ligand RMSD is less than 1 . 5 and 2 . 0 Å , respectively , versus 42% and 63% of the dissimilar pockets . In addition , the same conformer of a ligand may interact with different pockets in different poses [28] . They are the most challenging cases for a structure-based prediction on ligand-protein interactions .
Our study demonstrates a complicated picture of protein-ligand interactions . First , from mainly a structural prospective , the space of the protein pockets is degenerate . The growth rate of novel pockets deposited in the PDB has been steadily decreasing over the past decade , approaching a plateau . At a PS-score of 0 . 40 ( P<0 . 01 ) , one can find a structural match for all known pockets by using about 1 , 300 representative pockets . The number is higher than that reported in an earlier study [11] , which was limited to proteins with less than 250 residues and employed a less stringent pocket similarity criterion . Perhaps , this result is not that surprising given that the structural space of protein folds themselves is also finite [9] , [10] . Like protein fold space , the structural space of protein pockets is continuous in the sense that a significant set of structural features in one pocket can be found in another pocket , which may not share any evolutionary relationship . Interestingly , at a high structural similarity level ( PS-score 0 . 50 ) , a phase transition occurs in pocket space ( Fig . 2B ) , yielding mostly isolated clusters of pockets that could share an evolutionary relationship . However , this is not to say pockets at a lower similarity level do not share an evolutionary origin or that those at a higher similarity level have a common ancestor . Instead , it means that it is difficult to establish their evolutionary relationship using structural information alone . This observation is analogous to a “continuous-to-discrete” view of protein fold space [47] . Like the classification of protein folds , classification of protein pockets is dependent on the similarity criteria employed . We note that there is no perfect metric or criteria that gives a universally agreed upon classification . In our study , pocket similarity comparison focused on the position of Cα and Cβ atoms of the pocket-lining residues , as well as their chemical similarity . The similarity criteria we selected are based on estimation of the statistical significance , ranging from P<0 . 05 to highly significant P<1×10−6 according to APoc [28] . One major purpose of pocket comparison is to develop a structure-based method for predicting protein-ligand interactions . The rationale behind is that similar pockets attract similar ligands . This is certainly true to some extent; as shown in Fig . 5 , for 72% and 50% of ligand-bound pockets one can find another similarly shaped pocket ( at P<0 . 01 ) that interacts with a similar ligand at a Tc>0 . 4 and 0 . 7 , respectively . One advantage based on structural comparison of pockets is that one may uncover ligand-protein interactions that are undetectable from sequence or global structural comparison . However , there is one limitation to this approach . As we shown here , one type of pocket shape can accommodate multiple types of ligands , which could introduce false positives . To address this issue , it is necessary to increase the level of pocket similarity to reduce false positives , at the cost of sensitivity . This explains why current methods have relatively low coverage in benchmark tests [23] , [28] . How to improve sensitivity and maintain a low false positive rate remains a challenge for predicting protein-ligand interactions on the basis of pocket similarity . Many protein pockets are promiscuous . More than 1/3 of pockets in our data set belong to those promiscuous pockets that interact with multiple , chemically different ligands . Considering that only a tiny fraction of protein-ligand interactions are captured in the PDB , the results shown here likely represent a lower bound , and it is very likely that promiscuous protein pockets are more common . From an analysis of protein-ligand interactions observed in promiscuous protein pockets , we showed that a large fraction ( ∼60% on average ) of these interactions share similar types of interactions , e . g . , hydrogen bonding , hydrophobic , or aromatic . Moreover , the plasticity of protein pockets may also provide alternative , viable interaction modes [11] . Therefore , these promiscuous interactions may be understood from a physical chemical point of view . In principle , if one could design a scheme that matches similar ligands based on their physico-chemical properties regardless of their chemical scaffolds , then it could provide a means of predicting novel protein-ligand interactions . In practice , however , this is a highly challenging problem because many physical interactions such as hydrophobic interactions are not very specific , thus allowing many possible solutions that increase the chance of hitting a false positive . The complexity of protein-ligand interactions is also reflected in ligand promiscuity . That is , a ligand with different poses may interact with differently shaped pockets . One main reason is that ligands with multiple rotatable bonds are flexible , thus yielding different conformations selected by different pockets . In some cases , different poses fit different physico-chemical environments [48] . These observations further help explain polypharmacology or the unexpected “off-target” interactions found in many drug molecules [17] . From a prediction point of view , for a compound of interest , it is unlikely to predict all its protein partners based on only one template because of ligand structural diversity . In this regard , a catalog of structures of many-faceted protein-ligand interactions could significantly improve the prediction of side-effects or repurposing of drugs . In summary , we find that both protein pocket promiscuity and ligand promiscuity are common . The relationship of protein pockets and ligands is often not one to one but many to many . A given ligand may interact with a number of proteins whose structures are globally unrelated but contain similar pockets . Or it might interact with proteins having different pockets . Conversely , a given pocket can have similar physico-chemical interactions with ligands that may or may not have similar linear structures . For the case of dissimilar ligand pairs , they can adopt conformations that have similar interaction surfaces . Based on this and prior work [7] , we conclude that promiscuous ligand interactions of differing specificity are inherent to proteins and living cells . This has a number of implications: It provides a mechanism for a living cell to select for useful biochemical functions as such low level function is likely inherent to a soup of quasi stable protein structures which can then be optimized [14] , [15] . It also provides biological robustness [49] . On the other hand , it could cause difficulty in the control of biological processes and in assessing the accuracy of predicted protein-ligand interactions , since we are far from knowing all protein-ligand interactions . This work clearly argues that the notion of one ligand-one protein target that implicitly underlies many drug discovery efforts is fundamentally incorrect .
We collected a set of 20 , 414 ligand-bound pockets from holo-protein structures in the PDB [6] . The data set is curated from all 81 , 756 entries in a May 2012 PDB release . The program LPC [50] was applied to analyze protein-ligand complex structures . For each protein-ligand complex , the program returns a table of protein residues contacting with the ligand . A protein-ligand contact is defined based on the distance between heavy atoms from the protein and from the ligand , respectively . If the distance of a pair of atoms is less than the sum of the Van der Waals radii of the two atoms plus 2 . 8 Å , which is the diameter of a probing solvent molecule , then the residue that the protein atom belongs to is considered a pocket residue . All such residues collectively compose a protein pocket . In this study , we consider small molecule ligands that have at least ten and fewer than 200 heavy atoms , but do not consider polypeptides , DNA , or RNA molecules . In the PDB , each type of ligand is represented by a unique three-letter name known as the HET code . If one PDB entry contains multiple ligands with an identical HET code , we arbitrarily select the ligand making the most contacts with the protein . The primary protein chain that a ligand associates with is clustered at 90% sequence identity . In each cluster , we subsequently select a representative for each type of ligand , using X-ray structure resolution and number of contacts as the selection criteria . Finally , we discarded pockets with l0 or fewer residues . This yields 20 , 414 ligand-bound pockets , which are bound to 9 , 485 unique ligands . The chemical similarity of ligands is measured by their pairwise Tanimoto coefficient ( Tc ) , calculated using the 1 , 024-bit version of Daylight like 2D-fingerprints with the Open Babel package [51] . For two ligands A and B with fingerprints fA and fB , Tc = fA∩fB/fA∪fB , where symbols ∩ and ∪ represent intersection and union of non-zero bits , respectively . Structural comparison of pockets was conducted using the program APoc described previously [28] . Here , we give a brief description of the main ideas . Given two input pockets , a template and a target , APoc evaluates their Pocket Similarity score ( PS-score ) , which measures the similarity in their backbone geometries , side-chain orientations , and the chemical similarities between the aligned pocket-lining residues . The length of a pocket is the number of Cα atoms of the pocket residues . Suppose an alignment is obtained between a query ( target ) of length LQ and a template of length LT . The PS-score of the alignment defined as ( 1 ) ( 2 ) ( 3 ) ( 4 ) where Na is the number of aligned residue pairs , di is the distance in Å between the Cα atoms of the ith aligned residue pair , and the empirical scaling factor . The constants in d0 were obtained by fitting the distribution of Cα distances in random alignments of pockets . pi measures the directional similarity between two Cα to Cβ vectors in the two pockets , which span an angle θi at the ith alignment position of two non-Glycine residues . For Glycine , the value of pi is assigned 1 if both amino acids are Glycine and 0 . 77 if only one residue is Glycine . The latter is the mean pi derived from random alignments . ri measures the chemical similarity of the two aligned amino acids . has a value of 1 if the two amino acids belong to the same group ( I–VIII ) defined as: I ( LVIMC ) , II ( AG ) , III ( ST ) , IV ( P ) , V ( FYW ) , VI ( EDNQ ) , VII ( KR ) , VIII ( H ) [52] , and 0 otherwise . The scaling factor ensures that the mean score of two aligned random pockets is independent of their length . To calculate the distances used in di and pi , aligned residues are superimposed using the Kabsch algorithm [53] to minimize the RMSD of the full or subset of aligned residues . In principle , the number of all possible superpositions exponentially increases as the alignment length grows . The notation “max” in Eq . 2 indicates that the PS-score corresponds to the superposition that gives the maximum of all scores . In practice , a heuristic iterative extension algorithm is employed to calculate the PS-score , similar to that used for calculating the TM-score [33] . Note that identical pocket structures have a PS-score of 1 . 0 , which is the upper bound of the PS-score . APoc optimizes the pocket structural alignment through three phases: In the first phase , several guessed solutions are generated from gapless alignments , secondary structure comparisons , fragment alignments , and local contact pattern alignments . Starting from these guessed “seed” alignments , dynamic programming is iteratively applied in the second phase . This yields an “optimal” sequential ( viz . protein sequence order dependent ) alignments between two pocket structures . In the third phase , an iterative procedure searches for the best non-sequential alignment between two pockets , which is then selected if this alignment has a better PS-score than the “optimal” sequential alignment . The problem of finding an optimal non-sequential alignment ( or match ) is converted to the Linear Sum Assignment Problem ( LSAP ) , which is a special case of integer programming and is also equivalent to the problem of finding a maximum weight matching in a weighted bipartite graph . To efficiently solve LSAP , we implemented the shortest augmenting path algorithm [54] , which has a polynomial time complexity of O ( N3 ) , where . Since the PS-score is an optimal score from many alignment trials , its distribution can be modeled by the type I extreme value distribution ( Gumbel distribution ) . Using this statistical model , the statistical significance , i . e . P-values , of the PS-score is estimated . Parameters of the statistical models were obtained through comparing millions of randomly selected pocket pairs [28] . Given a graph G , the domination number N is defined as the cardinality of the smallest dominating set of the graph . Since the calculation of N is a NP hard problem , we implemented a greedy algorithm to estimate this value as follows [55]: For a given set of nodes , the node with the largest number of matched nodes is selected first ( two nodes are considered matched if they are connected in both directions in a directed graph ) . Then , after removing the selected node , the node in the remaining set with the highest number of matched nodes among unmatched nodes is selected . The process is iterated until all nodes that can be matched to the selected set of nodes are identified . The resulting number of this selected set is N . A strongly connected graph is a subgraph where all nodes are bidirectionally connected . The size of the LSCC was calculated using the igraph package for the statistical platform R . The fraction of matching pockets is the ratio of the number of pockets assigned to the dominating set divided by the total number of pockets . The classification of atomic ligand-protein interactions is obtained from the LPC [50] . For each atomic contact , the associated contact surface area is used to calculate the fraction of conserved contacts . The overall contribution of each type of interaction is calculated as the total contact surface area of each type divided by the total contact surface area for all pockets . When comparing two pairs of protein-ligand interactions , the fraction of conserved interactions for interaction type i is defined as , where and are the total contact surface areas for pocket p1 and p2 , respectively , and is the contact surface area of conserved contacts . The data set is available at http://cssb . biology . gatech . edu/pocketlib . | The life of a living cell relies on many distinct proteins to carry out their functions . Most of these functions are rooted in interactions between the proteins and metabolites , small-molecules essential for life . By targeting specific proteins relevant to a disease , drug molecules may provide a cure . A deep understanding of the nature of interactions between proteins and small-molecules ( or ligands ) through analyzing their structures may help predict protein function or improve drug design . In this contribution , we present a large-scale analysis of a non-redundant set of over 20 , 000 experimental protein-ligand complex structures available in the current Protein Data Bank . We seek answers to several fundamental questions: How many representative pockets are there that serve as ligand-binding sites in proteins ? To what extent can we infer a similar protein-ligand interaction by matching the structures of protein pockets ? How different are the ligands found in the same pocket ? For a promiscuous protein pocket , how does a pocket maintain favorable interactions with very different ligands ? Conversely , how different are those pockets that interact with the same ligand ? We find the structural space of protein pocket is small and that both protein promiscuity and ligand promiscuity are very common in Nature . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | A Comprehensive Survey of Small-Molecule Binding Pockets in Proteins |
Translesion synthesis ( TLS ) polymerases are specialized DNA polymerases capable of inserting nucleotides opposite DNA lesions that escape removal by dedicated DNA repair pathways . TLS polymerases allow cells to complete DNA replication in the presence of damage , thereby preventing checkpoint activation , genome instability , and cell death . Here , we characterize functional knockouts for polh-1 and polk-1 , encoding the Caenorhabditis elegans homologs of the Y-family TLS polymerases η and κ . POLH-1 acts at many different DNA lesions as it protects cells against a wide range of DNA damaging agents , including UV , γ-irradiation , cisplatin , and methyl methane sulphonate ( MMS ) . POLK-1 acts specifically but redundantly with POLH-1 in protection against methylation damage . Importantly , both polymerases play a prominent role early in embryonic development to allow fast replication of damaged genomes . Contrary to observations in mammalian cells , we show that neither POLH-1 nor POLK-1 is required for homologous recombination ( HR ) repair of DNA double-strand breaks . A genome-wide RNAi screen for genes that protect the C . elegans genome against MMS–induced DNA damage identified novel components in DNA damage bypass in the early embryo . Our data suggest SUMO-mediated regulation of both POLH-1 and POLK-1 , and point towards a previously unrecognized role of the nuclear pore in regulating TLS .
DNA damaging agents from both endogenous and exogenous sources can induce replication-blocking DNA lesions that threaten cell cycle progression and , consequently , cell viability . To remove these DNA lesions cells are equipped with various specialized repair mechanisms [1] , including nucleotide excision repair ( NER ) that deals with helix-distorting obstructions [2] . However , during embryogenesis , which entails phases of rapid cell division , only a limited time window is available for repair processes [3] . Consequently , unrepaired DNA damage may delay replication and cell cycle progression . In the nematode Caenorhabditis elegans a delay in replication is detrimental for the developmental program; timing of cell division is fixed and strictly regulated by the homologues of the checkpoint genes CHK1 and ATR [4] . Indeed , replication stress caused by depletion of nucleotide pools causes fatal errors in the correct timing of the first asynchronous divisions [5] . However , early embryos of C . elegans appear to be remarkably resistant to DNA damaging agents , suggesting an efficient way to prevent the induction of replication stress by DNA damage [6] . To be able to deal with replication obstructions , organisms evolved ways that allow bypass of the damaged template , thus ensuring continuity of the replication process [7] . Specialized TLS polymerases are capable of direct bypass of DNA lesions in an error free or error prone fashion , depending on their affinities for the specific lesion site . In eukaryotes , TLS is mediated by the DNA polymerases of the Y-family: Polη , Polκ , Polι and Rev1 , and the B-family member Polζ . All members of the Y-family polymerases lack proofreading activity and share a conserved active site , which is different from the high-fidelity polymerases in its open and less sterically constrained structure . It allows for accommodation of a DNA lesion , but is also the basis for reduced fidelity [8] . The functional specificities of TLS polymerases are due to minor differences in the structural features of the active site . The C . elegans genome encodes several Y-family TLS proteins , including POLH-1 and POLK-1 , homologs of mammalian Polη and Polκ , respectively . Purified Polη of yeast and vertebrates is capable of replicating across a wide variety of DNA damages , including UV-induced cyclobutane pyrimidine dimers ( CPDs ) , 7 , 8-dihydro-8-oxoguanine , O6-methylguanine , thymine glycol , cisplatin-induced intrastrand crosslinks , acetylaminofluorene-adducted guanine and benzo[a]pyrene-N2-guanine [9] . In humans , defective Polη has clinical implications: Polη is the product of the gene mutated in Xeroderma Pigmentosum complementation group “Variant” ( XPV ) , a syndrome that is associated with a high predisposition towards developing skin cancers [10] . In addition to a role in damage bypass some studies have suggested a role for Polη in homologous recombination , as the polymerase responsible for extension of the invading strand in the D-loop recombination intermediate [11] , [12] . Recently , it was reported that Polη plays a prominent role in early stages of nematode embryogenesis in C . elegans [6] , [13] . Polκ displays structural similarity to Polη but is considered to be the most evolutionarily conserved member of the Y-family showing homology to prokaryotic DinB [8] , [9] . Its substrate specificity in vitro is limited , although Polκ is an efficient extender of mispaired primer termini and some guanyl adducted lesion sites [14] , [15] . Furthermore , Polκ has been suggested as one of the gap-filling polymerases in NER , explaining a moderate sensitivity of Polκ-deficient mammalian cells to UV [16] , [17] . Here , we characterize the involvement of Polη and Polκ in various aspects of genome protection during animal development , using the model organism C . elegans . The advantages of this animal model are its spatial and temporal organization of gametogenesis and its rapid growth properties that allow monitoring DNA repair or lesion bypass during different developmental stages . We found that POLH-1 is involved in protection against a surprisingly wide range of DNA lesions , whereas the substrate specificity of POLK-1 is much more restricted . Both proteins can act redundantly on some lesions , since double mutants were extremely sensitive to the alkylating agent MMS , whereas both single mutants displayed profoundly less sensitivity to this carcinogen . In spite of their error proneness , POLH-1 and POLK-1 appear to be highly important in protection against DNA damage during embryonic development , while their role in later somatic development is limited . Finally , we used genome-wide RNAi to screen for factors that have a similar sensitivity profile leading to the identification of new factors that may play a role in the regulation of TLS .
To study the function of Y-family TLS polymerases in the DNA damage response at different stages of animal development , we set out to isolate mutants for the C . elegans homologs of the Polη and Polκ genes . Figure 1A illustrates a phylogenetic tree of the Y-family polymerase members from several species including C . elegans . The C . elegans genome encodes Polη , Polκ and Rev-1 , but not Polι . Full alignment of the C . elegans polh-1 and polk-1 gene products with their mammalian and yeast homologs reveals their well-conserved catalytic core ( Figure S1 ) . In addition , POLH-1 contains a C-terminal PIP box motif , which is essential for interaction with PCNA , and more recently , has also been shown to target the protein for degradation [13] , [18] . The remaining part of the C-terminus is evolutionary less conserved . Human Polη/yeast Rad30 and human Polκ contain ubiquitin binding zinc finger ( UBZ ) domains , mediating their interaction with PCNA [19] . A UBZ domain was found in C . elegans POLK-1 but not in C . elegans POLH-1 ( Figure S1 ) . Furthermore , C . elegans and yeast Polη and Polκ lack previously identified mammalian motifs that mediate an interaction with the deoxycytidyl transferase Rev1 [20] , [21] . Using a targeted mutagenesis approach [22] we isolated mutants for polh-1 and polk-1 ( Figure 1B ) . polh-1 ( lf31 ) has a single nucleotide substitution in the splice acceptor site of the fourth exon of the polh-1 gene; polk-1 ( lf29 ) contains a premature stop in the fourth exon and encodes a severely truncated version of POLK-1 , missing at least part of the catalytic domain . In the course of this study we obtained another polh-1 mutant from the Gene Knockout Consortium . This allele ( ok3317 ) carries a deletion in polh-1 that results in a fusion of upstream sequences of exon 2 to downstream sequences of exon 3 , removing 549 coding nucleotides ( Figure 1B ) . These mutant strains were backcrossed to remove background mutations that resulted from the mutagenic treatment . No obvious abnormal phenotypes were observed for the mutant strains . Neither the number of progeny , embryonic survival rate nor post-embryonic development was affected by the absence of POLH-1 or POLK-1 . However , double mutants of polh-1 ( ok3317 ) ; polk-1 ( lf29 ) and of polh-1 ( lf31 ) ; polk-1 ( lf29 ) show a minor but significant reduction in both brood size and embryonic survival ( up to five percent of the progeny died , data not shown ) , suggesting some level of functional redundancy in promoting fecundity . Because UV-induced CPDs are excellent substrates for Polη-mediated TLS in yeast and mammals [23] , [24] , we tested the sensitivity of polh-1 mutant animals to UV light by irradiating young adults and scoring progeny survival ( Figure 2A and Figure S2A ) . In contrast to Polη-defective yeast and mammalian cells , that display only a mild hypersensitivity to UV [25] , [26] , POLH-1 deficiency leads to extreme sensitivity to UV irradiation . Both polh-1 ( lf31 ) and polh-1 ( ok3317 ) mutants are more sensitive to UV than animals carrying mutations in xpa-1 , the worm homolog of NER gene XPA , which is essential for repair of UV damage [26] , [27] . NER contributes to UV survival also in polh-1 compromised conditions as animals defective in both xpa-1 and polh-1 are more sensitive than either of the single mutants . ( Figure S2B ) . In line with mammalian data , we observed that the protective role of POLH-1 is not restricted to UV-induced damage . polh-1 worms are severely sensitized to cisplatin treatment ( Figure 2B and Figure S2C ) . This sensitivity was even more pronounced than for dog-1 mutant animals which are defective in the homolog of the Fanconi Anemia gene FANCJ , involved in crosslink repair [28] . Vertebrate Polη has been implicated as the polymerase responsible for extension of HR intermediates [12] , [29] . Since HR is the predominant repair pathway in C . elegans for γ-irradiation-induced breaks in germ cells [30] , [31] , we exposed L4 animals to γ-irradiation and scored survival of the progeny ( Figure 2C and Figure S2D ) . We found that the sensitivity of polh-1 ( lf31 ) and polh-1 ( ok3317 ) mutants to irradiation was comparable to the sensitivity of animals that carry a mutation in brc-1 , the worm homolog of the HR gene BRCA1 ( Figure 2C ) . Worms defective for both polh-1 and brc-1 were more sensitive to γ-irradiation than either of the single mutants ( Figure S2E–S2F ) , suggesting a brc-1-independent role for POLH-1 in protection against γ-irradiation . This conclusion is strengthened by data showing that POLH-1 and BRC-1 protect cells against radiation at very different developmental stages ( see below ) . To further test whether the sensitivity of the polh-1 mutants to γ-irradiation is due to a possible defect in HR of DSBs , we determined the role of Polη in response to DSBs endogenously produced by DNA transposition . Transposition is desilenced in the germline of rde-3 mutants [32] and the ensuing DSBs predominantly rely on HR for their repair [33] . However , embryonic lethality was not increased in polh-1; rde-3 double mutants , in contrast to increased lethality in brc-1; rde-3 doubles ( Table S2 ) . As an independent and a direct method to address a possible in vivo role of C . elegans Polη in HR , we measured repair of a site-specific DSB using a somatic HR reporter assay ( Figure 3 ) . In this assay , which will be described in more detail elsewhere , heat shock-induced expression of the yeast endonuclease I-SceI leads to a DSB in the coding sequence of a GFP transgene that is driven by the intestinal elt-2 promoter . This transgenic setup monitors intrastrand HR , specifically in E-lineage cells , which are still proficient to enter S-phase post embryonically ( in contrast to many other post embryonic cells that arrest in G1 and rely on non-homologous end-joining to repair DSBs ) . A functional GFP transgene is generated following DSB induction only when repair uses a downstream GFP fragment as donor sequence ( Figure 3A ) . This outcome will manifest as GFP expressing intestinal cells . While brc-1 ( tm1145 ) mutation resulted in a profound reduction in the number of cells that expressed GFP , polh-1 ( ok3317 ) mutant animals displayed similar numbers of cells expressing GFP with similar intensities as compared to wild type worms ( Figure 3B and 3C ) . These data further support the notion that the observed sensitivity of polh-1 mutants to γ-irradiation is not caused by a defect in HR . We thus explored an alternative explanation , in which the increased cytotoxicity of polh-1 mutant animals towards γ-irradiation is the result of failed bypass of other ( non-DSB ) DNA lesions . Apart from DSBs , γ-irradiation induces single strand breaks ( SSBs ) as well as 8-Oxo-dG sites and thymine glycols [34] . We reasoned that base adducts in the DNA caused by γ-irradiation may resemble helix-distorting lesions that are substrates for NER and TLS . To address this hypothesis , we tested xpa-1 animals as well as animals defective for both xpa-1 and polh-1 for sensitivity to γ-irradiation ( Figure 3D ) . Strikingly , a redundant effect of both factors was observed after exposure to γ-irradiation similar to the effect seen after UV-irradiation ( Figure S2B ) . These results suggest that γ-irradiation of the germline causes replication-blocking lesions that are substrates for NER and can be bypassed by Polη . It also implies that genes previously found to be involved in γ-irradiation protection are not necessarily involved in the repair of DNA breaks [35] . C . elegans polh-1 mutants are far more sensitive to various DNA damaging agents as compared to vertebrate cells . We hypothesized that the dependence on POLH-1 for damage tolerance might be specific for early embryonic development , when TLS by POLH-1 is the predominant mechanism to avoid checkpoint activation by replication fork blocks on damaged DNA [6] . In differentiated cells , NER or other repair pathways may dominate the damage response . We therefore tested at which stage during development of C . elegans either POLH-1 mediated damage bypass or NER dominate the response to UV-irradiation . First , we exposed synchronized larvae of the L1 stage to UV light and quantified survival and growth ( Figure 4A and Figure S3A ) . L1 larvae already contain 558 of the total 959 somatic cells that make up the adult animal , and thus mainly grow by cellular volume expansion as opposed to mitotic proliferation [36] . Although xpa-1 mutants completely arrest in L1 after a low dose of UV ( Figure 4A , [27] ) , in polh-1 mutants L1 development is only slightly delayed ( Figure S3A ) . Ultimately polh-1 mutants displayed similar survival as found for wildtype L1s following UV exposure ( Figure 4A ) , indicating that in contrast to XPA , POLH-1 plays hardly any role in the UV damage response in L1 . Second , we found that germ cell maturation in polh-1 mutants was comparable to wildtype following UV exposure ( Figure 4B–4E ) , in contrast to xpa-1 mutants that ( i ) display an UV-induced expansion of the pachytene region and ( ii ) fail to generate normal-sized oocytes ( Figure 4D ) , [27] . In addition we determined the apoptotic response in the germline after UV irradiation using a ced-1::GFP transgene that marks germ cells in the process of apoptosis [37] . In contrast to xpa-1 deficient animals [38] , we found no reduction in the UV-dependent apoptotic response in polh-1 mutants as compared to wildtype animals ( Figure S4 ) . Together , these data indicate that NER is essential for normal gametogenesis and L1 development following UV exposure . Apparently , in polh-1 mutants there is sufficient time for repair of UV lesions in these developmental stages to prevent replication stress . However , limited time for DNA repair is available immediately upon fertilization , when a C . elegans embryo goes through a 3-hour period of rapid divisions , according to a fixed and time-constrained lineage program [31] . Thus , in this developmental stage incomplete removal of DNA damage could account for the severe embryonic lethality of UV-exposed polh-1 mutants . To test this hypothesis , we studied the persistence of CPDs - the most abundant lesion type caused by UV – in pronuclei of oocytes , just after fertilization . We irradiated adults with 200 J/m2 and after 24 hours stained developing embryos for CPDs . Remarkably , in wildtype embryos CPDs were still present in the paternal pronucleus , while no CPDs were observed in the maternal pronucleus ( Figure 4F–4G ) . We next assayed xpa-1 and polh-1 mutants after a dose of 50 J/m2 ( leading to comparable levels of embryonic lethality ) . Mutants defective in xpa-1 displayed CPD staining in both pronuclei ( Figure 4H ) , suggesting that in wildtype animals NER-dependent removal of CPDs has occurred during meiotic maturation of the germ cells . In contrast to xpa-1 mutants , but similar to wildtype animals , polh-1 mutants were proficient in removal of CPDs from the maternal pronucleus , whereas CPDs were clearly detectable in the paternal pronucleus ( Figure 4I ) . Before migration and fusion with the maternal pronucleus , the paternal genome decondenses and is replicated in less than 12 minutes [39] . This time span is insufficient for NER to remove DNA damage . We hypothesize that the presence of unrepaired damage from the paternal DNA poses a problem on the first mitotic divisions in polh-1 early embryos . To address this hypothesis , we mated UV-irradiated wildtype or mutant hermaphrodites with untreated males , providing a source of undamaged sperm DNA ( Figure 4J ) . To mark the progeny we used a transgenic line expressing Pmyo-2::GFP . Indeed , lethality in the progeny of irradiated polh-1 hermaphrodites is almost fully rescued by providing a source of undamaged sperm DNA . In contrast , mating of xpa-1 hermaphrodites with untreated males does not affect survival of the progeny . Together , these data indicate that correct progression of early embryonic cell divisions strongly relies on POLH-1 when the genome contains DNA damage . This dependency is not restricted to UV-induced damage but also extends to DNA damage induced by γ-irradiation . The increased sensitivity of polh-1 mutants to γ-irradiation can also be completely rescued by crossing irradiated hermaphrodites with untreated males , thus providing a non-damaged paternal genome ( Figure S5 ) . Importantly , this is in stark contrast to the sensitivity of brc-1 mutants , which cannot be rescued by providing non-damaged sperm . This developmental separation of the modes of action of these proteins further substantiates our findings that polh-1 and brc-1 act independently in protecting cells against γ-irradiation-induced DNA damage . We next wondered whether a similar developmentally restrained function could be attributed to TLS polymerase POLK-1 . To address this question , we exposed polk-1 ( lf29 ) mutant worms to different doses of UV , cisplatin or γ-irradiation ( Figure 2A–2C ) , but found no difference in sensitivity as compared to wildtype animals , indicating that POLK-1 is not involved in protection against these sources of DNA damage in C . elegans . However , akin to the outcome of published RNAi experiments [6] , both polk-1 and polh-1 mutants are sensitive to chronic exposure to the alkylating agent MMS , albeit that the sensitivity in polh-1 mutants was much more pronounced ( Figure 5A , Figure S6 ) , indicating that both POLH-1 and POLK-1 play a role in bypass of MMS-induced DNA damage . We next assayed polh-1 ( lf31 ) ; polk-1 ( lf29 ) double mutants and polh-1 ( ok3317 ) polk-1 ( RNAi ) animals for MMS sensitivity ( Figure 5A , Figure S6A ) . Interestingly , double mutants were extremely sensitive to MMS , and complete lethality was observed at a dose that was 100 times lower than the effective dose for any of the single mutants ( Figure 5A , Figure S6A ) . We did not observe any synergistic effect for any of the other types of lesions we tested ( Figure 2 ) . POLH-1 has previously been shown to be involved in avoiding DNA damage-induced checkpoint activation [6] . In C . elegans embryogenesis , checkpoints - mediated by the C . elegans homologs of the checkpoint genes ATR and CHK-1 - are used to time the first asynchronous cell divisions that are essential for embryonic patterning and thus embryonic viability [5] . Checkpoint activation due to DNA damage interferes with the developmental role of the checkpoint , causing patterning defects and embryonic lethality . Our results with null mutants for polh-1 and polk-1 suggest that both POLH-1 and POLK-1 can act to avoid checkpoint activation . To test the involvement of POLK-1 in checkpoint avoidance directly , we timed the first embryonic division of polk-1 embryos after exposure to MMS . Figure 5B illustrates a delayed first embryonic division in polk-1 mutants when compared to wildtype embryos . Importantly , we also observed examples of polk-1 embryos that after MMS treatment fully arrested at the 1-cell stage ( Videos S1 and S2 ) , while we never observed such cases for MMS-treated wildtype embryos . Two other phenotypes are also indicative of replication stress during early embryonic divisions of MMS treated polh-1 and polk-1 mutant animals . First , polh-1; polk-1 double mutant embryos displayed foci of the DSB repair marker RAD51 ( Figure 5C–5J ) , indicative of DSBs resulting from trying to replicate damaged genomes [40] . Second , DAPI staining revealed chromatin bridges and a disrupted nuclear morphology in the early embryo ( Figure 5J ) , suggesting division of disentangled or incompletely replicated genomes . These phenotypes were less profound , but noticeable , in both single mutants , while never observed in wild type embryos exposed to similar MMS concentrations ( Figure 5C–5H ) . To investigate whether the dependency on POLH-1 and POLK-1 for tolerance to MMS was restricted to embryogenesis - similar to the requirement of POLH-1 in UV tolerance - we followed the outgrowth of L1 animals exposed to increasing concentrations of MMS ( Figure S6B ) . The development of polh-1 larvae was mildly affected , while no delay was observed for polk-1 animals . As for UV , NER deficient xpa-1 larvae were profoundly more sensitive to MMS than either polk-1 or polh-1 deficient larvae ( Figure S6B ) , while the opposite is true for embryonic stages: xpa-1 embryos are less sensitive to MMS than polh-1 embryos [6] . This again argues that TLS is more important than DNA repair at developmental stages that are characterized by fast replication cycles . Since POLH-1 and POLK-1 together appear to be extremely important in protecting the developing embryo against MMS , we wondered whether there might be a general pathway underlying the regulation of the two TLS enzymes . To identify new factors regulating TLS in the early embryo , we performed a genome-wide RNAi screen for genes sensitizing embryos to MMS . Out of 16 , 757 genes tested ( covering ∼86% of all predicted C . elegans genes ) , we found 87 genes that resulted in sensitivity to MMS upon knockdown , including polk-1 . polh-1 was not identified in this screen , probably due to insufficient knockdown by the RNAi clone targeting this gene . We next inspected these RNAi knockdowns for phenotypes reminiscent of polh-1;polk-1 double mutants . All 87 hits were analysed by DAPI for altered nuclear morphology after exposure to MMS ( Figure 5A and Figure S7 ) . Four clones were selected for follow-up analysis based on perturbed embryonic divisions as indicated by chromatin bridges and malformed nuclei . These clones targeted the genes gei-17 , ulp-1 , npp-2 and npp-22 ( Figure 6 ) . gei-17 encodes a SUMO-protease that was previously shown to interact with POLH-1 after DNA damage [13] . ulp-1 encodes a ubiquitin-like protease ( ULP ) that deconjugates SUMO moieties from their target proteins [41] . npp-2 and npp-22 encode two components of the C . elegans nuclear pore complex ( NPC ) [42] . Null alleles of gei-17 , npp-2 and npp-22 are embryonic lethal . Knockdowns of the four genes reduced tolerance to MMS to a similar extent as mutations in polh-1 and polk-1 ( Figure 6K ) . In line with published data [6] we found that gei-17 knockdown led to abundant RAD51 foci in embryos treated on MMS , indicative of replication stress . Also knockdown of ulp-1 , npp-2 and npp-22 lead to MMS-induced RAD51 foci , although to a lesser extend than gei-17 knockdown . This is consistent with the observation that these knockdowns also display less dramatic effects on progeny survival . Foci formation was never observed in mock-treated knockdowns or wild type controls ( Figure 6A–6J ) . We hypothesized that if these genes were in a common pathway with POLH-1 and POLK-1 , then knockdown of these factors would not further increase sensitivity of polh-1;polk-1 worms to a low dose of MMS . Indeed , MMS sensitivity was not further increased when ulp-1 , gei-17 , npp-2 and npp-22 were knocked down in polh-1;polk-1 double mutant animals ( Figure 6L ) , placing all four factors in an epistatic relation to the TLS genes in the response to alkylating damage . To substantiate this epistatic relationship we also studied another source of DNA damage infliction , by exposing young adults to UV light . We previously showed that polh-1 is important for embryonic development in the presence of UV damage , and that an additional mutation in the NER factor xpa-1 renders the animals even more sensitive to low doses of UV ( Figure 2A and Figure S2B ) . We argued that if these factors act in a common pathway with Polη in the response to UV , we would expect their knockdowns to be epistatic with a polh-1 mutation , but increase the sensitivity of xpa-1 defective animals . Indeed , knockdowns of gei-17 , ulp-1 , npp-2 or npp-22 all further increased the sensitivity of xpa-1 mutant animals to a low dose of UV ( Figure 6M ) , but did not change sensitivity of polh-1 mutants ( Figure 6N ) . Together these data indicate that ulp-1 , npp-2 and npp-22 are novel factors in TLS mediated by POLH-1 and POLK-1 in response to DNA damage during early embryogenesis in C . elegans . The SUMO protease gene gei-17 has previously been shown to promote damage tolerance by sumoylating POLH-1 [13] . Our results suggest that GEI-17 is implicated in TLS mediated by both POLH-1 and POLK-1 .
Here we demonstrate that there is modulation of the choice between repair and bypass of damaged template DNA in a developing organism . A priori one would expect error-free repair by NER to be the favoured option in germ cells to prevent the accumulation of mutations in subsequent generations . Indeed , we and others found that both for germ cell maturation and post-embryonic somatic development , NER is indispensable in response to specific DNA damages [26] , [27] , [38] . However , and in line with previously published data [6] , we found that immediately after fertilization of the oocyte , during stages of rapid cell divisions in the early embryo , survival is determined by TLS factors and not by NER . The need for efficient TLS must be viewed in light of the strict timing of the developmental program , which likely does not allow time to “wait” for repair processes to be completed . Our observation that wild type animals can easily survive UV doses up to 50 J/m2 without substantially repairing CPDs from their sperm or decondensed paternal pronucleus indicates that TLS-proficient zygotes can replicate a damaged genome containing 10–50 . 000 UV lesions in less than 12 minutes - the time it takes for the paternal nucleus to double its genome content - without delaying cell division [39] . We found that C . elegans POLH-1 has a broader substrate specificity than POLK-1; POLH-1 is involved in bypass of damage induced upon exposure to UV light , γ-irradiation , cisplatin and MMS . We considered the possibility that all treatments may lead to a common substrate that causes the observed cytotoxicity , such as DSBs brought about by replication fork obstruction and collapse . This notion has been supported by studies in vertebrates , in which Polη was suggested to act in HR repair of DSBs by extending the D-loop intermediate structure [11] , [12] . However , we observed a wild type response to either transposon-mediated or ISceI-induced DSBs , thus arguing against a role for POLH-1 in DSB repair , in either germline or somatic tissue of C . elegans . We ascribe the sensitivity of polh-1 mutants towards γ-irradiation to the induction of other non-DSB lesions , which may be NER substrates . Consistent with this interpretation we observed a synergistic relationship between xpa-1 and polh-1 with respect to IR sensitivity . The induction of free radicals by ionizing radiation causes a plethora of lesions in the DNA , such as 8-oxo-dG sites , which may require Polη-mediated bypass to prevent checkpoint activation [43] . An explanation for the broad substrate specificity of POLH-1 may reside in the flexible active site of POLH-1 , which may allow for bypass of lesions that are structurally very different . Indeed , studies in chicken DT40 cells indicate that Polη is a much more versatile polymerase than the phenotype of XPV cells would suggest [44] . Alternatively , Polη could have an indirect role by recruiting other TLS proteins to the damage site . In human cells Rev1 is recruited to UV damages via an interaction with Polη [45] . Interestingly , Polκ has been shown to serve as a ‘backup’ polymerase in XPV cells in the bypass of both UV-induced CPDs as well as cisplatin adducts [46] , [47] . We here show that in nematodes this genetic interaction is restricted to damage induced by the Sn1 methylating agent MMS . The molecular effects of MMS include the formation of N-7 methylguanine ( which by spontaneous depurination can lead to an abasic site ) , N3- methyladenine , N3-cytosine and O6-methylguanine [48] . Although we cannot deduce from our in vivo analysis which of these damages underlies the cytotoxicity observed in nematodes , all of these base damages have residual coding capacity , and are less structurally perturbing than some of the DNA lesions induced by cisplatin or IR treatment . This notion may explain the redundant role of the functionally more restricted POLK-1 on MMS , while no contribution was seen following UV , IR or cisplatin treatment . In order to find novel factors that are directly or indirectly involved in TLS , we screened for RNAi knockdowns that rendered cells sensitive to MMS and UV , only in the context of TLS functionality . Out of ∼17 , 000 clones we identified four genes whose knockdown sensitized wildtype but not TLS-deficient animals to MMS treatment . One of these genes , gei-17 , was previously reported to regulate Polη; GEI-17 was shown to sumoylate POLH-1 near its PIP-box motif resulting in protection of the protein from degradation [13] . The profound effects of gei-17 RNAi on cellular tolerance to MMS suggest that this SUMO-ligase most likely acts on both POLH-1 and POLK-1 ( Figure 5A and 5I ) ; we note that C . elegans POLK-1 may also contain a PIP-box motif ( Figure S1 ) . In addition to GEI-17 we also identified the SUMO protease ULP-1 as a factor in TLS-mediated MMS- and UV-sensitivity . This result suggests that , apart from sumoylation , also desumoylation may play a role in the regulation of TLS proteins . Additional studies that identify targets of ULP-1 needs to establish whether its role is direct , by desumoylation of the TLS polymerases , or indirect . Ubiquitin-like proteases ( ULPs ) deconjugate SUMO from their target proteins and therefore the damage sensitivity of ULP-1 knockdown may also be explained by disturbed regulation because of a shortage of SUMO . SUMO proteases and ligases may anchor to the NPCs in order to sumoylate or desumoylate their targets [49] . Here we show that , similar to gei-17 and ulp-1 , RNAi against nuclear pore components npp-2 and npp-22 is compatible with viability but results in sensitivity to UV lesions and MMS . However , sensitivity was not further increased in the absence of POLH-1 and POLK-1 . This finding suggests a role for the NPC ( or NPC subunits ) in TLS mediated damage tolerance , possibly in the localization of SUMO-regulation . npp-2 encodes the C . elegans homolog of yeast Nup85 , which is one of the proteins of the scNup84 scaffolding complex . In yeast , mutants in the Nup84 and Nup60/Mlp1-2 complexes have similar phenotypes in the response to DNA damage as Ulp1 mutants [50] . A direct link of NPCs to the DNA damage response in yeast was also suggested by Nagai et al . , who showed relocation of damaged DNA to nuclear pores [51] and recently by Bermejo et al . who showed involvement of inner basket proteins in replication fork stability [52] . npp-22 encodes the C . elegans homolog of yeast and mammalian NDC1 , which is crucial for nuclear pore assembly [53] . Future work on gei-17 , ulp-1 and the nuclear pore components npp-2 and npp-22 is needed to substantiate the role of sumoylation and desumoylation processes and a possible link to the NPC ( subunits ) in regulating TLS .
All strains were cultured according to standard methods [54] . Wildtype N2 ( Bristol ) worms were used in all control experiments . The polh-1 ( lf31 ) and polk-1 ( lf29 ) mutants were isolated in our own laboratory . polh-1 ( ok3317 ) worms , that were kindly provided by Joel Meyer ( Duke University , Durham NC , USA ) , have been generated by the C . elegans knock-out consortium . BCN2081 , carrying a single copy integrated Pmyo::GFP transgene , was a gift from Ben Lehner ( EMBL Centre for Genomic Regulation , Barcelona , Spain ) [55] . All other alleles ( xpa-1 ( ok698 ) ; rde-3 ( ne298 ) ; brc-1 ( tm1145 ) ; dog-1 ( gk10 ) ) and the transgenic line MD701 ( bcIs39[P ( lim-7 ) ced-1::GFP+lin-15 ( + ) ] ) were obtained from the C . elegans Genetics Center ( St Paul , MN , USA ) . All mutant strains were backcrossed six times before performing experiments . Newly generated strains are listed in Table S1 in the supplementary information . Staged animals were exposed to different doses of various DNA damaging agents . To assess germline sensitivity three plates with three worms were allowed to lay eggs for 24–48 hrs per experimental condition . 24 hrs later , the number of unhatched eggs and the number of surviving progeny was determined . All experiments were performed in triplicate . To measure germline sensitivity to UV , staged young adults one day post L4 were transferred to empty NGM plates and exposed to different doses of UV-C ( predominantly 254 nm , Philips ) . Animals were placed on fresh OP50 plates and allowed to lay eggs for 32 hrs . To determine whether lethality could be rescued by the supply of undamaged sperm , UV irradiated hermaphrodites were mated with untreated BCN2081 worms , which have Pmyo-2::GFP transgenes integrated in their genomes . After 24 hrs of male contact , the hermaphrodites were transferred to individual plates and allowed to lay eggs for 24 hrs . The mother was subsequently removed and 24 hrs later the number of non-hatched eggs and the number of GFP+ and GFP- progeny was determined . The sensitivity of L1 larval stage animals to UV was measured as described previously [27] . L1s were synchronized by bleaching , and exposed to UV-C on empty NGM plates . Per plate , at least 100 L1 animals were counted . For three subsequent days the development of L1-treated animals was monitored . To measure germline sensitivity to γ-irradiation , different doses were delivered by an X-ray generator ( dose rate 7 Gy/min; YXLON International ) to L4 animals . Animals were allowed to lay eggs for 48 hrs , and scored 24 hrs later for hatching . Sensitivity to cisplatin was determined by incubating staged L4 animals for 3 hrs in M9 containing different concentrations of cisplatin ( Sigma-Aldrich ) . After 1 hr recovery on OP50 plates , animals were placed on fresh OP50 plates and allowed to lay eggs for 48 hrs . The mother was removed and the survival of the progeny was scored 24 hrs later . To measure sensitivity to chronic exposure to MMS , staged L4 animals were placed for 24 hrs on NGM plates containing different concentrations of MMS ( Sigma-Aldrich ) . After 24 hrs , the number of non-hatched eggs and surviving progeny was determined . A HR reporter plasmid was constructed consisting of a GFP/LacZ fusion under the control of the intestinal specific elt-2 promotor [56] . An ISceI recognition sequence was inserted that disrupted the GFP ORF . To provide a template for homologous recombination , part of the GFP coding region was PCR amplified and inserted downstream of the disrupted GFP/LacZ locus . The ISceI expressing plasmid pRP3001 ( hsp-16 . 41::ISceI ORF ) [57] , was modified to include the mCherry ORF leading to a functional ISceI/mCherry protein to visualize and monitor the expression of the ISceI endonuclease . A detailed description of the reporter system and its validation will be published elsewhere . For reading out HR , synchronized L4 animals were transferred and incubated for 1 . 5 hrs at 34°C . After 24 hrs , GFP expression in the intestine was analyzed on a Leica DM6000 microscope . Nuclear stainings on germlines and embryos were performed by incubation of staged young adults for 10 minutes in ethanol containing 10 µg/mL 4′ , 6-diamidino-2-phenylindole ( DAPI ) . After two washes with PBS , worms were mounted on object slides in 30% glycerol . To detect CPDs , eggs were liberated from UV-irradiated worms and fixed with 3% paraformaldehyde . Fixed eggs were permeabilized by freeze cracking and subsequently washed with 1% Triton and methanol ( −20°C ) . CPDs were visualized by subsequent staining with an anti-CPD mouse monoclonal antibody and an Alexa488-labelled goat-anti-mouse secondary antibody ( Molecular Probes Inc ) combined with 10 µg/mL DAPI . Dissected worms and eggs were mounted on object slides in Vectashield . To study RAD51 foci formation , a similar procedure as described for CPD staining was followed . Fixed eggs were permeabilized by freeze cracking and subsequently washed with 1% Triton and methanol ( −20°C ) . RAD51 was visualized by subsequent staining with an anti-RAD51 rabbit monoclonal antibody and an Alexa488-labelled goat-anti-rabbit secondary antibody ( Molecular Probes Inc ) combined with 10 µg/mL DAPI . Dissected worms and eggs were mounted on object slides in Vectashield . For the analysis of apoptosis , transgenic MD701 animals , expressing a ced1::GFP fusion behind a lim-7 promotor , were used to visualize sheath cells surrounding apoptotic germ cells [37] . All microscopy was performed with a Leica DM6000 microscope . Using the Ahringer Lab RNAi feeding library a genome-wide screen was performed for clones sensitizing animals to MMS . The procedure is an adaptation from a genome-wide RNAi screen for radiation sensitivity by Van Haaften et al , described in detail in their supplementary data [35] . Briefly , L1 worms were grown to L4s in liquid on RNAi food . At the L4 stage MMS was added to a concentration of 0 . 01% . After three days survival of the progeny was scored by visual inspection . For knockdown of polk-1 , gei-17 , ulp-1 , npp-2 and npp-22 genes , individual Ahringer clones were grown on IPTG containing NGM plates . Staged L4s were transferred to RNAi plates; analysis was performed on the progeny of these animals . | Unrepaired DNA damage on the template strand poses a problem for the progression of the replication fork . Specialized translesion synthesis ( TLS ) polymerases are capable of bypassing DNA lesions without repairing them . Here , we use the nematode C . elegans , to show that there is modulation of the choice between repair and bypass during development . We show that during gametogenesis and later development repair dominates , while there is a short phase during embryonic development where resistance to damage depends heavily on TLS polymerases . The rapid divisions at this stage do not allow for delay in which repair processes can occur . Furthermore , we identify new factors that may play a role in the regulation of TLS during early embryogenesis . | [
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] | 2012 | A Broad Requirement for TLS Polymerases η and κ, and Interacting Sumoylation and Nuclear Pore Proteins, in Lesion Bypass during C. elegans Embryogenesis |
Long INterspersed Element-1 ( LINE-1 or L1 ) is the only active autonomous retrotransposon in the human genome . To investigate the interplay between the L1 retrotransposition machinery and the host cell , we used co-immunoprecipitation in conjunction with liquid chromatography and tandem mass spectrometry to identify cellular proteins that interact with the L1 first open reading frame-encoded protein , ORF1p . We identified 39 ORF1p-interacting candidate proteins including the zinc-finger antiviral protein ( ZAP or ZC3HAV1 ) . Here we show that the interaction between ZAP and ORF1p requires RNA and that ZAP overexpression in HeLa cells inhibits the retrotransposition of engineered human L1 and Alu elements , an engineered mouse L1 , and an engineered zebrafish LINE-2 element . Consistently , siRNA-mediated depletion of endogenous ZAP in HeLa cells led to a ~2-fold increase in human L1 retrotransposition . Fluorescence microscopy in cultured human cells demonstrated that ZAP co-localizes with L1 RNA , ORF1p , and stress granule associated proteins in cytoplasmic foci . Finally , molecular genetic and biochemical analyses indicate that ZAP reduces the accumulation of full-length L1 RNA and the L1-encoded proteins , yielding mechanistic insight about how ZAP may inhibit L1 retrotransposition . Together , these data suggest that ZAP inhibits the retrotransposition of LINE and Alu elements .
Long INterspersed Element-1 ( LINE-1 , also known as L1 ) sequences comprise ~17% of human DNA and represent the only class of autonomously active retrotransposons in the genome [1] . L1s mobilize ( i . e . , retrotranspose ) throughout the genome via an RNA intermediate by a copy-and-paste mechanism known as retrotransposition [reviewed in 2] . The overwhelming majority of human L1s are retrotransposition-deficient because they are 5' truncated , contain internal rearrangements ( i . e . , inversion/deletion events ) , or harbor point mutations that compromise the functions of the L1-encoded proteins ( ORF1p and ORF2p ) [1 , 3] . Despite these facts , it is estimated that the average diploid human genome contains ~80–100 L1 elements that are capable of retrotransposition [4–6] . It is estimated that a new L1 insertion occurs in approximately 1 out of 200 live human births [reviewed in 7] . On occasion , L1 retrotransposition events can disrupt gene expression , leading to diseases such as hemophilia A [8] , Duchenne muscular dystrophy [9] , and cancer [10 , 11] . Indeed , L1-mediated retrotransposition events are responsible for at least 96 disease-producing insertions in man [reviewed in 12] . A full-length human L1 is ~6 kb in length and encodes a 5' UTR that harbors an internal RNA polymerase II promoter that directs transcription from at or near the first base of the element [13–15] . The 5' UTR is followed by two open reading frames ( ORFs ) that are separated by a short 63 bp inter-ORF spacer , and a 3' UTR that ends in a variable length poly adenosine ( poly ( A ) ) tract [16 , 17] . The first L1 ORF encodes an ~40 kDa protein ( ORF1p ) that has nucleic acid binding [18–22] and nucleic acid chaperone activities [22 , 23] . The second L1 ORF encodes a much larger ~150 kDa protein ( ORF2p ) [24–26] , which exhibits single-strand endonuclease ( EN ) [27] and reverse transcriptase ( RT ) [28 , 29] activities . Experiments in cultured cells have revealed that activities associated with both ORF1p and ORF2p are required for efficient L1 retrotransposition [27 , 30] . During a cycle of L1 retrotransposition , a full-length L1 is transcribed and the resultant bicistronic L1 mRNA is exported to the cytoplasm where it undergoes translation . Notably , L1 RNA is translated in a cap-dependent manner by an unconventional termination-reinitiation mechanism that facilitates translation of both L1 ORFs [31–34] . Following translation , ORF1p and ORF2p preferentially bind to their respective encoding L1 mRNA template ( a phenomenon known as cis-preference [35 , 36] ) to form an L1 ribonucleoprotein particle ( RNP ) [18 , 19 , 26 , 37 , 38] . Components of the L1 RNP gain access to the nucleus by a process that does not strictly require cell division [39] , although L1 retrotransposition seems to be enhanced in dividing cells [40 , 41] . Once the L1 RNP has entered the nucleus , the L1 RNA is reverse transcribed and inserted into genomic DNA by a process known as target-site primed reverse transcription ( TPRT ) [27 , 42 , 43] . Briefly , the ORF2p endonuclease generates a single-strand endonucleolytic nick in genomic DNA at a thymidine rich consensus sequence ( e . g . , 5'-TTTT/A , 5'-TCTT/A , 5'-TTTA/A , etc . ) [27 , 44 , 45] . The resulting 3' hydroxyl group then is used by the ORF2p reverse transcriptase as a primer to initiate ( - ) strand L1 cDNA synthesis from the L1 mRNA template [27 , 44] . The completion of L1 integration requires elucidation , but likely involves host proteins involved in DNA repair and/or replication [45–48] . Notably , the L1-encoded proteins also can work in trans to retrotranspose other cellular RNAs such as Short Interspersed Elements ( SINEs ) ( e . g . , Alu [49] and SINE-R/VNTR/Alu ( SVA ) elements [50–52] ) . L1 also can mobilize uracil-rich small nuclear RNAs ( e . g . , U6 snRNA [48 , 53 , 54] , small nucleolar RNAs ( e . g . , U3 snoRNA [55] ) , and messenger RNAs , which results in the formation of processed pseudogenes [35 , 36] ) . Since L1 retrotransposition can be mutagenic , it stands to reason that the host cell employs multiple mechanisms to restrict L1 mobilization [reviewed in 56] . For example , cytosine methylation of the L1 5' UTR suppresses L1 expression [57 , 58] . In addition , piwi-interacting RNAs ( piRNAs ) suppress L1 expression in germ line cells [reviewed in 56 , 59 , and 60] . Finally , emerging studies have demonstrated that several cellular proteins restrict L1 retrotransposition . These proteins include several APOBEC3 family members [61 , reviewed in 62] , TREX1 [63] , MOV10 [64–66] , hnRNPL [67] , SAMHD1 [68] , RNase L [69] , and the melatonin receptor 1 ( MT1 ) [70] . To gain a more complete understanding of the interplay between the L1 retrotransposition machinery and the host cell , we used liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) to identify proteins that co-immunoprecipitate with L1 ORF1p in HeLa cells , reasoning that some of these proteins may affect L1 retrotransposition . We next analyzed the effects of ORF1p-interacting proteins on L1 retrotransposition by overexpressing a subset of them in a cultured cell retrotransposition assay [30 , 71] . Here , we report that the zinc-finger antiviral protein ZAP [72] interacts with L1 RNPs and inhibits L1 retrotransposition in cultured cells . ZAP also inhibits human Alu retrotransposition and the retrotransposition of mouse and zebrafish LINE elements . Molecular genetic and biochemical analyses suggest that ZAP inhibits retrotransposition by suppressing the accumulation of full-length L1 RNA and L1-encoded proteins in the cell .
To identify proteins that interact with L1 ORF1p , we transfected HeLa cells with a human L1 construct , pJM101/L1 . 3FLAG , which expresses a version of ORF1p containing a FLAG epitope at its carboxyl-terminus ( ORF1p-FLAG ) ( Fig 1A ) . The pJM101/L1 . 3FLAG construct exhibits robust retrotransposition activity in HeLa cells , albeit at a lower efficiency ( ~50% ) than the untagged L1 construct , pJM101/L1 . 3 ( S1A Fig ) . Briefly , HeLa cells were transfected with pJM101/L1 . 3FLAG or pJM101/L1 . 3 , a similar construct that lacks the FLAG epitope sequence ( Fig 1A ) . Whole cell lysates from transfected cells then were incubated with anti-FLAG coated agarose beads to immunoprecipitate ORF1p-FLAG ( see Methods ) . Immunoprecipitated fractions were analyzed by SDS-PAGE and proteins were visualized by silver staining ( Fig 1B ) . The analysis of silver-stained gels revealed a prominent band of ~40 kDa ( the theoretical molecular weight of ORF1p ) in the pJM101/L1 . 3FLAG immunoprecipitation lane ( Fig 1B; asterisk ) , which was not apparent in the pJM101/L1 . 3 lane . Western blot analysis with an antibody specific to L1 . 3 ORF1p ( amino acids 31–49 ) confirmed the enrichment of ORF1p-FLAG in the pJM101/L1 . 3FLAG lane ( Fig 1C and S1B Fig; bottom panel ) . We also observed a complex pattern of bands between ~25 kDa and ~150 kDa that was present in the pJM101/L1 . 3FLAG lane that was not evident in the pJM101/L1 . 3 lane ( Fig 1B; black vertical bars ) . A similar pattern of protein bands was produced on silver-stained gels from pJM101/L1 . 3FLAG immunoprecipitation reactions using different wash and/or lysis conditions ( S1B and S1C Fig , respectively ) . To determine the identity of cellular proteins that associated with ORF1p-FLAG , the bands from the lanes corresponding to the pJM101/L1 . 3FLAG and pJM101/L1 . 3 immunoprecipitation experiments were excised from SDS-PAGE gels and submitted for LC-MS/MS ( see Methods ) . An LC-MS/MS-identified protein was selected as an ORF1p-interacting candidate if it met the following criteria: 1 ) the protein was unique to the pJM101/L1 . 3FLAG immunoprecipitation , and 2 ) the protein was identified by two or more unique peptide sequences ( peptide error rate ≤0 . 05; protein probability ≥0 . 95 ) ( S1 Table and Methods ) . Thirty-nine ORF1p-interacting protein candidates were identified that met these criteria ( S1 Table ) . To confirm the interactions between LC-MS/MS-identified proteins and ORF1p-FLAG , we evaluated 13 of the 39 ORF1p-FLAG interacting proteins for which there were commercially available antibodies and/or cDNA expression clones . Western blot analyses confirmed that these proteins associated with ORF1p-FLAG ( Fig 1D ) . The 13 ORF1p-interacting proteins are involved in a variety of cellular processes including antiviral defense ( ZAP [72] and MOV10 [73] ) , nonsense-mediated decay ( UPF1 [74] ) , RNA splicing ( hnRNPL [75] and DHX9 [76 , 77] ) , and transcription ( PURA [78] , CDK9 [79] , and ILF3 [80] ) . Notably , gene ontology [81] and global analyses of RNA binding proteins in human cell lines [82 , 83] revealed that the 13 validated ORF1p-FLAG interacting proteins are RNA binding proteins ( RBPs ) . Consistently , immunoprecipitation experiments of ORF1p-FLAG conducted in the presence of RNaseA disrupted the association between ORF1p and each of the 13 ORF1p-interacting proteins ( Fig 1D ) . Thus , the majority of ORF1p-interacting proteins associate with ORF1p by binding to L1 RNA and/or other RNAs present within the L1 RNP [84] . We next investigated whether overexpression of nine of the validated ORF1p-interacting proteins , as well as nine unvalidated ORF1p-interacting proteins , affects L1 retrotransposition [30 , 71] . Briefly , HeLa cells were co-transfected with a cDNA plasmid expressing one of the ORF1p-FLAG interacting proteins and an engineered human L1 construct ( pJJ101/L1 . 3; [85] ) marked with a blasticidin retrotransposition indicator cassette ( mblastI ) ( Fig 2A and 2B; top panel ) . The mblastI cassette contains an antisense copy of the blasticidin deaminase gene , which is cloned into the L1 3' UTR . The blasticidin deaminase gene also is interrupted by an intron in the same transcriptional orientation as L1 . This arrangement ensures that the blasticidin deaminase gene is expressed only when the L1 transcript is spliced , reverse transcribed , and inserted into genomic DNA . The resulting blasticidin-resistant foci then provide a visual , quantitative readout of retrotransposition activity [30 , 45] . To monitor potentially toxic side effects of cDNA overexpression , HeLa cells also were co-transfected in a parallel assay with a cDNA expression vector and a control plasmid ( pcDNA6/TR ) that expresses the blasticidin deaminase gene ( Fig 2B; bottom panel ) . Following blasticidin selection , the resulting foci provide a visual , quantitative readout of the effect of cDNA overexpression on colony formation ( Fig 2B; bottom panel ) . This control is essential to determine if a cDNA affects L1 retrotransposition or cell viability and/or growth . We co-transfected HeLa cells with each of the 18 ORF1p-FLAG interacting candidates and pJJ101/L1 . 3 ( Fig 2C ) . An empty pCEP4 vector that was co-transfected with pJJ101/L1 . 3 served as a normalization control ( Fig 2B and 2C ) . As a negative control , we demonstrated that a plasmid that expresses the humanized renilla green fluorescence protein ( pCEP/GFP ) did not affect pJJ101/L1 . 3 retrotransposition . As a positive control , we demonstrated that a plasmid that expresses human APOBEC3A ( pK_A3A ) reduced pJJ101/L1 . 3 retrotransposition to ~18% of control levels ( Fig 2C ) , which is in agreement with previous studies [61 , 86–88] . Four of the cDNA-expressing plasmids that we tested ( ZAP-S ( ZAP short isoform ) , hnRNPL , MOV10 , and PURA ) each reduced pJJ101/L1 . 3 retrotransposition to less than 50% of pCEP4 control levels . Notably , ZAP-S ( ~30% of control ) , hnRNPL ( ~30% of control ) , MOV10 ( ~13% of control ) , and PURA ( ~10% of control ) inhibited retrotransposition to levels similar to that of pK_A3A ( ~18% of control ) ( Fig 2C ) . By comparison , the majority of the cDNA-expressing plasmids ( 14/18 ) did not significantly affect pJJ101/L1 . 3 retrotransposition levels ( less than 50% inhibition when compared to pCEP4 control levels ) ( Fig 2C ) . Thus , the data suggest that ZAP-S , hnRNPL , MOV10 , and PURA inhibit L1 retrotransposition in cultured cells . The above data ( Fig 2C ) imply that ZAP , hnRNPL , MOV10 , and PURA may function as host factors that restrict L1 retrotransposition . Notably , hnRNPL [67] , MOV10 [64 , 65] , and PURA [89] previously were shown to inhibit L1 retrotransposition . However , the effect of ZAP on L1 retrotransposition has not been studied; thus , we sought to determine how ZAP inhibits L1 retrotransposition . ZAP is a poly ( ADP-ribose ) polymerase ( PARP ) family member [90] initially characterized as an antiviral protein that inhibits murine leukemia virus ( MLV ) replication in cultured rat cells [72] . Previous studies identified two human ZAP isoforms that resulted from alternative splicing [90] ( Fig 3A; top panel ) . The long ZAP isoform ( ZAP-L ) is 902 amino acids in length and contains an amino-terminus CCCH zinc-finger domain and an inactive carboxyl-terminal PARP-like domain [90] . The short ZAP isoform ( ZAP-S ) is 699 amino acids in length and lacks the carboxyl-terminal PARP-like domain [90] . The HA-tagged human ZAP-L isoform restricted pJJ101/L1 . 3 retrotransposition to ~40% of control levels ( Fig 3A; black bars ) and the human ZAP-S isoform restricted pJJ101/L1 . 3 retrotransposition to ~30% of control levels ( Figs 2C and 3A; black bars ) . Notably , overexpression of ZAP-L or ZAP-S did not dramatically affect the ability of HeLa cells to form blasticidin-resistant colonies in pcDNA6/TR control assays ( Fig 3A , white bars ) . Western blot control experiments confirmed the overexpression of ectopic ZAP-L and ZAP-S compared to untransfected controls ~48 hours post-transfection ( S2A and S2B Fig ) . Thus , ZAP inhibits L1 retrotransposition in cultured cells and the ZAP-L PARP-like domain is not required for L1 restriction . Putative ZAP orthologs are present in several species [90]; thus , we tested whether a rat ZAP cDNA ( rZAP ) [72] , that is orthologous to human ZAP-S [72 , 90] could restrict pJJ101/L1 . 3 retrotransposition . Overexpression of rZAP efficiently reduced retrotransposition to ~40% of control levels ( Fig 3A; black bars ) . Thus , the ability to restrict L1 retrotransposition is not limited to human ZAP . The ZAP zinc-finger domain binds to RNA and is required for antiviral activity [91 , 92] . To analyze the role of the ZAP zinc-finger domain in L1 restriction , we tested the effects of a truncated ZAP-S mutant that expresses the ZAP zinc-finger domain ( ZAP-S/1-311; containing amino acids 1–311 ) as well as a ZAP-S mutant that lacks the zinc-finger domain ( ZAP-S/Δ72–372; lacking amino acids 72–372 ) in pJJ101/L1 . 3 retrotransposition assays ( Fig 3A; above graph ) . ZAP-S/1-311 restricted retrotransposition to ~10% of control levels ( Fig 3A; black bars ) , whereas ZAP-S/Δ72–372 had little effect on retrotransposition ( ~80% of control levels ) ( Fig 3A; black bars ) . The overexpression of the wild type or mutant ZAP-S/Δ72–372 expression constructs did not adversely affect the ability of HeLa cells to form blasticidin-resistant colonies in pcDNA6/TR control assays ( Fig 3A; white bars ) . Notably , transfection with ZAP-S/1-311 resulted in an ~50% decrease in the ability of HeLa cells to form blasticidin-resistant colonies; however , this effect has been accounted for through normalization ( Fig 2B ) and thus is independent of the ability of ZAP-S/1-311 to restrict L1 retrotransposition . Indeed , similar off-target effects have been reported for A3A cDNA expressing plasmids in HeLa cell-based L1 retrotransposition assays [61] . Western blot control experiments revealed that wild-type ZAP-S and the two mutant ZAP-S isoforms were expressed at similar levels ~48 hours post-transfection ( S2B and S2C Fig ) . Thus , the ZAP zinc-finger domain is necessary and sufficient to inhibit L1 retrotransposition . To determine if ZAP-S was able to restrict other non-long terminal repeat ( non-LTR ) retrotransposons , we tested whether ZAP-S expression affected human Alu retrotransposition . Unlike L1 , Alu is a 7SL-derived non-autonomous retrotransposon that does not encode its own proteins [93] . Instead , Alu elements must parasitize L1 ORF2p in trans to mediate their retrotransposition [49] . Briefly , HeLa cells were co-transfected with a full-length L1 element ( pJM101/L1 . 3Δneo ) , an Alu retrotransposition reporter plasmid ( pAluneoTet ) , and a ZAP-S expression plasmid . Notably , ZAP-S potently reduced Alu retrotransposition to ~25% of control levels ( Fig 3B ) . In contrast , the expression of the L1 restriction-deficient ZAP-S/Δ72–372 mutant did not negatively affect Alu retrotransposition ( Fig 3B ) . Thus , ZAP-S is able to restrict the mobility of the two most prolific retrotransposons present in the human genome . We next tested if human ZAP-S could restrict the retrotransposition of a natural mouse L1 ( pGF21 ) [94] , a zebrafish LINE-2 ( pZfL2-2 ) [95] , or a synthetic mouse L1 ( pCEPsmL1 ) [96] that has been extensively mutagenized to alter 24% of the nucleic acid sequence without disrupting amino acid sequence . Human ZAP-S inhibited the retrotransposition of human L1 ( pJM101/L1 . 3; ~43% of control levels ) , natural mouse L1 ( pGF21; ~24% of control levels ) , zebrafish L2 ( pZfL2-2; ~19% of control levels ) , and synthetic mouse L1 ( pCEPsmL1; ~70% of control levels ) ( Fig 3C ) . The restriction-defective ZAP-S mutant , ZAP-S/Δ72–372 , did not significantly affect the retrotransposition activity of these retrotransposons ( Fig 3C ) . Notably , the milder inhibition of ZAP-S on pCEPsmL1 may be due to the elevated efficiency of pCEPsmL1 retrotransposition , the increased steady-state level of pCEPsmL1 mRNA and proteins , and/or the GC-rich nature of pCEPsmL1 [96] . Thus , ZAP-mediated restriction of retrotransposition is not specific to human non-LTR retrotransposons . To test if endogenous ZAP restricts L1 retrotransposition , we used small interfering RNA ( siRNA ) to deplete endogenous ZAP from HeLa cells . Following siRNA treatment , cells were transfected with an L1 plasmid ( pLRE3-mEGFPI ) tagged with an EGFP indicator cassette ( mEGFPI ) , which allows retrotransposition activity to be detected by EGFP fluorescence [97] . As a negative control , HeLa cells were transfected with the L1 retrotransposition-defective plasmid pJM111-LRE3-mEGFPI , which carries two missense mutations that adversely affect ORF1p RNA binding [22 , 30 , 98] . Treatment of HeLa cells with an siRNA pool against ZAP resulted in an ~80% and ~ 90% reduction of ZAP-L and ZAP-S protein levels , respectively , when compared to HeLa cells treated with a non-targeting control siRNA pool ( Fig 3D; top left panel ) . ZAP siRNA treatment led to an approximately two-fold increase in pLRE3-mEGFPI retrotransposition activity when compared to assays conducted in the presence of a control siRNA ( Fig 3D; bottom panel and S2D Fig ) . We further demonstrated that siRNA-mediated depletion of endogenous MOV10 ( Fig 3D; top right panel ) from HeLa cells resulted in an approximately two-fold increase in pLRE3-mEGFPI retrotransposition ( Fig 3D; bottom panel and S2D Fig ) , which is in agreement with previous studies [64 , 65] . These data suggest that endogenous ZAP may restrict L1 retrotransposition . To investigate how ZAP restricts L1 retrotransposition , we analyzed the effect of ZAP-S expression on the accumulation of the L1 RNA . HeLa cells were co-transfected with pJM101/L1 . 3Δneo and either ZAP-S or ZAP-S/Δ72–372 . Polyadenylated RNA from whole cell extracts then was analyzed by northern blot using RNA probes complementary to sequences within the L1 . 3 5' UTR ( 5UTR99 ) and ORF2 ( ORF2_5804 ) ( Fig 4A ) . Co-transfection with ZAP-S resulted in a reduction of full-length polyadenylated L1 RNA levels ( ~13% of pCEP4 control ) compared to cells co-transfected with either the restriction-defective ZAP-S/Δ72–372 ( ~47% compared to pCEP4 control ) or an empty pCEP4 control vector ( Fig 4B; black arrow in blot; black bars in graph ) . Interestingly , ZAP-S expression did not have a pronounced effect on the accumulation of smaller L1 RNA species , which may have resulted from cryptic splicing and/or premature polyadenylation ( Fig 4B; top panel: blue and yellow arrows , bottom panel: blue and yellow bars ) [99–101] . Finally , control experiments revealed that ectopic ZAP-S expression did not affect endogenous actin RNA levels ( Fig 4B ) . Thus , ZAP-S expression reduces the accumulation of full-length L1 mRNA in cultured cells . We next examined the effect of ZAP-S expression on the accumulation of ORF1p and ORF2p . We co-transfected HeLa cells with either ZAP-S or ZAP-S/Δ72–372 and the L1 plasmid , pJBM2TE1 , which expresses an L1 . 3 element marked with a T7 gene10 epitope tag on the carboxyl-terminus of ORF1p and a TAP epitope-tag on the carboxyl-terminus of ORF2p ( Fig 4C ) . Following co-transfection , HeLa cells were treated with puromycin to select for cells expressing pJBM2TE1 . Both whole cell lysates ( WCL ) and RNP fractions were collected 5 days post-transfection and subjected to western blot analyses to monitor ORF1p and ORF2p expression levels . Expression of ZAP-S led to a decrease in the level of ORF1p and ORF2p in both WCL and RNP fractions , whereas the expression of the restriction-defective ZAP-SΔ/72-372 mutant or an empty pcDNA3 vector did not dramatically affect ORF1p or ORF2p expression levels ( Fig 4D ) . The reduction in ORF1p and ORF2p was most evident in the RNP fraction , likely because both ORF1p and ORF2p are enriched in RNPs [19 , 26 , 37 , 38 , 102] . Control experiments revealed that ZAP-S expression did not affect the level of eIF3 protein ( Fig 4D ) and that ZAP-S and ZAP-S/Δ72–372 are expressed at similar levels in whole cell lysates ( Fig 4D: top WCL panel ) . By comparison , ZAP-SΔ/72-372 is present at much lower levels in the RNP fraction compared to wild-type ZAP-S ( Fig 4D; bottom RNP panel ) , suggesting that the zinc-finger domain is responsible for ZAP-S localization to the RNP fraction . To determine if ZAP-S affects the expression of non-L1 proteins , we examined the effect of ZAP-S on EGFP expression . We co-transfected ZAP-S with an L1 plasmid ( pLRE3-EF1-mEGFPΔIntron ) [103] that expresses the L1 element , LRE3 and an intact copy of the EGFP gene ( S3A Fig ) . In this case , LRE3 and EGFP are under the control of convergent promoters , which allows the simultaneous expression of LRE3 and EGFP from pLRE3-EF1-mEGFPΔIntron . Thus , EGFP expression is not dependent on retrotransposition . Forty-eight hours post-transfection , flow cytometry was used to isolate EGFP-positive cells ( i . e . , cells expressing pLRE3-EF1-mEGFPΔIntron ) ( S3C Fig ) . Western blotting demonstrated a marked reduction in ORF1p when compared to EGFP levels in cells that were co-transfected with ZAP-S ( S3B Fig ) . By comparison , ORF1p and EGFP were present at comparable levels in cells that were co-transfected with either an empty pCEP4 vector or the restriction-deficient ZAP-SΔ/72-372 mutant ( S3B Fig ) . Control experiments revealed that ZAP-S did not affect endogenous tubulin protein levels ( S3B Fig ) . Thus , ZAP-S expression appears to preferentially restrict the expression of L1 ORF1p . ORF1p , ORF2p , and L1 RNA form RNP complexes that appear as discrete cytoplasmic foci when visualized by fluorescence microscopy [25 , 26 , 104] . Notably , previous studies have shown that ZAP predominantly is localized in the cytoplasm [105] and that ZAP antiviral activity also is localized to the cytoplasm [72] . To determine if ZAP co-localizes with ORF1p , we co-transfected HeLa cells with pJM101/L1 . 3Δneo and a plasmid that expresses a carboxyl-terminus turbo-GFP tagged ZAP-S protein ( ZAP-S-tGFP ) . Control experiments showed that ZAP-S-tGFP restricted pJJ101/L1 . 3 retrotransposition to ~55% of control levels ( S4A Fig ) . Confocal fluorescence microscopy revealed that ORF1p and ZAP-S-tGFP co-localized in discrete cytoplasmic foci in ~68% of cells that co-expressed both ORF1p and ZAP-S-tGFP ( Fig 5A ) . To test if transfected ORF1p co-localizes with endogenous ZAP , we transfected HeLa cells with pAD2TE1 , which expresses a human L1 ( L1 . 3 ) containing a T7 gene10 epitope-tag on the carboxyl-terminus of ORF1p [26] . Confocal microscopy revealed that endogenous ZAP co-localized with ORF1p-T7 in cytoplasmic foci in ~91% of cells that contained ORF1p-T7 foci ( Fig 5B ) . Next , to test if endogenous ORF1p co-localizes with transfected ZAP-S , we transfected PA-1 cells ( a human embryonic carcinoma-derived cell line that expresses endogenous ORF1p [106 , 107] ) with ZAP-S-tGFP . Confocal microscopy demonstrated that endogenous ORF1p co-localized with ZAP-S-tGFP in ~89% of PA-1 cells that expressed ZAP-S-tGFP foci ( Fig 5C ) . Thus , ORF1p and ZAP generally localize to the same region of the cytoplasm . To test if the ZAP-S zinc-finger domain is critical for the co-localization of ZAP-S with ORF1p , we co-transfected HeLa cells with pJM101/L1 . 3Δneo and a tGFP-tagged ZAP-S mutant that expresses the ZAP-S zinc-finger domain ( ZAP-S/Δ310-645-tGFP; lacking amino acids 310–645 ) , or a ZAP-S mutant that lacks the ZAP-S zinc-finger domain ( ZAP-S/Δ72-372-tGFP; lacking amino acids 72–372 ) ( S4A Fig ) . In control experiments , ZAP-S/Δ310-645-tGFP restricted pJJ101/L1 . 3 retrotransposition to ~32% of control levels whereas ZAP-S/Δ72-372-tGFP did not have a significant effect ( ~93% of control ) on retrotransposition activity ( S4A Fig ) . Confocal microscopy revealed that ORF1p and ZAP-S/Δ310-645-tGFP co-localized in cytoplasmic foci in ~74% of cells that co-expressed both ORF1p and ZAP-S/Δ310-645-tGFP ( Fig 5D ) . In cells transfected with pJM101/L1 . 3Δneo and ZAP-S/Δ72-372-tGFP , ORF1p and ZAP-S/Δ72-372-tGFP co-localized in only ~14% of cells that co-expressed both ORF1p and ZAP-S/Δ72-372-tGFP ( Fig 5C ) . Thus , the ZAP-S zinc-finger domain is necessary and sufficient for the co-localization of ZAP-S and ORF1p in cytoplasmic foci . To determine if ZAP co-localizes with L1 RNA , we co-transfected HeLa cells with pJM101/L1 . 3 and either ZAP-S-tGFP , ZAP-S/Δ310-645-tGFP , or ZAP-S/Δ72-372-tGFP . To visualize L1 RNA , transfected cells were probed with fluorescently labeled oligonucleotide probes complementary to sequences within the L1 5' UTR . As a control , cells were co-transfected with pJM101/L1 . 3 and an empty pCEP4 vector . In pCEP4 control experiments , fluorescence microscopy revealed that ORF1p co-localized with L1 RNA in cytoplasmic foci in ~88% of cells that contained ORF1p cytoplasmic Foci ( Fig 6A and S5A Fig ) . In HeLa cells co-transfected with pJM101/L1 . 3 and ZAP-S-tGFP , L1 RNA co-localized with ORF1p and ZAP-S-tGFP in cytoplasmic foci in ~23% of foci-containing cells ( Fig 6B and S5A Fig ) . Thus , ZAP and L1 RNA co-localize in cytoplasmic foci . Fluorescence microscopy further revealed that in cells co-transfected with pJM101/L1 . 3 and ZAP-S/Δ310-645-tGFP that L1 RNA was detected in ORF1p and ZAP-S/Δ310-645-tGFP foci in only ~18% of foci-containing cells ( Fig 6C and S5A Fig ) . In contrast , in cells co-transfected with pJM101/L1 . 3 and ZAP-S/Δ72-372-tGFP , L1 RNA co-localized with ORF1p in ~77% of cells that expressed ZAP/Δ72-372-tGFP and contained ORF1p cytoplasmic Foci ( Fig 6D and S5A Fig ) . Thus , the data suggest that ZAP prevents the accumulation of L1 RNA in cytoplasmic foci . We next determined the effect of ZAP-S on ORF1p expression using confocal microscopy . HeLa cells were co-transfected with pJM101/L1 . 3Δneo and either ZAP-S-tGFP , ZAP-S/Δ310-645-tGFP , or ZAP-S/Δ72-372-tGFP . As a control , cells were co-transfected with pJM101/L1 . 3Δneo and an empty pCEP4 vector . In pCEP4 control experiments , confocal microscopy revealed that ~10 . 8% of cells expressed ORF1p after ~ 48 hours ( S5B Fig ) . In contrast , only ~2 . 8% of cells that were co-transfected with ZAP-S-tGFP expressed ORF1p and ~2 . 8% of cells that were co-transfected with ZAP-S/Δ310-645-tGFP expressed ORF1p ( S5B Fig ) . Approximately 8 . 0% of cells that were co-transfected with ZAP-S/Δ72-372-tGFP expressed ORF1p ( S5B Fig ) . Thus , the data suggest that the ZAP-S zinc-finger domain is necessary and sufficient to inhibit the accumulation of ORF1p in HeLa cells . L1 cytoplasmic foci also co-localize with an array of RNA binding proteins , including markers of cytoplasmic stress granules ( SGs ) [26 , 89 , 104] . Notably , ZAP also localizes to cytoplasmic SGs [108] . To determine whether ZAP-S/ORF1p foci co-localize with cytoplasmic SGs we transfected HeLa cells with pJM101/L1 . 3Δneo and ZAP-S-tGFP . Confocal microscopy revealed that ZAP-S-tGFP/ORF1p co-localized with the endogenous SG associated protein eIF3 ( S4B Fig ) . Additionally , endogenous ZAP also co-localized with the SG marker , G3BP ( S4D Fig ) . In contrast , ZAP-S-tGFP/ORF1p foci did not co-localize with endogenous tubulin ( S4C Fig ) , and endogenous ZAP did not co-localize with the processing body associated protein , DCP1α ( S4E Fig ) . Thus , L1 ORF1p , ZAP , and SG associated proteins partition to the same cytoplasmic compartment .
In this study , we identified 39 cellular proteins that interact with L1 ORF1p and validated 13 of these interactions in biochemical assays . Our data showed that the 13 validated ORF1p-interacting proteins associate with ORF1p via an RNA bridge ( Fig 1D ) . Notably , 33 out of 39 of the ORF1p-interacting proteins also were detected in recent studies ( S1 Table; [47 , 67 , 89] ) . Importantly , we discovered that ZAP restricts human L1 and Alu retrotransposition . We also showed that hnRNPL , MOV10 , and PURA inhibit L1 retrotransposition , which is in agreement with previous studies [64–67 , 89] . Thus , our data both confirm and extend those previous analyses and will help guide future studies that endeavor to determine how L1 retrotransposition impacts the human genome . ZAP inhibits the mobility of both human and non-human non-LTR retrotransposons . The overexpression of the human and rat orthologs of ZAP restricts human L1 retrotransposition ( Fig 3A ) . Human ZAP-S overexpression restricts the retrotransposition of an engineered human SINE ( Alu ) ( Fig 3B ) , an engineered mouse L1 ( GF21 ) , and an engineered zebrafish LINE-2 element ( ZfL2-2 ) ( Fig 3C ) . Although our studies primarily involved the overexpression of ZAP , we also demonstrated that the depletion of endogenous ZAP in HeLa cells led to an ~2-fold increase in L1 retrotransposition ( Fig 3D ) . This observed increase in L1 retrotransposition activity is similar to increases in L1 activity that were observed upon depletion of MOV10 and hnRNPL proteins in other studies [64 , 65 , 67] . Thus , in principle , physiological levels of ZAP may be sufficient to influence retrotransposition in certain cell types . The ZAP CCCH zinc-finger domain is required to both bind and mediate the degradation of viral RNA [92 , 109] . Our data indicate that ZAP binding to L1 RNA is critical for L1 restriction . We demonstrated that ORF1p-FLAG and ZAP interact via an RNA bridge ( Fig 1D ) . Moreover , we showed that overexpression of the ZAP zinc-finger domain more potently inhibits L1 retrotransposition than overexpression of wild type ZAP-L or ZAP-S ( Fig 3A ) , and that the ZAP-S zinc-finger domain is required to inhibit L1 retrotransposition ( Fig 3A and S4A Fig ) . In addition to our genetic and biochemical data , fluorescence microscopy revealed that: 1 ) co-transfected ZAP-S , L1 RNA , and ORF1p co-localize in the cytoplasm of HeLa cells; 2 ) the ZAP-S zinc-finger domain is necessary and sufficient for the co-localization of ZAP-S , L1 RNA , and ORF1p; 3 ) endogenous ZAP co-localizes with transfected ORF1p in HeLa cells; and 4 ) endogenous ORF1p interacts with transfected ZAP-S in human PA-1 embryonic carcinoma cells ( Figs 5A–5E and 6A–6D ) . Thus , the data suggest that ZAP interacts with L1 RNA in order to mediate L1 restriction . Notably , the zebrafish ZfL2-2 retrotransposon lacks a homolog to ORF1 and only encodes a single ORF that contains an apurinic/apyrimidinic endonuclease-like ( EN ) and a reverse transcriptase ( RT ) domain [95] . The finding that ZAP-S efficiently restricts ZfL2-2 retrotransposition further indicates that ZAP-S likely restricts retrotransposition by interacting with LINE RNA . Although a ZAP consensus RNA target sequence/motif has not yet been identified , evidence suggests that ZAP recognizes long RNA stretches ( >500 nucleotides ) and/or specific RNA tertiary structure [91 , 92] . The ability of ZAP to inhibit non-human LINE elements suggests that ZAP may not recognize a particular LINE linear consensus RNA sequence , but instead may recognize an unidentified structural feature common to certain LINE RNAs [91 , 92] . Evidence suggests that ZAP prevents the accumulation of viral RNAs in the cytoplasm [72] . ZAP-S overexpression significantly reduced the amount of polyadenylated , full-length L1 RNA ( Fig 4B ) , which would be expected to inhibit retrotransposition by limiting the supply of L1 mRNA available for translation and as a template for TPRT . Notably , while ZAP-S selectively inhibited the accumulation of full-length L1 transcripts , it did not dramatically affect the accumulation of shorter , spliced and/or polyadenylated L1 RNAs ( Fig 4B ) [99–101 , 110] . Thus , ZAP does not appear to affect L1 transcription per se , but instead likely affects the post-transcriptional processing of full-length L1 mRNA . In addition to biochemical data , fluorescence microscopy revealed that L1 RNA was depleted from L1 ORF1p cytoplasmic foci in the presence of ZAP ( S5A Fig ) . The depletion of RNA from L1 cytoplasmic foci was dependent on the ZAP-S zinc-finger domain . Based on these data it is likely that ZAP prevents the accumulation of cytoplasmic L1 mRNA . Previous studies have shown that ZAP also suppresses the expression of viral proteins [111–113] . Western blot experiments demonstrated that the overexpression of ZAP-S inhibited the accumulation of L1 ORF1p and L1 ORF2p in whole cell lysates and RNPs derived from transfected HeLa cells ( Fig 4D and S3B Fig ) . In agreement with western blot experiments , confocal microscopy experiments showed that tGFP-tagged ZAP-S inhibited the expression of ORF1p in transfected HeLa cells and that the ZAP-S zinc-finger domain is critical for the inhibition of ORF1p expression ( S5B Fig ) . ZAP-S expression also inhibited Alu retrotransposition ( Fig 3B ) , which depends on ORF2p to be supplied in trans by L1 [49] . In contrast to these data , ZAP-S overexpression did not significantly affect the expression and/or accumulation of EGFP or other endogenous proteins ( e . g . , eiF3 and tubulin ) ( Fig 4D and S3B Fig ) . Thus , ZAP may preferentially limit the accumulation of ORF1p and ORF2p by interacting with L1 mRNA . In sum , our data suggest that ZAP restricts L1 retrotransposition by preventing the accumulation of cytoplasmic L1 RNA . Notably , a recent study suggests that ZAP may interfere with translation of viral RNA , and that translation inhibition may precede viral RNA destruction [113] . Although a reduction in L1 RNA could explain the observed decrease in L1 protein expression , it also is conceivable that the interaction between ZAP and L1 RNA could interfere with L1 translation ( Fig 7 ) . It remains unclear how ZAP might destabilize full-length L1 RNA to restrict retrotransposition . Evidence suggests that ZAP recruits exosome components [109] along with other proteins involved in RNA degradation [111 , 114] to destroy viral RNA . Interestingly , immunofluorescence microscopy experiments revealed that ORF1p and ZAP co-localize with components of cytoplasmic SGs ( S4B and S4D Fig ) , which contain numerous RNA binding proteins involved in cytosolic RNA metabolism [reviewed in 115] . Indeed , ZAP previously has been shown to localize to SGs [108] and SGs have been suggested to play a role in regulating L1 retrotransposition [104] and viral pathogenesis [reviewed in 116] . The co-localization of ORF1p and ZAP with SGs also suggests that ZAP may possibly inhibit L1 translation , as SG assembly is stimulated by translational arrest [reviewed in 115] . Thus , we propose that ZAP interacts directly with L1 RNA in the cytoplasm , which likely results in the recruitment in SG components and/or other cellular factors involved in RNA metabolism to destabilize L1 RNA and/or block translation ( Fig 7 ) . ZAP exhibits antiviral activity against a variety of viruses such as MLV [72] , alphaviruses [117] , filoviruses [118] , HIV-1 [111] , and hepatitis-B virus [119] . Interestingly , many putative L1 restriction factors also are involved in antiviral defense ( i . e . , a subset of APOBEC3 proteins , TREX1 , MOV10 , SAMHD1 , and RNaseL ) . L1 elements have been active in mammalian genomes for ~160 million years [120–122] . Thus , it is tempting to speculate that some host factors , such as ZAP , may have first evolved to combat endogenous retrotransposons and subsequently were co-opted as viral restriction factors [90 , 123–125] . Indeed , identifying host factors that modulate L1 retrotransposition may prove to be an effective strategy to identify host antiviral factors .
HeLa-JVM cells were grown in high-glucose DMEM ( Gibco ) supplemented with 10% FBS ( Gibco ) , 100 U/mL penicillin-streptomycin ( Invitrogen ) , and 0 . 29 mg/mL L-glutamine ( Gibco ) [30] . HeLa-HA [126] and PA-1 [107] cells were grown in MEM ( Gibco ) with 10% FBS , 100 U/mL penicillin-streptomycin , 0 . 29 mg/mL L-glutamine , and 0 . 1 mM nonessential amino acids ( Gibco ) . Cell lines were maintained at 37°C with 7% CO2 in humidified incubators ( Thermo Scientific ) . Oligonucleotide sequences and cloning strategies used in this study are available upon request . All human L1 plasmids contain the indicated fragments of L1 . 3 ( accession no . L19088 ) [5] DNA cloned into pCEP4 ( Invitrogen ) unless otherwise indicated . A CMV promoter augments expression of all L1 and cDNA expressing plasmids unless noted otherwise . L1 plasmids also contain an SV40 polyadenylation signal that is located downstream of the native L1 polyadenylation signal . All plasmid DNA was prepared with a Midiprep Plasmid DNA Kit ( Qiagen ) . The following cDNA expression plasmids were obtained from OriGene: CDK9 ( SC119344 ) ; DDX21 ( SC108813 ) ; GNB2L1 ( SC116322 ) ; hnRNPDL ( SC107613 ) ; MOV10 ( SC126015 ) ; MATR3 ( SC113375 ) ; ZAP-S ( ZC3HAV1 transcript variant 2 ) ( SC101064 ) ; ZAP-S-tGFP ( GFP-tagged ZC3HAV1 transcript variant 2 ) ( RG208070 ) ; hnRNPA2B1 ( SC313092 ) ; IGF2BP3 ( SC111161 ) ; PURA ( SC127792 ) ; UPF1 ( SC118343 ) . The following cDNA expression plasmids were obtained from Open Biosystems: hnRNPL ( 6174088 ) ; LARP2 ( 5164712 ) ; LARP4 ( 5219803 ) ; SYNCRIP ( 5495201 ) . The following cDNA expression plasmids were obtained from Addgene: rZAP ( pcDNA4-TO-Myc-rZAP; Addgene plasmid#: 17381 , kindly provided by Dr . Stephen Goff ) ( Gao et al . , 2002 ) and ZAP-L ( pcDNA4 huZAP ( L ) ; Addgene plasmid#: 45907 , kindly provided by Dr . Harmit Malik ) [90] . pJM101/L1 . 3: is a pCEP4-based plasmid that expresses a human L1 ( L1 . 3 ) equipped with an mneoI retrotransposition indicator cassette . L1 expression is augmented by a CMV promoter located upstream of the L1 5' UTR and an SV40 polyadenylation signal that is located downstream of the native L1 polyadenylation signal [5 , 30 , 127 , 128] pJM101/L1 . 3FLAG: was derived from pJM101/L1 . 3 and contains a single FLAG epitope on the carboxyl-terminus of ORF1p . Dr . Huira Kopera ( University of Michigan Medical School ) constructed the plasmid . pAluneoTet: expresses an Alu element cloned from intron 5 of the human NF1 gene [129] that is marked with the neoTet reporter gene . The reporter [130] was subcloned upstream of the Alu poly adenosine tract [49] . pCEP/GFP: is a pCEP4 based plasmid that expresses the humanized renilla green fluorescent protein ( hrGFP ) coding sequence from phrGFP-C ( Stratagene ) , which is located downstream of the pCEP4 CMV promoter [33] . pJJ101/L1 . 3: is a pCEP4 based plasmid that contains an active human L1 ( L1 . 3 ) equipped with an mblastI retrotransposition indicator cassette [85] . pJJ105/L1 . 3: is similar to pJJ101/L1 . 3 , but contains a D702A missense mutation in the RT active site of L1 . 3 ORF2 [85] . pJM101/L1 . 3Δneo: is a pCEP4 based plasmid that contains an active human L1 ( L1 . 3 ) [35] . pLRE3-EF1-mEGFPΔIntron: is a pBSKS-II+ based plasmid that expresses an active human L1 ( LRE3 ) that is tagged with an EGFP cassette ( mEGFPI ) containing an antisense , intronless copy of the EGFP gene . A UbC promoter drives EGFP expression . An EF1α promoter drives L1 expression [103] . pAD2TE1: is similar to pJM101/L1 . 3 except that it was modified to contain a T7 gene10 epitope-tag on the carboxyl-terminus of ORF1p and a TAP epitope-tag on the carboxyl-terminus of ORF2p . The 3′-UTR contains the mneoI retrotransposition indicator cassette [26] . pJBM2TE1: is similar to pAD2TE1 except that the pCEP4 backbone was modified to contain the puromycin resistance ( PURO ) gene in place of the hygromycin resistance gene . pLRE3-mEGFPI: is a pCEP4 based plasmid that contains an active human L1 ( LRE3 ) equipped with an mEGFPI retrotransposition indicator cassette [97 , 106] . The pCEP4 backbone was modified to contain a puromycin resistance ( PURO ) gene in place of the hygromycin resistance gene . The CMV promoter also was deleted from the vector; thus , L1 expression is driven only by the native 5′ UTR [97] . pJM111-LRE3-mEGFPI: is identical to pLRE3-mEGFPI except that it contains two missense mutations in ORF1 ( RR261-262AA ) , which render the L1 retrotransposition-defective [30] . Mr . William Giblin ( University of Michigan Medical School ) constructed the plasmid [69] . pGF21: contains an 8 . 8 kb fragment which includes a full length mouse GF21 L1 element that contains the mneoI indicator cassette [94] . pZfL2-2: is a pCEP4 based plasmid that contains the ZfL2-2 ORF ( ZL15 , accession no . AB211150 ) cloned upstream of the mneoI indicator cassette [95] . pCEP4smL1: contains a codon optimized full-length mouse element ( derived from L1spa ) containing the mneoI indicator cassette [96] . ZAP-S/1-311: encodes the ZAP-S amino acid sequence from 1–311 and the following sequence of non-templated amino acids ( IIIYTGFLFCCGFFFFFFFLEGVSLCCPGWS ) . ZAP-S/Δ72–372: was derived by deleting the SfoI-XhoI fragment from ZC3HAV1 transcript variant 2 ( OriGene , SC101064 ) , and expresses a ZAP-S mutant protein that lacks amino acid sequence from 72–372 . ZAP-S/Δ310-645-tGFP: expresses a ZAP-S mutant protein that lacks amino acid sequence from 310–645 and contains a carboxyl terminus tGFP epitope tag . ZAP-S/Δ72-372-tGFP: expresses a ZAP-S mutant protein that lacks amino acid sequence from 72–372 and contains a carboxyl terminus tGFP epitope tag . LARP5: was derived by cloning LARP5 cDNA ( Open Biosystems , 40118844 ) into pcDNA3 ( Invitrogen ) . LARP1: was constructed by cloning the LARP1 cDNA ( Open Biosystems , 3138935 ) into pcDNA3 ( Invitrogen ) . pK_A3A: expresses HA-tagged APOBEC3A and was a generous gift from Dr . Brian Cullen [131] . pDCP1α-GFP: expresses a GFP-tagged version of DCP1α and was a generous gift from Dr . Gregory Hannon [132] . pG3BP-GFP: expresses a GFP-tagged version of G3BP and was a generous gift from Dr . Jamal Tazi [133] . pcDNA6/TR: expresses the blasticidin resistance gene and was obtained from Invitrogen . HeLa-JVM cells were seeded in T-175 flasks ( BD Falcon ) at ~6–8×106 cells/flask and transfected the next day with 20 μg of plasmid DNA using 60 μL of FuGENE HD ( Promega ) . Approximately 48 hours post-transfection , hygromycin B ( Gibco ) ( 200 μg/mL ) was added to the medium to select for transfected cells . After approximately one week of hygromycin selection , cells were washed 3 times with ice cold PBS and collected with a rubber policeman into 50 mL conical tubes ( BD Falcon ) . Cells were then pelleted at 1 , 000×g and frozen at -80°C . To produce whole cell lysates ( WCL ) , frozen cell pellets were rapidly thawed and then lysed in ~3 mL ( 1 mL lysis buffer per 100 mg of cell pellet ) of lysis buffer ( 20 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 10% glycerol , 1 mM EDTA , 0 . 1% IGEPAL CA-630 ( Sigma ) , 1X complete EDTA-free protease inhibitor cocktail ( Roche ) ) on ice for 30 minutes . WCLs were then centrifuged at 15 , 000×g for 15 minutes at 4°C . Supernatants were transferred to a clean tube and protein concentration was determined using the Bradford reagent assay ( BioRad ) . For the IP , ~1 mL of the supernatant ( ~3 mg total protein ) was pre-cleared with ~15 μL ( packed gel volume ) of mouse IgG-agarose beads ( Sigma ) for 4 hours at 4°C . Pre-cleared supernatants were then mixed with ~15 μL ( packed gel volume ) of EZview Red ANTI-FLAG M2 Affinity Gel ( Sigma ) and incubated overnight with rotation at 4°C . The beads then were rinsed 3x with 0 . 5 mL of lysis buffer , and then washed 3 times with 0 . 5 mL of lysis buffer for 10 minutes per wash on ice with gentle agitation . Protein complexes were eluted from the beads by adding ~70 μL of 2X NuPAGE LDS Sample Buffer ( Novex ) , supplemented with NuPAGE Sample Reducing Agent ( Novex ) , directly to the washed beads and incubating for 10 minutes at 70°C . Following incubation , the beads were pelleted and the sample was transferred to a fresh tube . For SDS-PAGE analysis , 20 μL of the IP were loaded onto a 4–15% gradient midi-gel ( BioRad ) and run under reducing conditions . Gels were silver stained using the SilverQuest Silver Staining Kit ( Novex ) to visualize proteins . The Proteomics Facility at the Fred Hutchinson Cancer Research Center ( Seattle , WA ) conducted protein identification experiments . Excised silver-stained gel slices were destained and subjected to in-gel proteolytic digestion with trypsin as described [134] . Following gel-slice digestion , the digestion products were desalted using C18-micro ZipTips ( Millipore ) and were dried by vacuum centrifugation . The resultant peptide samples were resuspended in 7 μL of 0 . 1% formic acid and 5 μL were analyzed by liquid chromatography coupled to tandem mass spectrometry ( LC-MS/MS ) . LC-MS/MS analysis was performed using an LTQ Orbitrap XL mass spectrometer ( Thermo Scientific ) . The LC system , configured in a vented format [135] , consisted of a fused-silica nanospray needle ( PicoTip emitter , 50 μm ID ) ( New Objective ) packed in-house with Magic C18 AQ 100A reverse-phase medium ( 25 cm ) ( Michrom Bioresources Inc . ) and a trap ( IntegraFrit Capillary , 100 μm ID ) ( New Objective ) containing Magic C18 AQ 200A reverse-phase medium ( 2 cm ) ( Michrom Bioresources Inc . ) . The peptide samples were loaded onto the column and chromatographic separation was performed using a two mobile-phase solvent system consisting of 0 . 1% formic acid in water ( A ) and 0 . 1% acetic acid in acetonitrile ( B ) over 60 min from 5% B to 40% B at a flow rate of 400 nL/minutes . The mass spectrometer operated in a data-dependent MS/MS mode over the m/z range of 400–1800 . For each cycle , the five most abundant ions from each MS scan were selected for MS/MS analysis using 35% normalized collision energy . Selected ions were dynamically excluded for 45 seconds . For data analysis , raw MS/MS data were submitted to the Computational Proteomics Analysis System ( CPAS ) , a web-based system built on the LabKey Server v11 . 2 [136] and searched using the X ! Tandem search engine [137] against the International Protein Index ( IPI ) human protein database ( v3 . 75 ) , which included additional common contaminants such as BSA and trypsin . Search results were compared between the pJM101/L1 . 3FLAG lane and the pJM101/L1 . 3 lane to generate a list of candidate L1 ORF1p associated proteins unique to the pJM101/L1 . 3FLAG immunoprecipitation . The search output files were analyzed and validated by ProteinProphet [138] . Peptide hits were filtered with PeptideProphet [139] error rate ≤0 . 05 , and proteins with probability scores of ≥0 . 95 were accepted . Suspected contaminants ( e . g . keratin ) were filtered from the final L1 RNP candidate list . The cultured cell retrotransposition assay was carried out essentially as described [30 , 71] . For retrotransposition assays with L1 constructs tagged with mblastI , HeLa-JVM cells were seeded at ~1–2×104 cells/well in a 6-well plate ( BD Falcon ) . Within 24 hours , each well was transfected with 1 μg of plasmid DNA ( 0 . 5 μg L1 plasmid + 0 . 5 μg cDNA plasmid or pCEP4 ) using 3 μL of FuGENE 6 transfection reagent ( Promega ) . Four days post-transfection , blasticidin ( EMD Millipore ) containing medium ( 10 μg/mL ) was added to cells to select for retrotransposition events . Medium was changed every two days . After ~8 days of selection , cells were washed with PBS , fixed , and then stained with crystal violet to visualize colonies . To control for transfection efficiency and off-target effects of cDNA plasmids , in parallel with retrotransposition assays , HeLa-JVM cells were plated in 6-well plates at 500–1 , 000 cells/well and transfected with 0 . 5 μg pcDNA6/TR ( Invitrogen ) plasmid + 0 . 5 μg cDNA plasmid using 3 μL of FuGENE 6 transfection reagent ( Promega ) . The pcDNA6/TR control assays were treated with blasticidin in the same manner as for retrotransposition assays . For retrotransposition assays with L1 constructs tagged with mneoI , HeLa-JVM cells were transfected as described above . Two days after transfection , cells were treated with medium supplemented with G418 ( Gibco ) ( 500 μg/mL ) for ~10–12 days . As a control , HeLa cells were plated at ~2×104 cells/well in a 6-well plate and transfected with 0 . 5 μg pcDNA3 ( Invitrogen ) plasmid + 0 . 5 μg cDNA plasmid using 3 μL of FuGENE 6 transfection reagent ( Promega ) . The pcDNA3 control assays were treated with G418 in the same manner as for retrotransposition assays . For Alu retrotransposition assays [49] , ~4×105 HeLa-HA cells were plated per well of a 6-well plate ( BD Falcon ) and transfected with 0 . 67 μg of pJM101/L1 . 3Δneo + 0 . 67 μg of pAluneoTet + 0 . 67 μg of cDNA plasmid using 6 μL FuGENE HD ( Promega ) . Three days post-transfection , cells were grown in the presence of G418 ( 500μg/mL ) to select for Alu retrotransposition events . As a control , HeLa-HA cells were plated at ~4×105 cells/well in a 6-well plate and transfected with 0 . 67 μg of pcDNA3 ( Invitrogen ) + 0 . 67 μg of pAluneoTet + 0 . 67 μg of cDNA plasmid using 6 μL of FuGENE HD ( Promega ) . The pcDNA3 control assays were treated with G418 in the same manner as for Alu retrotransposition assays . In experiments to study the effect of endogenous proteins on L1 retrotransposition , HeLa cells ( ~8×105 cells ) were plated in 60 mm tissue culture dishes ( BD Falcon ) . The next day , the cells were transfected with 50 nM of a control siRNA pool ( D-001810-10 , ON-TARGETplus Non-targeting Pool , Thermo Scientific ) or siRNA against ZAP ( L-017449-01-0005 , ON-TARGETplus Human ZC3HAV1 ( 56829 ) siRNA—SMARTpool , Thermo Scientific ) or MOV10 ( L-014162-00-0005 , ON-TARGETplus Human MOV10 ( 4343 ) siRNA—SMARTpool , Thermo Scientific ) using the DharmaFECT 1 transfection reagent ( Thermo Scientific ) . Twenty-four hours after siRNA treatment , cells were transfected with pLRE3-mEGFPI or pJM111-LRE3-mEGFPI ( 5 μg ) , using 15 μL of FuGENE HD transfection reagent ( Roche ) . After 48 hours , cells were trypsinized and an aliquot of the cells ( ~2×106 cells ) was used to monitor endogenous protein levels ( 72 hours after siRNA treatment ) by western blot analysis ( see below for list of primary antibodies ) . Blots were analyzed using an Odyssey CLx ( LI-COR ) with the following secondary antibodies: IRDye 800CW Donkey anti-Rabbit IgG ( 1:10 , 000 ) ( LI-COR ) and IRDye 680RD Donkey anti-Mouse IgG ( 1:10 , 000 ) ( LI-COR ) . Knockdown efficiencies were calculated using LI-COR Image Studio Software ( v3 . 1 . 4 ) and are the average of three independent experiments . Endogenous tubulin was used as the normalization control . The remaining cells were re-plated at ~2×105 cells/well of a 6-well plate and cultured in medium supplemented with puromycin ( 5 μg/ml , Gibco/Life Technologies ) to select for cells transfected with pLRE3-mEGFPI . After 4 days of puromycin selection , the percentage of GFP positive cells was determined by flow cytometry using an Accuri C6 flow cytometer ( BD Biosciences ) . RNPs were isolated as previously described [37] . Briefly , HeLa-JVM cells were seeded onto 60 mm tissue culture dishes ( BD Falcon ) and 24 hours later cells were co-transfected with 2 . 5 μg of pJBM2TE1 and 2 . 5 μg of the indicated cDNA plasmid using 15 μL of FuGENE HD ( Promega ) . Approximately two days after transfection , puromycin ( 5 μg/mL ) was added to culture medium to select for cells transfected with pJBM2TE1 . After ~3 days of puromycin selection ( 5 days after transfection ) , cells were lysed in RNP lysis buffer ( 150 mM NaCl , 5 mM MgCl2 , 20 mM Tris-HCl ( pH 7 . 5 ) , 10% glycerol , 1mM DTT , 0 . 1% NP-40 , and 1x complete EDTA-free protease inhibitor cocktail ( Roche ) ) . Following lysis , whole cell lysates were centrifuged at 12 , 000xg for 10 minutes at 4°C , and then the cleared lysate was layered onto a sucrose cushion ( 8 . 5% and 17% sucrose ) and subjected to ultracentrifugation at 4°C for 2 hours at 178 , 000xg . The supernatant was discarded and the resulting pellet was resuspended in water supplemented with 1x complete EDTA-free protease inhibitor cocktail ( Roche ) . Approximately 20 μg ( total protein ) of the RNP sample or ~30 μg ( total protein ) of the cleared whole cell lysate ( supernatant post 12 , 000xg centrifugation ) were then analyzed by western blot . Blots were analyzed using an Odyssey® CLx ( LI-COR ) with the following secondary antibodies: IRDye 800CW Donkey anti-Rabbit IgG ( 1:10 , 000 ) ( LI-COR ) and IRDye 680RD Donkey anti-Mouse IgG ( 1:10 , 000 ) ( LI-COR ) . To simultaneously analyze the effects of ZAP-S on ORF1p and EGFP protein expression , HeLa-JVM cells were seeded onto 10 cm dishes ( ~2 . 7×106 cells/dish ) ( BD Falcon ) and transfected with 10 μg of plasmid DNA ( 5 . 0 μg pLRE3-EF1A-mEGFPΔIntron + 5 . 0 μg cDNA plasmid or pCEP4 ) using 30 μL of FuGENE HD . After 48 hours , cells were harvested with trypsin and then subjected to flow cytometry to isolate GFP expressing cells . Approximately 1 . 2–1 . 7×106 GFP positive cells were collected for each transfection condition using a MoFlo Astrios cell sorter ( Beckman Coulter ) . The GFP gate was set using untransfected HeLa-JVM cells . The sorted cells were lysed as described in the IP procedure and lysates were then subjected to western blotting using standard procedures . For all other protein expression analyses , HeLa-JVM cells were seeded at ~4×105 cells/well in 6-well plates and transfected with 2 μg of plasmid DNA with 6 μL of FuGENE HD . Cells were collected 48 hours after transfection using a rubber policeman and lysates were prepared as described above . Western blots were visualized using either the SuperSignal West Femto Chemiluminescent Substrate ( Pierce ) or SuperSignal West Pico Chemiluminescent Substrate ( Pierce ) and Hyperfilm ECL ( GE Healthcare ) . HeLa-JVM cells were seeded in T-175 flasks ( BD Falcon ) and transfected with 20 μg of plasmid DNA ( 10 μg pJM101/L . 13Δneo + 10 μg cDNA plasmid ) using 60 μL FuGENE HD . Two days after transfection , cell pellets were collected and frozen at -80°C . Frozen cell pellets were then thawed and total RNA was extracted with TRIzol reagent ( Ambion ) , and then poly ( A ) + RNA was prepared from total RNA using an Oligotex mRNA kit ( Qiagen ) . Each sample ( ~1 . 5 μg of poly ( A ) + RNA ) was subjected to glyoxal gel electrophoresis and northern blotting using the NorthernMax-Gly Kit ( Ambion ) according to the manufacturer’s protocol . Following electrophoresis , RNA was transferred to BrightStar Nylon membranes ( Invitrogen ) and then cross-linked using UV light . For northern blot detection , membranes were prehybridized for ~ 4 hours at 68°C in NorthernMax Prehybridization/Hybridization Buffer ( Ambion ) , and then incubated with a strand specific RNA probe ( final concentration of probe ~ 3×106 cpm ml-1 ) overnight at 68°C . For band quantification , northern blot films were analyzed using ImageJ software [140] . Strand-specific RNA probes were generated using the MAXIscript T3 system ( Invitrogen ) . The 5UTR99 [100] probe corresponds to bases 7–99 of the L1 . 3 5' UTR and the ORF2_5804 probe corresponds to nucleotides 5560–5804 of the L1 . 3 sequence . RNA probe templates for T3 reactions were generated by PCR using pJM101/L1 . 3Δneo as a PCR template with the following primer pairs: ( 5UTR99: 5'-GGAGCCAAGATGGCCGAATAGGAACAGCT-3' and 5'-AATTAACCCTCAAAGGGACCTCAGATGGAAATGCAG-3' ) ; ( ORF2_5804: 5'- GACACATGCACACGTATGTTTATT-3' and 5'- AATTAACCCTCACTAAAGGGTGAGTGAGAATATGCGGTGTTT-3' ) . The T3 promoter sequence ( underlined ) was added to the reverse primer of each primer pair . The pTRI-β-actin-125-Human Antisense Control Template ( Applied Biosystems ) was used in T3 reactions as a template to generate the β-actin RNA probe . Each northern blot experiment was independently repeated three times with similar results . Immunofluorescence microscopy was performed essentially as described [26] with modifications . Briefly , cells were plated on round glass cover slips ( Fisher ) in a 12-well plate or into 4-well chambered glass slides ( Fisher ) and transfected ~24 hours later with 0 . 5 μg of plasmid DNA using 1 . 5 μL of FuGENE 6 transfection reagent . To visualize proteins , approximately 48 hours post-transfection cells were washed with 1x PBS , fixed with 4% paraformaldehyde for 10 minutes and then treated with ice-cold methanol for 1 minute . Next , cells were incubated for 30 minutes at 37°C in 1x PBS + 3% BSA . Cells then were incubated with primary antibodies in 1x PBS + 3% BSA for 1 hour at 37°C . Cells were washed three times with 1x PBS ( 10 minutes per wash ) and then incubated with appropriate , fluorescently-labeled secondary antibodies diluted in 1x PBS for 30 minutes at 37°C . The following secondary antibodies were used for indirect immunofluorescence: Alexa Fluor 488 conjugated Goat anti-Mouse and Goat anti-Rabbit ( Invitrogen ) ( 1:1000 ) , Alexa Fluor 546 conjugated Goat anti-Mouse and Goat anti-Rabbit IgG ( Invitrogen ) ( 1:1000 ) , and Cy5 conjugated Donkey anti-Rabbit IgG ( H+L ) ( Jackson ImmunoResearch ) ( 1:100 ) . To obtain images , a cover slip and/or slide was visually scanned and representative images were captured using a Leica SP5X confocal microscope ( 63x/1 . 4 objective; section thickness 1 μm ) . Cells were plated on round glass cover slips ( Fisher ) in a 12-well plate and transfected ~24 hours later with 0 . 5 μg of plasmid DNA using 1 . 5 μL of FuGENE 6 transfection reagent . Approximately 48 hours after transfection , cells were fixed with 4% paraformaldehyde for 10 minutes and then permeabilized with 0 . 2% Triton X-100 in 1x PBS for 7 minutes . Following permeabilization , coverslips were incubated for 5 minutes in FISH ( fluorescence in situ hybridization ) wash buffer ( 2x SSC , 10% formamide ) for 5 minutes . To visualize L1 RNA , coverslips were then incubated with 300 nM FISH probes ( sequences below ) in FISH hybridization buffer ( 2x SSC , 10% formamide , 1% dextran sulphate ) for ~4 hours at 37°C . Following hybridization , cells were incubated for 30 minutes in FISH wash buffer at 37°C and then incubated with FISH wash buffer + 3% BSA for an additional 30 minutes at 37°C . To visualize L1 ORF1p by immunofluorescence , coverslips then were incubated with αORF1p antibodies ( 1:2000 ) in 1x PBS + 3% BSA for 1 hour at 37°C . Cells were washed three times with 1x PBS ( 10 minutes per wash ) . Cells were incubated with Alexa Fluor 546 conjugated Goat anti-Rabbit IgG ( Invitrogen ) ( 1:1000 ) in 1x PBS + DAPI ( 50 ng/mL ) for 30 minutes at 37°C . Coverslips were mounted on slides with VECTASHIELD mounting media ( Vector Laboratories ) . Combined RNA FISH/immunofluorescence samples were imaged with a Zeiss Axioplan2 microscope ( 63x objective; Axiovision 4 . 8 software ) . RNA FISH/immunofluorescence images ( Fig 6A–6D ) were globally processed using the Photoshop CS6 ( version 13 . 0 x64 ) Levels tool to adjust input levels . The L1 RNA was labeled using 21 Quasar670-labelled anti-sense oligonucleotide probes complimentary to sequences within the L1 . 3 5' UTR ( probes were designed and produced by Biosearch Technologies , Petaluma , CA ) . The sequences of the 21 L1 probes are as follows: 5'-aaatcaccgtcttctgcgtc-3' , 5'-ggtacctcagatggaaatgc-3' , 5'-cactccctagtgagatgaac-3' , 5'-ccctttctttgactcagaaa-3' , 5'-aatattcgggtgggagtgac-3' , 5'-cttaagccggtctgaaaagc-3' , 5'-caggtgtgggatatagtctc-3' , 5'-tgctagcaatcagcgagatt-3' , 5'-ttgcagtttgatctcagact-3' , 5'-tttgtttacctaagcaagcc-3' , 5'-cagaggtggagcctacagag-3' , 5'-ctgtctttttgtttgtctgt-3' , 5'-cacttaagtctgcagaggtt-3' , 5'-ctctcttcaaagctgtcaga-3' , 5'-ttgaggaggcagtctgtctg-3' , 5'-ctgcaggtctgttggaatac-3' , 5'-ttctaacagacaggaccctc-3' , 5'-cctttctggttgttagtttt-3' , 5'-gatgggttttcggtgtagat-3' , 5'-gtctttgatgatggtgatgt-3' , 5'-tttgtggttttatctacttt-3' . Polyclonal antibodies against peptide sequences 31–49 of L1 . 3 ORF1p ( αORF1p ) were raised in rabbits and affinity-purified ( Open Biosystems ) . αCDK9 ( 2316 ) , αUPF1 ( 9435 ) , and αGFP ( 2955 ) were obtained from Cell Signaling Technology . αhnRNPL ( NBP1-67852 ) , αILF3 ( EPR3627 ) , αLARP1 ( NBP1-19128 ) , αMATR3 ( NB100-1761 ) , αNCL ( NB100-1920SS ) , and αDHX9 ( NB110-40579 ) were obtained from Novus Biologicals . αFAM120A ( ab83909 ) , αPURA ( ab79936 ) , and αHA tag ( ab9110 ) were obtained from Abcam . αMOV10 ( SAB1100141 ) , αZAP ( Anti-ZC3HAV1 ( HPA047818 ) ) , and αTubulin ( T9026 ) were obtained from Sigma . αZC3HAV1 ( 16820-1-AP ) was obtained from Proteintech . αeIF3 ( p110 ) ( sc-28858 ) was obtained from Santa Cruz Biotechnology . αT7-Tag mouse monoclonal ( 69522–3 ) was obtained from Novagen . αTAP rabbit polyclonal ( CAB1001 ) was obtained from Thermo Scientific . | Long INterspersed Element-1 ( LINE-1 or L1 ) is the only active autonomous retrotransposon in the human genome . L1s comprise ~17% of human DNA and it is estimated that an average human genome has ~80–100 active L1s . L1 moves throughout the genome via a “copy-and-paste” mechanism known as retrotransposition . L1 retrotransposition is known to cause mutations; thus , it stands to reason that the host cell has evolved mechanisms to protect the cell from unabated retrotransposition . Here , we demonstrate that the zinc-finger antiviral protein ( ZAP ) inhibits the retrotransposition of human L1 and Alu retrotransposons , as well as related retrotransposons from mice and zebrafish . Biochemical and genetic data suggest that ZAP interacts with L1 RNA . Fluorescent microscopy demonstrates that ZAP associates with L1 in cytoplasmic foci that co-localize with stress granule proteins . Mechanistic analyses suggest that ZAP reduces the expression of full-length L1 RNA and the L1-encoded proteins , thereby providing mechanistic insight for how ZAP may restricts retrotransposition . Importantly , these data suggest that ZAP initially may have evolved to combat endogenous retrotransposons and subsequently was co-opted as a viral restriction factor . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | The Zinc-Finger Antiviral Protein ZAP Inhibits LINE and Alu Retrotransposition |
The unintended consequences of gene targeting in mouse models have not been thoroughly studied and a more systematic analysis is needed to understand the frequency and characteristics of off-target effects . Using RNA-seq , we evaluated targeted and neighboring gene expression in tissues from 44 homozygous mutants compared with C57BL/6N control mice . Two allele types were evaluated: 15 targeted trap mutations ( TRAP ) ; and 29 deletion alleles ( DEL ) , usually a deletion between the translational start and the 3’ UTR . Both targeting strategies insert a bacterial beta-galactosidase reporter ( LacZ ) and a neomycin resistance selection cassette . Evaluating transcription of genes in +/- 500 kb of flanking DNA around the targeted gene , we found up-regulated genes more frequently around DEL compared with TRAP alleles , however the frequency of alleles with local down-regulated genes flanking DEL and TRAP targets was similar . Down-regulated genes around both DEL and TRAP targets were found at a higher frequency than expected from a genome-wide survey . However , only around DEL targets were up-regulated genes found with a significantly higher frequency compared with genome-wide sampling . Transcriptome analysis confirms targeting in 97% of DEL alleles , but in only 47% of TRAP alleles probably due to non-functional splice variants , and some splicing around the gene trap . Local effects on gene expression are likely due to a number of factors including compensatory regulation , loss or disruption of intragenic regulatory elements , the exogenous promoter in the neo selection cassette , removal of insulating DNA in the DEL mutants , and local silencing due to disruption of normal chromatin organization or presence of exogenous DNA . An understanding of local position effects is important for understanding and interpreting any phenotype attributed to targeted gene mutations , or to spontaneous indels .
In mammalian systems , improvements of transgenic technologies have focused on methods to reduce the influence of surrounding DNA , also called position effects at the genomic insertion site , to ensure reliable and consistent expression of the transgene . To this end , strong reliable ubiquitously expressing promoters have been identified and engineered to provide appropriate and consistent transgene expression [1–3] . Exogenous DNA promoting gene silencing has been identified and removed from constructs [4] , and insulating DNA has been utilized to reduce the influence of flanking DNA at the insertion site [5–6] . In addition , targeting vectors into safe havens with open chromatin [7] ensures that other genes are not disrupted and the targeting eliminates concatenation of the vector which may produce silencing [8] . The literature emphasizes a consideration of the impact of the local genome on the fidelity of the transgene [9–10] and generally doesn’t consider the effect of transgene integration on the local genome function . There are a few reports describing unintended position effects of either random integration or targeting events in mammals . And most of these reports relate to finding that a random integrant has disrupted either the regulatory domain [e . g . , 11–12] , or coding sequence of another gene . This lack of data may be a consequence of not looking . In transgenic studies of commercial crop and animal species due to food safety concerns , investigators have evaluated unintended consequences and pleiotropic effects of transgene integration [for reviews see 13–14] . Since the majority of commercial plant and animal transgenics are not targeted , the approach has been to look at physiological parameters such as blood , nutritional value , e . g . , carbohydrate composition of commercial crops , morphological changes by histology and transcriptome changes in multiple random integrants . And indeed they have been able to identify transgene specific vs . insertional position effects , primarily using transcriptome analysis [15] . Similarly , for viral delivery systems for human gene therapy , the unintended consequences of vector integration are of obvious concern for safety reasons [16–17] . The advent of expression profiling by the sequencing of cDNA libraries ( RNA-seq ) [18–20] enabled by NextGen sequencing has created an opportunity for using transcriptome analysis as a diagnostic tool to elaborate molecular mechanisms of disease [21] , drug response [22] , and response to toxins [23] . It is timely that we apply this technology to evaluate the consequence of the genomic insertion of targeted transgenic constructs . The International Knockout Mouse Consortium ( IKMC ) is an international effort to create a resource of targeted gene-specific knockout C57BL/6N mouse embryonic stem cells for ~ 17 , 000 protein coding genes [24] . The next step in this process is to use these stem cells to produce live animals for cryopreservation and phenotyping and the International Mouse Phenotyping Consortium was formed to complete this initiative ( IMPC ) [25–26] . The NIH Common Fund supported Knockout Mouse Project ( KOMP ) is part of the IKMC and IMPC projects and has already produced targeted stem cells for ~ 7 , 500 genes , and approximately 2 , 500 of these have now been used to generate live mice . The KOMP project uses two separate targeting strategies [27–28] . In one approach ( CHORI-Sanger-UC Davis , CSD ) the construct is a targeted gene trap ( TRAP ) which can be converted to a conditional allele ( see Methods ) . The TRAP is inserted into an intron 5’ to a critical exon and is designed not to disrupt any intronic regulatory elements . The vector construct is identical to that used by the European Conditional Mouse Mutagenesis Program ( EUCOMM ) [29] . In contrast , the Velocigene ( VG ) targeting strategy uses BAC recombineering to create a vector that deletes the entire open reading frame from the ATG of the targeted gene to the 3’ UTR creating a deletion allele ( DEL ) . The inserted vectors for the CSD and VG alleles are very similar . Both targeting strategies leave behind a bacterial beta-galactosidase reporter ( LacZ ) and a neo selection cassette usually driven by a heterologous promoter . In order to evaluate local targeting effects , the use of the same targeting vector to target many different genes is ideal to evaluate the potential off-target effects since the vector remains unchanged but the local targeted environment is varied . In addition , we have two different targeting strategies which can be compared . In this report we evaluate the local effects of TRAP and DEL targeting events in C57BL/6N adult mice by using RNA-seq to characterize the expression of the targeted gene , and genes flanking the targeted gene , in tissues from 44 unique mouse lines .
We present here the changes in gene expression by RNA-seq for genes within +/- 500 kb of the center of the targeted gene . In S1 Table we present the data for this analysis including coordinates of all genes relative to the target gene for each sequenced library , the normalized read counts from library sequencing , the Log2 difference between homozygous ( HOM ) and wild-type ( WT ) gene expression , and the significance of the differences as determined by the DESeq2 statistical analysis ( both the adjusted and unadjusted p value ) . Overall results are presented in Fig 1 for the gene trap ( TRAP ) mutants ( 15 targets and 64 sequenced mutant libraries ) , and for the deletion ( DEL ) mutants ( 29 targets and 94 sequenced libraries ) . Both TRAP and DEL targets had one or more down-regulated genes within 500kb of ~40–50% of the targeted genes . Whereas , 59% of the DEL targets , but only 7% of TRAP targets , had an up-regulated gene within 500kb . When the frequency of up- or down-regulated genes was expressed per library , similar differences were observed with more DEL libraries having both down- and up-regulated genes within 500kb compared with TRAP libraries ( Fig 1 ) . There was a clear spatial organization of the up- and down-regulated genes flanking the DEL targets ( Fig 2 ) . Down-regulated genes flanking DEL targets were approximately equally distributed 5’ and 3’ of the target and the median distance from the target was 34kb . For up-regulated genes flanking DEL targets , there was a clear preponderance of up-regulated genes 3’ to the target ( 77 . 4% ) vs . 5’ to the target ( 22 . 6% ) . The median distance of up-regulated genes 3’ to the target was 67kb , while the median distance of the up-regulated genes 5’ of the target was 321kb . For the TRAP targets , the down-regulated genes flanking TRAP alleles were approximately equally distributed 5’ and 3’ with the 5’ median distance of 224 kb , and the 3’ median distance from the target 8kb ( Fig 2 ) . There was only 1 up-regulated gene for one of the TRAP targets and it was located ~350kb 5’ to the target . There was no spatial organization of the genes with expression unaffected by the targeting event with approximately 5% of the genes ( range 3 . 2–6 . 8% ) found in each 50kb interval flanking the targets ( derived from S1 Table ) . We completed a genome wide assessment of the frequency of finding up-or down-regulated genes within 500kb of all genes down-regulated in the HOM mutant libraries . For this analysis all libraries were evaluated and a DESeq2 statistical significance of p < 0 . 05 was used as the criteria for identifying down-regulated genes . The overall genome-wide frequency for finding up-regulated and down-regulated genes within 500kb of genes down-regulated in the HOM libraries was 1 . 09% and 2 . 22% , respectively . This is much lower than the frequency of up- or down-regulated genes flanking the targeted genes . Using a chi-square statistic , we compared the actual frequency of up- and down-regulated genes flanking each of the mutant classes , with the genome-wide frequency ( see Table 1 ) . For DEL mutants , both up- and down-regulated genes were found flanking the targeted genes at a much higher frequency than the genome-wide frequency ( p < 10−6 ) . For TRAP mutants , the frequency of finding down-regulated genes was significantly greater than the genome-wide frequency ( p = 0 . 016 ) ; while the frequency of up-regulated genes flanking TRAP mutations was not significantly different from the rate predicted by the genome-wide survey ( p > 0 . 05 ) . Examples of local effects of targeting are provided for Fbxo44 , a TRAP targeted gene ( Fig 3 , Top Panel ) , and two DEL targeted genes , G6b ( Fig 3 , Middle Panel ) , and Tst ( Fig 3 , Bottom Panel ) . In Fbxo44 kidney , the flanking gene-family member Fbxo6 was significantly down-regulated 2–4 fold and this was observed in 4 of 5 tissues ( S1 Table ) . For the G6b HOM mutant spleen , three 3’ genes were significantly up-regulated , and 2 were down-regulated . In the G6b mutant , Clic1 was significantly down-regulated in 3 of 4 tissues profiled , and the other genes showed similar trends as observed in G6b HOM spleen . ( S1 Table ) . In Tst gonadal adipose , Tex33 was highly up-regulated 3’ to Tst , while Mpst and Kcdt17 were down-regulated 5’ , and this same pattern was found in all four tissues although the changes do not reach DESeq2 significance levels in all tissues ( S1 Table ) . Of note are examples of local effects of the mutations when the targeted gene was not normally expressed in that tissue . Three examples of this were provided by mutations in the Prkcdbp , G6b & Apof genes ( S1 Table ) . Prkcdbp was expressed at very low levels , but the neighboring gene Cnga4 was significantly up-regulated in Prkcdbp mutant liver . G6b was not expressed in liver , but the Clic1 gene was significantly up-regulated in G6b mutant liver as it was in all other mutant tissues examined . Apof was not expressed in the gastrocnemious muscle , but several genes in close proximity , including Timeless and Pan2 were significantly dysregulated in Apof mutant gastrocnemious muscle . Gene density was a predictor of finding genes up or down-regulated around the target . There were 27 mutants ( 21 DEL and 6 TRAP ) with one or more up- or down-regulated genes within 500kb of the target . The average number of genes found within 500kb of these 27 mutant targets was 30 . 0 per target . For the 17 mutants without local effects ( 8 DEL and 9 TRAP ) , the average number of genes within 500kb was 18 . 1 . For the 44 targeted genes , based upon an analysis of transcription factor binding sites ( TFBS ) , publicly available CHIPseq data , and coding sequence for miRNA , we conclude that there was a 6 . 7% frequency of the targeting event disrupting these regulatory elements in the 15 TRAP targets , while there was a 58 . 6% frequency for disrupting these intragenic regulatory elements in the 29 DEL targets . A chi-square test comparing the frequency of disruption of intragenic regulatory regions between DEL and TRAP alleles gave a p < 0 . 001 . Comparing only total read counts between HOM targeted and WT mouse RNA-seq libraries , and relying upon the DESeq2 statistic , was not sufficient to confirm targeting for all 44 mutants . Expression of exons proximal to the gene trap , non-functional splicing around the gene trap , expression of the 3’ UTR or distal exons for deletion mutants , and reads mapping to intronic non-coding sequence , may have resulted in apparent failure of the targeting . In addition , with small numbers of replicates the DESeq2 statistic may not find significance despite large differences in read counts . Therefore , we used the criteria of a minimum of 100 reads in either the WT or HOM library , and relative reads in the HOM libraries of 20% of wildtype or greater to identify those mutants with possible failed targeting for additional follow-up . We followed-up by examining the patterns of read alignment to introns , exons , and untranslated regions of each gene . Using the criteria described above , 8/15 TRAP mutants ( 47% ) were evaluated further for targeting . Inspection of reads mapping to the mouse genome helped to confirm targeting , for example where reads mapped to exons proximal to the gene trap , or to untranslated regions . However , in some cases for TRAP alleles with reads mapping to the gene , the pattern of reads mapping to exons was identical to that found for WT controls , albeit at lower total read counts ( e . g . , Slc1a3 , Fig 4 ) . For TRAP mutant Arap1 , the gene trap was complete in some tissues with no reads mapped 3’ to the gene trap in liver and brown adipose , while in other tissues the four exons 3’ to the Arap1 TRAP were skipped and then reads were mapped to the exons in the distal 3’ half of the gene ( Fig 5 ) . Six of twenty-nine HOM DEL mutants ( 21% ) had at least 20% of WT reads mapped to the targeted gene . When we examined the alignment of reads for 4/6 of these mutants the patterns of read mapping confirmed that targeting was successful . Mutant 2700097O09Rik had read counts mapped primarily to introns , Iqub had up-regulation of the distal exons and 3’ UTR with essentially no reads mapped to proximal exons in the mutant , Klf14 had up-regulation of the 3’ UTR , and Oxgr1 had low reads in the proximal exons but the distal exon had no reads . For Iqub and Klf14 , the pattern of up-regulation of distal exons/3’UTR was likely a consequence of deletion of the proximal exons only and a compensatory over-expression of the remaining exons and 3’ UTR in those genes ( e . g . , Iqub , Fig 6 ) . Only one DEL mutant line , Adam26a , had reads mapping to exons targeted for deletion ( S1 Table and Short Read Archive Project #PRJNA280546 ) . One gene , Mtnr1b , had no reads in either WT or HOM mutants mapping to the gene in any tissues and we could not conclude if it was correctly targeted based upon the RNA-seq data . Taking into account the location of the reads within each gene , we conclude that 97% of DEL alleles were correctly targeted .
Neighboring gene up- or down-regulation was found more frequently for DEL alleles than for TRAP alleles , and most of these differentially expressed genes were within 200 kb of the targeted gene . The frequency of down-regulated genes within +/- 500kb was significantly higher than expected from a genome wide survey for both DEL and TRAP mutants , whereas for up-regulated genes these only occurred above the genome wide expected frequency for DEL mutants and not for TRAP mutants . As would be expected , gene density around the target influenced the likelihood of local effects , since targets with local effects had approximately twice the number of genes in the 500kb flanking regions than genes with no local effects . Some of the local gene differential expression around targeted genes may have been due to the co-regulation of gene clusters . Genes tend to cluster in euchromatin due to the requirement of an open DNA structure and regions of generally up- or down-regulated genes have been identified in mammalian genomes [30] , but this doesn’t necessarily mean that the genes are co-regulated in response to developmental or cellular demands , or act in the same or related pathways . The presence of bidirectional promoters could also partly explain the co-regulation of flanking genes . Approximately 11% of human and mouse promoters are bidirectional [31 , 32] and regulate expression of flanking genes . The function of these bidirectional promoters may be to maintain an open chromatic structure and to coordinate expression of gene networks [33] . Therefore , at least some of the local effects we found in this study could be compensatory feedback in response to loss of the gene product and/or the presence of bidirectional promoters . However , the frequency of local effects around the DEL targets was much higher than the 11% frequency of bidirectional promoters and suggests some other mechanisms were involved . In some cases , we found local effects in a specific tissue , identical to the local effects found in other tissues , even when the targeted gene was not normally expressed in that tissue , e . g . , Prkcdbp , Apof , and G6b ( S1 Table ) . And , we found that there was a unique pattern to the clustering of dysregulated genes around the DEL targets . Up-regulated genes were primarily 3’ , with 77% 3’ of the targeted gene ( Fig 2 ) . Down-regulated genes were equally distributed 5’ and 3’ and were within 100kb of the targeted gene . The observations that gene dysregulation tended to cluster around the target even when the targeted gene was not expressed in that tissue , and the unique and different topography of up- and down-regulated genes , support the conclusion that some of these local effects were due to the targeting event and not a compensatory change due to the loss of targeted gene product . We hypothesize that the local transcriptional dysregulation , was due to three mechanisms: 1 ) the targeting event disrupted regulatory and insulating elements within the targeted gene and this had cis-acting effects on flanking genes; 2 ) the exogenous promoter in the neo selection cassette acted on 3’ genes to either increase or decrease gene expression; and 3 ) insertion of ~ 5 kb of exogenous DNA , and elements in the vector , promoted gene silencing . For DEL alleles , RNA-seq confirmed targeting for 97% of the targeting events . However , for DEL mutants with a significant numbers of reads mapping to the targeted gene , it was necessary to examine the mapping distribution of the reads across the gene to reach a conclusion . For TRAP alleles , confirmation of targeting by using only total read counts from RNA-seq was ~50% . For the 50% of the TRAP targets with significant numbers of reads from RNA-seq mapping to the gene in HOM tissues , usually the reads mapped to all exons . This does not necessarily mean that the targeted gene was expressing a functional protein . There are examples of splicing around gene traps in mice in the literature [54–60] . Some of these examples of alternative splicing , or splicing around the gene trap sequence , produced functional protein and were hypomorphs [56 , 58] , and some produced message which was not translated [57] . This alternative splicing may be a consequence of a weak splice acceptor at the gene trap , or the presence of cryptic splice donors and acceptors within the neo cassette [47] . Splicing around the gene trap appears to be tissue specific . In the case of Arap1 reported in this paper and described above with clear alternative splicing around the gene trap , this mouse gene has 19 splice variants , many of them protein coding , in the Ensembl database ( ENSMUSG00000032812 ) . Therefore , the Arap1 gene in particular may be transcriptionally organized to splice around the trap . In another more recent example of splicing around the gene trap , Hanstein et al . [61] evaluated the mRNA produced by a TRAP mutant for Panx1 . They showed that intact mRNA was produced , although it was ~ 30% of that found in wild type mice , and that this TRAP mutant was a functional hypomorph which was adequate to recapitulate the phenotype in a true -/- null mutant . We also evaluated by RNA-seq the pattern of mRNA expression in this same TRAP mutant and also showed that Panx1 mRNA was knocked down but not eliminated in the three tissues we sequenced ( S1 Table ) . For any gene trap mutant , relying upon the presence or absence of RNA mapping to the exons of the targeted gene is not necessarily evidence that the gene is functionally expressed . If mRNA is detected and maps to the exons in an RNA-seq experiment , ultimately it is necessary to assess the amount of protein , and sequence the cDNA , in order to conclude if the splicing around the gene trap is likely to produce a functional protein . RNA-seq is a useful method to assess gene targeting efficiency and to determine if follow-up studies with mRNA sequencing and protein assays are needed to confirm the mutation . In addition , RNA-seq readily identifies up- and down-regulation of genes in close proximity to the target which may be compensatory or a consequence of the targeting strategy and vector . We report here that local effects of targeting occur in both TRAP and DEL targeted mutations but occur with higher frequency in DEL mutations . It is likely that , in some cases , these local effects on gene transcription surrounding the targeted site are a consequence of the presence of the LacZ/selection cassette in the TRAP and DEL mutants , and/or the deletion of the open reading frame in DEL mutants . Future work comparing local gene expression changes in TRAP and DEL mutants having intact targeting vectors , with those following Cre excision of the neo cassette , will help us determine if the neo cassette is responsible for the local gene expression changes . We do not know if , or how frequently , local gene expression changes due to the vector insertion , affect phenotypes in targeted mutants . However , one recent report using the identical TRAP allele targeting Slc25a21 in the mouse revealed phenotypes that were a consequence of the down-regulation of a neighboring gene and not disruption of the targeted gene [62] . In some cases , up-regulation of a local gene may not have any physiological consequences unless the full pathway in which the protein product plays a role is active in that tissue . For genes flanking the target that are down-regulated , it is more likely that this would have a consequence in that tissue since the pathway is already active but expression is reduced in the mutant . When evaluating phenotypes in targeted mouse mutants , or in spontaneous indel mutations , one should consider the effect of the mutation on the expression and function of closely linked genes flanking the mutation .
This work was approved by the University of California Institutional Animal Care and Use Committee ( UCD IACUC Protocol #17328 ) and was performed in accordance to the guidelines of the National Institutes of Health , Institute of Laboratory Animal Research and Guide for the Care and Use of Laboratory Animals . The 44 genes for which homozygous ( HOM ) mutant tissues were evaluated by RNA-seq is: 2700097O09Rik , Aard , Adam26a , Adig , Apof , Arap1 , Arl10 , Atp6v1b1 , Ccdc116 , Ccl9 , Cd248 , Fastk , Fbxo44 , G6b , Gkn2 , Gnpda2 , Gpr182 , Il6ra , Inf2 , Iqub , Jazf1 , Kctd15 , Klf14 , Lancl2 , Lpin3 , Lrrc72 , Lyplal1 , Mtnr1b , Negr1 , Oxgr1 , Panx1 , Plekha8 , Ppapdc2 , Prkcdbp , Rgcc , Sik1 , Slc1a3 , Slc7a13 , Tex37 , Tmem248 , Tmem256 , Tst , Ube2e2 , Wtip . Tissues examined in each mutant and allele type are listed in S2 Table . Mutants were created with Knockout Mouse Project ( KOMP; [24 , 26 , 63] ) , or EUCOMM [64] , targeted embryonic stem cells derived from the C57BL/6N inbred mouse strain . The targeting strategies and cell lines have been described by Skarnes et al . [27] for the CSD and EUCOMM alleles , and by Valenzuela [28] for the Velocigene ( VG ) alleles . A description of each allele is available through the International Mouse Phenotyping Consortium ( IMPC ) [25]; or the Knockout Mouse Project ( KOMP ) Repository [65] . CSD/EUCOMM clones are gene trap ( TRAP ) alleles with the targeting vector expressing beta-galactosidase ( LacZ ) inserted 5’ to a critical proximal exon . The geometry of the CSD/EUCOMM allele was: splice acceptor , IRES , LacZ , SV40 polyA , hACTB promoter , neo , SV40 polyA . Two of the CSD/EUCOMM mutants we evaluated in this report did not have the standard hACTB promoter driving neo expression . One uses the mPgk promoter , and the other was promoterless and used the T2A self-cleaving strategy . VG targeting produced deletion alleles ( DEL ) that excised the entire gene between the translational start site and the 3’ UTR , leaving the LacZ at the ATG of the targeted gene . However , with very large genes only the 5’ exons/introns were removed . The geometry of the VG alleles was: LacZ cDNA , SV40 polyA signal , hUBC promoter , neo , Pkg polyA signal . Mice were produced by injection of targeted stem cells into blastocysts . Chimeras were backcrossed to C57BL/6NTac or C57BL/6NCrl inbred mice and the resulting founding heterozygous mutants were expanded by additional backcrosses . Targeting was confirmed by long-range PCR [27] or the loss-of-allele assay [66] and zygosity for the targeted allele was confirmed by a qPCR assay specific for the LacZ sequence . Additional tests were completed as per Ryder et al . [67] to confirm vector integrity . HOM mice and control , wild type ( WT ) littermates were produced by breeding of heterozygous mice and weaned at approximately 21 days of age . Breeders and weaned animals were housed in an environmentally controlled animal facility on a 12:12 hour , light:dark cycle , with lights on at 07:00Hr . Mice were fed Harlan Teklad Global Rodent Diet #2918 with a composition of 18% protein and 6% fat . Food and water were available ad libitum . Mice were euthanized at ~50-days of age by isoflurane anesthesia and thoracotomy . Tissues were rapidly removed and placed into RNAlater ( Qiagen Inc ) , or were quick frozen in liquid nitrogen . Tissues removed for RNA extraction included duodenum , kidney , liver , lung , spleen , testis , cardiac ventricle , gastrocnemius or abdominal skeletal muscle , interscapular brown adipose tissue , epididymal gonadal adipose and stomach antrum . RNA-seq analysis was completed on tissues from 15 mutant lines derived from CSD/EUCOMM clones ( TRAP mutants ) with an average of 4 . 2 tissues sequenced per mutant line , and on 29 mutant lines derived from VG clones ( DEL mutants ) with an average of 3 . 2 tissues sequenced per mutant line . For each HOM mutant mouse line , 4–5 male mice approximately 50 days of age were necropsied and tissues processed for RNA extraction . In addition , 5 age-matched WT mice were dissected and the tissues processed in parallel with the mutant mice . Tissues were homogenized with a bead mill in RNAzol-RT ( Molecular Research Center , Inc . ) . DNA and protein was separated from RNA by water precipitation and the resulting aqueous phase was mixed with an equal volume of isopropanol . Total RNA was isolated using the RNeasy kit ( Qiagen ) , including DNase treatment of the silica bound RNA to remove contaminating DNA . RNA quality was assessed by a Nucleic Acid Bioanalyzer 2100 ( Agilent ) and only RNA with an RNA Integrity Number > 8 . 0 was used for library production . For RNA-seq analysis , total RNA from homozygous biological replicates was pooled for each mutant tissue after normalizing for concentration . cDNA libraries from mRNA were created using the TruSeq kit from Illumina ( San Diego , CA ) . Single end sequencing with 50nt reads was performed on the Illumina HiSeq 2000 instrument using 4–5 multiplexed libraries per lane at the QB3 Sequencing Center at the University of California , Berkeley . The resulting sequence was parsed into individual libraries by barcode , and then preprocessed with the FastX Tool kit [68] to eliminate low quality and short reads using a minimum Phred score of 20 and a minimum read length of 18 . Generally 95% of reads passed our quality trimming and the average number of trimmed reads was greater than 20M per library . We then aligned using the Burrows-Wheeler Aligner tool [69] against the Ensembl mouse NCBI m38 build , mm10 transcriptome and MiRBase miRNA database [70–71] . Approximately 90% of quality-trimmed reads mapped to the mouse transcriptome/miRNA reference sequences . Comparisons between HOM mutant & WT reads were completed using the R Bioconductor package , DESeq2 [72] . RNAseq reads were mapped against the MM10 mouse reference genome using Tophat2 [73] . For verifying targeting , we used a criterion of normalized HOM targeted gene expression of less than 20% of WT gene expression . If the reads of the targeted gene in the HOM mutant exceeded this criterion , then we examined the distribution of HOM targeted gene reads mapped against the MM10 genome in order to determine the source of unexpected reads using the Integrated Genomic Viewer ( IGV , v 2 . 3 ) from the Broad Institute [74–75] . For the regional effects analysis , for each library we compared read counts for HOM and WT libraries , for each gene and miRNA coding sequence within +/- 500 kb of the target . Up- or down regulated genes were defined as significantly different by DESeq2 ( unadjusted p < 0 . 05 ) . The unadjusted p value was used as a less stringent criterion since the study lacked power due to the low number of biological replicates . In addition to examining local effects , we determined the genome-wide frequency of detecting up- or down-regulated genes within 500kb of each gene in every HOM library that was significantly down-regulated relative to the wild-type reference library using the DESeq statistic with an unadjusted p < 0 . 05 criterion . A conservative 500kb interval flanking targeted genes was decided upon by a preliminary analysis of the data which showed that the majority of local effects occurred between 0 and 200 kb of the targeted genes ( see results ) . All of the sequencing data generated for this study was deposited at the NCBI Short Read Archive ( SRA; Project #PRJNA280546 , KOMP Mouse Mutant Transcriptome Pilot ) . To analyze the likelihood there were regulatory elements deleted by targeting , we used the Swissregulon Database [76–77] . Transcription factor binding sites ( TFBS ) were mapped to the intron disrupted by the selection cassette for TRAP targeting , or intronic and exonic TFBS disrupted by the DEL allele . In addition , we evaluated the CHIPseq tracks for mouse tissues on the UCSC Genome Browser [78–79] . Eleven different CHIPseq tracks were utilized to screen for Pol2 , CTCF and p300 chromatin binding from 8 tissues . If the TRAP targeting vector was inserted into an intron between the promoter and 2 or more distal TFBS sites mapped with high confidence by Swissregulon , then that targeted event was scored as likely to have disrupted regulatory elements . Similarly , for the DEL targets , if two or more TFBS , along with corroborating data from CHIPseq tracks , were deleted by targeting , then that event was scored as likely to have disrupted regulatory elements . To compare the frequency of disruption of intragenic regulatory elements between TRAP and DEL targeting strategy , we used a chi-square goodness of fit test . The default criterion for the R package DESeq2 was used to determine if a gene was up- or down-regulated in HOM mutants relative to WT controls with an unadjusted p < 0 . 05 . For comparisons between DEL and TRAP mutants for the frequency of local up- or down-regulated genes , the presence of CpG or regulatory elements in the targeted promoter or disrupted introns , and for comparing the frequency of dysregulated genes within 500kb of the target , with the expected frequency based upon a genome-wide assessment , we used a chi-squared statistic . | Insertion of foreign DNA into mammalian genomes , and the deletion of DNA , may have unintended consequences extending beyond the site of the mutation . In the mouse , the insertion of foreign DNA , including foreign regulatory DNA , combined with the deletion of part of the targeted gene , had striking effects on the regulation of neighboring genes . And this ectopic local gene dysregulation occurred at high frequency in regions of high gene density . These findings emphasize the importance of evaluating the local effects on gene regulation following spontaneous insertions and deletions , and after engineering mutations , in mammalian systems . Phenotypes associated with mutations in a specific gene may be partially or entirely due to effects on neighboring genes . | [
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"genom... | 2016 | Transcriptome Analysis of Targeted Mouse Mutations Reveals the Topography of Local Changes in Gene Expression |
The evolution of altruism is a fundamental and enduring puzzle in biology . In a seminal paper Hamilton showed that altruism can be selected for when rb − c>0 , where c is the fitness cost to the altruist , b is the fitness benefit to the beneficiary , and r is their genetic relatedness . While many studies have provided qualitative support for Hamilton's rule , quantitative tests have not yet been possible due to the difficulty of quantifying the costs and benefits of helping acts . Here we use a simulated system of foraging robots to experimentally manipulate the costs and benefits of helping and determine the conditions under which altruism evolves . By conducting experimental evolution over hundreds of generations of selection in populations with different c/b ratios , we show that Hamilton's rule always accurately predicts the minimum relatedness necessary for altruism to evolve . This high accuracy is remarkable given the presence of pleiotropic and epistatic effects as well as mutations with strong effects on behavior and fitness ( effects not directly taken into account in Hamilton's original 1964 rule ) . In addition to providing the first quantitative test of Hamilton's rule in a system with a complex mapping between genotype and phenotype , these experiments demonstrate the wide applicability of kin selection theory .
One of the enduring puzzles in biology and the social sciences is the origin and persistence of altruism , whereby a behavior benefiting another individual incurs a direct cost for the individual performing the altruistic action . A solution to this apparent paradox was first provided by Hamilton [1] , who showed that a behavior increases in frequency when rb − c>0 , where c is the fitness cost to the altruist , b is the fitness benefit to the beneficiary , and r is their genetic relatedness . While this rule has provided an important framework in which to conceptualize social evolution [2]–[12] , it is based on several assumptions , including weak selection , additivity of costs and benefits of fitness components , and a special definition of relatedness that uses statistical correlations among individuals rather than genealogy to describe similarity . Several studies investigated how violations to these assumptions may lead to failures of Hamilton's original 1964 rule [13]–[21] , but it is yet unclear how the combined effects of these factors may affect the evolution of altruism in organisms with a complex mapping between genotype and phenotype . It also remains to be investigated to what extent Hamilton's original 1964 rule is influenced by factors such as drift and interactions between loci within genomes [22] , [23] . To investigate how a complex mapping between genotype and phenotype can affect the course of social evolution , we conducted artificial evolution with groups of robots in simulations by modifying a system recently developed to investigate the evolution of cooperative transport [24] . Eight small ( 2×2×4 cm ) Alice robots [25] and eight food items were placed in a foraging arena with one white wall and three black walls . The performance of robots was proportional to the number of food items successfully transported to the white wall and the robots were given the option to allocate the fitness rewards of successfully transported items to themselves ( selfish behavior ) or share them with other group members ( altruistic behavior—in this case the fitness reward of the food item was shared equally between the seven other robots in the group ) . By choosing appropriate fitness values for shared and non-shared food items ( see Materials and Methods ) , it was possible to precisely manipulate the benefits and cost of helping behavior ( i . e . , the c and b values of Hamilton's rule , see Materials and Methods ) . The robots were equipped with two motorized wheels and three infrared distance sensors that could detect food items up to 3 cm away , a fourth infrared distance sensor with 6 cm range allowing to distinguish food items from robots , and two vision sensors mounted on top of the robot to perceive the color of the arena walls ( Figure 1A ) . These six sensors were connected to a neural network comprising six input neurons , three hidden neurons , and three output neurons ( Figure 1B ) . Two output neurons determined the speeds of the wheels , while the third neuron determined whether the food items successfully collected were shared or not . The genome of the robots ( 33 genes ) encoded the 33 connection weights of the neural network ( see Materials and Methods ) and thus determined how sensory information was processed and how robots behaved . Our analyses reveal that this system resulted in both pleotropic and epistastatic effects as well as a high proportion of mutations having strong effects on behavioral traits ( i . e . , leading to deviations from the assumption of weak selection ) .
We conducted 500 generations of selection in a population consisting of 200 groups . The probability of robots to transmit their genomes from one generation to the next was proportional to their individual fitness ( see Materials and Methods ) . The selected genomes were randomly assorted and subjected to crossovers and mutations to create the 1 , 600 new genomes ( 200 groups of 8 robots ) forming the next generation [24] . This experimental setup allowed us to independently manipulate the relatedness between robots within a group and the cost-to-benefit ratios of helping . To quantitatively test Hamilton's rule for the evolution of altruism , we investigated how the level of altruism ( defined as the proportion of food items shared with other group members ) changed over generations in populations with five different c/b ratios and five relatedness values ( see Materials and Methods ) . For each of these 25 treatments , the selection experiments were conducted in 20 independently evolving populations . Because of the impossibility to conduct hundreds of generations of selection with real robots , we used physics-based simulations that precisely model the dynamical and physical properties of the robots . We previously showed that evolved genomes can be successfully implemented in real robots [26] that display similar behavior to that observed in the simulations . Because the 33 genes were initially set to random values , the robots' behaviors were completely arbitrary in the first generation . However , the robots' performance rapidly increased over the 500 generations of selection ( Figure 2 ) . The level of altruism also rapidly changed over generations with the final stable level of altruism varying greatly depending on the within-group relatedness and c/b ratio ( Figure 3 ) . When the c/b value was very small ( 0 . 01 ) , the level of altruism was very high in the populations where within-group relatedness was positive ( i . e . , 0 . 25 , 0 . 5 , 0 . 75 , and 1 . 00 ) and close to zero when robots were unrelated ( Figure 4 ) . In the treatments with other c/b values , the level of altruism was also very low when the relatedness was close to 0 and the level of altruism also rapidly increased when the relatedness became higher than a given value . In all cases , the transition occurred when r became greater than c/b , as predicted by Hamilton's rule . When the relatedness was equal to c/b , there was an intermediate level of altruism with the frequency of altruistic acts not differing significantly from the initial value , which was 0 . 5 ( four one-sample Wilcoxon tests , df = 19 , all p>0 . 368 ) . This is the expected pattern because the inclusive fitness of robots , comprising both their own fitness points and those gained from altruists , is independent of whether or not they behave altruistically when r = c/b . Under such conditions , the level of altruism should vary only as a result of drift over generations , thus leading to important between-population variation in the level of altruism . Consistent with this prediction , the standardized variance ( F = Var ( p ) /pq ) in altruism when r was equal to c/b ( F = 0 . 204 ) was significantly higher than when r was greater than c/b ( F = 0 . 018; Mann-Whitney , df = 13 , p = 0 . 002 ) and when r was smaller than c/b ( F = 0 . 015; Mann-Whitney , df = 13 , p<0 . 003 ) . The fact that the level of altruism remained slightly greater than 0 when r was smaller than c/b and slightly lower than 1 when r was greater than c/b can be explained by mutations maintaining some behavioral variability in the population . In line with this view of the level of altruism being at mutation-selection equilibrium , the level of altruism became significantly closer to zero ( Pearson's r = 0 . 643; Mann-Whitney , df = 13 , p<0 . 001 ) as the strength of selection increased ( i . e . , when the value r − c/b became more negative , only negative values of r − c/b considered for the correlation ) . Similarly , the level of altruism became significantly closer to 1 ( Pearson's r = 0 . 805; Mann-Whitney , df = 13 , p<0 . 004 ) as the strength of selection for higher levels of altruism increased ( i . e . , when the value r − c/b increased , only positive values of r − c/b considered in the correlation ) . To determine whether mutations in our neural network had pleiotropic and epistatic effects and whether there were departures from weak mutations effects , we conducted additional experiments at the last generation in two treatments with intermediate r and c/b values ( treatment 1: r = 0 . 25 , c/b = 0 . 75; treatment 2: r = 0 . 75 , c/b = 0 . 25 ) . First , for each treatment , we subjected 4 , 000 individuals ( one in each group ) to a single mutation of moderate effect ( see Materials and Methods ) . In the first experiment , performance was significantly affected by a much higher proportion of the mutations than the level of altruism ( Table 1 ) . Importantly , 1 . 36% of the mutations affecting the level of altruism also translated into a significant change in performance , indicating widespread pleiotropic effects . Similar results were obtained in the second experiment with 4 . 91% of the mutations affecting the level of altruism also significantly affecting performance . Second , we tested for epistatic effects by comparing the effect of a single mutation in 4 , 000 individuals with two allelic variants at another locus ( see Materials and Methods ) . The genetic background significantly influenced the effect of the mutation in 2 , 371 ( 59 . 3% ) of the cases in the first treatment and 2 , 336 ( 58 . 4% ) of the cases in the second treatment . These results demonstrate that epistatic interactions are also widespread . Finally , our experiments showed frequent departures from weak effects on behavior and fitness . Performance changed by more than 25% for 1 , 616 ( 40 . 4% ) of the mutations in the first treatment and 1 , 776 ( 44 . 4% ) of the mutations in the second treatment , and the level of altruism changed by more than 25% for 552 ( 13 . 8% ) and 1 , 808 ( 45 . 2% ) of the mutations in the first and second treatment , respectively .
Although Hamilton's original 1964 rule provides a general framework of how natural selection works [17] , [27] , its theoretical and empirical applications usually involve the limiting assumptions of weak selection and additivity of costs and benefits of fitness components as well as the absence of pleiotropic and epistatic gene interactions [15] , [16] , [28] ( but see [13] for relaxations of some of these assumptions in concrete applications ) , leading to the conclusion that the rb − c>0 rule should be used with caution when there are pleiotropic , epistatic , and non-additive effects [29] , [30] . Interestingly , the genetic architecture of the robots in our system also led to departure from all these assumptions with the exception of non-additivity of costs and benefits of fitness components . However , the occurrence of non-additive ( epistatic ) effects of mutations at several loci in the genome leads to a situation that is conceptually similar to non-additivity of costs and benefits of fitness components [22] . In both cases , the fitness depends non-additively on gene action , with the interaction involving alleles at two loci on the same genome in the case of non-additive ( epistatic ) gene effects , and alleles at two homologous loci on two different genomes in the case of non-additivity of costs and benefits of fitness components . Despite the fact that the assumptions mentioned above were not fulfilled , Hamilton's original 1964 rule always accurately predicted the conditions under which altruism evolved in our system . Whatever the c/b value used , altruism always evolved in populations where r was greater than c/b . This finding is important given that the assumption of weak selection , additivity of costs and benefits of fitness components and absence of pleiotropic and epistatic gene interactions are also likely to be violated in real organisms that also have a complex mapping between genomes and phenotypes . Another important issue relates to the measure of relatedness . There has been considerable confusion in the literature since relatedness coefficients actually measure more than pedigree coefficients and because different derivations of Hamilton's rule take as their focal trait a variety of different quantities [16] , [17] , [30] . In the original derivation of Hamilton's rule [1] and many that followed ( e . g . , [12] , [31] ) , the trait of interest was the genetic value at a single gene position and the regression coefficient of relatedness corresponded to an identity in state relative to the population average [31] . The interest in social evolution where social partners tend to be genealogical kin [1] has led to the use of Wright's F statistics as a measure of relatedness ( e . g . [12] , [22] , [32] ) . Alternatively , Hamilton's rule has been derived to express the change in the social behavior phenotype ( e . g . , [16] , [22] , [33] , [34] ) , often considered as a quantitative trait with many underlying gene positions contributing . In this case the coefficient of relatedness represents a regression of some measure of the individual's genetic value for that trait such as a breeding value [17] , p score [16] , gene frequency [1] , [12] , or partner phenotype on its own phenotype value [34] . Interestingly , the simple genetic structure of our groups leads to all these measures of relatedness being identical . In all our experiments groups were started by individuals randomly chosen from the previous generations . The relatedness between these founding individuals is therefore zero as they are not more genetically or phenotypically similar within groups than between groups . Positive within-group relatedness was created by cloning the founding individuals . Thus , positive relatedness was only due to one-generation coancestry and the probability that benefits of altruism being provided to a clone compared to an unrelated individual . Such a breeding system is conceptually very similar to that Hamilton had in mind when trying to explain the evolution of reproductive altruism in social insects where the sterile ( altruistic ) workers are the offspring of their mother queen ( the individual benefitting from the altruistic worker behavior ) . The relatedness in such a system can also be described in terms of identity by descent [35] , which provides an approximation of identity in state for rare genetic variants ( see [31] for a recent review ) . Of interest would be to test in future studies how the evolution of altruism is influenced by more complex population structures where the effect of strong selection may lead to variation in within-genome differences in the covariance between genes in different individuals . Because the rewards provided by the food items were either assigned to the focal individual who successfully transported it ( selfish behavior ) or shared equally between all the other group members ( altruistic behavior ) , the fitness effects were additive and there were no synergetic effects . Thus , the cost incurred by an individual sharing altruistically a food item and the benefits to the other group members was not dependent on the recipients' genotypes and the proportion of them being altruistic . The lack of such synergetic effects results in the costs and benefits associated with an altruistic act being independent of the genotypic composition of the groups and the overall level of altruism in the population ( i . e . , there are no frequency-dependent effects ) . In natural systems there are frequently synergetic effects and this is one of the main reasons why it is not possible to reliably quantify the cost and benefits associated with altruistic actions ( e . g . , [15] , [16] , [36] , [37] ) . From an empirical perspective , our study is therefore valuable because there have been many tests of Hamilton's rule , but these studies are usually not quantitative due to the impossibility of assessing the costs and benefits of altruistic acts , even in the most simple social systems such as those documented in some bacteria [10] , [38] , social amoebae [39] , or even synthetic microbial systems [36] . Our study also demonstrates that contrary to some misunderstandings [3] , kin selection does not require specific genes devoted to encode altruism or sophisticated cognitive abilities , as the neuronal network of our robots comprised only 33 neurons . More generally , this study reveals that a fundamental principle of natural selection also applies to synthetic organisms when these have heritable properties [40] .
Groups of eight Alice micro-robots and eight food items were placed into a 50×50 cm foraging arena . We chose a collective foraging task to investigate the evolution of altruism , because foraging efficiency is a key factor for many biological social groups such as ant or bee colonies [41] . Foraging required robots to locate a food item , to position themselves in front of the item , and to push it into a 4-cm-wide target zone along the white wall of the arena ( the three other walls were black ) . Robots were controlled by a feed-forward neural network consisting of six sensory input neurons , one bias input neuron , and six neurons with sigmoid activation . The robots had four infrared distance sensors , three of them sensing objects within a 3 cm range and the fourth , which was placed higher , having a 6 cm range . These sensors allowed robots to locate the food items and distinguish them from robots . Robots were also equipped with two vision sensors to see the white wall [24] . These six sensory inputs were scaled to a range of [−1; 1] . In addition to the sensory inputs the neural network also comprised a bias input set to a constant value of −1 , which was used to encode the neuron firing threshold . These seven inputs were connected to three neurons in a hidden layer , which in turn connected to three output neurons . The strength of these 33 connections was determined by 33 genes , whose values ranged from 0 to 255 ( i . e . , 8 bit resolution per gene ) . The activation of each of the six hidden and three output neurons was calculated by multiplying each of its input values by its associated connection weight , summing over all inputs , and passing the sum through the continuous tanh ( x ) function to obtain the neuron's activation value in the range of [−1; 1] . The activation value of the first output neuron controlled the left motor speed , the second the right motor speed , and the third whether or not the successfully pushed food items were shared with other group members . We used five different levels of relatedness in the experiments . To create groups of unrelated individuals ( r = 0 ) , we randomly distributed the 1 , 600 individuals in the 200 groups . To obtain groups with a relatedness of r = 1 , we cloned one individual 7 times and formed groups with 8 genetically identical individuals . To create groups with a relatedness of approximately r = 0 . 75 , we used two individuals ( A and B ) and cloned one seven times ( clone proportion A:B = 1:7 ) . The resulting relatedness in these groups was thus r , 0 . 7492 . To create groups with a relatedness close to r = 0 . 5 , we similarly composed each group of three types of clones but in proportions 6:1:1 , which led to r , 0 . 5357 . To create groups with a relatedness close to r = 0 . 25 , we again composed each group of three types of clones , but this time using proportions 3:3:2 , which resulted in a relatedness of r , 0 . 2468 . The genetic composition of groups thus differed from that of most animal groups in that some individuals were clones ( r = 1 ) rather than belonging to kin classes such as full siblings ( r = 0 . 5 ) or cousins ( r = 0 . 125 ) . However , in the absence of preferential interactions between kin , social evolution should be influenced by the average group relatedness . This is because genetic relatedness depends on interaction probabilities of genes [4] , which in our model is equivalent to interaction probabilities between clonal individuals . Our experimental setup prevented preferential interactions between individuals by randomizing starting positions , having all robots being identical , and using a neural network that did not allow individuals to memorize past interactions . To manipulate the c and b value of Hamilton's rule we modified the fitness values for shared and non-shared food items that were successfully transported . When non-shared , a food item provided a reward c to the selfish individual . When shared , the food item provided no direct benefit to the focal individual but a benefit b equally shared by the seven other robots in the group . The c/b ratios used were calculated using Queller's approach [42] . We used a value of 0 . 01 for the smallest c/b ratio because with a value of c/b = 0 , there is no selection for foraging efficiency when r = 0 , hence resulting in many populations going extinct ( because no items were successfully foraged ) . The foraging efficiency of each group was evaluated 10 times for 60 seconds and the inclusive fitness of each individual was estimated according to the number of food items collected and not shared + the number of food items that other group members collected and shared ( these values being multiplied by c and b/7 , respectively ) . The probability of the genome of a given robot to contribute to the next generation was directly proportional to the robot's inclusive fitness ( roulette wheel selection with replacement [43] ) . Selected genomes were paired to conduct a crossing over with a probability of 0 . 005 . The resulting genomes were subjected to mutation ( probability of 0 . 005 per bit; i . e . , 0 . 04 per gene ) . This process of selection , recombination , and mutation was repeated until there were enough genomes for the 1 , 600 individuals ( 200 groups ) of the next generation . The level of altruism was calculated for each group as the proportion of collected food items that was shared within a group: A = n ( a ) / ( n ( a ) + n ( s ) ) , where n ( a ) was the number of collected food items individuals shared and n ( s ) the number of items individuals did not share . All 25 selection experiments were repeated 20 times ( 20 independent replicates ) . Evolution lasted for 500 generations for each experimental condition . For statistic analyses , the fitness and the level of altruism of all 200 groups in each of the 20 replicates were averaged over the last 10 generations . Means were compared with Mann-Whitney tests as Shapiro-Wilk tests showed that in many treatments the data did not follow a normal distribution . In each of the individually evolving populations , altruistic interactions always occurred within groups , while the reproductive competition occurred at the level of the population . To manipulate the relatedness , we cloned genomes for each group and formed groups of different proportions of clones . Each group was composed of k different types of clones with respective frequencies xi , i = 1 … k , . The genetic relatedness r quantifies the greater ( or smaller ) genetic similarity between individuals compared to the population average . Using the regression definition of relatedness [16] , [42] , where j indexes the individuals in the population and l indexes the social partner of j . In our system corresponds to the average probability of a focal individual being genetically identical to another member of the population and to the average probability of a focal individual being a genetically identical clone of another member of its group . Assuming that populations contain m groups with n individuals each , In all experiments the independently evolving populations consisted of m = 200 groups , each composed of n = 8 individuals . Given that the evolution of social behavior is influenced by the relative rather than the absolute values of costs and benefits , we arbitrarily set and calculated the costs c and benefits b for the expected transition from selfish to altruistic behavior as To test for pleiotropic effects , we studied the outcome of a single mutation on two behavioral measures , performance and altruism . Performance was determined as the number of food items collected by an individual , and the level of altruism as the percentage of these food items shared with other group members . One mutation was performed on one individual in each of the 200 groups for each of the 20 replicates at the last generation for each of two treatments with intermediate values of relatedness and c/b ratio ( treatment 1∶ r = 0 . 25 , c/b = 0 . 75; treatment 2∶ r = 0 . 75 , c/b = 0 . 25 ) . All 8 , 000 individuals were subjected to a mutation of medium effect . This was achieved by flipping , for each individual , the third of the eight bits of a randomly chosen gene , hence always resulting in a mutation size ±32 . We chose this value because it was the median value of the mutations ( range ±128 ) the robots were subjected to in the 500 generations of selection . The performance and level of altruism of each mutated individual was then evaluated in 100 independent trials in its group and compared to its performance and level of altruism before the mutation ( Wilcoxon rank sum tests using a 5% significance level ) . For the first treatment ( r = 0 . 25 , c/b = 0 . 75 ) , rank sum tests could be conducted for 3 , 961 out of the 4 , 000 individuals as 39 individuals did not collect any food item either before or after the mutation , hence preventing determination of the level of altruism . For the second treatment ( r = 0 . 75 , c/b = 0 . 25 ) , rank sum tests could be conducted for 3 , 848 out of the 4 , 000 individuals , as 152 individuals did not collect any food item either before or after the mutation . To test for epistatic effects , we used the same individuals as used in the experiment on pleiotropic effects and assessed the performances of individuals without a mutation F ( 0 ) and with a mutation F ( A ) . We then subjected each of these 16 , 000 individuals ( 8 , 000 without and 8 , 000 with a mutation ) to a new mutation B ( also of median effect ) and assessed their fitnesses F ( B ) and F ( AB ) . We then compared whether this new mutation had a similar effect on the fitness of individuals with and without the first mutation by evaluating each of the resulting 32 , 000 individuals in 100 independent trials and calculating z scores based on the standard deviation ( SD ) and mean fitness Z scores could be calculated for 3 . 998 and 3 , 978 out of the 4 , 000 individuals for the first and second treatment , respectively ( 2 and 22 individuals , respectively , did not collect any food items ) . Statistics used a 5% ( z = 2 ) significance level . Models of social evolution , as most models in evolutionary biology , usually resort to weak selection , where different individuals have very similar fitness . To test whether the mutations frequently had large effects ( i . e . , whether there was departure from weak selection ) , we determined how frequently a mutation of median effect resulted in a greater than 25% change in performance and the level of altruism ( 4 , 000 individuals per treatment ) . Note that the value of 25% was arbitrarily chosen as there is no convention of what change in fitness can be assumed to be a departure of weak selection . Again Wilcoxon rank sum tests were performed on the 100 trials per individual with a 5% significance level . | One of the enduring puzzles in biology and the social sciences is the origin and persistence of altruism , whereby a behavior benefiting another individual incurs a direct cost for the individual performing the altruistic action . This apparent paradox was resolved by Hamilton's theory , known as kin selection , which states that individuals can transmit copies of their own genes not only directly through their own reproduction but also indirectly by favoring the reproduction of kin , such as siblings or cousins . While many studies have provided qualitative support for kin selection theory , quantitative tests have not yet been possible due to the difficulty of quantifying the costs and benefits of helping acts . In this study , we conduct simulations with the help of a simulated system of foraging robots to manipulate the costs and benefits of altruism and determine the conditions under which altruism evolves . By conducting experimental evolution over hundreds of generations of selection in populations with different costs and benefits of altruistic behavior , we show that kin selection theory always accurately predicts the minimum relatedness necessary for altruism to evolve . This high accuracy is remarkable given the presence of pleiotropic and epistatic effects , as well as mutations with strong effects on behavior and fitness . In addition to providing a quantitative test of kin selection theory in a system with a complex mapping between genotype and phenotype , this study reveals that a fundamental principle of natural selection also applies to synthetic organisms when these have heritable properties . | [
"Abstract",
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] | [
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] | 2011 | A Quantitative Test of Hamilton's Rule for the Evolution of Altruism |
Behavior and physiology are orchestrated by neuropeptides acting as central neuromodulators and circulating hormones . An outstanding question is how these neuropeptides function to coordinate complex and competing behaviors . In Drosophila , the neuropeptide leucokinin ( LK ) modulates diverse functions , but mechanisms underlying these complex interactions remain poorly understood . As a first step towards understanding these mechanisms , we delineated LK circuitry that governs various aspects of post-feeding physiology and behavior . We found that impaired LK signaling in Lk and Lk receptor ( Lkr ) mutants affects diverse but coordinated processes , including regulation of stress , water homeostasis , feeding , locomotor activity , and metabolic rate . Next , we sought to define the populations of LK neurons that contribute to the different aspects of this physiology . We find that the calcium activity in abdominal ganglia LK neurons ( ABLKs ) , but not in the two sets of brain neurons , increases specifically following water consumption , suggesting that ABLKs regulate water homeostasis and its associated physiology . To identify targets of LK peptide , we mapped the distribution of Lkr expression , mined a brain single-cell transcriptome dataset for genes coexpressed with Lkr , and identified synaptic partners of LK neurons . Lkr expression in the brain insulin-producing cells ( IPCs ) , gut , renal tubules and chemosensory cells , correlates well with regulatory roles detected in the Lk and Lkr mutants . Furthermore , these mutants and flies with targeted knockdown of Lkr in IPCs displayed altered expression of insulin-like peptides ( DILPs ) and transcripts in IPCs and increased starvation resistance . Thus , some effects of LK signaling appear to occur via DILP action . Collectively , our data suggest that the three sets of LK neurons have different targets , but modulate the establishment of post-prandial homeostasis by regulating distinct physiological processes and behaviors such as diuresis , metabolism , organismal activity and insulin signaling . These findings provide a platform for investigating feeding-related neuroendocrine regulation of vital behavior and physiology .
Animals continuously adjust to changes in their external and internal environment [1–3] and a central question is how homeostatically regulated behaviors and physiological processes critical for survival interact . In metazoans , neuropeptides play important roles in orchestrating homeostasis by mediating neuromodulation in circuits of the CNS and acting on peripheral tissues as circulating hormones [4–6] . We ask here whether a neuroendocrine system , using a single neuropeptide , can play a role in modulating complex behavioral and physiological processes . The neuropeptide leucokinin ( LK ) in the fly Drosophila is an excellent candidate to study modulation at multiple levels because it is expressed in three small sets of neurons and has been implicated in several homeostatically regulated functions , including sleep , feeding , water balance and response to ionic stress [7–13] . Previous in vitro work has suggested that one important function of LK in adult Drosophila and several other insect species is to regulate fluid secretion in the Malpighian ( renal ) tubules ( MTs ) , and , thus , to play an important role in water and ion homeostasis [9 , 14–17] . More recently , additional LK functions have been inferred from in vivo genetic experiments , such as roles in organismal water retention , survival responses to desiccation and starvation , subtle regulation of food intake , and chemosensory responses [10 , 13 , 18–21] . Furthermore , it has been shown that diminished LK signaling results in an increase in postprandial sleep [12] and impaired locomotor activity [11] . While we know that LK is critical for behavioral and physiological homeostasis , it is not clear how a relatively small population of less than 30 neurons can mediate diverse responses to environmental perturbation . Moreover , it remains unclear whether the different functions revealed are all part of a global orchestrating role of LK in which central and peripheral actions are coordinated at different levels . In the light of this , it is of interest to identify the functional roles of each of the three sets of LK neurons and to determine how these contribute to a coordinated modulation of homeostasis . To determine the role of LK signaling in adult post-feeding physiology and behavior , we generated novel Lk and Lkr mutant flies . By testing these mutants in various feeding-related physiological and behavioral assays , we demonstrate that LK signaling regulates water homeostasis and associated stress , feeding , locomotor activity , and metabolic rate . Based on these data , we propose that the homeostatic roles of LK can be linked to the regulation of post-feeding physiology and behavior . The abdominal ganglion LK neurons ( ABLKs ) , but not the two sets in the brain , display increased calcium-signaling activity in response to rehydration ( drinking ) following desiccation . Next , to reveal novel targets of LK peptide , we mapped the distribution of Lkr expression . Using two independent Lkr-GAL4 lines to drive expression of GFP , we show that Lkr is expressed in various peripheral tissues , including the gut , Malpighian tubules and chemosensory cells , which comports well with the functions suggested by the mutant analysis . In addition , the expression of the Lkr in the insulin-producing cells ( IPCs ) and the phenotypes seen after targeted receptor knockdown in these cells indicate interaction between LK and insulin signaling . Thus , the three different populations of LK neurons use LK to modulate post-prandial physiology by acting on different targets in the CNS , as well as cells of the renal tubules and intestine .
To investigate the role of Lk signaling in modulation of feeding-associated physiology and behavior , we utilized CRISPR-Cas9 gene editing to generate GAL4 knock-in mutants for Lk and Lkr ( Fig 1A ) . First , we tested the efficiency of the Lk and Lkr mutants by quantitative real-time PCR ( qPCR ) and immunolabeling . In qPCR experiments , we found an 80% reduction of Lk expression , whereas Lkr mRNA was reduced by about 60% ( Fig 1C ) , confirming the efficacy of these gene-edited mutants for Lk and Lkr ( residual expression presumably reflects some level of transcriptional read-through of the inserted GAL4 cassette ) . In the homozygous Lk mutants , LK immunolabeling is completely abolished in all cells of the CNS ( Fig 1B and 1D ) , establishing that Lk mutants do not produce a functional peptide . To verify that signaling by LKR is disrupted in Lkr mutants , we measured LK peptide levels by immunolabeling . The rationale for this was that we predicted that Lkr mutant flies would compensate for the diminished receptor expression , for instance in MTs , by increasing production of the peptide in neurosecretory cells to maintain homeostasis . Indeed , LK immunolabeling was elevated in the abdominal LK neurons ( ABLKs ) ( Fig 2A and 2B ) , and the cell bodies of these neurons were also enlarged ( Fig 2C ) , probably due to the increased peptide production [see [22]] . Interestingly , LK immunolabeling in the lateral horn LK ( LHLK ) neurons of the brain does not change in Lkr mutant flies ( Fig 2D and 2E ) , suggesting these neurons are not subjected to autoregulatory feedback . Thus , LK levels are differentially regulated in neurons of the brain versus those of the abdominal ganglion , and there appears to be feedback between receptor and peptide expression in abdominal ABLK neurons of Lkr mutant flies . A possible explanation for this is that the ABLKs are neurosecretory cells that target peripheral tissues such as MTs with hormonal LK ( see [10] ) and periphery-to-CNS feedback may be critical for homeostatic regulation . Having validated the loss of function in the Lk and Lkr mutants , we tested them for phenotypes that have been previously associated with LK signaling . Previous studies , in vitro or using different types of manipulations , have demonstrated a role of LK signaling in MT secretion [14 , 17] and a possible secondary effect of this on desiccation and starvation resistance [10 , 19 , 21] . We therefore recorded survival of Lk and Lkr mutant flies maintained under desiccation and starvation conditions . Both homozygous and heterozygous Lk ( Lk-GAL4CC9 ) and Lkr ( Lkr-GAL4CC9 ) mutants , survived longer under these stresses ( Fig 3A–3D ) . To determine whether changes in water content contributed to these survival differences , we assayed flies for their water content under normal conditions and after 9 hours of desiccation . As expected , Lk and Lkr mutant flies contained more water than control flies did under normal conditions as well as after desiccation ( Fig 3E ) . Therefore , loss of Lk/Lkr signaling promotes water retention and improves survival under desiccation conditions . Next , we asked which of the LK neurons might be responsible for these effects on water homeostasis and associated stresses . To determine which of the LK neurons display activity-dependent changes in response to starvation , desiccation , and/or water ingestion we monitored the calcium activity of LK neurons using the CaLexA system [23] . By expressing the CaLexA sensor with the Lk-GAL4 driver , we found that only the ABLKs , but not the LK neurons in the brain , were activated following re-watering ( drinking ) ( Fig 4A ) . The activation of ABLKs can be seen as increased GFP intensity as well as a greater number of detectable cells ( Fig 4B and 4C ) . Moreover , these cells did not display activation when the flies are placed under starvation , desiccation , or on a standard diet . These results further support the role of ABLKs in the regulation of water homeostasis . Having established a role for LK signaling in water homeostasis and activation of ABLKs in response to water intake , we asked whether LK signaling might affect other aspects of feeding-associated physiology and behavior . Hence , we examined Lk and Lkr mutants in various assays to monitor feeding propensity and food intake over different time scales . First , we tested the Lk and Lkr mutants for the strength of the proboscis extension reflex ( PER ) in response to different sucrose concentrations ( Fig 5A–5D and S1 Table ) to quantify gustation and/or the motivation to feed . The Lk mutant flies displayed a reduced PER ( Fig 5A ) and this phenotype was rescued by re-expressing the peptide by UAS-Lk in the homozygous GAL4-insertion mutants ( Fig 5B ) . This reduction in PER was also seen after inhibition of LK neurons by targeted expression of UAS-Tetanus toxin ( TNT ) ( Fig 5C ) . However , Lkr mutant flies displayed the opposite behavior , showing increased PER that could also be rescued by UAS-Lkr expression ( Fig 5D ) . This suggests a role for LK signaling in gustation ( see also [8 , 18] ) , but the opposite behavior seen in peptide and receptor mutant flies is difficult to explain . Maybe in the gustatory system LK acts through an alternative receptor type or different coupling to downstream signaling pathway . Next , we assayed for long-term defects in feeding by examining the mutants in a modified capillary feeding ( CAFE ) assay ( Fig 5E ) . Both , Lk and Lkr mutants exhibited a decrease in food intake compared to controls , with the homozygous mutants displaying a much larger decrease than the heterozygous ones ( Fig 5E ) . Finally , we used an assay for short-term feeding ( over 30 min ) , in which the amount of ingested blue-dyed food was measured in fly homogenates to determine differences in meal sizes . In this assay , there was no difference in food intake between mutant flies and controls , either in starved or fed conditions ( Fig 5F ) . This lack of effect was also seen when the LK neurons were inhibited by targeted expression of UAS-TNT ( Fig 5G ) . Therefore , LK neurons seem to regulate the propensity of animals to initiate reflexive feeding , without affecting total meal volume in the short-term , but probably contributes to reduced food intake over longer time frames . Physical activity and metabolic rate are acutely regulated by food availability and environmental stress . To determine whether LK regulates these processes we simultaneously recorded animal activity and metabolic rate using stop-flow indirect calorimetry [24] . Single Lk and Lkr mutant flies were tested for locomotor activity and metabolic rate ( vCO2 ) over a 24-hour period . The Lk mutants displayed reduced locomotor activity , with homozygotes displaying almost no morning or evening activity peaks ( Fig 6A and 6B ) . The metabolic rate of these mutant flies was also reduced over the entire period of observation ( Fig 6C and 6D ) . The Lkr mutants displayed a similar reduction in both locomotor activity and metabolic rate , except that the heterozygotes displayed no change in locomotor activity ( Fig 6E–6H ) . We also used the standard Drosophila activity monitor system ( DAMS ) to verify our locomotor-activity results from the above setup . Indeed , we obtained results similar to those above , with Lk and Lkr mutants displaying reduced activity ( S1A and S1B Fig ) . Taken together , these findings suggest that disruption of Lk-signaling leads to dysregulation of metabolic rate and altered locomotor activity . The expression of Lk and Lkr in the central nervous system ( CNS ) and periphery raises the possibility that distinct neuronal populations or neural circuits regulate different behaviors . The Lk and Lkr-GAL4 knock-in mutants ( GAL4CC9 ) that we generated using CRISPR-Cas9 gene editing enable simultaneous knockdown and visualization of the distribution of peptide- and receptor-gene expression in different tissues . Since the GAL4 is inserted within the gene itself , the retention of all the endogenous regulatory elements should in theory allow GAL4 expression to mimic that of the native Lk and Lkr . Indeed , the Lk-GAL4CC9 expression observed ( S2 Fig ) is very similar to that seen in earlier reports using conventional Lk-GAL4 lines [8 , 13] . With a few exceptions , the pattern of Lk-GAL4CC9 expression also matches that of LK immunolabeling ( S2C and S2D Fig ) . Notably , a set of 5 pairs of GFP-labeled lateral neurosecretory cells does not display LK immunolabeling in third instar larvae or adult flies ( S2C and S3A Figs ) . These neurons are known as ipc-1 and ipc-2a , and they co-express ion transport peptide ( ITP ) , short neuropeptide F ( sNPF ) and Drosophila tachykinin ( DTK ) [25 , 26] . Since the cellular expression pattern of Lkr in Drosophila is poorly understood we utilized our Lkr-GAL4CC9 line to drive GFP-expression and analyzed CNS and peripheral tissues . We compared the expression of our Lkr-GAL4CC9 to that of another Lkr-GAL4 ( Lkr-GAL4::p65 ) generated using a BAC clone as described previously [27] and found overlapping expression patterns between the two drivers . In the periphery , the stellate cells of the MTs express Lkr-GAL4CC9 ( Fig 7A ) as expected from earlier work that demonstrated functional expression of the Lkr in these cells [14 , 17] . Furthermore , Lkr-GAL4CC9 driven GFP was detected in endocrine cells of the posterior midgut ( Fig 7B ) , in the anterior midgut ( Fig 7C and 7D ) , and in muscle fibers of the anterior hindgut and rectal pad ( Fig 7E and 7F ) . Lkr-GAL4CC9>GFP expression was also present in peripheral neurons ( S4A Fig ) , the dorsal vessel , as well as axons innervating it ( S4A Fig ) , and sensory cells of the legs , mouthparts , and anterior wing margin ( S4B–S4D Fig ) . In third instar larvae , we could also detect Lkr-GAL4CC9 expression in the stellate cells of the MTs ( S5A and S5D Fig ) , in the ureter ( S5A Fig ) , in muscle fibers of the gastric caeca , midgut and hindgut ( S5A–S5C Fig ) , as well as in the endocrine cells of the midgut ( S5B and S5C Fig ) . The BAC-engineered Lkr-GAL4 had a much sparser expression pattern , with GFP detected in stellate cells of larval ( S6A Fig ) and adult ( S6C–S6E Fig ) MTs , and in the larval hindgut ( S6B Fig ) . Interestingly , the shape of the stellate cells in adults varied between cuboidal and the more typical star-shaped morphology ( S6C and S6D Fig ) . In general , the expression of the BAC/promoter fusion line is sparser than the new Lkr-GAL4CC9 line , but both are in agreement with available immunolabeling data on the MTs ( S5D and S6E Figs ) , suggesting that they largely recapitulate the endogenous receptor expression pattern . To further validate the authenticity of the GFP expression in the periphery , we examined Lkr expression in two publicly available resources for gene expression , FlyAtlas [28] and Flygut-seq [29] . FlyAtlas reveals that Lkr is expressed in the larval and adult hindgut , MTs and CNS ( Fig 7G ) . Moreover , the Flygut-seq data base shows that Lkr is expressed in enteroendocrine cells of the midgut , in visceral muscles near the hindgut , and in the gut epithelium [29] ( Fig 7H ) . Thus , the transcript expression data correlate well with the GAL4 expression pattern . The expression pattern of Lkr-GAL4CC9 and the Lkr-GAL4 also matched well within the brain . Both GAL4 lines drive GFP expression in a relatively large number of neurons in the larval ( S3B and S7A Figs ) and adult CNS ( S7B–S7C and S8 Figs ) , but we focus here on two sets of identified peptidergic neurons in the brain ( Fig 8 ) . Both Lkr-GAL4CC9 and Lkr-GAL4 , drove GFP expression in the brain IPCs , as identified by anti-DILP2 staining , and in the 5 pairs of brain ipc-1/ipc-2a cells , that display anti-ITP staining ( Fig 8 ) . This receptor expression is supported by analysis of a single-cell transcriptome dataset of the entire Drosophila brain [30] , which reveals coexpression between Lkr and DILP2 , 3 and 5 , as well as Lkr and ITP ( Fig 9 ) . The data set shows that Lkr is widely expressed in the Drosophila brain , with transcripts expressed in cells of various clusters , including the peptidergic cell cluster ( marked with dimm ) and the glial cell cluster ( marked with repo ) ( Fig 9A ) . Within the peptidergic cell cluster , Lkr is coexpressed with ITP ( Fig 9B ) and in IPCs along with DILP2 , 3 and 5 ( Fig 9C and 9D ) . Our receptor expression data further emphasizes the important interplay between LK signaling within the CNS and systemic LK action that targets several peripheral tissues , which together modulate feeding-associated physiology and behavior . To establish the nature of connections ( synaptic versus paracrine ) between LK neurons and the IPCs , and to identify other neurons downstream of LK signaling , we employed the trans-Tango technique for anterograde trans-synaptic labeling of neurons [31] . Using two independent Lk-GAL4 lines to drive expression of the system , we observed strong GFP labeling ( presynaptic marker ) in the SELK neurons for both lines ( Figs 10A and 10B and S9 ) but presynaptic staining in the lateral horn region for only one line ( Fig 10A and 10B ) . For both lines , expression of the postsynaptic marker ( visualized by mtdTomato tagged with HA ) was detected in several SEG neurons , some of which have axons that project to the pars intercerebralis ( Fig 10A and 10B; S9 Fig ) . Since Lkr is expressed in the IPCs , which have dendrites in the tritocerebrum and subesophageal zone where the LK post-synaptic signal is found ( S10 Fig ) , we asked whether the IPCs are postsynaptic to SELKs . However , no colocalization could be seen between the IPCs and postsynaptic signal of LKs ( S9 Fig ) . In addition , the post-synaptic signal is not coexpressed with Hugin neurons ( labeled with anti-CAPA antibody ) although these have similar axonal projections ( S11 Fig ) . Hence , these anatomical data indicate that the IPCs express the Lk receptor , but may receive non-synaptic ( paracrine ) inputs from LK neurons , or possibly LK signal via the circulation from the neurosecretory ABLKs . Since Lkr is expressed in the IPCs , we asked whether the expression of DILPs is altered in Lk and Lkr mutants . In Lk mutant flies , DILP3 immunolabeling is increased , and in Lkr mutants both DILP2 and DILP3 levels are significantly higher ( Fig 10C–10F ) , indicating that LK could affect the release of DILP2 and DILP3 ( as increased immunolabeling has been proposed to reflect decreased peptide release [32] ) . No effect on DILP5 levels was seen for any of the mutants , suggesting that LK selectively modulates DILP function ( S12 Fig ) . Next , we examined DILP2 , DILP3 , and DILP5 transcript levels by qPCR after targeted knockdown of the Lkr in the IPCs of flies using two different Lkr-RNAi lines and a DILP2-GAL4 driver . Also , different diets were tested since DILP expression in IPCs is influenced by carbohydrate and protein levels in the food [33] . The experimental flies developed to pupation on normal diet and were transferred as adults to three different diets: high sugar+high protein , low sugar+high protein , and normal diet . Knockdown of Lkr with UAS-Lkr-RNAi-#1 in IPCs had no effect on DILP transcripts and starvation survival ( S13 Fig ) , probably due to inefficient knockdown of Lkr with this construct . On the other hand , IPC-specific knockdown of Lkr with UAS-Lkr-RNAi-#2 ( referred to as Lkr-RNAi from here on ) impacted DILP transcripts and starvation survival in a diet-specific manner . Significant effects on DILP transcripts were only seen for DILP3 , which was increased in flies after Lkr-RNAi under normal and high-sugar+high-protein diets , and DILP5 , which was decreased in normal diet ( Fig 11A–11C ) . Moreover , there was an increase in survival during starvation with reduced Lkr in IPCs in adult flies that had been maintained on normal and high sugar-high protein diets ( Fig 11D–11F ) . Taken together , we identify roles for the signaling pathway comprising LK and its receptor within the CNS and that uniquely regulate physiological homeostasis . The Lkr expression in the periphery suggests that LK signaling is associated with water balance , gut function , and chemosensation ( Fig 12 ) . Within the CNS , LK signaling modulates specific neurosecretory cells of the brain that are known to regulate stress responses , feeding , metabolism , energy storage , and activity patterns , including sleep ( Fig 12 ) [25 , 34–38] .
In this study , we defined a set of effects caused by loss of LK signaling , which indicates that this neuropeptide homeostatically regulates physiology related to feeding , water homeostasis and metabolism , as well as associated stress , locomotor activity and metabolic rate . We suggest that LK regulates post-feeding physiology , metabolism , and behavior , as this seems to link most of the observed phenotypes observed after peptide and receptor knockdown . In S2 Table , we summarize effects of genetic manipulations of LK signaling from this study and earlier work and in Fig 12 , we propose a scheme of functions for the different LK-expressing neurons both in the CNS and in the periphery . Our model suggests that LK acts on peripheral targets such as the intestine and renal tubules , and via intermediate neuroendocrine cells in the brain , such as the IPCs and ITP-producing neurons , which in turn act on peripheral targets such as the fat body , crop , intestine , and others that are yet to be determined . In support of the physiological roles of LK signaling , we show distribution of Lkr expression in cells of the renal tubules and intestine , including the water-regulating rectal pads , as well as in the IPCs , which are known to signal with DILPs to affect feeding , metabolism , sleep , activity , and stress responses [34–37 , 39] . Lkr is also expressed by another set of brain neurosecretory cells ( ipc-1/ipc-2a ) known to regulate stress responses by means of three different coexpressed neuropeptides [25] . In the CNS of the adult fly , LK is produced at high levels by a small number of neurons of three major types: two pairs of interneurons in the brain ( SELK and LHLK ) and about 20 neurosecretory cells , ABLKs , in the abdominal ganglia [7 , 8] . Our data , taken together with earlier investigations ( see S2 Table ) , enable us to propose that each of the three types of LK neurons plays a different functional role by acting on distinct targets . However , they appear to act cooperatively to regulate post-feeding physiology and behavior . There is mounting evidence that the ABLKs use LK as a hormonal signal that targets peripheral tissues , including the renal tubules [10] , and that the brain LK neurons act in neuronal circuits within the CNS [11–13 , 40] . More specifically , the LHLK brain neurons are part of the output circuitry of the circadian clock in regulation of locomotor activity and sleep suppression induced by starvation [11 , 12 , 40] , and the SELKs of the subesophageal zone may regulate feeding [13] . In fact we show here that these SELKs have axons that exit through subesophageal nerves known to innervate muscles of the feeding apparatus . We found in this study that the ABLKs display increased calcium activity in response to drinking in desiccated flies , but not during starvation , desiccation , or regular feeding . This finding further supports a role for the ABLKs and hormonal LK in regulation of water balance . These neurons have also been implicated more broadly in control of water and ion homeostasis and in responses to starvation , desiccation , and ionic stress [10] . The LHLKs and SELKs did not display changes in calcium signaling under the tested conditions , emphasizing the unique function of ABLKs in diuresis ( see also [10] ) and aligning with earlier work suggesting that the brain neurons play roles in activity/sleep and feeding [11 , 13 , 40] . The regulation of metabolic rate , as determined by measurement of CO2 production , is a novel phenotype that we can link to LK signaling . This may be associated with the overall activity of the flies , as suggested by the correlation between activity and CO2 levels in our data . Thus , the regulation of activity and metabolic rate might be coordinated by means of the LK neurons . Using anatomical and experimental strategies , we identified a novel circuit linking LK to insulin signaling . Lkr expression was detected in the brain IPCs using two independently generated GAL4 lines plus single-cell transcriptome analysis . We also observed that Lk and Lkr mutants displayed increased levels of DILP2 and DILP3 immunoreactivity in the brain IPCs , and targeted knockdown of Lkr in IPCs increased DILP3 expression . Associated with this we found that Lkr-RNAi targeted to IPCs increased resistance to starvation . However , using the trans-Tango method for anterograde trans-synaptic labeling [31] , we could not demonstrate direct synaptic inputs to the IPCs from LK neurons . We found that LHLK neuronal processes do not overlap with those of IPCs in the brain . The SELKs drove postsynaptic marker signal in sets of neurons in the SEG , some of which have processes impinging on the IPCs . These findings suggest that LHLKs and SELKs form no conventional synaptic contacts with IPCs , but paracrine LK signaling to these neurons cannot be excluded since the SELK neurons have processes in close proximity to IPCs in the tritocerebrum and the subesophageal zone . Non-synaptic paracrine signaling with neuropeptides has been well established in mammals ( see [41–43] ) and is likely to occur also in insects [44] . Alternatively , the LK input to IPCs could occur systemically at the peripheral axon terminations of the IPCs after hormonal release from ABLKs . Whether acting in a paracrine or a hormonal fashion , LK appears to regulate the IPCs at the level of transcription and release of DILPs . Thus , some phenotypes seen after the global knockdown of LK and its receptor are likely to arise via secondary effects of insulin signaling . This suggests another layer of regulatory control whereby LK-driven modulation of DILP production and release could affect metabolism , stress responses , and longevity [reviewed by [39 , 45 , 46]] . Our findings , therefore , add LK as yet another regulator of the Drosophila IPCs , which have previously been shown to be under the influence of several other neuropeptides and neurotransmitters [reviewed in [39 , 45]] . It is noteworthy that at the levels of both transcription and presumed release the effect of LK on IPCs is selective , affecting DILP2 , DILP3 , and DILP3 only . We suggest that LK signaling may be nutrient-dependent and regulates post-feeding physiology and behavior , that can be observed in the mutants as reduced metabolic rate and locomotor activity , diminished PER , and reduced diuresis , as well as increased resistance to starvation and desiccation . Our data also indicate that in wild type flies , LK triggers release of IPC-derived DILPs that are required for post-feeding metabolism and satiety , and it acts on other cells to induce diuresis , and to increase activity ( especially evening activity ) and metabolic rate . An orchestrating role of LK signaling requires that the three types of LK neurons communicate with each other or are under simultaneous control by common sets of regulatory neurons . Alternatively , all the LK neurons could possess endogenous nutrient-sensing capacity whereby they can monitor levels of amino acids or carbohydrates in the organism . There is evidence for nutrient sensing in LHLK neurons [47] . This has also been shown for the brain neurosecretory cells expressing DH44 , DILP and corazonin [32 , 48–50] . Of the LK neurons , only the ABLKs and SELKs exhibit overlapping processes that could support direct communication , so it is more likely that other neurons form the link between these three sets of neuroendocrine cells . Such neurons are yet to be identified , but it has been shown that all the LK neurons express the insulin receptor , dInR [19 , 22] . This may suggest that the LK neurons could receive nutrient-related information from insulin-producing cells in the brain or elsewhere . In conclusion , we found that LK signaling is likely to modulate postprandial physiology and behavior in Drosophila . Food ingestion is followed by increased insulin signaling , activation of diuresis , increased metabolic rate , and lowered locomotor activity and increased sleep [12 , 15 , 32 , 45] . Flies mutated in the Lk and Lkr genes display phenotypes consistent with a role in regulation of insulin signaling , metabolic stress responses , diuresis , metabolic rate , and locomotor activity , all part of postprandial physiology .
All fly strains used in this study ( Table 1 ) were reared and maintained at 25°C on enriched medium containing 100 g/L sucrose , 50 g/L yeast , 12 g/L agar , 3 ml/L propionic acid , and 3 g/L nipagin , unless otherwise indicated . Experimental flies were reared under normal photoperiod ( 12 hours light: 12 hours dark; 12L:12D ) . Adult males 6–8 days post-eclosion were used for behavioral experiments . For some imaging experiments , females of the same age were also utilized . For trans-Tango analysis , flies were reared at 18°C , and adult males 2–3 weeks old post-eclosion were used . For DILP2>Lkr-RNAi qPCR , crosses were established in normal food ( NutriFly Bloomington formulation ) and eggs were laid for 24 hours . After adult eclosion , males were transferred to alternative diets ( normal diet described above; high-sugar high-protein: normal diet except with 20% sucrose and 10% yeast; low-sugar high-protein: normal diet except 5% sucrose and 10% yeast ) . After 5–7 days on these media , heads were dissected for qPCR , and other animals were transferred to starvation vials containing 1% agarose in water . Lk-/- and Lkr -/- were generated using the CRISPR/Cas9 system to induce homology-dependent repair ( HDR ) using one guide RNA ( Lk-/-: GATCTTTGCCATCTTCTCCAG and Lkr-/-: GTAGTGCAATACATCTTCAG ) . At gRNA target sites , a donor plasmid was inserted containing a GAL4::VP16 and floxxed 3xP3-RFP cassette . For Lk-/- , the knock-in cassette was incorporated immediately following the ATG translational start site ( +4bp to +10bp , relative to start site ) . For Lkr-/- , the knock-in cassette was incorporated upstream of the ATG ( -111bp to -106bp , relative to start site ) . All mutations were generated in the w1118 background . Proper insertion loci for both mutations were validated by genomic PCR . CRISPR gene editing was done by WellGenetics ( Taipei City , Taiwan ) . To prepare the Lkr-GAL4::p65 line , recombineering approaches based on previous methods [57] were used: in brief: a large genomic BAC with GAL4::p65 replacing the first coding region of Lkr , thereby retaining regulatory flanks and introns ) . First , a landing-site cassette was prepared: GAL4 and terminator homology arms were amplified from pBPGUw [58] and added to the flanks of the marker RpsL-kana [59] , which confers resistance to kanamycin and sensitivity to streptomycin . Lkr-specific arms were added to this landing-site cassette by PCR with the following primers , made up of 50 bases of Lkr-specific homology ( lower case ) plus regions matching the GAL4/terminator sequences: Lkr-F: tcatatcctcattaggatacacaactaaaactaaaaaacgaaaaagtgttATGAAGCTACTGTCTTCTATCGAACAAGC Lkr-R: tggatgagtcgcgtccccagttgcttgaagggattagagagtatacttacGATCTAAACGAGTTTTTAAGCAAACTCACTCCC Note the underlined ATG , reflecting the integration of GAL4 at the Lkr initiation site . The PCR product was recombined into bacterial artificial chromosome CH321-16C22 [60] ( obtained from Children’s Hospital Oakland Research Institute , Oakland , CA , USA ) , which contains the Lkr locus within 90 kb of genomic flanks . Recombinants were selected on kanamycin . Next , this landing pad was replaced by full-length GAL4::p65+terminators amplified from pBPGAL4 . 2::p65Uw [61] , and recombinants were screened for streptomycin resistance . Recombination accuracy was confirmed by sequencing , and the construct was integrated into attP40 by Rainbow Transgenic Flies ( Camarillo , CA , USA ) . To quantify Lk and Lkr transcript levels in mutant flies , the following method was used . Briefly , ten or more fed flies were flash frozen for each sample . Total RNA was extracted from whole flies using RNeasy Tissue Mini kit ( Qiagen ) according to the manufacturer’s protocol . RNA samples were reverse transcribed using iScript ( Biorad ) , and the subsequent cDNA was used for real-time RT-qPCR ( Biorad CFX96 , SsoAdvanced Universal SYBR Green Supermix qPCR Mastermix Plus for SYBRGreen I ) using 1 . 7 ng of cDNA template per well and a primer concentration of approximately 300 nM . The primers used are listed in Table 2 . Triplicate measurements were conducted for each sample . To quantify DILP2 , 3 and 5 transcript levels following DILP2>Lk-RNAi , the following method was used . DILP2-GAL4 and UAS-RNAi animals ( Lkr-RNAi-#1 and -#2 , plus a matched UAS-Luciferase as a control for effects of genetic background ) were mated and allowed to lay eggs for 24 hours in vials containing normal food; adult males from these crosses were then transferred to vials of normal food or high-sugar , high-protein or low-sugar high-protein diet . After 7 days , heads were dissected on ice into extraction buffer , and RNA was extracted with the Qiagen RNeasy Mini kit ( #74106 ) with RNase-free DNase treatment ( Qiagen #79254 ) . cDNA was prepared using the High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor ( ThermoFisher #4268814 ) , and qPCR was performed using the QuantiTect SYBR Green PCR Kit ( Fisher Scientific #204145 ) and an Mx3005P qPCR system ( Agilent Technologies ) . Expression levels were normalized against RpL32 ( Rp49 ) , whose levels have been determined to be stable under dietary modification [33 , 62] . The primers used are listed in Table 2 . Samples were prepared in four biological replicates of 10 heads each , and each biological replicate was assayed in two technical replicates . Immunohistochemistry for Drosophila larval and adult tissues was performed as described earlier [10 , 63] . Briefly , tissues were dissected in phosphate-buffered saline ( PBS ) and fixed in 5% ice-cold paraformaldehyde ( 2 hours for larval samples and 3 . 5–4 hours for adults ) . Samples were then washed in PBS and incubated for 48 hours at 4°C in primary antibodies diluted in PBS with 0 . 5% Triton X-100 ( PBST ) ( Table 3 ) . Samples were thereafter washed with PBST and incubated for 48 hours at 4°C in secondary antibodies diluted in PBST ( Table 3 ) . Following this incubation , some samples ( peripheral tissues ) were incubated with rhodamine-phalloidin ( 1:1000; Invitrogen ) and/or DAPI as a nuclear stain ( 1:1000; Sigma ) diluted in PBST for 1 hour at room temperature . Finally , all samples were washed with PBST and PBS , and then mounted in 80% glycerol . An alternative procedure was used for the adult gut to prevent tissues from rupturing . Briefly , intestinal tissues ( proventriculus , crop , midgut , hindgut , and MTs ) were fixed at room temperature for 2 hours , washed in PBS , incubated in rhodamine-phalloidin for 1 hour and washed in PBST and then PBS before mounting . Samples were imaged with a Zeiss LSM 780 confocal microscope ( Jena , Germany ) using 10X , 20X , or 40X oil immersion objectives . Images for the whole fly , proboscis , and wing were captured using a Zeiss Axioplan 2 microscope after quickly freezing the fly at -80°C . Cell fluorescence was measured as described previously [10] . Confocal and fluorescence microscope images were processed with Fiji [64] for projection of z-stacks , adjustment of contrast and brightness , and calculation of immunofluorescence levels . Calcium activity of LK neurons following various stresses was measured using the CaLexA ( Calcium-dependent nuclear import of LexA ) technique [23] . Briefly , the CaLexA sensor was expressed in LK neurons using the Lk-GAL4 . Next , 6-8-day-old males were transferred to a vial containing either nothing ( desiccation ) , aqueous 1% agar ( starvation ) or artificial diet ( normal food ) and incubated for 16 hours . In addition , one set of flies were desiccated for 13 hours and then transferred to a vial containing 1% agar ( re-watered ) . Following this period , the flies were fixed , dissected brains were processed for immunohistochemistry , and the GFP fluorescence was quantified as described above . To assay for survival under desiccation ( dry starvation ) and starvation , flies were kept in empty vials or vials containing 5 ml of 0 . 5% aqueous agarose ( A2929 , Sigma-Aldrich ) , respectively . Four biological replicates and 3 technical replicates for each biological replicate were performed for each experiment . For each technical replicate , 15 flies were kept in a vial and their survival was recorded every 3 to 6 hours until all the flies were dead . The vials were placed in incubators at 25°C under normal photoperiod conditions ( 12L:12D ) . For water-content measurements , 15 flies per replicate ( 4 biological replicates ) were either frozen immediately on dry ice or desiccated as above for 9 hours and then frozen . The samples were stored at -80°C until use . To determine their wet weight , flies were brought to room temperature and their weight was recorded using a Mettler Toledo MT5 microbalance ( Columbus , Ohio , USA ) . The flies were then dried for 24–48 hours at 60°C before their dry weight was recorded . The water content of the flies was determined by subtracting dry weight from wet weight . Long-term food intake of individual flies was quantified using a modified capillary feeding ( CAFE ) assay [19 , 69] . Capillaries were loaded with food comprising 5% sucrose , 2% yeast extract , and 0 . 1% propionic acid . Food consumption was measured daily , and the cumulative food intake over 3 days was calculated . The experiment consisted of 4 biological replicates and 10 flies per replicate for each genotype . Short-term food intake was measured as previously described [70] . Briefly , flies were starved for 24 hours on 1% agar ( Fisher Scientific ) or maintained on standard fly food . At ZT0 , flies were transferred to food vials containing 1% agar , 5% sucrose , and 2 . 5% blue dye ( FD&C Blue Dye No . 1 , Spectrum ) . Following 30 minutes of feeding , flies were flash frozen on dry ice , and four flies per sample were homogenized in 400 μL PBS ( pH 7 . 4 , Fisher Scientific ) . Color spectrophotometry was used to measure absorbance at 655 nm in a 96-well plate reader ( Millipore , iMark , Bio-Rad ) . Baseline absorbance was determined by subtracting the absorbance measured in non-dye fed flies from each experimental sample . Flies were collected and placed on fresh food for 24 hours , then starved for 24 hours in vials containing 1% agar . Flies were then anaesthetized under CO2 , and their thorax and wings were glued with nail polish to a microscopy slide , leaving heads and legs unconstrained . Following 1-hour recovery in a humidified chamber , the slide was mounted vertically under the dissecting microscope ( SM-3TX-54S , AmScope ) and proboscis extension reflex ( PER ) was observed . PER induction was performed as described previously [71] . Briefly , flies were satiated with water before and during experiments . Flies that did not water-satiate within 5 minutes were excluded from the experiment . A 1-ml syringe ( Tuberculin , BD&C ) with an attached pipette tip was used for tastant ( sucrose ) presentation . Tastant was manually applied to tarsi for 2–3 seconds 3 times with 10-second inter-trial intervals , and the number of full proboscis extensions was recorded . Tarsi were then washed with distilled water between applications of different concentrations of sucrose ( 0 . 1 , 1 . 0 , 10 , and 100 mM ) , and flies were allowed to drink water during the experiment ad libitum . Each fly was assayed for response to tastants . PER response was calculated as a percentage of proboscis extensions to total number of tastant stimulations to tarsi . Activity and metabolic rate ( MR ) was simultaneously recorded using the setup described earlier [24] . Briefly , MR was measured at 25°C through indirect calorimetry , measuring CO2 production of individual flies with a CO2 analyzer ( LI-7000 , LI-COR ) . Baseline CO2 levels were measured from an empty chamber , alongside five behavioral chambers , each measuring the CO2 production of a single male fly . The weight of a group of 10 flies was used to normalize metabolic rate since Lk mutants weighed significantly more than control w1118 flies . Flies were anesthetized using CO2 for sorting and allowed 24 hours acclimation before the start of an experiment . Flies were placed in glass tubes that fit a custom-built Drosophila Locomotor Activity Monitor ( Trikinetics , Waltham , MA ) , containing a single food tube containing 1% agar plus 5% sucrose with green food coloring ( McCormick ) . Locomotor activity data was calculated by extracting 10-minute activity periods for 24 hours using a custom generated Python program . CO2 output was measured by flushing air from each chamber for 75 seconds , providing readout of CO2 accumulation over the 10-minute period . This allowed for the coordinated and simultaneous recordings of locomotor activity and metabolic rate . Drosophila activity monitoring system ( DAMS; Trikinetics , Waltham , MA ) detects activity by monitoring infrared beam crossings for each animal . These data were used to calculate locomotor activity using the Drosophila Sleep Counting Macro [72] . Flies were anaesthetized under CO2 and loaded into DAMS tubes containing standard fly food for acclimation . After 24 hours acclimation in DAMS tubes with food , baseline activity was measured for 24 hours . Tubes were maintained in a 25°C incubator with 12:12 LD cycles . Lkr distribution in various tissues was determined by mining the FlyAtlas database [28] . Lkr expression in the different regions of the gut and its cell types was obtained using Flygut-seq [29] . A single-cell transcriptome atlas of the Drosophila brain was mined using SCope ( http://scope . aertslab . org ) to identify genes coexpressed with Lkr [30] . In all bar graphs , the data are presented as means ± s . e . m . In all box-and-whisker plots , each individual value has been plotted and the horizontal line represents the median . Unless stated otherwise , one-way analysis of variance ( ANOVA ) followed by Tukey’s multiple comparisons test was used for comparisons between three genotypes and an unpaired t test was used for comparisons between two genotypes . All statistical analyses were performed using GraphPad Prism with a 95% confidence limit ( p < 0 . 05 ) . Survival and stress curves were compared using Mantel–Cox log-rank test . All data sets are available in the S1 Data File . | Animals ranging from jellyfish to humans use multiple neuropeptides to orchestrate various aspects of behavior and physiology . A major question in biology is how animals are able to coordinate complex and competing behaviors to ensure maintenance of a stable internal environment . To address this , we delineated the functions of the neuronal pathways using the neuropeptide leucokinin ( LK ) in the fruit fly Drosophila melanogaster . We discovered that mutant flies lacking LK signaling exhibit defects in diverse but coordinated processes , including regulation of stress , water balance , gut function , activity , and metabolic rate . We also attribute these functions to different subsets of neurons that produce LK . Lastly , we show that this neuropeptide interacts with insulin signaling to affect stress tolerance and metabolism . This is of broad interest since stress , obesity and ensuing metabolic disorders , such as heart disease and diabetes , are immense problems in society . Our work provides a foundation for further investigation of neuroendocrine regulation of vital behavior and physiology associated with feeding . | [
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... | 2018 | Modulation of Drosophila post-feeding physiology and behavior by the neuropeptide leucokinin |
In eukaryotes , RNA species originating from pervasive transcription are regulators of various cellular processes , from the expression of individual genes to the control of cellular development and oncogenesis . In prokaryotes , the function of pervasive transcription and its output on cell physiology is still unknown . Most bacteria possess termination factor Rho , which represses pervasive , mostly antisense , transcription . Here , we investigate the biological significance of Rho-controlled transcription in the Gram-positive model bacterium Bacillus subtilis . Rho inactivation strongly affected gene expression in B . subtilis , as assessed by transcriptome and proteome analysis of a rho–null mutant during exponential growth in rich medium . Subsequent physiological analyses demonstrated that a considerable part of Rho-controlled transcription is connected to balanced regulation of three mutually exclusive differentiation programs: cell motility , biofilm formation , and sporulation . In the absence of Rho , several up-regulated sense and antisense transcripts affect key structural and regulatory elements of these differentiation programs , thereby suppressing motility and biofilm formation and stimulating sporulation . We dissected how Rho is involved in the activity of the cell fate decision-making network , centered on the master regulator Spo0A . We also revealed a novel regulatory mechanism of Spo0A activation through Rho-dependent intragenic transcription termination of the protein kinase kinB gene . Altogether , our findings indicate that distinct Rho-controlled transcripts are functional and constitute a previously unknown built-in module for the control of cell differentiation in B . subtilis . In a broader context , our results highlight the recruitment of the termination factor Rho , for which the conserved biological role is probably to repress pervasive transcription , in highly integrated , bacterium-specific , regulatory networks .
Transcription provides the basis for cellular development and metabolism in all living organisms by allowing the expression of the information stored in the DNA sequence of the genes . A different type of transcription not associated with classical , clearly delineated , expression units was discovered nearly fifteen years ago [1] . The term “pervasive transcription” was coined for this non-canonical type of transcription , found in all kingdoms of life , because of its generally genome-wide distribution , initiation from unexpected , often non-defined , or cryptic signals , or its arising from transcriptional read-through at weak or factor-dependent terminators [2–6] . From its discovery , the phenomenon of pervasive transcription raised questions concerning the biological functions of the associated RNA species . Indeed , this potentially futile process could have deleterious effects on cell physiology by interfering with sense transcription or chromosome replication or by compromising genome stability or cellular energy status [5–7] . However , turning it completely off may be difficult and counterproductive from an evolutionary stand-point , since mutations can continuously create new transcription initiation sites or alter the termination of existing transcription units . Indeed , this process may provide raw material for the evolution of novel functional biomolecules [8] . Pervasive transcription may thus be the result of a tradeoff between evolutionary forces and the production of essentially nonfunctional transcripts that are neutral or even slightly deleterious in terms of organism fitness . However , extensive studies in eukaryotes have established pervasive transcription as a fundamental component of the regulatory circuits that notably increases the complexity of gene control [7 , 9–11] . The produced non-coding RNAs ( ncRNAs ) are involved in a wide range of cellular processes , playing crucial roles in development , aging , disease , and the evolution of complex organisms [7 , 12 , 13] . Pervasive transcription has been found in various bacterial transcriptomes [14–21] , but its physiological role is still unclear . At the same time , mechanisms preventing pervasive transcription in bacteria are well known [6] . The transcription termination factor Rho , an ATP-dependent RNA helicase-translocase responsible for the main factor-dependent termination pathway in bacteria , plays an important role in preventing pervasive transcription [22–27] . In contrast to intrinsic terminators , the sequence features required for the function of Rho are complex and poorly defined [23 , 24] . Rho is nearly ubiquitous in bacterial genomes and the basic principles of Rho-dependent-termination are conserved across species , despite some structural differences between Rho proteins . Over the past decade , the importance of Rho in gene regulation and its conserved role in the enforcement of transcription-translation coupling , by interrupting transcription of untranslated mRNAs , has been substantiated by studies performed in several bacterial species [6 , 25–27] . The major role of Rho in the suppression of pervasive , primarily antisense , transcription has been demonstrated for the Gram-negative and Gram-positive microorganisms Escherichia coli , Bacillus subtilis , Staphylococcus aureus , and Mycobacterium tuberculosis , under conditions of Rho depletion [17 , 18 , 20 , 21] . Complete or even partial inactivation of Rho in these bacterial species causes widespread transcription originating from cryptic promoters and read-through of transcription terminators [17 , 18 , 20 , 21] . The biological relevance of this Rho-controlled component of the bacterial transcriptome is poorly understood . In E . coli , Rho inactivation is lethal , and a single amino acid substitution can cause changes in sense transcript levels and altered cellular fitness in the presence of various nutrients [28] . However , the increase of antisense transcription , due to partial inhibition of Rho , was reported to not interfere with sense transcription or gene expression in E . coli [18] . Similarly , no correlation between the levels of sense and antisense transcripts has been detected in M . tuberculosis . However , Rho inactivation in this bacterium significantly affected gene expression and caused cell death in cultures and during infection [21] . In contrast , a negative relationship between sense and antisense transcripts was observed in an S . aureus rho mutant [20] , suggesting that Rho-controlled asRNAs may influence gene expression . Nonetheless , the lack of Rho did not significantly modify the growth behavior of either B . subtilis or S . aureus cells under the growth conditions tested [17 , 20] . An increasing number of reports support a role of individual ncRNAs and antisense RNAs ( asRNAs ) in the regulation of gene expression in bacteria [5 , 16 , 29–33] , but it is still accepted that most pervasive transcription represents non-functional and relatively low-level transcriptional noise [18 , 34] . However , noise , or random fluctuations in gene expression due to the stochastic character of cellular processes involving low copy number cellular components ( e . g . transcription factors or mRNAs ) [35 , 36] , is also an important component of the fundamental processes of development and cell fate decision making in living organisms , from bacteria to mammals , as well as viruses [35–37] . Nonetheless , the influence of pervasive transcription on the regulation of developmental programs has not been experimentally addressed in bacteria . The Gram-positive , soil dwelling , bacterium B . subtilis is a model for studying phenotypic heterogeneity and transitional developmental programs in prokaryotes , as it can express distinct cell types associated with specific phenotypes: motility , production of lipopeptide surfactin , genetic competence , biofilm formation , protease production , and sporulation [37–40] . During exponential growth , one sub-population of B . subtilis cells can synthesize flagella and grow as individual motile cells . The motile state of B . subtilis populations is determined by the alternative sigma factor , SigD , which drives the expression of genes essential for the synthesis and regulation of the flagellar apparatus [40 , 41] . The transition from motility to an alternative type of cellular growth within surface-associated communities , known as biofilms , involves the repression of flagellar genes and activation of genes essential for production of biofilm extracellular matrix composed of polysaccharides , protein fibers and nucleic acids [41–43] . Under conditions of limiting nutrients , a sub-population of B . subtilis cells can initiate a multistage differentiation program to form highly resistant endospores ( spores ) [44 , 45] . The respective gene networks controlling these mutually exclusive developmental programs are interconnected and share common regulators and regulatory feedback loops , which prevent their simultaneous activation within a cell [41 , 43 , 44 , 46 , 47] . The key determinant in the regulation of biofilm formation and sporulation is the master regulator Spo0A , for which the activity depends on its gradually increasing phosphorylation state , determined by a multicomponent phosphorelay system [48–54] . When the concentration of phosphorylated Spo0A ( Spo0A~P ) is low , biofilm formation and sporulation are repressed by the transcriptional regulator of exponential growth , AbrB , and the biofilm-specific repressor , SinR [55 , 56] . This negative control is removed at intermediate levels of Spo0A~P , which activates the biofilm formation program [47 , 57–62] . Only cells expressing high levels of Spo0A~P can enter into sporulation [45 , 58 , 63] . At the same time , matrix production is blocked by high Spo0A~P [43 , 56–58] . Thus , the level of Spo0A~P determines heterogeneity of the matrix and spore production in populations of B . subtilis . Here , we investigated the impact of pervasive transcription on the physiology of B . subtilis cells taking advantage of the viability of B . subtilis rho-null mutant . Comparative transcriptome and proteome analyses of B . subtilis wild type ( WT ) and rho mutant ( RM ) strains revealed significant perturbation of the global gene expression landscape in the absence of Rho and highlighted potential alterations of the regulatory networks known to define cell fate in B . subtilis . Further functional studies demonstrated that at least three of the above-mentioned differentiation programs , motility , biofilm formation , and sporulation were altered in RM cells due to the loss of Rho-mediated control of pervasive transcription . We describe several mechanisms by which Rho directly or indirectly participates in the in fine regulation of cell fate decision-making . Rho-controlled transcription represents a new level of regulation of gene expression in the Gram positive bacterium B . subtilis and the termination factor Rho can be considered among the global transcriptional regulators .
We reanalyzed the dataset of genome-wide expression profiles previously established for B . subtilis 168 derivative strain 1012 and its isogenic rho mutant ( RM ) grown exponentially in rich medium as a starting point for dissecting the pathways by which the absence of Rho could affect B . subtilis physiology [17] . These tiling array data can be visualized on the B . subtilis expression data browser with expression profiles established for the BSB1 strain , a tryptophan-phototrophic ( trp+ ) derivative of 168 ( http://genome . jouy . inra . fr/cgi-bin/seb/index . py ) , [17] . The previous analysis of the RM strain was mainly focused on the detection of transcription outside of the transcribed regions ( TRs ) detected in the wild type strain ( native TRs ) [17] . The goal of the present reanalysis was to characterize global changes that affect functional regions ( in particular mRNAs ) and can lead to phenotypic variations . Therefore , we focused on the differential expression of sense and antisense expression levels aggregated according to a repertoire of 5 , 875 native TRs including mRNAs and ncRNAs identified in the BSB1 strain ( WT ) across 104 conditions . In this repertoire , 1 , 583 transcribed regions outside the Genbank-annotated genes were previously designated as S-segments and are numbered S1-S1583 , according to their chromosomal position [17] ( S1 Table ) . Significant changes in expression ( log2 RM/WT ≥ 1 or ≤ -1 ) consisted primarily in up-regulation of the antisense strand , in agreement with our initial observations [17] . The detected changes were decomposed into 456 up-regulations and 223 down-regulations on the sense strand ( Fig 1A ) and 1 , 446 up-regulations and 36 down-regulations on the antisense strand ( Fig 1B ) . Many of these changes exceeded a four-fold threshold ( log2 RM/WT ≥ 2 or ≤ -2 ) : 162 up- and 38 down-regulations on the sense strand , and 613 up- and seven down-regulations on the antisense strand . S1 Table presents the detailed results of this re-analysis . We used the non-domesticated NCIB 3610 strain for subsequent physiological analysis of rho mutants . This strain is a member of the 168-like group of strains originating from the Marburg ancestor and characterized by highly similar genome sequences [64] . Thus we also collected new transcriptome data to investigate the effect of rho deletion in the NCIB 3610 background . These new RNA-Seq-based data for the NCIB 3610 WT and NCIB 3610 RM strains ( Materials and methods ) are consistent with the previous data obtained by tiling array in the B . subtilis 1012 background . Differential expression analysis of the NCIB 3610 RM strain identified 1 , 029 up-regulations and 375 down-regulations of the sense strand , along with 2 , 115 up-regulations and 72 down-regulations of the anti-sense strand ( S1 Table ) . Approximately 80% of the up-regulations identified in 1012 RM were also found in NCIB3610 RM; the correspondence between data sets was lower for the sense strand ( ≈35% ) , but still highly statistically significant ( Fig 1A and 1B ) . Expression changes on the sense strand can be divided into three main categories ( Fig 2A ) : direct up-regulation downstream of Rho-dependent termination sites; indirect cis effects by which an up-regulated antisense transcription affects expression of the overlapping gene on the opposite strand; and indirect trans effects resulting from regulatory cascades initiated by direct effects . Disentangling these three types of effects can be difficult . Based on B . subtilis 1012 tiling arrays , up-regulation was detected for the sense expression levels of 456 ( 7 . 8% ) native TRs , including 181 S-segments and 275 protein coding genes . Most of these events were due to altered termination of transcription ( direct cis effect ) as previously reported [17] ( Fig 2B and 2C ) . In total , 90 up-regulated S-segments and 197 up-regulated protein coding genes clearly result from imperfect termination ( termination read-through and/or lack of termination; Fig 2D and 2E ) . However , 26 . 3% ( 120/456 ) of the detected up-regulated native TRs were associated with at least two-fold increased expression levels immediately after the corresponding promoters ( Fig 2F and 2G ) and thus likely result from indirect trans effects . Similarly , down-regulation of 163 of 223 ( 73 . 1% ) native TRs was associated with significantly decreased expression levels ( RM/WT log2 ≤ -1 ) immediately after promoters ( Fig 2H ) , which could reflect either decreased RNA synthesis , due to indirect trans effects , or increased RNA degradation . Although the tiling array analysis indicated that genes targeted in RM cells by anti-sense transcripts were down-regulated more often than would be expected at random , this result could not be entirely confirmed with the RNA-Seq data . Thus , it is still unclear whether the up-regulated antisense strands in B . subtilis cells contribute globally to down-regulation of the sense strands , in addition to indirect trans effects . We obtained further insights into the potential physiological consequences of the altered expression profiles in the RM strains by analyzing the distribution of up- and down-regulated native TRs in terms of regulons and functional categories ( sigma factor regulons as established in [17] , other regulons , and functional categories , as designated in the SubtiWiki database [65] ) . The complete list of statistically significant associations based on the analyses of B . subtilis 1012 tiling array and NCIB 3610 RNA-seq data ( Fisher exact test p-value ≤ 1e-4 ) is presented in S2 Table . Over-representation of the SigD regulon ( p-value 1e-59 ) among the down-regulated native TRs accounted for the strongest associations with known functional categories and regulons in both 1012 RM and NCIB 3610 RM strains . Most of the genes from functional category “motility and chemotaxis” were down-regulated in both RM strains , consistent with known functions of SigD . All genes from the Spo0A and CodY regulons , which were over-represented ( p-value 6 . 59e-15 and 1 . 57e-17 , respectively ) among the down-regulated TRs in both RM strains also belong to the SigD regulon . PBSX prophage genes and genes controlled by the specific sigma factor Xpf were down-regulated in the 1012 RM but not the NCIB 3610 RM strain . The up-regulated protein-coding genes and S-segments exhibited weaker biases towards specific regulons and functional categories . Nonetheless , we observed over-representation of genes from the SigK and SigG regulons ( p-value 4e-9 and 9 . 7e-5 , respectively ) , active during sporulation , and genes from the close functional category of “sporulation proteins” ( p-value 1 . 4e-7 ) [65] ( S2 Table ) . These genes were not expressed in WT cells during exponential growth in rich LB medium . SigA genes were statistically under-represented ( p-value 2e-9 ) , but still accounted for a majority of this set of up-regulated genes . Over-representation of the SigB regulon ( p-value 3 . 8e-7 ) was detected in the NCIB 3610 RM but not 1012 RM strain . We performed a comparative proteome analysis of the BSB1 WT and RM strains ( Materials and methods ) to complement the transcriptome data . Membrane and cytosolic fractions were prepared separately to maximize the chances of protein identification . Protein Abundance Index ( PAI ) values , calculated from mass spectrometry data , were used to compare the two proteomes [66] . In total , 1 , 619 proteins were identified ( see S1 Table for the PAI of identified proteins and S3 Table for raw proteome data ) , corresponding to 38% of the protein-coding genes . The log2 PAIs of detected proteins correlated with the abundance of cognate mRNAs both in WT and RM strains ( Pearson correlation coefficients > 0 . 60 ) . We evaluated the effect of Rho inactivation on the proteome using the same two-fold cut-off as for the transcriptome: abundance increased for 157 proteins and decreased for 101 proteins in RM , with 85 proteins detected only in RM and 38 detected only in WT cells ( S1 and S3 Tables ) . The proteome analysis confirmed the strong down-regulation of the SigD regulon in the absence of Rho: 42 of 44 SigD-controlled proteins detected in the WT proteome were under-represented in the RM proteome ( Table 1 ) . SigD protein itself was not detected in any proteome . The observed decrease of protein abundance in RM cells was variable and reached 26-fold in the case of the HemAT protein . In summary , transcriptome and proteome analyses documented notable alterations of the genome expression landscape in RM cells during exponential growth in rich medium . They highlight unscheduled expression of a number of sporulation genes and down-regulation of the SigD regulon , which could reflect important physiological changes . This prompted us to more thoroughly examine the impact of Rho inactivation on cell behavior during corresponding developmental programs . Analysis of the transcriptome and proteome data showed that SigD-controlled genes were significantly down-regulated in RM derivatives of 1012 and NCIB 3610 strains ( Table 1 and S1 Table ) . Further examination showed that genes belonging to the SigD regulon , but primarily expressed from SigA promoters [17 , 65] , displayed either a weak ( for example , log2 RM/WT ≤ −1 . 0 for the yjcP-yjcQ operon ) or intermediate ( for example , log2 RM/WT ≤ −1 . 5 for the fla-che operon ) decrease in expression . In contrast , genes exclusively controlled by SigD were strongly down-regulated ( for example , log2 RM/WT = −2 . 8 in 1012; log2 RM/WT = −2 . 1 in NCIB 3610 for the motA gene ) . The expression level of sigD itself was lower in both RM strains ( log2 RM/WT = −0 . 91 in 1012; log2 RM/WT = −1 . 69 in NCIB 3610 ) ( S1 Table ) . The down-regulation of 11 SigD-controlled genes was associated with the presence of asRNAs expressed above the cut-off level ( S4 Table ) . Four of these asRNAs have been previously detected and annotated in BSB1 WT: S1367 , S1403 , S125 , and S829 [17] . Expression of the non-annotated asRNAs facing the yvyC-fliD-fliS-fliT-yvzG operon and cheV and flhO genes was specific to RM ( can be visualized on http://genome . jouy . inra . fr/cgi-bin/seb/index . py ) . Several phenotypes are known to be associated with expression of the SigD regulon of B . subtilis cells , in particular , motility , the capacity to synthesize flagella and to swim in liquid , or to swarm over a solid surface [67] . SigD is a key determinant of the phenotypic switch between motile and sessile states within the exponentially growing cell population [40] . Cells with a high level of SigD ( SigD-ON ) are motile and those with a low level of SigD ( SigD-OFF ) are sessile . We compared the undomesticated strain NCIB 3610 and its isogenic rho mutant to investigate the impact of Rho inactivation on the SigD-controlled motility phenotype , as laboratory strains of B . subtilis do not exhibit swarming motility [68] . Swarming of the NCIB 3610 RM strain was significantly impeded ( Fig 3 ) . We restored the wild type rho allele at the chromosome of the RM strain to prove that the observed deficiency was directly linked to Rho inactivation . Conversion of the NCIB 3610 RM derivative back to wild type ( NCIB 3610 rho wt* ) restored motility , showing that the motility-deficient phenotype of the NCIB 3610 RM strain was due to the deletion of rho ( Fig 3A and 3B ) . Complex mechanisms , including several regulatory feedback loops , control expression of the genes from the SigD regulon and sigD gene itself , thus determining the SigD-ON or SigD-OFF state [40] . In particular , the anti-sigma factor FlgM antagonizes SigD activity by direct binding to SigD and inhibition of its interaction with RNA polymerase [69] . Additionally , genes from the SigD regulon are negatively controlled by the SinR , SlrR , and SlrA transcription factors , acting as SinR-SlrR and SlrR-SlrA heterodimers or a SlrA/SinR/SlrR functional complex [70 , 71]; global transcription regulator CodY [72]; stringent response regulator RelA [73]; and adaptive response regulator YmdB [74 , 75] . Global regulator DegU acts as a repressor or activator of the SigD regulon , depending on its phosphorylation state [76] . Finally , SwrA and SwrB proteins positively control SigD [68 , 77] . Expression of most of these regulatory genes was not significantly different between RM and WT cells , with the exception of lower expression of swrB ( log2 RM/WT = −0 . 88 in 1012; log2 RM/WT = −1 . 61 in NCIB 3610 ) and higher expression of slrR ( log2 RM/WT = 1 . 44 in 1012; log2 RM/WT = 3 . 16 in NCIB 3610 ) and slrA ( under the two-fold cut-off level , log2 RM/WT = 0 . 967 in 1012; log2 RM/WT = 0 . 60 in NCIB 3610 ) genes . Increased expression of both slr genes was due to 3’ extensions: of the asRNA targeting the epsA-O operon for the slrR gene ( see next section ) ; and of the S1475 segment for the slrA gene ( S2 Fig ) . Extension of the S1475 segment protects the slrA mRNA from enzymatic degradation [78] . An increase in slrA copy number down-regulates the fla/che operon containing the sigD gene and , consequently , the entire SigD regulon; this inhibition depends on active SlrR and SinR proteins [71 , 78] . Thus , we tested whether the non-motile phenotype of RM cells was due to increased expression of slrR and/or slrA . Inactivation of the slrR gene , which disables both regulators [71] , partially restored the motility of NCIB 3610 RM cells ( Fig 3 ) . The observed homogenous down-regulation of genes exclusively transcribed from SigD promoters suggests direct inhibition of SigD activity . This prompted us to consider the possible implication of FlgM in the observed phenotype of the RM strain . The anti-SigD activity of FlgM is dose-dependent and transcriptionally and post-translationally controlled [79–81] . Transcriptome analysis did not reveal any changes of flgM expression in RM strains ( S1 Table; http://genome . jouy . inra . fr/cgi-bin/seb/index . py ) . Post-translational regulation of FlgM is exerted via FlgM secretion from the cytoplasm by the flagellar export apparatus after assembly of the intermediate hook-basal body of flagellum [81] . Thus , SigD activity tightly correlates with the efficiency of FlgM secretion , which in turn depends on completion of the flagellar hook [67 , 81 , 82] . It is thus remarkable that expression of the flhO-flhP genes encoding the components required for hook completion was significantly lower in the RM strains ( for the flhO gene , log2 RM/WT = −2 . 06 in 1012; log2 RM/WT = –1 . 62 in NCIB 3610 ) . The decrease of flhO-flhP transcription correlates with the ~ 860 nucleotides ( nt ) long 3’-extension of the annotated S1403 asRNA ( log2 RM/WT = 2 . 77 for the flhO asRNA in 1012 , log2 RM/WT = 3 . 92 in NCIB 3610 ) , ( Fig 3C and S1 Fig ) . In the absence of Rho , transcription of S1403 extended through the RNA hairpin structure ( ΔG = −16 . 6 ) [83] within the flhP gene ( Fig 3C and 3D ) . The flhP asRNA , with the 3’-end matching the position of this hairpin , was detected by genome-wide 3’ end-mapping in the Rho-proficient B . subtilis PLBS802 strain [84] . In RM cells , the extended S1403 asRNA spreaded over the whole flhO gene , overlaped with the flhO-flhP promoter , and may have down-regulated expression of the flhO-flhP operon . This could impede the synthesis of flagellar hook , leading to FlgM accumulation in the cytoplasm and consequently , reduced expression of SigD-dependent genes , as observed for B . subtilis flhO and flhP mutants [82] . First , we tested this possibility by examining the contribution of FlgM to the motility-defective phenotype of the RM strain by inactivating the flgM gene . Deletion of flgM improved the motility of NCIB 3610 RM cells ( S3 Fig ) . We next compensated the down-regulation of the flhO-flhP operon in RM cells by inserting a copy of the flhO-flhP operon expressed from its own promoter , into the amyE chromosomal locus of NCIB 3610 RM ( NCIB 3610 RM amyE::PflhO-flhO-flhP ) . The expression of the flhO-flhP genes from the ectopic position improved the swarming motility of NCIB 3610 RM ( Fig 3A and 3B ) . Subsequent inactivation of the slrR gene ( NCIB 3610 RM slrR , amyE::PflhO-flhO-flhP ) had an additive effect , but yet did not restore cell motility to the WT level ( Fig 3A and 3B ) . This pinpoints the existence of additional factors that inhibit motility in RM cells . Rho-controlled sense transcripts associated with slrR and slrA and the antisense transcript associated with flhO-flhP genes represent newly identified components of the regulatory network that control cell motility . These findings provide additional evidence that read-through of Rho-dependent terminators affecting the expression of downstream genes can propagate into regulatory networks and cause phenotypic changes . The switch from the motile to sessile state in growing B . subtilis populations is associated with activation of an alternative developmental program , known as biofilm formation . Biofilms are multicellular aggregates assembled within a self-produced extracellular matrix . The main components of the biofilm matrix , exopolysaccharides ( EPS ) and amyloid-like protein fibers , are encoded by the 15-gene-long epsA-O operon and the tapA-sipW-tasA operon , respectively [85–87] . The global transcription regulator , Spo0A , indirectly controls the expression of matrix operons through the AbrB and SinI/SinR pathways [56 , 88–92] . Expression of both operons is activated when Spo0A~P is present at low and/or intermediate levels and suppressed by high levels of Spo0A~P [47 , 56 , 88] . Motility genes are involved in the initial stages of air-liquid interface biofilm ( pellicle ) formation , but not in the development of architecturally complex colonies ( colony biofilm ) on an agar surface [93 , 94] . Non-motile cells can also proceed to biofilm formation directly [39 , 41 , 61 , 88 , 95] . Thus , inactivation of Rho may affect the program of biofilm development due to altered SigD activity . In addition , up-regulation of slrR and slrA genes ( S1 Table ) could contribute to de-repression of the matrix operons and favor biofilm development in RM cells . We investigated the consequences of Rho inactivation on biofilm formation by comparing the dynamics of pellicle formation by the NCIB 3610 WT and RM strains in biofilm-promoting MSgg medium , as well as their capacity to develop architecturally complex colonies on an agar surface . The NCIB 3610 WT strain formed thick , robust pellicles and exhibited complex colony architecture as described in the literature [96] ( Fig 4A ) . In contrast , the isogenic RM strain formed only thin pellicles and flat unstructured colonies , which did not attain a phenotype similar to that of the wild type biofilms ( Fig 4A ) . There were no differences in the biofilm phenotypes between the NCIB 3610 WT and rho-restored NCIB 3610 rho wt* strains ( Fig 4A ) . This shows that the biofilm-deficient phenotype of the NCIB 3610 RM strain is primarily due to the deletion of rho , similarly to the motility defect . The defective architecture of colonies formed by the NCIB 3610 RM strain suggested that inefficient biofilm formation was not solely due to down-regulation of the SigD-regulon [43 , 93] . We thus examined biofilm formation by the strain NCIB 3610 RM amyE::PflhO-flhO-flhP . Indeed , ectopic expression of the flhO-flhP operon , which improved motility of the NCIB 3610 RM strain , did not improve biofilm formation ( S4 Fig ) . We next compared expression of the matrix genes between the WT and RM strains , using transcriptional fusions of the epsA and tapA promoters with the firefly luciferase ( luc ) gene [97] , to gain insight into the impaired capacity of RM cells to form biofilms . These fusions were introduced at the native eps or tapA chromosomal loci of BSB1 WT and RM strains . We monitored luciferase activity during growth in liquid MSgg medium with constant aeration ( [88] , Materials and methods ) . We observed maximal expression of the eps-luc and tapA-luc fusions in WT cultures at the end of the exponential growth phase , in accordance with previously published data [88] . At the same time , both the eps and tapA promoters were significantly less active in the RM strain ( Fig 4B and 4C ) , indicating inefficient de-repression of the matrix operons negatively controlled by SinR [56 , 91] . We therefore expected that inactivation of SinR would restore biofilm formation by the RM strain . We examined biofilms formed by the NCIB 3610 sinR mutant and observed the formation of vigorous pellicles , colonies with an elevated surface , and increased production of mucoid substances , as reported previously [51 , 56 , 91] ( Fig 4A ) . In contrast , the NCIB 3610 RM sinR strain formed fragile and shattered pellicles , resembling those of the NCIB 3610 eps mutants [43 , 86 , 98] , and colonies which were morphologically different from the Rho-proficient NCIB 3610 sinR mutant ( Fig 4A ) . Inactivation of AbrB , the second repressor of matrix operons in B . subtilis , did not restore biofilm formation by the NCIB 3610 RM or NCIB 3610 RM sinR mutant strains ( S5 Fig ) . Therefore , relieving matrix operons of SinR- and AbrB-mediated repression is not sufficient to restore normal biofilm formation by the RM strain . The transcriptome analysis revealed an additional factor that could potentially interfere with biofilm formation by B . subtilis RM cells . It is represented by an asRNA of ~15 , 740 nt , which starts near the 3’-end of the epsO gene , probably due to read through at an intrinsic terminator of the oppositely oriented yvfG gene , and overlaps the entire epsA-O operon ( for epsC asRNA , log2 RM/WT = 3 . 01 in 1012 and log2 RM/WT = 4 . 79 in NCIB 3610; Fig 4D , S1 Table ) . We tested whether the activity of eps asRNA contributes to the impaired biofilm formation of RM cells by blocking its synthesis . This was achieved by insertion of three Rho-independent transcription terminators within the epsO gene , with the active orientation blocking the synthesis of eps asRNA ( Materials and methods , Fig 4D and 4E ) . RT-PCR confirmed that the synthesis of eps asRNA was abolished in the NCIB 3610 RM epsO:Ter strain ( Fig 4F ) . Previously , the epsO gene was shown to be dispensable for pellicle formation [99] . Indeed , the NCIB 3610 derivative carrying the epsO:Ter insertion did not display any defect in biofilm formation ( Fig 4A ) . The NCIB 3610 RM epsO:Ter strain had somewhat stronger pellicles and more complex colony biofilms than the parental RM strain ( Fig 4A ) . Simultaneous prevention of antisense transcription and de-repression of the matrix operons in the NCIB 3610 RM sinR , epsO:Ter strain greatly improved development of pellicles and colony biofilms , which were similar to those formed by the NCIB 3610 sinR mutant ( Fig 4A ) . These results demonstrate that Rho-controlled eps asRNA negatively affects EPS production . Altogether , our results show that inactivation of Rho results in impaired biofilm formation . This phenomenon is mainly due to inefficient de-repression of both matrix operons and the negative effect of the eps-specific asRNA on the expression of the eps genes . Comparative transcriptome and proteome analysis of B . subtilis WT and RM cells revealed that Rho inactivation led to perturbations of the multi-component phosphorelay system responsible for Spo0A phosphorylation [48 , 49] , ( Table 2; S6 Fig ) . Among the genes coding for five sensor histidine protein kinases ( KinA—KinE ) , which are at the basis of the Spo0A phosphorelay , the kinB gene was strongly upregulated in RM cells ( log2 RM/WT = 2 . 02; Table 2 ) . We also detected the KinB protein in the RM , but not WT proteome , consistent with the transcriptome data . Analysis of the transcription profiles of WT cells during exponential growth revealed that kinB mRNA level was not constant across the gene but showed a marked down-shift at approximately one-third part of the open reading frame . In contrast , we did not observe this down-shift in RM cells ( http://genome . jouy . inra . fr/cgi-bin/seb/index . py ) . The level of gene expression is aggregated into a single value computed as the median for probes within the transcription region [17] . Thus , the presence of the down-shift strongly reduces the value of kinB mRNA expression in the WT relative to RM cells . The mechanism of an intragenic down-shift within kinB will be discussed later . RNA levels of the other kinase genes were not significantly affected in the RM strain ( Table 2 ) . However , the KinA and KinE proteins were detected in the RM but not the WT proteome , whereas the KinC and KinD kinases were detected at slightly decreased levels in the RM proteome . The transfer of the phosphoryl group from sensor kinases to Spo0A is catalyzed by two phosphotransferases , Spo0F and Spo0B [48] . Both spo0F and spo0B transcripts were up-regulated in RM ( log2 RM/WT = 0 . 895 and 1 . 185 , respectively ) . The amounts of Spo0F and Spo0B proteins were also increased in the RM proteome ( 1 . 5- and 2 . 9-fold , respectively ) , consistent with the transcriptome data . In addition to the main phosphorelay components , the expression of several genes encoding accessory proteins was modified in the absence of Rho . Transcript level of the kbaA gene , which encodes a positive effector of KinB [100] , was higher in RM cells ( log2 RM/WT = 2 . 04 ) due to the 3’ extension of the upstream salA mRNA . Transcript levels of the sivA and sivB genes , encoding factors that negatively control the level of Spo0A~P through inhibition of KinA autophosphorylation [101] , were lower in RM cells ( log2 RM/WT = −1 . 22 and −0 . 97 , respectively ) . For both genes , this was apparently due to lower activity of the corresponding promoters ( indirect trans effect ) . The genes of the rapA-phrA operon , encoding RapA phosphatase , which specifically dephosphorylates Spo0F~P , and its inhibitor , the PhrA peptide [102] , were equally up-regulated in the RM strain ( log2 RM/WT = 2 . 15 and 2 . 52 , respectively ) . We also detected higher levels of RapA in the RM proteome , consistent with the transcriptome data . The up-regulation of rapA-phrA mRNA levels in RM cells was associated with the disappearance of the down-shift within the rapA transcript observed in WT cells , similar to kinB transcription . In contrast , RapB , the second phosphatase active on Spo0F~P , was down-regulated in the RM strain , apparently due to lower activity of the rapB promoter . Transcript levels of the yisI gene , encoding a phosphatase specific for Spo0A~P [103] , increased in the RM strain ( log2 RM/WT = 2 . 50 ) . The remaining components of the B . subtilis phosphorelay system were not significantly affected by the lack of Rho ( S1 and S3 Tables ) and are not reported in Table 2 . We translationally fused KinA and KinB proteins with the SPA peptide and compared the levels of SPA-tagged proteins in WT and RM cells to assess expression of these main sensor kinases at different growth stages . RM cells contained higher levels of KinA and KinB proteins than WT cells during the exponential and stationary phases of growth in LB ( Fig 5 ) . The effect was more prominent for KinB , as no or very little protein was detected in WT cells grown in LB . The propagation of cells in sporulation-inducing DS medium stimulated the synthesis of both kinases with a prevailing effect in RM cells , especially for KinB ( Fig 5 ) . Taken together , these results highlight important changes in the expression of the multi-component phosphorelay system controlling the phosphorylation state of Spo0A in RM cells . The gradual activation of Spo0A~P by sequential phosphorylation might be shifted towards higher phosphorylation levels in the absence of Rho , given the known functions of the up- and down-regulated genes in this process . We sought to experimentally establish whether the observed changes of phosphorelay in RM cells results in modification of the phosphorylation level of Spo0A~P as this could contribute to their defect in biofilm formation . Indeed , a high level of Spo0A~P induces suppression of the matrix operons [47 , 56 , 88] . During the transition to stationary phase , accumulating Spo0A~P increases spo0A gene expression via several positive feedback loops [104] and a transcription switch from the SigA-dependent vegetative promoter to the SigH-controlled sporulation-specific promoter [105] . We postulated that a high level of Spo0A~P in RM cells would lead to detectable changes of spo0A expression . The real-time kinetics of spo0A expression was previously analyzed at a population-wide level using the luc reporter gene fused , in-frame , to the spo0A start codon at its natural locus [97] . The construction monitors the activity of both spo0A promoters while maintaining an intact copy of the spo0A gene [97] . We used this spo0A-luc fusion to compare spo0A expression between the WT and RM strains . Initially , we analyzed the cells growing in biofilm-promoting MSgg medium , in which Spo0A~P accumulates to an intermediate level , stimulating de-repression of the matrix operons [58 , 88] . We followed this event by simultaneous analysis of tapA-luc expression . The expression of spo0A in WT cells gradually increased during exponential growth and then remained relatively constant , producing a few weakly oscillating peaks ( Fig 6A ) . At the end of exponential growth , one of the spo0A expression peaks coincided with activation of the tapA-sipW-tasA operon , indicating that the cells accumulated an appropriate Spo0A~P level . In RM cells , spo0A expression was lower during exponential growth than in WT cells , but exhibited a spike at the end of exponential growth ( Fig 6A ) . Such a burst of spo0A activity might reflect activation of the sporulation-specific , SigH-dependent spo0A promoter by a high level of Spo0A~P [105]; the transition phase-specific SigH factor has been shown to be active in MSgg medium on other promoters [89] . Indeed , the spike of spo0A activity in RM cells correlated with decreased tapA-sipW-tasA expression , known to be suppressed by a high level of Spo0A~P [87 , 106] . Next , we assessed spo0A expression in WT and RM cells grown in sporulation-inducing DS medium . In WT cells , spo0A expression was characterized by several pulses during the exponential and stationary growth phases , closely resembling previously reported spo0A expression kinetics ( Fig 6B ) , [97] . A double-headed peak of spo0A expression observed at the moment of growth arrest has been previously shown to mark entry of the cells into sporulation ( T0 ) , as it coincides with activation of the early sporulation genes ( Fig 6B ) , [97] . This peak would reflect a sporulation-inducing high threshold level of Spo0A~P [107] . Inactivation of Rho had no significant effect on spo0A promoter activity during exponential growth of RM cells but modified it at T0 , when spo0A expression peaked at a higher level than in WT cells . This spike in the activity of the spo0A promoter at T0 was highly reproducible in RM cells and most likely resulted in a higher Spo0A~P level than in WT cells . We further established an increase in the level of Spo0A~P in sporulating RM cells by following luciferase expression from the SigH-dependent promoter of the spoIIAA-AB-sigF operon , which is activated by a high level of Spo0A~P [57 , 108] . In both WT and RM strains , spoIIA-luc induction coincided with pulses of spo0A activity , attributable to high Spo0A~P levels ( Fig 6C ) . However , spoIIA-luc expression in the RM culture initiated about one hour earlier and was notably more efficient than in WT cells . This indicates that a sub-population of cells , in which Spo0A~P reached the required threshold to activate early sporulation genes , was higher in the RM culture . We investigated whether the changes of spo0A activity in RM cells propagate further into the sporulation-specific cascade of gene expression by analyzing the expression of the gerE gene , which depends on the late mother cell-specific sigma factor , SigK , thus reflecting the final steps of sporulation [109] . The expression of gerE-luc in RM cells occurred within a narrow pulse starting ~1 . 5 hours earlier and reaching a ~10-fold higher maximum than in WT cells ( Fig 6D; note different ordinates for the WT and RM gerE-luc expression curves ) . Such kinetics would account for more synchronous sporulation in the RM population , most probably due to efficient initiation of the process by high Spo0A~P . In summary , different expression patterns of spo0A and Spo0A-regulated genes in WT and RM cells account for more efficient phosphorylation of Spo0A in the absence of Rho , both under biofilm- and sporulation-promoting growth conditions . In RM cells , the rapid increase of Spo0A~P to a high level in MSgg medium could inhibit matrix gene transcription and thus impair biofilm development , whereas in DS medium , higher Spo0A~P levels would trigger sporulation earlier and in a larger sub-population of cells . We then assessed whether the effects of the Rho mutation on the expression of sporulation genes leads to more productive sporulation . The laboratory B . subtilis 168-related strains are sporulation-proficient and could thus be used for this analysis . We used the exhaustion method to induce sporulation . The first heat-resistant spores were detected in BSB1 RM cultures four hours after entry into sporulation ( T4 ) , and by T7 , almost 100% of the RM cells had formed spores ( Fig 7A ) . Less than 20% of the WT cells had produced spores by the same timepoint , reflecting the well-known dichotomy of sporulation in B . subtilis [104 , 109 , 110] . We performed the same experiment using other B . subtilis strains: non-domesticated NCIB 3610; TF8A , a phage-cured derivative of 168; and PY79 , a laboratory prototroph strain genetically distant from 168 [64 , 111] . Deletion of the rho gene accelerated sporulation in all genetic backgrounds and most of the RM cells produced mature spores by T7 ( Fig 7B ) . We examined whether rho deletion can suppress sporulation defects of the kinA and kinB mutants to formally link more efficient sporulation by RM cells to increased activity of the Spo0A phosphorelay . KinA is commonly considered as the main sporulation kinase , as its inactivation severely inhibits this process . The inhibitory effect of kinB mutations is more variable and apparently depends on the genetic background of the cells [53 , 112 , 113] . We cultured the kinA and kinB mutants of BSB1 and PY79 under sporulation conditions and reproduced both reported trends: the kinA mutation strongly reduced sporulation in both strains , whereas the inhibitory effect of the kinB mutation was strong in BSB1 and weak in PY79 ( Fig 7C and S7 Fig ) . We used BSB1 derivatives for further experiments to remain consistent with the gene expression analysis , although the efficiency of sporulation was generally lower in the BSB1 than PY79 background . Inactivation of Rho improved the sporulation of BSB1 kinA and kinB mutants , although to different degrees: an increase of ~15 fold in RM kinA and ~2 . 5-fold in RM kinB strains relative to the BSB1 kinA and kinB mutants ( Fig 7C ) . Similarly , the rho mutation preferentially rescued the sporulation defect of the PY79 kinA mutant , although the effect was relatively small ( S7A Fig ) . Inactivation of Rho in the double kinA kinB mutants had no effect on the basal level of sporulation in either background ( S7B Fig; data presented for the PY79 mutant derivatives ) . Altogether , the observed genetic interactions indicate that the stimulatory effect of rho deletion on sporulation mostly involves the KinB kinase , suggesting its increased role in the Spo0A phosphorelay system in RM cells . We tested this hypothesis by analyzing the expression of the Spo0A~P-dependent spoIIAA-AB-sigF operon during sporulation of the kinase mutants . Expression of spoIIA-luc was similarly inhibited in BSB1 kinA and kinB mutants , indicating a strong decrease of Spo0A activity in the absence of either kinase ( Fig 7D ) , corroborating the results of the sporulation assay in these strains . The expression of spoIIA in RM kinA cells was higher than the wild-type level , nearly reaching the maximum observed in RM cells ( Fig 7D ) . However , spoIIA induction was delayed in RM kinA cells relative to RM cells ( Fig 7D ) , which might underlie their different sporulation efficiencies ( Fig 7C ) . Rho inactivation in the kinB mutant ( RM kinB ) also improved the expression of spoIIA , however it remained below the wild-type level ( Fig 7D ) . The pattern of spoIIA expression in RM kinB cells thus correlates with their low sporulation efficiency . Altogether , these results indicate that the increased phosphorylation of Spo0A in the RM strain is mainly due to KinB . As mentioned above , transcriptome analysis revealed an abrupt down-shift within the kinB gene in WT , but not RM cells , explaining the higher expression of KinB in the absence of Rho ( Fig 8A ) . The internal down-shift of kinB transcription is also observed in cells depleted of the RNaseY , RNase J1 , or RNase III ribonucleases [114 , 115] , excluding its formation by the action of these enzymes . Analysis of the kinB region using Petrin [17] and MFOLD software [83] did not reveal any putative secondary structures characteristic of intrinsic terminators . Altogether , these observations suggest that the internal down-shift of kinB transcription is due to the activity of an intragenic Rho-dependent terminator . We used a plasmid-based system for the detection of transcription terminators [116 , 117] to more firmly establish the role of Rho in termination within the kinB gene . Two DNA fragments containing the kinB promoter , translation initiation region ( TIR ) , and differently sized 5’-terminal regions of the kinB orf were cloned in front of the cat gene , encoding chloramphenicol ( Cm ) acetyltransferase ( Fig 8B ) . The transcript of the long fragment ( first 417 bp of kinB orf; plasmid pKinB-Long ) likely contained sequence features required for termination ( estimated to be ~350 ribonucelotides downstream of the kinB start codon ) , whereas the transcript of the small fragment ( first 157 bp of kinB orf; plasmid pKinB-Short ) did not ( Fig 8B ) . The truncated kinB orf ended with a stop codon in both plasmids to ensure that translation of the cat mRNA initiating from the kinB promoter depended on its own RBS . At the same time , a transcription terminator located upstream of the cat gene would decrease cat expression and consequently Cm-resistance [117] . As shown in Fig 8C , BSB1 WT and RM cells containing the pKinB-S plasmid were mostly resistant to the tested Cm concentrations . In contrast , the pKinB-L plasmid conferred similar Cm-resistance only to RM cells , whereas WT cells containing this plasmid were considerably more sensitive ( Fig 8C ) . These results are consistent with Rho-dependent termination within the large kinB fragment cloned into pKinB-L . According to the E . coli model , Rho loads onto nascent transcripts not protected by translating ribosomes [22–25] . Therefore , Rho-dependent intragenic termination is modulated by the efficiency of translation initiation [118] . Thus , the efficiency of Rho would depend on kinB translation if it has a direct role in transcription termination within kinB . We varied the translation initiation rate of kinB by replacing its TIR , containing an imperfect RBS sequence ( RBS wt ) , by a TIR with a strong RBS ( RBSm+ ) or a TIR with degenerated RBS ( RBSm-; Fig 8D ) [119] . The pKinB-S-RBSm+ and pKinB-S-RBSm- plasmids conferred similar levels of Cm-resistance in WT and RM cells as the pKinB-S plasmid ( Fig 8E , data shown for WT cells ) . This demonstrated that modifications of the kinB TIR had no effect on translation of the cat mRNA . In contrast , WT cells carrying the pKinB-L-RBSm+ plasmid were considerably more resistant to Cm than cells carrying pKinB-L plasmid , whereas the absence of an active RBS in pKinB-L-RBSm- resulted in much lower Cm-resistance ( Fig 8F ) . However , rho inactivation in cells bearing the pKinB-L-RBSm- plasmid significantly improved their resistance to the antibiotic and restored viability at high Cm concentrations ( Fig 8G ) . Altogether , these results confirm the major role of Rho in the premature termination of kinB transcription . Thorough analysis of the kinB transcription profile in B . subtilis BSB1 across a database of 104 different growth conditions [17] revealed the presence of an intragenic down-shift of kinB expression in a substantial proportion of the dataset ( S5 Table , Fig 9A and 9B ) . However , the expression level was remarkably similar between the 5’ and central segments of the kinB gene during the initial stages of sporulation , which correspond precisely to conditions in which kinB expression has been identified to be maximal ( S5 Table ) . However , expression of the 5’ segment of kinB appeared to be relatively constant between non-sporulating cells ( e . g . transition phase ) and cells entering sporulation ( e . g . S1; Fig 9B ) . This is in accordance with previous data , indicating that there is no specific regulation of the activity of the kinB promoter during sporulation [53] . Thus , intragenic transcription termination appears to be a mechanism for the control of kinB expression , which is weaker at the onset of sporulation . The amount or activity of Rho should decrease during the early stages of sporulation if it regulates kinB expression . We assessed changes in the cellular level of Rho protein by constructing a Rho-SPA translational fusion , which retains regulatory activities of non-modified Rho ( S8 Fig ) , and monitoring its expression in WT cells during growth in DS medium . Rho-SPA protein levels started to decrease after the mid-exponential growth phase and became barely detectable two hours after growth arrest ( Fig 9C and 9D ) . This is in accordance with previous transcription analyses , showing that the level of rho expression is relatively high during exponential growth in rich medium and decreases during sporulation [17] . Previously , B . subtilis Rho was found to auto-regulate its expression by transcriptional attenuation at the Rho-dependent terminator ( s ) located within the leader region of rho mRNA [120] . Our results suggest that regulation of Rho expression during sporulation is more complex . In conclusion , increased kinB expression due to the absence of intragenic transcription termination at early stages of sporulation appears to coincide with a decrease of Rho protein content . The mechanism ( s ) regulating Rho protein expression and/or stability during sporulation remain ( s ) to be elucidated . The negative correlation between Rho content and kinB expression during sporulation in WT cells suggests that Rho termination activity may be dose-dependent . We tested this possibility by investigating the physiological effects of Rho overexpression using the middle-copy number plasmid pDG148Rho ( hereafter pRho ) , which constitutively expresses Rho at a level which we estimate to be ~3-fold higher than normal ( S9 Fig ) . We started by testing the effect of Rho overproduction on the efficiency of the kinB intragenic Rho-dependent terminator . Introduction of the pRho plasmid into WT cells containing pKinB-L considerably decreased their Cm-resistance ( Fig 10A , data shown for Cm 3μg/ml; compare with Fig 8C ) . In contrast , pRho did not affect Cm-resistance of WT cells carrying the pKinB-S plasmid , which does not contain the Rho-dependent terminator ( Fig 10A ) . This indicated that the efficiency of the kinB intragenic transcription terminator increased with higher levels of Rho . Indeed , Western-blot analysis of the kinB-SPA translational fusion showed that cells grown in sporulation-inducing DS medium produced less KinB kinase in the presence of pRho than of a control vector ( Fig 10B ) . Next , we assessed Spo0A phosphorylation and cell commitment to sporulation using the reporter spoIIA-luc fusion . Introduction of pRho into WT and RM cells decreased activity of the spoIIA promoter by ~3 fold relative to respective vector-containing controls ( Fig 10C ) . These results suggest less efficient Spo0A phosphorylation when Rho is produced above its natural level . Of note , pRho-mediated inhibition of spoIIA activity was higher in the WT strain , which has the rho gene in the chromosome . The sporulation efficiency of the WT strain containing pRho was ~3 fold lower than that of control ( Fig 10D ) , consistent with low Spo0A activation . A similar ( ~ 5-fold ) inhibition of sporulation by pRho was observed in the BSB1 kinA mutant . In contrast , pRho had no significant effect on sporulation in BSB1 kinB cells , showing again that Rho preferentially targets kinB transcription ( Fig 10D ) . Altogether , these results show that Rho overexpression inhibits sporulation , most likely by decreasing activity of the Spo0A phosphorelay system . One of the determinant factors of this inhibition is reinforced intragenic termination of kinB transcription . Rho overexpression has the opposite effect of rho deletion , resulting in much less efficient sporulation . It is thus possible that Rho overexpression could stimulate the developmental programs for which Rho inactivation is inhibitory . We addressed this possibility by analyzing biofilm formation and swarming motility of strains containing pRho . Presence of the pRho plasmid rescued the negative biofilm phenotype of the NCIB 3610 RM strain , but had no global effect on naturally robust biofilms formed by NCIB 3610 WT ( Fig 10E ) . Apparent insensitivity of biofilm formation to Rho overexpression in WT cells was expected due to known functional redundancy between phosphorelay kinases and the minor involvement of KinB in biofilm development under conditions of MSgg growth [85 , 121] . However , the introduction of pRho not only suppressed the sessile phenotype of NCIB 3610 RM , but also increased swarming capacities of the parental swarming-proficient NCIB 3610 WT strain ( Fig 10F and 10G ) . This higher-than-natural motility phenotype suggests that Rho over-production increases a subpopulation of WT cells with active SigD . These experiments established that Rho inactivation and overexpression have opposite effects on B . subtilis physiology . This suggests that cells might be sensitive to intercellular variations of a naturally low level of Rho expression [120 , 122] .
The transcriptome and proteome analyses presented here demonstrate the important effects of rho deletion on the B . subtilis gene expression landscape , encompassing approximately one-tenth of the known functional regions in a given growth condition . Prominent alterations of sense-strand expression in RM cells are caused by a combination of direct cis up-regulation due to transcription read-through of Rho-dependent terminators , and , in some cases , cis down-regulation of genes confronted with antisense transcription . These primary events propagate into regulatory networks and cause other changes that can be considered to be indirect trans effects . Recently , another transcription factor , NusA , was shown to regulate global gene expression in B . subtilis by controlling transcription read-through at suboptimal intrinsic terminators . Depletion of this essential protein caused a substantial increase of antisense transcription and misregulation of many genes mainly involved in DNA replication and DNA metabolism [84] . Physiological analyses of RM strains demonstrated that numerous changes in gene expression caused by Rho inactivation are not fortuitous . Indeed , they are related to biologically relevant phenotypes linked to three distinct B . subtilis cell fates: the synthesis of flagella leading to cell motility , matrix production underlying biofilm formation , and sporulation . These mutually exclusive developmental programs are controlled by complex regulatory networks , which are organized in a way that avoids their simultaneous activation in the same cell . Each program is characterized by a high level of phenotypic heterogeneity and bistable expression of a specific set of genes [37–40 , 43 , 44 , 71] . The biological significance of Rho-controlled transcription is illustrated by the opposite phenotypes of RM and Rho over-expressing strains , corresponding to its high and low steady-state levels , respectively . RM cells are mostly sessile , exhibit low extracellular matrix production , and sporulate with high efficiency . In contrast , cells over-expressing Rho sporulate weakly , but are highly motile . These opposite phenotypes are determined by a specific architecture of the Rho-controlled transcriptome , of which the elements appear to be organized for the simultaneous stimulation of sporulation and repression of the principle alternative programs , once the control by Rho is removed . Several Rho-controlled transcripts within the motility differentiation program act to down-regulate expression and activity of the motility-specific sigma factor SigD . In the absence of Rho , independent events of read-through transcription at Rho-dependent terminators directly up-regulate slrR and slrA genes which products negatively control sigD expression [59 , 60–62 , 71 , 78] . Simultaneously , a Rho-dependent antisense transcript down-regulates the flhO-flhP operon , encoding components of the flagella export apparatus , which is essential for secretion of the anti-SigD factor FlgM [69 , 81] . Altogether , these events disturb the self-reinforcing circuit of sigD expression and bias the SigD-ON/SigD-OFF bistable switch of motility towards a SigD-OFF state . Other factors contributing to motility are also affected by Rho inactivation , but were not analyzed in this study . For example , the flagellar chaperons FliS and FliD , encoded by the yvyC-fliD-fliS-fliT operon , were down-regulated in RM cells , probably as a result of the Rho-controlled asRNA targeting this locus ( Table 1 , S1 and S4 Tables ) . Concomitant with the inhibition of flagellar synthesis , the absence of Rho severely alters the program of biofilm development . Both epsA-epsO and tapA-sipW-tasA operons encoding the main components of the extracellular matrix are down-regulated in RM cells . Weak expression of both matrix operons results from reinforcement of SinI/SinR-mediated repression of their promoters caused by a deregulated phosphorylation of Spo0A . Our analysis demonstrates that Spo0A~P rapidly accumulates beyond a level needed to induce matrix production and reaches a higher , sporulation-triggering , threshold , due to increased activity of Spo0A phosphorelay in RM cells . High Spo0A~P should inhibit expression of the SinI anti-repressor and , consequently , reinforce SinR-mediated repression of the matrix operons . Besides this indirect trans effect on the expression of matrix operons , inactivation of Rho provokes the generation of a new antisense transcript , which spans across the entire epsA-epsO operon and , as shown here , contributes to the inhibition of biofilm formation in RM cells . The exact mechanism by which eps asRNA inhibits expression of the eps operon remains to be established , but it may directly interfere with activity of the eps promoter and/or the RNA switch located between the epsB and epsC genes , which allows transcription of the long eps operon [123] , or act post-transcriptionally [30] . Additionally , we detected down-regulation of the skf operon ( S1 Table; http://genome . jouy . inra . fr/cgi-bin/seb/index . py ) involved in the production and release of the cannibalism toxin , Skf , known to stimulate biofilm development and delay sporulation [38 , 124] . Thus , inefficient production of Skf might have also contributed to reduced biofilm formation by RM cells . Expression of the skfA-H operon is inhibited by a high level of Spo0A~P [43 , 58] , similarly to the matrix operons . In addition , highly expressed asRNA overlapping the entire skf operon was revealed in the RM strains ( S1 Table ) . The putative role of the skf-specific asRNA in the regulation of the skfA-H operon merits further analysis . All Rho-controlled transcripts detected within the Spo0A phosphorelay are sense and lead to up-regulation of the cognate genes when de-repressed in the absence of Rho . Aside from the rapA gene , other targets of Rho-controlled transcription encode positive factors of Spo0A phosphorylation: sensor kinase KinB , its positive effector KbaA , and the phosphotransferase Spo0B . Pervasive transcription , together with several positive feedback loops of spo0A expression , intensifies phosphorelay activity . As a result , RM cells engage in differentiation more efficiently and synchronously than WT cells , characterized by broadly heterogeneous Spo0A phosphorylation [104] . Key factors involved in the increase of phosphorelay activity in RM cells are the up-regulated KinB and , to a lesser extent , KinA kinases , which are known to trigger sporulation if over-expressed [49 , 63] . In RM cells , kinA up-regulation is most likely due to increased Spo0A~P , acting within a positive auto-regulatory loop [104] , and thus would be an indirect trans effect of Rho-controlled transcription . In contrast , up-regulation of kinB in RM cells is the direct result of a read-through at intragenic transcription terminator . We experimentally proved that the 5’-terminal segment of the kinB transcript contains sequence features required for Rho-dependent transcription termination . We showed that the efficiency of intragenic kinB termination depends on Rho availability and negatively correlates with the kinB translation initiation rate in a Rho-dependent manner . This anti-correlation is in accordance with the well-documented preferential activity of Rho at untranslated RNAs and its role in the control of transcription-translation coupling [23–25 , 118] . The global transcriptional regulators , AbrB and CodY , and positive stringent control regulate the expression of KinB at the transcription initiation level [125–127] . Our results highlight a novel regulatory mechanism of kinB expression that acts through the premature termination of transcription . Inactivation of Rho and , consequently the lack of termination , generates a full-length kinB transcript , leading to an increased level of KinB , higher levels of Spo0A~P , and the activation of sporulation . In contrast , high Rho amounts strengthen intragenic termination of kinB , decrease cellular levels of KinB and phosphorelay activity , and lead to a weak-sporulation phenotype . The intragenic termination of kinB in WT cells is less efficient at early stages of sporulation , correlating with decreased rho expression . Programmed decrease of Rho amount might be needed to compensate probable strengthening of the premature kinB termination due to decrease of the translation efficiency under nutrient-limiting conditions . This emphasizes the biological significance of Rho-mediated control of kinB expression . By premature termination of kinB transcription , Rho delays sporulation , giving cells the possibility to continue exploring the environment . Rho might also influence other developmental programs , as KinB kinase has been found to be involved in the control of sliding motility and biofilm formation under particular growth conditions [47 , 128 , 129] . Thus , Rho-mediated control of kinB expression appears to be an integral part of the deterministic regulation of B . subtilis development , mediated by Spo0A . Recent study in E . coli has established that Rho acts as a global regulator of genes expression during the exponential growth by prematurely terminating transcription within the 5’ un-translated regions of hundreds of genes , including the global stress response sigma factor rpoS gene . Rho-mediated control of rpoS expression is modulated by the regulatory sRNAs and is relieved at the stationary phase of growth [130] . This and our analyses illustrate the diversity of strategies by which bacteria employ Rho-mediated transcription termination to adapt to the environmental and metabolic changes . Our data suggest another potential role of Rho: that as a factor of phenotypic heterogeneity within B . subtilis population . Heterogeneity based on intrinsic fluctuations of gene expression provides potential flexibility to a genetically homogenous population to respond to environmental changes [131] . We showed that population heterogeneity is considerably reduced when Rho-controlled transcription levels are artificially high or low . This suggests that pervasive transcription in WT cells varies depending on the intracellular concentration of Rho . This was already suggested by our previous study , which noted that the length of sense and antisense 3’extensions generated by read-through transcription negatively correlates with rho expression [17] . B . subtilis Rho is a low abundant protein present at 0 . 5 to 4 . 8% of the level of RNA polymerase [17 , 120 , 122] . According to the E . coli paradigm , Rho should function as a hexamer [22] . This suggests that even minor variations of Rho levels might have substantial impact on the efficiency of pervasive transcription . The stochastic generation of pervasive , often antisense , transcription targeting gene expression via various mechanisms [29 , 30 , 132] may increase intercellular heterogeneity and phenotypic variation within isogenic B . subtilis population . A similar hypothesis was recently proposed following analysis of the Clostridium botulinum Rho protein expressed in heterologous systems [133] . Comparative analyses of closely related bacterial species have revealed both conservation of and significant differences between respective non-coding transcriptomes . It has thus been proposed that pervasive transcription represents a major element of inter-strain divergence , providing a potential for physiological adaptation [8 , 134 , 135] . Here we show that several transcripts regulated by Rho are similarly functional in different B . subtilis strains ( BSB1 , NCIB 3610 , PY79 ) . Moreover , the nucleotide sequences of the corresponding genomic regions are conserved among B . subtilis strains ( ≥ 99% identity ) , while the conservation level is lower in other Bacillus species ( for example , the kinB and flhO-flhP genomic regions of B . subtilis and B . amyloliquefaciens present only 67%-73% of nucleotide identity , respectively ) . Thus , RNA features controlled by Rho might represent a specific trait fixed by evolution , at least in B . subtilis . It is important to note that the Rho-dependent regulatory network in B . subtilis may be broader than it emerges from our analysis , as Rho-controlled transcripts expressed under other conditions and/or dependent on alternative sigma factors , may have escaped identification . Future studies in B . subtilis and other bacteria will help to understand how elements of the Rho-controlled pervasive transcription are recruited to achieve important regulatory functions . Our results support the view that , in terms of gene regulation , transcription termination can be as important as the repression or activation of transcription initiation . They advance our understanding of the role of pervasive transcription in bacteria , considered for a long time as a “dark matter” of bacterial transcriptomes . Part of Rho-controlled transcription appears to constitute an integral module of the B . subtilis cell differentiation regulatory network instead of simply being non-functional transcriptional noise . This ranks termination factor Rho among the global regulators of B . subtilis .
B . subtilis strains used in the work are listed in S6 Table . When needed , cells contained plasmids as indicated in Results section . Cells were routinely grown in Luria-Bertani liquid or solidified ( 1 . 5% agar; Difco ) medium at 37°C . Standard protocols were used for transformation of E . coli and B . subtilis competent cells . SPP1 phage was used for transduction of NCIB 3610 strains as described [136] . Biofilm formation was analyzed in liquid ( for pellicles ) or solid ( for colony biofilms ) MSgg medium [96] . Sporulation was analyzed in supplemented Difco Sporulation medium ( Difco ) [137] . When required for selection , antibiotics were added at following concentrations: 100 μg per ml of ampicillin , 60 μg per ml of spectinomycin , 0 . 5 μg per ml ( for B . subtilis ) and 90 μg per ml ( for E . coli ) of erythromycin , 3 μg per ml of phleomycin , 5 μg per ml of kanamycin , and 5 μg per ml of chloramphenicol . B . subtilis strains and the plasmids used in the study are listed in S6 Table . The plasmids were constructed in E . coli TG1 strain . The used oligonucleotides are listed in S7 Table . To repair NCIB 3610 RM ( Δrho::phleo ) strain back to the wild type rho allele , a near-by DNA fragment containing ywjH gene was amplified using oligonucleotides eb460 and eb461 , digested by HindIII and BamHI endonucleases and cloned at pMutin4 plasmid [138] . The resulting plasmid was integrated by single crossover in the ywjH locus of BSB1 chromosome and subsequently transferred in NCIB 3610 RM with selection at erythromycin . NCIB 3610 RM ywjH::pMutin transductants were tested for sensitivity to phleomycin that indicated substitution of the rho deletion by the wild type rho allele . Phleomycin-sensitive clones were further selected for loss of the erythromycin-resistance that indicated excision of pMutin4 from the chromosome and restoration of the ywjH gene . Thus obtained NCIB 3610 rho wt* clones were controlled for integrity of the rho and ywjH wild type alleles by PCR . To inactivate kinB gene , the internal part of the gene was amplified using oligonucleotides veb608 and veb610 , digested with HindIII and EcoRI endonucleases and cloned at pMutin4 plasmid . The resulting plasmid was integrated by single crossover in the kinB locus of BSB1 chromosome leading to its disruption . The translational fusions of the kinA , kinB and rho genes with the sequential peptide affinity ( SPA ) tag sequence were constructed for immunoblot analysis of the proteins . The 3’- terminal parts of the kinA and kinB genes were amplified using pairs of oligonucleotides eb625 and eb626 and veb606 and veb607 , respectively . The amplified kinA and kinB DNA fragments were digested , respectively , by BamHI and NcoI and by Acc65I and NcoI endonucleases , and ligated with pMutin-SPA plasmid [139] cutted by BglII and NcoI for the kinA , and by Acc65I and NcoI for the kinB cloning . The rho-SPA fusion was constructed using ligation-independent cloning as described [140] . The 3’-terminal part of rho was amplified using oligonucleotides rhoSpa-Fwd and rhoSpa-Rev , treated with T4 DNA polymerase in the presence of dTTP and annealed to pMUTIN-LICSPA plasmid [141] , linearized by AscI endonuclease and treated with T4 DNA polymerase in the presence of dATP . The annealing mixture was transformed into E . coli cells . The resulting plasmids with the kinA- , kinB- and rho-SPA fusions were transferred in BSB1 cells where they integrated into respective chromosomal loci by single crossover . To express the flhO and flhP genes from ectopic position , the flhOP operon was PCR-amplified using oligonucleotides eb617 and eb618 , digested by Acc65I and BamHI endonucleases and cloned onto integrative plasmid pSG1729 [142] . The resulting plasmid was integrated in the amyE locus of BSB1 chromosome by double crossover with selection of the chloramphenicol-resistant transformants , which lost amylase activity . To suppress anti-sense transcription within the eps operon , the 5’-part of epsO gene was amplified using oligonucleotides veb680 and veb681 , cutted by EcoRI endonuclease and cloned at pMutin4 plasmid between PacI site filled-in by T4 polymerase and EcoRI site . In the resulting plasmid , the 3’ end of the inserted fragment is flanked by three intrinsic transcription terminators of the vector [138] . The plasmid was integrated in the epsO locus of BSB1 chromosome by single crossover and subsequently transferred in NCIB 3610 WT and RM cells . In the mutant epsO:Ter allele , the inserted transcription terminators are oriented to block transcription of the eps asRNA initiated near the 3’- end of epsO gene . To analyze genes expression , pUC18Cm-luc plasmid was used to construct genes transcriptional fusions with the butterfly luciferase gene luc [97] . The promoters of spoIIA , gerE , epsA and tapA genes were amplified together with the upstream chromosomal fragments of ~1 Kbp , using the corresponding pairs of oligonucleotides listed in S7 Table . The fragments containing spoIIA and gerE promoters were digested by HindIII and BamHI endonucleases and cloned at pUC18Cm-luc . The luc fusions with epsA and tapA promoters were obtained by the assembly Gibson’s procedure with linear vector amplified with oligonucleotides F- pUC18-luc and R- pUC18-luc [143] ( S7 Table ) . The obtained plasmids were used to transform B . subtilis where they integrated by single crossover at chromosomal loci of the targeted genes . This event reconstructs natural regulatory region of gene upstream the fusion and an intact copy of gene downstream . To inactivate the anti-SigD factor FlgM without inducing polar effects on the downstream genes , a marker-less in-frame flgM deletion was constructed by a two-step integration-excision method similar to the previously described [144] . Two chromosomal fragments of ~1 Kbp were amplified using oppositely oriented and partially over-lapping oligonucleotides veb687 and veb688 , which match close to the extremities of flgM gene , and their counterpart primers veb686 and veb689 , respectively . The amplified fragments were joined by PCR using primers veb686 and veb689 and cloned between the BamHI and SalI sites at the thermo-sensitive plasmid pMAD [145] . The resulting plasmid contains a fragment of B . subtilis chromosome with in-frame deletion of the most part of flgM gene ( flgMΔ63 ) . The flgMΔ63 structure was controlled by sequencing . The plasmid was transformed in BSB1 cells with selection for erythromycin-resistance at non-permissive for replication temperature 37°C . This led to plasmid insertion into the chromosome by single crossover and duplication of yvyF-csrA locus , which copies contained the wild type or flgMΔ63 alleles . The duplicated region was transferred in NCIB 3610 RM cells , which were further cultivated at permissive 30°C without erythromycin to stimulate excision of the yvyF-csrA duplicate from the chromosome . The resulting clones were controlled for the presence of flgMΔ63 allele by PCR using primers veb686 and veb689 . For Rho overproduction , rho gene was amplified using oligonucleotides eb423 and eb424; digested by NheI and BamHI endonucleases and cloned at pDG148 [146] plasmid between the XbaI and BamHI sites . The resulting plasmid pRho is deleted for lacI repressor gene and expresses Rho constitutively from Pspac promoter . To construct the control vector pDG148Δlac , the XbaI-BamHI double-cutted pDG148 was treated by T4 DNA polymerase and self-ligated . To estimate Rho production from Pspac promoter , pRho plasmid was modified to express a SPA-tagged protein . The 3’-terminal part of the chromosomal rho-SPA fusion ( see above ) was amplified using oligonucleotides eb423 and pdg148-rev , which matches the sequence behind the SPA-tag , and digested by XhoI endonuclease . The PCR product was cloned at pRho plasmid between BamHI site filled-in by T4 DNA polymerase and XhoI site . To analyze presence of the Rho-dependent terminator within kinB gene , two plasmids were constructed which contain kinB promoter and differently sized 5’-terminal parts of the gene . The kinB gene and the upstream 650 bp region were amplified using oligonucleotides veb610 and veb611 . The fragment was digested either by SmaI and NsiI or by SmaI and MboI endonucleases and the fragments of 390 and 650 bp , respectively , were gel-purified and cloned at the terminator-screening vector pGKV210 [116] digested , respectively , by SmaI and PstI or by SmaI and BamHI endonucleases . The resulting plasmids pKinB-S ( hort ) and pKinB-L ( ong ) were transformed into B . subtilis cells with selection at erythromycin . Both plasmids contain the promoter of kinB gene and the first 157 and 417 bp of kinB orf , respectively . To modify efficiency of kinB translation initiation at pKinB-L and pKinB-S plasmids , the whole pKinB-S was amplified using the oligonucleotides veb678 and veb679 or veb679 and veb690 designed to substitute natural kinB ribosome binding site ( RBS ) by a stronger or a weaker , respectively . PCR products were treated by DpnI endonuclease , to degrade template DNA , 5’-phosphorylated by T4 polynucleotide kinase , self-ligated and transformed in E . coli cells with selection at erythromycin . The resulting plasmids were used as templates for amplification of the modified kinB fragments using the oligonuclotides veb676 and eb407 . PCR products were controlled by sequencing , digested by EcoRI and NarI endonucleases and cloned at similarly digested pKinB-L or pKinB-S . The resulting derivative plasmids contain either canonic GGAGGA ( RBSm+ ) or a weak TGATAA ( RBSm- ) RBSs ( Fig 8D ) . Tiling array data obtained with a strand-specific resolution of 22 bp for exponential growth in LB [17] were reanalyzed . This re-analysis used the same signal processing and gene-level aggregation procedures as the initial study but differed by the normalization and differential expression analysis . Briefly , the raw log2-transformed hybridization signal was smoothed with an algorithm that accounts for probe-specific biases and changes in expression levels between adjacent regions that can take a form of abrupt shifts and more continuous drifts [17] . Then , a whole genome transcription profile was aggregated into sense and antisense gene level data by computing the median of the smoothed signal on the sense and antisense strand of a repertoire of native expression segments , i . e . detected as transcribed in the wild-type in one out of 269 hybridized RNA samples intended to capture the diversity of the lifestyles of the bacterium [17] . To allow precise between-sample comparison of expression levels on the both sense and antisense strands of the native expression segments , the quantile normalizing transformation fitted on aggregated sense strand levels was also applied to the antisense strand levels and to the smoothed transcription profiles as described [20] . Statistical comparison of the 3 biological replicates for RM and WT relied on moderated t-statistics computed with the functions “lmFit” and “eBayes” of R package “limma” [147] . Control the False Discovery Rate relied on q-values obtained with R package “fdrtool” [148] . Sense strand and antisense strand levels were considered simultaneously in these analyses . The same statistical procedure served here to examined the expression levels immediately downstream a repertoire of 3242 transcriptional up-shifts encompassing promoters of most genes [17] . We considered that an up-regulation was detected at a given promoter if the downstream smoothed-normalized signal exhibited differential expression according to the specified amplitude ( log2 RM/WT ≥ 1 ) and false discovery rate ( q-value ≤ 0 . 01 ) cut-offs and if the downstream transcription level was at least twice higher than the upstream level in the 3 biological replicates for RM ( indicating activity of this promoter as opposed to transcription from an upstream promoter ) . Reciprocally , down-regulation was considered detected when log2 RM/WT ≤ -1 , q-value ≤ 0 . 01 , and the downstream transcription level was at least twice higher than the upstream level in the 3 biological replicates for WT . RNA was extracted from B . subtilis WT and RM derivative strains grown in LB or MSgg medium at 37°C under vigorous agitation up to an OD600nm ~0 . 5 . RNA preparation and DNase treatment were done as described [17] . Quality and quantity of RNA samples were analyzed on Bioanalyzer ( Agilent , CA ) . For analysis of the antisense transcription of flhOP and eps operons by RT-PCR , cDNA was synthesized using flhO , epsL and 16S rRNA specific oligonucleotides eb700 , eb706 and eb715 , respectively , ( S7 Table ) and 50 ng of total RNA as a template in reaction with SuperScriptIV Reverse Transcriptase ( Invitrogen ) according to the supplied protocol , and treated with RNaseH ( Invitrogen ) for 20 min at 37°C . To amplify internal DNA fragments , PCR ( 35 cycles ) was performed by Thermo Scientific DreamTaq DNA Polymerase ( ThermoFicher ) with the oligonucleotides pairs specific for flhO ( eb705 and eb702 ) , epsK ( eb708 and eb710 ) and 16S rRNA genes ( eb715 and eb716 ) ( S7 Table ) . RNA samples of B . subtilis BSB1 , NCIB3610 , and the corresponding rho-deletion mutants were prepared as described in the previous section . Transcriptome analysis was performed by Transcriptome and EpiGenome platform ( Pasteur Institute , France ) . Briefly , RNA samples have been submitted first to ribosomal RNA depletion using the RIBOZero rRNA removal kit Bacteria ( Illumina , San Diego , California ) . Purified fraction was then treated for library preparation using the Truseq Stranded mRNA sample preparation kit ( Illumina , San Diego , California ) according to manufacturer’s instruction . Fragmented RNA samples were randomly primed for reverse transcription followed by second-strand synthesis to create double-stranded cDNA fragments . No end repair step was necessary . An adenine was added to the 3'-end and specific Illumina adapters were ligated . Ligation products were submitted to PCR amplification . Sequencing was performed on the Illumina Hiseq2500 platform to generate single-end 65 bp reads bearing strand specificity . Reads were trimmed based on sequencing quality using Sickle ( v1 . 200 ) and mapped on AL009126 . 3 reference genome assembly using Bowtie2 ( 2 . 2 . 6; options "-N 1 -L 16 -R 4" ) [149] before read-count aggregation on the sense and antisense strand of each transcribed region ( annotated genes and S-segments ) with Htseq-count ( 0 . 6 . 0; standard options ) . Raw sequencing data and aggregated counts have been deposited in GEO ( GEO submission number GSE94303 ) . Experiments were made in duplicates for B . subtilis NCIB3610 to allow statistical differential expression analysis . RPKM normalization [150] served for a first level of exploratory analysis . Differential expression analysis of B . subtilis NCIB3610 relied on R library “DESeq2” [151] and associated "median ratio method" normalization procedure . Normalization relied on a control set of 1152 always well expressed sense regions a priori less impacted by low-level transcriptional read-through typical of the rho-deletion mutants . These were ranking in the 25% highest density of mapped reads in each of the four NCIB3610 samples . DESeq2 p-values were converted into q-values using R library “fdrtool” [148] . While the initial differential expression analysis relied on the four NCIB3610 samples , we also performed another , more discriminative , differential expression analysis excluding one of the parental NCIB3610 sample which exhibited anti-sense transcription levels markedly higher than other RNA-Seq ( B . subtilis BSB1 and NCIB3610 ) and tiling array ( B . subtilis 1012 ) samples of the parental strains probably because of more advanced growth status . B . subtilis 168 derivative strains were grown in LB medium at 37°C under vigorous agitation up to an OD600nm ~0 . 6 . Cells were harvested by centrifugation ( 6 , 000g for 10 min at 4°C ) , washed once with 50 mL of buffer A ( 10 mM Tris-Cl pH 7 . 5 , 150 mM NaCl ) before being centrifuged again . The cell pellets were frozen in liquid nitrogen and kept at -80°C . Cell pellets were thawed on ice and resuspended with 5 mL of buffer A , and disrupted by French press ( pressure 2 . 7 MPa ) . Unbroken cells were removed by centrifugation at 15 , 000 RPM , and the supernatants were centrifuged at 100 , 000g for 1 hour at 4°C . The resulting supernatants were kept as the cytosolic fraction . The pellets were then washed twice with cold buffer A , and centrifuged twice at 100 , 000g for 1 hour at 4°C . The pellets were re-suspended in TE ( 20mM Tris , 2 mM EDTA ) and considered as the membrane fraction . All experiments were carried out in duplicate . Membrane and cytosolic protein concentrations were measured using the Bradford method ( Bio-Rad kit ) . Membrane and cytosolic samples were treated differently before separation by electrophoresis . Samples corresponding to the membrane fractions were mixed with a loading buffer containing 125 mM Tris-Cl pH 6 . 8 , 20% glycerol , 10% SDS and 0 . 1% bromophenol blue , and left overnight at room temperature . Equal amounts of cytosolic proteins for each sample were treated with a classic Laemmli loading buffer and boiled for 5 min . Samples were then loaded on a 10% Bis-Tris polyacrylamide NuPAGE gel ( Invitrogen ) and the electrophoresis was left running at 100V for 15 min . The gel was then stained with Bio-Safe Coomassie G-250 Stain ( Bio-Rad ) . After distaining the bands of 2 mm-wide along the protein migration lane were cut off and used as samples for the identification of the proteins by mass spectrometry . The gel pieces for each sample were washed twice with 0 . 2% TFA-50% acetonitrile , reduced by 10 mM DTT for an hour at 56°C , alkylated by 50 mM iodoacetamide for 1 hour at room temperature into darkness . Sequencing grade modified trypsin ( Promega ) diluted in 25 mM NH4HCO3 was added for 18 hours at 37°C . Tryptic peptides were recovered by washing the gel pieces twice with 0 . 2% TFA-50% acetonitrile , once with 100% acetonitrile and the supernatants were evaporated to dryness . The peptides were then re-suspended in 25 μL of pre-column loading buffer ( 0 . 05% trifluoroacetic acid ( TFA ) and 5% acetonitrile ( ACN ) in H2O ) , prior to LC-MS/MS analysis . Mass spectrometry was performed on the PAPPSO platform ( MICALIS , INRA , France , http://pappso . inra . fr/ ) . Protein identification was performed with X ! Tandem software ( DB: X ! tandem version 2013 . 09 . 01 . 1 ) against a protein database of B . subtilis as well as a proteomic contaminant database ( for details of the parameters used , see S2 Table ) . For quantification of the proteins , we used the number of spectra obtained during protein identification by mass spectrometry . The number of spectra is admitted to be proportional to the abundance of a given protein . For each protein , we calculated the relative abundance factor ( PAI ) as described in [66] . The PAI estimates the relative abundance of a protein and is calculated as the number of identified spectra divided by the number of theoretical peptides of the protein ( theoretical peptide number corresponds to the number of peptides resulting from the theoretical digestion of the protein by trypsin and that are visible in mass spectrometry [i . e . having a mass ranging between 800 and 2 , 500 D . ] ) . The PAI were log2-transformed after adding a pseudo count of 0 . 1 which corresponded approximately to quantile 10% of the PAI distributions . Analysis of the promoters’ activity using translational fusions with luciferase was performed as described by [97] with minor modifications . Cells were grown in LB medium to mid-exponential phase ( optical density OD600 0 , 4–0 , 5 with NovaspecII Visible Spectrophotometer , Pharmacia Biotech ) , after which cultures were centrifuged and resuspended in fresh DS or MSgg media to obtain OD600 1 , 0 . The pre-cultures were next diluted in respective media to OD600 0 . 025 . The starter cultures were distributed by 200μl in a 96-well black plate ( Corning ) and D-lucefirin ( PerkinElmer ) was added to each well to final concentration 1 . 5 mg/mL . The cultures were incubated with agitation at 37°C in PerkinElmer Envision 2104 Multilabel Reader ( PerkinElmer ) equipped with an enhanced sensitivity photomultiplier for luminometry ( data presented in Figs 4 , 6 and 10 ) or in Synergy 2 Multi-mode microplate reader ( BioTek Instruments; data presented in Fig 7 and S7 Fig ) . Relative Luminescence Units ( RLU ) and OD600 were measured at 5 min intervals . Each fusion-containing strain was analyzed at least three times . Each experiment included four independent cultures of each strain . Swarming and swimming motility tests were performed using NCIB 3610 strain and its derivatives as described by [68 , 152] with some modifications . The fresh plates ( 9cm ) were prepared from liquid LB medium ( Difco ) fortified by agar ( Invitrogen , Life Technologies ) at 0 . 3% or 0 . 7% concentration for swimming and swarming tests , respectively , and dried in a laminar flow hood for 15 minutes . B . subtilis cells were grown to an OD600 0 . 5 ( Biochrom Libra S11 Visible Spectrophotometter , Biochrom ) , 2 ml of cells were pelleted and gently resuspended in 100 μl of phosphate-buffered saline ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 2 mM KH2PO4 ) containing 0 . 5% of India ink ( Higgins ) . For motility assay , 5μl of cells were directly spotted on the plate and dried in a laminar flow hood for two minutes . Plates were incubated at 37°C and the extent of swimming or swarming was noted at defined time intervals . The images were acquired with the ChemiDoc MP system ( BioRad ) and treated using ImageLab 5 . 0 software ( BioRad ) after 5 or 20 hours of incubation for swimming or swarming motility tests , respectively . For each strain , from three to five independent cultures were analyzed in parallel during each experiment . At least four independent experiments were performed . Overnight bacterial cultures were diluted 200-times in fresh LB medium and grown at 37°C with agitation to an OD600 ∼0 . 6 . For the colony assay , 2μl of culture was spotted onto MSgg agar plate ( 1 . 5% agar , Invitrogen ) and incubated at 30°C for 72h . For pellicle assay , 2μl of culture was added to 2ml of MSgg medium in a well of 24-well sterile microtiter plate ( Evergreen Scientific ) . The plates were incubated without agitation at 30°C for 72h . Photographs were acquired with the Samsung Galaxy Tab E–SM-T560 . For each strain , four independent cultures were analyzed in parallel during each experiment . At least five independent experiments were performed . For sporulation assay , cells were diluted in LB in a way to obtain the exponentially growing cultures after over-night incubation at 28°C . The pre-cultures were diluted in pre-warmed liquid DS medium at OD600 0 . 025 and incubated at 37°C . The growth rates were the same for all strains . Starting from OD600 1 . 5 ( taken as T0 ) cultures were analyzed for the presence of spores at the indicated time . Samples were split in two and one part was heated at 75°C for 15 min; heated and unheated cultures were plated in sequential dilutions at LB-agar plates and incubated for 36 h at 37°C . The percentage of spores was calculated as the ratio of colony forming units in heated and unheated samples . Each experiment included three independent isogenic cultures . Four independent experiments were performed to establish sporulation efficiency of each strain . The crude cell extracts were prepared using Vibracell 72408 sonicator ( Bioblock scientific ) . Bradford assay was used to determine total protein concentration in each extract . Equal amounts of total proteins were separated by SDS-PAGE ( 12% polyacrylamide ) . The SPA-tagged Rho , KinA and KinB proteins were visualized using the primary mouse ANTI-FLAG M2 monoclonal antibodies ( Sigma-Aldrich; dilution 1:5 , 000 ) and the secondary goat peroxidase-coupled anti-mouse IgG antibodies ( Sigma-Aldrich; dilution 1:20 , 000 ) . MreB and Mbl proteins used as controls for samples equilibrium were visualized using primary rat anti-MreB and rabbit anti-Mbl antibodies ( a gift of X . Henry , dilution 1:10 , 000 ) and the secondary peroxidase-coupled anti-rat and anti-rabbit antibodies A9037 and A0545 , respectively ( Sigma-Aldrich; dilution 1:10 , 000 ) . Three independent experiments were performed , and a representative result is shown . | Bacillus subtilis is a widely used model to study cell differentiation in the bacterial world . This soil-dwelling bacterium can engage in several alternative developmental programs , which generate distinct cell types adapted to different lifestyles , to cope with its complex and changing natural environment . The underlying differentiation control mechanisms are interconnected and tightly regulated , because these physiological and morphological cellular states are mutually exclusive and a correct choice is crucial . Here , we describe a previously unrecognized mechanism that regulates cell fate decisions in B . subtilis . It is based on the elements of pervasive genome-wide transcription controlled by the termination factor Rho . Pervasive transcription originating from non-defined or cryptic signals is spread throughout bacterial transcriptomes , but its physiological role is not yet well understood . We show that the elements of the Rho-controlled transcriptome affect specific developmental programs in B . subtilis: cell motility , biofilm formation , and sporulation , by directly or indirectly targeting expression of the key factors of cellular differentiation . Our results illuminate how Rho plays a prominent role in complex and organism-specific regulatory networks by controlling pervasive transcription . These findings rank Rho among the global transcriptional regulators of B . subtilis and invite systematic exploration of its role in other microorganisms . | [
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"exper... | 2017 | Termination factor Rho: From the control of pervasive transcription to cell fate determination in Bacillus subtilis |
We report association mapping of a locus on bovine chromosome 3 that underlies a Mendelian form of stunted growth in Belgian Blue Cattle ( BBC ) . By resequencing positional candidates , we identify the causative c124-2A>G splice variant in intron 1 of the RNF11 gene , for which all affected animals are homozygous . We make the remarkable observation that 26% of healthy Belgian Blue animals carry the corresponding variant . We demonstrate in a prospective study design that approximately one third of homozygous mutants die prematurely with major inflammatory lesions , hence explaining the rarity of growth-stunted animals despite the high frequency of carriers . We provide preliminary evidence that heterozygous advantage for an as of yet unidentified phenotype may have caused a selective sweep accounting for the high frequency of the RNF11 c124-2A>G mutation in Belgian Blue Cattle .
Growth is one of the economically most important phenotypes in livestock production . While genetic variants with large effects on stature account for part of the between-breed variation [1] , within-breed variation is likely to be highly multifactorial and polygenic . Accordingly , quantitative trait loci ( QTL ) influencing growth are reported on all autosomes in the cattle QTL database ( http://www . animalgenome . org/cgi-bin/QTLdb/BT/index ) . The BBC breed is a beef breed that is famous for its “double-muscling” phenotype caused in part by a disruptive 11-bp deletion in the myostatin ( MSTN ) gene [2] . As in other breeds , growth performances are paramount in BBC as they control duration of the fattening period and final carcass weight , hence directly determining profit . In recent years , an increasing number of young animals with growth retardation as primary symptoms were reported to our heredosurveillance platform . We established this platform in 2005 to rapidly detect genetic defects emerging in the BBC , identify the culprit genes and mutations , and develop diagnostic tests to limit their negative impact [3] . Animals with growth retardation underwent a standard protocol including a genome-wide association study ( GWAS ) to identify putative causative loci . We herein report the mapping of a locus accounting for ∼40% of growth-retardation cases , and identify the causative loss-of-function mutation in the RING finger protein 11 ( RNF11 ) gene . Moreover , we perform a prospective study that indicates that as much as one third of homozygous mutants die from infection before six months of age . We finally present evidence that carriers of the mutation might benefit from a selective advantage that may account for its unexpectedly high frequency ( ∼13% ) in the BBC population .
Between 2008 and 2011 , we collected blood samples and epidemiological data from 147 BBC individuals , aged between 3 months and 3 years old , with pronounced ( ∼15% reduction in stature when compared to contemporaries ) yet proportionate growth retardation as primary distinctive feature . We initially genotyped 33 of these with a custom-designed 50 K medium-density bovine SNP array [3] . None of these animals would be homozygous or compound heterozygote for the previously identified c . 2904-2905delAG [4] and c . 1906T>C [5] MRC2 mutations causing Crooked Tail Syndrome and known to affect stature . Using the genotypes of the corresponding SNPs ( yet obtained with a distinct , high-density bovine SNP array ) from 275 healthy sires as control , we performed a GWAS using an approach based on hidden haplotype states with a generalized mixed model accounting for stratification ( Zhang et al . , submitted for publication ) . A genome-wide significant signal was obtained on BTA3 driven by haplotype state 17 , observed at a frequency of 52% in cases versus 12% in controls ( Figure 1A ) . Fourteen of the 33 cases ( 42% ) were homozygous for the corresponding haplotype , causing a significant deviation from Hardy-Weinberg expectations in cases ( expected: 27% , p<0 . 002 ) , hence suggesting recessivity . Retrospective phenotypic analysis of the 14 homozygotes revealed shared features: proportionate growth retardation appearing around 5–6 months of age ( not observed at birth ) , normal muscular development , close forehand , long and thin neck , hairy , long and thin head ( Figure 2 ) . Pedigree analysis indicated that the 14 individuals traced back to Galopeur des Hayons ( a once popular BBC sire ) on sire and dam side . Direct examination of the SNP genotypes of the 14 cases homozygous for hidden state 17 revealed a 3 . 3 Mb ( 100 , 727 , 788–104 , 017 , 608 - Btau 4 . 0 ) segment of autozygosity ( Figure 1B ) . It encompassed 19 annotated genes of which none was an obvious candidate ( Figure 1C ) . We thus undertook the systematic re-sequencing of all open reading frames ( ORF ) and intron-exon boundaries . During this process ( and after completion of 14/19 genes ) , we identified an A to G transition ( c124-2A>G ) mutating the intron 1 acceptor splice site of the RNF11 gene ( Figure 1D ) . RNF11 encodes a highly conserved , ubiquitously expressed protein with 154 amino-acids [6] , recently recognized as a subunit of the A20 ubiquitin-editing complex regulating NF-κβ signaling [7] . We developed a 5′-exonuclease assay and genotyped ( i ) the case-control cohort used for GWAS ( 33 cases , 275 controls ) , ( ii ) a diversity panel encompassing 141 animals from eleven breeds other than BBC , ( iii ) 549 additional normal adult BBC animals , and ( iv ) Galopeur des Hayons . The c124-2A>G variant appeared in near perfect linkage disequilibrium ( D′ = 1; r2 = 0 . 984 ) with haplotype state 17 in the case-control cohort . It was not present in non-BBC animals . It had an allelic frequency of 13% amongst the 824 genotyped healthy adult BBC animals , yet without a single animal being homozygous GG ( p<0 . 01 under Hardy-Weinberg equilibrium ) . Galopeur was indeed confirmed to be carrier of the c124-2A>G mutation . The effect of the c124-2A>G mutation on RNF11 transcripts was examined by RT-PCR using RNA extracted from skeletal muscle , spleen , mesenteric lymph node , thymus , lung , trachea of one GG and one AA animal . Using two primers located respectively in exon 1 and 3 and RNA from wild-type AA animals , we obtained a unique 360-bp RT-PCR product in all examined tissues , and showed by sequencing that it encompassed the expected exon 2 sequence ( data not shown ) . The same experiment performed with RNA from a homozygous mutant GG animal yielded ( i ) a major product of ∼190 bp , and ( ii ) a minor product of ∼360 bp ( Figure 3A ) . The major product was shown by sequencing to correspond to a transcript skipping exon 2 . The minor product missed the first seven base pairs of exon 2 , and resulted from the activation of a cryptic splice site in exon 2 . RT-PCR conducted with primers located respectively in exon 1 and 2 confirmed the existence of transcripts containing exon 2 in homozygous mutants ( Figure 3B ) . Both forms are expected to cause a frameshift , appending 29 ( major product ) and 14 ( minor product ) illegitimate residues to a severely truncated ( 41/154 amino-acids ) RNF11 protein missing the ubiquitin interaction and RING-finger domains . The transcript corresponding to the minor form is expected to undergo non-sense mediated RNA decay ( NMRD ) [8] , due to the occurrence of a stop codon in exon 2 of three . NMRD is not expected to affect the transcript corresponding to the major form as the corresponding open reading frame terminates in exon 3 of three . We compared the levels of RNF11 transcript in mesenteric lymph node and spleen of a wild-type AA and a mutant GG animals , using quantitative RT-PCR with primer sets targeting the second ( outside of the 7-bp deletion ) and third RNF11 exons , respectively , as well as three internal control genes . In spleen , we observed a 1 . 1-fold reduction ( p = 0 . 4 ) in the amount of exon 3 containing transcripts , and a 11-fold reduction ( p<0 . 005 ) in exon 2 containing transcripts . Assuming NMRD of the minor but not of the major product , this allows us to estimate ( i ) that ∼80% of the RNF11 pre-mRNAs skip exon 2 , while ∼20% use the exon 2 cryptic splice site , and ( ii ) that 55% of exon 2 retaining transcripts are being degraded by NMRD . The same analysis conducted in lymph node reveals a ∼2-fold reduction ( p<0 . 05 ) in exon 3 containing transcripts , and ∼37-fold reduction ( p<0 . 0005 ) in exon 2 containing transcripts , corresponding to ( i ) ∼44% of RNF11 pre-mRNAs skipping exon 2 and ∼56% using the exon 2 cryptic splice site , and ( ii ) ∼95% of exon 2 retaining transcripts being degraded by NMRD ( Supporting Information S1 ) . Taken together , our findings strongly support the causality of the c124A>G RNF11 mutation in determining stunted growth in homozygous GG animals . The ∼26% carrier frequency amongst healthy individuals is incompatible with the number of reported cases of stunted growth . As an example , ∼6% of offspring of known carrier bulls should be affected , and such high figures were never recorded . We reasoned that this lower than expected incidence of cases might reflect elimination of mutant animals either before or after birth . Embryonic mortality of homozygous mutant fetuses has been reported for deficiency in uridine monophosphate synthetase ( DUMPS ) [9] , Complex Vertebral Malformation ( CVM ) [10] , [11] and Brachyspina ( BS ) ( Charlier et al . , submitted for publication ) . To test these hypotheses we first examined field data and tested the effect of sire carrier status on ( i ) “non return ( in oestrus ) rate” of inseminated cows between 28 and 280 days after insemination , and ( ii ) rate of mortality , morbidity and culling of offspring between birth and 14 months of age [12] . Non-return rates tended to be slightly decreased when cows were inseminated with semen from carrier sires ( i . e . reproductive failure increased ) , but the effect was not significant ( p = 0 . 66 ) . Mortality , morbidity and culling tended to be increased in offspring of carrier sires , but this effect was not significant either ( p = 0 . 89 ) ( Supporting Information S1 ) . As analysis of field data did not provide conclusive results , we performed a prospective study . We identified 105 carrier dams in 22 farms that were pregnant following insemination with semen from known carrier sires . We followed the ensuing 105 calves up to 12 months after birth . The responsible veterinarian ( AS ) and the breeders were not aware of the calves' RNF11 genotype until completion of the study . Genotypic proportions at birth did not deviate significantly from Mendelian expectations ( AA: 26 ( = 24 . 8% ) ; AG: 56 ( = 53 . 3% ) ; GG: 23 ( = 21 . 9% ) ; p = 0 . 72 ) . All calves looked normal , and there was no significant effect of RNF11 genotype on weight or height at birth . However , one year after birth , 10 calves had died and eight had been culled for health-related reasons . Strikingly , all but one of these were homozygous mutant GG , while one was AG ( p<0 . 0005 ) ( Figure 4A ) . While the AG animal was euthanized with a limb fracture , the nine deceased GG animals died with severe inflammation ( primarily pneumonia ) ( Supporting Information S1 ) . The c124-2A>G genotype had a highly significant ( p≤0 . 001 ) effect on post-natal growth . Indeed , all surviving GG animals exhibiting stunted development after 6 months ( Figure 4B ) . A contrario , the growth pattern of AG and AA animals was indistinguishable . Taken together , our data indicate that as much as one third of homozygous GG calves die with major inflammation , while all remaining calves exhibit stunted growth and are hence systematically culled prematurely . The 26% carrier frequency amongst healthy BBC animals is puzzling given the observed purifying selection against GG animals . This suggests that heterozygotes might benefit from a selective advantage that would maintain the G allele at high frequency in the population . Such balanced polymorphism has been demonstrated for MRC2 loss-of-function mutations causing Crooked Tail Syndrome in homozygotes , yet increased muscle mass in carriers [4] , [5] . To test this hypothesis , we first used field data and examined the effect of RNF11 c124-2A>G sire carrier status on own and progeny performances for recorded traits including size , muscularity , type and general appearance [12] . We obtained conflicting results: carrier status appeared to negatively affect the perceived quality of sire , yet improve the quality of its offspring ( Supporting Information S1 ) . As an alternative approach to test for a putative selective advantage benefitting carriers , we evaluated whether the incidence of carriers amongst active AI sires was compatible with Mendelian ( 0 . 5∶0 . 5 ) inheritance of a neutral mutation from the founder bull Galopeur . Assuming that the c124-2A>G mutation improves zootechnical performances in heterozygotes , carriers should be over-represented amongst AI sires related to Galopeur . Two hundred and six of the 262 BBC AI sires born between 2003 and 2007 were related to Galopeur and 58 ( = 28% ) of these proved to carry the RNF11 c124-2A>G mutation . Using gene dropping in the known genealogies , we computed the probability that 58 or more descendants would be carrier in the absence of selection ( no systematic transmission distortion ) . This probability was 0 . 0002 , 0 . 0006 and 0 . 01 assuming a frequency of 0 , 0 . 01 and 0 . 05 for the c124-2A>G mutation outside the Galopeur lineage ( Figure 5A ) . These results suggest that the c124-2A>G mutation indeed underwent a recent selective sweep in the BBC population , although the phenotype that is being selected remains unclear . That 58/206 descendents of Galopeur carry the c124-2A>G mutation is best explained by assuming that the mutation has ∼10% excess probability ( i . e . 60% ) to be transmitted by a carrier parent to an AI sire or one of its ancestors ( Figure 5B ) . Homozygosity at the RNF11 c124-2A>G mutation accounted for 14 of the first 33 analyzed cases ( i . e . 42% ) , raising the question of what caused stunted growth in the others . To address this , we genotyped the remaining 114 cases for the c124-2A>G mutation . In agreement with genotypic proportions in the first 33 cases , 47/114 ( 41% ) were homozygous and 23/114 ( 20% ) heterozygous . Therefore , carrier frequency amongst non c124-2A>G homozygous cases was 34% ( 29/86 ) , which does not differ significantly ( p = 0 . 10 ) from the frequency of c124-2A>G carriers in the control cohort ( 211/829 = 26% ) . This suggests that the c124-2A>G mutation is the only common RNF11 mutation involved in stunted growth in BBC . To identify putative other loci involved in stunted growth , we genotyped the remaining 67 non c124-2A>G homozygous cases with a medium density 50 K SNP array ( Illumina ) , and rescanned the genome as described before using only non c124-2A>G homozygous cases ( 86 ) and the same control cohort ( 275 ) . As expected , there was no evidence for a residual effect of the RNF11 locus . Neither was there any genome-wide significant evidence for other loci on any one of the 29 autosomes ( Supporting Information S1 )
We herein demonstrate that a loss-of-function mutation in the RNF11 gene affects normal growth and disease resistance in calves . This is the first report of a phenotypic effect associated with RNF11 mutations in any organism , including human and mouse [7] . We postulate that the increased disease susceptibility of homozygous c124-2A>G calves is related to the demonstrated role of RNF11 in feedback down-regulation of NF-κB by the A20 complex [7] . Indeed , the nine c124-2A>G homozygous calves that underwent necropsy were affected by extensive inflammation of the respiratory tract ( eight ) or by polyarthritis ( one ) . Of note , A20 knock-out mice die prematurely from multi-organ inflammation [13] . The fact that only ∼1/3 of homozygous mutant calves died prematurely is compatible with a defect in the control or resolution of inflammation . External factors , including pathogens , may trigger an intendedly salutary innate and/or adaptive response , that evolves in pathogenic non-resolving inflammation [14] . The effects on growth may be secondary to hidden episodes of uncontrolled inflammation , as proposed for A20- and ITCH-deficient mice and human [13] , [15] , [16] . However , the fact that several of the surviving homozygous c124-2A>G calves appeared perfectly healthy upon clinical examination , suggest that growth retardation might be directly related to alternative functions of RNF11 as modulator of growth factor receptor signaling ( particularly TGF-β and EGFR signaling ) and transcriptional regulation [6] . It is also noteworthy , that RNF11 has been found to be highly expressed in bone cells during osteogenesis [17] . Calf mortality is an economically important trait . It is generally considered highly complex and multifactorial , and its heritability is always very low . It is thus difficult to improve using conventional selection strategies . We herein demonstrate that genomic approaches may help dissect such complex phenotypes in sub-components including some with simple Mendelian determinism amenable to effective “marker assisted selection” . The situation uncovered in this work is reminiscent of bovine leukocyte deficiency ( BLAD ) in Holstein-Friesian [18] , an immune deficiency resulting from CD18 deficiency and causing increased susceptibility to infection in young calves [19] . We provide suggestive evidence that the high incidence of the RNF11 c124-2A>G mutation in BBC is not only due to drift , but may be due to the superiority of heterozygotes for unidentified selection criteria . Such a situation would be reminiscent of previously described pleiotropic effects on conformation of mutations in the gene encoding the calcium release channel ( CRC ) in pigs ( causing malignant hyperthermia and porcine stress syndrome in homozygotes ) [20] and in the MRC2 gene in cattle ( causing Crooked Tail Syndrome in homozygotes ) [4] , [5] . These examples illustrate some of the issues resulting from the selection of animals with extreme performances .
Blood samples were collected from sires , cows and calves , by trained veterinarians following standard procedures and relevant national guidelines . Genomic DNA of cases was extracted from 350 µl of blood using the MagAttract DNA Blood Midi M48 Kit ( Qiagen ) . Genomic DNA of controls was extracted from frozen semen using the MagAttract Mini M48 Kit ( Qiagen ) . The 33 cases of the initial genome scan were genotyped using a custom-made 50 K SNP array [3] . The 67 cases of the second scan ( excluding RNF11 c124-2A>G homozygotes ) were genotyped with the BovineSNP50 v2 DNA analysis BeadChip ( Illumina ) . The 275 control sires were genotyped with the BovineHD BeadChip ( Illumina ) . SNP genotyping was conducted using standard procedures at the GIGA genomics core facility . Phasing of the SNP genotypes and assignment of the haplotypes to a predetermined number of hidden haplotype states was conducted with PHASEBOOK [21] . Hidden haplotype state-based association analysis was conducted using GLASCOW ( Zhang et al . , submitted for publication ) . GLASCOW uses generalized linear models and fits a random hidden haplotype state effect as well as a random polygenic effect to correct for population stratification . Locus-specific p-values were determined from 1 , 000 permutations assuming a gamma distribution of the used score test ( Zhang et al . , submitted for publication ) . We applied a conservative Bonferonni correction assuming 50 , 000 independent tests to determine the genome-wide significance thresholds . Coding exons of positional candidate genes were amplified from genomic DNA of a homozygous case and a healthy control using standard procedures . The primers used for the RNF11 gene are listed in the Supporting Information S1 . PCR products were directly sequenced using the Big Dye terminator cycle sequencing kit ( Applied Biosystem , Foster City , CA ) . Electrophoresis of purified sequencing reactions was performed on an ABI PRISM 3730 DNA analyzer ( PE Applied Biosystems , Forster City , CA ) . Multiple sequence traces from affected and wild-type animals were aligned and compared using the Phred/Phrap/Consed package ( www . genome . washington . edu ) . A 5′ exonuclease assay was developed to genotype the c124-2A>G RNF11 mutation , using 5′-AGG AAG AAA CAA AAG GAA AAC ATT ACC TAG A-3′ and 5′-TGT TGG ATG ATA GAC CGG AAC TG-3′ as PCR primers , and 5′-ACT TGT TCC TAA ATT TT-3′ ( wild type A allele ) and 5′-TTG TTC CCA AAT TTT-3′ ( mutant G allele ) as probes ( Taqman , Applied Biosystems , Fosters City , CA ) . Reactions were carried out on an ABI7900HT instrument ( Applied Biosystems , Fosters City , CA ) using standard procedures . Total RNA from RNF11 c124-2A>G AA and GG animals was extracted from lung , lymph nodes , spleen , skeletal muscle , thymus and trachea using standard procedures ( Trizol , Invitrogen ) . After DNase-treatment ( Turbo DNA-free , Ambion ) , cDNA was synthesized using the SuperScript III First-Strand Synthesis SuperMix ( Invitrogen ) . A cDNA segment was amplified using two RNF11 specific primers sets: one encompassing exon 2 with primers located in exon 1 and exon 3 ( E1–E3 ) and one encompassing the exon1-exon2 boundary ( E1–E2 ) ( Supporting Information S1 ) . PCR products were separated by electrophoresis on a 2% agarose gel containing 0 . 0001% of SYBR Safe DNA gel stain ( Invitrogen ) at 100 volts during 40 min and size was evaluated with SmartLadder 200 lanes ( Eurogentec ) . The PCR products were directly sequenced as described above . Total RNA from RNF11 c124-2A>G AA and GG animals was extracted from lymph node , spleen as described above . After DNase-treatment ( Turbo DNA-free , Ambion ) , 500 ng of total RNA was reverse transcribed in a final volume of 20 µl using SuperScript III First-Strand Synthesis SuperMix ( Invitrogen ) . PCR reactions were performed in a final volume of 10 µl containing 4 µl of 5-fold diluted cDNA ( corresponding to 100 ng of starting total RNA ) , 1X of ABsolute Blue QPCR SYBRE Green ROX Mix 2X ( Thermo Fischer Scientific ) , 0 . 3 µM forward and reverse primers and nuclease free water . PCR reactions were performed on an ABI7900HT instrument ( Applied Biosystems , Forster City , CA ) under the following conditions: 10 min at 95°C followed by 40 cycles at 95°C for 15 sec and 60°C for 1 min . Two primers sets were used to test RNF11 expression and three genes were included as candidate endogenous controls: ( 1 ) Beta-Actin ( ACTB ) , ( 2 ) Ribosomal Protein Large P0 ( RPLP0 ) , ( 3 ) Tyr-3- & Trp-5-Monooxygenase Activation Protein Zeta ( YWHAZ ) . The corresponding primer sequences are given in Supporting Information S1 . A standard curve with a five point two-fold dilution series ( total RNA = 100 , 200 , 400 , 800 and 1600 ng from lymph node and spleen from a AA wild-type individual ) for each RNF11 primer set was used to determine the amplification efficiency . All sample/gene combinations were analyzed in triplicate . ACTB and YWHAZ genes were selected as endogenous controls using geNorm [22] . Normalized relative RNF11 expression , for exon 2- and exon 3-containing transcripts , in the lymph node and the spleen of a wild-type AA and a mutant GG animal accounting for primer efficiency were computed using the qbaseplus software package ( Biogazelle ) [23] . The effect of the sire's RNF11 c124-2A>G genotype on non-return rate ( NRR ) of its mates was estimated using a mixed model including sire's RNF11 genotype ( fixed ) , year and month at insemination ( fixed ) , mate's herd ( random ) , individual animal effect of the offspring ( random ) and error . NRR are computed from the AI information collected by inseminators working with the Association Wallonne de l'Elevage ( AWE; http://www . awenet . be/ ) at seven time-points after AI . The analysis was performed on 479 , 674 cows mated to 340 AI sires . The effect of the sire's RNF11 c124-2A>G genotype on the rate of mortality , morbidity and culling of its offspring was estimated using a mixed model including sire's RNF11 genotype ( fixed ) , calf's gender ( fixed ) , year and month of calf's birth ( fixed ) , mate's parity ( fixed ) , calf's in utero position ( fixed; forward or backward ) , calf's herd ( random ) , individual animal effect of the calf ( random ) , and error . The corresponding phenotypes are collected by AWE technicians visiting farms , for ( i ) newborn calves , and ( ii ) calves having reached the age of 14 months since last visit . The number of records for newborn offspring was 317 , 350 from 332 AI sires , and for 14 month-old offspring was 126 , 098 from 288 AI sires . The effect of the sire's RNF11 c124-2A>G genotype on its own zootechnical performances was estimated using a mixed model including sire's RNF11 genotype ( fixed ) , sire's MRC2 genotype ( fixed ) [4] , [5] , year and month at scoring ( fixed ) , sire's body condition at scoring ( fixed ) , sire's age at scoring ( quadratic regression ) , individual animal effect for the sires ( random ) and error [24] . Zootechnical performances of AI sires are recorded between 15 and 56 months of age as 22 linear scores ( 0–50 score ) that are summarized as indexes evaluating size , muscularity , meaty type and general appearance [12] . Three hundred and eleven sires were used in this analysis . The effect of the sire's RNF11 c124-2A>G genotype on the zootechnical performances if its offspring was estimated using a mixed model including sire's RNF11 genotype ( fixed ) , sire's MRC2 genotype ( fixed ) [4] , [5] , offspring's gender ( fixed ) , year and month at scoring ( fixed ) , offspring's body condition at scoring ( fixed ) , offspring's age at scoring ( quadratic regression ) , offspring's herd ( random ) , individual animal effect for the offspring ( random ) and error [24] . The first data set corresponded to the same five global scores ( cfr . sire's own performances ) measured on 92 , 475 36-month-old daughters of 306 sires by AWE technicians . The second data set corresponded to weight ( Kg ) , size ( cm ) and conformation ( 1–9 score ) measured on 95 , 045 14-month-old offspring of 315 sires . Covariances between random individual animal effects were assumed to be proportionate to twice the kinship coefficient computed from known genealogies . Variance components and fixed effects were computed using MTDFREML [25] . | Recessive defects in livestock are common , and this is considered to result from the contraction of the effective population size that accompanies intense selection for desired traits , especially when relying heavily on artificial insemination ( as males may concomitantly have a very large number of offspring ) . The costs of recessive defects are assumed to correspond to the loss of the affected animals . By performing a molecular genetic analysis of stunted growth in Belgian Blue Cattle ( BBC ) , we highlight ( i ) that the economic impact of recessive defects may outweigh the only loss of affected animals and ( ii ) that some genetic defects are common for reasons other than inbreeding . We first demonstrate that a splice site variant in the RING finger protein 11 ( RNF11 ) gene accounts for ∼40% of cases of stunted growth in BBC . We then show that a large proportion of animals that are homozygous for the corresponding RNF11 mutation die at a young age due to compromised resistance to pathogens . We finally demonstrate that carriers of the mutation benefit from a selective advantage of unidentified origin that accounts for its high frequency in BBC . | [
"Abstract",
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] | 2012 | A Splice Site Variant in the Bovine RNF11 Gene Compromises Growth and Regulation of the Inflammatory Response |
We study local calcium dynamics leading to a vesicle fusion in a stochastic , and spatially explicit , biophysical model of the CA3-CA1 presynaptic bouton . The kinetic model for vesicle release has two calcium sensors , a sensor for fast synchronous release that lasts a few tens of milliseconds and a separate sensor for slow asynchronous release that lasts a few hundred milliseconds . A wide range of data can be accounted for consistently only when a refractory period lasting a few milliseconds between releases is included . The inclusion of a second sensor for asynchronous release with a slow unbinding site , and thereby a long memory , affects short-term plasticity by facilitating release . Our simulations also reveal a third time scale of vesicle release that is correlated with the stimulus and is distinct from the fast and the slow releases . In these detailed Monte Carlo simulations all three time scales of vesicle release are insensitive to the spatial details of the synaptic ultrastructure . Furthermore , our simulations allow us to identify features of synaptic transmission that are universal and those that are modulated by structure .
Exocytosis , the process by which vesicles bind to the membrane and release their neurotransmitter cargo , is primarily triggered by the VDCC calcium currents . The arrival of an axonal action potential ( See Fig . 3 in Text S1for the voltage waveform ) leads to a depolarization of the membrane potential in the presynaptic terminal and leads to the stochastic opening of VDCCs . The total calcium flux entering the terminal depends on the time course of the action potential , the number of channels present on the membrane , the calcium conductance of open channels , and the total time each of the channels remains open . The calcium ions diffuse away from their point of entry into the terminal , where they may encounter and bind to buffers such as Calbindin , the calcium sensors and the PMCA pumps . A vesicle release takes place if sufficient calcium ions bind to the calcium sensor enabling the sensor to transition into an appropriate active state . The geometrical arrangement of the parts of the calcium handling machinery and the calcium flux entering the pre-synaptic terminal tightly regulate the local calcium profile at the active zone and therefore control the neurotransmitter release probabilities . The canonical CA3-CA1 en passant synapse geometry used in our simulations is shown in Fig . 1A . The basic computational domain consists of a pre-synaptic terminal ( a bouton ) encompassing a rectangular box 0 . 5 µm wide and 4 µm long; this terminal represents a segment of axon making an en passant synapse , and the only information passing from axon shaft to bouton is the voltage . The dynamical model for calcium handling consists of ( Table 1 for rates accompanied by references ) 1 . a cluster of voltage-dependent calcium channels ( VDCCs ) of type P/Q [8] , which is known to be the main contributor to presynaptic Ca2+ current in mature hippocampal presynaptic terminals [9] , [10]; 2 . plasma membrane calcium ATPase ( PMCA ) pumps that work to keep the base level Ca2+ at 100 nM ; 3 . the mobile calcium buffer calbindin-D28k [11] ; 4 . an active zone populated by seven docked vesicles [3] , [12] , each endowed with its own calcium sensor for neurotransmitter release; and 5 . the calcium concentration was clamped at 100 nM at both ends of the axon segment . The active zone is placed at a specified co-localization distance , lc ( center-to-center distance: 20 nm–400 nm ) from the VDCC cluster ( source of Ca2+ flux ) [13] . Calcium buffers modify the calcium diffusion rate and ultimately the local calcium profile . The diffusion length for calcium ions in our system was measured over several hundred trials and fit to the diffusion equation to calculate the effective diffusion constant . This was ∼50 µm2/s , close to experimentally measured values [14] ( compared to the free diffusion constant of ∼220 µm2/s in the cytoplasm ) and our local calcium profiles compare well with those of other studies [15] ( See Fig . 1 in Text S1 ) . Our basic protocol is to simulate the sequence of events at the CA3-CA1 synapse beginning with the arrival of an action potential , the opening of the VDCC's , the diffusion of calcium from the VDCC's to the calcium sensor and the triggering of vesicle fusion and glutamate release [16] . The dynamics of these events were simulated in 3D using Monte Carlo methods ( MCell version 3 – see supplemental info for a description of this package ) . Because the simulations are stochastic , we perform 10000 trials of each test case to generate an average release profile that can be compared directly to experimental data . A detailed analysis shows that the most important source of stochasticity is the random opening and closing of the VDCC's [17] . Release at a single active zone with seven docked vesicles is governed by a dual calcium sensor kinetic scheme ( Fig . 1B ) . The dual sensor kinetic scheme used in these simulations is similar to that proposed ( for a different synapse – see discussion later ) by Sun et al . [18] , in which one of the sensors regulates synchronous release via Synaptotagmin II ( Syt II ) and has 5 calcium binding sites , while the other regulates slow , asynchronous release via an as yet unidentified molecule and has 2 calcium binding sites . To fit data from the hippocampal synapse of interest , we have adjusted the asynchronous sensor rate ( from its value in ref . [18] ) ( reduced unbinding rate by a factor of 5 ) . We have investigated other possible binding schemes for the asynchronous sensor ( data not shown ) and attempts to reproduce the asynchronous release were most successful when 2 binding sites were assumed . The vesicle fusion rate for asynchronous neurotransmitter release was taken as an independent parameter , not necessarily equal to the synchronous vesicle fusion rate; identical fusion rates for both sensors , as in the model of Sun , leads to inconsistencies , as discussed in detail later . We simulated the effects of varying the extracellular calcium concentration on the number of vesicles released ( See Fig . 4 in Text S1 ) in the first 20 ms for direct comparison with [1] . The results fit well with the Dodge and Rahamimoff equations with an exponent of 4 . Thus the apparent cooperativity is ∼4 even though there are 5 binding sites . The precise values of all our model parameters are given in the table in the supplementary information . In our baseline model , simultaneous release of multiple vesicles is prevented by imposing a refractory period of 6 ms after a release event takes place [5] , [6]; we also consider a variant with no refractory period , everything else being held constant . Finally , the model includes a readily-releasable pool ( RRP ) with 7 docked vesicles [3] , [12] , which is decremented after a release . This feature allows the model to accurately describe plasticity phenomenon such as depression and facilitation . All the results described below unless explicitly stated remain valid for a range of typical RRP sizes ( results not shown ) .
As mentioned above , the calcium is kept at a resting level of 100 nM by the action of the pumps . This resting level gives rise to a base level rate of neurotransmitter release in the absence of any stimulus . This level depends only on the sensitivity of the calcium sensors and not on any of the structural parameters ( such as lc ) which only effect stimulus response . We have verified that the spontaneous release rate in our model ( 1 . 2×10−4 per ms±0 . 2×10−4 per ms , Fig . 2A ) matches the release rate of 10−5 to 10−4 per ms reported in recordings from CA3-CA1 [19] , [20] . This agreement helps validate the values chosen for the forward and backward binding rates of the calcium sensor . Different hippocampal synapses can have rather different overall probabilities of successful vesicle release . Most hippocampal synapses have a low probability with an average baseline value of pr ∼0 . 2 [2] . However , the range of release probabilities at hippocampal synapses is high , from weak synapses ( pr<0 . 05 ) that rarely ever release to synapses with high release rates ( pr>0 . 9 ) [2] . Our model can accommodate this , since the peak value of calcium depends on two distinct parameters; the co-localization distance ( lc ) and the number of VDCC's . Fig . 2B shows the neurotransmitter release probability as a function of the peak of the local calcium transient ( measured at 10 nm from the sensor ) for multiple co-localization distances ( lc ) . The number of VDCCs present in the cytoplasmic membrane regulates the calcium flux at the specified lc . Small lc leads to sharper , narrower local calcium peaks at the active zone ( See Fig . 2 in Text S1 ) and the response curves for different lc are non-overlapping ( Fig . 2B ) . Our model synapse achieves pr = 0 . 20 with 48 VDCCs in a single cluster of 35 nm radius , at lc = 250 nm , which is compatible with estimates made at other central synapses [13] . In our model , a single action potential at a synapse with 20% release probability produces a roughly 400 msec long elevated release rate of neurotransmitter . The model thus correctly captures the release profile of hippocampal neurons reported by Goda and Stevens [1] , adapted figure shown in Fig . 3A . More specifically , the response to an action potential averaged over 10000 trials in 10 ms bins ( Fig . 3B , black line , 3E and 3F ) gives decay time constants of tfast ( 7 . 25±1 . 8 ms ) and tslow ( 140 . 0± 28 . 0 ms ) in agreement with the reported data [1] , ( Fig . 3A ) . Requiring this agreement enabled us to determine values for the dual sensor model . To show the sensitivity of these results , we have also plotted in Fig . 3B ( grey line ) the results that would hold for choosing the Sun et al . dual-sensor parameter set ( essentially using their sensor kinetic scheme in our spatially-extended simulation ) [18] . Clearly , there needed to be an increase in the overall contribution of asynchronous release , as well an increase in the rate of decay of the synchronous release ( tfast ) . Remarkably , we have been able to accomplish this fit without having to alter the binding affinity of the synchronous pathway , which remains at 38 µM . This affinity is the primary determinant of the calcium sensitivity , since the fast component contributes more than 90% to the overall release probability ( Table 1 ) . The first set of issues we address concern a more precise look at the timescales involved in the vesicle response . Fig . 3D ( red line ) shows the local [Ca2+]i 10 nm from the active zone ( units on right-hand axis of graph ) . The neurotransmitter release peaks after a typical latency of ∼3 ms . Note that here we measure the latency starting from the beginning of the action potential ( See Fig . 3 in Text S1 , i . e . t = 0 in Fig . 3D is at the beginning of the action potential ) , This latency is due mainly to the delay in opening the VDCCs after the action potential depolarizes the axon . The local [Ca2+]i peaks at 12±4 . 8 µM for pr = 0 . 2 . This rapid timescale response is present in the vesicular release curves as well . When the data from our standard pr = 0 . 20 simulation are binned at 1 ms ( Fig . 3D black line , units on left-hand axis ) , a third “super-fast” timescale of release is apparent . Its time constant , tsuperfast = 0 . 65±0 . 07 is obviously directly correlated with the aforementioned time course of the Ca2+ pulse . This phenomenon arises due to the fact that the vesicle fusion rate γ is chosen to be fast enough to track the calcium transient created by the fast P/Q calcium channels; this speed requirement is well within the range of measured release rates [15] , [18] . This result has yet to be observed in hippocampal synapses , due to the lack of sufficient data at this temporal resolution; it has however been found in other synapses ( see Fig . 3C and later discussion ) . The independent contributions of synchronous and asynchronous release are shown in Figs . 4A-C . Initially , the fast ( and superfast ) release dominates , but it decays rapidly and is soon overtaken by asynchronous release . The synchronous part of the release machinery is the primary contributor to the tsuperfast time scale , which should then be referred to as ‘phasic synchronous release’; the tfast time scale is also mainly driven by the synchronous pathway and is best referred to as ‘delayed synchronous release’; finally , the tslow release is the commonly named ‘asynchronous release’ . The asynchronous contribution to the release profile has a delayed peak compared to the synchronous contribution . As mentioned above , the model synapse achieves pr = 0 . 20 with 48 VDCCs in a single cluster of 35 nm radius , at lc = 250 nm . This is not unique , since other combinations of VDCC number and lc can also give pr = 0 . 20 . Changing the model in this manner does not lead to any significant modification in our findings . What happens if we alter the release probability , by changing either the VDCC number or lc ? We find that the maximum amplitudes of the synchronous and asynchronous contributions are indeed modulated by the varying pr , but the decay constants of the release profiles are unchanged ( Fig . 3E; pr = 0 . 6 , lc = 400 nm , 128 channels; Fig . 3F; pr = 0 . 92 , lc = 250 nm , 112 channels ) . This result of the model is consistent with reported data from high and low release probability synapses that show similar decay constants [1] , [21] , [22] for the different release probabilities . In other words , in our simulations the decay time scales ( other than the super-fast one ) are independent of the spatial organization of the synapse and are a consequence of the kinetics of the calcium sensor . As mentioned above , our model posits that multiple releases can take place from the active zone after a refractory time constant of ∼6 ms following each release [5] , [6] . To test the extent to which the finite available resource of docked vesicles ( i . e . the RRP ) is a limitation , we modify our simulation to contain an active zone in which a released vesicle is instantly replaced , i . e . a depletion free active zone . The probability distribution of number of quanta of neurotransmitter released in 400 ms is shown in Figs . 4D-F . For a synapse with a release probability pr = 0 . 2 , the likelihood that more than two vesicles are released was less than 5% . Furthermore , there is less than 20% chance of releasing more than 2 and almost never more than 6 vesicles for pr = 0 . 6 and a 33% chance of releasing more than 2 , and almost never more than 9 vesicles for pr = 0 . 95 . The size of readily release pool ( RRP ) has been estimated to be 5–10 vesicles at CA3-CA1 synapses [3] . Thus , the model prediction of the maximum number of vesicles that can be released is consistent with the typical RRP size at this synapse and both these numbers are positively correlated with release probability [23] . The model suggests that the typical RRP size at a CA3-CA1 synapse and the calcium sensitivity of the release machinery are well-matched , so that the number of docked vesicles is not a limiting factor at low stimulus frequencies . Stevens and collaborators introduced the idea that there is a short refractory time following vesicle release from an active zone . With such a refractory period more than one quantum of neurotransmitter can be released by an action potential , but the quanta are released one at a time . Several recent experimental studies have tried to address the question of refractoriness after release but with conflicting results . Explicit measurements at a wide variety of synapses conclude that there exists a “one active zone-one vesicle release” principle and hence provide direct evidence for functional coupling within the active zone [4]–[6] , [24]–[31] . However , other studies have presented evidence against uni-vesicular release due to such “lateral inhibition” [4] , [32]–[39] . Our basic strategy is to compare neurotransmitter release profiles with and without the existence of a 6 ms refractory time constant preventing simultaneous release of different vesicles . We do this comparison for different values of the overall release probability ( See Fig . 5 ) . For a release probability at CA3-CA1 of pr = 0 . 2 , the release transient for a synapse with a refractory period ( gray line ) is almost indistinguishable from a synapse without any refractoriness ( black line ) . Thus for this set of parameters , the presence or absence of refractoriness does not make any functional difference . For a release probability of pr = 0 . 2 for the whole active zone , each of the 7 individual docked vesicles must have a release probability of 0 . 031 so the probability that 2 or more vesicles being released is only 0 . 02 . This implies that although any single vesicle was released on 20% of the stimuli , two or more vesicles were released on only 2% of the trials . The detailed timing of release of the second vesicle relative to the refractory period has a negligible effect on the overall averaged release profile . The consequence of a refractory period was more prominent for pr = 0 . 95 . For a synapse with independent releases ( i . e . no refractory period ) and pr = 0 . 95 , 2 or more vesicles were released on 67% of the trials . The top panel in Fig . 5B shows the release transients over 400 ms when the release data were in 10 ms bins and the bottom panel ( Fig . 5D ) describes the same data with finer 1 ms bin . Now , there is a clear consequence to the inclusion of a refractory period . We have seen that our model can reproduce one of the important distinguishing characteristics of neurotransmitter release in hippocampal CA3-CA1 synapses , that the decay time scales are conserved across a wide range of release probabilities even as the overall amplitude of the transient is modulated [21] , [22] , This result depends on the inclusion of refractoriness . Without refractoriness , depletion overwhelms the release at high release probability synapses: The peak release rate is higher , the decay becomes significantly faster and the amplitude of later releases is much lower ( Fig . 3F , black line ) . We therefore conclude that existing experimental data strongly support the existence of the refractory period . We can also examine the differences in the release transients due to refractoriness separately for the synchronous and asynchronous release for pr = 0 . 95 ( see Fig . 6A and B ) . This analysis was possible because our sensor model treated these releases via independent pathways ( see Fig . 1B ) . Our model predicts that the synchronous release profile ( Fig . 6A ) should be lower in amplitude and decay more slowly for a synapse with a refractory period . Synchronous and asynchronous releases compete for the same RRP resources [40] leading to a net increase in asynchronous release ( 1511 total events in 400 ms , for 10000 trials ) for the synapse with refractoriness compared to the synapse without refractoriness ( 1379 total events in 400 ms ) ( Fig . 6B ) . Note that in the first ∼50 ms after the stimulus , when release via the synchronous pathway dominates , refractoriness slows the rate of depletion of the RRP ( Fig . 6A ) . Refractoriness also slows down asynchronous release initially ( Fig . 6B ) . But beyond 50 ms , when asynchronous release begins to dominate , the larger residual RRP ( because of slower depletion ) in synapses with refractoriness means that the net amount of release via the asynchronous pathway can be larger than in synapses without refractoriness . Gene knock-out experiments are now routinely used to quantify signaling pathways . Knocking out synaptotagmin ( KO ) , the calcium sensor for neurotransmitter release , eliminates the fast release component of the transient but leaves the slow component intact [18] , [41] . We can modify our model to allow for the study of the KO transgenics by removing all the states along the synchronous pathway . Since both pathways used the same resource pool of neurotransmitter [40] , knocking out the synchronous release sensor makes more vesicles available for release through the asynchronous release sensor . Augmentation of asynchronous release in genetically modified , fast sensor deficient mice has been previously reported in [42] , albeit pointing to a different mechanism . Simulation results for asynchronous release transients comparing synchronous sensor knock-out ( KO ) and wild type are shown in Fig . 6C and D . The results show that the genetic modification eliminates much of the effect of the refractory period ( grey solid line and black solid line respectively ) with almost the same number of release events for both in the 400 ms ( inset ) and 50 ms time windows . The genetic modification has a larger effect on the refractory synapse and is qualitatively more consistent with the aforementioned experimental data . We can understand this effect in more detail by focusing on the change in time-course brought about by the genetic modification . For a synapse without refractoriness , the ratio ( Fig . 6D ) between the release rate of the wild type and KO stays constant through the transient; however , for a synapse with refractoriness ( Fig . 6C ) , the model predicts that the ratio between wild type and KO would be larger in the first few milliseconds and then taper off with time . This happens because the large forward binding rate of the synchronous part of the sensor dominates release in the wild type and therefore acts to inhibit asynchronous release; this inhibition occurs through refractoriness that lasts a few milliseconds before the asynchronous channel reaches its normal release rate as defined by the binding kinetics . A 90% increase in release rate of asynchronous release in first 50 ms for synapse with refractoriness in a KO compared to the wild type is seen . While a synapse without refractoriness sees an increase of only 75% in a KO compared to the wild type . In a synapse without refractoriness , synchronous and asynchronous releases are independent and therefore they always occur at their normal rates . Refractoriness differentially affects synchronous and asynchronous release at early and late times after a single stimulus and this effect is sensitive to the initial release probability ( Fig . 5 ) . But what happens during a train of high-frequency stimuli ? We performed simulations to predict what might be seen in CA3-CA1 synapses when stimulated at 100 Hz for 200 ms ( 20 stimuli ) and we now examine the results for features that would distinguish between synapses with and without refractoriness . This same stimulus protocol was used in a previous study of a different synapse with many active zones [22] and was found to be sufficient to deplete the RRP . We surmised that such a stimulus might therefore be sufficient to deplete the RRP at our model CA3-CA1 synapse with a single active zone . The response of our model synapse for the different cases of initial release probabilities pr = 0 . 2 ( number of VDCCs = 48 , lc = 250 nm ) , pr = 0 . 6 ( number of VDCCs = 72 , lc = 250 nm ) , and pr = 0 . 95 ( number of VDCCs = 112 , lc = 250 nm ) is shown in Fig . 7 . For pr = 0 . 6 the facilitation ( ratio of first two release rates ) in the synapse with refractoriness ( black line ) was almost twice that of a synapse without refractoriness ( grey line ) . However for the synapse with refractoriness the background release level ( due to asynchronous release ) was much higher compared to a synapse without refractoriness . These predictions can be directly tested in future hippocampal synapse experiments . We now wish to investigate the role of the slow sensor in the presence of a spike train , the response to a 10 Hz stimuli for a total of 400 ms ( i . e . 4 triggers ) for a synapse with intrinsic release probability 0 . 2 is shown in Fig . 8 . Response to high frequency 100 Hz stimulus for high release probability synapse is described in the Supporting information ( See Fig . 5 in Text S1 ) . The simulations are carried out both for a simulated asynchronous sensor knock out ( SAKO ) ( Fig . 8B ) and wild type ( Fig . 8A ) . The response to higher frequency ( 100 Hz ) is discussed in the supplementary material . Unlike the SAKO ( Fig . 8B ) , the peak release rate ( data binned in 1 ms ) in the wild type ( Fig . 8A ) is facilitated with each subsequent stimulus . The same data ( grey line-SAKO , black line- wild type ) is shown on a log scale in Fig . 8C . In the wild type , response to subsequent stimuli rides on top of a higher base level release . This is due to the slow time scale of release of the asynchronous sensor ( the inherent memory of the sensor ) . This ensures greater facilitation for the wild type . Fig . 8D shows the total release rate for each stimulus ( grey line-SAKO and black line –wild type ) . We can see that for the facilitation in the wild type is more than 50% whereas for the SAKO it is limited to 35% . All the results given so far have used a model for which the parameter γa , the fusion rate of vesicles activated by the asynchronous sensor , is smaller than the corresponding rate for the synchronous one . To demonstrate why this is necessary , a sample release profile of the asynchronous pathway for our single active zone synapse with 7 docked vesicles [3] assuming equal release rates for both release pathways is shown in Fig . 9 ( Grey line , pr = 0 . 2 , number of VDCC = 48 , lc = 250 nm ) . The early peak in this figure , present for simulations at all values of the release probability , is clearly inconsistent with electrophysiological data [18] , [40] . If we demanded equal fusion rates , we were unable to eliminate this early peak in the asynchronous release while still reproducing all the other measured release properties; we tried ( to no avail ) to accomplish this by changing the binding affinities or by including additional calcium binding sites for the asynchronous pathway that would delay release ( data not shown ) . Thus , in order for our model to be consistent with measured asynchronous release transients , the value of γ needs to be significantly slower for the asynchronous pathway relative to the synchronous pathway . This introduces an additional parameter ‘a’ such that the neurotransmitter fusion rate is γa = aγ ( with a<1 ) for asynchronous release ( see Table 1 ) . The presence or absence of assumed refractoriness does not affect this early peak of the asynchronous pathway . For the choice a = 0 . 025 ( i . e . net asynchronous vesicle fusion rate = 50/s ) , the early release from the asynchronous pathway is suppressed and all the detailed characteristics of neurotransmitter release can be reproduced ( Fig . 9 , Black line ) . In the context of a model with independent vesicles comprising the active zone , we must assume that the asynchronous pathway has a slower release . An alternative approach to eliminate the early peak in the asynchronous release while implementing neurotransmitter fusion rates for synchronous and asynchronous release is to use a higher-scale phenomenological model for the entire active zone such that it has a single gating mechanism prescribed by kinetic rates given in Table . 1 . This type of model sets no a priori limit on the number of docked vesicles ( i . e . has an infinite RRP ) and multiple release events may occur , subject to the refractory time constant . With this framework , it is possible to consistently reproduce all our data , including the 3 timescales and a cumulative release well matched to the RRP ( data not shown ) . In short , an additional parameter ‘a’ is needed in the docked vesicle model with individual sensors on each vesicle , to directly suppress asynchronous release , whereas in an alternative phenomenological approach that treats the whole active zone as having a single gating mechanism , no such parameter is needed . We have chosen to focus on the individual vesicle model , as there is no obvious justification for such a strong vesicle coupling .
Neurotransmitter release at chemical synapses in response to electrical stimulus is tightly regulated over multiple time scales by mechanisms in the presynaptic terminal . Release takes place at specialized locations at the presynaptic membrane called active zones designated by the presence of SM ( Sec1/Munc18-like ) proteins [7] , [43] . Some of this machinery is ubiquitous for all exocytosis events and consists of SNARE ( soluble N-ethylmaleimade-sensitive factor attachment protein receptor ) proteins , SM ( Sec1/Munc18-like ) proteins , along with complexins and synaptotagmins that are needed to control the timing of neurotransmitter release [7] , [44] . Much of the molecular and structural details of this process have been elucidated; however , how each of the components interacts to execute precise dynamic control on the release has not yet been established . The goal of this study was to develop a detailed biophysical model of exocytosis that takes into account the spatial organization of the molecular components and the time courses of their kinetic states . We have chosen to carry out our study focusing on the CA3-CA1 synapse in the hippocampus . The advantage of using this synapse is its relative simplicity , consisting of only one or two active zones , and its starring role in many studies of plasticity . Even with this emphasis , varying results from different experiments have led to confusion regarding certain basic features of synaptic transmission . Our computational experiments have led to possible resolutions for some of these contentious issues , such as the existence of refractoriness between releases , cohesively bring together data from different sources that point to universal features of vesicle release and those that may be unique to the CA3-CA1 synapse [45] , [46] . In particular , our simulations have illuminated the observation in two separate sets of data [21] , [22] that changing the release probability modifies only the amplitudes of release transients and not the timing of release . An important prediction of this study is the new identification of three separate time scales of the release and that these time scales are all independent of the synaptic geometry . It has been reported in a recent study [47] that properties of the Ca2+ channels and relative location of Ca2+ do not modulate the relative dynamics of asynchrony to phasic release . This study strongly supports our own modeling results in which the calcium sensor governs all the relevant time scales . This result stands in contrast with other approaches [48] for which geometry governs slow release ( see later ) . Two decay timescales have indeed been observed in hippocampal synapses . Also , similar findings ( a slow decay component of ∼82 ms ) have been reported in parvalbumin-containing GABAergic interneurons expressing P/Q calcium channels [21] . However , the predicted super-fast timescale of release has yet to be observed in our hippocampal synapse of interest . It has apparently has been observed in calyx of Held ( see later ) by Scheuss et al . [22]; see Fig . 3D . Their ‘biphasic decay of release rate’ was comprised of a superfast component of release and a fast component ( 588 . 6 ±3 . 5 µs and 14 . 7±0 . 4 ms respectively ) . However , they were unable to distinguish the contribution of slow asynchronous release lasting up to 200 ms , from the effect of residual glutamate in the cleft . Thus , several different times scales of release by different labs ( τfast and τslow , ) or ( τsuperfast and τfast ) have been reported [1] , [18] , [21] , [22] . This disagreement can be reconciled by the coexistence of three time scales of release , as seen in Figs . 3B and 3C . As has been explained , our model for the calcium sensor is a modified stochastic version of the one introduced by Sun et al . [18] . That kinetic model is one of several that have been created to explain data from the calyx of Held . The calyx of Held is a giant pre-synaptic terminal with hundreds of active zones and can be probed directly because of its large size [49] , [50] . However , the active zones are separated from the points of calcium entry ( i . e . voltage-dependent calcium channels ) over a range of distances . This makes it difficult to disentangle the properties of vesicular release that arise due to the kinetics of the calcium sensors alone from those due to their complex spatial arrangement . Elegant calcium-uncaging experiments have been performed to ensure a uniform calcium concentration across the hundreds of docked vesicles [15] , [51] . However , the calcium concentration stays high for a long time in these protocols , depleting the docked vesicle resources and hence modifying the average vesicle release rates . Furthermore , uncertainties in actual number of docked vesicles introduce error in the kinetic models . These difficulties have led to disparate models with calcium sensitivities that vary over 500% [15] , [51] . For example Fig . 1 in [13] shows that 25% release probability corresponds to peak calcium of either 8 . 8 µMor ∼50 µMin two competing kinetic models for the calyx . These models provide a starting point but cannot be directly used to provide an accurate description of release at CA3-CA1 . A detailed comparison of our model for vesicle release and that of Sun et al . is outlined as follows . In contrast to the deterministic kinetic sensor model of Sun et al . , our model is a spatially explicit stochastic model of the entire bouton . In Sun et al . [18] the two sensors act completely independently to cause release and all releases are independent events . In our kinetic model for CA3-CA1 the release of one vesicle ( whether synchronously or asynchronously ) temporarily prevents the release of other vesicles within the active zone . A refractory period results with a recovery time constant of ∼6 ms [5] , [6] . Also , our model differs from Sun et al . [18] in the binding and unbinding rates while maintaining the binding affinity and cooperativity of the calcium sensor for synchronous release . To better match published data [1] the asynchronous release in our model lasts much longer and has a much higher amplitude suggesting that this synapse has a longer memory . This was achieved in the model by making the unbinding rate of the second sensor 5 times slower than that in Sun et al . [18] . Another significant distinguishing feature of the present model is that it includes a readily-releasable pool ( RRP ) with 7 docked vesicles [3] , which is decremented after a release . The calyx and the CA3-CA1 synapses subserve different functions . The calyx is a giant synapse in the auditory pathway that achieves reliable synaptic transmission with several hundred active zones . In comparison , most CA3-CA1 synapses in the hippocampus have an intrinsically low release probability but are highly plastic [23] to serve as a substrate for memory [52] , [53] . Despite these differences , the calcium sensor that governs fast temporally correlated signal transmission seems to be conserved . Asynchronous release transients may be more diverse , although at a particular calyx synapse that exhibited an exceptionally high level of asynchronous release , Scheuss et al . [22] reported a slow asynchronous decay with a time scale that was comparable to that in our model ( 79 . 3 ±29 . 7 ms ) . Furthermore , the global parameters of the synapse , such as the number of active zones , and their respective distance from the VDCCs , can give rise to apparently different calcium sensitivities that can be misleading ( see Fig . 2B ) . In fact , some researchers [48] have attributed the entire mechanism of asynchronous release in the calyx to vesicles that were further away from calcium sources . This is manifestly not the case in our hippocampal model , as we have repeatedly emphasized that the decay time scales were independent of the spatial organization of the synapse and were a consequence of the kinetics of the calcium sensor ( See Fig . 3E ) . Thus is it as yet unclear whether the asynchronous sensor is similar in different synapses . Whether universal or not , a Ca2+ sensor with a long memory as described in our hippocampal model can have a significant role in activity-dependent short-term synaptic plasticity ( Fig . 8 ) . We now return to the issue of the refractory period . The active zone is morphologically distinctive and has complex protein meshes spanning the entire length of the region connecting all the vesicles [54] . Recently , a diffusive protein trans-complex was identified that forms a continuous channel lining at the fusion site and is integral to exocytosis [55] . Therefore , it is reasonable to hypothesize that a local perturbation caused by exocytosis is likely to be spread through these diffusive molecules . It has also been suggested that the mechanical rearrangement of the lipid bilayer during exocytosis can also affect later release over a short enough time scale [56] . Given all these opportunities to influence each other , there are likely to be conditions under which docked vesicles interact cooperatively . Our simulations suggest that the release of a vesicle may trigger direct and indirect interactions between the synchronous and asynchronous release pathways , between individual sensors on the several docked vesicles , and between the microenvironment of the membrane of the active zone and the vesicles . These interactions occur on several time scales . In our model , “Lateral inhibition” a refractory period with a time constant of 5–7 ms [5] , [6] , [57] blocks simultaneous release from the active zone during the period of highest calcium concentration after opening of VDCCs . The exact biophysical mechanism for this refractory time window is unknown . Without such a refractory period of 6 ms after a release event , it would not be possible to maintain the same decay time scales across all release probabilities ( compare pr = 0 . 2 and pr = 0 . 9 shown in Fig . 5 ) . In addition , the prediction of the facilitation and base level release as illustrated in Fig . 7 can also be rigorously tested experimentally for further confirmation and exploration of the phenomenon . Some of the discrepancies leading to different conclusions about the refractoriness following vesicle release [4]–[6] , [24]–[29] , [31]–[37] , [39] , [58] could be due to differences in techniques and stimulation protocols . The proposed refractoriness originally measured by Dobrunz et al . [5] lasted only a few ms and did not impede subsequent release beyond that time window . Oertner et al . [37] reported multivesicular release accompanied by an increase of glutamate in the synaptic cleft . It is possible that more than one vesicle was indeed released but separated in time by the refractory period , since their methods lacked temporal resolution to resolve millisecond differences . Simultaneous release within synapses containing more than one active zone is also possible [32] , [35] . We have estimated that if release indeed operated independently at each docked vesicle , for pr = 0 . 9 there should be a 70% chance of releasing more than 2 vesicles in response to a single action potential , but in Christie et al . [33] multivesicular release was observed only in a paired pulse facilitation protocol . The accumulation of glutamate in the synaptic cleft could also give a misleading interpretation of multivesicular release . Abenavoli et al . [34] performed statistical analysis of release events where they observed that the output at long time intervals was not Poisson distributed . This phenomenon was attributed to a burst of release from the same synapse , which contradicted the refractory period hypothesis and led them to conclude that multivesicular release occurred at the CA3-CA1 synapse . An alternative explanation is the existence of long-time correlations in neural activation , perhaps by astrocytes acting to synchronize activity [59] , [60] . Furthermore , the quick freeze technique they used to image synaptic vesicles did not have the temporal resolution to distinguish between endocytotic and exocytotic events . In short , we feel that experiments all purporting to see simultaneous release from a single active zone have alternate interpretations . It has been suggested that synaptotagmins synchronize release rather than control it as an explanation of enhanced asynchronous release seen in transgenic mice with the fast sensor knocked out [42] . Elimination of the fast sensor makes more vesicles available for the asynchronous pathway leading to an augmented asynchronous release in our model . An alternate mechanism has been recently proposed , relying on the molecular zipping action of complexins with synaptotagmins that clamps down release in the wild type [61] . Binding of calcium releases the complexin clamp . However , in the KO this clamp is abolished , leading to an increase in spontaneous release [41] . Further experiments will be needed to test whether this more detailed mechanism is present and important , given that we can already obtain augmentation from the existing model . Finally , we return to the issue of the universality of fusion rates . Our model has an active zone with a RRP of vesicles that are coupled through a brief refractory period following each release via either the synchronous or asynchronous pathway . This differs from kinetic models for the calyx of Held [15] , [51] , including that of Sun et al . [18] , which assumed that every vesicle release was independent . In the calyx , Sun et al . used a vesicle fusion rate ( γ = 6000 s−1 , see kinetic scheme in Fig . 1 ) as measured by Schneggenburger and Neher [15] and made this rate equal for both the synchronous and asynchronous pathways . This is consistent with observations which showed that slow-to-release vesicles have the same release transients [48] as other vesicles , when calcium was un-caged so that calcium concentration was uniform across the presynaptic terminal of the calyx . This suggests equal neurotransmitter fusion release rates , γ , since in calcium-uncaging protocols , it is likely that calcium ion binding is not the rate-limiting quantity . However , it is only possible to fit all the release data for CA3-CA1 synapses when we set the value of the neurotransmitter fusion rate , γ , to be 40 times slower for the asynchronous pathway relative to the synchronous pathway , assuming that vesicles act independently aside from the refractory period . An alternative possibility is that there might be additional coupling in the active zone beyond the refractoriness , coupling that makes the active zone behave as if there were a single gate . This suggestion comes from our simulations with a phenomenological model ( mentioned earlier ) of the entire active zone where the spurious early peak in asynchronous release is eliminated without having to change the vesicle fusion rates . The overall effect of this inhibitory coupling is to reduce the effective asynchronous neurotransmitter fusion rate . Developing this possibility further would require a better understanding of the proteins that are responsible for the coupling and including the concomitant explicit sensor-sensor coupling in the kinetic scheme . Experimentally , one would need to develop knock-outs of the coupling proteins and test these for evidence of enhanced asynchronous release rates , especially the existence of an early release peak not present in wild-type synapses . Finally , our study is built upon an underlying assumption that spontaneous release , synchronous release and asynchronous release take place from the same RRP [40] , [62] . This has been questioned recently [63] , [64] . We do not explicitly address any alternate possibilities in this present study .
Simulations were performed using MCell , version 3 [65] , [66] . MCell uses Monte Carlo algorithms to simulate volume and surface reaction-diffusion of discrete molecules in complex spatial environments with realistic cellular and sub-cellular geometry . This allows for detailed study of the effect of the spatial organization and stochastic reaction-diffusion dynamics on the temporal evolution of key system variables . We modelled a 0 . 5 µm×0 . 5 µm×4 µm volume of simplified en passant axon segment with physiologic spatial distributions and concentrations of ligands and molecules . Initial concentrations , locations , diffusions constants , and rates and their sources used for the MCell model are specified in Table 1 . Further validation of the parameters used comes from the shape and amplitude of the calcium response to action potential in our simulations which is consistent with experimental data [8] , [9] . The apparent diffusion constant of calcium , a key parameter for physiological relevance of our results , was matched in the model to the measured value ( 50 µm2/Sec ) [14] . This value is substantially slower than the initial cytoplasmic free diffusion constant of 220 µm2/sec specified for the simulation and arises because our model has an accurate description of the calcium binding kinetics of mobile calcium binding proteins in the synapse ( See Fig . 10 for kinetic schemes ) . The calcium concentration was clamped at 100 nM at both ends of the axon segment . The simulation time step for calcium was specified to be 0 . 1 µsec and for all other molecules was 1 . 0 µsec . The release transients presented in the figures is a result of N = 10000 simulations for each parameter set . For our stochastic simulations the standard deviation of the vesicular release number is √r where r is the total number of release events observed in a temporal bin , tb ( tb = 10 ms or 1 ms ) . The value of ‘r’ in every bin can be determined by r = release rate . N . tb . The docked vesicles were clustered in a hexagonal array with largest center-to-center distance between vesicles of 35 nm . | Chemical synaptic transmission in neurons takes place when a neurotransmitter released from a nerve terminal of the presynaptic neuron signals to the postsynaptic neuron that an event has occurred . The goal of our research was to model the release at a type of synapse found in the hippocampus , a part of the brain that is involved with learning and memory . The synapse model was simulated in a computer that kept track of all of the important molecules in the nerve terminal . The model led to a better understanding of the extant experimental data including exact conditions that lead to the release of a single packet of neurotransmitter . According to our model , the release of more than one packet can be triggered by a single presynaptic event but the packets are released one at a time . Furthermore , we uncovered the mechanisms underlying an extremely fast form of release that had not been previously studied . The model made predictions for other properties of the synapse that can be tested experimentally . A better understanding of how the normal synapses in the hippocampus work will help us to better understand what goes wrong with synapses in mental disorders such as depression and schizophrenia . | [
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... | 2010 | Modelling Vesicular Release at Hippocampal Synapses |
Highly pathogenic avian influenza A viruses ( HPAIV ) of the H5N1 subtype occasionally transmit from birds to humans and can cause severe systemic infections in both hosts . PB1-F2 is an alternative translation product of the viral PB1 segment that was initially characterized as a pro-apoptotic mitochondrial viral pathogenicity factor . A full-length PB1-F2 has been present in all human influenza pandemic virus isolates of the 20th century , but appears to be lost evolutionarily over time as the new virus establishes itself and circulates in the human host . In contrast , the open reading frame ( ORF ) for PB1-F2 is exceptionally well-conserved in avian influenza virus isolates . Here we perform a comparative study to show for the first time that PB1-F2 is a pathogenicity determinant for HPAIV ( A/Viet Nam/1203/2004 , VN1203 ( H5N1 ) ) in both mammals and birds . In a mammalian host , the rare N66S polymorphism in PB1-F2 that was previously described to be associated with high lethality of the 1918 influenza A virus showed increased replication and virulence of a recombinant VN1203 H5N1 virus , while deletion of the entire PB1-F2 ORF had negligible effects . Interestingly , the N66S substituted virus efficiently invades the CNS and replicates in the brain of Mx+/+ mice . In ducks deletion of PB1-F2 clearly resulted in delayed onset of clinical symptoms and systemic spreading of virus , while variations at position 66 played only a minor role in pathogenesis . These data implicate PB1-F2 as an important pathogenicity factor in ducks independent of sequence variations at position 66 . Our data could explain why PB1-F2 is conserved in avian influenza virus isolates and only impacts pathogenicity in mammals when containing certain amino acid motifs such as the rare N66S polymorphism .
Direct bird-to-human transmission of highly pathogenic avian influenza A viruses ( HPAIV ) of the H5N1 subtype was first reported in 1997 [1] . Since then , 518 human cases have been confirmed ( WHO: updated January 20th , 2011 ) . Unlike seasonal influenza A viruses , HPAIVs do not yet have the ability to spread directly from human-to-human , a likely consequence of multiple species barriers , including their preferential attachment to alpha 2 , 3-linked sialic acids which are rarely present in the upper regions of the human respiratory tract [2] . However , human infections with these viruses often clinically manifest with an unusual hyperactivation of the host immune response , vast overproduction of cytokines and chemokines , severe inflammatory response syndrome , fever , pneumonia , pulmonary hemorrhage , acute respiratory distress syndrome , lymphopenia , prominent hemophagocytosis , disseminated intravascular thrombosis , diarrhea and multiorgan failure [3] , [4] , [5] , [6] , [7] . Despite the low number of cases so far , the overall human mortality rate of HPAIV ( ∼60% , WHO: updated January 20th , 2011 ) is of serious concern , and the possibility that these HPAIVs might reassort with a circulating human strain , thereby potentially integrating lethality and transmissibility , is a continuous public-health risk . Waterfowl , particularly ducks , are an important natural reservoir for a variety of influenza A viruses [8] . Infections with low pathogenic avian influenza A virus are usually asymptomatic in ducks [9] . The virus mainly replicates in enterocytes in the digestive tract of infected animals and is shed in the feces [10] , [11] . Even most HPAIV infections only cause mild clinical signs in ducks and immune competent animals usually recover from infection [12] . In contrast , chickens often die abruptly by infection with HPAIV . Despite similar multi-organ tropism in ducks and chicken , HPAIV apparently replicates better in chicken , as higher viral titers are commonly observed [12] . Exceptions to this are clade 2 . 2 H5N1 HPAIVs , which cause death in experimentally infected ducks [13] . PB1-F2 was identified ∼10 years ago during the screening of MHC presented epitopes of alternative ORF gene products from influenza A virus infected cells [14] . This 90 amino acid polypeptide is translated from the PB1 mRNA from an alternative start codon in the +1 reading frame , approximately 100 base pairs downstream of the PB1 start codon . Initially PB1-F2 was characterized as a pro-apoptotic peptide , integrating into the inner mitochondrial membrane and disrupting mitochondrial membrane potential , apparently with a preference for monocytic cells [14] , [15] , [16] , [17] . In this regard , later studies showed that PB1-F2 can interact with the mitochondrial membrane proteins VDAC-1 and ANT3 [18] , both of which are involved in maintaining the mitochondrial membrane potential [19] . For the mouse-adapted human H1N1 isolate A/PR/8/1934 ( PR8 ) , PB1-F2 can increase the viral polymerase function , presumably due to its interaction with the polymerase subunit PB1 [20] . Recent studies have confirmed both the pro-apoptotic and polymerase enhancing functions of PB1-F2 , but have clearly shown that these activities are highly dependent upon the specific virus isolate used [21] . With parallels to the current sporadic bird-to-human transmissions of H5N1 HPAIV , the causative agent of the devastating 1918 “Spanish flu” ( 1918 H1N1 virus ) was proposed to have originated in humans as a direct transmission from birds . The PB1-F2 of this virus is unusual in that it contains a serine at position 66 instead of asparagine . A recombinant mutant 1918 virus with a substitution of asparagine for serine at position 66 ( S66N ) showed increased MLD50 , which was approximately two to three orders of magnitude higher than for the wild type recombinant virus [22] . The molecular mechanism for enhanced pathogenesis caused by the 66S polymorphism is not fully understood . However , recent experiments with PB1-F2 derived peptides suggest that the C-terminus of PB1-F2 , which includes residue 66 , can induce proinflammatory cytokines [23] . In addition , PB1-F2 of 1918:H1N1 has been shown to increase secondary bacterial infections of mice [24] . Descendents of the 1918 H1N1 virus still circulate in humans to date , but soon after their introduction into humans they acquired an asparagine at position 66 , and in the early 1950s much of the PB1-F2 ORF was lost by introduction of a premature stop codon at position 58 , thus leaving the polypeptide without its C-terminal mitochondrial targeting sequence . Furthermore , an identical truncation occurred independently in human isolates after the reintroduction of H1N1 viruses expressing a 90aa PB1-F2 in the late 1970s . For human H2N2 and H3N2 viruses , the original pandemic viruses had a PB1 gene derived by reassortment from an avian influenza virus strain , containing a full-length PB1-F2 ORF . Recently published data suggest that at least the PB1-F2s encoded by current human H3N2 viruses might not be functional [21] . Thus , it has been suggested that in humans there is no selective pressure to maintain full-length and/or functional PB1-F2 and that PB1-F2 might have a different role in mammalian versus avian hosts [25] . In this regard , reintroduction of the PB1-F2 ORF by reverse genetics into the novel 2009 pandemic H1N1 virus did not significantly increase pathogenicity of this virus in mouse [26] . In stark contrast to the situation in human influenza A viruses , full length PB1-F2 ORFs are highly conserved in viruses isolated from avian species [25] , especially waterfowl . For example , in duck virus isolates the prevalence of full length PB1-F2 is almost 96% . Nevertheless , the role of PB1-F2 in avian species is unknown and comprehensive studies in avian systems have been limited . The PB1-F2s of all human influenza A virus pandemics of the last century were derived from avian strains . In this study , we compared effects of PB1-F2 expression and sequence variation at position 66 on viral pathogenesis and host response in two prototype mammalian and avian model organisms , mice and ducks . We sought to delineate host dependent functions of PB1-F2 from a highly pathogenic H5N1 avian influenza virus that has crossed the bird-mammal species barrier and caused severe infections in both host systems .
This study was carried out in strict accordance with recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All mouse procedures were approved by Institutional Animal Care and Use Committee ( IACUC ) of Mount Sinai School of Medicine and performed in accordance with the IACUC guidelines ( Protocol # LA08-00594: “Contribution of NS1 to pathogenicity and evasion of innate immunity” ) . Duck experiments were conducted under ABSL-3 conditions in a USDA approved facility and performed according to protocol R-09-93 “Transmissibility of Influenza A Viruses” , approved by the Institutional Animal Care and Use Committee of the University of Maryland . A customized polyclonal serum against the N-terminal 37aa of A/Viet Nam/1203/2004 PB1-F2 was raised in rabbits ( Genescript ) . Monoclonal mouse anti-influenza A virus anti-NP ( clone 28D8 ) was generated against RNPs of A/Puerto Rico/8/1934 in the hybridoma facility of MSSM , NY . Mouse monoclonal antibody against prohibitin was purchased from Abcam ( clone II-14-10 ) . All recombinant influenza A/Viet Nam/1203/2004 ( VN1203 ) viruses and the low pathogenic version thereof were described previously [27] . The PB1-F2 deletion mutant of this virus was generated by substitution of bases T96C ( in PB1-F2-ORF: ATG->ACG ) , C129G ( TCA->TGA ) , C198G ( TCA->TGA ) , T210C ( ATG->ACG ) and T231C ( ATG->ACG ) in the PB1 ORF of pPol1-PB1 A/Viet Nam/1203/2004 , thus eliminating all 3 ATGs and introducing 2 additionally two stop codons in the PB1-F2 ORF , using the Quick Change Site directed mutagenesis kit ( Stratagene ) according to the manufacturer's instructions . The PB1-F2 N66S mutant was generated by introducing the A291G substitution ( in PB1-F2-ORF: AAT->AGT ) . None of the mutations result in non-synonymous changes in the ORF of PB1 or N40 . The same mutations were introduced into a low pathogenic mutant of VN1203 lacking the multi-basic cleavage site of HA ( HAlo ) . All viruses were plaque purified and successful PB1-F2 mutagenesis was confirmed by sequencing the viral RNA of stocks used for infection experiments . No additional mutations were found in any other gene segment of the recombinant viruses . Madin Darby canine kidney ( MDCK ) cells , murine lung epithelial adenoma cells ( LA-4 ) and duck embryonic fibroblasts ( DEF ) were purchased from ATCC and cultured according to the manufacturer's instructions . Murine bone marrow derived macrophages ( BMDMs ) were generated and cultured as described previously [28] . Murine bone marrow derived dendritic cells ( BMDDCs ) were isolated as BMDMs , but differentiated in the presence of recombinant murine GM-CSF at 20 ng/ml ( R&D ) [29] . C57/BL/6 mice were purchased from Jackson Laboratories . C57/BL/6/A2G-Mx1 , a mouse line expressing functional Mx1 , was kindly provided by Peter Staeheli , University of Freiburg , Germany . These mice were generated in a fashion similar to the previously described BALBc/A2G-Mx1 mice [30] . All viral infections of mice were performed in accordance with CDC and USDA guidelines in the ABSL-3+ facility of Mount Sinai School of Medicine , New York , NY . Briefly , mice were anesthetized by intraperitoneal injection of ketamine-xylazine and infected intranasally with 50 µl of virus diluted in PBS . The mice were monitored for clinical signs and weight loss daily . Animals were humanely euthanized upon reaching 75% of initial body weight . MLD50 was calculated according to the method of Reed and Muench . Viral lung titers were determined by standard plaque assay on MDCK cells [31] . Two-week old White Peking ducks ( McMurray Hatchery , IA , USA ) were randomly divided into four treatment groups ( n = 16 ) and housed in separate isolators . Following the acclimatization period , animals were mock inoculated with PBS or infected through natural routes with 104 pfu of the recombinant viruses diluted in 1 ml PBS ( 0 . 5 ml intratracheal , 0 . 3 ml into the nares , and 0 . 2 ml into the eyes ) . Clinical signs ( depression , cyanosis of the skin , respiratory involvement , diarrhea , edema of the face and/or head , and neurological signs ) were monitored and scored daily . A pathogenicity index was calculated as previously described [32] . Tracheal and cloacal swabs were taken on days 1 , 3 and 5 post-infection . Viral load of multiple organs were determined on day 1 and 3 post infection using the TCID50 method as described previously [33] . Total RNA was isolated by Qiagen RNeasy Mini Kits ( Qiagen ) or TRIzol ( Invitrogen ) according to the manufacturer's instructions . 1–5 µg of total RNA was used for reverse transcription by oligo-dT priming in a 20 µl volume ( 1st Strand cDNA synthesis kit , Roche ) . 0 . 5 µl of 1∶10 diluted cDNA , 2 µl of 10 µM gene specific primer mix ( sequences for murine cDNAs , duck cDNAs and influenza segment 7 see Table 1 ) and 5 µl of 2x SYBR-Green qPCR Mastermix ( Roche ) were used for qPCR in a Light-Cycler480 ( Roche ) . Mean N-fold expression levels of cDNA from three individual biological samples , each measured in triplicates , were normalized to 18S rRNA levels and calibrated to mock treated samples according to the 2−ΔΔCT method [34] . Immunofluorescence microscopy was performed using a Zeiss Axioplan fluorescence microscope and Axiovision image acquisition and analysis software at the MSSM-Microscopy Shared Resource Facility , supported with funding from NIH-NCI shared resources grant ( 5R24 CA095823-04 ) , NSF Major Research Instrumentation grant ( DBI-9724504 ) and NIH shared instrumentation grant ( 1 S10 RR0 9145-01 ) . SDS PAGE and western blot were performed as described previously [35] . Briefly , total cell lysates were obtained by collecting adherant and floating cells in disruption buffer containing 6 M urea , 2 M b-mercaptoethanol and 4% SDS . Genomic DNA was fragmented by brief sonication on ice . Lysates were not centrifuged before gel electrophoresis since this method of disruption does not yield a visible pellet after centrifugation . Total protein lysates were separated on 4–15% or 4–20% gradient Ready-Gels ( Biorad ) and transferred to PVDF membranes for western blot analysis . The murine RNA-polymerase I driven negative sense luciferase reporter plasmid ( pcDNA3 . 1-IVAR-Luc ) was cloned by replacing the CMV promotor of pcDNA3 . 1 ( Invitrogen ) with bp -2150 - +1 of the murine 45S ribosomal RNA promotor . We used pMrTsp-9-T10 plasmid , kindly provided by Dr . Gernoth Längst , University of Regensburg , Germany , as template for amplification of murine RNA polymerase I promotor and terminator sequences [36] . A negatively orientated firefly luciferase coding sequence , flanked by influenza NP promotor elements was inserted downstream . The murine RNA Pol1 terminator of 45S ribosomal RNA was introduced downstream of the negatively oriented reporter gene . Murine 3T3 fibroblasts were transfected in 24 well format with 50–400 ng of pCAGGS PB1 ( VN1203 WT or mutants PB1-F2 generated as described for the recombinant viruses ) , 100 ng pCAGGS-PB2 and -PA and 200 ng of pCAGGS-NP ( all VN1203 ) , 100 ng of pcDNA3 . 1-IVAR-Luciferase and 50 ng of pCMV-Renilla-luciferase , using Lipofectamin 2000 ( Invitrogen ) according to the manufacturers instructions . Cells were lysed 24 h post transfection according to the manufacturers instruction ( Dual Luciferase Kit Promega ) . Firefly-Luciferase activity was normalized by Renilla activity . Samples were measured in independent biological triplicates . All statistical analyses were performed using GraphPad Prism Software Version 5 . 00 ( GraphPad Software Inc . , San Diego , CA ) . Comparison between two treatment means was achieved using a two-tailed Student t-test , whereas multiple comparisons were carried out by analysis of variance ( ANOVA ) followed by Dunnett's test using the WT virus as the control . Survival analyses were carried out following the Kaplan Meier procedure . The differences were considered statistically significant at p<0 . 05 .
We first studied the replication kinetics of VN1203 WT , dF2 and N66S viruses in the murine lung epithelial adenoma cell-line LA-4 . Although similar or higher levels of viral nucleoprotein ( NP ) were observed for the mutant viruses as compared to WT , the expression of the N66S mutant form of PB1-F2 was lower than that of WT and barely detectable at 24 h post infection ( Fig . 1A , panel 2 , long exposure ) . Treatment with 10 µM lactocystin resulted in increased levels of PB1-F2 N66S but not WT , suggesting specific proteasomal degradation of the mutant PB1-F2 ( Fig . 1B ) . Interestingly , the N66S substituted PB1-F2 migrates slower in the gel , possibly indicating a posttranslational modification . Interestingly , the PB1-F2 deficient virus generates higher amounts of NP 4 h post infection . These levels adjust to WT at later timepoints , implying an early deregulation of protein expression . However , deletion of the PB1-F2 ORF does not change polymerase activity ( Fig . 1C ) or viral multi-cycle growth in these cells: the WT and PB1-F2 deficient viruses grow to identical titers . Notably , the N66S substituted virus grows to 10-fold higher titers than WT or dF2 after 72 h ( Fig . 1D ) . Previous studies have suggested that PB1-F2 may play a role in monocytic cells . To address the potential cell-type specific effects of VN1203 PB1-F2 , we infected murine bone marrow derived macrophages ( BMDM ) and dendritic cells ( BMDDC ) as a model system for monocytic cells ( Fig . 1E ) . Notably , in macrophages and dendritic cells , we were unable to detect PB1-F2 expression ( data not shown ) , possibly as a result of low protein stability and/or lower levels of protein expression . Interestingly , in both these bone marrow derived cells , the N66S virus appeared to replicate better than the WT and dF2 , as estimated by western blot against viral nucleoprotein at 8 and 16 h post-infection . PB1-F2 was initially described to localize predominantly to mitochondria ( as shown for A/Puerto Rico/8/1934 [14] ) but also in the cytoplasm and nucleus [39] , depending on the virus strain studied . The mitochondrial localization and interaction with mitochondrial membrane proteins VDAC-1 and ANT-3 were proposed to be the basis for the PB1-F2 pro-apoptotic function [18] . Since the N66S substitution is localized in close proximity to the mitochondrial localization sequence , we tested whether this substitution alters the sub-cellular localization of VN1203 PB1-F2 in mouse epithelial cells . By immunofluorescent staining of LA-4 murine lung epithelial cells infected with the WT , dF2 or N66S mutants of VN1203 , we could detect WT PB1-F2 as early as 8 h p . i . ( Fig . S1A , panel 2 ) . At 4 h p . i . no PB1-F2 specific signal was detectable ( data not shown ) . In agreement with our western blot data the N66S substituted PB1-F2 was not detectable at 8h p . i . . WT PB1-F2 was expressed predominantly in the nucleus , with a few cells showing diffuse staining of the whole cell body . Remarkably , co-staining against the NP of influenza virus revealed that only a minor portion of infected ( NP positive ) cells showed PB1-F2 staining , underlining the low expression or stability levels of the PB1-F2 peptide . In agreement with our western blot data , the N66S substituted PB1-F2 was not detectable at 8 h p . i . However , at 24 h post infection both WT and N66S variants of PB1-F2 were detectable , although again not all NP positive cells were positive for PB1-F2 , especially for the N66S mutant ( Fig . S1B ) . At this time point both variants of PB1-F2 were distributed throughout the cytoplasm and nucleus . To test if the cytoplasmic PB1-F2 is localized at mitochondria , we co-stained the infected LA-4 cells for prohibitin , an inner mitochondrial membrane protein . As shown in Fig . S1C ( and enlarged in Fig . S1D ) , we did not detect predominant co-localization of PB1-F2 WT or N66S with prohibitin , but rather a diffuse distribution throughout the cytoplasm and nucleus . Recent findings implicate an interferon-antagonistic function as a new role for PB1-F2 during viral infection . Using whole genome transcriptome analysis , Connenello et al showed increased levels of type I interferon and interferon stimulated genes ( ISGs ) in BALB/c mice infected with a recombinant A/WSN/1933 virus containing a A/Hong Kong/483/97 PB1 segment with a N66 PB1-F2 substitution , as compared to a virus containing the S66 PB1-F2 [40] . Thus , we tested whether the substitution ( N66S ) in a wild type VN1203 H5N1 background would also lead to altered cellular innate responses in LA-4 cells as well as in BMDMs and BMDDCs . In all cell types , we could not detect major differences in the mRNA levels of types I and III interferon induced by VN1203 WT or dF2 virus . Accordingly , the induction levels of ISGs ( IP10 , MxA , ISG15 ) did not significantly depend on presence of PB1-F2 ( Fig . 2A , B , C ) . In both BMDMs and BMDDCs , the dF2 virus induced significantly higher levels of proinflammatory cytokines IL1beta and IL6 . We could not see these differences in epithelial cells ( Fig . 2A ) . Interestingly , despite higher levels of replication , the N66S mutant virus clearly induced lower levels of interferon dependent antiviral responses in BMDMs and BMDDCs ( Fig . 2B and C ) . Presumably as a consequence of this , mRNA levels of ISGs and cytokines and chemokines were significantly reduced in N66S virus infected BMDMs and BMDDCs . Similar results were seen at early time points of infection in vivo by Connenello and colleagues [40] . Surprisingly , this effect was not visible in LA-4 cells , suggesting a monocytic cell type specific effect of PB1-F2 with regards interferon antagonism . Next we were interested in determining whether deletion of the PB1-F2 ORF or the N66S substitution would affect viral pathogenesis in vivo . The mouse 50% lethal dose ( MLD50 ) of recombinant WT VN1203 in C57/BL/6 is ∼3 pfu ( John Steel , unpublished data ) . Since we hypothesized that the N66S substitution might potentially reduce the MLD50 , ( based on the higher replication observed in murine lung epithelial cells ) , we decided to conduct this experiment in C57/BL/6/A2G-Mx1 mice that express a functional Mx1 gene product . Previous studies have shown a dramatically increased resistance of Mx1+/+ mice to both highly pathogenic avian influenza H5N1 viruses and the 1918 pandemic H1N1 virus [38] . As expected , the MLD50 for recombinant WT VN1203 was more than 200 , 000 times higher in C57/BL/6/A2G-Mx1 mice ( MLD50 = 6 . 7×10E5 pfu ) compared to C57/BL/6 ( MLD50 = 3 . 16 pfu ) ( Fig . 3G ) . Interestingly , only animals infected with 2 . 5×10E6 of the wild type virus showed weight loss and decreased survival ( Fig . 3A and D ) . The mice infected with lower doses of virus were completely asymptomatic . Deletion of the PB1-F2 ORF resulted in a slightly decreased pathogenicity as compared with WT ( Fig . 2B , E and G: MLD50 = 1 . 7×10E6 pfu ) . Interestingly , the N66S substituted virus showed an increased pathogenicity compared to the wild type virus ( Fig . 3C and F; MLD50 = 1 . 1×10E5 pfu ) . Moreover , we observed sustained weight loss , clear signs of sickness ( ruffled fur , lethargy ) and death in these animals , even in the groups infected with 2 . 5×10E4 and 2 . 5×10E3 pfu . Interestingly , in these lower dose infected animals ( 2 . 5×10E3 and 2 . 5×10E4 ) onset of death occurred unusually late around day 10 post infection . We noted that prior to death , starting at day 9 post infection , 3 out of 6 mice infected with 2 . 5×10E3 pfu VN1203 N66S showed neurologic symptoms ( e . g . hemi-paralysis ) indicating a potential viral infection of the central nervous system ( CNS ) . Notably , this was not observed for the WT or dF2 viruses at any dose used . Also , it was not observed in animals infected with higher doses of the N66S virus , likely due to death from pulmonary infection . Surprisingly , when using a low pathogenic mutant of VN1203 ( HAlo ) lacking the multi-basic cleavage site , we did not observe differences in lethality ( Fig . 3G right panel ) or viral lung titers ( data not shown ) between WT , dF2 or N66S viruses . Given that without the multi-basic cleavage site HAlo viruses are likely to be confined to the respiratory tract , these data suggest that tissue tropism has a role in determining the impact of PB1-F2 . In the murine lung epithelial cells ( LA-4 ) the N66S virus replicated to titers 10-fold higher than WT and dF2 ( Fig . 1B ) . Similarly , we observed almost 100-fold higher titers in N66S virus infected mouse lungs on days 2 and 5 post infection , as compared to WT and dF2 infected mice ( Fig . 4A ) . The late neurological symptoms observed in low dose VN1203 N66S virus infected mice suggested spreading of the virus to the CNS at later stages of infection . We thus conducted a separate infection study in C57/BL/6/A2G-Mx1 mice using a sub lethal dose of 2 . 5×10E3 pfu and measured viral titers in the lung , spleen and brain on day 8 post infection , a day before the onset of neurological symptoms . At this time-point none of the groups showed any detectable levels of virus in the lung ( data not shown ) . As described for highly pathogenic influenza A virus infections in Mx1 positive mice [38] , we did not detect systemic spread of WT or dF2 VN1203 into brain or spleen by plaque assay . However , in 6 out of 7 animals infected with N66S virus , we detected virus in the brain ( Fig . 4B ) , but not in spleen or lung ( data not shown ) , suggesting a specific function of N66S PB1-F2 in neurotropism . To further show the replication advantage of the N66S substituted virus in neuronal tissue , we infected mice intracranially with either 10 or 100 pfu and measured viral titers in the brain 2 days post infection ( Fig . 4C ) . The N66S substituted virus replicated significantly better in brain tissue compared to the WT or dF2 viruses in the mice inoculated with 100 pfu ( Fig . 4C lower panel ) . We even observed replication of N66S virus in one out of three animals inoculated with 10 pfu . It should be mentioned that 2 out of 4 animals of the WT infected group showed viral brain titers , but this difference was not significant compared to the PB1-F2 deficient virus infected mice . In summary , substitution of serine for asparagine at position 66 of PB1-F2 enhances replication of VN1203 in murine in vitro models and reduces the IFN response in infected murine monocytes . In an in vivo mouse model , this substitution enhances pathogenicity , replication and neurotropism . However , we could not detect major differences in viral lung titers and only a mildly altered LD50 in mice when comparing the WT and PB1-F2 deficient viruses . Nevertheless , we observed enhanced mRNA levels of the proinflammatory cytokines IL-6 and IL-1b in dF2 virus infected monocytic cells . The PB1-F2 ORF is highly conserved in viruses isolated from ducks . More than 95% of viruses express a PB1-F2 of 87 amino acids or longer ( NCBI Influenza virus resource: http://www . ncbi . nlm . nih . gov/genomes/FLU/FLU . html and Influenza Research Database ( IRD ) : http://www . fludb . org/brc/home . do ? decorator=influenza ) . We thus hypothesized a specific need to retain PB1-F2 in an avian host environment and consequently envisaged a different outcome to our infection study in an avian host as compared to a mammalian host . To test the growth properties of the three recombinant VN1203 viruses in avian cells we first performed infection experiments in duck fibroblast cells ( DEF ) . As shown for the murine lung epithelial cells , the N66S mutant protein was present in lower amounts in infected cells and expression levels increased in the presence of the proteasome inhibitor lactocystin ( Fig . 5B ) . In stark contrast to the murine cell models , we did not observe any increase in viral replication or viral NP levels by substituting serine for asparagine at position 66 of VN1203 PB1-F2 ( Fig . 5A and B ) . Similar to what we observed in the murine lung cell line , in the infected DEFs the WT PB1-F2 is expressed to higher levels ( already detectable after 4 h post infection , Fig . 5A ) , than the N66S variant ( first detectable after 24 h ) . Interestingly , the N66S PB1-F2 shows a slower migrating form in western blot , as shown in murine cells , which may indicate a post-translational modification . Of note , the proteasome inhibition mainly affected the levels of low molecular weight PB1-F2 , again indicating that the higher molecular weight form could be protected from degradation ( Fig . 5B ) . As shown in LA-4 the polyclonal serum raised against the N-terminus of PB1-F2 detects a higher migrating band only present in infected duck cells ( 24 h post infection ) . Next we examined the localization of PB1-F2s in infected DEFs . The WT PB1-F2 was localized mainly to the nucleus at 8 h pi ( Fig . S2A ) . Similar to infected LA-4 cells , not all NP positive cells were also positive for PB1-F2 . At 24 h post infection , we could detect both the WT and the N66S PB1-F2 distributed throughout the whole cell ( Fig . S2B ) . Co-staining for the mitochondrial protein prohibitin did not reveal predominant mitochondrial localization of WT or N66S PB1-F2 ( Fig . S2C and enlarged in Fig . S2D ) as observed previously in LA-4 cells . This suggests that the inability of this PB1-F2 to localize to mitochondria is not a species-specific phenomenon . To address a potential impact of PB1-F2 deletion or N66S substitution in vivo , we next conducted pathogenicity studies in two-week-old white Peking ducks . The infected ducks were monitored for clinical signs and scored daily as described previously [32] , [33] . In the first two days of infection , we could clearly detect differences in the onset of clinical signs between the three groups of animals ( Table 2 and Fig . 6A and B ) . The WT virus infected animals show sickness ( n = 7 ) or severe sickness ( n = 3 ) by day 2 of infection ( pathogenicity index ( PI ) of 2 . 28 ) . In contrast the N66S virus infected ducks show a slightly faster disease progression ( day 1: sick n = 9 , severely sick n = 1 , ( PI = 2 . 4 ) ) . However , both viruses efficiently killed the ducks ( 9/10 animals ) after 10 days of infection . In contrast , the dF2 virus infected animals progressed showed significantly slower progression of clinical signs of sickness ( day 2: sick n = 1 , ( PI = 1 . 66 ) as compared to the WT and N66S virus infection . Furthermore , the dF2 virus infected animals showed a greater survival rate ( 3/10 ) , although this difference is not statistically significant . It has to be pointed out that all three viruses clearly have a pathogenicity index of >1 . 2 , defining each as a highly pathogenic virus in birds . To test if PB1-F2 plays a role in viral dissemination to organs in infected ducks , we measured viral titers in different organs at various time post infection . By day 1 post infection TCID50 in colon , liver , spleen and kidney are higher in WT and N66S virus infected animals compared to dF2 virus infected animals ( Fig . 6C ) . However , likely due to the use of outbred animals , the variation within each group was high and by day 3 post infection no statistically significant differences were detectable ( Fig . 6D ) . Surprisingly , we did not observe enhanced viral titers in cloacal swabs of WT or N66S virus infected ducks compared to dF2 virus infected animals ( Fig . 6E and F ) implicating similar shedding rates . In ducks , we could not detect different levels of type I interferon mRNA on day 1 or 3 post infection in spleen , lung or colon of the three groups of infected animals ( data not shown ) . Thus it is possible , at least for VN1203 , that PB1-F2 has an interferon-antagonist independent role in enhancing pathogenicity in avian hosts . Overall , we observed that the VN1203 WT and N66S viruses behave very similarly in ducks with respect to replication and pathogenicity . Both viruses spread systemically to spleen and kidney by day 1 post infection and onset of severe clinical signs occurs by day 1 or 2 post infection . In contrast the deletion of PB1-F2 resulted in delayed onset of clinical symptoms and systemic spread of the virus is first detectable only by day 3 post infection . Our data suggest that the full length PB1-F2 ORF is important for rapid systemic viral dissemination of HPAIV in birds . This contrasts with our mammalian host data , in which deletion of PB1-F2 has only a minor impact on replication and pathogenicity , although variations at position 66 can enhance virulence .
HPAIV infections are associated with hyper-production of inflammatory cytokines and chemokines , systemic viral spreading and severe multi-organ damage in both mammalian and avian hosts . The viral and host factors contributing to this unusually severe outcome have been studied for years , but are still not completely identified or understood . Here we analyzed the impact of PB1-F2 , a non-structural viral protein , on viral pathogenesis and host response in mammalian and avian model systems . PB1-F2 is present in the majority of avian isolates of all subtypes , but the full length open reading frame is lost over time in many mammalian isolates . To our knowledge this is the first comparative study using an iso-genic approach to test the function of PB1-F2 in a HPAIV in birds and mammals . We further analyzed the impact of a N66S substitution in PB1-F2 , which was previously shown to be associated with viral pathogenesis of the 1918:H1N1 pandemic virus [22] and that was recently proposed to promote interferon antagonistic properties of PB1-F2 in mice [40] . In the murine cell system , the three tested viruses showed differences in growth and host response depending on the cell model used . In monocytic cells ( BMDMs and BMDDCs ) the N66S substituted virus replicated faster as indicated by higher levels of NP and induced lower levels of type I interferon , ISGs and proinflammatory cytokines and chemokines . Interestingly , the PB1-F2 deleted virus induced higher levels of IL-6 and IL-1beta in monocytic cells . In contrast , in lung epithelial cells the host response was similar among the three viruses . A cell type specific function of PB1-F2 was already proposed when PB1-F2 was initially described ∼10 years ago . Chen et al provided experimental evidence for a pro-apoptotic function of PR8 PB1-F2 that predominantly affects monocytes [14] . We found that the tested VN1203 PB1-F2 used in this study does not predominantly localize to mitochondria . Chen et al showed , that two leucine residues in the C-terminus of PB1-F2 are essential for mitochondrial localization of PB1-F2 ( L69 and L75 ) [39] . They could show that PB1-F2 of A/Hong Kong/156/1997 does not localize to mitochondria due to alternative amino acids at position 69 and 75 ( Q69 and H75 ) . Interestingly substitution of these residues with leucine changes the subcellular localization of PB1-F2 to the mitochondria , but does not affect virus induced apoptosis levels in vitro or viral pathogenicity in mice when tested in a 7+1 PR8 system with segment 2 of A/Hong Kong/156/1997 . Notably , PB1-F2 of VN1203 does not contain the essential leucines for mitochondrial localization at position 69 ( 69Q ) and 75 ( 75R ) , which is in full agreement with what we have observed by immunofluorescence in infected murine and duck cells . It is thus unclear if VN1203 shares the published pro-apoptotic function of PB1-F2s from other viruses ( e . g . PR8 ) or if the cytoplasmic PB1-F2 can still interact with mitochondrial host factors such as VDAC and ANT3 [18] . When PB1-F2 was initially described [14] , it was shown that this short polypeptide is highly unstable and degraded in proteasome dependent way . We did not observe changes in the levels of WT PB1-F2 when blocking proteasome function . However , we show here in murine and duck cells that the protein levels of the N66S mutant can be increased by exposing the infected cells to lactocystin . This increase mainly affects the faster migrating protein species and could implicate that a modified version of PB1-F2 N66S is protected from proteasomal degradation . At this point it is unclear if and how the stability of N66S affects its function in increasing pathogenicity of the virus . We consistently observed an increase of viral proteins and/or viral titers in the N66S virus infected murine cells . Mazur et al have shown that PB1-F2 of PR8 can interact with the polymerase subunit PB1 and enhance polymerase function . Nevertheless , this does not affect viral replication and pathogenicity in vitro and in vivo [20] , [21] . McAuley et al could show that PB1-F2 of VN1203 can increase the polymerase function of a PR8 derived polymerase complex in human 293T [21] . However , it did not affect pathogenesis of a recombinant PR8 with a VN1203 PB1-F2 in mice . Nevertheless , none of these studies addressed a potential effect of sequence variations at position 66 . In our mouse fibroblast system ( 3T3 cells ) , we did not detect changes in viral polymerase function in the presence or absence of PB1-F2 . Moreover the N66S substitution did not enhance viral polymerase activity in this in vitro assay , as could be suspected from the enhanced replication and enhanced viral protein levels . This is contradictory to the data presented by McAuley et al . However , it has to be pointed out that this group combined PR8 polymerase subunits with PB1-F2 from different viruses [21] . We cannot exclude that the PR8 polymerase function is specifically modulated by certain PB1-F2s . We clearly observed enhanced replication of the N66S substituted virus in vitro and in vivo , resulting in increased pathogenicity and higher lung titers in mice , consistent with earlier work using a 7+1 influenza A/WSN/33 virus with an HK/483 PB1 segment [22] . Interestingly , the N66S variant was the only virus that was able to spread to the CNS of Mx1 positive mice , leading to detectable viral brain titers 8 days post infection . This infection of the CNS is most likely responsible for the observed paralysis in mice infected with low doses of virus and the unusual second wave of deaths in infected mice around day 10 of infection . When Mx1 positive mice were initially infected with H5N1 and the 1918 H1N1 virus [38] , no systemic replication could be detected . We also did not detect systemic viral spreading after infection with low doses of WT H5N1 virus . Mechanistically it is not clear at this point how PB1-F2 potentiates spread of the N66S virus to the central nervous system . It is also intriguing that we only detected virus in the brain and not in other organs . Direct viral inoculation of the brain resulted in sustained replication of the N66S substituted virus , while the WT and dF2 mutant did not replicate or replicated poorly . This implies that PB1-F2 N66S supports neuronal dissemination of the virus , and whether this is due to its specific interaction with a host factor in the brain remains to determined . Very recently Shinya et al investigated neuropathogenicity of different H5N1 influenza A virus isolates in ferrets [41] . Interestingly the authors could show that influenza A/Hong Kong/483/1997 , which contains a serine at position 66 of PB1-F2 disseminates wider throughout the brain tissue , most likely by infecting vascular cells , since severe vascular lesions were observed . These lesions were not found in ferrets infected with a closely related isolate , A/Hong Kong/486/1997 , which contains an asparagine at positione 66 of PB1-F2 . It remains elusive if the observed phenotype is based on the N66S substitution or other changes between the two isolates . To our surprise , we did not observe enhanced viral replication or pathogenicity of the N66S virus when using a low pathogenic mutant background of VN1203 lacking the multi-basic cleavage site in HA . The multi-basic cleavage site is essential for systemic spreading of HPAIV in mammals , allowing HPAIV to replicate independently of lung resident extracellular proteases [32] . It appears then that both a multi-basic cleavage site in HA and a S66 amino-acid residue in PB1-F2 are required to achieve high levels of replication and spread to the brain in Mx1 competent mice . At this juncture , we cannot exclude that the enhanced lethality of the N66S mutant virus and the neurotropism in mice are linked . Newly developed viral tracking systems , may allow us to find more details on the type of cells infected by these viruses in vivo [42] . In the Mx1 positive mouse system , the expression of a PB1-F2 with a serine at position 66 clearly enhances viral replication and pathogenicity in vivo . Interestingly , deletion of the PB1-F2 ORF had little effect on replication or lethality of the virus , when compared to the WT virus . This was described earlier for PR8 virus , where the deletion of PB1-F2 did not affect replication and lethality in mice [24] . In the human H1N1 isolates , the full length PB1-F2 ORF is lost by introduction of a premature stop codon at position 59 , [25] . This could indicate , that full length PB1-F2 per se is not essential for viral fitness in humans/mammals and could explain why the deletion of PB1-F2 had no major effect on pathogenicity of the viruses tested here and in other studies [21] , [24] . Accordingly , the currently circulating swine origin H1N1 virus has no functional PB1-F2 ORF , and is nevertheless spreading successfully [26] . At this point , the function of a C-terminally truncated PB1-F2 in humans remains elusive . PB1-F2 is expressed from a +1 ORF of segment 2 of IAV . Besides PB1 and PB1-F2 this segment also encodes for N40 , an N-terminally truncated version of PB1 that was first described in 2009 [43] . Deletion of the PB1-F2 ORF , by mutation of the start AUG enhances expression of N40 as a consequence of ribosomal shifting . Deletion of N40 results in attenuation of the virus , but this also changes the PB1 sequence . In contrast , enhanced expression of N40 , as shown for PB1-F2 deficient virus mutants , has no effect on viral replication in vitro and in ovo [43] . Thus , the function of N40 remains elusive . We cannot exclude the possibility that in our systems deletion of the PB1-F2 ORF might cause phenotypes that are indirectly influenced by the levels of N40 . Despite the lack of differences in replication in vitro and in vivo or major impact on pathogenicity in mice , we observed increased mRNA levels of IL1beta and IL6 in murine BMDMs and BMDDCs infected with PB1-F2 deficient viruses in vitro . So far , no correlations have been made between PB1-F2 and inflammatory responses . It is striking that this effect is more pronounced in monocytic cells , since these cells have previously been shown to be more susceptible to the pro-apoptotic function of PB1-F2 [14] . In contrast to the wildtype and PB1-F2 deficient virus , the N66S virus induces significantly lower amounts of type I and type III interferon and consequently ISGs in BMDM and BMDDCs . Recent work showed that in a WSN/33 virus encoding the HK/483 PB1 , the N66S substitution in the PB1-F2 ORF resulted in reduced type I interferon levels by day one of infection , despite higher viral titers [40] . Since pDCs and alveolar macrophages are the main producers of type I interferon and proinflammatory cytokines , it is possible that PB1-F2 primarily acts in these cells by a yet undetermined mechanism . In parallel to this work , a study by Varga et al ( accepted for publication ) , showed that PB1-F2 expression can decrease RIG-I and MAVS induced IFN-β production . The detailed mechanism is still unknown , but PB1-F2 seems to act at the level of MAVS . It is so far unclear to what extent PB1-F2 contributes to antagonism of the type I interferon response and why deletion of PB1-F2 ( as in the pandemic 2009 H1N1 strains ) does not result in diminished viral replication . In ducks , deletion of PB1-F2 had a more striking effect on pathogenesis . We clearly observed a delayed onset of clinical symptoms in the animals infected with the PB1-F2 deficient virus . Moreover , the systemic spreading of the virus was significantly delayed , when compared to the wild type and N66S virus . Nevertheless , the deletion of PB1-F2 did not reduce lethality to the levels of low pathogenic avian viruses . Moreover , we did not observe statistically significant differences in the type I interferon levels present in infected animals . Although this could be a consequence of the high inner group variation , due to the outbread nature of the animals , it may also be that the proposed anti-interferon function of PB1-F2 is specific for mammals . We are currently testing this hypothesis . We also did not see major differences in viral loads in the cloacal swabs of these animals , indicating that PB1-F2 does not affect viral shedding . A recent study by Marjuki et al has shown that three point mutations in PB1-F2 of VN1203 can affect polymerase function in chicken cells and pathogenicity in mallard ducks [37] . Interestingly , these three mutations change the PB1-F2 of the highly pathogenic VN1203 into the PB1-F2 of the low pathogenic A/chicken/Vietnam/C58/04 . The same group showed in a previous study that the PB1 segment essentially contributes to pathogenicity of VN1203 in birds [44] . Taken together , these data and our data imply that PB1-F2 is important for the pathogenicity of H5N1 HPAIV . Moreover , the high conservation of PB1-F2 in avian isolates suggests a yet to be determined , but essential , function of PB1-F2 for these viruses . It is intriguing that the N66S substitution of PB1-F2 , a polymorphism associated with increased pathogenicity of the 1918 H1N1 and the highly pathogenic H5N1 isolates from Hong Kong 1997 , significantly enhances the pathogenicity of VN1203 in mice , but has little detectable effect in ducks . Intriguingly , in many avian isolates the serine at position 66 is common . This could mean that the molecular mechanism for the enhanced pathogenicity of viruses with this substitution in mammals does not apply to avian hosts . In summary , our study shows that PB1-F2 is an important , evolutionary conserved pathogenicity factor of HPAIV in avian species that supports faster viral spreading into different organs . In mammals the 1918:H1N1-like N66S substitution supports spreading and replication in the CNS , by a yet to be defined mechanism . The increase in viral pathogenicity of N66S mutant is possibly due to inhibition of type I interferon and proinflammatory responses in monocytic cells . | Influenza A viruses can infect avian and mammalian hosts . Human infections with seasonal influenza virus strains are usually confined to the respiratory tract and are cleared within days by the immune system . In contrast , highly pathogenic avian influenza viruses can spread throughout the whole body , usually resulting in multi-organ failure and even death in immune competent hosts . Here , we investigated the species-specific function of an influenza A virus protein , PB1-F2 , that is highly conserved in avian influenza virus strains but which is lost in many isolates from mammalian hosts . Our data indicate that PB1-F2 allows successful spreading of the virus throughout the body in experimentally infected ducks . In contrast , PB1-F2 does not contribute to the severity of HPAIV infections in mice . Nevertheless , a polymorphism at position 66 of PB1-F2 ( N66S ) that was found in the devastating 1918 pandemic virus and in several early H5N1 HPAIV isolates clearly increased pathogenicity of a HPAIV influenza virus in mice . Our findings might explain why the whole PB1-F2 ORF is conserved in avian influenza viruses , since it contributes to viral dissemination and pathogenicity , but can be lost in mammalian hosts as it has minimal effects on virulence . | [
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] | 2011 | Differential Contribution of PB1-F2 to the Virulence of Highly Pathogenic H5N1 Influenza A Virus in Mammalian and Avian Species |
The bHLH transcription factor , PHYTOCHROME INTERACTING FACTOR 3 ( PIF3 ) , interacts specifically with the photoactivated , Pfr , form of Arabidopsis phytochrome B ( phyB ) . This interaction induces PIF3 phosphorylation and degradation in vivo and modulates phyB-mediated seedling deetiolation in response to red light . To identify missense mutations in the phyB N-terminal domain that disrupt this interaction , we developed a yeast reverse-hybrid screen . Fifteen individual mutations identified in this screen , or in previous genetic screens for Arabidopsis mutants showing reduced sensitivity to red light , were shown to also disrupt light-induced binding of phyB to PIF3 in in vitro co-immunoprecipitation assays . These phyB missense mutants fall into two general classes: Class I ( eleven mutants ) containing those defective in light signal perception , due to aberrant chromophore attachment or photoconversion , and Class II ( four mutants ) containing those normal in signal perception , but defective in the capacity to transduce this signal to PIF3 . By generating a homology model for the three-dimensional structure of the Arabidopsis phyB chromophore-binding region , based on the crystal structure of Deinococcus radiodurans phytochrome , we predict that three of the four Class II mutated phyB residues are solvent exposed in a cleft between the presumptive PAS and GAF domains . This deduction suggests that these residues could be directly required for the physical interaction of phyB with PIF3 . Because these three residues are also necessary for phyB-imposed inhibition of hypocotyl elongation in response to red light , they are functionally necessary for signal transfer from photoactivated phyB , not only to PIF3 and other related bHLH transcription factors tested here , but also to other downstream signaling components involved in regulating seedling deetiolation .
As sessile photoautotrophic organisms , plants live their lives entirely at the mercy of their environment . Therefore , they have evolved the ability to detect even subtle changes in environmental conditions , and to adjust their developmental programs accordingly , to optimize growth , survival and reproduction . Because plants depend on sunlight for energy to drive photosynthesis , they are particularly adapted to detect and respond to changes in light conditions . To this end , plants have three types of sensory photoreceptors , the blue light sensing cryptochromes and phototropins , and the red/far red light sensing phytochromes [1]–[4] . There are five phytochromes in Arabidopsis thaliana , designated phyA-phyE [5] , [6] . The phytochromes are photoreversible molecular switches , that undergo rapid interconversion between inactive , Pr ( for red-light ( R ) -absorbing ) and active , Pfr ( for far-red-light ( FR ) -absorbing ) conformations upon sequential absorption of photons of the appropriate wavelength [7] . Upon photoconversion to the Pfr form , the phytochromes undergo translocation to the nucleus where they initiate developmental programs characteristic of growth in the light , resulting in , among other things , an inhibition of hypocotyl elongation , stimulation of cotyledon expansion and greening . phyA is a light labile phytochrome , highly abundant in the dark , but quickly degraded following photoconversion . This phy is responsible for seedling deetiolation in response to continuous far-red light , and for events occurring very rapidly upon initial exposure to red light . The Type II phytochromes , phyB-phyE , are more stable than phyA in light-grown plants and seem to play more prominent roles under longer-term red-light irradiation conditions , with phyB being the predominant photoreceptor under these conditions [6] , [8] . phyA and phyB have been shown to physically interact with the basic helix-loop-helix ( bHLH ) transcription factor PIF3 [9] , [10] , as well as a number of other PIF3-related bHLHs , PIF1 , PIF4 , PIF5 , PIF6 and PIF7 [11]–[15] . Following photoconversion and nuclear translocation , the interaction of the phytochromes with these factors and nuclear body formation are thought to be the earliest events in phytochrome signaling . PIF3 is necessary for the light-induced regulation of a subset of rapidly light-responsive genes , and plays an important role in greening during seedling establishment [16] . Furthermore , PIF3 is phosphorylated in a manner dependent on interaction with phytochromes in red light , and this phospohorylation precedes PIF3 degradation via the ubiquitin proteasome system [17] , [18] . It appears that phy-mediated proteasomal degradation of PIF3 is crucial for the proper timing of expression of light-induced gene expression [19] . Similarly rapid phy-induced phosphorylation and degradation have been reported recently for PIF1 [20] , [21] , PIF4 [22] and PIF5 [22]–[24] . Due to the importance of the PIF3-phytochrome interaction to early events in photomorphogenic development , studies have been carried out to molecularly dissect the phyA and phyB binding sites on PIF3 . The binding site for phyB , termed the Active PhyB ( APB ) binding site , is located near the N-terminus of PIF3 and was initially identified by sequence similarity of this region of PIF3 to other bHLHs that bind phyB [13] . phyA binds downstream of phyB on PIF3 at a region termed the Active PhyA binding site ( APA ) [17] . Similarly separate binding sites for phyA and phyB on PIF1 have also been recently reported [21] . The region of the phytochromes to which the phytochrome interacting bHLHs bind is poorly defined , and the N-terminal residues of phyB that are required for the interaction are unknown . The phytochromes consist of an N-terminal photosensory domain and a C-terminal dimerization domain [5] , [7] , [25] . The N-terminal domain has four subdomains: an N-terminal extension found only in higher plants , a Per/Ant/Sim ( PAS ) -like domain ( PAS ) , a cGMP phosphodiesterase/adenyl cyclase/FhlA ( GAF ) domain , and a phytochrome ( PHY ) domain . Although PIF3 was originally identified by its ability to bind the C-terminal domain of phyB in a yeast 2-hybrid screen [9] , it was later shown that PIF3 photoreversibly binds more strongly to the N-terminal phyB domain in vitro , albeit with somewhat reduced affinity compared to binding to full-length phyB [10] , [26] . A previous reverse genetic study aimed at identifying regions of phyB required for its signaling activity in vivo , examined deletion derivatives transgenically expressed in Arabidopsis for their biological activity . In planta analyses of these derivatives showed strikingly that the C-terminal domain of phyB is not required for phytochrome activity per se , but is required for dimerization and possibly to attenuate phyB-signaling activity [27] . These findings , taken together with the abovementioned binding studies [10] , [26] , provide compelling evidence that PIF3 , and other phytochrome signaling partners , bind the phyB N-terminal domain . Missense mutations in phyB have also been identified for the purpose of defining regions of the photoreceptor which are required for particular aspects of phytochrome signaling . Several such mutations , identified in screens for mutants with long hypocotyls , are located in the N-terminal domain . Specifically , Krall and Reed [28] identified G118R , S134G and I208T , and Reed and colleagues [29] identified H283T . In a screen for phyB mutants deficient in nuclear speckle formation , Chen et . al . [30] identified point mutations in the phyB N-terminal domain ( C327Y , A372T , and A587T ) . Furthermore , Kretsch et al . [31] identified a phyB point mutation ( G564E ) that is able to adopt the Pfr conformation , but failed to revert back to the Pr form in the dark ( reduced dark reversion ) resulting in a hypersensitive phenotype in the light . Interestingly , Oka et al . [32] later found that a different residue substitution at this position , G564A , showed faster dark reversion . However , overall , the spectral characteristics were examined for only three of these mutations by these or other authors [31]–[33] leaving open the question of whether signal perception or signal transfer were affected . More recently , Oka et al . [34] performed a genetic screen with a previously characterized transgenic Arabidopsis line expressing a transgene-encoded , phyB-N-terminal-domain fusion-protein , designated N651G-GUS-NLS [27] . This protein consists of the N-terminal 651 amino acids of phyB translationally fused in series to green fluorescent protein ( GFP ) , b-glucuronidase ( GUS ) ( which promotes dimerization ) , and a nuclear localization signal ( NLS ) ( to ensure proper subcellular localization ) , and is expressed in a phyB null mutant background . The transgenic line was mutagenized with ethyl methyl sulfonate ( EMS ) and screened for a tall hypocotyl phenotype when grown in dim red light . The rationale for this screen , aimed specifically at the identification of mutations in the phyB N-terminal domain , was that this domain alone is sufficient for phyB signaling , provided that it is capable of nuclear translocation and dimerization , as shown earlier by Matsushita et al . [27] . Oka and colleagues [34] reported the identification of 14 novel phyB missense mutations that resulted in long hypocotyl phenotypes , bringing the total such mutants to 22 when combined with the 8 previously identified , as mentioned above [28]–[30] , [32] , [35] . Oka et al . [34] examined all 22 mutant phyBs and showed that most were disrupted in their ability to undergo normal photoconversion . Of the remainder exhibiting normal spectral properties , four ( R110Q , G111D , G112D , and R352K ) were of particular interest because they localized to the “light-sensing” knot of the recently solved crystal structure of Deinococcus radiodurans bacteriophytochrome [36] . Here , to identify phyB mutants which are defective in binding to PIF3 , we performed a yeast reverse-hybrid screen designed to recover phyB missense mutations which abrogate light-induced interaction of the N-terminal domain of the photoreceptor with PIF3 . Such mutants were then examined for loss of normal spectral activity , indicative of loss of signal perception capability , and for loss of their ability to physically interact with PIF3 and other bHLH transcription factors , suggestive of the loss of their ability to transduce the light signal . Functional importance to phyB signaling in vivo was assessed for the spectrally active phyB mutants , by evaluating the capacity of the mutant molecule to inhibit Arabidopsis hypocotyl elongation in response to continuous red light ( Rc ) . Conversely , we examined the previously identified phyB missense mutations of Oka et al . [34] , shown to lack normal signaling activity in vivo by hypocotyl assays , but to retain normal spectral activity , for their ability to bind PIF3 in vitro .
We developed a yeast reverse-hybrid screen that allowed the identification of the desired missense mutations in the N-terminal domain of phyB . This screen was based on a previously developed modification of the yeast two-hybrid system in which we showed that the phyB N-terminal domain ( phyBNT ) fused to the Gal4 DNA binding domain of yeast ( DBD ) interacts photoreversibly with PIF3 fused to the Gal4 activation domain ( GAD ) in transformed yeast cells [26] . Yeast reverse hybrid screens have been used extensively to identify point mutations that abolish the interaction of two normally interacting proteins . Such screens are based on a negative selection where protein-protein interaction in a yeast 2-hybrid context results in cell death [37]–[42] . Most yeast reverse-hybrid screens reported to date have sought to dissect interactions between yeast or mammalian proteins . To our knowledge , this study represents the first report of a yeast reverse-hybrid screen performed on plant proteins . In principle , the mutations isolated in this type of screen may either affect amino acid residues that are directly involved in the physical interaction of the target protein and its binding partner , or may result in localized structural changes that consequently indirectly interfere with binding . In addition here , because only the Pfr form of phyB is able to bind PIF3 , this screen provides the potential to identify mutations that disrupt photoconversion . Mutations in phyBNT were generated randomly by error-prone PCR and were screened in red light for loss of interaction with PIF3 in yeast on media supplemented with 5-fluoroorotic acid ( 5-FOA ) and the chromophore phycocyanobilin ( PCB ) . In the event of interaction between phyBNT-DBD and GAD-PIF3 , transcription at the LacZ and URA3 genomic loci are activated , resulting in the accumulation of β-galactosidase and URA3 protein ( URA3p ) . The accumulation of URA3p results in death in the presence of 5-FOA . However , a mutation in phyBNT that disrupts binding to GAD-PIF3 prevents transcription of LacZ and URA3 resulting in growth even on 5-FOA . With this yeast reverse-hybrid screening technique , we were able to easily and rapidly obtain large numbers of mutations in phyBNT that disrupt binding to PIF3 . A schematic representation of the screening technique is shown in Figure 1 , where Figure 1A represents the case in which phyB and PIF3 interact , and Figure 1B represents the case in which a mutation in phyB disrupts binding to PIF3 . Several hundred yeast colonies , co-transformed with mutated phyBNT-DBD and GAD-PIF3 , were obtained in the presence of 5-FOA and the chromophore , PCB , in red light , indicative of loss of phyB-binding to PIF3 . However , growth on 5-FOA/PCB-containing media alone is an insufficient assay to eliminate mutations that result in the introduction of a premature stop codon in the phyBNT coding sequence , as these would be expected to result in a lack of reporter gene expression during screening . To eliminate this type of false positive from further analysis , immunoblots were performed on crude protein extracts of ninety-five putative positive colonies using an anti-DBD antibody for detection of the transgene-encoded protein . As shown in Figure S1 , 35 yeast colonies were identified that accumulated full-length phyBNT-DBD fusion protein . For the remaining colonies examined , no protein was detected with the anti-DBD antibody , presumably due to a stop codon being introduced upstream of DBD in the phyB coding region . The presence of a stop codon in clone #50 , which fails to accumulate full length phyBNT-DBD , was confirmed by sequencing . It is expected that any mutation isolated here that disrupts phyBNT binding to PIF3 would fall within the phyBNT coding region , for the simple reason that this was the only region subjected to mutagenesis . However , the possibility of spontaneously arising second-site mutations resulting in decreased binding affinity or decreased reporter gene expression needed to be ruled out . To this end , plasmid was isolated from each positive yeast colony , recycled through E . coli , and re-transformed into the progenitor yeast strain carrying a GAD-PIF3 plasmid . The level of interaction between phyBNT-DBD and GAD-PIF3 in response to saturating 5-minute pulses of either red or far red light was quantified as a function of β-galactosidase activity in liquid assays using ortho-Nitrophenyl-β-galactoside ( ONPG ) as a substrate . As shown in Figure 1C , all of the yeast plasmids tested exhibited either reduced or completely abolished phyBNT-PIF3 interaction . Specifically , while essentially no interaction significantly greater than the baseline was detected when yeast cells were treated with far red light , 70% of the wild-type level of phyBNT Pfr interaction with PIF3 was detected in clone #6 in response to red light . Clone #s 18 , 28 , 52 , 67 , and 93 had approximately 15% of residual binding , whereas clone # 2 had ∼25% of residual binding . The remaining 17 clones tested for interaction with PIF3 using β-galactosidase activity assays , showed no binding in response to red light significantly higher than was detected with the negative control , the Pr form of the wild-type phyB N-terminus ( Figure 1C ) . Yeast plasmids were sequenced to identify the mutations responsible for loss of phyB binding to PIF3 in the 24 clones examined by β-galactosidase assays . In many cases , more than one point mutation was identified in a given clone . To assay the identified mutations in the context of the full-length phyB protein , and to distinguish between multiple mutations , individual point mutations were introduced by site-directed mutagenesis into full-length phyB for in vitro translation with a rabbit reticulocyte lysate transcription and translation system ( TNT ) . In vitro co-immunoprecipitation assays for 47 phyB missense mutations were performed with radiolabeled GAD-PIF3 as bait and radiolabeled phyB as prey as described previously [13] . Due to the relatively large number of mutations identified in each original yeast plasmid , it was not surprising that many of the phyB missense mutations examined did not affect binding to PIF3 , when re-assayed as single amino acid changes in an otherwise wild-type protein . However , 13 mutations were identified that reduced binding to 50% or less of the wild-type Pfr level in the co-immunoprecipitation assays . Twelve of these are shown in Figure 2 . Of these , six ( C119Y , R415W , V264E , S343Y , V273L and I308T ) displayed undetectable or severely reduced light-induced binding to PIF3 , whereas the remainder showed varying levels of limited binding . The thirteenth mutation , G111D , shown in Figure 3A and 3B , also exhibited essentially complete loss of light-induced PIF3 binding . This mutant was of particular interest because , coincidently , it had also been independently identified as one of four in vivo-signaling mutants in the genetic screen for functionally compromised phyB Arabidopsis mutants by Oka et al . [34] , thereby providing a convergence point for the two studies based on complementary strategies . To determine whether the three other mutants of Oka et al . [34] ( R110Q , G112D and R352K ) were affected in PIF3 binding , we generated full-length phyB constructs containing these missense mutations and tested them by co-immunoprecipitation assay . The data show that both R110Q and R352K , like G111D , displayed little or no light-induced PIF3 binding , whereas G112D appears to have been only marginally affected in this capacity by the mutation ( Figure 3A and 3B ) . The seedling deetiolation phenotypes of the three PIF3-binding-deficient mutants generated by Oka et al . [34] are shown in Figure 3C . Each of these mutants displays reduced sensitivity to prolonged continuous R , but responds normally to continuous FR , as demonstrated by Oka et al . [34] . These data indicate that this subset of mutant phyB molecules , disrupted in their capacity to bind PIF3 , are also compromised in their capacity to inhibit hypocotyl elongation in response to R light signals . Conversely , the absence of a strong effect of the G112D mutation on PIF3 binding is also consistent with the data of Oka et al . [34] where this mutation was found to have only a weak effect on R-induced hypocotyl inhibition . Because wild-type phyB in its inactive , red-light-absorbing , Pr form cannot bind GAD-PIF3 in vitro , one explanation for loss of binding of phyB missense mutants to GAD-PIF3 could be that the mutations disrupt normal phyB photoconversion , thereby preventing the establishment of the Pfr form following irradiation with red light . To test whether the phyB missense mutations identified here are able bind the chromophore , PCB , we performed zinc blot assays , and included the additional two mutants of Oka et al . [34] , R110K and R352K , for comparison . Zinc blot assays are based on the fluorescence displayed by bilin-linked polypeptides when they are complexed with zinc ions . As shown in Figure 4 , nine phyB mutants ( indicated in Table 1 ) are disrupted in their ability to bind chromophore , despite the chromophore attachment site ( C357 ) being intact . Disruption in this case is defined arbitrarily as a 25% or greater reduction in fluorescence relative to wild-type protein in the zinc-blot assay , although most mutations tested disrupted chromophore binding by greater than 75% ( Figure 4E ) . We refer from here on to these mutants , deficient in chromophore binding , as Class I mutants . Six other mutants ( R110Q , G111D , I308T , G348D , R352K , and S367P ) were largely unaffected in chromophore binding , as shown quantitatively in a scatter plot ( Figure 4D ) and bar graph ( Figure 4E ) relative to wild-type phyBNT . This result is consistent with the previous finding that R110Q , G111D , and R352K all bind PCB [34] . Because zinc blots simply assess chromophore binding , not phytochrome photoreversibility , we measured the Pr-Pfr difference spectra of recombinant phyBNT for each missense mutant ( Figure 5 ) . As expected , the mutants that were negative for chromophore binding yielded strongly reduced or no detectable changes in absorbance by difference spectrum analysis , clearly distinct from wild-type ( Figure 5A , 5C , and 5D ) , and consistent with their classification as Class I mutants ( Table 1 ) . Strongly reduced in this case is defined arbitrarily as a 50% or greater reduction in absorbance change relative to the wild-type photoreceptor , although most mutations tested disrupted photoconversion by greater than 75% ( Figure 5C and 5D ) . One missense mutant , phyB S367P , which was positive for chromophore binding , failed to show evidence of normal photoreversibility . Similarly , another chromophore-binding-positive mutant , I308T , did display photoreversibility , but the Pfr form was partially bleached and therefore considered to be spectrally aberrant ( Figure S2 ) . This behavior is consistent with that reported recently for mutation of the homologous residue ( I208 ) in Deinococcus phy [43] . These two phyB mutants , therefore also fall into Class I ( Table 1 ) . On the other hand , four mutants , ( G348D from the yeast screen , R110Q , and R352K from the previously reported hypocotyl screen , and G111D from both screens ) that were positive for chromophore attachment , also showed normal absorbance spectra and photoconversion ( Figure 5A , 5C , 5D , and 5E ) , and are therefore defined as Class II mutants , as indicated in Table 1 . The locations of all 15 mutations within the N-terminal domain of phyB are shown schematically in Figure 6A . The data show that all 15 are confined to the PAS ( 4 mutations ) and GAF ( 11 mutations ) subdomains ( Table 1 ) . Recently , the chromophore binding domain of phytochrome from the bacterium Deinococcus radiodurans was crystallized , and its three dimensional structure solved [36] . This domain is equivalent to the PAS and GAF subdomains of the plant phys ( Figure 6A ) . A pair-wise sequence alignment between Arabidopsis phyB and the phytochrome sequence from Deinococcus shows that the two proteins have 29% identity over the crystallized region of the Deinococcus protein ( Figure S3 ) . Given this sequence similarity between Arabidopsis phyB and Deinococcus phy , it is possible that their structures are also similar . To predict the location of the phyB missense mutations identified here in the context of a three dimensional structure , we mapped them onto the solved Deinococcus structure . The residues corresponding to the missense mutants are indicated on the sequence alignment in Figure S3 . A schematic representation of the three dimensional structure of Deinococcus phytochrome , published by Wagner et al . [36] , is reproduced in Figure 6B showing the locations to which the point mutations identified here map , in addition to the mutant class designation for each highlighted residue . As shown , five Class I mutants ( those that fail to bind PCB in zinc blot assays and have abnormal spectral properties ) fall in the GAF domain , and two fall in the PAS domain . In addition the Class I mutants , I308T and S367P , which bind PCB but nonetheless have abnormal phyB spectral properties , are also located in the GAF domain , consistent with a function in chromophore-protein interaction . In contrast , all four photoconvertible ( Class II ) mutants that are affected in PIF3 binding and result in a long hypocotyl phenotype in Rc are located in a trefoil loop , at the junction of the PAS and GAF domains , also referred to previously as the light-sensing knot [36] , [43] ( Figure 6B; Table 1 ) . To gain further insight into the potential locations of the mutated residues within the three dimensional structure of Arabidopsis phyB , the PAS-GAF segment of the phyB sequence corresponding to the crystallized chromophore-binding domain of Deinococcus was threaded onto the Deinococcus crystal structure ( pdb: 1ztu ) . The homology model was produced using the program “nest” [44] which was found to make the fewest mistakes overall in a comparison of available homology modeling programs [45] . The resultant homology model is shown in Figure 7A , with green ribbons indicating the Deinococcus crystal structure , and blue ribbons indicating the predicted Arabidopsis phyB structure , with the position of the chromophore in Deinococcus superimposed in gold . The close agreement between the Deinococcus structure and the homology model is consistent with a high level of conservation in the critical structural residues . The sulfhydryl group of the Arabidopsis chromophore-binding cysteine residue is co-ordinated with the position of the ethylidene moiety on the chromophore sufficiently closely and in the correct conformation to form the thioether bond by which the chromophore is known to be covalently attached [25] . This is true despite the fact that the cysteine residue is in a very different position in the primary sequence of the protein from that in Deinococcus , and that the cysteine residue approaches the chromophore from the opposite side of the plane of the bilin from the side from which it binds in the Deinococcus structure ( Figure 7A ) . This is consistent with the predictions of Wagner et al . [46] on the position of the plant chromophore-binding site . Examination of the predicted phyB structure using the software PyMol revealed that three of the Class II phyB residues in the knot region , R110Q , G111D and G348D , described here as being required for binding to PIF3 , may be solvent exposed . These presumptive surface residues appear to be clustered near each other at the interface between the PAS and GAF domains as shown in Figure 7B . As shown in the alignment in Figure S3 , one of these residues , G348D , also appears to be close to the chromophore binding site ( C357 ) in the primary phyB sequence . Figure 7C shows a 3D ribbon diagram of the predicted phyB structure with the three surface residues and the chromophore attachment site shown in space-filling format . The distance between G348 and the chromophore attachment site is predicted to be ∼19 . 2 Å . To determine if any of the phyB residues identified here as being required for binding to PIF3 are conserved among Arabidopsis phytochromes , a multiple sequence alignment of phyA-phyE was constructed using the Muscle algorithm . As shown in Figure S4 , all but four of the 15 residues described here are conserved amongst all five phytochromes . Given the differential affinity of PIF3 for phyB compared to the other phytochromes , especially phyA which has been shown to bind a different region of PIF3 than that to which phyB binds ( APA vs . APB ) [17] we might predict that the phyB residues directly involved in binding to PIF3 would not be conserved in phyA . Of the four Class II mutations that had normal photoconversion but led to a disruption in phyB binding to PIF3 , one residue , R110 is a lysine in phyA . As shown above , the same substitution in phyB results in lack of binding to PIF3 , suggesting that this residue may make a significant contribution to the differential affinity of phytochromes for PIF3 . Disruption of the PIF3 binding site on phyB may disrupt binding to all the phyB-interacting bHLHs , because phyB has been shown to bind to the APB domain present in all phy-interacting bHLHs [13] . To test this hypothesis , phyB G111D and R352K , two Class II phyB mutants ( those that photoconvert but do not bind PIF3 ) , were tested for binding to PIF1 , PIF3 , PIF4 , PIF5 , and PIF7 by in vitro co-immunoprecipitation assays . As shown in Figure 8 , these two mutations do indeed disrupt binding to all of the bHLH PIFs tested , and would therefore be predicted to be qualitatively more impaired in red light signaling than plants deficient in individual bHLH PIFs , all of which have overlapping and unique roles in phytochrome signaling [5] , [11] , [16] , [19] , [20] , [22] , [47]–[51] .
The data from our yeast-reverse-hybrid screen presented here have identified a set of amino acid substitutions in the N-terminal domain of phyB that disrupt the Pfr-specific interaction with PIF3 and related bHLH transcription factors . A number of phyB missense mutants have been identified previously in genetic screens , but they have not been characterized for their ability to bind signaling partners [28]–[30] , [32] . The success of our screening strategy suggests its potential utility in studying other plant signaling systems that depend on protein-protein interactions . Moreover , the remarkable convergence of our molecular screen and the independent phenotypic screen of Oka et al . [34] on at least one critical signaling residue , together with our demonstration that , conversely , two additional long-hypocotyl phyB-signaling mutants from the latter screen also fail to bind PIF3 , is compelling evidence that the residues identified by the molecular-interaction assay are functionally relevant to seedling deetiolation . The phyB mutations identified here fall into two functionally distinct categories: those disrupted in light-signal perception ( Class I ) , because of defective chromophore function , and those normal in signal perception , but defective in the capacity of the Pfr form to bind to PIF3 ( Class II ) . One subset of the Class I mutants are defective in chromophore ligation , and therefore lack the capacity to absorb the light signal , whereas a second subset appear to support normal ligation but display a lack of , or abnormal , photoconversion activity . In either case , the photoreceptor is unable to undergo normal light-activated conversion to the active Pfr conformer necessary for PIF3 interaction . By contrast , the Class II mutants exhibit normal light-induced conversion to the Pfr form , indicating normal photoperception , but the mutant molecule is altered in determinant ( s ) necessary for the correct physical interaction between the two signaling partners , with the result that signal-transfer to PIF3 is abrogated . However , because of the well-established observation that simple mutation of PIF3 does not phenocopy mutation of phyB [14] , [16] , [19] , [52] , PIF3 alone cannot be responsible for transducing all phyB signaling involved in controlling the multiple , pleiotropic facets of the overall seedling deetiolation process . In fact , the pif3 single mutant is hypersensitive to Rc , the converse of the extreme hyposensitive phenotype of the phyB mutant [14] , [16] , [19] , [52] . Thus , the apparent pleiotropic loss of phyB signaling activity in planta by the Class II mutants identified here suggests that these residue substitutions cause a global disruption of the phyB signaling necessary for the overall deetiolation process . This implies in turn , that these mutations disrupt productive signaling interactions with one or more factors in addition to PIF3 , and that these factors collectively or alone transduce the signals to multiple downstream pathways necessary for deetiolation . Other potential candidates for this role include the other known phy-interacting , PIF3-related bHLH transcription factors . Consistent with this possibility , both Class II mutants tested here ( G111D and R352K ) displayed loss of Pfr-specific binding capacity for several of these factors , PIF1 , PIF4 , PIF5 and PIF7 . However , while this result indicates the broader importance of these residues for phy recognition of this general class of signaling partner , both single and higher-order mutations in these factors , like PIF3 , also cause Rc-hypersensitivity [14] , [53] , [54] , or have no effect [12] , rather than the hyposensitivity expected for loss of phyB signaling . One possible mechanism explaining this apparent contradiction is suggested by the recent twin observations that PIF1 , PIF3 , PIF4 and PIF5 act collectively to repress seedling deetiolation [55] , and that interruption of phyB-PIF interaction , through PIF mutation , leads to increased levels of the PIF protein [19] . Consequently , the concomitant loss of phyB binding to these multiple PIFs , through phyB mutation , in the present study , would be expected to result in increased levels of these factors in the light [19] , without the complication of antagonism imposed by the increased levels of active phyB known to occur under these conditions [14] , [19] , [54] . These increased levels of the PIFs would then be predicted to repress the normally phyB-induced facets of deetiolation , resulting in hyposensitivity , as observed here for the Class II phyB mutant lines ( Figure 3C ) . A second possible alternative mechanism would suggest the potential existence of yet additional components that , at least partially , utilize the same interaction site on phyB as the known PIFs for the primary signal transfer process from the activated photoreceptor necessary for inducing seedling deetiolation . Regardless of the specific underlying mechanism , this binding site appears to have a very fundamental pleiotropic function in the primary , intermolecular signaling process . The alternative , that the mutations at this site could significantly alter the phyB three-dimensional structure , such that binding of other factors at a distant location is disrupted seems less likely . This is because chromophore binding and photoconversion - processes known to be sensitively dependent on an intact three-dimensional structure [7] , [43] - do not seem to be affected by these mutations . Regardless , although the interaction with the PIFs identified here appears unlikely to be responsible for all phyB signaling , these interactions do appear to provide a useful marker for the apparently broader ensemble of signal-transfer interactions collectively responsible for all phyB signaling necessary for inducing normal seedling photomorphogenesis . Based on sequence similarities to known protein structural domains , the phy protein has been defined as being composed of a series of subdomains [7] , [25] , as shown in Figure 6A . The discovery , therefore , that all of the mutations identified here are confined to the PAS and GAF subdomains of the phyB N-terminal domain ( Figure 6A ) is evidence of the centrally important role played by these domains in the photoreceptor's function . This prompted us further to examine the locations of the presumptive homologous residues by utilizing the crystal structure of the PAS-GAF domains of the bacteriophytochrome from Deinococcus radiodurans [36] , [43] . This analysis led to intriguing insights into the possible location of the postulated PIF-interaction surface within the three-dimensional structure of the photoreceptor and its functional importance to phyB signaling in the cell . The majority ( eleven ) of phyB mutants identified here in the yeast reverse-hybrid screen were found to be Class I ( Table 1 ) . Of these , nine have reduced , or lack detectable , chromophore ligation to the apoprotein , suggesting that the affected residues are either directly necessary for the catalytic chromophore-ligase activity intrinsic to the molecule , or indirectly necessary for the structural integrity of the catalytic site . The remaining mutants , I308T and S367P , have normal chromophore ligation but fail to undergo normal photoconversion . Regardless , both subclasses of mutations eliminate or severely reduce the light-induced conversion of the photoreceptor to its active Pfr form , thereby abrogating the photoperception function of the molecule . Nine of these mutations are located either in the GAF domain ( seven residues ) , which contains the chromophore attachment site and surrounding binding pocket [36] , [43] , considered to be required to stabilize the protein-chromophore interaction [56] , or in the adjacent knot region ( two residues ) of the protein , in the vicinity of the chromophore [36] . It is perhaps not surprising , therefore , that these residues might have an important functional role in maintaining the structural and/or catalytic integrity of the protein interface with the chromophore . Only two Class I residues are in the PAS domain ( Table 1 ) . In contrast to the Class I mutants , all four Class II mutants ( R110Q , G111D , G348D and R352K ) are strikingly clustered in the “light-sensing” trefoil knot at the interface between the PAS and GAF domains of the Deinococcus phy molecule [36] ( Figure 6B ) . As indicated above , three of these residues ( R110Q , G111D , and R352K ) were also identified in the screen for long hypocotyl mutants . The fact that all of our Class II mutants appear to be physically clustered , provides compelling additional support for the idea that the trefoil knot region of phyB is required for normal phyB signaling capabilities , as suggested previously [34] . To further examine the potential three-dimensional spatial distribution of the mutant residues described here , a predicted Arabidopsis phyB three-dimensional structure was generated based on homology to the Deinococcus phytochrome with a solved crystal structure ( Figure 7 ) . This analysis revealed that three of the four Class II residues are not only predicted to be clustered near each other , but also to be solvent exposed in a cleft formed by the junction of the PAS and GAF domains ( Figure 7 ) . This finding suggests that these residues ( R110 , G111 , and G348 ) are surface exposed . Although it is possible that other domains of phyB not included in the homology model cover this region in the full protein structure , the role of these residues in interaction with PIF3 suggests that they are exposed in at least the Pfr form of the molecule . They are , therefore , potentially positioned within the photoreceptor molecule where local conformation and/or accessibility may be switchable upon reversible light-induced conversion between the two phyB conformers , thereby providing at least part of the conformer-specific binding site on phyB for interacting signaling partners . The data presented here have identified two classes of amino acid residues with functionally distinct roles in the photoregulatory activity of the phyB photoreceptor molecule: Class I residues which are necessary for the correct perception of incoming light signals , and Class II residues which are functionally necessary for the transfer of perceived signals to one or more categories of interacting partners in the intracellular transduction chain controlling light-induced seedling development . The apparent surface location of three of the Class II residues , clustered in the predicted PAS-GAF knot interface region of the phyB molecule , and their necessity for conformer-specific binding to the PIF bHLH transcription factors , suggest that these residues may comprise at least part of the signal-transfer site on the activated photoreceptor .
The yeast strain MaV103a was used for the reverse-hybrid screen . This strain was maintained on YPD plates or on L-W- SD-media for plasmid selection . Media was prepared according to BD Biosciences Clontech ( Palo Alto , CA ) . The phyB N-terminal domain ( phyBNT ) corresponding to the first 1863 nt of phyB from the start codon , was mutagenized by PCR with Mutazyme™ ( Stratagene , La Jolla , CA ) . The PCR primers contained phyB sequence flanked by sequence from the D153 ( DBD-containing ) vector . Primer sequences were as follows: The underlined portion of primer sequences indicates phyB sequence . PCR product was purified with a Qiaquick PCR purification Kit ( Qiagen , Valencia , CA ) . To insert the PCR product into the D153-DBD yeast vector , D153 was digested with NotI and overhangs were filled in by Klenow . GAD-PIF3 carrying Mav103a yeast cells were then transformed with PCR product and linearized D153 vector . Circular plasmid was reconstituted by GAP repair in yeast as described previously [57] , [58] . MaV103a cells transformed with GAD-PIF3 , phyBNT mutagenized PCR product , and linearized D153 vector were grown for 3 d under 1 µmol/m2s Rc on L-W- SD plates containing 0 . 035% 5-FOA and 25 µM PCB . Colonies were then transferred to L-W- SD plates for maintenance , or W- SD plates to lose the GAD-PIF3 plasmid . Yeast plasmids were isolated from overnight liquid cultures using the YEASTMAKER™ yeast plasmid isolation kit according to the manufacturer's instructions ( Clontech , Palo Alto , CA ) . When large numbers of yeast transformants were required , as in screening , yeast transformation was performed as previously described [59] . When fewer transformants were required , transformation was performed with the Fast™-Yeast Transformation Kit according to the manufacturer's instructions ( Genotech , St . Louis , MO ) . β-galactosidase assays were performed as described previously [26] , with some modifications in growth condition and light treatments . 1 mL cultures were grown overnight in the dark in L-W- media containing 25 µM PCB . Overnight cultures were divided into two equal parts and treated with saturating pulses of 5 minutes of either red or far red light . 2 mL of YPD media containing 10 µM PCB was added to each culture and cultures were incubated for 2 hrs in the dark . Light treatments were then repeated and cultures returned to the dark for 1 hr . From that point on , assays were performed with technical triplicates as described by Clontech [60] . Site-directed mutagenesis of phyB was performed using the Quick Change Site-Directed Mutagenesis kit from Stratagene ( La Jolla , CA ) using complementary sense and anti-sense oligonucleotides containing the desired mutation . SDS-polyacrylamide gel electrophoresis , protein blotting , and immunodetection were performed as described [14] . In vitro co-immunoprecipitation assays were performed as described previously [13] . For phyBNT protein expression , the wild type and mutant phyBNT fragment was cloned into the pTYB2 vector containing an Intein/CBD tag ( New England Biolabs ) . Escherichia coli transformation and expression of wild type- and mutant phyBNT-Intein/CBD fusion proteins were performed as previously described [32] , [34] . For Zn blot assays , the phyBNT-Intein/CBD fusion proteins were affinity-purified with a chitin column according to the manufactuer's instructions ( New England Biolabs ) . Purified phyBNT-Intein/CBD fusion proteins were incubated with PCB at 4°C for one hour and subjected to the assay for chromophore attachment as previously described [32] , [34] . For difference spectra analysis , the crude extracts from Escherichia coli were directly incubated with PCB at 4°C for one hour . Zn blot and difference spectra analysis were performed as described previously [32] , [34] . Seeds were sterilized , stratified and induced for germination as described [16] , then grown in darkness , Rc or FRc at 21°C for 5 days . Seedlings were pressed gently onto the surface of agar medium before photographs were taken . Throughout this work , the phyB sequence of Arabidopsis ecotype Columbia ( GenBank accession number: NP_179469 ) was used . The PyMOL package ( http://pymol . sourceforge . net/ ) was used for visualization of protein structures . Homology models were generated using the nest [44] program of the Jackal package . The 1ztu PDB accession of the Deinococcus structure [36] was used to generate all structural diagrams and homology models . | Plants monitor their environment for informational light signals that are used to direct adaptive morphogenic responses . The phytochrome ( phy ) family of photoreceptors are central to this process . Upon photoperception , phy molecules rapidly translocate to the nucleus where they interact with basic helix-loop-helix transcription factors , termed PIFs ( phy-Interacting Factors ) , and induce gene-expression changes that control morphogenic responses . The molecular determinants in the phy protein responsible for direct intermolecular signal transfer from the activated photoreceptor to transduction partners are undefined . Using random mutagenesis of Arabidopsis phyB , coupled with a reverse-hybrid protein-interaction screen , we identified missense mutations in the N-terminal domain that abrogate the binding of the photoreceptor molecule to PIF3 . A subset of these mutated phyB molecules retain the capacity for light-signal perception but are defective in the capacity to transduce that signal to PIF3 and other related PIFs . The mutated residues in these molecules are predicted to cluster at the surface of the protein in a structure termed the “light-sensing knot . ” These residues are necessary for phyB-regulated growth in the living plant , establishing that the protein region identified appears to function as a component of the molecular interface responsible for direct signal transfer to transduction partners in the cell . | [
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] | 2009 | Residues Clustered in the Light-Sensing Knot of Phytochrome B are Necessary for Conformer-Specific Binding to Signaling Partner PIF3 |
Chronic Obstructive Pulmonary Disease ( COPD ) is a complex disease . Genetic , epigenetic , and environmental factors are known to contribute to COPD risk and disease progression . Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation , gene expression , and phenotype data in lung tissue from COPD and control samples . Our integrative analysis identified 126 key regulators of COPD . We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity . EPAS1 is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes . Genes predicted to be regulated by EPAS1 were enriched for biological processes including signaling , cell communications , and system development . We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that Epas1 gene expression is reduced in mice chronically exposed to cigarette smoke . As EPAS1 downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells , we tested EPAS1 function in human endothelial cells . EPAS1 knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with EPAS1 downstream genes in lung tissue including hypoxia responsive genes , and genes associated with emphysema severity . Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a ‘causal’ role in the molecular pathophysiology of COPD , but it can be leveraged to directly identify novel key mediators of this pathophysiology .
Chronic Obstructive Pulmonary Disease ( COPD ) is a common lung disease . It is the fourth leading cause of death in the world and is expected to be the third by 2020 [1] . COPD is a heterogeneous and complex disease consisting of obstruction in the small airways , emphysema , and chronic bronchitis [2] . Patients with COPD generally have an increased level of systemic inflammation and progressive loss of lung function by irreversible airflow limitations [3] . COPD is generally caused by exposure to noxious particles or gases , most commonly from cigarette smoking [4]–[6] . However , only 20–25% of smokers develop clinically significant airflow obstruction [7] , which suggests that inter-individual differences related in part to genetic susceptibility play an important role in modifying the risk of disease in individuals [8] . Genome-wide association studies ( GWAS ) have recently identified several risk loci for COPD and/or smoking associated genes [4] , [9]–[13] . While these studies have provided an initial look into the genetic architecture of COPD , they have been limited by size , by heterogeneity of disease phenotype , and by potential confounders relating to the amount of cigarette smoking . The smaller genetic variance component for COPD identified to date could be due to environmental factors and/or epigenetic regulation . Indeed , epigenetic changes are a factor in many diseases , including many different types of cancer [14] . In the lung DNA methylation is an important factor for normal lung function [15] , and several studies have recently confirmed that DNA methylation is significantly associated with lung cancer [16]–[18] . Moreover , smoking , which is one of the major risk factors of COPD , is considered as one of important modifiers of DNA methylation [19] , [20] and it is also known to cause epigenetic changes in lung tissue [21] , [22] . Therefore , understanding the transcriptional regulation by epigenetic factors such as DNA methylation may shed light on understanding the biological processes associated with COPD susceptibility , severity , and COPD comorbidities such as lung cancer . Recently , DNA methylation is shown to be associated with COPD and lung function , suggesting that genetic and epigenetic pathways may contribute to COPD [23] , [24] . While previous studies have provided potentially important CpG loci associated with COPD , they have not yet clarified the role variations in methylation play in regulating global gene expression and the biological consequences of such regulation . In this study we present a novel systematic approach for identifying key regulators in COPD by integrating functional genomic , epigenetic data , and higher order phenotypic data . We studied 100 COPD and 52 control ( CTRL ) lung samples to investigate the relationship between methylation status of DNA and expression level of a gene either in close ( cis ) or far ( trans ) proximity to the methylated site . The primary focus of this study is not only to identify those regions that are differentially methylated between COPD cases and controls , but to resolve the gene expression changes that follow as a result of these differentially methylated regions and the biological consequences that these regulatory changes induce with respect to disease development or progression . Our integrative analysis of DNA methylation and gene expression validates the importance of DNA methylation in COPD and enables the direct identification of novel key regulators modulated by epigenetic changes in this multifactorial , systematic disease . When comparing downstream genes controlled by the key regulators with gene sets related to COPD disease severity , we identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple gene sets related to COPD . We further show that EPAS1 protein levels are lower in lung tissues of COPD patients . Epas1 is down-regulated transcriptionally by chronic smoke exposure in mice , and the EPAS1 knockdown signature in human endothelial cells significantly overlaps with our predicted EPAS1 downstream genes . These data combined suggest that our systematic approach can provide important insights into understanding the mechanisms underlying epigenetic regulation , via DNA methylation , that in turn alters transcriptional programs that lead to COPD pathogenesis and progression .
Prior to integrating the molecular traits , we first characterized the differentially expressed and methylated genes between the COPD and CTRL groups . We identified 1 , 594 genes as differentially expressed between the COPD and CTRL groups ( t-test p-value<0 . 01 , corresponding to a false discovery rate , or FDR , of 0 . 09 based on permutation tests ) . We also identified 92 , 606 methylation probes corresponding to 8 , 848 genes that were differentially methylated between the COPD and CTRL groups ( t-test p-value<0 . 01 , FDR = 0 . 06 based on permutation tests ) . There are 990 genes overlapping the set of differentially methylated and expressed genes ( Fisher's exact test p-value = 0 . 009 ) . Given methylation data are known to be noisy [26] , [27] , we focused on methyl probes located in CpG island defined by a hierarchical hidden Markov model [28] , resulting in 26 , 143 differentially methylated probes corresponding to 6 , 416 genes for further analyses . Among them , 704 genes overlapped with differentially expressed genes ( Fisher's exact test p-value = 6 . 6×10−6 ) . Additional constraints could be applied to further enrich for biologically relevant methylation , such as 1 ) self-consistent methyl probes ( at least two methyl probes differentially methylated for a single gene ) , and 2 ) methyl probes close to transcription start sites ( <1 kb ) . These filters were potentially useful for enriching for genes that are both differentially methylated and expressed ( S3 Table ) , but were not used in the analysis because they were too stringent , resulting in smaller signature sizes . In general , when DNA methylation levels of methyl probes in CpG islands for COPD were compared with those of CTRL samples , the COPD samples were predominantly hypermethylated ( S1A Fig . ) . Results based on probe-by-probe comparison also showed that CpG islands were more likely to be hypermethylated than hypomethylated in lung tissues of COPD patients ( S4 Table ) . However , when comparing non-CpG island methyl probes , the pattern was very different and the numbers of hyper- and hypo- methylated probes were evenly distributed ( S1B Fig . and S4 Table ) . This different pattern suggested there was no global methylation level difference between COPD and CTRL samples . The main difference between them was methylation levels of CpG islands , suggesting biological importance of methylation of CpG islands in transcription regulation . The hypermethylation pattern in CpG islands was observed in a lung cancer study [16] . Recently , Vucic et al . reported that DNA methylation levels in COPD were different from ones in CTRL samples and 90% of differentially methylated CpG island probes were hypermethylated in small airways epithelium cells from COPD patients [24] . The differentially methylated or differentially expressed genes were not associated with potential biological subtypes in the samples ( S1 Text ) . These results suggest that there were significant differences in methylation levels between the CTRL and COPD groups and , hence , these differences may be involved in epigenetic regulations causing pathogenesis and progression of COPD . Of the 704 differentially methylated genes within CpG islands that are also differentially expressed between the CTRL and COPD groups , most ( 696 out of 704 ) were hypermethylated in COPD , whereas only about half of the corresponding gene expression levels ( for 378 genes ) were downregulated ( S5 Table ) . The remaining 318 genes were upregulated even when their promoter regions were hypermethylated . While this pattern does not match the expected classical inverse relationship between DNA methylation and gene expression levels , a number of studies have shown that the DNA methylation – gene expression relationship may be more complicated [29]–[31] . While promoter methylation most often leads to gene silencing , DNA methylation of promoter regions , in some cases , can be associated with transcription activation; for example through blocking repressor proteins binding to the promoter region [32] , [33] . Vucic et al . also shows that methylation levels of many genes were positively associated with gene expression levels when comparing small airways epithelium cells of non-COPD controls and COPD patients [24] . Genes that are hypermethylated and downregulated in COPD , including genes related to lung function , such as EP300 , EPAS1 , FOXF1 , FOXA2 , KDR , LAMA5 , SHH , NKX2-1 , VEGFA , FZD1 , NUMB , and PKDCC [34]–[37] , are enriched for GO biological processes ( S6 Table ) such as regulation of cell communication ( p-value = 1 . 98×10−8 ) , regulation of multicellular organismal development ( p-value = 2 . 13×10−7 ) , and tissue morphogenesis ( p-value = 4 . 43×10−7 ) . The other set of genes that are hypermethylated and upregulated in COPD are enriched for a number of GO categories as well , including co-translational protein targeting to membrane ( p-value = 2 . 27×10−13 ) , protein targeting to ER ( p-value = 3 . 07×10−12 ) , translational initiation ( p-value = 5 . 34×10−9 ) , translational termination ( p-value = 1 . 23×10−8 ) , and cellular protein complex disassembly ( p-value = 1 . 06×10−6 ) ( S7 Table ) . These results indicate that both epigenetic and transcriptional regulations contribute to COPD pathogenesis . Hence , knowing the causal relationship between DNA methylation and gene expression is critical to understand the complex and systematic molecular underpinnings of COPD . While CpG islands in COPD are hypermethylated in general , variations in the expression levels of individual genes are mainly influenced by cis-acting methylation levels in a given gene's promoter region [38] , [39] . The association of DNA methylation and gene expression was computed in a non-parametric fashion using Spearman correlation statistics [40] . Cis regulation was defined as significant correlation between the expression levels of a gene and methylation levels in the promoter region of the gene ( Fig . 1A ) . DNA methylation levels in the promoter region of a gene may also influence the expression of genes that are distal to the given promoter region ( trans regulation described in Fig . 1B ) [41] , [42] . At Spearman correlation p-value<0 . 01 , we identified 7 , 353 and 2 , 825 cis regulated methylation-mRNA probe pairs for COPD and CTRL , respectively . The corresponding cis regulated genes significantly overlapped with the 704 differentially methylated and differentially expressed gene set above ( Fisher Exact Test p-values = and for COPD and CTRL , respectively ) . We also identified 8 , 335 , 177 and 1 , 338 , 232 trans methylation-mRNA probe pairs at p-value < ( FDRs = 0 . 04 and 0 . 2 ) for COPD and CTRL , respectively . There were 859 , 430 and 52 , 033 trans methyl-mRNA probes pairs in COPD and CTRL where a genes's methylation cis regulates its own expression and trans regulates other genes' expression . These pairs were subjected to the causality test below . While the differences in the numbers of cis and trans pairs in the CTRL and COPD groups may be at least partially due to power differences ( 52 versus 100 sample pairs in CTRL and COPD , respectively ) , we observed similar differences after constraining each group to have the same number of samples ( S2 Fig . ) . There are 218 cis pairs in common between the CTRL and COPD groups , a statistically significant enrichment ( Fisher's exact test p-value = 1 . 6×10−7 ) . However , there are only 171 trans pairs shared between the CTRL and COPD groups ( Fisher's exact test p-value = 1 ) . Therefore , the relationships between gene expression and DNA methylation are likely different between the COPD and non-disease CTRL groups . While it is reasonable to assume that methylation variation in the promoter region of a gene is causal for changes in the gene's expression ( cis regulation ) , changes in gene expression may also be causal for methylation changes via trans regulations that can affect processes such as the transfer of methyl groups [43] , [44] . These can be represented as two possible causal models of cis and trans regulation ( shown in Fig . 1C ) : in model I , the expression of a trans gene is regulated by gene expression that is cis modulated by variations of the methylation levels in the corresponding gene's promoter region; and model II , the methylation levels for a gene in cis is regulated by the expression level of a gene in trans . In addition to these causal relationships , the cis and trans regulation can be independent , regulated by an unknown factor X ( model III ) . To infer the causal relationship between gene expression and methylation variations , we developed a causality test similar to previously developed causality tests [45]–[47] ( see Methods for details ) . By applying the causality test , we identified 30 , 177 trans pairs in the CTRL group ( FDR = 0 . 03 ) and 362 , 095 trans pairs in the COPD group ( FDR = 0 . 0014 ) whose methylation levels likely regulated the expression of trans genes ( Fig . 2A and 2B ) . For all of these trans pairs , the strong correlation between methylation and trans gene expression was predicted by our modeling to be mediated by the expression levels of the cis gene ( Model I in Fig . 1C ) . Of the putative causal relationships identified by our approach , only 1 , 241 and 19 , 173 trans gene-methylation pairs identified in the CTRL and COPD groups , respectively , were of the Model II type ( Fig . 1C ) in which trans gene's expression → methylation in cis gene → cis gene's expression ( Fig . 2C and 2D ) . These results indicate that the association between trans gene expression and methylation in a cis gene , is overwhelmingly driven by changes in cis gene expression which is regulated by methylation changes in the cis gene . We next assessed whether there were any epigenetic hotspots in which the expression levels of many genes in trans varied as a consequence of a single cis gene whose expression levels were altered by methylation events in cis . Such cis genes can be considered as key regulator genes . Towards this end we characterized the number of trans genes causally associated with cis genes as determined by the causality test above and found that the numbers of cis genes and their causally regulated trans genes follow a scale-free distribution ( Fig . 3; linear in the log-log plot ) . That is , most cis genes regulate a small number of trans genes , but there are a few cis genes that regulate a large number of trans genes as downstream targets . We defined key regulators as genes whose number of downstream targets is larger than the mean plus two standard deviations across all cis genes . Given this definition , we identified 67 genes as key regulators in the CTRL group and 126 in the COPD group ( S8–S9 Tables ) . These key regulators influence a significant number of downstream genes . There are 6 regulators in common between the CTRL and COPD groups: FOXK2 , HEATR2 , EPAS1 , PLXNB2 , GAK , and YOD1 . However , only a small portion of their downstream target genes is shared , with the biological enrichments revealed by these downstream targets are different between the CTRL and COPD groups . These results suggest that epigenetic regulation of gene expression mediated by DNA methylation has different biological consequences between the COPD and CTRL groups . More detailed analyses of these key regulators in terms of methylation patterns , downstream targets , and their regulated biological processes are presented in the S1 Text . In summary , we reveal that multiple key regulators target similar sets of genes indicating that the epigenetic control of gene expression by methylation is seemingly complex ( S3–S4 Fig . ) . The patterns were similar if more stringent definition ( the number of downstream targets> the mean plus 3 standard deviations ) of key regulator was used ( S5 Fig . ) . There were multiple key regulators in COPD regulating genes involved in metabolic processes and immune response , which are processes known to be involved in COPD pathogenesis and progression . To further investigate key regulators in COPD development and progression we compared the expression levels of the key regulators and their downstream genes with genes associated with COPD disease severity related clinical features . Five COPD related severity measures were available in the LGRC data set , including DLCO ( Diffusing capacity of the Lung for Carbon Monoxide ) [48] , BODE ( Body mass index , airflow Obstruction , Dyspnea and Exercise capacity ) index [49] , FEV1 ( Forced Expiratory Volume ) percentage predicted [50] , FEV1/FVC ( Forced Vital Capacity ) ratio , and emphysema percentage . DLCO , FEV1 percentage predicted , and FEV1/FVC ratio decrease as COPD severity increases , while BODE index and emphysema percentage increase with disease severity . At p-value<0 . 05 , methylation levels of the promoter regions of 3 of the 126 key regulators in COPD groups , ACSF3 , SELO , and EPAS1 , significantly correlated with all 5 disease severity phenotypes ( Fig . 4A; S10 Table ) . At p-value<0 . 01 , we identified 572 expression traits in the COPD group as significantly correlated with DLCO ( FDR = 0 . 24 ) , 1164 genes with BODE ( FDR = 0 . 12 ) , 545 genes with FEV1 percentage predicted ( FDR = 0 . 27 ) , 333 genes with FEV1/FVC ( FDR = 0 . 40 ) , and 1702 genes with emphysema percentage ( FDR = 0 . 09 ) . There was no key regulator gene whose expression levels consistently correlated with all 5 COPD severity phenotypes . Therefore , to strengthen the association between key regulators and COPD , we compared the disease phenotype gene expression signature sets with each of the key regulator's downstream targets ( Fig . 4B; S11 Table ) . Of the 126 key regulator genes , EPAS1 was the only gene whose downstream genes were significantly overlapping with all disease phenotype gene expression signature sets ( Fig . 4B ) . We further compared the downstream genes of key regulators in COPD with known COPD signatures . Recently , Campbell et al . reported a set of 127 genes whose expression levels were significantly associated with regional emphysema severity in a mouse model [51] . Our human mRNA dataset includes 104 orthologous genes out of these 127 mouse emphysema severity associated genes . When directly comparing the emphysema associated genes in mouse and our emphysema percentage related genes in human , only 10 of them overlap ( Fisher's exact test p-value = 0 . 76 ) . When comparing these 104 genes to the downstream target genes of all key regulators in COPD , only the downstream genes of EPAS1 significantly overlap with this emphysema severity associated gene set ( S12 Table ) ; EPAS1 itself is one of the emphysema severity associated genes in mouse . Among the 104 emphysema severity associated genes in mouse , 30 of them overlap with the downstream genes regulated by EPAS1 ( p-value = 5 . 1×10−15 ) . Expression levels of 4 of the 30 overlapping genes are positively correlated with EPAS1 methylation levels indicating that their expression levels increase as emphysema severity increases . One of the four genes , CD79B , was positively correlated with EPAS1 methylation levels , which is consistent with previous reports that B cell abundance increases as emphysema severity increases [51] , [52] . Expression levels of the remaining 26 genes are anti-correlated with EPAS1 methylation levels; their expression levels are expected to decrease as emphysema severity increases . For example , gene expression levels of members of the TGF-beta pathway such as ACVRL1 are inversely correlated with EPAS1 methylation levels . This observation agrees with previous reports in which TGFBR2 was shown to be down regulated in regions of severe emphysema [53] . EPAS1's methylation profile and its downstream genes are distinct from ones of other regulators ( S4 Fig . ) . Only 6% of downstream targets of the key regulator GAK , which regulated the largest number of downstream target genes , overlapped with the EPAS1 downstream target genes ( Fisher's exact test p-value = 1 ) . EPAS1 downstream target genes are enriched for multiple GO biological processes ( S13 Fig . ) including anatomical structure formation involved in morphogenesis ( p-value = 1 . 17×10−6 ) , adherens junction assembly ( p-value = 3 . 53×10−6 ) , locomotion ( p-value = 5 . 92×10−6 ) , angiogenesis ( p-value = 1 . 22×10−5 ) , and cell division ( p-value = 1 . 52×10−5 ) . EPAS1 is differentially expressed and methylated between the CTRL and COPD groups ( S6 Fig . ) . The putative causal relationships identified between EPAS1 and trans genes associated with methylation changes in the EPAS1 promoter region , the association of EPAS1 with COPD severity measures , and its differences between the CTRL and COPD groups indicate that EPAS1 is a putative key causal regulator of multiple COPD severity phenotypes in human and emphysema severity associated genes in mouse . EPAS1 is a hypoxia-responsive transcription factor and is also known as Hypoxia-inducible Factor 2 alpha ( HIF2α ) [54] , . It is regulated by oxygen through enzymatic post-translational hydroxylation of the α subunit [56] . With a sufficient supply of oxygen , HIF genes are degraded . But under hypoxic conditions , HIF genes bind directly to DNA and enhance transcription of target genes [57] , [58] . While several studies have revealed that HIF2α has been implicated in cancer [59]–[62] , the specific physiological functions of EPAS1 are not yet fully understood . There have been several studies regarding hypoxic response genes in different tissues including breast , kidney , head and neck , and lung [63]–[67] . From these data we found that our predicted EPAS1 downstream target genes significantly overlapped with HIF regulated genes only in primary human pulmonary artery endothelial cells ( Fisher's exact test p-value = 0 . 004 ) [63] , but not with the other hypoxia signatures defined in other tissues such as breast cancer , head and neck cancer , and normal kidney ( p-values = 0 . 74 , 0 . 24 , and 0 . 15 , respectively ) . These results suggest that the regulation of hypoxia responsive genes by EPAS1 may be a unique characteristic of COPD lung samples . In addition to directly binding to HIF response elements , EPAS1 may regulate downstream gene expression by regulating or interacting with other transcription factors such as AREB6/ZEB1 or miRNAs ( see S1 Text ) . EPAS1 expression levels are lower in COPD lung tissue compared to CTRL lung ( S6A Fig . ) . To test whether EPAS1 protein abundance concordantly changes with EPAS1 gene expression levels in lung tissues of COPD patients , we stained lung tissue blocks from 5 COPD patients and 4 non-COPD patients using a polyclonal anti-EPAS1 antibody ( NB10-122; Novus Biologicals , CO , USA ) and categorized EPAS1 abundance . All 4 slides from non-COPD patients contained high levels of EPAS1 , and 3 of 5 slides from the COPD patients contained low levels of EPAS1 as shown in S7 Fig . , so that a statistically significant difference in EPAS1 protein levels was observed between the two groups ( p-value = 0 . 03 ) . The difference was similar for endothelial cells ( EPAS1 high in 4 of 4 non-COPD samples and low in 3 of 5 COPD samples ) and alveolar ( EPAS1 high in 4 of 4 non-COPD samples and low in 3 of 5 COPD samples ) cells . The EPAS1 target genes we predicted significantly overlap with genes associated with emphysema caused by smoking in mouse , as indicated above . To investigate whether EPAS1 expression levels change when mice start to develop emphysema after chronic smoking exposure , we checked Epas1 expression levels in two different chronic smoking mouse models using C57BL/6J and A/J mice . C57BL/6J mice start to develop emphysema after 6 month exposure to chronic smoking [68] while A/J mice start to develop emphysema after only 2 months of exposure to chronic smoking [69] . Epas1 expression levels in smoking mice ( 6 months of smoking for C57BL/6J and 2 month for A/J ) are significantly lower than levels in corresponding age-matched non-smoking mice ( Fig . 5 , p-value of the t-test = 0 . 009 and 0 . 007 for the C57BL/6J and A/J models , respectively ) . We also checked the Epas1 downstream target gene vascular endothelial growth factor ( Vegfa ) , given it is also a hypoxia responsive gene . Smoke exposed mice had lower amount of Vegfa expression as well ( Fig . 5 , p-value of the t-test = 4 . 0×10−7 and 0 . 01 for the C57BL/6J and A/J smoking models , respectively ) , which suggests that Epas1 downstream target genes were down regulated in the smoke exposed mice at the time when emphysema develops in these models . These results are consistent with our causal predictions relating to EPAS1 . To test whether EPAS1 causally regulates the downstream target genes we predicted , we knocked down EPAS1 expression via siRNA in human umbilical vein endothelial cells ( HUVEC ) and mouse endothelial cell line C166 ( see Methods for details ) and then performed RNASeq analysis to quantify genome wide gene expression changes . When comparing endothelial cells treated with EPAS1 siRNAs and scrambled siRNAs , we identified an EPAS1 siRNA signature consisting of 2796 and 3730 genes in human and mouse endothelial cell lines , respectively , whose expression levels significantly changed ( t-test p-value<0 . 05 ) , including EPAS1 itself ( p-value = 0 . 002 and 0 . 02 ) and the EPAS1 downstream target gene VEGFA ( p-value = 0 . 03 and 0 . 01 ) . The EPAS1 siRNA signatures derived from human and mouse cell lines were highly consistent , with 695 genes in common to both signatures ( p-value = 7 . 2×10−65 ) . Both signatures not only significantly overlapped with EPAS1 downstream genes ( p-value = 7 . 3×10−7 and 1 . 5×10−12 ) , but also with hypoxia response genes in endothelial cells ( Fisher's Exact Test p-value = 5 . 8×10−8 and 1 . 2×10−12 in the human and mouse signatures , respectively ) . Moreover , the EPAS1 siRNA signatures consistently overlapped genes associated with the COPD severity phenotypes ( Table 1 ) . These results together validate that EPAS1 causally regulates the downstream target genes we predicted , and that these genes in turn affect COPD development and progression .
Genetic , epigenetic , and environmental factors are known to contribute to COPD risk and disease progression . Therefore to elucidate more comprehensive molecular regulations of COPD disease , we developed a novel systematic approach to identify key regulators in COPD and CTRL lung tissue by integrating genome-wide DNA methylation and gene expression patterns . Using our causality test , we link the variation of the expression of numerous genes to only a few key regulators that are systematically regulated by variations in DNA methylation including 126 for COPD and 67 for non-disease lung . These key regulators such as EPAS1 can be targets of potential therapeutic intervention . We also highlighted important biological pathways associated with these key regulators in normal and diseased lung by hierarchical clustering of their common downstream genes . We observed common epigenetic regulations in both CTRL and COPD samples in expression of genes involved in metabolic- and cilium related- biological processes . Although cilium-related genes display the most varying expression levels both in CTRL and COPD samples they are not associated with disease phenotypes . This is an interesting observation as in the lung ciliary-related proteins keep the airways clear of mucus and dirt , allowing one to breathe easily and without irritation . Key regulators of these genes are WDR90 in CTRL and PAX9 in COPD . Since mRNA levels of PAX9 are associated with WDR90 methylation in CTRL , this suggests the wide variance of expression of the cilium related genes are explained by epigenetic regulations via methylation level of the regulators in the same pathway . Similarly , we observed common epigenetic regulations with metabolic processes , including RNA processing and chromatin modifications , by key regulators both in CTRL and COPD . However , unlike the ciliary-related pathway; the key regulators are not exactly the same for COPD and CTRL . These observations highlight in part potential mediators of COPD pathophysiology . In COPD , there are three groups of key regulators obtained based on their shared downstream genes ( S4B Fig . ) . The key regulators in the two large clusters control similar downstream genes involved in metabolic processes , RNA processing , chromatin modification , immune response and cell cycle . This type of coordinated yet diverse pathway regulation seems fitting with the current view of COPD , in that the disease pathologically is not limited to the lungs , but rather a disorder with systemic features . This view is driven in part by the strong associations of COPD with increased CVD risk , anemia , musculoskeletal diseases as well as the metabolic syndrome and Type 2 diabetes mellitus [70] . While the underlying molecular basis linking COPD with these comorbidities is still not fully understood , alterations in several pathophysiological features have been considered important such as systemic inflammation , oxidative stress , adipokine metabolism , insulin resistance and obesity . Importantly , beyond the pathway level , we were able to identify genes of importance through looking at the key regulators associated with these cluster . One interesting gene was GAK , as it was predicted to regulate the largest number of downstream genes . GAK is a cyclin G associated kinase , and is known to regulate clathrin-mediated membrane trafficking [71] . Recently , it has also been shown that the disruption of the kinase domain of GAK , in mice , causes embryonic lethality due to pulmonary dysfunction including notable alterations in the distribution of lung surfactant protein A [72] , a known biomarker of COPD disease severity [73] . These studies in mice were prompted by the fact that gefitinib , which is an inhibitor of the epidermal growth factor receptor and used to treat non-small cell lung cancer in humans , has significant adverse side effects in therapy , such as respiratory dysfunction , which in part has been attributed to the fact the gefitinib also inhibits GAK [72] . While a role for GAK in COPD has not been previously linked , our observations would suggest further investigation is warranted . Importantly , about 87% of key regulators in COPD ( 111/126 ) share similar downstream genes with GAK . In addition , some of their methylation levels are highly correlated each other , indicating overall that regulation of downstream genes may be mediated by multiple key regulators in a systematic way rather than by single master controllers . Other potentially relevant mediators of COPD pathophysiology are those key regulators that showed a different methylation profile and different downstream target gene set as compared to all other regulators , such as was the case with EPAS1 . To our knowledge EPAS1 has not been previously linked with COPD pathophysiology . This is despite the fact that EPAS1 is one of the major mediators of the transcriptional response to physiological hypoxia , an environment typical of lung alveolar as progressive airflow limitation increases with COPD severity . EPAS1 is a hypoxia-responsive transcription factor and is also known as hypoxia-inducible factor 2 alpha ( HIF-2α ) [54] , [55] . Interestingly , compared to the ubiquitous expression of HIF1a , another key mediator of hypoxic responses , EPAS1 has relatively high levels of expression in the placenta , heart , lung and endothelial cells . Importantly , a previous study reported alveolar hypoxia increases in prevalence as disease severity increases [74] and mounting evidence suggests , hypoxia is more than a signifier of advanced COPD but rather a key player in many of the maladaptive processes as well as the systemic comorbidities associated with COPD . Since sustained exposure of cultured lung alveolar epithelial cells to hypoxia maintained the induction of EPAS1 expression as induced by short-term hypoxic exposure , the decreased EPAS1 expression observed in COPD may in fact result in maladaptive hypoxia responses [75] . Thus understanding the contribution of EPAS1 to disease and its mechanisms in it would be very promising for treatment of disease . In this study we demonstrate that EPAS1 methylation level is significantly associated with disease severity and that an increase in methylation decreases EPAS1 gene expression . Thus we hypothesize that disease severity may be systematically controlled by altered regulation of a large set of EPAS1 downstream genes . Several observations in humans and mouse have demonstrated that altered EPAS1 expression can affect lung physiology . Specifically gain-of-function mutations in humans were associated with pulmonary hypertension , increased cardiac output and heart rate as well as increased pulmonary ventilation relative to metabolism [76] . However , in a heterozygous EPAS1 mutant mouse , haploinsufficiency for the oxygen-sensing factor resulted in augmented carotid body sensitivity to hypoxia , including irregular breathing , apneas , hypertension and elevated norephinephrine levels on one mouse strain background , but protection against pulmonary hypertension on a different strain [77] , [78] . There are several consequences of hypoxia in COPD which may contribute to disease severity , with pulmonary hypertension in part due to hypoxic pulmonary vasoconstriction driven by alveolar hypoxia , being one of them . Another possible link between hypoxia mediated COPD disease severity and EPAS1 may be the fact that EPAS1 is a known transcriptional activator of the VEGF [55] , which was shown in our study to be one of EPAS1 downstream genes and one of EPAS1 siRNA signature genes . VEGF expression level is associated with COPD phenotypes and downregulated in COPD samples in the LGRC dataset . VEGF is involved both in the regulation of the bronchial microvascular changes as well as in the inflammatory airway changes in COPD . In patients with emphysema , low levels of VEGF are thought to promote the destruction of alveoli , since VEGF normally acts to induce the expression of anti-apoptotic proteins and acts as a survival factor for endothelial cells . The importance of VEGF in survival signals necessary for the maintenance of normal lung structure and consequences characteristics of emphysema has also been confirmed in animal studies disrupting VEGF signaling either through genetic deletion of lung VEGF or through VEGF receptor blockage . VEGF is also thought to play a dual role in the lung by regulating not only apoptosis but also efferocytosis , which is the process involved in phagocytosis of apoptotic cells . The net effect of efferocytosis is anti-inflammatory because dying cells are removed before they undergo postapoptotic necrosis and anti-inflammatory mediators are released thereby suppressing further adaptive immune responses . Therefore , dysregulation of VEGF via altered EPAS1 regulation could link hypoxia to mechanisms of COPD severity [79] . One other point of interest is the fact that neonatal mice lacking complete EPAS1 expression have deficient lung surfactant , such as surfactant D ( SP-D ) , in addition to other lung abnormalities and die of respiratory failure [80] . This deficiency has been attributed to reduced expression of VEGF as VEGF rescue therapy resulted in restoration of surfactant production and less respiratory distress in the EPAS1 null mice compared to wildtype . Surfactants , such as SP-D have many functional properties including anti-inflammatory and anti-oxidant capacities , and protection against respiratory infections . In various mouse models , SP-D appears to play a distinct role in protecting murine lungs from the development of emphysematous changes possibly by reducing inflammation and oxidative stress in the lungs . While in humans , elevated serum SP-D level is an apparent biomarker of COPD , there is a reported inverse relationship with bronchoalveolar lavage fluid levels , whether elevated or decreased levels of SP-D are important in pathogenesis are still unclear [81] . Nonetheless , this is another clear example of how EPAS1 , through modulation of VEGF , may contribute to the chronic inflammatory response and tissue destruction in COPD through augmented apoptosis , impaired efferocytosis , and abnormal tissue remodeling . Many studies focus on genetic contribution to COPD development and phenotypes [82]–[84] and a recent review paper provides an updated list of COPD associated genes [85] . There are 140 COPD susceptible genes identified in at least one of COPD GWAS studies . When we overlapped these CODP susceptible genes with EPAS1 downstream genes , the overlap is marginal significant ( Fisher's exact test p-value = 0 . 053 ) , but it is the best overlap comparing with other regulator's downstream genes ( the second best p-value is above 0 . 1 ) . This enrichment of COPD GWAS genes in EPAS1 downstream further substantiates critical role of EPAS1 in the disease . At present it is still unclear how the methylation level of key regulators , in particular the predominant hypermethylation seen in COPD is regulated upstream . A recent study has also demonstrated that DNA methylation is widely disrupted and predominantly hypermethylated in small airway epithelia of COPD patients [86] . In addition to cigarette smoking , evidence has shown that hypoxia is also an important regulator of a cell's global epigenetic profile . For an example , chronic hypoxia induces a significant increase in global DNA methylation such as in human pulmonary fibroblasts [87] . Some of the underlying mechanisms that may account for global epigenetic alterations in DNA methylation include changes in the activity of epigenetic modifying enzymes such as DNA methyltransferases ( DNMT ) or in levels of the methyl-donor S-adenosylmethionine ( SAM ) . DNA hypermethylation has also been demonstrated in PwR-1E prostate cell cultures in response to chronic hypoxia , a consequence linked to increased de novo DNMT activity due to elevated expression of DNMT3B as well as a hypoxia-induced decrease in levels of SAM suggesting an increase in SAM usage in hypoxic cells [88] . Interestingly , low circulating levels of folate and increased homocysteine levels , which are involved in the generation of SAM via the one-carbon cycle , have been associated with COPD patients [89] . Compared with the number of inferred relationships that methylation variations causally regulate gene expression ( methylation → gene expression ) in trans , the number of inferred relationships that gene expression variations causally regulate methylation variations ( gene expression →methylation ) in COPD is small ( 362 , 095 vs . 19 , 173 ) . Similar to the methylation → gene expression relationships , the numbers of genes' methylation levels regulated by a gene's expression level in trans follow a scale-free distribution ( S8 Fig . ) . The top putative causal regulator CDK5RAP1 controls methylation levels of 152 genes in COPD . CDK5RAP1 is a RNA methyltransferase [90] . Both DNA and RNA transmethylations are affected by the availability of the universal methyl donor substrate S-adenosylmethionine ( SAM ) . Interesting , 44 of 126 key methylation → gene expression regulators overlap with CDK5RAP1 downstream genes ( p-value = 4 . 9×10−57 ) . And among the 44 genes in the overlap , 32 genes methylation levels negatively correlate with CDK5RAP1 expression level and 12 of them positively correlate . This result suggests that one possible mechanism CDK5RAP1 regulating methylation levels of key COPD regulators is through affecting availability of SAM . It is worth to note that there are differences between statistical causal and biological causal relationships . Similar to other causal inference studies [45]–[47] , [91] , all causal relationships inferred from the causality test in this study imply statistical causal relationships . Perspective validations are needed to convert statistical relationships into biological causal relationships [92] . It is also worth to note that the causal relationship does not imply gene regulates gene by direct physically interact even the causal relationship is biologically validated . Gene might regulate gene through gene . There are some limitations in the array-based technologies used for measuring gene expression and methylation profiles . Transcript levels of different splicing isoforms were not uniquely measured in the Agilent arrays . Different splicing isoforms of genes , such as NOD2 [93] and RAGE [94] , associate with COPD severity and progression . Differential splicing is as prevalent as differential gene expression based on RNAseq analysis of other complex lung diseases such as idiopathic pulmonary fibrosis [95] . Similarly , methylation arrays can't differentiate methylation forms of cytosine , 5-methylcytosine ( mC ) and 5-hydroxymethylcytosine ( hmC ) . DNA demethylation in mammals involves oxidizing mC to hmC followed by deamination or oxidation steps [96] . It was shown that hmC can offset mC's repression on gene expression [97] and hmC plays an important role in embryogenesis and brain development [98] . However , hmC level in lung tissues is low [99] so that we can assume that the DNA methylation level measured by arrays was mainly due to mC level . RNA sequencing technologies are needed to precisely quantify contributions of transcript splicing isoforms or hmC levels in COPD pathogenesis or progress . In summary , we propose a potential epigenetic mechanism of COPD using a novel systematic approach integrating cis and trans regulation between DNA methylation and gene expression . This approach provides mechanisms of how variation of the expression of genes is systematically regulated by DNA methylation level of key regulators in COPD . The severity of COPD can be regulated by methylation level of EPAS1 and , in turn , it regulates large numbers of gene expression variations . Therefore , if lowering methylation level of EPAS1 or increasing EPAS1 expression level might be very useful to treat patients with this irreversible disease . This approach can be applied to other diseases where DNA methylation can contribute to disease development such as lung cancer to find key epigenetic contribution to the diseases .
The LTRC is a resource program of the NHLBI that provides human lung tissues to qualified investigators for use in their research . The program enrolls donor subjects who are anticipating lung surgery , collects blood and extensive phenotypic data from the prospective donors , and then processes their surgical tissues for research use . The diagnoses of COPD are based on clinical , imaging and pathological data including chest CT images , pulmonary function tests , exposure and symptom questionnaires , and exercise tests . The COPD class in this study was based on having a FEV1/FVC< . 7 on pulmonary function testing . The “control” lungs consist of adjacent histologically normal lung tissues obtained at time of nodule resection from patients with normal lung function testing parameters . In terms of tissue collection procedure , all lung tissue cores were collected at the time of surgical resection , surgical biopsy or transplantation and flash frozen in liquid nitrogen prior to being stored at −80 . Data used in the study were obtained from the publicly available LGRC data portal ( http://www . lung-genomics . org ) . All LGRC lung mRNA data were generated using Agilent V2 human whole genome arrays and were deposited into GEO database as GSE47460 by LGRC consortium . All RNA samples subjected to gene expression profiling were with RIN>7 . 0 . Due to the number of samples , multiple batches of arrays were necessary , so 10% of the arrays were picked at random to have replicates throughout each batch to account for possible batch effects . The feature extracted data was normalized using a pairwise cyclic loess approach , and the probes were collapsed to one probe per gene by selecting the probe with the highest average signal . The processed mRNA arrays data were directly downloaded from the LGRC data portal . DNA methylation data were generated using Nimblegen 2 . 1 M Whole-Genome Tiling array . The quality of each probe was compared with the background probe signals and probes with low quality were removed from the dataset . The DNA methylation level ( β value ) of each tiling probe was estimated using the CHARM method [27] , [100] . The estimated methylation level for each sample from the raw data was almost identical with the processed methylation level downloaded from the LGRC data portal . For COPD and controls there are 218 and 94 gene expression arrays , respectively . There are 179 and 76 methylation arrays for COPD and controls , respectively . To check for potential errors in labeling of the sample name , we applied the MODMatcher ( Multi-Omics Data Matcher ) procedure to identify matched methylation and gene expression samples based on the assumption that the correlation of methylation mRNA profiles from the same individual was significantly higher than ones from randomly paired samples [25] . The matching result was stable after 25 iterations of sample alignments with 100 COPD sample pairs and 52 control sample pairs selected for further analysis ( S14 Table ) . The demographic characteristics of these samples are listed in S1 Table . Both gene expression and methylation profiling data were adjusted for covariates as where is gene's expression or methylation level . Means plus residuals were used for further analysis . Potential biological subtypes in the samples were compared with disease status S9 Fig . , S15–S19 Tables , detailed in S1 Text ) . There are 5 different measurements of lung function for patients in the LGRC cohort: 1 ) DLCO ( Diffusing capacity of the Lung for Carbon Monoxide ) [48] , 2 ) BODE ( Body mass index , airflow Obstruction , Dyspnea and Exercise capacity ) index [49] , 3 ) FEV1 ( Forced Expiratory Volume ) percentage predicted [50] , 4 ) FEV1/FVC ( Forced Vital Capacity ) ratio , and 5 ) emphysema percentage . For each clinical phenotype , only a part of COPD patients were measured: 85 for DLCO , 98 for BODE , 81 for FEV1 and FEV1/FVC ratio , and 62 for the emphysema percentage . Each methyl probe was mapped to the nearest transcript starting site . Transcription information of hg18 was fetched from UCSC Genome browser database and further processed using the Bioconductor GenomicFeature package . A probe was mapped to the nearest gene if the distance between the probe and the nearest gene's transcription starting site in was less than 10 kilobases . FDRs were estimated in multiple statistic tests based on permutation tests . For differential gene expression analysis between COPD and control samples , we permuted sample labels ( COPD or CTRL ) , then applied the t-test to the permuted data to identify significant differentially expressed genes . We performed the permutation test 100 times to estimate FDRs . Similarly , for differentially methylation analysis between COPD and control samples , we permuted sample labels , and applied the permutation scheme 100 times to estimate FDRs for differentially methylated genes at each p-value of the t-test . For estimating FDRs of cis or trans acting methylation-mRNA probe pairs in COPD or control samples we permuted genome-wide gene expression data 5 times , calculated pairwise correlation between methylation and permuted gene expression profiles for all possible pairs , and then counted cis or trans acting pairs in permuted data at each p-value cutoff . Similarly , for association analysis between gene expression and phenotypical data we permuted genome-wide gene expression data 5 times , calculated pairwise correlation between gene expression and phenotypical data for all possible pairs , and then counted significant pairs in permuted data at each p-value cutoff . To identify potential functions of selected gene sets , we compared these gene sets with each GO biological process [101] and computed functional enrichment using the hypergeometric test . For the annotation , Agilent hgug4845a annotation data corresponding to the mRNA microarray was used in the Bioconductor GOstats package [102] . The embedded function called “geneIdsByCategory” was used to fetch the list of genes overlapping with each GO term . Any GO biological process consisting of more than 1500 genes was considered non-specific and was removed from the analysis . For simplification purposes , we describe the causality test using the COPD dataset , the corresponding values for the control dataset were generated in a similar fashion . Given a significant cis methylation-mRNA relationship for gene ( empirical probability estimate ) and a significant trans methylation-mRNA relationship between genes and ( empirical probability estimate ) , where is the methylation level of CpG islands within gene's promoter region , and and are mRNA expression levels of genes and , there are multiple causal reactive relationships among , , and ( Fig . 1C ) . We focused on two possible causal/reactive models: model I ( ) , where the methylation level of gene causally regulates trans gene expression of gene through cis regulation on gene's expression level; and model II ( ) , where the expression level of gene trans regulates the methylation level of gene . As there are many potential models with hidden regulators [47] we can't enumerate all possible causal reactive models , therefore , we modeled the causality test as an empirical Bayesian estimation of the significance of each causal relationship [46] , [47] instead of a model selection problem [45] . As shown by Chen et al [46] and Millstein et al [47] , the probability of can be decomposed as a product of probabilities of a chain of statistic tests . Instead of calculating for all possible trios ( 171 , 750*15 , 260 ) , we required each association test ( p-values<0 . 01 and for cis and trans regulations determined above ) to be significant so that only a small fraction of all possible trios were subjected to the causality test . If assuming that all methylation levels and gene expression levels are normally distributed and that all causal relationships are linear , the probability ofcan be estimated analytically . However , the empirical data never perfectly fit to the underlying model assumption . Thus , we applied a permutation approach to estimate a null distribution at each step similar to Chen et al [46] . In all permutation tests , we permuted only the gene expression data . Note that all our tests are non-parametric . The p-values based on permutation tests were similar to the nominal p-values . For example , given a cis association , >0 . 99 based on permutation tests . The two models and are equally possible given that and are associated . Given a significant cis regulation and a trans association and a non-informative prior of , we got . Thus , was mainly determined by . was calculated as residuals from the linear regression of trans gene expression on cis gene expression . At Spearman correlation p-value>0 . 01 , 42 . 1% of tested pairs were independent . When checking pairs selected from permuted data sets , only 21 . 4% of tested pairs were independent . To estimate the FDR of the causality test , , we permuted the whole gene expression data 5 times . At the cutoff values noted above , we identified 362 , 095 pairs of causal relationships in COPD and 518 pairs in the permuted data on average , with the corresponding FDR . It is possible to set more stringent p-value cutoffs for the conditional independent test . At p-value>0 . 05 and 0 . 1 , the corresponding FDRs were 0 . 001 and , respectively . As at the independent test p-value>0 . 01 , the corresponding FDR for the causality test was far less than 0 . 05 , so that we chose this set of causal relationships for further analyses . Similarly , to test causality in the opposite direction , , where trans gene expression regulates gene 's DNA methylation level , we decomposed as . At the same cutoff values noted above , we identified 19 , 173 pairs of causal relationships in COPD and 2 pairs in the permuted data , corresponding to an FDR . In the CTRL data set , we identified 30 , 177 causal relationships as ( FDR = 0 . 03 ) and 1 , 241 causal relationships as ( FDR = 0 . 006 ) . A similar causality test can be applied to infer genes that are potentially causal to COPD ( see S1 Text for details ) . Immunohistochemistry staining of paraffin embedded human lung tissue of de-identified patients was carried out with the IRB approval ( HS#12-00171 ) from Mount Sinai Hospital . Institutional Animal Care and Use Committee ( IACUC ) approval ( FO0501 ) was obtained for the chronic smoke exposure mouse model systems at St . Lukes Roosevelt Hospital . C57Bl/6J and A/J mice ( Jackson Labs , Bar Harbor , ME ) were exposed to cigarette smoke for 6 or 2 months respectively in a specially designed chamber ( Teague Enterprises , Davis , CA ) for 4 hours a day , 5 days per week at a total particulate matter concentration of 80 mg/m3 . Animals were sacrificed 12 hours after the last smoke exposure . Comparative analyses were made with age-matched air-exposed C57Bl/6J and A/J mice that were treated in an identical manner . The total RNA from mouse lung tissues was extracted using RNeasy Mini Kit ( QIAGEN , Germany ) . Then cDNA was synthesized with SuperScript III ( Life Technologies , CA , USA ) . For quantitative PCR , we utilized TaqMan gene expression assays ( Applied Biosystems , Canada ) , which contain prevalidated primers and TaqMan probe for the individual genes . TaqMan Gene Expression Assay IDs are Mm01236112_m1 and Mm01281449_m1 for mouse Epas1 and Vegfa , respectively . The real-time PCR reactions were carried out following the manufacturer's protocol , and the gene expressions were normalized to Rn18s ( Mm03928990_g1 ) . The paraffin sections of human lung tissues were provided by Histology Shared Resource Facility of Mount Sinai Hospital with the IRB approval . The immunostaining was performed using Vectastain ABC Elite Kit ( Vector Laboratories , CA , USA ) with polyclonal anti-EPAS1 antibody ( NB10-122; Novus Biologicals , CO , USA ) . Following deparaffinization and hydration of sections , antigen retrieval with 10 mM citrate buffer and blocking of endogenous peroxidase with 0 . 3% H2O2-methanol were performed . The tissue sections were blocked with 5% goat serum diluted in 0 . 1% Tween-20 in phosphate buffered saline ( PBS-T ) for 30 minutes , and then incubated in anti-EPAS1 ( 1∶100 ) at room temperature for 1 hour . The tissue sections were washed and incubated with the secondary antibody anti-rabbit-HRP . After washing , DAB substrate ( 3 , 3′-diaminobenzidine ) was utilized to obtain positive reactions . The cell lines of HUVEC ( Lonza , MD , USA ) and C166 ( American Type Culture Collection , VA , USA ) were cultured in the appropriate media at 37°C with 5% CO2 . The cells were transfected with EPAS1 siRNA and non-targeting negative control siRNA ( Life Technologies , CA , USA ) using Lipofectamine RNAiMAX as recommended transfection protocols by the manufacturer . After the treatments with 5 nM Silencer Select siRNA ( s4700 for EPAS1 , s65525 for Epas1; Life Technologies , USA ) for 48 hours , the total RNA was purified with RNeasy Mini Kit ( QIAGEN , Germany ) . The efficiencies of knocked down the EPAS1 expression were assessed by qPCR with 1 . 4% for HUVEC , 3 . 2% for C166 . Approximately 250 ng of total RNA per sample were used for library construction by the TruSeq RNA Sample Prep Kit ( Illumina ) and sequenced using the Illumina HiSeq 2500 instrument with 100 nt single read setting according to the manufacturer's instructions . The RNAseq data set was deposited in GEO as GSE62974 . Sequence reads were aligned to human genome assembly hg19 and mouse genome assembly mm10 , respectively , using Tophat [103] . Total 23 , 228 human and 22 , 609 mouse genes were quantified using Cufflinks [103] . siRNA signatures were derived by comparing expression profiles of EPAS1 or Epas1 siRNAs with non-targeting siRNAs at paired t-test p-value cutoff 0 . 05 with resulting signature sizes of 2 , 796 and 3 , 730 , and corresponding q-values [104] 0 . 11 and 0 . 07 for HUVEC and C166 , respectively . | Chronic Obstructive Pulmonary Disease ( COPD ) is a common lung disease . It is the fourth leading cause of death in the world and is expected to be the third by 2020 . COPD is a heterogeneous and complex disease consisting of obstruction in the small airways , emphysema , and chronic bronchitis . COPD is generally caused by exposure to noxious particles or gases , most commonly from cigarette smoking . However , only 20–25% of smokers develop clinically significant airflow obstruction . Smoking is known to cause epigenetic changes in lung tissues . Thus , genetics , epigenetic , and their interaction with environmental factors play an important role in COPD pathogenesis and progression . Currently , there are no therapeutics that can reverse COPD progression . In order to identify new targets that may lead to the development of therapeutics for curing COPD , we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation , gene expression , and phenotype data in lung tissue from COPD and control samples . Our integrative analysis identified 126 key regulators of COPD . We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity . | [
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] | 2015 | Integrative Analysis of DNA Methylation and Gene Expression Data Identifies EPAS1 as a Key Regulator of COPD |
Rates of spontaneous mutation have been estimated under optimal growth conditions for a variety of DNA-based microbes , including viruses , bacteria , and eukaryotes . When expressed as genomic mutation rates , most of the values were in the vicinity of 0 . 003–0 . 004 with a range of less than two-fold . Because the genome sizes varied by roughly 104-fold , the mutation rates per average base pair varied inversely by a similar factor . Even though the commonality of the observed genomic rates remains unexplained , it implies that mutation rates in unstressed microbes reach values that can be finely tuned by evolution . An insight originating in the 1920s and maturing in the 1960s proposed that the genomic mutation rate would reflect a balance between the deleterious effect of the average mutation and the cost of further reducing the mutation rate . If this view is correct , then increasing the deleterious impact of the average mutation should be countered by reducing the genomic mutation rate . It is a common observation that many neutral or nearly neutral mutations become strongly deleterious at higher temperatures , in which case they are called temperature-sensitive mutations . Recently , the kinds and rates of spontaneous mutations were described for two microbial thermophiles , a bacterium and an archaeon . Using an updated method to extrapolate from mutation-reporter genes to whole genomes reveals that the rate of base substitutions is substantially lower in these two thermophiles than in mesophiles . This result provides the first experimental support for the concept of an evolved balance between the total genomic impact of mutations and the cost of further reducing the basal mutation rate .
It has become increasingly clear that the basal rate of spontaneous mutation per genome per replication is remarkably invariant in DNA microbes: using a classical correction factor for estimating the ratio of all base-pair substitutions ( BPSs ) to detected base-pair substitutions , genomic mutation rates ( mutations per genome per replication ) vary by less than twofold while genome sizes vary by ≈6 , 000-fold ( Table 1 ) . Thus , when mutation rates are expressed per average base pair , they also vary by a similarly large factor . Therefore , basal mutation rates characteristic of unstressed microbial populations can evolve to finely tuned values . The theory of mutation rates has its roots in Haldane's 1927 formulation of the impact of selection and mutation on fitness [1] , followed by Sturtevant's 1937 conjecture that the deleterious character of most mutations would generate selective pressures that should lower mutation rates indefinitely [2] . In 1967 , Kimura offered the hypothesis that there would be a “physiological cost” to each reduction in rate , leading to an equilibrium value when that cost outweighs the gain in fitness [3] . The surprise has been that the observed genomic rates are so narrowly distributed among DNA microbes despite a wide variety of life histories and genome sizes . An even deeper mystery , not to be addressed here , is why the particular microbial genomic rate of about 0 . 003–0 . 004 has been adopted by microbes of such diverse life histories and genome sizes . If the Kimura conjecture is correct , then increasing the average deleterious impact of a spontaneous mutation ( and thus converting many neutral or nearly-neutral mutations to deleterious mutations ) would lower the rate of mutation , at least on an evolutionary time scale . The concept of an equilibrium basal mutation rate is difficult to test in a laboratory context because any imposed resetting of the equilibrium would probably require numbers of generations large even by microbial standards , and is difficult to test convincingly because only one or a few habitats could be explored . However , it has recently proven possible to test the concept by examining a natural evolutionary experiment , life at high temperatures . Those who gather mutants for fun or profit have often observed that the most common class of mutations is to temperature sensitivity , indicating that many missense mutations are well tolerated at the standard growth temperature but become much more deleterious , often to the point of lethality , at a temperature only 5°C–10°C higher . This widespread anecdotal observation implies that macromolecular stability becomes increasingly dependent on structural integrity as temperatures rise , a reasonable conjecture in keeping with the considerable constraints observed in the proteins of thermophilic microbes ( e . g . , [4] ) . It is therefore likely that the average missense mutation harms thermophiles more than mesophiles ( the hypothesis of dangerous missense ) . This simple prediction was supported by the observation that missense mutations accumulated to a lesser extent ( compared to synonymous mutations ) in thermophiles than in mesophiles during the course of molecular evolution ( dN/dS falling from 0 . 14 to 0 . 09 ) , implying stronger purifying selection in thermophiles [5] . Here , direct measurements of the rate and character of spontaneous mutation are compared for mesophilic and thermophilic microbes .
The first phase of determining genomic mutation rates involves measuring a mutation frequency , converting the frequency to a rate , and taking precautions to exclude or take into account the impact of perturbations such as differential growth rates of mutants versus wild type and delayed expression of the mutant phenotype . In addition to measuring rates , it is crucial to identify the kinds of mutations that arise in order to exclude biases due to massive mutational hotspots or to bizarre classes of mutations . The typical result is a rate for a mutation-reporter gene , which is then extrapolated to the whole genome provided that the spectrum of mutations is fairly ordinary . However , there is a substantial problem here: while most indels are detected , most BPSs fail to produce a phenotypic change detectable in the laboratory . One must therefore estimate their full frequencies . ( An exception is the still rare case that mutation detection is achieved with phenotype-blind genomic DNA sequencing . ) Two methods have been applied . Both make the reasonable assumption that almost all indels and chain-termination ( CT ) BPSs are detected with high efficiency in protein-coding sequences . ( Although exceptions occur , they are infrequent and tend to occur at the extreme downstream end of a gene . ) The first method was based in part on the average relative frequencies of CT and non-CT BPSs in a handful of spectra and provided a correction factor for base substitutions of 4 . 726 [6] . This method was used for almost all of the entries in Table 1; however , the range of values averaging to 4 . 726 was large , reducing reliability . The second method is based exclusively on CT mutations . It involves examining the reporter sequence for all possible BPSs capable of generating CTs and then dividing the observed CT mutation frequency by that reduced target size and multiplying by 3 ( to account for the three BPSs that can arise at any site ) to obtain an average mutation rate per base pair . The CT method also has drawbacks . First , it cannot report A·T→G·C mutations , but these generally arise at approximately average BPS rates , suggesting a minimal problem . Second , CT mutations are typically a minority of all mutations , so that many spectra sport only a few CTs , reducing sampling accuracy . The other major barrier to accurate extrapolation from a mutation-reporter gene to the whole genome becomes manifest when sequencing reveals a major hotspot . Mutation rates at particular sites vary greatly , but most mutational spectra display a range of site-specific numbers of mutations ranging from 1 to hotspots with from several percent to even a quarter of the whole collection . The impact of a hotspot containing a quarter of all the mutations is modest , but some genes contain single hotspots bearing the large majority of mutations; the classic example is the E . coli lacI gene , where ∼72% of all mutations are indels arising at a stretch of 13 BPSs consisting of 3 . 25 repeats of a tetramer [7] . However , such massive indel hotspots are infrequent among genes , and it is reasonable to post occasional genomic rates both including and removing them . All informative microbial mutation rates obtained before 2000 were for mesophilic species , but rates and spectra are now available for two genes in each of two very different thermophiles , the crenarchaeon Sulfolobus acidocaldarius [6] and the bacterium Thermus thermophilus [8] , both growing at close to 75°C . In the first study , with S . acidocaldarius , BPSs were a smaller fraction of the spectrum than in mesophiles , and this observation prompted the hypothesis of dangerous missense . Note , however , that if greater fractions of missense mutations are phenotypically detectable in thermophiles than in mesophiles , then the historical method of correcting for undetected BPSs becomes inappropriate when based on mesophiles . It is therefore advisable to resort exclusively to the CT method for estimating total BPS rates , which is the central result for this report . Table 2 lists genomic mutation rates estimated using the CT method ( or its lacZα equivalent ) , sometimes based on the same sources as for Table 1 but excluding some reports whose sequencing information was inadequate for the CT method . The nine entries at the top are for mesophiles and reveal no significant departures from the values in Table 1 , providing empirical confidence in the robustness of the CT method . The two entries at the bottom are for thermophiles , whose numbers of CTs are small . ( The data for the two mutation-reporter genes are combined in each organism because of the small number of CTs . ) The thermophile BPS rates are substantially lower , by about 10-fold , than their mesophile counterparts . When major indel hotspots are included , indel rates are less than twofold lower in thermophiles , while total genomic rates are about fivefold lower . ( When the indel hotspots are removed from the analysis , the indel rate decrease is three-fold and the total genomic rate decrease is seven-fold . ) Although these ratios are somewhat uncertain because of the small numbers of CTs for five of the seven mesophiles and both thermophiles , the mean difference is large enough to support the inference that BPS rates are lower in thermophiles . The mesophile and thermophile values were compared using randomization t-tests [9] , a nonparametric test that requires no assumptions about normality or equal variances of the mutation rates . The resulting one-sided p values are 0 . 018 for both the total mutation rate and its BPS component , and 0 . 27 for the indel values that include the hotspots .
Genomic mutation rates have long been suspected to evolve as a balance between the deleterious impact of the average mutation and the cost of further reducing the mutation rate . A test of this conjecture on the evolutionary scale could consist of estimating mutation rates in organisms whose environment increases the impact of the average mutation . Because many base substitutions do greater harm at higher temperatures , thermophiles were suitable candidate organisms . For both a bacterium and an archaeon , the thermophiles display sharply reduced rates of base pair substitutions compared to the typical mesophile . The lower mutation rates in thermophiles are likely to reflect their higher optimal growth temperatures . There is no obvious hint of a particular aspect of life history other than temperature that sets the two thermophiles apart from the mesophiles . The % ( G+C ) values for the ten organisms in Tables 1 and/or 2 , listed monotonically with the two thermophile values in bold , are 35–36–37–38–41–50–50–51–68–69 , providing no hint of a role for this variable , as also noted in the earlier molecular-evolution study [5] . Thus , the Kimura conjecture , that the equilibrium mutation rate reflects a balance between the impact of the average mutation compared to the cost of keeping mutations in check , is supported in a natural experiment . The hypothesis of dangerous missense predicts that BPS rates will be reduced in thermophiles but does not speak directly to indel rates . However , indel rates are also reduced , although less strongly than are BPS rates and with a p value of 0 . 27 for these data . One candidate explanation for this difference is that the reduction in BPS rates is achieved by the accumulation of modifiers selected to target BPS mutagenesis but at most incidentally targeting indel mutagenesis . Because single-base additions and deletions tend to be the large majority of indels in mesophiles ( 35 single-base indels/38 total indels in phage λ , 20/23 in phage T4 , 45/45 in Herpes simplex virus , 604/641 in E . coli , 88/97 in S . cerevisiae , and 24/32 in S . pombe ) and are similarly frequent in thermophiles ( 84/95 in S . acidocaldarius and 46/54 in T . thermophilus ) , these small indels must be the main targets of antimutagenic modifiers acting on indels generally . Both single-base indels and BPSs result from errors of insertion followed by failures of proofreading and DNA mismatch repair in well studied model organisms such as E . coli and S . cerevisiae , but little is known about the sources of spontaneous mutations in S . acidocaldarius and T . thermophilus . Are there likely to be other outliers with informative deviations from the mutational pattern that is consistently displayed among the mesophilic microbes examined to date with respect to either the mutation rate or the BPS:indel ratio ? Mutations to cold sensitivity are rarely reported and are anecdotally described as difficult to discover . If they are indeed rare , perhaps fewer missense mutations produce mutant phenotypes in psychrophiles than in mesophiles . One evolutionary consequence might then be a relaxation to a higher spontaneous rate of BPS mutation , perhaps with little effect on the rate of indel mutation . Because of incomplete buffering against the impacts of their environments , halophiles and acidophiles experience relative high internal concentrations of Na+ and H+ , respectively , compared to other microbes . These ionic environments might be unusually stressful to mutants carrying missense mutations , resulting in adjustments to their mutational patterns in the same direction as seen for thermophiles . Although without significance because of sampling constraints , Table 2 attributes a five-fold lower BPS mutation rate to the acidophile S . acidocaldarius than to the non-acidophile T . thermophilus . Unfortunately , an attempt to characterize mutation in the halophilic archaeon Haloferax volcanii failed , probably because this mesophile is highly polyploid [10] . The lactic acid bacterium Oenococcus oeni , used in wine making to convert malic acid to lactic acid , lacks the usual bacterial DNA mismatch repair ( MMR ) system and has a high mutation rate as judged by mutations conferring resistance to rifampin and erythromycin , as does Oenococcus kitaharae [11] . These results suggest a powerful genus-wide mutator condition , which would normally be highly deleterious . The question then arises whether the lack of MMR is so strongly adaptive in these species as to outweigh the sharply decreased fitness of the mutator condition , or whether the species have been unable to re-acquire the MMR genes by horizontal transfer . Whereas the above two species lack MMR function and display mutator phenotypes , the crenarchaeons as a whole , including S . acidocaldarius , lack all known bacterial MMR genes , but S . acidocaldarius , at least , displays an antimutator phenotype compared with mesophiles . How can this be ? In Escherichia coli , the mutation rate per average base pair ≈8×10−10 ( Tables 1 and 2 ) . Based on the strengths of mutator mutations , replication infidelity can be estimated as the product of three components during DNA replication: insertion errors ≈0 . 9×10−5 , proofreading failures ≈1 . 7×10−2 , and MMR failures ≈5×10−3 [12] , [13] . In bacteriophage T4 , which does not employ a general MMR system , the mutation rate per average base pair ≈2×10−8 ( Tables 1 and 2 ) . Based on the strengths of mutator mutations , replication fidelity can be estimated as the product of two components during DNA replication: insertion errors ≈1×10−5 and proofreading failures ≈2×10−3 [13] . Thus , T4 makes up for the lack of MMR by a proofreading potency about an order of magnitude greater than that operating in E . coli . The mutation rate per base pair for S . acidocaldarius ≈3×10−10 , which might be achieved by a product of factors applied to the T4 insertion and proofreading accuracies that together produce a 70-fold improvement . Alternatively , S . acidocaldarius may possess an MMR system so distinct from the standard mutHLS model as to have escaped recognition by genomic scans . Note also that both thermophiles have genomes about twofold smaller than the E . coli genome .
We begin in possession of values for the following: G = the genome size in bases or base pairs . T = the number of bases or base pairs in the target ( the mutation-reporter sequence ) . μT = the measured mutation rate at T , corrected where necessary for mutants expressing the characteristic phenotype but revealed by sequencing to lack mutations in the reporter gene , but not corrected for mutants with two or more mutations ( which are infrequent and sometimes absent ) . In many cases , μT = f/ln ( μTN ) where f = the measured mutation frequency for the given target , N = the final population size , and the median μT over several cultures is used [14] , a method that is robust compared to the classical fluctuation test provided the average number of mutational events per culture is ≥30 [15] . M = number of sequenced mutants = B+I , where B = number of BPS mutants and I = number of indel mutants , the latter also including complex mutants ( a minority , if present at all ) regardless of their components . For the “historical” method , we correct for undetected BPSs by multiplying the number of detected BPSs by 4 . 726 [6] . Then the average mutation rate per base or base pair μb = [μT corrected upwards by ( I+4 . 726B ) /M]/T = ( I+4 . 726B ) ( μT/MT ) . The genomic mutation rate μg = Gμb . For the “CT” method , the indel genomic mutation rate μg ( I ) is calculated as above ignoring the BPS component , B becomes BCT = number of mutations to a chain-terminating codon ( TAG , TGA , or TAA ) , and P = number of possible mutational pathways to a CT mutation within T ( there being three mutational BPS pathways per base or base pair ) . Then the BPS genomic mutation rate μg ( B ) = μT ( 3BCT/MP ) G . The total genomic rate μg = μg ( I ) +μg ( B ) . | Spontaneous mutations are key drivers of evolution and disease . In microbes , most mutations are deleterious , some are neutral ( without significant impact ) , and a few are advantageous . Because deleterious mutations reduce fitness , there should be constant selection for antimutator mutations that reduce rates of spontaneous mutation . However , such reductions are necessarily achieved at some cost . Therefore , a mutation rate should converge evolutionarily on a value that reflects this trade-off . For DNA microbes , the observed genomic mutation rate is remarkably ( and mysteriously ) invariant , in the neighborhood of 0 . 003–0 . 004 , with a range of less than two-fold despite huge variation per average base pair in organisms with a wide diversity of life histories . Would an environmental condition that increased the average deleterious impact of a mutation be balanced by additional investments in antimutator mutations ? It is widely observed that many mutations with mild impacts become strongly deleterious at higher temperatures , so mutation rates were measured in two thermophiles , a bacterium and an archaeon . Remarkably , both displayed average mutation rates reduced by about five-fold from the characteristic mesophilic value , most of the decrease reflecting a 10-fold reduction in the rate of base substitutions . | [
"Abstract",
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] | 2009 | Avoiding Dangerous Missense: Thermophiles Display Especially Low Mutation Rates |
It is often assumed that parasites are not virulent to their vectors . Nevertheless , parasites commonly exploit their vectors ( nutritionally for example ) so these can be considered a form of host . Trypanosoma cruzi , a protozoan found in mammals and triatomine bugs in the Americas , is the etiological agent of Chagas disease that affects man and domestic animals . While it has long been considered avirulent to its vectors , a few reports have indicated that it can affect triatomine fecundity . We tested whether infection imposed a temperature-dependent cost on triatomine fitness . We held infected insects at four temperatures between 21 and 30°C and measured T . cruzi growth in vitro at the same temperatures in parallel . Trypanosoma cruzi infection caused a considerable delay in the time the insects took to moult ( against a background effect of temperature accelerating moult irrespective of infection status ) . Trypanosoma cruzi also reduced the insects’ survival , but only at the intermediate temperatures of 24 and 27°C ( against a background of increased mortality with increasing temperatures ) . Meanwhile , in vitro growth of T . cruzi increased with temperature . Our results demonstrate virulence of a protozoan agent of human disease to its insect vector under these conditions . It is of particular note that parasite-induced mortality was greatest over the range of temperatures normally preferred by these insects , probably implying adaptation of the parasite to perform well at these temperatures . Therefore we propose that triggering this delay in moulting is adaptive for the parasites , as it will delay the next bloodmeal taken by the bug , thus allowing the parasites time to develop and reach the insect rectum in order to make transmission to a new vertebrate host possible .
A long-standing implicit assumption in the literature on vector-borne diseases is that the parasite does little or no harm to its vector ( see [1] for a review ) . This makes considerable intuitive sense as the parasite relies on the vector for its transmission , so negative effects on the vectors’ fitness could be expected to reflect negatively on the parasites’ fitness . This was perhaps best formulated ( verbally rather than mathematically ) in Ewald’s classic treatise on the evolution of virulence [2] . With the rapid development of theory on the evolution of virulence in recent years [3] , it has become clear that the vector should to a large degree be considered an alternative host for the parasite , one in which a certain degree of host exploitation ( and consequent virulence to this ‘host’ ) is to be expected [1] . Meanwhile , empirical studies that are aimed at detecting fitness effects of parasite infection have become more refined , looking beyond fecundity and mortality to hunt for more subtle life history or behavioral effects , for example . This can be seen particularly in studies of mosquito ( Culicidae ) infection with pathogens , such as negative effects of dengue virus on fecundity and oviposition success in Aedes [4] . Perhaps the most elegant demonstration that the interests of parasite and vector are not entirely aligned is parasite-induced increases in biting rates in mosquitoes [5–9] , sand flies [10] and tsetse flies [11 , 12]—this is likely to increase transmission ( and thereby fitness ) of the parasite while the vector is liable to suffer a reduction in fitness due to excessive energy expenditure and increased risk of mortality when attempting to bite . Meanwhile , evidence of an interplay between parasite and vector strategies towards one another can be seen in the case of several parasites of plants that are transmitted by insect vectors . In several systems where parasite and vector are believed to have shared a coevolutionary history , the parasite increases its vector’s fitness indirectly via effects on the host plant ( e . g . [13 , 14] ) . This positive interaction is illustrative as the vector will likely spend several generations on the main host ( the plant ) , a situation very different from most vectors of parasite diseases of humans that interact only briefly with the main hosts and in which negative effects can be expected . For vector-borne diseases of humans , such considerations are of great importance for vector management , especially when novel technologies are under consideration . In strategies such as the release of transgenic vectors , paratransgenesis or use of biocontrol agents that interfere with transmission , the life history and behavior of the vector are key factors [15 , 16] , as are possible evolutionary responses of vector and parasite [17] . It is vital , then , to understand how vector and parasite interact in terms of their respective fitnesses and possible patterns of selection . Chagas disease is one such example . Trypanosoma cruzi is a digenetic protozoan that infects mammals and triatomines in the Americas . As a result of anthropic activities this enzootic infection affects man and domestic animals , causing to the first a disease with different levels of pathology . As a comparatively recently described disease ( Chagas disease was first described by Carlos Chagas in 1909 ) research has focused on interactions between the parasite and man , with little consideration of parasite effects on the invertebrate hosts . Further , as earlier studies showed no parasite-induced alterations in triatomine physiology [18] , the parasite has long been considered avirulent to its vectors [19–21] . Few studies showing alterations on fecundity rates of infected females have been conducted [22 , 23] . Furthermore , our group has recently shown that T . cruzi affects fecundity and fertility rates of R . prolixus depending on the temperature at which insects are raised [24] . We sought , then , to investigate how T . cruzi might affect its triatomine hosts . As the parasite does not invade the insects’ body but develops rather in its intestine , we might expect effects on the insects’ fitness to be marginal . Previous studies showed no effect of T . cruzi on the development of Nocardia sp . and Rhodococcus rhodnii , gut symbionts of Triatoma infestans and Rhodnius prolixus , respectively [25] . However , as a consequence of living only in the insect intestinal tract , T . cruzi probably competes with its host for nutritional resources . In addition , most laboratory studies of T . cruzi-triatomine interactions have evaluated fitness parameters under conditions that aim to maximize vector development and survival . Changes in mortality rates in mosquitoes under glucose deprivation have been demonstrated for Plasmodium [26 , 27] and dengue virus infections [4] . Therefore , our prediction is that the parasite might have negative effects on its host’s fitness under less than optimal ( and therefore more realistic ) environmental conditions [28] . In addition , temperature is a factor of particular importance in host-parasite interactions , especially when the host is ectothermic . It can be a key factor in determining whether a host-parasite interaction eventually favors host or parasite , while in some instances the nature of the interactions can only really be understood by observing the host-parasite interaction under different thermal conditions [29 , 30] . We therefore chose to conduct our study under four thermal regimes , and to use a comparatively narrow range of temperatures to keep the test conservative .
All experiments using live animals were performed in accordance with FIOCRUZ guidelines on animal experimentation and were approved by the Ethics Committee in Animal Experimentation ( CEUA/FIOCRUZ ) under the approved protocol number L-058/08 . The protocol is from CONCEA/MCT ( http://www . cobea . org . br/ ) , which is associated with the American Association for Animal Science ( AAAS ) , the Federation of European Laboratory Animal Science Associations ( FELASA ) , the International Council for Animal Science ( ICLAS ) and the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . Rhodnius prolixus used in assays were obtained from a laboratory colony which is derived from insects collected in Honduras around 1990 . The colony was maintained by the Vector Behaviour and Pathogen Interaction Group in Centro de Pesquisas René Rachou , FIOCRUZ , Brazil . Insects were reared at 26 ± 1°C and relative humidity of 65 ± 10% , with natural illumination . They were fed on chicken and mice anesthetized with an intraperitoneal injection of a ketamine ( 150 mg/kg; Cristália , Brazil ) /xylazine ( 10 mg/kg; Bayer , Brazil ) mixture . When insects were infected , they were fed on an artificial feeder containing a suspension of freshly collected and inactivated human blood ( 56°C/30min ) [24] , using standard aseptic procedures . Therefore , the parasites did not enter into contact with the anesthetic mixture . Note also that as a routine procedure , T . cruzi cultures were checked for bacterial contamination in every passage under the microscope . Therefore , these procedures assured no bacterial contamination in the blood or T . cruzi cultures . The T . cruzi used was ‘CL’ strain , originally isolated from naturally-infected T . infestans from southern Brazil [31] and subsequently kept in laboratory cultures . Epimastigote forms were cultured at 27°C in liver infusion tryptose ( LIT ) medium supplemented with 15% fetal bovine serum , 100mg/ml streptomycin and 100units/ml penicillin . Parasite passages were performed twice a week , i . e . ca . once every three days . As it has been shown that trypanosomes tend to lose infectivity if they are not frequently exposed to hosts [32] , parasites were passed through mice and triatomines every 6 months . Briefly , 5th instar nymphs were infected with culture epimastigotes through artificial feeding . One month after infection , these insects were fed and their urine containing metacyclic trypomastigotes was collected and inoculated into a Swiss mouse . Two weeks after inoculation the parasites were recovered by cardiac puncture and used to perform a hemoculture . For infection assays , 50–100μl of culture were washed in sterile PBS ( 0 . 15M NaCl , 0 . 01M sodium phosphate , pH 7 . 4; 2 , 000 RPM ) and resuspended in a final volume of 50μl . Seven day old second instar nymphs ( n = 126 ) were fed on a suspension of freshly collected and inactivated human blood ( 56°C/30min ) with culture epimastigotes . Aiming to prepare 5ml of inoculum at 1x107 parasites/ml of blood , we took a volume of culture that would give us 5x107 parasites total . This volume of culture was washed in PBS , centrifuged and resuspended in 50μl of PBS . This was then added to 5ml of blood . Since a second instar nymph ingests between 20–24μl of blood , we estimated that each one ingested approximately 200 , 000 parasites . Insects used for the control group were fed on the same inactivated blood at the same conditions , except for the parasite presence ( n = 132 ) . One day after feeding , the insects were transferred to Petri dishes whose bases were lined with filter paper discs ( up to seven insects per plate; 5 plates/treatment ) . These were placed in temperature control chambers at 21±0 . 2 , 24±0 . 2 , 27±0 . 2 or 30±0 . 2°C and no further food was offered during the experiment . The times taken to reach third instar and mortality rates were recorded up to 90 days after the first moult . Insect mortalities were recorded for both treatments at 30 , 60 and 90 days after ecdysis to the third instar . The entire intestinal tracts of infected insects—dead or alive at the end of the experiment—were macerated and examined to confirm parasite infection . Culture epimastigotes were transferred at an initial concentration of 1x106/ml to cell culture flasks ( 25cm2 ) containing fresh LIT medium to a final volume of 8ml . The flasks were immediately transferred to four independent controlled temperature chambers ( 21±0 . 2 , 24±0 . 2 , 27±0 . 2 and 30±0 . 2°C ) and kept there for seven days . Two replicate culture samples were simultaneously tested for each temperature . Daily , a 50μl sample was collected from each flask and stained for flow cytometry absolute counts and viability analysis . Dual label fluorescent staining procedures were performed per sample to determine the absolute counts of live and dead parasites in each sample . For this purpose , 50μl of culture were incubated in the presence of 120μl of PBS and 25μl of fluorescein diacetate ( FDA ) at 7μg/ml plus 5μl of propidium iodide ( PI ) at 25μg/ml , both from Sigma ( St Louis , MO , USA ) for 10 min at room temperature . FDA ( Sigma 7378 ) stock solution was prepared at 1mg/ml in acetone and stored at −20°C until use . PI stock solution was prepared in ddH2O at 1 mg/ml and stored at −20°C until use . Following incubation , 20μl of fluorosphere suspension were added to each tube immediately before flow cytometric acquisition . As many flow cytometers cannot directly provide the cell concentration or absolute count of cells in a sample , the Flow-Count Fluorosphere ( lot #7548025 bead counts of 986 beads/μl , Beckman Coulter , Inc . , Miami Lakes , FL , USA ) were used as a calibration device to directly obtain absolute counts of parasites using flow cytometry . Quantitative flow cytometric double labeling assay , calibrated with fluorospheres , was used to simultaneously determine the number of parasites along the growth curve , as well as to calculate the mortality rate . In order to obtain the number of total epimastigotes/μL of LIT cultures , following flow cytometer acquisition of approximately 5 , 000 fluorospheres per sample , data analysis was carried out as follows: A bidimensional pseudocolor graph of granularity ( SSC ) versus non-related fluorescence 3 chart was created to exclude autofluorescent ( FL3 positive ) events outside the region R1 . Following this , the events inside the R1 were displayed on size versus granularity plots to select and quantify the bead cluster ( BEADS ) and epimastigote population ( EPI ) as illustrated in S1 Fig . EPI gated events were then analyzed further on FL1 ( FDA ) versus FL2 ( PI ) charts to quantify the frequency of PI+FDA positive events ( DEAD EPI = MORTALITY RATE ) as well as FDA single positive cells ( LIVE EPI ) ( S1 Fig ) . The calculation of the final concentration of TOTAL EPI and the VIABLE EPI counts were achieved with the following equations: Totalepi=epi50×beads÷19 , 720 where EPI = number of epimastigote event counts for a given tube , 50 = volume of culture suspension added to each tube; BEADS = number of fluorosphere beads aspirated in a given tube and 19 , 720 = number of fluorosphere beads added to each tube , considering the volume of 20ml of bead suspension . Viableepi=totalepi×liveepi100 where liveepi = percentage of FDA single positive events and 100 = the percentage conversion factor . A dye-free sample was used as a control . A FACScan Becton Dickinson flow cytometer ( La Jolla , CA , USA ) was used for acquisition and the FlowJo software 9 . 6 . 3 ( San Diego , CA , USA ) used for data analysis using pseudocolor charts . Representative flow cytometry charts are provided in the figures . The times that infected and uninfected insects took to die were estimated using Kaplan—Meier survival analyses . Comparisons were made with log-rank tests . Intermoult periods were compared through a nested ANOVA with Petri dish groups nested within both feeding and infection status . As no significant differences were found among the Petri dishes ( F = 1 . 178 , p = 0 . 286 ) post hoc comparisons ( Tukey HSD test ) were performed adding all data of the five groups of the respective temperatures . Analyses of temperature effects on in vitro parasite growth were conducted in R version 2 . 13 . 0 [33] . The first step was to determine growth rates ( i . e . regression slopes ) for each replicate ( bottle ) for each temperature treatment . For this , live parasite population sizes were log-transformed ( i . e . log10 of parasite number +1 ) and linear mixed effects models were used to account for the repeated measures ( i . e . days 1 , 2 , 3 and so on ) . Eight growth rate values were therefore obtained ( two replicates x four temperatures ) . These were subjected to regression analyses aimed at detecting temperature effects on growth rates , in particular , to test whether growth rates could be seen to peak at different temperatures .
The time taken to moult from second to third instar was affected by both infection ( nested ANOVA , F = 67 . 445 , p = 0 . 00001 ) and temperature ( nested ANOVA , F = 68 . 967 , p = 0 . 00001 ) . The period was reduced by increasing temperatures up to one third for uninfected insects and half for infected insects ( Fig . 1A-D ) . Meanwhile , infection with T . cruzi delayed moult by 6–11 days ( Fig . 1A-D , Tukey HDS; 32 . 1±8 . 3 ( control ) vs . 43 . 5±9 . 2 ( infected ) days for 21°C ( p = 0 . 00003 ) , 23 . 2±9 . 0 ( control ) vs . 30 . 0±7 . 7 ( infected ) days for 24°C ( p = 0 . 017 ) , 17 . 8±8 . 9 ( control ) vs . 23 . 6±6 . 3 ( infected ) days for 27°C ( p = 0 . 08 ) , and 13 . 3±3 . 2 ( control ) vs . 23 . 3±10 . 4 ( infected ) days for 30°C ( p = 0 . 00008 ) ) . At the lowest temperature ( 21°C ) , mortality in uninfected control insects was more than 20% after 30 days ( Fig . 1H ) . This initial mortality of uninfected insects was much reduced at the higher temperatures but by the end of the observations ( 90 days ) , these uninfected insects had almost all died at the higher temperatures . Against this background , infection with T . cruzi was found to accelerate mortality in the two intermediate temperatures , 24 ( P = 0 . 02 ) and 27°C ( P = 0 . 0001 ) ( Fig . 1F & G ) , but not at 21 or 30°C ( Fig . 1E & H ) . The population growth of T . cruzi in vitro increased consistently with increasing temperature ( Fig . 2; p<0 . 0001 for temperature effect ) . The best-fit regression of growth rates against temperature ( Fig . 2B ) was not curved , so peak growth would have occurred at or above 30°C . Fluorescein diacetate-propidium iodide staining made it possible to distinguish live cells from those that had already started to die , these last being stained by both dyes ( S1 Fig ) . Mortality rates of T . cruzi were below 5% at 27 and 30°C , reaching ca . 15% at 24°C and over 20% at 21°C ( S1 Fig ) .
To the best of our knowledge , the present study is the first report of temperature-modulated mortality caused by a protozoan parasite of medical importance to its arthropod vector . In the case of malaria-mosquito systems , decreased mosquito survival during infection is only seen in unnatural combinations , although natural combinations exhibit a tendency towards such increases in mortality [34] . More recently , a natural combination of Plasmodium-mosquito ( in this case an avian malaria system ) , showed an increase in longevity associated with a decrease in fecundity in infected mosquitoes [35] . It is now well-established that arboviruses can be virulent to their culicid vectors , depending on the taxonomic groups and the mode of virus transmission [36] . In dengue virus-Aedes associations it has been observed that the virus presence affected several mosquito fitness parameters such as survival , fecundity and oviposition success [4] . This accelerated mortality of R . prolixus infected with T . cruzi , under conditions of starvation ( commonly experienced by these insects—[28] ) , was seen over a narrow range of temperatures ( i . e . , at 24 and 27°C but not at 21 or 30°C ) . High temperatures associated with prolonged starvation were lethal to insects , independently of parasite infection . It is well known that high temperatures promote an increase in the metabolism of insects ( reviewed by [37] ) . Therefore , an increased mortality would be expected in starved insects submitted to higher temperatures , as already seen in previous studies [38] . According to the effect of temperature on T . cruzi growth in culture media , the mortality of infected insects would be expected to occur as a consequence of large parasite populations developed at higher temperatures . Nevertheless , there were no differences in mortality rates between infected and healthy insects kept at 30°C . This was probably a result of a lack of nutritional resources for parasite development in starved insects . In fact , it has already been demonstrated that triatomines can eliminate T . cruzi infections after long periods of starvation [39] . Curiously , R . prolixus prefers temperatures of 25 . 0–25 . 4°C when offered a choice and performs best around these temperatures [40] . Furthermore , temperatures in the sylvatic ecotopes in which this insect is to be found oscillate closely around 25°C [41] . While we might have expected the vector to be less affected by the parasite at temperatures near to its optimum ( as is the case with locusts infected with the fungus Metarhizium anisopliae , [29] ) we might also expect the parasite to be adapted to perform optimally at exactly these temperatures . If this is the case , then we must conclude that T . cruzi’s strategy , in its vector , results in direct physiological harm to its vector , that can be observed as vector mortality . At this range of temperatures , the parasite has a high in vitro growth rate ( Fig . 2 ) so we hypothesize that its strategy in the vector is close to unrestrained growth , trading off an increased chance of transmission ( due to a high population density in the intestine ) with the cost of killing its vector and so effecting zero transmission . Given these insects are able to display temperature preferences [40 , 42–45] , we might expect them , when infected with T . cruzi , to alter their thermal preferences . All of these factors are liable to affect T . cruzi transmission dynamics and ultimately , epidemiology . The number of T . cruzi parasites increased in direct relation to temperature . In fact , after seven days , parasites kept at 30°C increased their numbers close to 28 times , almost doubling their growth rate at 27°C . Previous studies have shown that both T . cruzi epimastigote and trypomastigote forms grow when exposed to 37°C [46] . The lowest temperature tested here seemed to have a harmful effect on T . cruzi , since their mortality at this temperature was close to 20% . It has been suggested that low temperatures affect the endocytic processes in T . cruzi epimastigotes [47] . Low-temperature blockage of endocytosis has also been reported in many eukaryotic cells [48–51] . Whether these effects of low temperature on parasite endocytosis are related to the poor performance of T . cruzi at 21°C deserves to be analyzed in future experiments . Temperature is important in the development and within-host dynamics of several other protozoan parasites . Leishmania species differ in their susceptibility to temperature stress , as reflected in their ability to establish infections at different sites in the mammalian body [52] . The temperature resistance of Leishmania spp . has been related with the parasite tropism , as visceral species are more temperature resistant than cutaneous species [53] . Temperature has also been shown to be important to regulate the membrane potential across the plasma membrane and the internal pH in Trypanosoma brucei [54] . In addition , the reduction in temperature from 37 to 27°C and the addition of cis-aconitate are enough to trigger the transformation of the monomorphic T . brucei from bloodstream to procyclic trypomastigotes in culture medium [55] . Beyond mortality , infection with T . cruzi considerably delayed moult in R . prolixus , across the range of temperatures tested . In contrast to results observed with other triatomine species [18–22] , moulting in R . prolixus second instar nymphs was delayed by more than 10 days in a single developmental stage . In an entire life cycle the accumulation of this effect could possibly prolong by more than a month the time needed to reach the adult stage . It is highly likely that such a delay would affect insect fitness . While it will be interesting to look for physiological explanations for this ( there is some evidence indicating a possible competition for lipids in this host-parasite system [56] ) , there may be a very good adaptive explanation , in terms of the parasite’s fitness . Trypanosoma cruzi has been reported to take approximately up to month at 28°C to colonize the intestine , reach the rectum and differentiate into infective stages [57 , 58] . As triatomines will only feed again after they have moulted , it would benefit the parasite if their moult , and thus the next bloodmeal , were delayed until such a time as the parasite is in the right place and life stage to be transmitted to a vertebrate host . Such a delay would then favor parasite transmission . Effects of T . cruzi infections on triatomine fitness have previously been described in the literature . Schaub and Lösch [59] observed that the resistance of infected T . infestans was reduced when insects were starved . However , in subsequent studies from the same group the parasite was considered subpathogenic to its invertebrate hosts since , apparently , it does not damage the vector under optimal conditions [60 , 61] . Meanwhile , Botto-Mahan [62] evaluated the time to moult during the ontogeny of Mepraia spinolai infected by T . cruzi ( kept at 26°C ) and showed that infected insects presented a delayed moult and an increased mortality when compared with control ones . However , insects from the infection treatment were always fed on infected mice , and as mentioned by the author , it is not possible to be sure that the observed effects were not a result of differences in blood quality between infected and non infected mice . Nevertheless , the deleterious effects of T . cruzi described in these studies altogether with the results presented in this study and the alteration of the reproductive fitness of R . prolixus induced by T . cruzi recently demonstrated by our group [24] represent a bulk of evidence confirming fitness costs induced by this parasite . To conclude , we have shown that the medically-important parasite T . cruzi can exert virulence effects on the vector R . prolixus . This effect is strongest over exactly the temperature range preferred by the insect and in which it is to be found in the wild ( often infected with T . cruzi ) . The ability of T . cruzi to develop over a broad temperature range might have contributed to its adaptation to a larger number of triatomines . It will be important to investigate virulence effects in other vector species , behavioural responses of the insects to infection ( see [63] for example ) and impacts on transmission dynamics . | Parasites are often assumed to cause little harm to their arthropod vectors , even though they commonly reproduce inside the arthropods and exploit their nutrients , even causing lesions when crossing internal barriers . Thus , the interests of parasite and vector may well not be aligned and we can expect the parasite to exploit its vector just as it does with its main host , with consequent negative effects on the vector’s fitness . Here , we show that this occurs with Trypanosoma cruzi in its bug vector ( T . cruzi causes Chagas disease , affecting ca . 8 million people and disease management is principally attained via vector control ) . Our results indicate that the parasites delay insect moulting , which is likely beneficial to them as they need time to develop in the insect before the next bloodmeal ( that only occurs post-moult ) . We also show parasite-induced mortality over the narrow range of temperatures which the insect prefers and over which it performs best . In vitro growth of the parasite increases with temperature and we discuss how this may help explain the effects in vivo . Overall , these results will be important to understand the epidemiology of Chagas disease and provide an evolutionary context to explain the parasite′s interaction with its vector . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Trypanosoma cruzi, Etiological Agent of Chagas Disease, Is Virulent to Its Triatomine Vector Rhodnius prolixus in a Temperature-Dependent Manner |
Multiciliated cells of the airways , brain ventricles , and female reproductive tract provide the motive force for mucociliary clearance , cerebrospinal fluid circulation , and ovum transport . Despite their clear importance to human biology and health , the molecular mechanisms underlying multiciliated cell differentiation are poorly understood . Prior studies implicate the distal appendage/transition fiber protein CEP164 as a central regulator of primary ciliogenesis; however , its role in multiciliogenesis remains unknown . In this study , we have generated a novel conditional mouse model that lacks CEP164 in multiciliated tissues and the testis . These mice show a profound loss of airway , ependymal , and oviduct multicilia and develop hydrocephalus and male infertility . Using primary cultures of tracheal multiciliated cells as a model system , we found that CEP164 is critical for multiciliogenesis , at least in part , via its regulation of small vesicle recruitment , ciliary vesicle formation , and basal body docking . In addition , CEP164 is necessary for the proper recruitment of another distal appendage/transition fiber protein Chibby1 ( Cby1 ) and its binding partners FAM92A and FAM92B to the ciliary base in multiciliated cells . In contrast to primary ciliogenesis , CEP164 is dispensable for the recruitment of intraflagellar transport ( IFT ) components to multicilia . Finally , we provide evidence that CEP164 differentially controls the ciliary targeting of membrane-associated proteins , including the small GTPases Rab8 , Rab11 , and Arl13b , in multiciliated cells . Altogether , our studies unravel unique requirements for CEP164 in primary versus multiciliogenesis and suggest that CEP164 modulates the selective transport of membrane vesicles and their cargoes into the ciliary compartment in multiciliated cells . Furthermore , our mouse model provides a useful tool to gain physiological insight into diseases associated with defective multicilia .
Cilia are evolutionarily conserved , microtubule-based organelles that project from the apical cell surface and perform a wide array of cellular functions [1–3] . Reflecting their diverse cellular tasks , many types of cilia exist , but they are generally categorized into two broad classes , primary and multicilia . Immotile primary cilia , which have a 9+0 microtubule arrangement , are present on most mammalian cell types , mediate signaling of multiple pathways including Hedgehog signaling , and sense the cellular environment [1] . Motile multicilia , on the other hand , have a 9+2 microtubule arrangement and are responsible for clearing mucus and debris from the airways , circulating cerebrospinal fluid in the brain ventricles , and providing the motive force for ovum transport along the oviduct ( also called the fallopian tube ) [4 , 5] . Sperm flagella are also motile with a 9+2 axonemal structure . In recent years , the identification of human mutations in cilia-related genes , causative for a group of disorders known as ciliopathies , has highlighted the importance of primary cilia to human health and created great interest in the field [1–3] . On the other hand , multicilia have been linked to genetic disorders such as primary ciliary dyskinesia ( PCD ) . Although there are exceptions , in most cases , PCD is caused by the immotility or abnormal motility of multicilia of normal length and number per cell [6 , 7] . In addition , multicilia have been associated with several chronic respiratory disorders including chronic obstructive pulmonary disease ( COPD ) and asthma [6 , 7] . These findings underscore the importance of elucidating the molecular mechanisms underlying the formation and function of multicilia . Although primary and multicilia are thought to be produced through largely analogous pathways , differences exist [4 , 5 , 8] . Primary cilia are nucleated in a quiescent cell from the mother centriole , which is distinguished from the daughter centriole by the presence of the subdistal and distal appendages . The two centrioles surrounded by amorphous pericentriolar material constitute the centrosome . However , multiciliated cells must generate hundreds of centrioles through the direct duplication of existing centrioles and an acentriolar pathway via fibrogranular structures termed deuterosomes [4 , 9] . After centrioles are formed in multiciliated cells , they mature through the acquisition of accessary structures , such as the subdistal and distal appendages [2 , 10] . In both primary and multiciliogenesis , small vesicles then dock to the distal appendage and coalesce to form the larger ciliary vesicle [9 , 11] . The ciliary vesicle is thought to promote docking of the centriole , now termed a basal body , to the apical cell surface by fusing with the apical cell membrane [12] . At this point , the axoneme extends from the basal body via the action of an intraciliary trafficking mechanism , called intraflagellar transport ( IFT ) [13] . The distal appendages , or transition fibers as referred to at the ciliary base , are nine radial fibrous extensions originating from the B-tubule at the distal end of the mother centriole or basal body [10] . A core unit composed of at least five proteins , CEP83/CCDC41 , CEP89/CEP123 , SCLT1 , FBF1 , and CEP164 , has been reported [14] . Several functions have been ascribed to these proteins in primary ciliogenesis [15]; for example , FBF1 has been linked to IFT particle entry into the cilium while CEP83 , CEP89 , and CEP164 are critical for vesicle recruitment and ciliary vesicle biogenesis [16–19] . Rab small GTPases are known vesicle trafficking effectors and facilitate the assembly of ciliary vesicles and membranes at the distal appendage [10 , 17 , 20–22] . Specifically , during primary ciliogenesis , Rab11-positive vesicles are transported to the pericentrosomal region . Rabin8 , a guanine nucleotide exchange factor ( GEF ) for Rab8 , is then recruited by Rab11 to promote the local activation of Rab8 , which in turn facilitates the efficient formation of ciliary vesicles and membranes . In addition to Rabs , ADP-ribosylation factor ( Arf ) /Arf-like ( Arl ) small GTPases also regulate primary ciliogenesis as well as targeting of ciliary proteins [23] . Interestingly , a recent report describes a novel role for the Eps15 homology domain ( EHD ) proteins EHD1 and EHD3 in ciliary vesicle formation in primary ciliogenesis [24] . Although these studies provide clear evidence that the distal appendage/transition fiber and its associated protein networks are necessary to build a primary cilium , little has been explored regarding their roles in multiciliogenesis . We previously demonstrated that the 15-kDa coiled-coil protein Chibby1 ( Cby1 ) localizes to the distal appendage/transition fiber and plays a key role in ciliogenesis [25–31] . Cby1-knockout ( KO ) mice display chronic sinusitis and otitis , polycystic kidneys , and sub-fertility as well as polydactyly and hydrocephalus at low frequency , due to defective primary and multicilia [25–27 , 30 , 31] . Recent studies in Drosophila melanogaster and Xenopus laevis highlight an evolutionarily conserved role for Cby1 in ciliogenesis [32 , 33] . We further showed that CEP164 , which is mutated in nephronophthisis and Bardet-Biedl syndrome ( BBS ) , both of which are classified as ciliopathies [34 , 35] , directly interacts with and recruits Cby1 to the distal appendage/transition fiber of the mother centriole/basal body during primary ciliogenesis [25] . Cby1 then binds Rabin8 and facilitates an interaction between CEP164 and Rabin8 . This leads to the recruitment and activation of Rab8 to promote the efficient assembly of ciliary vesicles and subsequent basal body docking to the apical plasma membrane . A crucial role for Cby1 in membrane association with and docking of basal bodies has been further demonstrated by studies in D . melanogaster [36] . Recently , we identified novel Cby1-interactors , the membrane-binding Bin/Amphiphysin/Rvs ( BAR ) -domain containing proteins , family with sequence similarity 92 members A and B ( FAM92A and FAM92B ) [37] . FAM92A and FAM92B are recruited to mother centrioles/basal bodies by Cby1 to facilitate ciliogenesis likely through regulation of membrane remodeling processes . Centrosomal protein of 164 kDa ( CEP164 ) was originally identified in a proteomic analysis of centrosomal proteins and a screen for modulators of ciliogenesis [38 , 39] . CEP164-knockdown ( KD ) experiments in mammalian cultured cells revealed its functions in small vesicle docking to the distal appendage , at least in part , via the direct interactions between its C-terminal region and Rabin8 [17] . During primary ciliogenesis , the N-terminal WW motif of CEP164 has also been shown to bind and recruit Tau-tubulin kinase 2 ( TTBK2 ) to the mother centriole [40 , 41] . TTBK2 then phosphorylates the distal end-capping protein CP110 to promote the removal of CP110 from mother centrioles for the initiation of ciliogenesis . Thus , CEP164 is a key regulator of primary ciliogenesis; however , its role in multiciliogenesis remains largely unexplored . Here , we report a novel conditional mouse model in which CEP164 is ablated from multiciliated cells . These mice show a severe reduction in the number of airway , ependymal , and oviduct multicilia , and ~20% die around weaning age with profound hydrocephalus . We found that CEP164 is important for proper multiciliogenesis by regulating ciliary vesicle formation and basal body docking . Experiments using primary cultures of mouse tracheal epithelial cells ( MTECs ) revealed that CEP164 is required for the normal basal body localization of Cby1 and its interactors FAM92A and FAM92B . Moreover , we provide evidence that CEP164 plays distinct roles in primary vs . multiciliogenesis and differentially controls the ciliary trafficking of membrane-associated proteins in multiciliated cells . Taken together , our study establishes a novel mouse model for multicilia-associated diseases and sheds light on the multiple indispensable roles of CEP164 in airway multiciliated cell differentiation .
CEP164 is composed of 1460 amino acids and contains a WW domain along with three coiled-coil domains ( S1A Fig ) . To elucidate the physiological function of CEP164 in mammals , we obtained the CEP164 KO-first mouse line from the MRC-Harwell ( S1B Fig ) [42 , 43] . This mouse line contains the promoter-driven Tm1a allele that carries lacZ gene and neomycin-resistance cassettes . As we initially expanded our CEP164 KO-first mouse colony , we noted that mice heterozygous for the KO-first allele appeared healthy and fertile while no homozygous mice were born , suggesting embryonic lethality . To address this possibility , we examined embryos from heterozygous intercrosses at various stages of gestation . At embryonic day ( E ) 7 . 5 , CEP164-KO embryos showed no obvious morphological abnormalities; however , at E9 . 5 and E10 . 5 , they exhibited holoprosencephaly , cardiac looping defects , an edematous pericardial sac , and a truncated posterior trunk ( Fig 1A ) . These phenotypes are similar to those reported for mouse mutants for KIF3A and KIF3B [44–46] , which are major components of the kinesin-II ciliary anterograde motor , providing further evidence for the essential role of CEP164 in primary ciliogenesis . Resorptions were consistently observed at E12 . 5 and all later stages examined . These data demonstrate that CEP164 is necessary for mammalian embryogenesis . CEP164 has been shown to be essential for primary ciliogenesis in mammalian cultured cells and zebrafish embryos [17 , 39 , 40 , 47 , 48] . To determine whether CEP164 is necessary for primary ciliogenesis in vivo , we assessed the status of primary cilia in the neural tube of E9 . 5 CEP164-KO embryos using immunofluorescence ( IF ) staining for the ciliary marker Arl13b ( Fig 1A ) . Primary cilia were abundant in the neural tube of control embryos but almost completely absent in that of CEP164-KO embryos . Consistent with this , mouse embryonic fibroblasts ( MEFs ) prepared from E8 . 5 CEP164-KO embryos showed a dramatic loss of primary cilia ( 2 . 7±0 . 3% ciliated KO MEFs vs . 62 . 3±4 . 1% ciliated control MEFs ) ( n>200 cells for each of three independent MEF preparations per genotype ) ( Fig 1B ) . These findings suggest that loss of primary cilia is , at least in part , responsible for the embryonic phenotypes observed . We previously demonstrated that CEP164 physically interacts with Cby1 and is responsible for the recruitment of both Cby1 and FAM92A to the ciliary base to facilitate primary ciliogenesis in mammalian cultured cells using siRNA-mediated KD experiments ( Fig 1C ) [25 , 37] . Indeed , in contrast to control MEFs , neither Cby1 nor FAM92A was detected at the centrioles of CEP164-KO MEFs ( Fig 1D ) . We also confirmed the loss of CEP164 at the centrioles of CEP164-KO MEFs using IF staining ( Fig 1D ) . Thus , our results validate previous data suggesting a fundamental role for CEP164 in the recruitment of Cby1 and FAM92A to basal bodies . CEP164 plays an essential role in primary ciliogenesis; however , the role of CEP164 in multiciliogenesis had not been elucidated , and no CEP164-KO animal models were available to investigate its physiological functions in vivo . We therefore employed the CEP164 KO-first mouse line to generate a mouse model that lacks CEP164 in multiciliated cells ( S1B Fig ) . To this end , a heterozygous CEP164 KO-first mouse was crossed with a flippase ( Flp ) deleter mouse to remove both the lacZ and neomycin-resistance cassettes . The resultant mouse ( CEP164fl/fl ) has two loxP sites flanking exon 4 of the CEP164 gene , which encodes a part of the WW domain ( S1A Fig ) . FOXJ1 is a forkhead transcription factor expressed in multiciliated cells in the airways , brain ventricles , and oviducts as well as in the testis [49 , 50] . In airway multiciliated cells , FOXJ1 is expressed early during multiciliogenesis in ciliating cells that still possess a primary cilium and are initiating production of centrosomal proteins for centriole amplification [51] . Thus , we bred the CEP164fl/fl mouse with a FOXJ1-Cre transgenic mouse that expresses Cre recombinase under the control of the FOXJ1 promoter [52] . Cre-mediated recombination results in the excision of exon 4 and a frameshift , leading to a truncation at amino acid position 65 ( S1A Fig ) . Correct genotypes were verified by PCR ( S1C and S1D Fig ) . A majority of FOXJ1-Cre;CEP164fl/fl mice lived to adulthood without gross abnormalities , except for ~20% that succumbed to death due to severe hydrocephalus around weaning and another ~20% that exhibited mild hydrocephalus , which resolved itself later . Histological assessment of the trachea and sinus ( Fig 2A ) from FOXJ1-Cre;CEP164fl/fl adult mice showed a marked decrease in the number of airway multicilia in comparison to control specimens from CEP164fl/fl mice . IF staining of tracheal sections for the ciliary marker acetylated α-tubulin ( A-tub ) showed significant loss of multicilia upon CEP164 deletion ( S2 Fig ) . Indicative of impaired mucociliary clearance , these mice frequently produced coughing- or sneezing-like noises . As noted above , 19% of FOXJ1-Cre;CEP164fl/fl mice displayed severe hydrocephalus with a prominently domed head around weaning ( 11 out of 58 mice ) ( Fig 2B , left panels ) ; however , all FOXJ1-Cre;CEP164fl/fl adult mice examined ( n = 10 ) showed substantial ventricular enlargement ( middle panels ) . The high penetrance of hydrocephalus prompted us to examine the status of ependymal multicilia by IF staining of whole mounts of the subventricular zone ( SVZ ) . As expected , IF staining for A-tub demonstrated a clear reduction in the number of ependymal multicilia in FOXJ1-Cre;CEP164fl/fl SVZ whole mounts compared to control CEP164fl/fl samples ( Fig 2B , right panels ) . Consistent with this , quantification of basal body patch area and displacement revealed significant perturbations in the organization of basal bodies at the apical surface of CEP164-KO ependymal multiciliated cells ( S3 Fig ) . We next examined reproductive tissues in FOXJ1-Cre;CEP164fl/fl mice as FOXJ1 is highly expressed in the multiciliated cells of the oviduct epithelium as well as in the testis [49 , 52] . In the oviduct of adult FOXJ1-Cre;CEP164fl/fl mice , multicilia were reduced in number as evaluated by both histology ( Fig 3A ) and IF staining for A-tub ( Fig 3B ) compared to control CEP164fl/fl tissues . However , FOXJ1-Cre;CEP164fl/fl females were fertile , suggesting that the remaining multicilia are sufficient to sustain normal function . Alternatively , ciliary motility is not strictly required for female fertility . In stark contrast , FOXJ1-Cre;CEP164fl/fl males were completely infertile . Histological analysis revealed variable degrees of degenerative changes in the seminiferous tubules of FOXJ1-Cre;CEP164fl/fl adult testes . In general , we noticed a substantial reduction in the number of late-stage germ cells ( Fig 3C ) . In a subset of seminiferous tubules , germ cells were entirely depleted with solely Sertoli cells present ( Fig 3C , asterisk ) . Additionally , no mature sperm were detectable in the epididymis of FOXJ1-Cre;CEP164fl/fl mice . In support of these extensive phenotypes , X-gal staining of testis sections from heterozygous CEP164 KO-first mice carrying a lacZ reporter showed broad CEP164 expression with particularly intense staining in differentiating spermatids ( Fig 3D ) . Overall , these results demonstrate that FOXJ1-Cre;CEP164fl/fl mice exhibit phenotypes consistent with impaired multi- and motile ciliogenesis and provide a useful model system to study the mechanisms of multiciliogenesis and its associated diseases . To gain insight into the molecular basis of defective multiciliogenesis in the absence of CEP164 , we employed primary cultures of MTECs , a well-characterized in vitro model for airway differentiation and ciliogenesis [53] . MTEC cultures are created by seeding isolated tracheal epithelial cells at low density onto a semipermeable , collagen-coated membrane and permitting them to proliferate until confluent for ≤7 days . Differentiation then proceeds in a semi-synchronous manner after an air-liquid interface ( ALI ) is established with low-serum media . At 14 days post-ALI induction ( ALId14 ) , the cultures contain both multiciliated and non-ciliated cells and resemble the native tracheal epithelium . Using the MTEC system , we first sought to determine the efficiency of Cre-mediated CEP164 removal as well as whether it has any impact on the multiciliated cell lineage . Hence , we performed IF staining of ALId14 MTECs for CEP164 and FOXJ1 . While intense CEP164 signals were detectable at the ciliary base of FOXJ1-positive multiciliated cells in CEP164fl/fl MTEC cultures at ALId14 , CEP164 expression was lost or greatly reduced in ~90% of FOXJ1-positive multiciliated cells in FOXJ1-Cre;CEP164fl/fl MTEC cultures ( n>800 ciliated cells ) , revealing highly efficient Cre-mediated recombination ( S4A Fig ) . On the other hand , it is possible that the efficiency of Cre recombination might vary among individual cells , leading to a partial/variable phenotype especially at early stages of multiciliogenesis . Interestingly , there was a modest decrease in the number of FOXJ1-positive cells in FOXJ1-Cre;CEP164fl/fl ( 37 . 1±2 . 7% ) vs . CEP164fl/fl ( 47 . 6±3 . 0% ) MTEC cultures ( n>500 cells for each of three independent MTEC preparations per genotype ) ( S4B Fig ) . These data suggest that CEP164 may play some role in the maintenance and/or survival of multiciliated cells . Clearly , this requires further detailed investigation in the future . Next , we assessed the extent of multiciliogenesis in ALId14 MTECs from CEP164fl/fl and FOXJ1-Cre;CEP164fl/fl mice by IF staining for CEP164 and A-tub ( Fig 4A ) . As expected , CEP164-KO multiciliated cells showed profound defects in ciliogenesis . However , we noticed that CEP164-KO multiciliated cells were able to extend cilia , albeit short and sparse ( Fig 4A , zoomed image ) , in contrast to the absolute requirement for CEP164 in primary ciliogenesis [17 , 39] . Four different stages of centriole formation and ciliogenesis in multiciliated cells are defined: Stage I , appearance of centrosomal protein foci; Stage II , centriole replication; Stage III , centriole dispersion and migration; Stage IV , axonemal elongation ( Fig 4B ) [8] . To precisely quantify the percentages of multiciliated cells at each stage , we fixed CEP164fl/fl and FOXJ1-Cre;CEP164fl/fl MTECs at ALId5 , d7 , and d14 and conducted IF staining for A-tub . As shown in Fig 4C , impaired multiciliogenesis in FOXJ1-Cre;CEP164fl/fl MTEC cultures was evident at ALId5 and more pronounced at ALId14 with a large decrease in the number of stage IV multiciliated cells and concomitant increases in the numbers of early stage multiciliated cells ( n>225 total cells for each ALI day from each of three independent MTEC preparations per genotype ) . The increased number of non-ciliated cells at ALId14 was in line with the decreased number of FOXJ1-positive cells in ALId14 FOXJ1-Cre;CEP164fl/fl MTECs ( S4B Fig ) . Moreover , a vast majority of the stage IV multiciliated cells in FOXJ1-Cre;CEP164fl/fl MTEC cultures extended only short and scarce cilia at ALId14 ( Fig 4D ) . Corroborating this observation , CEP164fl/fl MTEC cultures had 46 . 5±1 . 4% of total cells that were fully ciliated with abundant cilia , whereas only 4 . 9±1 . 1% of cells in FOXJ1-Cre;CEP164fl/fl MTEC cultures appeared fully ciliated ( n>250 total cells from each of three MTEC independent preparations per genotype ) , which most likely corresponds to CEP164-positive multiciliated cells that escaped Cre-mediated recombination . Collectively , these data indicate that loss of CEP164 in airway multiciliated cells results in defective ciliogenesis and multiciliated cell differentiation . During primary ciliogenesis , CEP164 plays a pivotal role in recruitment of small vesicles to the distal appendages of mother centrioles for assembly of ciliary vesicles [17] . To examine whether CEP164 similarly regulates vesicle recruitment and subsequent basal body docking during multiciliogenesis , we performed transmission electron microscopy ( TEM ) on both CEP164fl/fl and FOXJ1-Cre;CEP164fl/fl adult tracheas . In control CEP164fl/fl multiciliated cells , 98% of basal bodies were properly docked to the apical cell surface with cilia extending into the lumen ( n = 167 basal bodies from 12 ciliated cells ) ( Fig 5A ) . In contrast , 48–83% of basal bodies were found undocked in the cytoplasm of FOXJ1-Cre;CEP164fl/fl multiciliated cells with only a few cilia ( n = 176 basal bodies from 10 ciliated cells ) . In agreement with the IF staining results of MTECs ( Fig 4A ) , we frequently noted shortened cilia in FOXJ1-Cre;CEP164fl/fl adult tracheas ( S5B Fig ) . We also confirmed the presence of many undocked , cytoplasmic basal bodies in ALId14 MTECs from FOXJ1-Cre;CEP164fl/fl mice using TEM ( Fig 5B ) . Furthermore , we found that the transition fibers as well as the Y-linkers of the transition zone were present in the absence of CEP164 ( S5A , S5C and S5D Fig ) , suggesting that CEP164 is not an essential structural component of the transition fibers and does not influence transition zone ultrastructure . Basal body docking defects often result from the inability of distal appendages to recruit small vesicles in order to assemble ciliary vesicles [10 , 15] . CEP164 has been shown to be responsible for the recruitment of small vesicles to distal appendages during early stages of primary ciliogenesis in human retinal pigment epithelial ( RPE1 ) cells [17] . To test if this is the case in multiciliated cells , P8 tracheas were cultured ex vivo in the presence of the microtubule-stabilizing agent taxol and subjected to TEM analysis . Taxol has previously been shown to block apical migration of basal bodies and enrich for basal bodies bound to vesicles in multiciliated cells [54] . As shown in Fig 5C , in control CEP164fl/fl tracheas , 68% of cytoplasmic basal bodies were associated with vesicles , whereas only 35% of basal bodies in FOXJ1-Cre;CEP164fl/fl tracheas were attached to vesicles ( n = 68 and 81 basal bodies for CEP164fl/fl and FOXJ1-Cre;CEP164fl/fl , respectively , from three tracheas per genotype ) . Of note , without 3D reconstruction , these numbers do not precisely represent the actual number of centrioles associated with vesicles , but rather the numbers detected on thin TEM sections . Collectively , our TEM data support the notion that CEP164 plays key roles in small vesicle recruitment and ciliary vesicle formation during multiciliogenesis . We previously reported that Cby1 is important for ciliary vesicle formation and basal body docking in airway multiciliated cells [25] . We also demonstrated that , during primary ciliogenesis , CEP164 is essential for recruitment of Cby1 to the distal appendages of mother centrioles via protein-protein interactions . Cby1 then recruits the BAR domain-containing proteins FAM92A and FAM92B to basal bodies to facilitate primary ciliogenesis [37] . IF staining of MTECs revealed that Cby1 recruitment to basal bodies was lost or substantially reduced at both early and fully differentiated stages ( Fig 6A ) . At ALId14 , ~65% of CEP164-KO multiciliated cells showed diminished recruitment of Cby1 to basal bodies ( n = 300 ciliated cells from three independent MTEC preparations ) . Similarly , the basal body recruitment of FAM92A and FAM92B was clearly diminished in 50–65% of CEP164-KO multiciliated cells ( n>250 ciliated cells at ALId14 for each protein from three independent MTEC preparations ) ( Fig 6B and 6C ) . These data indicate that CEP164 lies upstream of Cby1 , FAM92A , and FAM92B and recruits them to the distal appendages/transition fibers to promote ciliary vesicle formation , basal body docking , and multiciliated cell differentiation . It was demonstrated that CEP164 KD leads to a significant reduction in the levels of IFT components at the base of primary cilia in RPE1 cells [17 , 40] . We therefore examined the effects of CEP164 loss on the localization of the IFT components IFT88 and IFT20 in multiciliated cells ( Fig 7A ) . Surprisingly , in contrast to primary cilia , both IFT proteins were clearly detectable at basal bodies in CEP164-KO multiciliated cells at similar levels to control multiciliated cells . During primary ciliogenesis , CEP164 recruits TTBK2 to mother centrioles [40 , 41] . TTBK2 in turn promotes removal of the distal end-capping protein CP110 and recruitment of IFT proteins to initiate ciliogenesis . Thus , CEP164-KD RPE1 cells fail to remove CP110 from the mother centriole , thereby preventing ciliogenesis from proceeding upon serum starvation [40 , 41] . In contrast , we found that , in multiciliated cells , CP110 was constitutively present at nascent centrioles as well as at the basal bodies of elongating and mature cilia ( Fig 7B ) . The basal body localization of CP110 was not overtly affected in CEP164-KO multiciliated cells . TTBK2 was weakly detectable at the ciliary base and more brightly at the tip of a subset of cilia at comparable levels in both control and CEP164-KO multiciliated cells ( S6A Fig ) . These findings suggest that CEP164 is dispensable for the proper localization of IFT particles , CP110 , and TTBK2 to centrioles/basal bodies in multiciliated cells and highlight potential differences between primary and multiciliogenesis . During primary ciliogenesis , the small GTPase Rab11 recruits Rab8 GEF Rabin8 , which in turn recruits and activates Rab8 at centrosomes [20–22] . Rab8 then promotes membrane trafficking to the base of cilia to facilitate ciliary membrane assembly . CEP164 is known to bind Rabin8 and mediates Rab8 recruitment and activation [17] . Furthermore , Cby1 binds CEP164 to facilitate the CEP164-Rabin8 interaction and Rab8 activation , thereby promoting ciliary vesicle formation and subsequent basal body docking during airway multiciliated cell differentiation [25] . We therefore hypothesized that CEP164 might affect the Rab11-Rab8 cascade in multiciliated cells and immunostained ALId14 MTECs from CEP164fl/fl and FOXJ1-Cre;CEP164fl/fl mice with antibodies for Rab8 and Rab11 ( Fig 8A ) . Utilizing super-resolution structured illumination microscopy ( SIM ) , we found that the ciliary and basal body localization of both Rab8 and Rab11 was substantially reduced in CEP164-KO compared to control multiciliated cells . Of particular note , Rab11 has been reported to predominantly localize to a pericentrosomal compartment in cycling cells or a peri-basal body region in quiescent cells with primary cilia [17 , 20 , 22] . In contrast , Rab11 localization extended to a proximal region of multicilia , again highlighting differences between primary and multicilia . These data point to potential alterations in the trafficking and formation of ciliary membranes in CEP164-KO multiciliated cells . Next , we sought to determine if other ciliary membrane proteins exhibit altered localization patterns upon CEP164 loss . The ciliary protein ADP-ribosylation factor-like 13b ( Arl13b ) is a small GTPase that specifically associates with the ciliary membrane via palmitoylation and functions in vesicle and ciliary trafficking as well as multiple other cellular processes [55–57] . Additionally , Arl13b forms a functional complex with CEP164 to target the lipid phosphatase inositol polyphosphate-5-phosphatase E ( INPP5E ) to the primary cilium [58] . INPP5E is a prenylated protein important for primary ciliogenesis and maintenance of proper ciliary membrane lipid composition [59–61] . Furthermore , genetic mutations in Arl13b and INPP5E are linked to the ciliopathy Joubert syndrome [62 , 63] . Hence , we investigated whether the loss of CEP164 has any effect on the ciliary localization of Arl13b and INPP5E . Surprisingly , SIM imaging revealed that , in CEP164-KO multiciliated cells , Arl13b robustly accumulated in the short cilia and that the ciliary localization of INPP5E was modestly , yet consistently , increased along the entire length of the short cilia ( Fig 8B ) . The ciliary accumulation of Arl13b was also observed at early ciliation phases in ALId5 MTEC cultures , although that of INPP5E was not clearly detectable ( S6B Fig ) . In CEP164-KO MEFs , weak to moderate signals for Arl13b and INPP5E were consistently observed at centrioles ( S7 Fig ) . These results , combined with the diminished ciliary recruitment of Rabs in CEP164-KO multiciliated cells , imply that CEP164 might be involved in the trafficking and formation of ciliary membranes in multiciliated cells .
In spite of a large portion of the population affected by genetic and/or chronic disorders involving multicilia , only a few surviving mouse models exist to interrogate the mechanisms of their formation and physiology . Here , we report a viable mouse model that lacks the distal appendage/transition fiber protein CEP164 in FOXJ1-positive cells of the airways , brain , oviduct , and testis ( S1 Fig ) . We demonstrated that CEP164 removal in these tissues results in a profound loss of multicilia in the airway , ependymal , and oviduct epithelia as well as development of hydrocephalus and male infertility ( Figs 2–4 ) . Therefore , our FOXJ1-Cre;CEP164fl/fl mouse model provides a powerful tool to study diseases of multicilia , such as PCD , and to further elucidate how defective multicilia contribute to the pathology of chronic respiratory diseases , such as cystic fibrosis , asthma , and COPD . Cilia play essential roles in various aspects of embryonic development such as tissue patterning and organogenesis [1 , 64] . CEP164-KO embryos exhibit holoprosencephaly , an edematous cardiac sac , heart looping defects , and a truncated posterior trunk at E9 . 5–10 . 5 ( Fig 1A ) , leading to embryonic lethality . Intriguingly , these phenotypes resemble those of the mouse mutants for KIF3A [44 , 45] and KIF3B [46] , which are components of the plus end-directed kinesin-II microtubule motor that carries IFT particles and cargoes to the tip of cilia . These similarities in phenotype indicate that CEP164 may play an essential role in primary ciliogenesis during early embryogenesis . In agreement with this notion , we found that CEP164-KO MEFs fail to develop primary cilia ( Fig 1B ) . Our findings also revealed a critical role for CEP164 in male reproductive development ( Fig 3C and 3D ) . Interestingly , mature sperm were not present in the epididymis of FOXJ1-Cre;CEP164fl/fl mice . In severe cases , germ cells were completely depleted , suggesting that CEP164 may have a fundamental function in spermatogonial stem cells . Besides sperm flagella , germ cells in mammalian testes lack primary cilia [65 , 66] . Thus , CEP164 may play cilia-independent roles during spermatogenesis . Future studies are clearly warranted to address the biological functions of CEP164 in the testis . In line with its function during primary ciliogenesis , our results suggest that CEP164 is critical for the recruitment of small vesicles to the distal appendages of centrioles and subsequent assembly of ciliary vesicles during multiciliogenesis . Furthermore , CEP164 is required for the proper basal body localization of the downstream effectors Cby1 , FAM92A , and FAM92B in airway multiciliated cells ( Fig 6 ) . At present , we cannot rule out the possibility that , besides its role in basal body docking , CEP164 may play an additional role in other aspects of multiciliogenesis such as centriole amplification and cilium elongation . Our work also highlights differences in the requirements for CEP164 in primary vs . multiciliogenesis . While CEP164 has been shown to be necessary for IFT88 recruitment to basal bodies in primary cilia [17] , both IFT88 and IFT20 localize to the ciliary base in CEP164-KO multiciliated cells ( Fig 7A ) . Additionally , CEP164 physically interacts with TTBK2 to promote the removal of the centriolar distal end-capping protein CP110 to initiate primary ciliogenesis [40] . In contrast , CP110 is clearly present at the basal bodies of mature cilia in airway multiciliated cells in a CEP164-independent manner ( Fig 7B ) . Consistent with these findings , a recent report , using Xenopus epidermal multiciliated cells , demonstrated that CP110 localizes to basal bodies and may have unique functions in basal body apical transport and ciliary adhesion complex formation during multiciliogenesis [67] . Hence , it will be of importance to determine shared and distinct mechanisms between primary vs . multiciliogenesis . This may contribute to the development of targeted therapies for symptoms associated with defective primary vs . muticilia . Interestingly , we found that the ciliary localization of membrane-associated proteins is perturbed in CEP164-KO multiciliated cells . We observed a significant reduction in the levels of ciliary Rab8 and Rab11 ( Fig 8A ) . Previously , CEP164 has been shown to interact with Rab8 GEF Rabin8 and recruit Rab8 to primary cilia [17] . Our data support this model during multiciliogenesis . Distinct from primary cilia where Rab11 is detectable at pericentrosomal regions [20 , 22] , Rab11 localization extends into the proximal portion of multicilia . It is possible that Rab11 is also present in primary cilia at very low levels beyond the detection limit of fluorescence microscopy . Alternatively , Rab11 may have additional unique functions in multiciliogenesis . In contrast to the Rabs , we observed increases in the ciliary localization of Arl13b and INPP5E in CEP164-KO multiciliated cells ( Fig 8B ) . While we cannot completely exclude the possibility that the increased ciliary localization of Arl13b and INPP5E in the absence of CEP164 results from the recruitment of all the protein to a few remaining immature cilia , these findings are still surprising in light of a prior report that CEP164 forms a multiprotein complex with Arl13b and INPP5E and is important for their trafficking to primary cilia [58] . Based on these data , we propose that , in multiciliated cells , CEP164 functions in the selective transport of certain vesicle types carrying unique cargos into the cilium . In doing so , CEP164 recruits Rab-positive membrane vesicles and limits the proportion of Arl13b- and INPP5E-containing vesicles . This model also concurs with the notion that the transition fibers act as a ciliary gate that regulates the entry and exit of ciliary proteins and vesicles [10 , 15] . FBF1 , another distal appendage/transition fiber protein , has been shown to facilitate the entry of IFT particles into the cilium [19] . Therefore , CEP164 may function in an analogous manner to FBF1 for ciliary membrane vesicles and ciliary membrane proteins . In summary , our data support a crucial role for CEP164 in multiciliogenesis . CEP164 recruits Cby1 , FAM92A , and FAM92B along with the Rab11-Rab8 axis to basal bodies to facilitate ciliary vesicle formation and subsequent basal body docking . During cilium elongation and maintenance , CEP164 may play a role in selective transport of certain types of vesicle with distinct cargos to the ciliary compartment . Finally , our CEP164 conditional KO mouse model will provide a basis for future investigations into the molecular mechanisms of primary and multiciliogenesis in vivo as well as the pathogenesis and mechanisms of ciliopathies .
All mice were handled in accordance with NIH guidelines , and all protocols were approved by the Institutional Animal Care and Use Committee ( IACUC ) of Stony Brook University ( #2010–1393 ) . CEP164 KO-first mice , which contain the promoter-driven Tm1a allele , were obtained from the MRC-Harwell , which distributes these mice on behalf of the European Mouse Mutant Archive [42 , 43] . CEP164 KO-first mice were crossed with the Flp deleter mouse line B6 ( C3 ) -Tg ( Pgk1-FLPo ) 10Sykr/J ( The Jackson Laboratory , #011065 ) to generate CEP164fl/fl mice [68] . Removal of the lacZ and neomycin-resistance cassettes was confirmed by polymerase chain reaction ( PCR ) genotyping analysis and subsequent electrophoresis . Subsequently , CEP164fl/fl mice were crossed with FOXJ1-Cre mice [52] to generate FOXJ1-Cre;CEP164fl/fl mice lacking CEP164 in multiciliated cells and the testis . A colony of CEP164 KO-first mice was maintained by intercrossing heterozygous mice while FOXJ1-Cre;CEP164fl/fl mice were generated by breeding FOXJ1-Cre;CEP164fl/+ with CEP164fl/fl mice . Primers for genotyping were: WT allele for CEP164 KO-first , 5’-CCATCTGTCCAGTACCATTAAAAA-3’ and 5’-CCCAGAATACAACATGGGAGA-3’ ( 215 bp ) ; KO allele for CEP164 KO-first , 5’-CCATCTGTCCAGTACCATTAAAAA-3’ and 5’-GAACTTCGGAATAGGAACTTCG-3’ ( 148 bp ) ; CEP164 floxed allele , 5’-CCATCTGTCCAGTACCATTAAAAA-3’ and 5’-CCCAGAATACAACATGGGAGA-3’ ( WT allele , 215 bp; floxed allele , 415 bp ) . Trachea , testis , and oviduct from adult mice were fixed with 4% paraformaldehyde ( PFA ) in phosphate-buffered saline ( PBS ) , pH 7 . 4 , overnight at 4°C , paraffin-embedded , sectioned at 5 μm , stained with hematoxylin and eosin using standard protocols , and mounted with Permount ( Fischer Scientific ) . For X-gal staining , testes from control WT or heterozygous CEP164 KO-first mice were fixed with 2% PFA and 0 . 25% glutaraldehyde in PBS overnight at 4°C , embedded in Optimal Cutting Temperature ( OCT ) compound ( Fisher Scientific ) , and snap-frozen in liquid nitrogen-cooled 2-methylbutane . The tissues were then sectioned at 5 μm , washed twice for 5 minutes each in wash buffer ( 0 . 01% sodium deoxycholate , 2 mM MgCl2 , and 0 . 02% NP-40 in PBS ) , incubated with X-gal ( 1 mg/ml ) in wash buffer for 48 hours at room temperature , washed twice for 5 minutes in wash buffer , and mounted with Permount . MEFs were prepared from E8 . 5 embryos of intercrosses between heterozygous CEP164 KO-first mice as previously described [28 , 69] , and extra-embryonic tissue was used for genotyping analysis . In brief , embryos were placed in 0 . 05% trypsin-EDTA , minced , and incubated in 0 . 05% trypsin-EDTA for 20 minutes at 37°C . Dissociated cells were plated out on glass coverslips in a 48-well plate and cultured in Dulbecco’s Minimum Essential Medium ( DMEM ) supplemented with 10% FBS ( Invitrogen ) and 100 U/ml penicillin/streptomycin . MEF cultures were allowed to grow until confluent , at which point ciliogenesis was induced by serum starvation for 48 hours . MTECs were isolated and cultured as previously described [25 , 28 , 53] . Briefly , tracheas were dissected from 2- to 6-month-old CEP164fl/fl and FOXJ1-Cre;CEP164fl/fl mice ( typically 4 tracheas per genotype per preparation ) , and tracheal epithelial cells were harvested after overnight incubation with 1 . 5 mg/ml pronase ( Roche ) at 4°C . Isolated MTECs were seeded onto collagen-coated Transwell permeable membranes made of either polycarbonate or polyester ( 6 . 5-mm diameter and 0 . 4-μm pore size; Corning Costar ) . Cultures were then allowed to proliferate in MTEC Plus media with retinoic acid ( RA ) until confluent , at which time an ALI was established and 2% NuSerum media with RA was provided only in the basal chamber of the Transwell ( ALId0 ) . MTECs were cultured until ALId14 to ensure full differentiation , unless otherwise noted . IF staining was achieved using standard protocols as previously described [25 , 28] . Briefly , MEF coverslips and MTEC membranes were fixed in either 4% PFA in PBS or ice-cold methanol-acetone ( 1:1 ) for 20 minutes at 4°C , washed three times with PBS for 10 minutes at 4°C , and blocked for 1 hour at room temperature with antibody diluent ( 5% bovine serum albumin [BSA] and 0 . 2% Triton X-100 in PBS ) and 5% goat serum . MEF samples were incubated with primary and secondary antibodies for 1 hour each at room temperature . MTEC membranes were incubated with primary antibody overnight at 4°C , followed by 1 hour of blocking with 5% goat serum in antibody diluent prior to secondary antibody incubation for 1 hour at room temperature . Subsequently , samples were washed three times with PBS for 5 minutes each . Finally , DAPI counterstain was performed for 2 minutes at room temperature , followed by two 5-minute PBS washes . Specimens were then mounted with Fluoromount-G ( SouthernBiotech ) . For analysis of primary cilia in the neural tube , E9 . 5 embryos were fixed in 4% PFA , cryoprotected with 30% sucrose and embedded in OCT compound for sectioning , followed by the IF staining procedure as described above . For IF staining of oviducts , paraffin sections were subjected to antigen retrieval with citrate buffer ( pH 6 . 0 ) , blocked with normal horse serum , and incubated with primary and secondary antibodies , followed by mounting with Prolong Gold with DAPI ( Invitrogen ) . For primary antibody information , see S1 Table . The secondary antibodies used were: goat anti-rabbit IgG conjugated with either DyLight 488 or DyLight 549 and horse anti-mouse IgG conjugated with either DyLight 488 or DyLight 549 ( Vector Laboratories ) . SVZ whole mounts were dissected as described previously [70] . Briefly , adult mice were anesthetized and decapitated . After brain removal , the lateral wall of the lateral ventricle was dissected and fixed in 4% PFA in PBS for 30 minutes on ice . Whole mounts were washed with PBS , blocked in blocking solution ( 10% donkey serum with 0 . 1% Triton X-100 in PBS ) , and incubated with primary antibodies for 24 hours at 4°C and secondary antibodies for 2 hours at room temperature in blocking solution . The secondary antibodies used were: goat anti-mouse IgG1 conjugated with DyLight 549 and goat anti-mouse IgG2b conjugated with DyLight 549 ( Jackson ImmunoResearch ) . Whole mount fields were randomly selected for imaging from the anterior-dorsal region of the SVZ . Images were processed and quantified using the FIJI/ImageJ software as previously described [70] . Outlines of the apical borders of ependymal multiciliated cells and the borders of basal body patches were traced manually in FIJI/ImageJ . Absolute areas were directly calculated and reported whereas fractional areas were calculated by dividing the basal body patch area by the apical cell surface area . The centroid of each area was calculated in FIJI/ImageJ , and the vector from the center of the cell and center of the basal body patch was then calculated based on those values . Basal body patch displacement was calculated by taking the magnitude of this vector . Fractional displacement was calculated by dividing the magnitude of the vector running from the center of the cell to the center of the basal body patch by the magnitude of a manually drawn vector running from the center of the cell through the center of the basal body and terminating at the cell border . Epifluorescence images were taken on a Leica DMI6000B epifluorescence microscope with an HCX PL Fluotar 100X/1 . 3 NA oil objective equipped with a DFC300FX camera . Confocal images were acquired from either a Leica SP5 or SP8X confocal microscope with a HC PL APO 100X/1 . 4 NA oil objective . For SIM imaging , MTECs were imaged using a Nikon N-SIM with a 100x/1 . 49 NA oil objective equipped with an Andor iXon3 897 EMCCD camera . All confocal and epifluorescence images were analyzed with Leica Application Suite X software while SIM images were analyzed with Nikon NIS-Elements image analysis software . Finally , all images were further processed with Adobe Photoshop and Illustrator . Samples used for TEM were processed using standard techniques [25 , 31] . Briefly , MTEC membranes and adult tracheas were fixed by immersion in 2 . 5% PFA and 2% glutaraldehyde in PBS overnight at 4°C . After fixation , samples were washed in PBS , placed in 2% osmium tetroxide in PBS , dehydrated in a graded series of ethanol , and embedded in Embed812 resin ( Electron Microscopy Sciences ) . Ultrathin sections of 80 nm were cut with a Leica EM UC7 ultramicrotome and placed on Formvar-coated slot copper grids . Sections were then counterstained with uranyl acetate and lead citrate and viewed with a FEI Tecnai12 BioTwinG2 electron microscope . Digital images were acquired with an XR-60 CCD digital camera system ( Advanced Microscopy Techniques ) . Centrioles in tracheal multiciliated cells were analyzed for the presence or absence of docked vesicles as previously described [25] . In brief , tracheas were dissected from P8 mice and cultured for 16 hours in a 5% CO2 atmosphere at 37°C in DMEM media supplemented with 10% FBS , 100 U/ml penicillin/streptomycin , 1 μg/ml insulin ( Sigma-Aldrich ) , and 300 ng/ml dexamethasone ( Sigma-Aldrich ) in the presence of 1 μM paclitaxel ( Sigma-Aldrich ) . Tracheas were then processed for TEM as described above . Two-tailed Student’s t-tests were used for quantification analysis as indicated , and p<0 . 05 was considered significant . In the figures , asterisks indicate p-values as follows: * , p<0 . 05; and ** , p<0 . 01 . | Lining the airways , brain ventricles , and oviducts , multicilia are small hair-like structures that beat in a whip-like motion to propel fluids , such as mucus , over cell surfaces . Dysfunction of multicilia arising from genetic perturbations is most prominently associated with a devastating disorder called primary ciliary dyskinesia ( PCD ) . PCD is a rare genetic disease characterized by hydrocephalus , chronic airway infection , and infertility . Furthermore , defective airway multicilia have been implicated in several respiratory diseases , including cystic fibrosis , asthma , and chronic obstructive pulmonary disorder ( COPD ) . While important to human health , the detailed molecular mechanisms of how multiciliated cells develop remain largely unknown . Here , we establish a new mouse model that lacks the key ciliary protein CEP164 in multiciliated cells . These mice recapitulate many symptoms of PCD patients such as hydrocephalus and infertility . We show that , in the absence of CEP164 , differentiation of airway multiciliated cells is severely perturbed at multiple steps . Importantly , our data also suggest that CEP164 differentially regulates the proper recruitment of membrane-associated ciliary proteins . In summary , we have developed a powerful mouse model to study diseases affecting multicilia and shed light on novel roles of CEP164 in multiciliogenesis . | [
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"trachea"... | 2017 | Conditional knockout mice for the distal appendage protein CEP164 reveal its essential roles in airway multiciliated cell differentiation |
Vaccination represents an important instrument to control typhoid fever in humans and protects mice from lethal infection with mouse pathogenic serovars of Salmonella species . Mixed infections with tagged Salmonella can be used in combination with probabilistic models to describe the dynamics of the infection process . Here we used mixed oral infections with tagged Salmonella strains to identify bottlenecks in the infection process in naïve and vaccinated mice . We established a next generation sequencing based method to characterize the composition of tagged Salmonella strains which offers a fast and reliable method to characterise the composition of genome-tagged Salmonella strains . We show that initial colonization of Salmonella was distinguished by a non-Darwinian selection of few bacteria setting up the infection independently in gut associated lymphoid tissue and systemic compartments . Colonization of Peyer's patches fuels the sustained spread of bacteria into mesenteric lymph nodes via dendritic cells . In contrast , infection of liver and spleen originated from an independent pool of bacteria . Vaccination only moderately reduced invasion of Peyer's patches but potently uncoupled bacterial populations present in different systemic compartments . Our data indicate that vaccination differentially skews the capacity of Salmonella to colonize systemic and gut immune compartments and provide a framework for the further dissection of infection dynamics .
While infections with Salmonella enterica serovar Typhi and Paratyphi are estimated to affect some 27 million individuals each year [1] nontyphoidal strains of Salmonella can also cause life-threatening invasive disease , in particular in immunocompromised patients and by selected Salmonella lineages in African countries [2] . Infection with Salmonella typically occurs upon consumption of contaminated water or food and thus safe water supply is the best measure to control typhoid and paratyphoid fever [3] . Consequently , due to lack of proper sanitation in developing countries , vaccines remain an important instrument to control typhoid fever [4] . At present two licensed typhoid fever vaccines are available , an injectable capsular polysaccharide vaccine and an orally applied live attenuated bacterial mutant . Parenteral as well as oral vaccines are well tolerated and similarly effective . Still , parenteral and oral vaccines differ with respect to the quality of the induced immune response . Oral vaccines in first instance target gut associated lymphoid tissue ( GALT ) , foremost Peyer's patches ( PP ) , and therein induce a strong gut-directed secretory Ig ( SIg ) A response [5] . In contrast , parenteral vaccination does not trigger PP and results in comparably weak SIgA responses . The functional relevance of this difference remains uncertain . Intuitively , oral vaccines that recapitulate the natural route of infection might be considered superior compared to the more artificial parenteral route of immunization . Indeed , early studies using mice carrying hybridoma tumors producing IgA directed against a surface epitope of Salmonella demonstrated protection after oral but not systemic Salmonella infection [6] . Along the same line , infection with a self-limiting invasive Salmonella strain yielded high levels of SIgA in the intestine and conferred protection against challenge with fully virulent Salmonella , whereas a non-invasive variant that failed to elicit strong SIgA responses did not provide protective immunity [7] . These observations suggest that vaccine-induced SIgA exerts a substantial protective effect at the mucosal surface . Still this interpretation contrasts with other reports . Oral infection with an attenuated Salmonella strain protected polymeric Ig receptor ( pIgR ) -deficient mice that fail to transport and secrete Ig into the gut lumen [8] , against secondary infection . Similarly , mice incapable of producing class switched Ig , were protected against infection with fully virulent Salmonella by immunization with attenuated Salmonella [9] . In humans , a couple of well controlled studies documented antibody and cell-mediated immune responses after natural Salmonella infection and vaccination ( reviewed in [10] ) . High serum Immunoglobulin ( Ig ) levels against Salmonella antigens correlated with better protection [11] and antibodies protect against bacteremia caused by non-typhoidal Salmonella strains [2] , [12] . Still , since Salmonella mostly resides within host cells , cell-mediated immune responses are considered to take the lion share in limiting the progression of typhoid infection . The difficulty to gauge the contribution of different immune defense mechanisms such as cell-mediated versus humoral immunity to the protection against Salmonella is linked to limited insight into the dynamics of the infection process . Bacterial loads are commonly described as colony forming unit ( CFU ) , i . e . the number of bacteria recovered under a given condition . In combination with kinetic studies , the CFU adequately describes overall changes in bacterial numbers in a given organ . Still , changes in CFU do not allow disentangling distinct aspects of the infection process such as bacterial growth and death rates , immunological activity , pathogen dissemination and others . However , access to such information could identify critical steps in the infection process and guide the development of vaccines and anti-infective therapies . One way to overcome the limitations of classical infection experiments is to perform co-infections with tagged pathogens and to apply probabilistic models . Such approaches offer unprecedented insights into the dynamics of bacterial populations and may change fundamentally our understanding of bacterial infections [13] , [14] . However , so far the potential of such approaches has not been fully exploited . Pioneering studies used PCR or array hybridization to identify the tagged pathogens [15]–[18] . However , allocation of tagged pathogens by PCR or hybridization is a tedious process that seriously limits efficiency and resolution of these approaches . Here we used next-generation sequencing ( NGS ) of nucleotide-tagged Salmonella to identify bottlenecks in the infection process in naïve and vaccinated mice . We exploit stochastic variations in the presence of individual sequence-tagged strains to estimate the number of Salmonella setting up the infection and compare the composition of tags in different compartments to describe their relatedness and deduce routes of pathogen dissemination . We reveal vaccination-sensitive and insensitive steps of the infection process in otherwise non-manipulated wild type mice . Our results suggest that vaccination has only a modest effect on initial colonization but potently restricts pathogen spread in systemic compartments .
Vaccination may limit initial invasion , affect subsequent growth of bacteria and/or affect pathogen dissemination . To describe the dynamics of the Salmonella infection process in mice , we performed infections with mixed inocula of genome-tagged strains of Salmonella enterica serovar Typhimurium ( S . Typhimurium ) . For creating such tagged strains , primers carrying a random nucleotide sequence were used to generate genomic integrations , disrupting the endogenous proV gene and introducing an artificial stop codon . A total of 23 strains carrying individual sequence tags was selected for easy-to-discriminate tag sequences and used for further experiments . The proV gene is one of three redundant genes under the proU operon , which encodes a glycine-betaine/L-proline binding transport system during high osmolarity situation . Genetic disruption of proV is thought to have no effect on Salmonella virulence [19] . Still , before applying our tagged collection of strains in infection experiments , we compared the fitness of the wild type and its isogenic genome-tagged strains for their virulence in C57/BL6 mice . After either oral or intra peritoneal infection the parental S . Typhimurium strain and its tagged variants revealed comparable bacterial loads in the mesenteric lymph nodes ( mLN ) , PP , livers and spleens ( Fig . S1 ) . This confirmed that disruption of the proV gene did indeed not affect colonization or expansion of S . Typhimurium in vivo . According to a previous suggestion for naming such collections of strains [17] , we refer to our library of tagged strains as wild type isogenic tagged strains of S . Typhimurium , or WITS . To increase the recovery rate of a given WITS , we first used an equal mixture of 10 instead of 23 WITS to orally infect C57BL/6 mice . Two days after infection , cecum contents and minced Peyer's patches ( PP ) , mesenteric lymph nodes ( mLN ) , livers and spleens were plated . Individual colonies were picked up , the tagged proV gene was amplified by PCR and sequenced by Sanger technology . When analysing the representation of the individual WITS , we observed that within single compartments , some WITS were strongly over- or underrepresented , and some WITS were entirely undetectable ( Fig . 1 ) . Since we analysed only a low numbers of colonies , we evaluated whether absence of some WITS might simply reflect sampling and analysing a low number of colonies . However , in silico simulation suggested that most experimentally observed WITS compositions were highly unlikely to occur by chance ( data not shown ) . This indicated that already 2 days post challenge selection processes profoundly shaped the bacterial population . Moreover , we observed striking differences between compartments , and individual WITS dominating one particular compartment could be less frequent or undetectable in others ( Fig . 1 ) . In particular , the representation of individual WITS as observed in the cecum did not show a congruent match to the situation observed in liver , spleen or mLN of the same mouse . Similarly , when comparing the distribution of WITS among liver , spleen and mLN , no consistent representation of WITS was found . This suggested that independent selection processes occur within the intestinal lumen and beyond the gut . Notably , when we pooled sequences from all infected animals and compartments , we observed a fairly equal contribution of the 10 WITS . Moreover , all 10 WITS were represented in most compartments analysed and each of the WITS was absent in some rare cases ( Fig . 1 ) . This further confirmed the equal biological properties of the individual WITS . Over-represented WITS might arise from stochastic variations in the infection process in combination with an overall low number of bacteria initially invading the tissue . Alternatively , over-represented WITS might have acquired beneficial mutations , which allowed them to outgrow other WITS in vivo and thereby resulting in a disproportionate representation of individual WITS . To distinguish between these alternatives , we performed sequential infection experiments in 129Sv mice . 129Sv mice , which are more resistant than C57BL/6 mice and survive S . Typhimurium infection , were infected with an equal mixture 10 WITS for 60 days . Similar to the findings described in the acute infection , individual WITS were unevenly represented in the mLN 60 days after infection ( Fig . S2 ) . Subsequently , we picked one over- and one under-represented WITS from two chronically infected mice , and used them at a 1∶1 ratio for a second round of infection in naïve 129Sv mice . Two days later , the contribution of both WITS was determined . In one case we observed a scenario opposite to the first round of infection/selection , in another case both WITS contributed roughly equally in the second infection ( Fig . S2 ) . Even though we did not perform extensive analysis on larger groups of mice , this results hints that the disproportional contribution of individual WITS might not be caused by acquirement of stable genetic mutations in the bacteria . Instead , a seemingly non-Darwinian selection process appeared to favour or repress individual WITS early after oral infection . We speculated that the seemingly random presence or absence of WITS within individual host compartments and their disproportional contribution to the bacterial load could be used to describe population dynamics of infecting bacteria . In case of numerous invasion events , i . e . if the number of bacteria entering a given compartment largely exceeds the number of WITS , all WITS should be evenly represented in the tissue . In contrast , our data rather suggested that a few founder bacteria set up the infection , effectively resulting in a heavily skewed representation of WITS and the absence of several WITS in a given compartment . Thus , we propose that determining the frequency of samples that lack one or more WITS allows an estimate of the number of bacteria that initially seed a given compartment and successfully contribute to the observed population . We refer to this number of bacteria that initially invade an organ and initiate a productive infection , as “tissue seeding units” , TSU . To translate experimentally observed frequencies of ‘one or more missing WITS’ into TSU values , i . e . the most probable number of bacteria contributing to the infection , we applied a stochastic model . As input parameters we allowed variable numbers of WITS to be represented at definable proportions . As output parameter the model provides the most likely TSU , i . e . the most likely number of invasive bacteria that corresponds to a given frequency of experimentally observed ‘missing WITS’ . Since real life experiments are limited to comparably small sample sizes/numbers of mice , we additionally allowed to define the number of samples . This enables us to determine confidence intervals besides the mean value expected for very large sample sizes . To calculate a TSU estimate on the basis of the frequencies of ‘one or more missing WITS’ , we established a software to determine TSU and confidence intervals . A detailed description of the model and the visual basic application ( VBA ) -coded macro , which runs in the Microsoft Excel environment , are available as supplementary material . Mixing the WITS at different proportions allows to shift the range of TSU that can reasonably be determined ( Fig . 2A ) . Irrespective of the number of samples analysed , only a single frequency of ‘one or more missing WITS’ in all samples can be determined and consequently each experiment will yield only a single TSU value . Still , the explanatory power of TSU data depends on the number of samples analysed and confidence intervals associated with a given TSU narrow with increasing numbers of samples ( Fig . 2A , right panel ) . With increasing numbers of WITS , the approach can be further modified to include the information of how many WITS are missing , instead of the ‘one or more missing WITS’ . With large numbers of WITS used in the mixed inocula , reasonably robust TSU values can be determined even for individual samples ( Fig . 2B ) . Picking and sequencing of individual clones is poorly suited to determine the frequency of ‘one or more missing WITS’ , because a high number of colonies need to be analysed by a low-throughput method . One straightforward method to reliably determine the frequency of ‘missing WITS’ is provided by fluorescence-tagged bacteria . Characterization of nucleotide-tagged bacteria by saturating sequencing techniques , i . e . when all clones are sequenced , represents a second approach ( see below ) . To challenge the TSU concept , we performed mixed inocula infections with fluorescent S . Typhimurium strains , here referred to as fluorescent WITS ( fWITS ) . To minimize the impact of the fluorescent marker on Salmonella virulence , all reporter proteins were expressed under control of an inducible promoter that is transcribed at very low rate in vivo but readily inducible in vitro by addition of arabinose to the medium . Two days after i . v . infection with a mixture of three fWITS , all fWITS were represented in individual PP at fairly similar proportions compared to the inoculum and we did not observe ‘one or more missing WITS’ ( Fig . 2C , left panel ) . Similarly , when an equal mixture of the fWITS was used for oral infection , the vast majority of all PP harboured all fWITS ( data not shown ) . To increase the occurrence of ‘one or more missing WITS’ , we used the fWITS at a 90∶5∶5 ratio that compared to the equal mixture is more suitable to observe higher TSU ( Fig . 2A ) . In this setting again all fWITS were represented in pooled PP ( data not shown ) . Yet , the situation changed when individual PP were analysed and similar to our observations with the nucleotide-tagged WITS library ( Fig . 1 ) , within single PP the representation of individual fWITS frequently differed from the inoculum . Additionally , at a 90∶5∶5 ratio we observed ‘one or more missing WITS’ in roughly half of all analysed PP , i . e . at least one fluorescent strain could not be detected ( Fig . 2C ) . Notably , for this analysis only samples/plates were considered that showed a sufficient number of colonies , so to exclude false ‘one or more missing WITS’ observations with a p value of 0 . 05 . For oral infections with 109 Salmonella cells , experimentally observed frequencies of ‘one or more missing WITS’ translated into 42 TSU per PP ( confidence interval 23–71 ) . Reducing the infection dose from 109 to 108 and 107 attenuated Salmonella resulted in a reduction of the TSU per PP ( Fig . 2D ) . However , the number of bacteria entering the PP did not decline proportionally with the infection dose . Similar TSU were observed at day 2 in PP of C57BL/6 and 129Sv mice as well as comparing SL1344 parental strain to the attenuated SL1344ΔaroA strain ( Fig . 2D and data not shown ) . This indicated that differences in host susceptibility to S . Typhimurium had no detectable effect on TSU . In contrast a Salmonella pathogenicity island-1 ( SPI-1 ) -deficient strain ( SL1344ΔhilA ) or a Salmonella strain lacking a fimbrial subunit ( SL1344ΔfimH ) showed very low TSU ( Fig . 2D ) . These results are consistent with the known function of SPI-1 in tissue invasion [20] and of FimH in binding to M cells [21] . While already the use of two WITS allows determining TSU , the practicability of this approach is limited because robust values can only be obtained for comparably large sample sizes/numbers of mice ( Fig . 2A ) . We therefore performed mixed infections with our nucleotide-tagged WITS library and used NGS to determine the representation of individual WITS . Mice were orally infected with an equal mixture of 23 WITS . 2 or 4 days post infection PP were minced and plated . Instead of picking individual clones , we recovered all grown colonies , extracted genomic DNA , bulk amplified the tagged proV gene by PCR and analysed the amplicons by NGS ( Fig . 3A ) . To ensure sufficient coverage for each sample , at least ten-fold more sequences than ‘input colonies’ present on the plate were analysed . Technical replicates showed that PCR amplification and NGS were highly reproducible and did not affect our results ( Fig . S3 ) . Consistent with our previous observations , we observed a disproportional representation of WITS in single PP and frequently one or more WITS were absent ( Fig . 3B ) . Since NGS analysis of WITS composition ( Fig . 3 ) and characterisation of WITS composition by colony picking and Sanger sequencing ( Fig . 1 ) both show similar patterns , including strongly overrepresented as well as missing WITS , we suggest that these features of the WITS composition are unlikely to be caused by PCR bias or during NGS . TSU determined by NGS for individual PP stayed well within the TSU range predicted on the basis of fWITS . Moreover , the TSU predicted for PP did not depend on the day of analysis , i . e . 2 and 4 days post infection we observed comparable TSU , even though CFU were much higher at day 4 compared to day 2 post infection ( Fig . 3C ) . In addition , the higher resolution offered by a more complex WITS library allowed to estimate TSU for mLN = 25 ( 13–36 ) , spleen = 24 ( 1–46 ) and liver = 36 ( 8–65 ) . Considering that each mouse has 8–12 PP , 2 days post infection the overall number of S . Typhimurium entering PP by far exceeds the number of bacteria reaching mLN and/or liver and spleen . However , we also noted that 4 days after infection TSU in mLN tended to be higher than at day 2 ( Fig . 3C ) , whereas no such difference was observed in PP ( Fig . 3C ) . Such increase in TSU observed in mLN might indicate sustained dissemination of Salmonella from the gut into mLN . Since Salmonella-caused lethality prevented the analysis of later time points , we used the attenuated SL1344ΔaroA fWITS to compare TSU at 2 , 4 and 7 days post infection . Indeed we observed that comparing day 2 and 7 post infection , TSU were higher in mLN but not in PP ( Fig . 3D ) . Comparing TSU for different PP along the axis of the small intestine , we noted a tendency to higher TSU in distal compared to proximal PP that also reflected in moderately higher CFU ( Fig . 3E ) . In aggregate , these observations indicated that firstly , after primary infection between 2 and 4 days post infection there is no on-going invasion of PP from luminal Salmonella and secondly , no ‘new holes’ appeared in the WITS pool , i . e . WITS present at day 2 were also present at day 4 . This observation emphasizes that TSU do not correlate with CFU but represent an independent measure to describe the infection . Besides determining holes in the WITS pool , NGS characterization of WITS populations can be used to track S . Typhimurium dissemination routes . Compartments successively colonized from a joined set of bacteria should show a similar representation of WITS . In contrast , a dissimilar composition of WITS in two compartments rather indicates their independent colonization . Two days after oral infection the WITS representation in individual PP appeared random , i . e . the frequency of a given WITS present in one PP was not predictive for the frequency of the respective WITS in another PP of the same mouse ( Fig . 4B ) . To systematically compare the WITS composition in different compartments , we performed Pearson correlation analysis and calculated Morisita-Horn indices ( MHI ) to describe population similarity . For Pearson correlation analysis the frequency of each WITS in one compartment was plotted versus its frequency in a second compartment ( Fig . 4A and S4 ) . Consistent with the visual impression , comparison of proximal PP to distal PP showed a very low correlation coefficient and likewise only slightly stronger correlations were observed comparing middle to distal PP . Still , the Pearson's correlation coefficient is sensitive to skewed distributions and outliers that are an inherent feature of the WITS data discussed here . We therefore used the Morisita-Horn index ( MHI ) to quantify similarities in the WITS composition seen in different compartments . The MHI gives weight to unique species such as missing WITS and provides a weighted measure of population similarity . A MHI of one identifies identical populations , whereas a MHI of zero reflects entirely dissimilar populations . Comparing the WITS composition between different PP low MHI were observed for the comparison of proximal to distal and slightly higher MHI were observed for middle to distal PP . In sum , these results indicate that S . Typhimurium invading PP did not undergo strong selection in the gut lumen and colonization of individual PP is an independent process . We next characterized Salmonella dissemination and invasion frequencies in S . Typhimurium aroA vaccinated mice as compared to non-vaccinated age matched controls . Mice were vaccinated with SL1344ΔaroA 40–50 days before secondary infection with either a fully virulent SL1344 or an SL1344ΔaroA strain . CFU in target organs were consistently higher in naïve mice as compared to mice vaccinated with the attenuated aroA-deficient S . Typhimurium strain . However , the level of protection as seen as fold reduction in CFU differed for the various compartments ( Fig . 5 ) . In particular , after challenge with the virulent SL1344 strain , the drop in CFU observed in vaccinated compared to non-vaccinated mice was less pronounced in PP compared to mLN and spleen . This indicates that vaccination did not protect all compartments to the same extent . When infecting vaccinated and non-vaccinated mice with the WITS library and probing proportions of WITS 2 days post infection , uneven WITS composition and holes in the WITS library were observed in mLN , liver and spleen of both vaccinated and non-vaccinated mice ( Fig . 6A and S4 ) . When comparing the WITS composition between various compartments in non-vaccinated mice , the highest MHI were observed at PP to mLN and liver to spleen comparisons ( Fig . 6B and S4 ) . In contrast , the WITS composition in PP and mLN was largely dissimilar to the composition noted for the liver and spleen . This indicates that S . Typhimurium colonizing systemic compartments did not originate from PP or mLN . Consistently , we observed that depletion of CD11c-expressing cells resulted in decreased TSU in mLN but not in liver and spleen ( Fig . S5 ) . Depletion of CD11c-expressing cells foremost affects dendritic cell ( DC ) numbers . Thus , reduced TSU after DC depletion in mLN but not systemic compartments supports our previous suggestion that dissemination from gut/PP to mLN but not to liver and spleen relies on DC-mediated transport of Salmonella [22] . Instead , colonization of liver and spleen seems to originate from an independent pool of bacteria . In contrast to non-vaccinated mice , in orally vaccinated mice similarity of WITS composition was reduced for all compartments . Still the strongest effect was apparent for the comparison of liver and spleen , whereas the association of PP and mLN was less affected ( Fig . 6B ) . This indicates that vaccination does not only affect killing of Salmonella-infected cells by cell-mediated immunity but also alters Salmonella migratory routes , potentially by the action of vaccination-induced Salmonella-specific antibodies . Antibodies might be particularly effective in uncoupling colonization of liver and spleen , whereas the lymphogenic spread of Salmonella from PP to mLN might be less sensitive to antibody-mediated protection . Besides serum antibodies , oral and to a lesser extent parenteral vaccination induces SIg , predominantly SIgA , secreted into the gut lumen . SIg may shield host tissues from pathogens but also modulate sampling of SIgA-bound antigen [23] . The invasion frequency of Salmonella into PP of vaccinated mice was significantly reduced compared to non-vaccinated age-matched controls but still substantial numbers of bacteria were able to invade ( Fig . 6C ) . This indicates that even though vaccination is insufficient to completely shield PP from invasion , vaccinated mice will still be confronted with a reduced number of Salmonella after oral infection . This difference might be decisive in natural settings that , compared to the very high number of Salmonella used in experimental infections , typically are distinguished by lower numbers of Salmonella taken up with the diet – an observation that warrants to be considered in the development of novel anti-Salmonella vaccines .
Within-host changes in bacterial loads are influenced by numerous factors , including bacterial replication , dissemination and killing by the host . To understand how these and other factors determine bacterial loads is critical to develop anti-infective strategies . Thus , sophisticated technical approaches have been established to dissect the impact of distinct bacterial and host factor on the course of bacterial infections . One approach to distinguish bacterial replication and killing of bacteria used non-replicating elements ( reviewed in [24] ) . Similar to this study , other reports used mixed infection with tagged bacteria . Fluorescently tagged Yersiniae enterocolotica [25] and oligonucleotide-tagged Y . pseudotuberculosis [15] have been used to describe the dynamics of systemic infection in mice . A chief finding from such studies is that systemic infection may originate from clonal expansion of only few individual bacteria residing in infection foci [25] . While being chiefly an intracellular pathogen during the systemic phase of the murine infection , microscopic studies on S . Typhimurium-infected murine livers have also revealed individual infection foci of infected macrophages [26] . Likewise , seminal studies using two or three genetically tagged S . enterica strains in combination further imply that also productive infection is initiated from selected infective units rather than through an uniform progression of the infection from the mixed inoculum [27] . Manipulation of host and pathogen factors can be expected to further alter the outcome of infection dynamics , and to provide fundamental insights into infection pathogenesis . However , the approaches listed above may fall short of describing the fine architecture of pathogen dynamics and relevant information required for the rational design of anti-infective therapies [14] . Here we used mixed inocula of tagged genetically tagged S . Typhimurium ( wild type isogenic tagged strains; WITS ) , identifiable by rapid next-generation DNA sequencing , to detail in vivo population dynamics in naïve and vaccinated mice after oral infection . Following oral infections with a WITS library , we observed a highly uneven representation of WITS in target organs and frequent holes in the WITS library . NGS offered an affordable and fast method to characterize such WITS compositions . In this study typically more than 10-fold more sequences than bacteria present in the sample were analysed – still depending on total CFU and the number of different WITS , far fewer sequences are sufficient to reliably describe the WITS representation in target organs . We propose firstly that holes in the WITS pool can be used to estimate the number of bacteria productively seeding a given compartment; and secondly , that comparison of WITS representation between samples allows describing routes of Salmonella dissemination . Both properties of the WITS pool , ‘holes’ and skewed representation of individual WITS , seem interconnected and a consequence of an overall low number of S . Typhimurium initially seeding target organs . We assume that detectable WITS regularly expand from single bacteria and subsequently grow up to either overrepresented WITS clones or show low net growth and eventually contribute to the overall bacterial load to a much lower degree . Thus , the particular nature of the infected host cell might determine the contribution of the respective bacteria to the overall bacterial population . This observation is consistent with the previously described ‘dormant’ Salmonella , bacteria which are infecting host cells but not undergoing direct replication [28] . Consequently , we did not use the frequency of a given WITS to estimate invasion frequencies but instead only considered absence/presence of a given WITS . The occurrence of ‘holes’ in the WITS representation can be used to estimate the number of bacteria that initially seeded a compartment . We refer to such values as tissue seeding units – TSU . TSU for PP , mLN , liver and spleen were typically below 50 , i . e . less than 50 founder bacteria set up the infection . Considering the number of 109 Salmonella used for oral infection these values seem surprisingly low . Yet , similar observations were recently reported in the “streptomycin model” of Salmonella infection . In this model despite presence of 109 Salmonella in the cecum only about 300 Salmonella reached the draining lymph nodes per day [18] . TSU were similar for attenuated ( ΔaroA ) and wild type S . Typhimurium as well as in susceptible C57BL/6 mice and more resistant 129Sv mice ( data not shown ) . This indicates that metabolic attenuation as well as relevant host factors affecting the killing of incoming Salmonella did not affect the number of bacteria initially breaching the gut barrier . Still the TSU is based on detectable bacteria in a specific organ and we cannot rule out that higher numbers of bacteria initially entered host tissues but are undetectable even in susceptible hosts . Irrespectively , TSU measurement allows uncoupling initial invasion from later steps of the infection process and reveals that entry from the gut lumen into host tissues represents the first bottleneck in oral infections that is overcome by only few bacteria . Comparing the TSU at different time points after infection provides information on the spreading of the infection over time , i . e . increasing TSU with time might indicate sustained colonization of a given compartment . Such effect could be observed in mLN but not liver and spleen , i . e . in mLN TSU at later time points were higher compared to day 2 after infection . Increasing TSU in mLN over time might be fuelled by infected PP , which in sum encompassed all 23 WITS present in the library . Salmonella can be transported from the gut to mLN within DC . Colonization of the mLN is reduced in CCR7-deficient mice that display impaired migration of DC [22] , [29] and also in the streptomycin model about 10-fold reduced rates of Salmonella entering draining lymph nodes were observed [18] . Here we show that depletion of DC reduces the number of Salmonella entering mLN but not liver and spleen and TSU in mLN but not systemic compartment gradually increased during the infection . A higher resolution of S . Typhimurium dissemination within the host could be achieved by comparing the WITS frequencies between different compartments . Comparison of WITS representation between individual PP showed that each PP becomes colonized by a unique set of WITS . This indicates that the invasive WITS pool is not shaped within the intestinal lumen . The WITS representation within pooled PP was most similar to gut draining mLN and considerably less similar to spleen and liver . This indicates that colonization of liver and spleen does not originate from GALT and/or gut draining mLN . Barnes and colleagues made a similar observation for the spread of Y . pseudotuberculosis [15] . In their report a set of individually tagged Y . pseudotuberculosis strains was generated and their presence was determined by PCR amplification and hybridization [15] . Comparing the presence or absence of tagged strains in spleen and mLN very little overlap was observed , indicating that similar to the situation for Salmonella described herein , for both pathogens , infection of spleen and liver does not route through gut lymphoid tissues . The technical approach used in the Yersinia study did not allow determining the frequency of the tagged strains but only considered the presence or absence of a strain . Similarly , in our study we used the number of ‘missing WITS’ to estimate TSU . In contrast , however , we used NGS to avoid problems with the detection of low abundant clones and to reliably determine holes in the WITS pool . Underrepresented WITS might easily be missed by hybridization- or PCR-based approaches . Yet , in case of S . Typhimurium , the detection of low abundant WITS seems particularly important , because we found that WITS compositions were highly skewed . In further contrast to the Yersinia approach , the characterization of WITS composition by NGS allows to determine the frequency of individual WITS among the non-missing clones , which provides relevant information to compare the WITS composition between compartments . As another technical approach , quantitative PCR was used by Grant and colleagues to identify WITS after systemic infection . Minutes after systemic infection , all WITS were detectable in blood , whereas at later time points individual WITS were absent , indicating that host killing of bacteria and concomitant bacterial replication can result in the formation of subpopulations of bacteria within tissues [17] . Here we did not investigate any time point earlier than day 2 post infection and thus cannot rule out that such processes can contribute to the formation of holes in the WITS pool . Indeed the highest similarity in WITS representation was observed for the comparison of liver and spleen , an observation that is easiest explained by an early mixing of WITS between both organs . Notably , similarity in WITS representation between liver and spleen was lost in vaccinated mice . In vaccinated mice , exchange of WITS between liver and spleen might be limited by the action of Salmonella-directed antibodies . In this case , the TSU observed in liver and spleen would represent the sum of bacteria seeding the individual compartments and TSU should be lower in vaccinated mice compared to naïve mice . Indeed we observed a tendency to lower TSU in the spleen of vaccinated mice compared to non-vaccinated mice . Still mixing of bacterial subpopulations generally does not seem to occur freely . In 129Sv mice that survive S . Typhimurium infection and become chronically infected , the WITS composition in spleen and liver was dissimilar and retained a disproportional WITS distribution similar to the situation observed at day 2 of acute infection . Comparing the TSU in PP of vaccinated and naïve mice , we observed lower TSU in the former group . This shows that vaccination reduces but does not shield PP from Salmonella invasion and contrast with other reports suggesting that SIg was dispensable for vaccine-mediated protection [8] , [30] . Still , reduction in TSU observed in vaccinated mice is difficult to explain other than by Salmonella-directed SIgA . We therefore propose that experimental infections using CFU and/or clinical symptoms as readout might have missed the vaccine-mediated protection of PP . However , since infection of PP does not carry on to spleen and liver , reduced entry into PP might have only little effects with respect to colonization of spleen and liver . Similarly , there is little evidence to support a role of gut epithelial cell invasion for Salmonella infection in vivo and its overall impact to disease progression . Instead systemic compartments seem to be colonized by a low number of bacteria presumably directly entering the blood circulation within gut tissues . In conclusion , we have established and validated a NGS-based method to describe Salmonella population dynamics . NGS offers a robust method to characterize WITS compositions and in contrast to other methods benefits from the high resolution offered by a complex WITS library . We propose that the technical approach described in this study may help the characterization of critical steps during bacterial infection and spur the development of new anti-infective therapies . We show that infection in PP fuels spread to the mLN but not liver and spleen . Vaccination uncoupled colonization of liver and spleen and moderately reduced colonization of PP , a result that can best be explained by the presence of Salmonella-directed SIg in orally vaccinated mice .
Experiments involving animals were performed in accordance with the German Law for the Protection of Animal Welfare ( Tierschutzgesetz ) . Experiments were approved by the Lower saxony state office for consumer protection and food safety ( Landesamt für Verbraucherschutz und Lebensmittelsicherheit , LAVES ) under the file numbers TVA 09/1771 , TVA 10/0164 and TVA 12/0915 . Salmonella strains were derivatives of S . enterica serovar Typhimurium strain SL1344 . SL1344ΔaroA and SL1344ΔhilA ( SPI-1 deficient ) were described previously [22] , [31] . Fluorescence-tagged variants were generated by transforming plasmid pBAD18 , containing the desired fluorochrome gene ( dsRed , GFP or mPlum ) under control of an arabinose inducible promoter , by electroporation . A 23-clones library of nucleotide-tagged S . Typhimurium SL1344 was established . Each clone was generated by integrating of a PCR product into the proV gene in the Salmonella chromosomal genome . The PCR product contains a degenerate four nucleotide sequence and a chloramphenicol resistance cassette ( amplified from the pKD3-plasmid ) , flanked by parts of the proV gene . As described previously [32] , integration was performed with the aid of a phage λ Red recombinase-containing plasmid pKD46 which was already present in the parental Salmonella strain . Each clone differed from one another by only 4 bp in the nucleotide tag gene sequence . To select suitable clones for the WITS library , individual colonies were picked , the tagged site amplified by PCR and sequenced . Clones to be included in the library were selected for easy to identify tag sequences with sufficient differences to avoid erroneous assignment of tags . In particular we avoided tag sequences , which by single nucleotide differences/sequencing errors can be interconverted . S . Typhimurium were grown in LB broth , until the culture had reached a density of OD6001 . 0–1 . 2 . Bacteria were washed with 3% NaHCO3/LB medium , and resuspended to final cell density of 1×109 bacteria ( SL1344 parental strain , ΔaroA and ΔhilA ) or 2 . 3×109 bacteria ( nucleotide-tagged Salmonella , each clone is represented by 108 bacteria ) per 100 µl NaHCO3/LB . Mice were infected by oral gavage with 100 µl bacterial suspension for 2 days ( acute infection experiment ) or immunized orally with 1×109 live attenuated SL1344 ΔaroA for at least 40 days . Vaccinated mice were given one dose of enrofloxacin ( Baytril , Bayer , Leverkusen ) orally at 2 . 5 mg in 100 µl PBS to clear any remaining Salmonella in the lumen , 2 days before the mice were re-infected orally with 1×109 CFU of SL1344 . The actual numbers of inoculated bacteria and of bacteria recovered from tissues were determined by serial dilution plating on selective LB agar plates with appropriate antibiotics ( 90 µg of streptomycin/ml or/and 100 µg of ampicillin/ml ) . Organs were dissected and homogenized with Ultra Turrax T18 ( Carl Roth , Karlsruhe ) . Aliquots from these homogenized tissue samples or the whole cell suspensions , were first mixed and vortexed with 3% Triton X-100 ( Sigma-Aldrich ) , then plated on selective LB agar plates and incubated overnight at 37°C . For fluorescence-tagged Salmonella clones colonies were counted under a fluorescence stereomicroscope ( Leica MZ16FA ) . Colonies were washed off in 4 ml PBS on a horizontal shaker . Bacteria suspensions were collected , centrifuged and pellets resuspended . For genomic DNA extraction 4 µl of the cell pellet was transferred to 1 ml of 0 . 1% SDS , 10 mM Tris HCL , 5 mM EDTA , and incubated at 60°C for 30 minutes to lyse the cells . Genomic DNA was extracted from each sample , by the conventional ‘Phenol-chloroform’ DNA isolation method . Ten ng of gDNA from each sample was used as template for a two step PCR reaction creating an amplicon library fully compatible to the multiplexing Illumina TruSeq DNA sequencing protocol ( size of first product = 138 bases , inclusive of the target sequence 70 bases: ACAGGACGAAGACCGTGAATATGGTTACGTCATTGAGCNNNNTGTGTAGGCTGGAGCTGCTTCGAAGTTC; Forward adapter primer sequence: ACACTCTTTCCCTACACGACGCTCTTCCGATCTACAGGACGAAGACCGTGAATATGG; Reverse adapter primer sequence: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGAACTTCGAAGCAGCTCCAG ) . The second step PCR was performed to add the multiplex tag ( MID ) assigned to each sample , while it extended the final PCR product to 193 base pairs in total length ( Forward multiplex adapter primer: AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTAC; Reverse multiplex adaptor primer: CAAGCAGAAGACGGCATACGAGATNNNNNNGTGACTGGAGTTCAGAC . Underlines indicate MIDs ) . QIAquickGel extraction kit ( Qiagen ) was used to extract and purify the PCR amplicons from the gel according to the manufacturer's instruction . The concentration of the final PCR amplicon library was adjusted and sequenced on Illumina a MiSeq system using MiSeq Reagent Kits ( 73cycles/323cycles ) at the Genome Analytics Group ( GMAK ) in Brunswick , Germany . The fluorescent images were processed to sequences and transformed to FastQ format using the Real Time Analysis Software RTA 1 . 17 . 22 ( Illumina ) . Low quality reads were discarded . Sequences were further processed with in house Microsoft Excel VBA ( Visual Basic for Applications ) based macros . Only sequences that comprised intact flanking regions up- and downstream of the nucleotide tag were included in the analysis and the frequency of every individual nucleotide tag was determined . Tag-sequences not represented in the WITS library are likely to be a consequence of sequencing errors and were discarded . CD11c-DOG mice [33] were injected intraperitoneally with diphtheria toxin ( DT ) at 1 µg per mouse 18 hours before Salmonella infection . The efficacy of DT-induced CD11c+ DC depletion in PP , mLN and spleen was confirmed by flow cytometry ( data not shown ) . Mice were bred at the central animal facility of Hannover Medical School under specified pathogen-free conditions . C57BL/6 mice and 129Sv mice were purchased from the Charles River Laboratory ( Sulzfeld , Germany ) . TSU were determined by a custom made Microsoft Excel VBA ( Visual Basic for Applications ) based macro ( Text S1 ) and provided as supplementary material . MHI was analysed with BiodivR 1 . 2 software ( Hardy , O . J . 2010 . BiodivR 1 . 2 . A program using rarefaction principles was applied to compute statistically unbiased indices of species diversity within sample and species similarity between samples ( http://ebe . ulb . ac . be/ebe/Software . html ) . MHI = 0 indicates that 2 samples do not overlap at all in terms of clonal representation , whereas MHI = 1 implies that both samples share the same set of clones and in similar proportions for each clone . Statistical analysis was performed with GraphPad Prism software . Unpaired nonparametric two-tailed t test ( Mann-Whitney test ) was used to determine inter-group significant values . Whisker bar lines represent 95% confidence interval range . Statistical differences of the mean values are indicated as follows: * , P value is <0 . 05; ** , P value is <0 . 01; and *** , P value is <0 . 001 . | Pathogens have evolved strategies to invade , replicate and spread within their hosts . On the contrary , vertebrates have developed sophisticated immune defence mechanisms that limit , and ideally clear , the infection . This dynamic interplay between host and pathogens determines the course of the infection and the development of clinical disease . Knowledge on particularly vulnerable steps in the infection process , i . e . the “Achilles heel” of a pathogen , may guide the development of anti-infective therapies and vaccines . However , for most pathogens we lack detailed information on the dynamics of the infection process . Here we determined bottlenecks , i . e . critical steps during pathogen invasion and spread , after oral Salmonella infection in non-manipulated and vaccinated mice . We infected mice with mixtures of tagged Salmonella strains and analysed the strain composition in different compartments by high throughput sequencing . This information allowed us to estimate the number of Salmonella invading a given tissue and to describe routes of pathogen dissemination . We show that vaccination only modestly reduces invasion of intestinal lymphoid tissue but had a profound effect on the spread of Salmonella to systemic compartments . | [
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"an... | 2014 | Independent Bottlenecks Characterize Colonization of Systemic Compartments and Gut Lymphoid Tissue by Salmonella |
Ongoing climate change can alter conditions for plant growth , in turn affecting ecological and social systems . While there have been considerable advances in understanding the physical aspects of climate change , comprehensive analyses integrating climate , biological , and social sciences are less common . Here we use climate projections under alternative mitigation scenarios to show how changes in environmental variables that limit plant growth could impact ecosystems and people . We show that although the global mean number of days above freezing will increase by up to 7% by 2100 under “business as usual” ( representative concentration pathway [RCP] 8 . 5 ) , suitable growing days will actually decrease globally by up to 11% when other climatic variables that limit plant growth are considered ( i . e . , temperature , water availability , and solar radiation ) . Areas in Russia , China , and Canada are projected to gain suitable plant growing days , but the rest of the world will experience losses . Notably , tropical areas could lose up to 200 suitable plant growing days per year . These changes will impact most of the world’s terrestrial ecosystems , potentially triggering climate feedbacks . Human populations will also be affected , with up to ~2 , 100 million of the poorest people in the world ( ~30% of the world’s population ) highly vulnerable to changes in the supply of plant-related goods and services . These impacts will be spatially variable , indicating regions where adaptations will be necessary . Changes in suitable plant growing days are projected to be less severe under strong and moderate mitigation scenarios ( i . e . , RCP 2 . 6 and RCP 4 . 5 ) , underscoring the importance of reducing emissions to avoid such disproportionate impacts on ecosystems and people .
Plant growth is a fundamental biological process that is strongly controlled by climate variables [1–6] . Plant productivity influences the functioning of ecosystems [7] , fuels the global food web [8] , and is the foundation for some of the most diverse habitats in the world [9] . Vegetation also sustains humanity [10–12] , directly providing oxygen , food , fiber , and fuel ( e . g . , an estimated ~30%–40% of the biosphere production is currently consumed or coopted by humans [13–18] ) and indirectly supporting livelihoods through jobs and revenue [19] . However , plant growth is strongly limited by climate variables such as air temperature , water availability , and solar radiation [1–4 , 20–22] , which are changing in response to ongoing climate change [23–26] . These changes are concurrent with a greater human demand on the planet’s resources , which could further stress natural ecosystems and lead to shortages in important goods and services [13 , 15 , 27–29] . While there have been considerable advances in understanding the extent to which individual [30] and multiple [4 , 6 , 31] climate variables limit plant growth [e . g . 20 , 23–25 , 32–34] , comprehensive analyses integrating climate , biological , and social sciences are less common . Here we provide a global-scale perspective , using climate model projections ( S1 Table ) and available socioeconomic and ecological data ( S2 Table ) , to assess how projected climate change will affect the suitability of the planet for plant growth and evaluate potential implications of these changes for ecosystems and people .
To assess the future limiting roles of temperature , water availability , and solar radiation on plant growth , we calculated changes in the number of days in a given year that are within suitable climate conditions for plant growth ( i . e . , suitable plant growing days ) under different climate projections ( see Methods; data used are described in S1–S2 Tables ) . We first estimated climatic thresholds ( i . e . , for temperature , soil moisture , solar radiation , and the interactions of these three factors ) within which 95% of the terrestrial vegetative matter in the world is produced ( Moderate Resolution Imaging Spectroradiometer [MODIS] Net Primary Production [NPP] from 2004–2013; S2 Table; see Methods; Fig 1 ) . We then used daily climate projections ( from the Coupled Model Intercomparison Project Phase 5 [CMIP5] ) under strong ( i . e . , representative concentration pathway [RCP] 2 . 6 ) , moderate ( i . e . , RCP 4 . 5 ) , and business-as-usual ( i . e . , RCP 8 . 5 ) mitigation scenarios to quantify the number of days in a given year that fall within climate thresholds for plant growth . We analyzed each climate variable independently as well as their interactions . We describe results based on multimodel averages because they are more accurate at predicting observed suitable growing days than most models alone ( results for precision and accuracy are shown in S2–S3 Figs ) . When we analyzed the limiting roles of temperature , soil moisture , and solar radiation independently , global average trends masked regional differences in the gains and losses of suitable plant growing days . As expected , we found that at mid- and high latitudes , projected warming will reduce the number of days below freezing , resulting in more suitable growing days ( the average global number of days above freezing will increase by 2% , 5% , and 7% under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively; Fig 2A , S5A–S5D Fig , S6A–S6C Fig ) [35] . However , we also found that warming will more often exceed the upper thermal threshold for plant growth , which will decrease the number of suitable growing days , mainly in the tropics ( Fig 2A , S5A–S5D Fig , S6D–S6F Fig , see also [35] ) . By 2100 , the decreasing number of suitable growing days in the tropics will offset optimistic projections at mid- and high latitudes , resulting in minimal changes in the global average number of suitable days under RCP 2 . 6 and RCP 4 . 5 but a ~26% reduction in the number of suitable growing days under RCP 8 . 5 ( solid blue lines in Fig 3 ) . For soil moisture and solar radiation , regional differences in the number of suitable plant growing days averaged out globally under all scenarios ( solid green and yellow lines in Fig 3 ) . Notably , projected changes in soil moisture ( Fig 2B ) and solar radiation ( Fig 2C ) showed contrasting spatial patterns . Areas that gained suitable days because of water availability also lost days because of solar radiation , and vice versa; this could be explained by coupled dynamics between rainfall and cloud cover [3] . Plant growth is strongly mediated by the extent to which multiple interacting climate variables remain within suitable conditions . When looking at the interaction between temperature and solar radiation , we found that the number of suitable plant growing days will decline more so than either variable independently ( 5% , 9% , and 29% under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively; dashed yellow lines in Fig 3 ) . This steeper decline is driven mainly by patterns at high latitudes , where gains in suitable plant growing days due to higher temperatures are offset by the fact that those places remain limited by light ( compare the intensity of blue colors in Fig 2A and 2F ) . In contrast , the interaction between temperature and soil moisture resulted in a smaller reduction in suitable plant growing days than the losses due solely to temperature ( 0% , 5% , and 19% under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively; dashed blue lines in Fig 3 ) . This smaller decline is driven mainly by patterns in arid regions ( e . g . , northern Africa , Australia , and the Middle East ) , where losses in suitable plant growing days due to higher temperatures are reduced because those locations are already limited by water availability ( compare yellow- and white-colored areas in Fig 2A and 2D ) . Changes in suitable plant growing days due to the interaction between solar radiation and soil moisture were minimal ( -2% , 0% , and 2% under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively; dashed purple lines in Fig 3 ) , although there was considerable spatial variability ( Fig 2E ) due to the coupling between rainfall and cloud cover . When looking at the interaction among all three climate variables , we found that the global average number of suitable days still decreased under RCP 8 . 5 but less so than when temperature was considered alone or in interaction with solar radiation or soil moisture ( -2% , 1% , and 11% under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively; dashed red lines in Fig 3 ) . Gains and losses in suitable plant growing days due to projected temperature changes alone are lessened because some regions are already limited by either solar radiation ( reducing gains at high latitudes ) or water availability ( reducing losses in arid regions ) . However , there is still an overall loss in suitable plant growing days , with some regions facing unsuitable conditions for multiple reasons . In addition to fewer plant growing days , unsuitable plant climate conditions will occur sporadically throughout the year , as indicated by our metric of continuous suitable plant growing days . We found that the longest uninterrupted number of days when all three climate variables remained within suitable climate ranges reduced considerably under RCP 4 . 5 and RCP 8 . 5 ( 5% , 13% , and 35% under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively; solid red lines in Fig 3 ) . While some areas at high latitudes ( most noticeably in Russia , China , and Canada ) will gain days with suitable conditions in all three climate variables ( Fig 2G , S5 Fig ) , many other areas will actually become limited by multiple climatic variables . For example , areas across the Sahel that are already limited by water availability will become increasingly limited by high temperatures by 2100 ( Fig 2 ) . These results highlight the risk for synergistic responses and concerns over biological and societal adaptations given the suite of physiological traits and social capacity needed to cope simultaneously with future changes in several climate variables . Reductions in the number of days with suitable climate conditions for plant growth also underscore an internal discrepancy of Earth System Models: while these models project dramatic enhancements of NPP [5 , 20 , 36] , our results show multiple climate variables becoming limiting for plant growth , particularly in tropical areas , which could result in considerable reductions in future NPP ( S4 Fig ) . This discrepancy likely reflects an overemphasis of CO2 fertilization in modeling NPP while failing to account for the limiting roles of other climatic variables and disturbances [5 , 22 , 36] . Furthermore , reductions in plant growth due to unsuitable growing days could lead to feedbacks whereby climate change is even more extreme , leading to even less suitable conditions for plant growth . The fact that unsuitable climatic conditions will occur more sporadically throughout the year highlights the potential for extreme events ( e . g . , heat waves or drought ) to truncate the growing period , which may impair plant growth and even cause mortality [5 , 21 , 37] . Reichstein et al . [5] recently concluded that “climate extremes…can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake , ” further highlighting an important research area for improvement of Earth System Models . Most of the world’s ecosystems and cultivated areas will be negatively affected by changes in the number of suitable growing days if climate change continues , possibly triggering climate feedbacks . Tropical ecosystems in particular ( e . g . , broadleaf evergreen forests; Fig 4 ) will lose suitable growing days due to temperatures exceeding the upper limit of the thermal range in combination with water failing to meet plant growth requirements . By 2100 , for example , broadleaf evergreen forests will lose about 3 wk of suitable growing days under RCP 2 . 6 ( Fig 4A ) but lose nearly 3 mo under RCP 8 . 5 ( Fig 4C ) . Prolonged unsuitable climatic conditions can prevent development of tropical forests [37] and result in tree die-offs , either directly from intolerance to altered climate conditions or indirectly through increased vulnerability to infestations by insects and pathogens [1 , 2 , 21] . In turn , such increased tree mortality can trigger ecological responses , including changes in plant community composition ( e . g . , from sensitive to less-sensitive species ) and range contractions or expansions [2] . Unsuitable climate conditions can lead to increased plant respiration , potentially turning forests into carbon sources rather than carbon sinks [4 , 5] . At the same time , fewer freezing days at higher latitudes could potentially accelerate carbon releases through microbial decomposition [38 , 39] , and this excess carbon might not be sequestered by plants , as higher latitudes will remain limited by insufficient solar radiation ( S6G–S6I Fig ) . Finally , the impacts of climate change on plant growth could alter ecological interactions among species with potential cascading effects on food webs; integrating changes in suitable plant growing days and NPP within recently developed General Ecosystem Models [40] could provide some insights into the magnitude of these changes . Losses in suitable plant growing days can translate into losses of food , fiber , fuel , and associated jobs and revenue , with potentially negative effects in countries with high reliance on those goods and services , particularly those with minimal capacity to adapt . Here , we assessed human vulnerability to changes in the number of suitable plant growing days by using a common method that distinguishes populations depending on their ( i ) “exposure” to environmental change , ( ii ) “dependency” on potentially impacted goods and services , and ( iii ) social “adaptability” [41–44] . We used changes in suitable plant growing days ( i . e . , between contemporary and 2100 , Fig 2G ) as our metric of exposure and collected agriculture-related and economic data to quantify dependency and adaptability ( see Methods ) . Under RCP 2 . 6 , no country will experience high losses or high gains in suitable plant growing days ( i . e . , reductions or gains greater than 30% of the current suitable growing period , S3 Table ) . However , human vulnerability will be much greater under RCP 8 . 5 . If climate change were to continue under this scenario , ~3 , 400 million people will live in countries facing reductions of 30% or more suitable plant growing days; of those people , ~2 , 900 million are highly dependent on plant-related goods and services , and ~2 , 100 million of those are in low-income countries ( S3 Table ) . A few countries in the Americas and all countries in Oceania , Asia , and Africa , with the exception of Australia , New Zealand , Russia , South Africa , Namibia , Algeria , and Libya , are highly vulnerable to reductions in plant growing days ( Fig 5 ) . Under RCP 8 . 5 , only ~270 million people live in countries projected to experience medium to high gains ( i . e . , greater than 10% ) in the number of suitable plant growing days ( e . g . , Iceland , Norway , Sweden ) . Vulnerability for all countries is shown in Fig 5 , S3 Table , and S2 Data . Our study adds to the understanding of projected changes in climate suitability for plant growth , highlighting where ecosystems and human populations could be more vulnerable to such changes . Although our study confirms a benefit of ongoing climate change on plant growing conditions at higher latitudes because of fewer freezing days , this considerably underestimates the full extent of consequences of projected climate changes , particularly under business-as-usual projections . Consideration of an upper thermal limit and interactions with plant growth thresholds in additional climatic variables resulted in the opposite trend: global decreases in the number of suitable plant growing days by 2100 ( Fig 2 ) . The unprecedented rate and number of climate variables becoming limiting for plant growth could challenge the capacity of species to adapt , with the potential to negatively impact terrestrial ecosystems and trigger climate feedbacks . Potential reductions in plant growth associated with fewer plant growing days are particularly worrisome given that the largest impacts are expected to affect the poorest and most agriculturally dependent countries in the world ( Fig 5 ) . These effects will be further exacerbated by increasing human appropriation of NPP associated with human population growth ( Fig 6 , S4 Fig ) . On a positive note , our study also indicated that projected changes in suitable plant growing days are minimal under RCP 2 . 6 , underscoring the importance of reducing emissions to avoid such disproportionate impacts on ecosystems and people .
We used the rate at which terrestrial vegetative matter is produced ( NPP ) as a proxy for plant growth . Derived values of NPP were obtained from 8-d averaged MODIS data ( the finest temporal resolution available; data source in S2 Table ) . MODIS NPP data are modelled using remotely sensed satellite data and have been cross-validated by other studies [45] . To estimate climate thresholds for plant growth , we overlaid 8-d maps of derived NPP onto 8-d maps of observed temperature ( i . e . , near-surface air temperature ) , water availability ( using soil moisture in upper 10 cm of the soil column as proxy ) , and solar radiation ( i . e . , surface downwelling shortwave radiation ) ( sources provided in S2 Table ) . This allowed us to calculate the total amount of 8-d NPP produced along gradients of each of the three climate variables and their interactions . We defined NPP climatic thresholds as the boundaries that surround the climatic conditions under which 95% of the world’s NPP occurs for each variable ( Fig 1A–1C ) and their interactions ( Fig 1D–1G ) , for each year between 2004 and 2013 . For our analysis , we used the boundaries encompassing all of the yearly boundaries ( Fig 1 ) and define suitable growing days as those days in which projected climatic conditions fall within that multiyear boundary . While some plants grow under extreme conditions , relatively little NPP occurs in these primarily cold and arid places ( as noted by the steep declines of NPP along climatic variables in Fig 1 ) ; using more than 95% of global NPP to include these extremist plants will considerably broaden the climate thresholds and overestimate global suitability for the majority of plant growth . An upper threshold for radiation was rarely exceeded ( S7B Fig ) , but we retained it to maintain consistency with the analysis of other climatic variables . To compare global thresholds to ecosystem-specific thresholds , we repeated the above approach using NPP within cells that overlap each of 14 land-cover types ( based on satellite-derived maps of dominant ecosystem type; S8 Fig , data sources provided in S2 Table under “Land use data” ) . We used global thresholds to calculate suitable plant growing days , as they encompassed the bulk productivity of most ecosystems ( S8 Fig ) . However , some ecosystems that already frequently experience extreme conditions surpassed global thresholds ( e . g . , semidesert wooded grassland/shrubs , S8 Fig ) , suggesting that these ecosystems could better cope with future climate projections . Climatic thresholds were also very similar if they were weighted by the area where climatic conditions occur ( S1 and S8 Figs ) . All data sources are listed in S2 Table . To estimate the number of suitable days for plant growth each year , we counted the total or consecutive number of days in a year in which climatic conditions ( i . e . , temperature , soil moisture , solar radiation , and the interactions of these three variables ) fall within the global thresholds for plant growth . We obtained daily projections of temperature , soil moisture , and solar radiation from recent Earth System Models developed as part of the Coupled Model Intercomparison Project Phase 5 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ( S1 Table ) . Daily projections run from 1950 to 2005 simulating anthropogenic and natural climate forcing ( i . e . , “historical” experiment ) and from 2006 to 2100 under three alternative representative concentration pathways: RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 . CO2 concentrations will reach ~400 , ~530 , and ~930 ppm by 2100 , under RCP 2 . 6 , RCP 4 . 5 , and RCP 8 . 5 , respectively . As of November 2014 , there were 14 Earth System Models from 12 centers in eight countries that modeled temperature , soil moisture , and solar radiation at a daily resolution for at least one of the three RCPs ( S1 Table ) ( Note: all Earth System Models that we used include feedbacks of plant production on water balance ) . In total , for all variables and projections , we processed ~1 . 8 million daily global maps . We quantified the number of suitable plant growing days independently for each model and averaged the results to estimate the multimodel average . Changes in the number of suitable plant growing days ( Fig 2 ) were calculated by subtracting contemporary ( 1996 to 2005 ) from future averages ( 2091 to 2100 ) ; decadal averages were chosen to minimize aliasing by interannual variability . To assess exposure of different terrestrial ecosystems to projected changes in climate suitability ( Fig 4 ) , we calculated the mean and frequency distribution of changes in suitable plant growing days ( Fig 2A–2C and 2G ) for cells dominated by each of 14 land-cover types . All data sources are indicated in S2 Table . “Vulnerability” was assessed in the traditional sense of determining human “exposure” to environmental change , “dependency” in terms of food , jobs , and revenue at stake , and “adaptability” in terms of wealth , assuming that richer countries will have more capacity to respond [41–43] . “Exposure” was quantified as changes in climate suitability for plant growth categorized for each country as follows: “high loss” for countries experiencing reductions in suitable plant growing days in excess of 30% , “medium loss” for countries experiencing losses of 30% to 10% , “no change” for countries that gain or lose up to 10% , “medium gain” for countries gaining 10% to 30% , and “high gain” for countries gaining in excess of 30% more days . “Dependency” was quantified by adding three proportional metrics for each country: percentage of gross domestic product contributed by agricultural revenue , percentage of the workforce in the agricultural sector , and percentage of NPP appropriated by people ( from food , paper , wood , meat , fiber , and animal by-products ) [14] . Countries were categorized as having “low , ” “medium , ” or “high” dependency if their cumulative percentages in those three goods and services ranged from 0% to 33% , >33% to 66% , or >66% , respectively . Finally , “adaptability” was quantified as per capita gross domestic product , under the assumption that richer countries will have greater access to a wider range of adaptive strategies . For the purpose of classification , we used the World Bank categorization of low- , medium- , and high-income countries depending on whether annual per capita gross domestic product was less than US$4 , 000 , between US$4 , 000 and US$12 , 000 , or greater than US$12 , 000 , respectively . All data sources are shown in S2 Table . | Ongoing greenhouse gas emissions can alter climate suitability for plant growth , in turn affecting biological and social systems . Using the latest generation of available climate projections we show that there will be fewer days with suitable climates for plant growth , despite an increase in days above freezing . This decline in suitable plant growing days is due to interactions among unsuitable temperatures , light , and water availability . Our analysis shows that reductions in suitable plant growing days will be most pronounced in tropical areas and in countries that are among the poorest and most highly dependent on plant-related goods and services . Changes in suitable plant growing days will be less severe under strong and moderate mitigation scenarios , highlighting the importance of reducing emissions to ameliorate the biological and social impacts of these changes . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Methods"
] | [] | 2015 | Suitable Days for Plant Growth Disappear under Projected Climate Change: Potential Human and Biotic Vulnerability |
Genetic makeup of the host plays a significant role in the course and outcome of infection . Inbred strains of mice display a wide range of sensitivities to Listeria monocytogenes infection and thus serve as a good model for analysis of the effect of genetic polymorphism . The outcome of L . monocytogenes infection in mice is influenced by the ability of this bacterium to induce expression of interferon beta mRNA , encoded in mouse by the Ifnb1 ( interferon beta 1 , fibroblast ) gene . Mouse strains that lack components of the IFNβ signaling pathway are substantially more resistant to infection . We found that macrophages from the ByJ substrain of the common C57BL/6 inbred strain of mice are impaired in their ability to induce Ifnb1 expression in response to bacterial and viral infections . We mapped the locus that controls differential expression of Ifnb1 to a region on Chromosome 7 that includes interferon regulatory factor 3 ( Irf3 ) , which encodes a transcription factor responsible for early induction of Ifnb1 expression . In C57BL/6ByJ mice , Irf3 mRNA was inefficiently spliced , with a significant proportion of the transcripts retaining intron 5 . Analysis of the Irf3 locus identified a single base-pair polymorphism and revealed that intron 5 of Irf3 is spliced by the atypical U12-type spliceosome . We found that the polymorphism disrupts a U12-type branchpoint and has a profound effect on the efficiency of splicing of Irf3 . We demonstrate that a naturally occurring change in the splicing control element has a dramatic effect on the resistance to L . monocytogenes infection . Thus , the C57BL/6ByJ mouse strain serves as an example of how a mammalian host can counter bacterial virulence strategies by introducing subtle alteration of noncoding sequences .
Bacterial pathogens utilize a wide range of approaches to down-modulate or subvert host immune responses . L . monocytogenes is an intracellular pathogen that , following invasion of the host cell , is capable of escaping the host phagolysosomes and replicating in the cytoplasm . Within the cytoplasm , the bacterial DNA is thought to be recognized by an unknown host receptor , activating a signaling cascade that rapidly induces Ifnb1 expression [1] . This signaling cascade relies on TANK-binding kinase 1 ( TBK1 ) -mediated phosphorylation of IRF3 , a transcription factor that , following dimerization and translocation to the nucleus , induces expression of Ifnb1 [2–4] . In a murine model of infection , activation of host IFNβ signaling is an important L . monocytogenes virulence strategy . Mouse lines that lack components of the IFNβ signaling pathway ( Ifnb1 , Ifnar1 ) are significantly more resistant to L . monocytogenes infection [2 , 4–6] . A similar protective effect of a Irf3 knockout suggests that Toll-like receptor ( TLR ) –independent induction of IFNβ is detrimental to control of listeriosis [7] . Several independent observations suggested that IFNβ signaling sensitizes lymphocytes for cell death , leading to an increase in sensitivity to L . monocytogenes [8] . L . monocytogenes activates such proapoptotic genes as Trail ( Tnfsf10 ) , Pkr ( Eif2ak2 ) , and Daxx in spleen and bone marrow macrophages of wild-type , but not Ifnar-deficient mice [4] . This is consistent with the observation that Trail knockout mice are more resistant to L . monocytogenes infection [9] . It has also been noted that following infection , mice lacking components of the IFNβ signaling machinery have higher total numbers of macrophages . This could be due to the ability of Type I interferon signaling to accelerate cell death of L . monocytogenes–infected macrophages [3] . Inbred mouse strains are extensively used as a model system to study host immune response throughout the course of L . monocytogenes infection . In addition , common strains display a wide range of sensitivities to intravenous infection with L . monocytogenes [10] . Our initial analysis of genetic determinants affecting susceptibility to L . monocytogenes infection was carried out using a pair of differentially susceptible inbred mouse strains: BALB/cByJ and C57BL/6ByJ [11] . These strains were selected based on the ancestry of the 13-member CXB Recombinant Inbred ( RI ) Panel , which serves as a useful tool for mapping single gene traits [12] . While our study identified two major genetic loci that controlled differential sensitivity to L . monocytogenes infection , it was clear that there were additional genetic factors that we did not detect due to the limited size of our cross . Here , we report identification and characterization of one of these additional factors , a polymorphism in the C57BL/6ByJ inbred mouse strain that affects expression of Ifnb1 and results in increased resistance to L . monocytogenes infection . Our data demonstrate that a single base-pair polymorphism in intron 5 of Irf3 reveals an important role for splicing in control of IFNβ induction and innate immune function .
Our analysis of L . monocytogenes infection of macrophages derived from bone marrow ( i . e . , bone marrow macrophages; BMMs ) of BALB/cByJ and C57BL/6ByJ strains revealed strain-specific differences in infection-induced cell death . Across a range of time points and infectious doses , BALB/cByJ BMMs had consistently higher cell death then BMMs from C57BL/6ByJ mice ( Figure 1A and 1B ) . Interestingly , 18 h following infection with L . monocytogenes , there were also significant ( p < 0 . 001 ) differences in death of BMMs from J and ByJ substrains of the common C57BL/6 lineage ( Figure 1C ) . The observed differences in cell death could be due to small differences in replication of bacteria in infected BMMs ( Figure 1D ) . However , recent studies that have demonstrated a role for IFNβ signaling in the outcome of L . monocytogenes infection have also suggested that it plays a role in the survival of macrophages [2] . Therefore , we chose to test if there are mouse strain-specific differences in IFNβ signaling , and we analyzed the course of Ifnb1 mRNA induction in L . monocytogenes–infected BMMs . We found that Ifnb1 expression was rapidly induced in BALB/cByJ BMMs , increasing 400-fold by the 4-h time point . By contrast , in C57BL/6ByJ BMMs , there was very slight induction of Ifnb1 mRNA , reaching only 12-fold by the 4-h time point ( Figure 2A ) . This low level of Ifnb1 induction in C57BL/6ByJ mice was surprising , as the role of Ifnb1 signaling in conferring susceptibility to L . monocytogenes infection has been analyzed using mice of the C57BL/6 background [4] . To test the possibility that the defect in Ifnb1 induction was specific to the C57BL/6ByJ substrain we also monitored expression of Ifnb1 in BMMs from C57BL/6J mice . Indeed , in response to L . monocytogenes infection , C57BL/6J BMMs had a similar magnitude of Ifnb1 mRNA induction as BALB/cByJ BMMs ( Figure 2A ) . Ifnb1 mRNA induction in F1 progeny of the J and ByJ substrains of mice was similar to that observed in the C57BL/6J strains ( unpublished data ) , suggesting that the C57BL/6ByJ strain carries a recessive polymorphism that prevents upregulation of Ifnb1 expression . There is at least one additional inbred mouse strain , SPRET/Ei , that has a similar naturally occurring defect in induction of Ifnb1 expression [13] . However , the genetic basis for this defect in SPRET/Ei remains to be elucidated . Several lines of evidence indicate that inhibition of IFNβ signaling in mice leads to a significant increase in resistance to intravenous L . monocytogenes infection [4 , 5] . To test if the Ifnb1 induction defect in the C57BL/6ByJ strain promotes resistance to infection , we compared survival and bacterial loads in C57BL/6 substrains infected intravenously with a high dose ( 105 colony-forming units [cfu] ) of L . monocytogenes strain 10403S . There was no detectable Ifnb1 expressed in liver and spleen tissue in either mouse strain up until the 24-h time point . At the 24-h time point , Ifnb1 mRNA was present in both livers and spleens of C57BL/6J animals but only in spleens of C57BL/6ByJ animals ( Figure S1 ) . We found that at the 24- and 48-h time points , there was a significantly higher number of bacteria in the livers ( Figure 3A ) and spleens ( Figure 3B ) of C57BL/6J mice , indicating the infection was controlled better in C57BL/6ByJ mice . Consistent with this idea , spleens from C57BL/6ByJ animals had nearly twice as many Mac1-positive cells than spleens from C57BL/6J mice at the 48-h time point ( Figure 3C ) . In fact , this dose of bacteria led to death in C57BL/6J animals within 72 h after infection , whereas most of the C57BL6/ByJ mice survived indefinitely ( Figure S2 ) . Collectively , these results indicate that C57BL/6ByJ mice were significantly more resistant to intravenous L . monocytogenes infection than C57BL/6J animals . Intracellular L . monocytogenes is thought to induce Ifnb1 expression by activating an as-of-yet unidentified cytoplasmic receptor that initiates signaling through the TBK1 and inhibitor of kappaB kinase epsilon ( IKBKE ) kinases [1] . TBK1 and IKBKE also participate in transducing signals from various TLRs in response to viral and bacterial infections [14] . Therefore , we tested if the defect in C57BL/6ByJ mice was in a L . monocytogenes–specific component of the TBK1/IKBKE signaling pathway or in a component shared with other pathways . Treatment of C57BL/6ByJ BMMs with lipopolysaccharide or poly I:poly C , which induce Ifnb1 expression through TLR4 and TLR3 , respectively , failed to induce Ifnb1 mRNA at the same levels as observed in C57BL/6J BMMs ( Figure 2B and 2C ) . On the other hand , C57BL/6ByJ BMMs treated with 200 hemagglutinizing units ( HU ) of Sendai virus , which induces Ifnb1 expression through a RIG-I/MAVS–dependent pathway [15] , had levels of Ifnb1 mRNA comparable to those observed in C57BL/6J BMMs at the later stages of infection but nevertheless had a noticeable delay at the earlier stages ( Figure 2D ) . These observations indicate that the defect in Ifnb1 induction in C57BL/6ByJ mice is likely to lie in a shared component of the signaling pathway . However , our initial analysis Tbk1 , Ikbke , and Irf3 failed to identify differences in the coding sequence or overall expression levels of these mRNAs in C57BL/6ByJ versus C57BL/6J mice ( unpublished data ) . As mentioned above , our original choice of mouse strain for genetic analysis was based on the availability of the CXB RI mapping panel . However , analysis of the transcriptional response to L . monocytogenes infection in macrophages from all 13 CXB strains revealed no differences in induction of Ifnb1 ( unpublished data ) , precluding the use of the panel for mapping . Because C57BL/6ByJ mice carry a recessive mutation , we therefore chose a backcross as our mapping strategy to identify the locus in the C57BL/6ByJ mouse genome that harbors the mutation preventing induction of Ifnb1 . C57BL/ByJ and C57BL/6J mice have virtually no polymorphisms that can be used to monitor allelic segregation in a cross . On the other hand , BALB/cByJ mice are similar to C57BL/6J mice in their induction of Ifnb1 , and we therefore chose C57BL/6ByJ and BALB/cByJ as parental strains for our cross . We backcrossed F1 male progeny of C57BL/6ByJ and BALB/cByJ mice to C57BL/6ByJ females and used the resulting 54 C ( B . C ) N2 progeny to construct a genetic map with 56 microsatellite markers evenly distributed throughout the mouse genome [16] . To generate phenotypic data , we first used real time reverse transcriptase PCR ( RT-PCR ) to analyze the dynamics of L . monocytogenes-induced Ifnb1 expression in BMMs isolated from 43 backcrossed mice ( unpublished data ) . We then used transformed real time RT-PCR Ct values representing the levels of Ifnb1 mRNA at the 4-h time point directly as a quantitative trait . Mapping of this trait using MapManger QTX identified a peak likelihood ratio statistic score of 35 . 1 ( logarithm of the odds [LOD] = 7 . 6 ) at the D7Mit229 marker [17] ( Figure S3 ) . Using the MapManger QTX built-in permutation test function , we established that the identified linkage is highly significant with an experimental p-value less then 10−4 . QTL support interval was approximated as 1 . 5 LOD drop-off from the peak score [18] and extended from D7Mit27 to D7Mit158 markers , spanning empirical genetic distance of 6 . 9 cM ( 0 cM MGI ) and a physical fragment of 2 . 35 Mb . The D7Mit229 marker is located on mouse Chromosome 7 adjacent to Irf3 [16] , identifying Irf3 as the primary candidate gene . Interestingly , our initial analysis of C57BL/6J and C57BL/6ByJ substrains did not find any differences in overall Irf3 mRNA levels or polymorphisms in the coding region of Irf3 ( unpublished data ) . However , analysis of the structure of Irf3 mRNA using a series of overlapping primers revealed differences in the Irf3 transcripts between the two substrains . As expected , in C57BL/6J mice the majority of Irf3 transcripts were completely spliced , whereas in C57BL/6ByJ mice , splicing of Irf3 was not complete and the majority of transcripts retained intron 5 ( Figure 4A and quantified in Figure 4B ) . Retention of intron 5 introduces a premature stop codon at amino acid 243 , rather than producing the full-length 419 amino-acid protein . To test if the observed differences in splicing had functional significance , we analyzed activation of IRF3 in BMMs by monitoring the formation of IRF3 dimers following bacterial ( L . monocytogenes ) or viral ( Sendai virus ) infection . We found that untreated BMMs from C57BL/6ByJ mice had significantly lower levels of IRF3 protein than untreated BMMs from C57BL/6J mice ( Figure 4C and 4D ) . Moreover , following a 2-h infection with L . monocytogenes or Sendai virus , there were no detectable IRF3 dimers in C57BL/6ByJ BMMs , although IRF3 dimers were readily detectable in C57BL/6J BMMs . Nevertheless , we observed that Sendai virus–infected C57BL/6ByJ BMMs are capable of inducing Ifnb1 expression ( see Figure 2D ) . This is consistent with earlier observations that Irf3-deficient cells rely on IRF7 to have a normal interferon response to several viral infections [19 , 20] . Interestingly , our polyclonal antibodies failed to detect a truncated form of IRF3 even in the presence of proteasome inhibitor ( MG132 ) , suggesting that the unspliced form of Irf3 might not be efficiently translated ( Figure 4C ) . Overall , these results show that C57BL/6ByJ BMMs have dramatically lower amounts of functional IRF3 protein , and in conjunction with the existing Irf3 knock-out data [2 , 4] , explain the increased resistance of C57BL/6ByJ mice to L . monocytogenes infection ( see Figure 3 ) . Sequencing of the entire 7 . 2-kb genomic region of Irf3 [21] , including 1 kb of upstream and downstream sequences , revealed a single A to T polymorphism in the middle of intron 5 in C57BL/6ByJ mice ( Figure 5 ) . To establish if this polymorphism altered the splicing efficiency of the intron , we monitored splicing using both cell culture–based and in vitro approaches . For cell culture–based experiments , we derived minigene constructs containing the complete intron 5 ( from either C57BL/6J or C57Bl/6ByJ ) flanked by exons 5 and 6 , and expressed them under the control of the heterologous CMV promoter . In order to rule out the possibility that C57BL/6ByJ mice carry additional mutations that affect splicing , we first tested our constructs in a C57BL/6ByJ fibroblast-like cell line ( Y5 ) . Following transfection into Y5 cells , the efficiency of splicing of intron 5 was monitored by real time RT-PCR using primers specific to the vector and exon 5–6 junctions , and the total amount of RNA expressed from each construct was measured using primers specific to the exon fragment , which is identical in both constructs ( see Figure 6A schematic ) . When normalized for the total amount of expressed RNA , there was significantly more spliced product generated from the C57BL/6J construct than from the C57BL/6ByJ construct ( Figure 6A ) . This result indicates that in C57BL/6ByJ cells , C57BL/6J Irf3 intron 5 is spliced more efficiently than the C57BL/6ByJ version of the intron . The effect of the A to T substitution on splicing efficiency of intron 5 was further confirmed using an in vitro splicing assay , in which a uniformly radioactively labeled Irf3 pre-mRNA containing intron 5 flanked by 50 bp of exon 5 and exon 6 was incubated in HeLa nuclear extract . As expected , the C57BL/6J-derived Irf3 pre-mRNA substrate was spliced efficiently , as evidenced by the appearance of both intermediate and fully spliced products ( Figure 6B ) . By contrast , there was no detectable splicing of the C57BL/6ByJ-derived Irf3 pre-mRNA substrate even when incubated for 60 min in the splicing reaction mixture . These results confirmed that the A to T substitution had a direct effect on efficiency of Irf3 splicing . Pre-mRNA splicing occurs in a ribonucleoprotein complex called the spliceosome [22] . Splicing is initiated through recognition of several intron-defining splicing signals , including the 5′ and 3′ splice sites and the branchpoint , which is usually located near the 3′ end of the intron . Introns can be classified into two categories: U2-type introns , which comprise the major class of introns , and U12-type introns . U2-type introns are characterized by the presence of a conserved GU dinucleotide at the 5′ end of the intron and a conserved AG dinucleotide at the 3′ end , whereas U12-type introns can harbor AT–AC , AT–AG , and GU–AG dinucleotides at their 5′ and 3′ boundaries , respectively . Furthermore , on U12-type introns , the 5′ and 3′ splice sites and branchpoint are highly conserved and differ from those of the conventional U2-type introns [23 , 24] , and the characteristic polypyrimidine tract is typically absent [25] . Because the A to T polymorphism is located within an intron and affects splicing efficiency , we hypothesized that it might alter the function of a splicing signal . The polymorphism is located 46 bp upstream of the 3′ splice site , within a region where the branchpoint is typically found . The 5′ boundary of the murine Irf3 intron 5 matches the U2-type intron GTRAGT consensus sequence ( Figure 5 ) [23 , 24] . However , the region surrounding the polymorphism more closely resembled the U12 branchpoint consensus ( TCCTTAACY ) than the U2 branchpoint consensus ( YURAY ) . To determine whether intron 5 of the Irf3 gene was a U2-type or U12-type intron , we monitored splicing of the wild-type C57BL/6J-derived Irf3 pre-mRNA substrate following inactivation of U2 or U12 snRNA by oligonucleotide-directed RNase H cleavage . The in vitro splicing assay in Figure 6C shows that splicing of the C57BL/6J-derived Irf3 pre-mRNA substrate occurred following inactivation of U2 snRNA but not following inactivation of U12 snRNA . As expected , splicing of the control U2-type intron–containing adenovirus major late ( Ad ML ) pre-mRNA substrate was fully dependent on the presence of U2 snRNA . These observations indicate that splicing of intron 5 of the Irf3 gene relies on the U12-dependent mechanism . Irf3 transcripts that retain intron 5 are detected in both C57BL/6ByJ and C57BL/6J strains ( see Figure 4A ) . Therefore , it appears that even in the C57BL/6J strain splicing of intron 5 is somewhat inefficient , whereas in the C57BL/6ByJ strain , intron 5 splicing is substantially impaired . To test if the observed phenotypic differences in induction of Ifnb1 expression in the two mouse strains are due to differences in splicing efficiency of Irf3 intron 5 , we performed complementation experiments . To achieve this , we transfected BMMs from Irf3 knockout mice with full-length in vitro transcribed Irf3 mRNA species harboring either the C57BL/6J or C57BL/6ByJ version of intron 5 . Fully spliced Irf3 mRNA was used as a positive control , and mRNA containing a partial deletion of IRF domain ( ΔXma ) was used as a negative control . Previous experiments had shown that introduction of single-stranded RNA into the cell cytosol leads to Irf3-dependent induction of Ifnb1 expression [26–28] ( O . G . , unpublished data ) . Therefore , transfection of intron 5–containing Irf3 mRNAs into BMMs that lack Irf3 should lead to a level of Ifnb1 induction that is proportional to the splicing efficiency of intron 5 . We measured the levels of Ifnb1 mRNA by real time RT-PCR 19 h following transfection of BMMs , and found that there was ∼5-fold more ( p < 0 . 01 ) Ifnb1 mRNA expressed in BMMs transfected with the C57BL/6J-derived Irf3 mRNA than in BMMs transfected with C57BL/6ByJ-derived Irf3 mRNA ( Figure 7 ) . Following a 4-h infection with L . monocytogenes , BMMs transfected with either mRNA showed further induction of Ifnb1 expression . Nevertheless , the C57BL/6J-derived Irf3 mRNA induced significantly ( p < 0 . 01 ) higher amounts of Ifnb1 mRNA than the C57BL/6ByJ-derived version . As expected , BMMs transfected with Irf3 mRNA lacking the IRF domain induced only low levels of Ifnb1 mRNA expression . Because the Irf3 knockout mice used in this experiment are on the C57BL/6J background [19] , this experiment also ruled out the possibility that impaired splicing of Irf3 intron 5 could be due to a linked C57BL/6ByJ polymorphism . Therefore , we conclude that decreased splicing efficiency of Irf3 intron 5 is directly responsible for the reduction in Ifnb1 expression observed in C57BL/6ByJ mice .
Previous studies have demonstrated an important role of IFNβ signaling in host defense against L . monocytogenes infection . L . monocytogenes evolved to take advantage of the host signaling pathways and is capable of inducing Ifnb1 expression in order to down-modulate the antibacterial host defense . Here , we show that at least one inbred strain of mice can resist this pathogen's tactic by carrying a single nucleotide polymorphism that changes the efficiency of splicing of its Irf3 transcription factor . While this naturally occurring polymorphism does not eliminate IRF3 activity , the resulting reduction in IRF3 protein levels is sufficient to confer 10-fold higher resistance to L . monocytogenes infection . Considering that complete loss of IRF3 function is detrimental to immune defense , and Irf3 knockout mice are more sensitive to encephalomyocarditis infection [19] , it would be interesting to determine if the level of IRF3 in C57BL/6ByJ mice is sufficient to maintain protection against viral infections . Nevertheless , our finding indicates that genetic changes in noncoding regions of the host genome is one of the mechanisms that can be used to fine tune the effectiveness of host defenses against infections . It has been suggested that in the process of evolution , U12-type introns are either lost or undergo subtype switching ( from AT–AC to GT–AG ) and are eventually converted to U2-type introns [25] . Our data provide additional support for this hypothesis . Although splicing of IRF3 intron 5 is dependent on the U12 spliceosome , the splice donor site is a typical U2 site . Even more interesting is the fact that the region of the human and rhesus intron 5 that is homologous to the putative murine U12 branchpoint site contains a G in place of the T that is found in rodents ( Figure 5 , inset box ) . This substitution places a purine residue in front of a putative branchpoint adenosine , thus creating a perfect match to the canonical U2 branchpoint consensus sequence . Therefore , intron 5 of the murine Irf3 gene might represent one of the final steps in the conversion of a U12-type intron to a U2-type intron . The amount of IRF3 available in the cell is tightly controlled and overproduction of IRF3 is lethal to BMMs [29 , 30] ( Figure S4 ) . Recent evidence indicates that splicing of U12-type introns could be a rate-limiting step in gene expression [31] . Our analysis suggests that this may also be the case for intron 5 in the murine Irf3 gene . Macrophages from common strains , such as C57BL/6J and BALB/cByJ , have two populations of Irf3 transcripts: a major , fully spliced species and a minor species that retains intron 5 ( see Figure 4A ) . The presence of multiple Genbank ( i . e . , BC082274 , BC003233; http://www . ncbi . nlm . nih . gov/Genbank/index . html ) and EST entries of Irf3 mRNAs that retain intron 5 further supports this hypothesis . Therefore , the rate of intron 5 splicing could control the amount of IRF3 available in the cell . Interestingly , it has been shown that activity of human IRF3 is also regulated at the level of splicing [32] . However , in contrast to rodent Irf3 , regulation of human IRF3 involves alternative intron 1 splice acceptor sites that can produce an active or a dominant negative , IRF3a , form of IRF3 [33] . Because it appears that by converting to a U2 intron , human IRF3 intron 5 lost its rate-limiting function , it is intriguing to contemplate that this led to emergence of an alternative splicing control mechanism for human IRF3 . Our choice of mouse strains for genetic analysis of susceptibility to L . monocytogenes infection was based on the existence of the ByJ-based CXB RI mapping panel . The C57BL/6By substrain has been used to generate at least seven of the 13 CXB RI strains , but none of the CXB strains appear to have a defect in induction of Ifnb1 . To resolve this discrepancy , we sequenced Irf3 intron 5 in all C57BL/6By-derived CXB strains . None of the sequenced Irf3 introns contained the mutation found in the C57BL/6ByJ strain ( unpublished data ) . Therefore , it appears that the A to T mutation rose in the C57BL/6ByJ background only recently , after the generation of the CXB RI strains . It is possible that the return of D . W . Bailey's substrains to the Production Department of Jackson Laboratories in 1974 could have created a bottleneck that fixed the mutation in the current C57BL/6ByJ population . Splicing of mRNA is a critical step in protein expression , and in humans , genetic polymorphisms that produce aberrant or alternate splicing products have been associated with a wide range of diseases [34] . We used genetic analysis of the mouse model system to provide definitive evidence of the important role of splicing in control of infection . We found that a mouse substrain-specific defect in induction of Ifnb1 is due to a single nucleotide polymorphism in intron 5 of Irf3 . Our analysis of this polymorphism revealed that splicing is a critical step in the control of Irf3 expression and , as a result , in the course and outcome of L . monocytogenes infection . While intron 5 of murine Irf3 has features of both U2 and U12 introns , we provide evidence that its splicing is dependent on the U12 spliceosome . Therefore , it appears that in rodents the U12 spliceosome can use U2 splice sites . This suggests that the spectrum of U12-type introns present in mammalian genomes could be wider than previously thought . Finally , our comparison of rodent and primate Irf3 genomic sequences also revealed the intriguing possibility that we have identified an intermediate step in the process of conversion from a U12- to U2-type intron .
Six-to-twelve-week-old animals were used in all experiments . BALB/cByJ , C57BL/6J , and C57BL/6ByJ mice were obtained from Jackson Laboratories ( http://www . jax . org/ ) . B/ByJ . C N2 mice were created by backcrossing B/ByJ . C F1 males to C57BL/6ByJ females . All mouse strains were bred and maintained under specific pathogen-free conditions in the animal facilities at the University of Massachussetts Medical School . All experiments involving live animals were carried out in accordance with the guidelines set forth by the University of Massachussetts Medical School Department of Animal Medicine and the Institute Animal Care and Use Committee . Pre-titered TSB-glycerol stocks of L . moncytogenes strain 10403S were stored at −80 °C . Prior to infection , 1-ml bacterial aliquots were recovered for 1 h at 37 °C in 9 ml of TSB ( BD Biosciences , http://www . bdbiosciences . com/ ) , washed , and resuspended to the desired cfu in PBS . Mice were injected with a defined dose of L . monocytogenes strain 10403S in 0 . 4 ml of PBS . At defined time points , infected animals were killed by CO2 asphyxiation . Livers and spleens of infected animals were aseptically harvested , weighed , and homogenized in 0 . 02% Triton X-100 . Aliquots of serial 5-fold dilutions in sterile water were plated in duplicate on TSB agar ( BD Biosciences ) plates containing 10 μg/ml streptomycin . After overnight incubation , the number of bacteria per milligram of tissue was determined by counting colonies at the appropriate dilution . BMMs were generated by differentiating bone marrow calls in a complete BM medium ( DMEM , 10% heat-inactivated FCS ( Invitrogen , http://www . invitrogen . com/ ) , 100 U/ml penicillin ( Invitrogen ) , 100 μg/ml streptomycin ( Invitrogen ) , and 10% L929 fibroblast-conditioned medium as a source of M-CSF ) for 6 d in 10-cm Petri dishes ( VWR , http://www . vwr . com/ ) . Fibroblast-like YF5 and macrophage-like YM14 cell lines were generated by immortalization of C57BL/6ByJ bone marrow cells as previously described [35 , 36] . Clones were selected based on morphology . Differentiated BMMs were detached from the Petri plates by incubation in cold PBS , washed , resuspended in BM medium without antibiotics and used to seed multiwell dishes at 1 × 105 cells/cm2 . Following overnight incubation the medium was replaced with DMEM containing the agent used to stimulate BMM . For L . monocytogenes , after 1 h the medium was replaced with BM medium containing 10 μg/ml gentamicin ( Fisher ) to remove extracellular bacteria . Lipopolysaccharide ( Sigma ) was added at 1 μg/ml , poly I:poly C ( Sigma ) at 25 μg/ml and Sendai Virus ( generous gift of Kate Fitzgerald , University of Massachussetts ) at 200 U/ml . At defined timepoins BMMs were lysed in TRIzol ( Invitrogen ) and RNA was isolated according to manufacturer's protocol . 5 × 104 BMMs were seeded in 96-well tissue culture plate and following overnight incubation the cells were infected with a defined multiplicity of infection ( MOI ) of L . monocytogenes . After 1 h incubation , the cell culture medium was replaced with medium containing 10 μg/ml of gentamicin ( MP Biomedicals , http://www . mpbio . com/ ) . At defined time points the supernatants were collected and the remaining cells were lysed for 20 min at room temperature in 50 μl/well of complete medium plus 1μl of cell lysis solution from CytoTox-ONE Homogeneous Membrane Integrity Assay kit ( Promega , http://www . promega . com/ ) . Lactose dehydrogenase ( LDH ) assay for supernatants and cell lysates was performed according to the manufacturer's protocol . Fluorescence was recorded at 560/590 nm using a Synergy HT microplate reader ( Bio-Tek , http://www . biotek . com/ ) . The relative concentration of LDH in supernatants was calculated by the equation: 100% × ( LDHsup/ ( LDHsup+LDHcell ) ) . Relative mRNA levels were quantified by real time RT-PCR on an ABI 7300 instrument ( http://www . appliedbiosystems . com/ ) utilizing SYBR Green chemistry ( ABI SYBR master mix + RT reagents ) . Generally , 50 ng of total RNA was used in 20 ml one-step reactions that incorporated a 30-min reverse transcription step prior to cycling . Primers used to detect specific mRNAs are described in Table S1 . Ribosomal protein S17 ( Rps17 ) and actin mRNA were used as a loading controls . To quantify the relative amounts of mRNA in each experiment , 2-fold dilutions were used to create calibration curves . Each experiment included at least two biological and three experimental replicates . BMMs from 46 B/ByJ ( C . B/ByJ ) N2 mice were infected in duplicate with L . monocytogenes strain 10403S at MOI = 5 . Four hours following infection , total RNA was isolated and used for real time RT-PCR analysis of Ifnb1 mRNA induction . Ifnb1 Ct values from duplicate samples were adjusted for variation in total RNA concentration using Rps17 Ct values , transformed by subtracting the average C57BL/6ByJ parental value , and used directly as trait values for mapping using MapManger software . Under ideal conditions , such transformed Ct values can be viewed as log2 of the fold difference in Ifnb1 expression compared to the C57BL/6ByJ parent . A genetic map was constructed using 56 microsatelite markers [16] . Experimental p-value for linkage was evaluated using a built-in permutation test and was found to be less than 10−4 . BMMs were infected with L . monocytogenes ( MOI = 5 ) or Sendai Virus ( 600 HU ) for 4 h . Cells were lysed in the presence of protease inhibitors ( Roche , http://www . roche . com/ ) and following centrifugation , supernatant aliquots containing 20 μg of protein were loaded per lane of a native polyacrylamide gel . IRF3 was detected by immunoblotting with an anti-IRF3 antibody ( Invitrogen ) . For total IRF3 protein analysis , lysates were obtained from infected and uninfected BMMs treated with either 10 μM MG-132 proteasome inhibitor or DMSO vehicle . Three micrograms of total protein were separated on denaturing SDS polyacrylamide gel and immumoblotted with anti IRF-3 antibobody ( Invitorgen ) . The cDNAs of fully spliced Irf3 , Irf3 lacking internal XmaI fragment , and strain-specific Irf3 species retaining intron 5 were amplified with IRF3F+1 and IRF3dT32SspIR oligonucleotides ( Table S1 ) . IRF3dT32SspIR was designed to introduce poly ( A ) 32 and a unique SspI site at the 3′ end of Irf3 coding sequences . Amplified Irf3 fragments were cloned in front of T7 promoter of pCR2 . 1 vector ( Invitrogen ) . To generate Irf3 mRNAs , 1 μg of respective SspI linearized plasmids was used as templates in in vitro transcription reactions ( Maxiscript T7; Ambion , http://www . ambion . com/ ) . One microgram of DNAse treated , reprecipitated RNA was used for nucleofection of 1 to 1 . 5 × 106 BMMs in 100 μl of complete mouse macrophage nucleofector solution ( 82 μl mouse macrophage nucleofector solution plus 18 μl supplement ) from Mouse Macrophage Nucleofector kit ( Amaxa Biosystems , http://www . amaxa . com/ ) . Following electroporation using a Nucleofector II electroporation device ( Amaxa ) with program Y-001 , cells were split into two wells of a 24-well culture plate , each containing 1 ml of RPMI/10% FBS/10% LCCM medium . Splenocytes were isolated by disrupting aseptically harvested spleens in RPMI 10% FCS using a 70-μm pore size mesh cell strainer ( BD Biosciences ) to obtain single-cell suspensions . Red blood cells were lysed in ACK buffer ( Sigma-Aldrich , http://www . sigmaaldrich . com/ ) . Splenocytes were resuspended to obtain 2 × 107 cells/ml in HBSS supplemented with 0 . 5% and 0 . 1% NaN3 . Each sample containing 1 × 106 cells was blocked with FcR-blocking antibody ( CD16/CD32 ) and stained with respective directly conjugated antibodies and their isotype controls . Stained cells were fixed in 1% paraformaldehyde ( Sigma-Aldrich ) for 24 h prior to analysis using a FACScan flow cytometer ( BD Biosciences ) . C57BL/6J and C57BL/6ByJ minigene constructs were generated by cloning Irf3 intron 5 flanked by 250 bp of exonic sequences into the vector pcDNA3 ( Invitrogen ) , to create pINT5J and pINT5ByJ , respectively . For ex vivo splicing assays , the C57BL/6ByJ-derived fibroblast-like cell line Y5 was transfected with either pINT5J or pINT5ByJ , and splicing was monitored 12 h later by real time RT-PCR using primers specific to the transcribed vector sequence ( pCDNA3F3 ) and either intron 5 ( IRF3IntR ) or the exon 5–6 junction ( IRF3Ex5/6R5 , ) . For in vitro splicing assays , minigene Irf3 pre-mRNA templates were PCR-amplified from pINT5J and pINT5ByJ using a T7-containing primer , purified , and transcribed in vitro using T7 polymerase in the presence of [alpha-32P]UTP . In vitro splicing reactions were performed essentially as described previously [37] , except that 30% HeLa nuclear extract was used . Spliced products were resolved on 12% denaturing polyacrylamide gels ( 19:1 ) in 8 M urea in Tris-Borate-EDTA buffer , and visualized using a Fujifilm FLA-500 phosphorimager ( http://www . fujifilm . com/ ) . U2 and U12 snRNAs were inactivated by RNase H–directed cleavage as described previously [38] using DNA oligonucleotides complementary to nucleotides 27–49 of the U2 snRNA or to nucleotides 11–28 of the U12 snRNA . All genes mentioned in the text and their corresponding National Center for Biotechnology Information ( NCBI ) GeneID ( http://www . ncbi . nlm . nih . gov/sites/gquery ) and Ensembl ( http://www . ensembl . org/ ) identifiers are described in Table S2 . | Specific variances in an individual's DNA , known as genetic polymorphisms , can play a significant role in determining susceptibility to an infectious disease . To identify the genetic polymorphisms that are associated with resistance to the common human bacterial pathogen L . monocytogenes , we have carried out a series of genetic and molecular biology experiments using closely related strains of mice that are differentially susceptible to Listeria infection . Through this analysis , we have identified a spontaneous mutation in an intron of the Irf3 gene , which encodes a key transcription factor involved in innate immunity . This single nucleotide change affects the efficiency with which Irf3 mRNA is spliced , thus limiting the ability of bacteria to induce interferon beta expression in order to suppress innate immune defense . By analyzing this mutation , we found that processing of mouse Irf3 mRNA relies on an atypical U12 splicing mechanism that has been suggested to be a rate-limiting step in gene expression . Our findings not only provide an additional example of an important role of noncoding polymorphisms in control of gene function , but also demonstrate how such polymorphisms can fine tune innate immune response . | [
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] | 2007 | Irf3 Polymorphism Alters Induction of Interferon Beta in Response to Listeria monocytogenes Infection |
Mutations in the gene encoding the methyl-CG binding protein MeCP2 cause several neurological disorders including Rett syndrome . The di-nucleotide methyl-CG ( mCG ) is the classical MeCP2 DNA recognition sequence , but additional methylated sequence targets have been reported . Here we show by in vitro and in vivo analyses that MeCP2 binding to non-CG methylated sites in brain is largely confined to the tri-nucleotide sequence mCAC . MeCP2 binding to chromosomal DNA in mouse brain is proportional to mCAC + mCG density and unexpectedly defines large genomic domains within which transcription is sensitive to MeCP2 occupancy . Our results suggest that MeCP2 integrates patterns of mCAC and mCG in the brain to restrain transcription of genes critical for neuronal function .
Methylation at the C5 position of cytosine is an epigenetic mark implicated in gene regulation and disease [1] . In mammals , DNA methylation occurs most often in a CG di-nucleotide context , but in neuronal cells and embryonic stem cells ( ESCs ) mCA is detected at significant levels [2 , 3] . Like mCG , mCA is negatively correlated with transcript abundance , hinting at a repressive function in the brain [2 , 3] . Highest levels of non-CG methylation are observed in the human and mouse brain , where mCA accumulates postnatally at the same time as a phase of active synaptogenesis [3] . In mice , the increase in neuronal mCA coincides with accumulation of the DNA methyltransferase Dnmt3a [3] , which is able to methylate CA , albeit at a low rate [4–7] . Brain mCA occurs most frequently in the tri-nucleotide mCAC [2 , 8] , whereas in ESCs mCAG is the preferred sequence context . The biological significance of these preferences has yet to be elucidated [2 , 3] . A potential mechanism for interpreting the DNA methylation signal is the recruitment of methyl-CG binding domain ( MBD ) proteins including MeCP2 , MBD1 , MBD2 and MBD4 [9] . Of these , MeCP2 has attracted most attention as mutations involving the MECP2 gene cause the X-linked autism spectrum disorder Rett syndrome [10] and MECP2 duplication syndrome [11] . Rett missense mutations cluster in two domains of MeCP2: the MBD and the NCoR/SMRT co-repressor Interaction Domain ( NID ) [12 , 13] . These observations raise the possibility that loss of binding to methylated DNA and/or failure to recruit the NCoR/SMRT repressor complex are primary causes of Rett syndrome . MeCP2 has a high affinity in vivo and in vitro for binding to mCG [14–16] , but the determinants of its targeting to DNA have recently diversified to include mCA , whose postnatal accumulation is paralleled by an increase in MeCP2 protein [2 , 3] . In addition , it has been reported that MeCP2 binds to hydroxymethylcytosine ( hmC ) , the major oxidized form of mC , which is abundant in neurons [17] . Finally , there have been suggestions that MeCP2 can bind chromatin in a DNA methylation-independent manner [15 , 18–20] . The mutational spectrum and biochemical interactions of MeCP2 suggest that it behaves as a transcription repressor [13 , 21] . Changes in the mouse brain transcriptome when the protein is absent , however , involve both up- and down-regulation of genes [17 , 22 , 23] . Accordingly MeCP2 has been proposed to act as an activator of transcription or as a multifunctional hub that effects diverse aspects of cellular metabolism [12 , 24 , 25] . An additional model proposes that MeCP2 primarily functions by globally modifying the architecture of chromatin via multifaceted interactions with DNA [16 , 19] . An inverse correlation between levels of CA methylation and expression of long genes has recently re-emphasized the role of MeCP2 in transcriptional inhibition [26] . On the other hand a separate study reported that mCA is enriched within genes that are mis-regulated regardless of the direction of the transcriptional change in response to MeCP2 depletion or excess [27] . Despite progress , therefore , a consensus view regarding the role of MeCP2 in transcriptional regulation has been elusive . Here we define the DNA binding specificity of MeCP2 using in vitro and in vivo approaches . We show for the first time that MeCP2 binding to non-CG methylated sites is primarily restricted to the tri-nucleotides mCAC or hmCAC in vitro and in vivo . Modeling based on the X-ray structure of the MBD of MeCP2 suggests that mCAC and mCG interact with a common protein conformation and may therefore lead to indistinguishable down-stream biological effects . MeCP2 binding across the adult brain genome reveals long genomic domains of high and low occupancy that match the distribution of mCAC + mCG binding sites . The results have important implications for regulation of gene expression , as we uncover a strong correlation between MeCP2 binding , mCAC + mCG density and the direction of gene mis-regulation when MeCP2 is absent or over-expressed . Our findings shed new light on the binding properties of MeCP2 and implicate MeCP2 as a global negative modulator of neuronal transcription .
The predominant methylated sequence is the di-nucleotide CG , but in adult brain mCA [3] and hmCG [17 , 28] are implicated as binding partners of MeCP2 . A recent study concluded that in addition to mCG , MeCP2 binds both mCA and hmCA [26] and confirmed earlier reports that hmCG is a low-affinity binding site for MeCP2 [26 , 29–31] . In addition MeCP2 has been reported to bind in vitro to DNA in which every cytosine was substituted with hmC [17] . To comprehensively analyze the DNA sequence determinants of MeCP2 binding in vitro we performed EMSA analysis . As full-length MeCP2 binds to DNA poorly in vitro , most likely due to masking of the MBD by unstructured C-terminal sequences , these experiments utilized the 1–205 N-terminal domain of MeCP2 which contains the MBD and only robustly detectable AT-hook [32] . The MBD is the dominant DNA binding motif in MeCP2 and most missense mutations that cause RTT disrupt its ability to bind methylated DNA . Unexpectedly , the EMSA data revealed a novel constraint on MeCP2 binding , as the third base following mCA strongly affected MeCP2 binding affinity in vitro ( Fig 1A ) . Probes containing the mCAC tri-nucleotide sequence bound with high affinity to MeCP2 , whereas probes containing mCAA , mCAG and mCAT bound much less strongly . This result was confirmed in EMSA experiments using all possible mCXX tri-nucleotide sequences as unlabeled competitors against a labeled mCGG-containing probe ( Fig 1B ) . Quantification showed that mCAC and , to a lesser extent mCAT , are both effective competitors , but mCAG and mCAA compete no better than non-methylated control DNA ( Fig 1C ) . All mCGX oligonucleotide duplexes competed strongly indicating that the base following mCG on the 3’ side does not have a large effect on binding , although we note that mCGA was reproducibly a weaker competitor than mCGC , mCGG or mCGT . Neither mCCX nor mCTX tri-nucleotides had a significant affinity for MeCP2 in vitro . As hmCA is reported to bind MeCP2 in vitro [26] , we asked whether the third base is also important for hmC binding . Using hmCXX tri-nucleotides as probes in EMSAs , we found that hmCAC bound with a much higher affinity than hmCAA , hmCAG and hmCAT DNA ( Fig 1D ) . It is notable that the great majority of hmC in the brain and elsewhere is in the hmCG di-nucleotide , with hmCAC being extremely rare [3] . The DNA binding specificity of MeCP2-MBD deduced from these in vitro experiments is summarized in a matrix of di- and tri-nucleotide sequences that bind to MeCP2 ( red lettering , Fig 1E ) . A notable feature of the mCAC binding site is that mC is only present on one strand , whereas mCG possesses symmetrically placed mC moieties . Within mCAC the position normally taken by mC across the dyad is occupied by thymine , which is effectively 5-methyluracil ( Fig 1F ) . We therefore speculated that the mC methyl group in the MeCP2-mCAC complex is provided by thymine . To test this , we replaced thymine with uracil in the labeled probe and performed EMSA analysis ( Fig 1G ) . Loss of the thymine methyl group abolished binding to MeCP2 . The data suggest that a symmetrical pair of 5-methyl pyrimidines , one of which is mC , offset by one base pair is an essential pre-requisite for MBD binding to DNA . This finding also raises the possibility that the mode of binding to mCAC and mCG may be similar ( see Discussion section for informal structural modeling based on this possibility ) . To determine whether the binding specificities established in vitro apply to full-length MeCP2 protein in living cells , we developed a novel assay using transfection followed by chromatin IP ( ChIP ) [33] . Synthetic DNA duplexes containing specific cytosine modifications were transfected into HEK293 cells expressing human full length MeCP2 tagged with GFP ( Fig 2A and S1A and S1B Fig ) . Levels of endogenous MeCP2 in these cells are negligible and therefore do not interfere with the assay ( S1C Fig ) . We tested oligonucleotide duplexes containing a single modified cytosine in either a mCG , hmCG , mCAX or hmCAX context ( Fig 2B ) . The results showed that mCAC , mCAT , hmCAC and mCG all bound MeCP2 efficiently , whereas hmCG , mCAA and mCAG binding was indistinguishable from background binding to non-methylated DNA ( Fig 2C ) . The same outcome was seen with different DNA sequences containing three CAC motifs per oligonucleotide , either all unmodified , all methylated or all hydroxymethylated ( Fig 2D and 2E ) . These in vivo results with full-length protein reinforce the evidence obtained using EMSAs that MeCP2 requires specific tri-nucleotide settings to recognize mC or hmC in a non-CG context . While in vivo and in vitro data match in nearly all respects , we noted a quantitative difference regarding mCAT binding , which bound relatively weak in EMSAs , but was robustly detected in the transfection assay ( Fig 2C ) . To test whether the DNA binding specificities established in vitro and in transfected cells also apply in native tissues , we analyzed MeCP2 ChIP-seq and whole genome bisulfite ( WGBS ) datasets derived from adult mouse brain ( references [3 , 26 , 27] and WGBS from sorted neurons and hypothalamus [this study] ) . CG is under-represented in the mouse genome ( ~4% of CX ) , but highly methylated ( ~80% ) , whereas CA is the most abundant CX di-nucleotide ( 36% of CX ) , but even in brain only a small fraction of CA is methylated ( <2% ) ( Fig 3A–3C ) . Bisulfite analysis of neurons sorted by NeuN staining as described in reference [16] confirmed previous reports that CAC is the most methylated tri-nucleotide [2 , 8] , being ~12% methylated ( Fig 3C ) . The finding that the MeCP2 tri-nucleotide binding specificity matches the most abundantly methylated non-CG sequence in brain encourages the view that this interaction is biologically relevant . Previous ChIP-seq analyses have concluded that MeCP2 read coverage tracks the density of CG methylation [15 , 16 , 26 , 27] . Re-analysis of several MeCP2-ChIP data sets for which the antibody used has been rigorously verified , indicates , that the profile of Input–that is , DNA derived from the fragmented chromatin sample used for ChIP–is closely similar to that of MeCP2 ( Fig 3D ) . Using a ChIP-seq dataset for hypothalamus in which replicate Input and ChIP samples are sequenced at high depth [27] , we fitted a linear model to predict MeCP2 read coverage from Input reads alone and found a coefficient of determination of 0 . 84 ( Fig 3E ) . If we removed windows with relatively increased ( purple ) or decreased ( green ) MeCP2 read coverage ( Fig 3F ) and analysed only the remaining 94% of the genome , the variance in MeCP2 signal was 90% predictable by the Input coverage . This means that MeCP2 is relatively uniformly distributed across most of the genome at this resolution . These results are in line with the high binding site frequency and a previous report that the number of MeCP2 molecules in mature neurons is sufficient to almost ‘‘saturate” mCG sites in the genome [16] . Given the similarity between ChIP and Input , we used Input corrected MeCP2 signals ( log2 ( MeCP2 ChIP/Input ) ) and complemented genome-wide analysis by examining regions that deviate from the Input profile regarding enrichment or depletion of MeCP2 . First , we investigated genomic regions that are depleted of potential binding sites , e . g . unmethylated CpG islands ( CGIs ) . As the ChIP dataset was derived from mouse hypothalamus , we derived a DNA methylome for this brain region by performing WGBS on three biological replicates ( see Online Methods ) . Using ChIP and DNA methylation datasets from the same brain regions , we observed a pronounced drop in the log2 ( MeCP2 ChIP/Input ) signal across CGIs in both datasets , in line with previous analyses [16 , 26 , 27] ( S2A Fig ) . We next examined regions where the MeCP2 signal was higher than expected by applying the MACS [34] tool to detect summits of MeCP2 binding peaks relative to Input [26] . As expected , the di-nucleotides mCG and mCA showed a sharp peak at MeCP2 ChIP summits in the hypothalamus dataset ( S2B Fig and [26] ) . Further , the tri-nucleotide mCAC , but not other mCAX tri-nucleotides , coincided strikingly with MeCP2 peak summits , confirming that mCAC provides a focus for MeCP2 binding ( Fig 3G and 3H ) . Using random regions as a negative control , we did not detect any sequence or methylation dependency ( S2C Fig ) . Regarding targeting of mCAT , which bound relatively weakly in EMSA , but strongly in the transfection assay , the ChIP-seq data suggest that this is a low affinity binding site in native brain ( Fig 3G and 3H ) . To further explore MeCP2 occupancy in vivo , we analyzed MeCP2 binding preferences in protein coding genes . In the Input sample , both mCG density and the density of unmethylated CGs strongly correlated with coverage revealing a methylation-independent CG sequencing bias in both hypothalamus and cortex datasets ( Fig 4A and S3A Fig ) . However , MeCP2 ChIP coverage was clearly sensitive to DNA methylation , with mCG being positively correlated , while unmethylated CG density was anti-correlated ( Fig 4B and S3B Fig ) . Importantly , DNA methylation-sensitivity was also observed in the Input-corrected signal ( log2 ( MeCP2 ChIP/Input ) ) , strongly supporting the view that mCG and mCAC are targets for MeCP2 binding ( Fig 4C and S3C Fig ) . To complement the analysis of protein coding genes , we found once again that the Input-corrected signal is strongly correlated with the density of both mCG and mCAC ( green and pink lines ) but gives the best correlation upon summation of both individual binding motifs ( grey line ) ( Fig 4D ) . We identified similar binding preferences when adopting a sliding window approach to 1kb regions across the genome ( Fig 4E and 4F ) . This mCG binding preference was independent of the third DNA base ( S3D Fig ) . In agreement with the in vitro and in vivo results reported above , the density of mCAC also correlated strongly with increasing MeCP2 enrichment , whereas a much weaker trend was observed for other methylated tri-nucleotide sequences ( Fig 4F and S3E and S3F Fig ) . In contrast to published reports [17 , 35] , we found no evidence for MeCP2 binding to hmCG in native brain ( S3G and S3H Fig ) . We also failed to detect MeCP2 binding to unmethylated C’s in any sequence context ( S3I–S3L Fig ) . Taken together , our analysis of ChIP data strongly supports the view that MeCP2 binding is determined by the combined density of mCG and mCAC sites . Given the high abundance and global distribution of MeCP2 in the neuronal genome , we looked for domains of MeCP2 occupancy that might reflect long-range variation in binding site abundance . To avoid pooling data in arbitrary windows , we used a multiscale representation method ( MSR ) that identifies patterns of signal enrichment or depletion across scales spanning several orders of magnitude [36] . MSR identified a large number of long domains moderately enriched for MeCP2 of up to 1 Mb in length , indicating that regions of high MeCP2 occupancy extend beyond the scale of a single gene ( Fig 4G ) . These regions share common sequence features , in particular high mCG and mCAC densities ( Fig 4H and S3M and S3N Fig ) . We also identified many short regions that have high GC content ( <1 kb ) but are strongly depleted in MeCP2 binding . As expected , these latter regions significantly overlap with CGIs ( S3O Fig ) . Lastly , we found a group of relatively long regions ( 10 kb—1 Mb ) which are moderately depleted in MeCP2 binding ( MeCP2 enrichment score < 0 to -2 ) . These regions are relatively enriched for mCG but lack mCAC ( Fig 4H and S3M–S3O Fig ) . As part of this binding site analysis we re-visited an earlier report that used SELEX to demonstrate MeCP2 binds preferentially to mCG flanked by an AT-rich run of 4–6 base pairs in vitro [37] . To look for this preference in brain , we asked whether isolated mCG and mCAC flanked by a run of 4 or more A or T base pairs within 13 base pairs showed greater MeCP2 ChIP-seq signal than sequences lacking an AT-run . In summary , we find no evidence for an effect of AT-flanks on MeCP2 binding site occupancy ( S3P and S3Q Fig ) . We speculate that SELEX , which requires repeated cycles of MBD binding followed by PCR amplification , has detected a real but weak preference that is of questionable biological relevance . The robust association of MeCP2 with both methylated sites in the genome and the co-repressor complex NCoR [13] suggests that the protein can function to inhibit transcription . If so , a relationship would be expected between MeCP2 occupancy and the transcriptional mis-regulation when MeCP2 is either depleted by deletion of the gene ( KO ) [38] or over-expressed ( OE ) [39 , 40] . Before making use of published datasets for mouse hypothalamus [27] , we first asked using HPLC whether the absence or overexpression of MeCP2 alters total RNA levels . Previous studies using MeCP2-deficient neurons differentiated in vitro from mouse ES cells or human iPS cells reported reduced total RNA and transcriptional capacity [25 , 41] , but comparable measurements in brains of MeCP2-deficient mice have not been reported . Using a sensitive RNA quantification technique , we observed that total RNA per cell in KO hypothalamus is reproducibly 15% lower than WT ( S4A Fig ) . Overexpression of MeCP2 , however , did not significantly affect total RNA . As whole cell RNA is ~98% ribosomal RNA , we asked whether mRNA levels were also reduced in KO hypothalamus . Quantitative RT-PCR ( qPCR ) , using spiked-in Drosophila cells to control for experimental error and normalized to brain cell number in each sample ( S4B Fig ) , confirmed that genes previously reported to be up- or down-regulated in MeCP2-deficient hypothalamus [27] were similarly mis-regulated in our samples ( S4C–S4F Fig ) . We then measured the transcript abundance for three housekeeping genes and found that all were down-regulated by approximately 15% ( S4G–S4I Fig ) . These results suggest that total RNA and mRNA levels are coordinately reduced and we have therefore applied this normalization to all hypothalamus RNAseq datasets . The mechanisms responsible for reducing total RNA , and whether this effect is a primary or secondary consequence of MeCP2 deficiency , are currently unknown . We next examined gene expression by separating genes whose expression was increased , unaltered or decreased in response to changing levels of MeCP2 . Normalizing ChIP signals against Input , we found that up-regulated genes in KO hypothalamus were within domains enriched in MeCP2 , whereas down-regulated genes were within relatively MeCP2 depleted regions ( Fig 5A ) . Unchanged genes showed an intermediate level of MeCP2 occupancy . The reciprocal result was seen in OE hypothalamus , as down-regulated genes had high MeCP2 occupancy , whereas up-regulated and unchanged genes bound relatively less MeCP2 ( Fig 5B ) . This relationship , which was not observed in a previous analysis of this gene expression and ChIP-seq dataset , disappeared altogether if gene body binding of MeCP2 was normalized to binding levels in gene flanking regions [27] . Adjustment of the data in this way obscures the relationship between DNA methylation and gene expression because enhanced or depleted binding of MeCP2 is not confined to gene bodies , but extends up- and down-stream of the transcription start and end sites ( Fig 5A and 5B ) . We also asked whether the increased MeCP2 occupancy measured by ChIP-seq in hypothalamus correlated with an elevated level of the two target sequences mCAC and mCG . The distribution of mCAC strikingly matched the pattern of MeCP2 binding ( Fig 5C and 5D ) , but mCG , which occurs at much higher density , correlated less obviously ( Fig 5E and 5F ) . Possible reasons for this difference are considered below ( see Discussion ) . The strong reciprocal relationship between MeCP2 occupancy and the direction of gene mis-regulation in KO and OE hypothalamus respectively , is compatible with the notion that MeCP2 binding is inhibitory to transcription . Excess MeCP2 preferentially inhibits genes with most binding sites whereas its depletion preferentially de-represses highly occupied genes . Elevated or depleted MeCP2 binding extended up- and down-stream of the TSS , suggesting that these genes are embedded within the extended MeCP2 domains identified by our MSR analysis ( Fig 4G and 4H ) . This was confirmed by mapping MeCP2-enriched and depleted domains onto the genome in relation to mis-regulated genes ( Fig 5G ) . Approximately 80% of genes up-regulated in KO hypothalamus were within or overlapped domains of high MeCP2 occupancy ( dark and pale lilac ) , whereas only 19% of down-regulated genes were associated with MeCP2 enrichment ( Fig 5G , 5H and S5A and S5B Fig ) . In order to visualize the relationship between transcription and MeCP2 occupancy comprehensively , transcript fold change levels were plotted against MeCP2 ChIP signal normalized to Input ( Fig 5I and 5J and S5C and S5D Fig ) . The 15% global reduction in mRNA and ribosomal RNA relative to DNA automatically means that in KO hypothalamus , expression of most genes ( 8 , 363 ) is reduced ( Fig 5I , green ) . Despite this overall trend , however , a small number of genes ( 403 ) significantly increased their expression per cell compared with WT ( Fig 5I , purple ) . These genes , which shared high MeCP2 occupancy ( Fig 5I , lower panel ) , showed reciprocal behavior in OE hypothalamus where they were down-regulated ( Fig 5J ) . This inverse relationship is readily apparent in a plot of gene expression changes in KO/OE hypothalamus ( S5E Fig ) . De-regulated genes shared similar levels of mCG density , but only the up-regulated genes displayed increased mCAC ( S5F and S5G Fig ) . We noted that the genes up-regulated in KO hypothalamus were significantly longer than average , in agreement with a report that long genes are preferentially up-regulated in MeCP2 KO mice [26] ( S5H Fig ) and most were implicated in brain function by gene ontology analysis ( S5I Fig ) . In addition to this group of genes , a gene set that has been implicated in a variety of neurological disorders [42] also showed behavior compatible with repression by MeCP2 , as the magnitude of mis-regulation in KO and OE hypothalamus correlated reciprocally with MeCP2 occupancy ( Fig 5K and 5L ) .
In this study , we comprehensively analyzed the modified DNA sequences that determine MeCP2 binding . To ensure the reliability of our conclusions we used three independent experimental approaches: in vitro EMSA using amino acids 1–205 of MeCP2; in vivo ChIP in transfected cultured cells using full length MeCP2; and in vivo ChIP-seq of native MeCP2 in mouse brain . Three methylated DNA motifs consistently recruited MeCP2: mCG , mCAC and hmCAC . Interestingly , mCAC is the predominant methylated non-CG sequence in brain , comprising 15–30% of all methylated cytosine in sorted mouse neurons , probably due to the action of the de novo DNA methyltransferase Dnmt3a [2 , 4] . The tri-nucleotide mCAT gave inconsistent results in our assays; being well bound in the transfection assays , weakly bound in vitro and undetectably bound in brain . We speculate that overexpression in transiently transfected cells may have exaggerated an otherwise weak interaction . It is likely that the brain ChIP data give the most reliable indication of biologically relevant binding specificity . Although the hydroxymethylated sequence hmCAC binds MeCP2 in all assays , it is reportedly extremely rare in brain perhaps due to the preference of Tet enzymes for mCG as a substrate [3] . Given the inability of MeCP2 to bind hmCG and the rarity of hmCAC , it seems unlikely that hmC is a major target for MeCP2 . The data in fact suggest a negative role , as oxidation of the abundant mCG methyl group by Tet enzymes would “switch off” MeCP2 binding , thereby preventing recruitment of corepressor complexes at this site . Our data also indicate that the methylation required for MBD binding to DNA can be supplied on one strand by thymine rather than 5-methylcytosine . We previously observed that replacement of a mC at a methylated CG di-nucleotide with T , forming a T:G mispair , had a negligible effect upon the binding affinity of MeCP2 [29] . This indicates that hydrogen bonded base pairs are not essential and that the interaction is flexible enough to accommodate T:G wobble geometry . Here we report that in duplex DNA one pyrimidine-methyl group can be provided by either the mC or T , as replacement of T with U , which lacks the T methyl group , results in loss of MeCP2 binding . To explore the structural basis for the tri-nucleotide specificity of MeCP2 binding , we asked whether the X-ray structure of the MeCP2-MBD [43] could suggest why mCAC or mCAT binding is permitted while mCAA , mCAG , mCCX and mCTX are forbidden . Surprisingly , informal modeling indicated that altering the conformation of only one amino acid side chain , R133 , while leaving all other coordinates of the established X-ray protein structure unchanged , could hypothetically account for the observed interactions ( Fig 6 and S6 Fig plus accompanying extended legend ) . Thus , the observed tri-nucleotide binding specificity of MeCP2 can hypothetically be explained with minimal perturbation of the established structure of the MBD-DNA complex . Although structure determination is essential to test these predictions , their potential impact on MeCP2 function is of interest . Formally , mCAC and mCG may be read by MeCP2 as independent signals with distinct biological outcomes . Alternatively , they may lead to functionally identical consequences when MeCP2 is bound . Favoring the second possibility , published evidence indicates that MeCP2 can repress transcription via mCH and mCG sites [2] . If , as our modeling implies , binding to either mCG or mCAC is accompanied by a minimal conformational shift in the MBD structure , we anticipate that the biological consequences of binding to either motif will be the same . Further experiments are required to test these conjectures rigorously . It was shown previously that DNA methylation is the primary determinant of MeCP2 binding in cultured cells by genetic ablation of the DNA methyltransferases [15 , 44] . DNA methylation-independent binding has been detected when the MBD is mutated [15] , but as individuals lacking a functional MBD nevertheless exhibit severe RTT , the significance of this non-specific interaction is questionable . Our re-analysis of MeCP2 ChIP datasets greatly strengthens the argument that mC-directed binding is a critical function of MeCP2 . Mapping bound MeCP2 in vivo has been problematic , as the high frequency of mCG and mCAC binding sites throughout the neuronal genome ( one per ~100 bp ) poses challenges for conventional ChIP-seq analysis . Unlike transcription factors , whose binding sites tend to be widely spaced compared with the size of chromatin-derived DNA fragments ( 200–500 bp ) , MeCP2 binding sites are short and frequent . As a result of this difference , only a minority of DNA fragments is immunoprecipitated by transcription factor antibodies , leading to recovery of discrete peaks , whereas in the case of MeCP2 , most genomic DNA fragments contain mCAC and/or mCG ( CGIs being a conspicuous exception ) , leading to a relatively uniform recovery of genomic DNA . Thus , the difference between Input and ChIP signal , which is the measure of MeCP2 density , provides an undulating continuum in which peaks are broad . In spite of these limitations , our careful normalization of ChIP versus Input reads detects a robust relationship between MeCP2 binding and the density of mCAC + mCG in brain nuclei . Contrasting with this conclusion , a recent study using olfactory bulb neurons , reported that MeCP2 is enriched at non-methylated CGIs and that DNA methylation is a minor determinant of binding [20] . This conflicts with the findings of our study and several previously published MeCP2 ChIP experiments , all of which found a dramatic drop in MeCP2 binding at CGIs in cultured cells [15] , whole mouse brain [16] , hypothalamus [27] , cortex and cerebellum [26] coupled with DNA methylation-dependent occupancy of the genome . It is notable that these reports of DNA methylation-dependence were achieved using a diversity of validated antibodies . One potential explanation for the discrepant recent results in olfactory bulb is that the rules governing MeCP2 binding in this region of the brain differ from those operating in the remainder of the nervous system . Alternatively , there may be a technical issue regarding antibody specificity or differential PCR amplification of ChIP and Input samples prior to sequencing that has led to inconsistent findings . A striking feature of the MeCP2 KO hypothalamus is the reduced level of total RNA , in agreement with reports in cultured mouse and human neurons [25 , 41] . The mechanism responsible is unknown , but one proposal is that MeCP2 is a direct global activator of transcription [24 , 25] . Arguing against this possibility , we found that most KO down-regulated genes lie in domains of low MeCP2 occupancy . Also , it might be expected that two-fold overexpression of an activator would lead to increased levels of RNA compared to WT , but this is not observed ( S4A Fig ) . An alternative explanation is that reduced RNA reflects reduced cell size , perhaps as a secondary consequence of sub-optimal neuronal gene expression . In this case , the change in total RNA and the relative mis-regulation of genes within the transcriptome may be separate phenomena . Given the close relationship between gene mis-regulation , MeCP2 binding site density and MeCP2 occupancy , we favor the view that effects of MeCP2 concentration on the balance of neuronal gene expression are primary , whereas the downward shift in total RNA is a secondary effect . To test this rigorously it will be important to track down the origins of the total RNA deficiency . By re-analyzing ChIP-seq and RNA-seq datasets from hypothalamus of WT mice , Mecp2-null mice and mice over-expressing MeCP2 we were unable to confirm reports that MeCP2 is more highly bound to the transcription units of mis-expressed genes regardless of up- or down-regulation [27] . Instead we found that MeCP2 is bound at higher levels within and surrounding genes that are up-regulated when MeCP2 is missing , or down-regulated by MeCP2 over-expression . In other words , mis-regulated genes belong to large domains that are relatively rich in mCG and mCAC . These findings fit well with the evidence that MeCP2 may link methylated DNA with the NCoR/SMRT corepressor [13] . Co-repressor recruitment to a genomic domain would be expected to down-regulate genes within that domain according to the density of recruiter binding sites . In the absence of the recruiter , therefore , up-regulation of gene expression would also be proportional to binding site density , as we observe . In addition the results fit with the conclusions of Gabel and colleagues [26] who found repression of long genes by MeCP2 . They furthermore accord with a previously published study indicating that transcriptional repression by MeCP2 depends upon domains of DNA methylation , notably in gene bodies [45] . Although we emphasize the correlation with binding site density per unit of DNA length , it is possible that the absolute number of binding sites per gene also contributes to the MeCP2 response . Further work is required to disentangle the roles of these related variables . The tri-nucleotide mCAC , despite its lower abundance compared with relatively uniformly distributed mCG , correlates strongly with MeCP2 binding and transcriptional mis-regulation in response to altered levels of MeCP2 . Superficially , mCG correlates less well than mCAC with changes in gene expression . However , this effect may be exaggerated by the difference in their average densities across the genome . Mis-regulated genes have on average one extra mCAC per 2000 bp when compared with those changing in the opposite direction . Although this is a very small increment , it is robustly measurable compared with the average density of <2 mCAC sequences per kb . In contrast , addition of one mCG per 2000 bp , though an identical change , makes a much smaller difference ( <4% ) to the already high mCG density in the genome ( ~15 per kb ) and would not therefore be reliably measurable . These considerations leave open the possibility that the small differences in gene expression in response to changing amounts of MeCP2 are equally affected by both mCG and mCAC binding sites . While there is no direct evidence that aberrant gene expression is the proximal cause of Rett syndrome or MeCP2 overexpression syndrome , it is noteworthy that thousands of genes , including many implicated in human neuronal disorders , are sensitive to altered levels of MeCP2 . Mild mis-regulation on this scale may destabilize neuronal function [26] . It is worth recalling that Rett neurons , though sub-optimal , are viable for many decades . In this sense , the biological defect can be seen as mild , despite the profound effects on higher functions of the brain . The challenge now is to determine how brain function might be affected by a multitude of small discrepancies in gene expression . Overall , the results presented here sustain a coherent view of MeCP2 function: namely that MeCP2 binding at mCG and mCAC sites determines the magnitude of a repressive effect on transcription that is exacerbated by MeCP2 excess and relieved by MeCP2-deficiency . With the benefit of a comprehensive list of MeCP2 target sequences at the molecular level , the predictions of this model can be experimentally tested , clarifying further the role of MeCP2 in regulating transcription in the brain .
All mouse studies were approved and licensed under the UK Animals ( Scientific Procedures ) Act 1986 and conducted in accordance with guidelines for use and care of laboratory animals . Male Mecp2STOP/y and corresponding WT littermates [46] , male Mecp2 -/y and WT littermates [38] and Mecp2 overexpression ( OE ) mice [40] were used as Western blotting and Real Time PCR controls or for HPLC estimation of RNA/DNA ratio . C57Bl6 male WT 10 week old mice were used for FACS sorting experiments and consecutive WGBS and TAB-seq . Protein was prepared as described [33] . When examining MeCP2 [1–205] specificity , DNA sequence was varied at the tri-nucleotide indicated in bold ( S1 Table ) . The primary cytosine of this tri-nucleotide was either non-methylated , methylated , or hydroxymethylated . All oligonucleotides were annealed to their complement , 32P-labelled and electrophoretic mobility shift assays performed on ice for 30 min using conditions described previously [37] . In competition assays to assess tri-nucleotide-binding preferences of MeCP2 [1–205] a parent 58 bp Bdnf-probe , containing the centrally methylated sequence mCGG , was 32P-labelled and co-incubated with a 2000-fold excess of cold-competitor DNA bearing one of the sequences described in Fig 1B and 1C . Bound complexes were resolved as described above and levels of competition visualized by Phosphorimager analysis and ImageJ quantification . These experiments were performed in triplicate . HPLC purified oligonucleotides and corresponding antisense oligonucleotides were purchased from biomers . net . Some oligonucleotides containing 5hmC were synthesized and characterized as described previously [47] . All oligonucleotide sequences used are listed in S1 Table . Equal amounts of sense and antisense oligonucleotide stocks ( 100μM ) were mixed with 10x Ligation Buffer ( NEB ) in 50μl volumes . Oligonucleotide mix was boiled in a water bath for 8 minutes and cooled to room temperature . Annealed oligonucleotides were cleaned-up with MSB Spin PCRapace cleanup kit ( Invitek ) and diluted to 10μM stocks for transfections . HEK293FT cells ( 1 . 5 x 106 ) were transfected overnight , with 0 . 5μg of full-length MeCP2 tagged at the N-terminus with EGFP , using Lipofectamine 2000 ( Lifetechnologies ) according to manufacturer’s instructions . After assessment of transfection efficiency ( described in [33] ) , the medium was changed and replaced with annealed unmodified , methylated or hydroxymethylated oligonucleotides [100nM final concentration] using TransIT Oligofect reagent ( Mirus ) for 4 hours . Cells were washed with PBS and harvested by scraping . Cells were then crosslinked with formaldehyde to 1% final concentration for 5 minutes at room temperature and quenched by the addition of glycine to a final concentration of 0 . 125M for 5 minutes followed by another two washes in PBS . Cell pellets were flash frozen in liquid nitrogen and stored at -80°C or directly used for chromatin isolation and consecutive ChIP with 4μg MeCP2 M6818 antibody ( Sigma ) . Isolated chromatin from transfection ChIP assay was also used for Western blotting to estimate MeCP2 expression levels ( S1C Fig ) . Primer sequences can be found in S2 Table . Dot blots of modified oligonucleotides and control DNA ( Methylated standard kit , Active Motif ) were generated with Bio-Dot Microfiltration Apparatus ( BioRad ) using manufacturer’s recommendations . Oligonucleotides and control DNA were denaturated by the addition of [0 . 4M] NaOH , [10mM] EDTA in a total volume of 50μl and boiled for 10 minutes . DNA was neutralized by addition of an equal volume of ice-cold 2M ammonium acetate . Control DNA and oligonucleotides were spotted in duplicate serial dilutions ( Control DNA: 50ng , oligonucleotides: 10μM starting concentration ) . Nitrocellulose membrane was UV auto-crosslinked and then blocked for 30 minutes in 5% non-fat dried milk powder , 0 . 05% Tween 20/ 1x TBS . Primary antibodies were incubated for 45 minutes at room temperature ( 5hmC: 1:10 . 000 Active Motif; 5mC: 1:500 Eurogentech ) . Secondary LI-COR antibodies were incubated in the dark for 30 minutes ( donkey anti mouse IRDye 800Cw; donkey anti rabbit IRDye 680; LI-COR ) . Membranes were scanned with a LI-COR Odyssey instrument . Modeling was based on the X-ray structure of MeCP2 ( PDB code 3C2I ) using the program COOT [48] . Atomic coordinates for DNA bases were generated using the ‘mutate’ option . To optimize hydrogen-bonded and van der Waals contacts between protein and different base pair sequences , the conformation of the side chain of R133 was adjusted manually ( all other atoms in the structure were left unchanged ) . The potential role of water molecules in the recognition of different base-pair sequences by MeCP2 was examined by placing a water molecule in the highly conserved and most probable sites of hydration in the major groove of B-DNA as described [49] . All figures were prepared using the graphics program PyMol ( DeLano Scientific , San Carlos , CA ) . Brain nuclei isolation and consecutive FACS sorting according to NeuN expression was performed as described previously [16] . 10 week old WT Bl6 male mice were used and 4 brains were pooled for each replicate . Genomic DNA of three replicates of 10 week old male Bl/6 WT dissected hypothalamus samples was prepared with the DNeasy Blood & Tissue Kit ( Qiagen ) and 0 . 5% unmethylated λ DNA ( Promega ) was spiked in . Equal amounts of genomic DNA were bisulfite converted with the EZ DNA Methylation Gold Kit ( Zymo Research ) and libraries prepared with the TruSeq DNA methylation kit ( Illumina ) according to manufacturer’s instructions . WGBS and TAB sequencing from NeuN positive sorted neuronal nuclei was as described previously [50] . For TAB treatment , half the DNA was glycosylated , TET oxidized and spiked with control DNA . The other half was left untreated and spiked with unmethylated λ DNA ( Promega ) . NGS libraries were prepared with TruSeq DNA Sample Preparation Kit ( Illumina ) according to manufacturer’s instructions . After size selection , all libraries were bisulfite treated with EpiTect Bisulfite Kit ( Qiagen ) and amplified with Pfu Turbo Cx Polymerase ( Stratagene ) for 7 PCR cycles . Cleaned-up libraries were validated on a Bioanalyzer High Sensitivity DNA Chip ( Agilent ) and 100 bp paired-end sequencing performed on an Illumina HiSeq 2000 platform ( Wellcome Trust Sanger Institute , Hinxton , UK ) . Hypothalamus was isolated from 6 week old male WT and Mecp2 -/y mice in 5 replicates . RNA and DNA were co-isolated with the AllPrep DNA/RNA Mini kit ( Qiagen ) according to manufacturer’s instructions with some modifications . In short , tissue was homogenized in 1ml RLT buffer ( spiked with 3 x 106 Drosophila S2 cells/10ml RLT buffer ) and centrifuged in a Qiashredder column ( Qiagen ) for 2 minutes at full speed . The eluted RNA was next subjected to treatment with the DNA-free DNA removal kit ( Ambion ) and reverse transcribed with the iScript cDNA synthesis kit ( BioRad ) . Real Time quantitative PCR was performed on cDNA and DNA with Drosophila and mouse specific mRNA and genomic DNA primers . For analysis , mouse mRNA was normalized to Drosophila RNA and analogous mouse DNA was normalized to Drosophila DNA . In the final step , corrected mRNA levels were normalized to corrected DNA values . Primer sequences can be found in S3 Table . Dissected hypothalamus tissue was homogenized in lysis buffer ( 10mM Tris HCl [pH 7 . 4] , 0 . 5% SDS , 100mM EDTA , 300μg/ml proteinase K ) and incubated at 50°C for 2 hours . Total nucleic acid was recovered from the completely lysed sample by ethanol precipitation in 2 volumes of 100% ethanol at room temperature ( for 30 minutes ) , and pelleting by centrifugation . The pellet was washed once in 2 volumes of 70% ethanol , and the nucleic acid pellet was resuspended in hydrolysis buffer containing 1x DNase I buffer ( NEB ) , 1mM zinc sulphate , DNase I ( NEB ) and Nuclease P1 ( Sigma ) . After 4 hours , the sample was mixed thoroughly and digested for a further 8 hours . After 12 hours at 37°C , the sample was heated to 92°C for 3 minutes and cooled on ice . Two volumes of 30mM sodium acetate , 1mM zinc sulphate [pH 5 . 2] were added plus additional Nuclease P1 and the nucleic acids were further digested to deoxyribonucleotide and ribonucleotide 5’ monophosphates for a further 24 hours at 37°C . The samples were then subjected to HPLC as set out below . UV absorbance was recorded at 276 nm ( dCMP , elution time 9 . 4 minutes ) , 282 nm ( 5mdCMP , elution time 17 minutes ) , 268 nm ( dTMP , elution time 21 . 9 minutes ) , 260nm AMP and dAMP ( elution times 27 minutes and 62 . 47 minutes ) and 254 nm ( GMP and dGMP , elution times 11 . 1 minutes and 29 . 7 minutes ) . Extinction coefficients used in nucleotide quantifications were dCMP , 8 . 86 x 103; 5mdCMP 9 . 0 x 103; dTMP , dGMP/GMP 12 . 16 x 103; dAMP/AMP 15 . 04 x 103 . Quantifications were calculated from the area under each peak estimated using Chromeleon software using the respective extinction coefficients . | Rett Syndrome is a severe neurological disorder found in approximately 1:10 . 000 female births . The gene causing most cases of Rett Syndrome has been identified as methyl-CG binding protein 2 ( MeCP2 ) which is an epigenetic reader protein , classically characterized as binding to CpG methylated ( mCG ) di-nucleotides . Although much research has focused on the binding capacities of MeCP2 , its exact mode of action is still controversial . Here we show , that in addition to the classical mCG motif , frequently occurring mCAC tri-nucleotides are also bound by MeCP2 . We additionally discover large genomic regions of high mCG + mCAC density that contain neuro-disease relevant genes sensitive to MeCP2 loss or overexpression . Our results re-emphasize MeCP2’s original proposed function as a transcriptional repressor whose purpose is to maintain the delicate balance of neuronal gene expression . | [
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"meth... | 2017 | MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain |
Superantigens ( SAgs ) are potent exotoxins secreted by Staphylococcus aureus and Streptococcus pyogenes . They target a large fraction of T cell pools to set in motion a “cytokine storm” with severe and sometimes life-threatening consequences typically encountered in toxic shock syndrome ( TSS ) . Given the rapidity with which TSS develops , designing timely and truly targeted therapies for this syndrome requires identification of key mediators of the cytokine storm’s initial wave . Equally important , early host responses to SAgs can be accompanied or followed by a state of immunosuppression , which in turn jeopardizes the host’s ability to combat and clear infections . Unlike in mouse models , the mechanisms underlying SAg-associated immunosuppression in humans are ill-defined . In this work , we have identified a population of innate-like T cells , called mucosa-associated invariant T ( MAIT ) cells , as the most powerful source of pro-inflammatory cytokines after exposure to SAgs . We have utilized primary human peripheral blood and hepatic mononuclear cells , mouse MAIT hybridoma lines , HLA-DR4-transgenic mice , MAIThighHLA-DR4+ bone marrow chimeras , and humanized NOD-scid IL-2Rγnull mice to demonstrate for the first time that: i ) mouse and human MAIT cells are hyperresponsive to SAgs , typified by staphylococcal enterotoxin B ( SEB ) ; ii ) the human MAIT cell response to SEB is rapid and far greater in magnitude than that launched by unfractionated conventional T , invariant natural killer T ( iNKT ) or γδ T cells , and is characterized by production of interferon ( IFN ) -γ , tumor necrosis factor ( TNF ) -α and interleukin ( IL ) -2 , but not IL-17A; iii ) high-affinity MHC class II interaction with SAgs , but not MHC-related protein 1 ( MR1 ) participation , is required for MAIT cell activation; iv ) MAIT cell responses to SEB can occur in a T cell receptor ( TCR ) Vβ–specific manner but are largely contributed by IL-12 and IL-18; v ) as MAIT cells are primed by SAgs , they also begin to develop a molecular signature consistent with exhaustion and failure to participate in antimicrobial defense . Accordingly , they upregulate lymphocyte-activation gene 3 ( LAG-3 ) , T cell immunoglobulin and mucin-3 ( TIM-3 ) , and/or programmed cell death-1 ( PD-1 ) , and acquire an anergic phenotype that interferes with their cognate function against Klebsiella pneumoniae and Escherichia coli; vi ) MAIT cell hyperactivation and anergy co-utilize a signaling pathway that is governed by p38 and MEK1/2 . Collectively , our findings demonstrate a pathogenic , rather than protective , role for MAIT cells during infection . Furthermore , we propose a novel mechanism of SAg-associated immunosuppression in humans . MAIT cells may therefore provide an attractive therapeutic target for the management of both early and late phases of severe SAg-mediated illnesses .
Bacterial exotoxins known as superantigens ( SAgs ) constitute a family of virulence factors deployed by common bacterial pathogens such as S . aureus and S . pyogenes [1] . SAgs cause a variety of illnesses , including but not limited to food poisoning , scarlet fever , and menstrual and non-menstrual toxic shock syndrome ( TSS ) . Certain SAg-mediated illnesses inflict severe morbidity or even death and are , as such , considered serious clinical emergencies [2] . Also , alarmingly , SAgs can be weaponized and used against civilian populations . As a matter of fact , staphylococcal enterotoxin B ( SEB ) , a major cause of non-menstrual TSS , is listed by the Centers for Disease Control and Prevention among “category B priority” bioterrorism agents [3] . As intact and unprocessed proteins , SAgs bind to lateral surfaces of MHC class II molecules found on antigen ( Ag ) -presenting cells [4] and to T cell receptor ( TCR ) Vβ regions of many T cells [5] . These unorthodox interactions short-circuit the normal sequence of events that typically activates only a tiny proportion of T cells with unique TCR specificities for cognate peptide:MHC complexes , which is approximately 1 in every 10 , 000 T cells . By defying the rule of MHC restriction , SAgs activate as many as 20% of all exposed T cells , regardless of their TCR specificity [1] . This , in turn , leads to a massive “cytokine storm” and hyperinflammation and , under certain circumstances , to organ failure . In addition , in vivo exposure to SAgs punches “holes” in the T cell repertoire by deleting many T cells [5] , while other SAg-responsive T cells may undergo anergy [6] . Consequently , a fraction of pathogen-specific T cells are physically or functionally removed from action in the battle against microbes , hypothetically including the very bacteria that produce SAgs . Of note , SAg-induced T cell deletion and anergy have been extensively studied in mouse models . Whether human conventional T ( Tconv ) cells or innate-like T cells are similarly affected by SAgs remains poorly understood . CD4+ and CD8+ Tconv cells are known targets of SAgs . In contrast , in what capacity non-MHC-restricted T cells may participate in SAg-mediated immunopathology is far from clear . γδ T cells have been implicated in host responses to SAgs [7 , 8] . We and others also reported that invariant natural killer T ( iNKT ) cells can be directly activated by group II bacterial SAgs in a CD1d-independent fashion [9 , 10] . However , human iNKT cells are infrequent , especially in comparison with mucosa-associated invariant T ( MAIT ) cells that comprise 1%–10% of all peripheral blood T cells , up to 10% of intestinal T cells , and approximately 45% of all hepatic lymphocytes in humans [11 , 12] . MAIT cells are innate-like T lymphocytes that express an invariant TCRα ( iTCRα ) chain with a unique Vα19-Jα33 rearrangement in mice and Vα7 . 2-Jα33 in humans [13 , 14] . They are restricted by MHC-related protein 1 ( MR1 ) [15] , a monomorphic MHC class I—like molecule that is highly conserved among mammalian species [16] and presents microbe-derived vitamin B metabolites [17] . These discoveries underpinned the recent invention of MR1 tetramer reagents that enable MAIT cell identification [18 , 19] . Human MAIT cells are also phenotyped as CD3+Vα7 . 2+CD161high cells . MAIT cells can be viewed as “emergency responders” to infection . This is because: i ) they occupy strategic locations at the host—pathogen interface; ii ) they quickly amass at infection sites where they respond to a variety of bacteria and fungi [20–22]; iii ) they exhibit an effector memory-like phenotype [11] and are capable of producing pro- and/or anti-inflammatory cytokines ( e . g . , IFN-γ , TNF-α , IL-4 , IL-10 ) readily , amply and promptly after iTCR stimulation . The nature of cytokines released by MAIT cells is likely to influence the function ( s ) of various downstream effector cell types . This , in turn , either promotes immunity to or immunopathology caused by microbial intruders . It is noteworthy that MAIT cells can also be activated by a combination of IL-12 and IL-18 [23] , which are released during many infections . Despite all the above attributes , it is unclear whether MAIT cells respond to bacterial SAgs . This is an important question in light of the enormous immunomodulatory properties of these cells . MAIT cells are enriched in the intestine and in the human liver , which receive continuous , heavy exposure to microbes , including SAg-producing bacteria . Equally important , cells expressing “SAg-responsive” Vβs are present within the MAIT cell iTCR repertoire [24] . In this work , we have utilized MAIT hybridoma cell lines , wild-type mice , HLA-DR4-transgenic ( DR4 tg ) mice , MAIT cell-enriched bone marrow chimeric mice , and humanized NOD-scid IL-2Rγnull ( NSG ) mice , as well as human peripheral blood mononuclear cells ( PBMCs ) and non-parenchymal hepatic mononuclear cells ( HMNCs ) to investigate the responsiveness of MAIT cells to a wide panel of bacterial SAgs in vitro and/or in vivo . We report for the first time , to our knowledge , that select staphylococcal and streptococcal SAgs trigger rapid activation of MAIT cells in an MR1-independent manner . In addition , MAIT cell activation by SAgs occurs through iTCR triggering and/or IL-12/IL-18 signaling . Interestingly , the responses launched by human MAIT cells were far greater in magnitude than those elicited by Tconv cells , iNKT cells , or γδ T cells . However , the generation of multifunctional , hyperinflammatory MAIT cells by SAgs was followed by a state of anergy that hampered their cognate response to bacterial pathogens . We propose a novel mechanism of immunosuppression in the aftermath of exposure to bacterial SAgs , which involves a distinct subset of unconventional , innate-like human T cells .
While Tconv cells are considered the main targets of bacterial SAgs , the effector functions of “innate-like” T cells following their exposure to SAgs have been largely overlooked , due perhaps to their lower frequencies in the circulation . When investigating the relative contribution of various human T cell subsets to SEB-triggered production of IFN-γ , a pro-inflammatory cytokine that is key to the pathogenesis of SAg-mediated illnesses , we found the vast majority of IFN-γ-secreting CD3+ T cells to strongly express CD161 ( Fig 1A ) . CD161 is a C-type lectin that is more abundantly expressed by innate-like γδ T , iNKT , and MAIT cells than by Tconv cells [25] . Therefore , we evaluated the contribution of these cell types to IFN-γ production . Our experiments revealed that MAIT cells are the major source of this cytokine after stimulation with SEB ( Fig 1B and 1C ) . The frequency of iNKT and γδ T cells with detectable intracellular IFN-γ was increased upon SEB stimulation ( Fig 1C ) . However , MAIT cells were clearly the predominant IFN-γ+ population ( Fig 1B and 1C ) . Of note , MAIT and iNKT cells synthesized more IFN-γ on a per cell basis , as judged by their mean fluorescence intensity ( MFI ) of IFN-γ expression , in comparison with γδ T cells ( Fig 1C ) . Importantly , when compared with other CD3+ T cell fractions , MAIT cells elicited a more robust IFN-γ response to SEB ( Fig 1D ) . Finally , using data from a relatively large cohort of blood donors , we found a positive correlation between MAIT cell and IFN-γ+ cell frequencies following SEB stimulation of PBMCs ( Fig 1E ) . It is important to note that in control experiments , we have ruled out the possibility of a role for endotoxin contamination in MAIT cell activation . We demonstrated that adding lipopolysaccharide ( LPS ) to PBMC cultures does not increase SEB-induced IFN-γ production by MAIT cells ( S1A Fig ) . Furthermore , adding polymyxin B to PBMC cultures before SEB stimulation did not lower the response to SEB ( S1A Fig ) . To make sure polymyxin B was effective in blocking the action of LPS in our hands , we stimulated the human monocytic cell line THP-1 with LPS in the presence or absence of this antibiotic . As expected , treatment with polymyxin B dramatically reduced LPS-induced TNF-α production by THP-1 cells ( S1B Fig ) . Given that MAIT cells have an effector memory-like phenotype [11] , we next made comprehensive , head-to-head comparisons between MAIT cells and effector memory Tconv cells among other T cell subsets in a separate cohort ( n = 7 ) . We found that CD3+CD8+CD45RO+CCR7-Vα7 . 2- effector memory T cells ( TEM ) and CD3+Vα7 . 2+CD161high MAIT cells make significant contributions to the overall IFN-γ response ( Fig 2A ) . The contribution of MAIT cells was particularly impressive given their lower frequency . To be exact , MAIT cells were responsible for 22 . 5% of total IFN-γ production , despite their average frequency of 1 . 3% . In contrast , CD8+ and CD4+ TEM comprised 7 . 1% and 25 . 1% of all CD3+ cells and could account for 22 . 1% and 9 . 5% of the IFN-γ response , respectively ( Fig 2A ) . We verified the above results by MR1 tetramer staining . Accordingly , MR1 tetramer+ MAIT cells constituted only 1 . 3% ( ± 0 . 5% ) of CD3+ PBMCs , but 17 . 4% ( ± 5 . 2% ) of all CD3+IFN-γ+ cells . MAIT cells can be divided into several subsets based on the type of co-receptor ( s ) they express , and it was of interest to determine whether treatment with SEB affects the subset distribution of MAIT cells . We found the majority of SEB-exposed blood MAIT cells to be CD8+ , which is similar to their resting , steady state [18] . In addition , most IFN-γ-producing MAIT cells fell within the CD8+ subset ( Fig 2B ) . Next , we demonstrated that SEB-induced activation of both MAIT and Tconv cells follows a dose-dependent pattern and reaches its plateau at around 1 ng/mL of SEB ( Fig 3A ) . The readout in these experiments was the expression of CD69 , an early activation marker , which also allowed us to compare the kinetics of MAIT and Tconv cell responses to SEB . Both cell types exhibited readily detectable CD69 expression as early as 6 h post-SEB exposure ( Fig 3B ) . However , the magnitude of MAIT cell activation in bulk cultures was far greater than that of Tconv cells at multiple time points ( 6 h , 12 h , and 24 h ) , thus recapitulating our intracellular IFN-γ results . In the next series of experiments , we asked whether and to what extent human MAIT cell responses to SEB may depend on HLA class II or MR1 . Unlike a mouse IgG2a isotype control , an HLA-DR-blocking monoclonal antibody ( mAb ) , which was added to PBMC cultures before the SEB challenge , decreased the frequency of IFN-γ+ MAIT cells ( Fig 3C ) . In contrast , blockade of MR1 failed to alter this response ( Fig 3D ) . The anti-MR1 mAb used in these experiments was functional , as evidenced by its ability to attenuate MAIT cell activation by K . pneumonia lysate ( Fig 3D ) , which was used as a crude source of MAIT cell cognate Ags [26 , 27] . Collectively , the results summarized in Figs 1–3 demonstrate that MAIT cells are hyperresponsive to bacterial SAgs and rapidly produce high levels of IFN-γ as a result . This response does not require MR1 participation and is more robust than those mounted by Tconv cells or innate-like T cell types other than MAIT cells . Exposure to SEB triggers the release of multiple inflammatory cytokines from multiple cell types . To begin to address the role of MAIT and Tconv cells in pro-inflammatory cytokine production , we first performed cytokine multiplexing on PBMC culture supernatants harvested at 2 h , 6 h , 12 h , and 24 h post-SEB stimulation ( Fig 4A ) . We found a gradual increase in the IFN-γ , IL-2 , IL-17A , and TNF-α content of these samples . Since these cytokines can be of Tconv and MAIT cell origin , we set out to examine the relative contribution of these cells to cytokine responses . IFN-γ+ , IL-2+ , and TNF-α+ MAIT cells accumulated gradually and dramatically within SEB-stimulated PBMC cultures , thus yielding frequencies that far exceeded those of Tconv cells ( Fig 4B ) . To our surprise , however , IL-17A+ MAIT cells were barely detectable ( Fig 4B ) . Consistent with this observation , while the expression of T-bet , a transcription factor linked to a Th1 phenotype and IFN-γ production , doubled in SEB-exposed MAIT cells , the intracellular levels of RORγt , a master regulator of Th17-type responses , decreased , rather than increased , after SEB stimulation ( Fig 4C ) . The inflammatory cytokine profiles of MAIT cells can be impacted by the anatomical location of these cells and by the experimental conditions and stimuli employed for their activation [11 , 12 , 28] . Therefore , we sought to examine how exposure to SEB affects the production of pro-inflammatory cytokines by liver-resident MAIT cells . We chose to work with HMNCs because the liver accommodates a large number of MAIT cells [11 , 12] . To ascertain whether hepatic MAIT cells behave similarly in response to SEB , we first confirmed that tumor-free liver tissue samples obtained from colorectal carcinoma patients contained many CD3+Vα7 . 2+CD161+ MAIT cells ( Fig 4D ) . HMNCs were isolated and exposed to SEB for 12 h or 24 h before IFN-γ+ and IL-17A+ MAIT cells were enumerated . Similar to their blood counterparts , hepatic MAIT cells launched a strong IFN-γ response and only a negligible IL-17A response to SEB ( Fig 4E ) . Next , we extended our investigation to assess the cytokine secretion capacity of purified MAIT cells . Sorted CD3+Vα7 . 2+CD161+ cells were co-incubated for 2 h , 6 h , and/or 12 h with autologous CD14+ monocytes , as accessory cells , in the presence or absence of SEB ( Fig 4F ) . As with bulk PBMC cultures , SEB stimulation of purified MAIT cells led to substantial IFN-γ production but no IL-17A secretion . Taken together , these results indicate that: i ) peripheral blood and hepatic MAIT cells respond similarly to SEB; ii ) MAIT cell activation by SAgs results in selective , as opposed to global , pro-inflammatory cytokine release , with the notable and surprising absence of an IL-17A component . To determine whether MAIT cells expressing “SEB-responsive” TCR Vβ chains are directly activated by this SAg , we took advantage of several well-characterized mouse MAIT hybridomas , namely lines 8D12 , 6C2 , and 17E6 [14 , 16] . 8D12 and 6C2 cells express Vβ8 ( Fig 5A ) , a known target of SEB in mice , whereas 17E6 is a Vβ2+ hybridoma [14] that should not respond to SEB . When stimulated with SEB in the presence of wild-type bone marrow-derived dendritic cells ( BMDCs ) , neither 8D12 nor 6C2 cells released IL-2 into the culture supernatant ( Fig 5B ) . We posited that this was merely a reflection of the low affinity of mouse MHC class II molecules for SEB [29] . Indeed , when we used BMDCs generated from DR4 tg mice as accessory cells , both these hybridomas , but not 17E6 , were responsive to SEB ( Fig 5B and 5C ) . The failure of 17E6 cells to produce IL-2 was not due to defective iTCR expression or function because an agonistic anti-CD3ε mAb was able to trigger their activation ( Fig 5C ) . To confirm that DR4 tg BMDCs were only physically required for mouse MAIT cell responses—that is , to mainly supply HLA DR4 and perhaps cell-surface costimulatory molecules of mouse origin—we compared γ-irradiated and nonirradiated BMDCs in 8D12 stimulation cultures . While γ irradiation of 8D12 cells nearly abrogated their ability to secrete IL-2 , as expected , γ-irradiated DR4 tg BMDCs could still prompt an impeccable response that was only marginally weaker than that elicited by nonirradiated BMDCs ( Fig 5D ) . To definitively show that iTCR engagement by SEB was a prerequisite for IL-2 secretion by 8D12 and 6C2 , we used SEBN23A , a mutated version of SEB with a partially impaired TCR binding capacity [30] , in parallel cultures . As anticipated , SEBN23A did not equal SEB in eliciting an IL-2 response ( Fig 5B ) . We also demonstrated that as with human MAIT cells , mouse MAIT cell activation by SEB was MR1-independent ( Fig 5E ) . Finally , we extended our study to examine MAIT cell responses to a relatively wide panel of staphylococcal and streptococcal toxins belonging to multiple evolutionary groups of SAgs [1] . These included toxic shock syndrome toxin -1 ( TSST-1 ) , streptococcal pyrogenic exotoxin A ( SpeA ) , staphylococcal enterotoxin A ( SEA ) , and SpeI , which represent groups I , II , III , and V SAgs , respectively [1] . Interestingly , SEB and SpeA , which originate from 2 different Gram-positive pathogens but are grouped together under the same phylogenetic branch of SAgs ( i . e . , group II ) with known reactivity to mouse Vβ8 [31] , provoked MAIT cell activation ( Fig 5F ) . The majority of human MAIT cells have a remarkably stable TCRβ repertoire that is biased towards Vβ2 and Vβ13 families [14 , 18 , 24] . On the other hand , SEB targets human Vβ13 . 2 but not Vβ2 . This presented a unique opportunity to explore the direct responsiveness of the respective human MAIT cell subpopulations to SEB . Using PBMCs obtained from 12 healthy donors , we first confirmed the higher frequencies of TCR Vβ13 . 2+ and TCR Vβ2+ fractions among MAIT cells in comparison with Tconv cells ( Fig 5G ) . We then purified these fractions and stimulated them with SEB in the presence of autologous monocytes . As hypothesized , Vβ13 . 2+ , but not Vβ2+ , MAIT cells upregulated CD69 and secreted IFN-γ 12 h after they were exposed to SEB ( Fig 5H ) . Therefore , both mouse and human MAIT cells that express “SAg-responsive” TCR Vβ families can be directly activated by bacterial SAgs . Our experiments using purified MAIT cells demonstrated that iTCR ligation contributes to SAg-mediated responses ( Fig 5 ) . However , in vivo T cell responses occur in the presence of other cell types and amid an intricate cytokine milieu , which can be simulated in bulk cultures . To determine the impact of the microenvironment in which MAIT cells encounter SAgs , we compared Vβ13 . 2+ and Vβ2+ MAIT fractions in unfractionated human PBMC cultures . When exposed to SEB , Vβ2+ cells were capable of making IFN-γ ( Fig 6A ) . Intriguingly , IFN-γ synthesis by Vβ13 . 2+ and Vβ2+ MAIT cells was equally vigorous and followed a similar kinetics ( Fig 6A ) . When we used the expression of the early activation marker CD69 as a readout , a similar pattern emerged , although , interestingly , the activation of Vβ2+ MAIT cell appeared to lag slightly behind that of the Vβ13 . 2+ subset ( S2 Fig ) . These results suggested that a cytokine-mediated pathway was operational and fully capable of compensating for a lack of Vβ2+ iTCR cross-linking by SEB . Therefore , we considered the possibility of MAIT cell transactivation by a combination of IL-18 and IL-12 or by IFN-γ , which contribute to innate lymphocyte activation in other settings [23 , 32] . In fact , IL-18 is known to potentiate IFN-γ responses—hence its historical name “IFN-γ-inducing factor” [33] . Several other cytokines have also been implicated in direct or indirect activation of MAIT cells . These include IL-7 [12 , 34] , IL-15 [35] , and IFN-α [36] . Therefore , we first assayed for these cytokines in SEB-stimulated PBMC cultures . We found substantial quantities of IL-18 and IL-12p70 , some IFN-α2 , and only negligible quantities of IL-7 and IL-15 ( Fig 6B ) . Next , we assessed what proportion of SEB-stimulated CD3+IFN-γ+ cells expressed CD218a , the IL-18Rα chain and an integral part of the high affinity receptor for IL-18 . We found a disproportionate pattern whereby the majority of IFN-γ+ T cells were among the CD218a+ population ( approximately 80% at 24 h ) ( Fig 6C ) . This suggested that SEB-hyperresponsive T cells fell within a population that either constitutively expressed CD218a or had upregulated this molecule upon exposure to SEB . In our receptor expression analyses , SEB failed to increase the frequency of CD3+CD218a+ cells among PBMCs or the intensity of CD218a expression in T lymphocytes ( S3A and S3B Fig ) . Furthermore , in the absence of SEB stimulation and within total peripheral blood T cells , we found coincident expression of CD218a and CD161 ( Fig 6D ) . Equally important , CD218ahighCD161high cells were almost exclusively Vα7 . 2+ MAIT cells ( Fig 6D ) . In their steady state , most MAIT cells expressed CD218a along with CD212 , the β1 subunit of the receptor for IL-12 , a potent cytokine that cooperates with IL-18 to induce innate T cell activation [23 , 37] . In addition , and consistent with our findings in unfractionated CD3+ cells , SEB did not alter the expression levels of CD218a or CD212 in MAIT cells ( Fig 6E ) . We found only a small fraction of resting Tconv cells to express CD218a and CD212 ( S4 Fig ) . The frequency of CD218a+ Tconv cells only marginally increased upon SEB stimulation , and that of CD212+ cells was slightly reduced ( S4 Fig ) . Consistent with this finding , neutralizing IL-18 and/or IL-12 failed to decrease the minute , but still detectable , TCR-dependent response of Vβ13 . 2+ Tconv to SEB ( S5 Fig ) . In the subsequent series of experiments , we evaluated the functional contribution of IL-18 , IL-12 and IFN-γ signaling to MAIT cell responses to SAgs . Neutralizing IFN-γ did not prevent the accumulation of CD69+ ( Fig 6F ) , IFN-γ+ , TNF-α+ , or IL-2+ MAIT cells in PBMC cultures ( S6 Fig ) . In contrast , the frequency of cytokine-secreting MAIT cells was diminished partially by an anti-IL-12 mAb and almost completely by an IL-18-neutralizing mAb ( Fig 6G and 6H ) . Moreover , co-neutralization of IL-12 and IL-18 led to a complete or near complete inhibition of cytokine production and CD69 expression by MAIT cells ( Fig 6F and 6G ) . The inhibitory effects of IL-12 and IL-18 neutralization were evident not only for unfractionated MAIT cells but also for their Vβ13 . 2+ and Vβ2+ subsets ( Fig 6H ) . Finally , we compared MAIT cell responses to SEB , recombinant human IL-12 ( rIL-12 ) , and/or recombinant human IL-18 ( rIL-18 ) in parallel . Treatment with rIL-12 or rIL-18 alone gave rise to CD69+ MAIT cells in culture ( Fig 6I ) . When combined , these cytokines induced a stronger response , which was quantitatively similar to that triggered by SEB . When IFN-γ was used as the readout , rIL-18 alone did not induce a response above the background but further boosted the response to rIL-12 , thus closely mimicking the SEB response ( Fig 6I ) . Altogether , the above findings indicate that: i ) MAIT cells constitutively express high levels of IL-12R and IL-18R and are thus poised to respond to these cytokines during infection with SAg-producing bacteria; ii ) the cytokine-mediated pathway of MAIT cell activation is dominant over the iTCR-dependent pathway during exposure to SAgs; iii ) and this pathway is driven by IL-18 and IL-12 but not by IFN-γ at the outset . Mitogen-activated protein kinases ( MAPKs ) have been implicated in cytokine-driven IFN-γ production by NK , iNKT , effector and memory Th1 cells [38–41] . However , whether they control MAIT cell transactivation is unknown . To delineate the intracellular pathway ( s ) governing MAIT cell responses to SAgs , we used pharmacological inhibitors of key intermediates of the MAPK signaling network . We found a marginal inhibition of the IFN-γ response when either SB203580 or PD98059 was present in cultures ( Fig 7A ) . However , a combination of these 2 inhibitors completely disabled IFN-γ production by MAIT cells ( Fig 7A ) . Therefore , p38 and MEK1/2 work synergistically in MAIT cells to allow for a powerful IFN-γ response to SEB . SEB stimulation led to p38 phosphorylation within MAIT cells , which was inhibited by mAbs to IL-12 and IL-18 ( Fig 7B ) . In addition , while rIL-18 generated a minute but still consistently detectable level of p38 phosphorylation , rIL-12 was much more efficient in this capacity ( Fig 7C ) , and combined cytokine treatment was similar to SEB stimulation in inducing p38 phosphorylation in many MAIT cells . Therefore , MAIT cell activation by SEB that leads to IFN-γ production requires p38 and MEK1/2 kinases . We found that human MAIT cells respond much more rigorously to SEB or to a combination of rIL-12 and rIL-18 than they do against lysates prepared from several bacterial pathogens known to harbor MR1-restricted Ags ( S7 Fig ) . These microbes include K . pneumoniae [26 , 27] , E . coli [42–44] , Pseudomonas aeruginosa [45] , and Salmonella typhimurium [17 , 42] . Therefore , we became curious if the enormity and the speed with which MAIT cells respond to SEB result in their exhaustion or anergy . We tested the ability of SAg-pre-exposed MAIT cells to respond to cognate stimulation . Twenty-four hours after incubation with SEB ( or in medium as a control ) , PBMCs were washed and rested for an additional 72 h before they were challenged with Klebsiella lysate ( Fig 8A ) . A leading cause of community-acquired and nosocomial Gram-negative bacterial pneumonia [46] , K . pneumoniae contains MAIT cell cognate Ags [26 , 27] . Despite resting in culture for 4 days , previously unstimulated MAIT cells were still capable of producing IFN-γ , TNF-α , IL-2 , or IL-17A to Klebsiella Ags ( Fig 8A ) . In stark contrast , SEB-exposed MAIT cells failed to produce these cytokines ( Fig 8A ) . This was not due to cell death since only a very small fraction of MAIT cells stained positively with Annexin V ( 3 . 6%-7 . 2% ) or retained the Fixable Viability Dye eFluor 780 ( 0 . 64%-1 . 75% ) at the 24-h time point . Therefore , they were neither undergoing apoptosis nor dead . In a separate cohort ( n = 13 ) , we reduced the resting period to 24 h and additionally used E . coli lysate as a secondary challenge . Consistent with the data we obtained in the previous setup , SEB-exposed , but not unstimulated , MAIT cells were unresponsive to either bacterial challenge ( Fig 8B ) . We also detected no tangible difference between the 3 groups in terms of Annexin V positivity or Fixable Viability Dye retention ( Fig 8B ) . It is noteworthy that in a reverse experimental setting , stimulation of PBMCs with Klebsiella lysate prevented the MAIT cell response to SEB ( S8 Fig ) . Therefore , it appears that repeated TCR triggering incapacitates MAIT cells . This theory is supported by our additional finding that priming PBMCs with Klebsiella lysate could also prevent the MAIT cells’ recall response to the same bacterial preparation ( S8 Fig ) . On the contrary , TCR-independent signaling through IL-12 and IL-18 receptors enhanced , rather than attenuated , the IFN-γ response to Klebsiella ( S8 Fig ) . To explore the possibility of MAIT cell anergy or exhaustion in our system , we evaluated the expression of co-inhibitory molecules associated with these phenomena , including LAG-3 , TIM-3 and PD-1 . We found that a large proportion of CD3+ cells that co-expressed LAG-3 at 24 h post-SEB exposure were Vα7 . 2+CD161high cells , thus fitting the phenotypic definition of MAIT cells ( Fig 8C ) . Interestingly , while only a tiny subpopulation ( approximately 1% ) of Tconv cells were CD69+LAG-3+ , more than 70% of MAIT cells co-expressed CD69 and LAG-3 ( Fig 8D ) , suggesting that MAIT cell hyperactivation and anergy programs are simultaneously set in motion by SEB stimulation . By the same token , a combination of SB203580 and PD98059 prevented SEB-induced LAG-3 up-regulation ( Fig 8E ) , indicating that both p38 and MEK1/2 were required for the observed phenotype . This was reminiscent of the role played by MAPKs in IFN-γ production by MAIT cells ( Fig 7A ) , which supports the notion that SEB-induced MAIT cell activation and anergy go hand in hand . SEB-induced up-regulation of LAG-3 on MAIT cells appears to rely on IL-12 and IL-18 . First , SEB and a combination of these cytokines elevated the population size of LAG-3+ MAIT cells to a comparable level ( Fig 8F ) . Second , co-neutralizing IL-12 and IL-18 led to a 50% reduction in the frequency of this population ( Fig 8G ) . Since the observed inhibition was incomplete , we asked whether iTCR signaling was also required for full induction of LAG-3 . Indeed , when we used the nuclear factor of activated T cells ( NFAT ) inhibitor cyclosporine A ( CsA ) in conjunction with anti-IL-12 and anti-IL-18 , LAG-3+ MAIT cell proportions dropped further ( Fig 8G ) . Investigating the potential role of other co-inhibitory molecules , we found that most LAG-3+ MAIT cells did not co-express TIM-3 at earlier time points ( Fig 8H ) . However , after 12 h of SEB stimulation , LAG-3+TIM-3+ cells were easily detectable within the MAIT cell population but not among Tconv cells ( Fig 8H ) . Finally , only few MAIT cells ( approximately 10% ) expressed PD-1 by 48 h post-SEB stimulation . To ascertain the significance of early LAG-3 upregulation in SEB-induced MAIT cell anergy , we added a LAG-3-blocking mAb or a mouse IgG1 isotype control to PBMC cultures 24 h after SEB stimulation . Cells were then rested for 24 h before they were challenged with Klebsiella and examined , 24 h later , for their intracellular IFN-γ content ( Fig 8I ) . Importantly , the blockade of LAG-3 restored the ability of MAIT cells to respond to Klebsiella Ags , further indicating that exposure to SEB does not simply kill MAIT cells but renders them anergic . In summary , we conclude that: i ) hyperactivation of MAIT cells by SEB interferes with their ability to respond to bacterial pathogens; ii ) the failure of MAIT cells to produce cytokines in response to cognate Ags is accompanied by LAG-3 and TIM-3 upregulation; iii ) SEB-induced MAIT cell anergy can be reversed by blocking LAG-3; iv ) and a cytokine-dominant pathway dictates both the hyperactivation and the anergy of MAIT cells in the aftermath of an SEB challenge . In the next series of experiments , we investigated whether SAgs induce MAIT cell anergy in vivo . SEB exhibits poor affinity for MHC class II molecules expressed in certain mouse strains , such as B6 mice [29] . Therefore , we and others have routinely used DR4 tg mice in which high-affinity interactions with SEB induce rigorous host responses to this SAg , thus simulating many aspects of clinical TSS [9 , 47–50] . A single , low-dose ( 10 μg ) intraperitoneal ( i . p . ) injection of SEB into DR4 tg mice caused morbidity in these animals as judged by their weight loss . In contrast , SEB-injected wild-type B6 mice and PBS-injected DR4 tg mice showed no signs of morbidity as anticipated ( Fig 9A ) . We noticed a significant numerical increase in non-parenchymal lung mononuclear cells ( MNCs ) in SEB-exposed DR4 tg mice ( Fig 9A ) . This was of interest since lungs can attract a substantial number of MAIT cells during infection [21] . We found dramatic increases in PD-1+ , LAG-3+ and TIM-3+ cells among lung MAIT cells in DR4 tg mice 4 days ( Fig 9B ) or 7 days ( Fig 9C ) after SEB administration . Mouse MAIT cells in these experiments were identified through co-staining with an anti-mouse TCRβ mAb ( or anti-mouse CD3 mAb ) and 5- ( 2-oxopropylideneamino ) -6-D-ribitylaminouracil ( 5-OP-RU ) -loaded mouse MR1 tetramer reagents [19] . Unlike in humans , MAIT cells are infrequent in laboratory mouse strains such as B6 mice [51] and in DR4 tg mice ( Please read below ) . This is with the exception of CAST/EiJ mice that harbor an unusually high number of MAIT cells , approximately 20 times more than B6 mice , in their T cell repertoire [51] . We found CAST-EiJ mice to be responsive to SEB ( S9 Fig ) . However , CAST-EiJ and DR4 tg mice could not be directly compared due to their different genetic backgrounds . A MAIThigh congenic strain on the B6 background ( B6 . CAST mice ) has been recently generated [51] . However , these mice express MHC class II molecules of B6 origin that have inadequate affinity for SEB . Therefore , to confirm MAIT cell anergy in SAg-responsive , MAIT cell-sufficient mice , which more closely resemble humans , we generated B6 . CAST×DR tg bone marrow chimeras . We first verified the expression of HLA-DR4 in DR4 tg mice , B6 . CAST×DR tg chimeras , and B6×DR tg chimeric controls , but not in wild-type B6 and B6 . CAST mice ( Fig 10A ) . Using 5-OP-RU-loaded mouse MR1 tetramer reagents , we also confirmed heightened MAIT cell frequencies in the lungs of B6 . CAST×DR tg chimeras in comparison with B6×DR tg controls ( Fig 10B ) . There was no reactivity with 6-formylpterin ( 6-FP ) -loaded MR1 tetramer , which was used as a staining control . Consistent with our findings in DR4 tg mice ( Fig 9 ) , B6 . CAST×DR tg chimeras gradually lost weight and exhibited lung mononuclear hypercellularity following SEB administration ( Fig 10C ) . In addition , we observed a trend towards higher proportions of PD-1+ , LAG-3+ and TIM-3+ cells among lung MAIT cells in these animals ( Fig 10D ) . Together , the above results indicate that mouse MAIT cells are anergized after in vivo exposure to SEB . Human PBMC-reconstituted NSG ( hPBMC-NSG ) mice provide a valuable model in which to study human immune responses in an in vivo setting . This model offers an additional advantage for our purpose—that is , cellular responses to SAgs can be deciphered in the absence of severe morbidity . This is because non-hematopoietic cells ( e . g . , endothelial cells , intestinal , and respiratory epithelial cells ) of hPBMC-NSG mice lack human receptors that mediate the adverse manifestations of SAg-mediated illnesses . However , it is still possible to quantify human cytokines in the circulation and to assess human leukocytes for phenotypic changes indicative of anergy/exhaustion . We found that a single i . p . injection of SEB results in an approximately 3-fold increase in the serum concentration of IFN-γ ( Fig 11A ) and also confirmed that both the human CD45+ hematopoietic cell population and the human CD3+ T cell population are expanded by SEB ( Fig 11B ) . Vα7 . 2+CD161high MAIT cells were present in hPBMC-NSG mice but did not show any sign of expansion by SEB , at least on day 9 post-stimulation , a time point at which the general T cell population size was markedly enlarged ( Fig 11B ) . Unlike PBS-injected control mice , SEB-primed mice exhibited substantial expression of human LAG-3 , TIM-3 and PD-1 on their splenic MAIT cells ( Fig 11C ) . Finally , as with our in vitro human PBMC cultures , LAG-3 was apparently the main co-inhibitory molecule expressed by SEB-exposed MAIT cells . Therefore , the hPBMC-NSG mouse model validates our in vitro findings pertaining to human MAIT cell anergy .
MAIT cells are thought to participate in host defense against a wide range of bacteria and fungi . These include K . pneumoniae , E . coli , P . aeruginosa , S . typhimurium , S . aureus , Staphylococcus epidermidis , Candida albicans , C . glabrata , and Saccharomyces cerevisiae among other microbes [17 , 20–22 , 26 , 27 , 42–45] . In this work , we have defined a pathogenic , as opposed to protective , role for MAIT cells during infection . While investigating the early sources of IFN-γ among human PBMCs following exposure to SEB , a prototypical staphylococcal SAg , we found a subpopulation of T cells that expressed a very high level of CD161 ( >100-fold higher than that expressed by the general T cell population ) and also harbored a Vα7 . 2 TCR α chain . This finding and our subsequent experiments led to the identification of MAIT cells as extremely potent and fast-acting producers of pro-inflammatory cytokines in response to bacterial SAgs . Therefore , MAIT cells are likely to be a key effector of the characteristic cytokine storm associated with these potentially deadly toxins . The tissue distribution of MAIT cells may affect their inflammatory cytokine profiles . In addition , the type of cytokine sets secreted by MAIT cells is dictated by the experimental conditions or by the nature of the stimuli employed . For instance , Dusseaux et al . reported that human blood MAIT cell stimulation with phorbol 12-myristate 13-acetate ( PMA ) and ionomycin leads to IFN-γ , TNF-α , IL-2 and IL-17 production [11] . In contrast , human MAIT cell co-culture with E . coli-fed autologous monocytes resulted in secretion of IFN-γ , but not IL-2 , and a mixed IL-17 response [11] . A subsequent study by Tang et al . found that hepatic MAIT cells produce more IL-17 in response to PMA and ionomycin than their blood counterparts do [12] . However , when liver or blood MAIT cells were incubated with anti-CD3/CD28-coated beads , IL-17 production was not induced at either mRNA or protein level . As another example , MAIT cells isolated from the fetal small intestine , but not those harvested from the fetal liver or lungs , respond to E . coli and an agonistic anti-CD28 mAb by secreting large quantities of IL-22 , a cytokine that modulates parenchymal tissue responses during inflammation [28] . The findings of these studies suggest that iTCR-dependent and -independent stimulation of MAIT cells yield distinct cytokine responses . In the current study , stimulation with SEB resulted in robust induction of IFN-γ and TNF-α and moderate up-regulation of IL-2 in both peripheral blood and liver MAIT cells . However , there was no appreciable IL-17A response , and the frequency of RORγt+ MAIT cells , in fact , decreased after exposure to SEB . We found that SEB-provoked MAIT cell activation does not involve MR1 but requires MHC class II molecules . Since human MAIT cells express HLA-DR [12] , a scenario can be envisaged in which MAIT cells cross-activate each other when incubated with SEB . However , we observed no cytokine production by SEB-exposed , purified MAIT cells in the absence of autologous monocytes . This rules out the above possibility and indicates a requirement for accessory molecules , which would perhaps supply costimulatory signals , for maximal MAIT cell responses to SAgs . SEB activates MAIT cells through iTCR ligation and IL-18R/IL-12R signaling , although the latter pathway appears dominant . Therefore , whether MAIT cells produce IL-17 is not simply a matter of the presence or absence of iTCR signaling . Our preliminary findings suggest that the choice to launch the RORγt/IL-17 program is not influenced by internalization of a critical number of iTCRs in MAIT cells either . This is because stimulation with anti-CD3/CD28 mAbs , but not SEB , resulted in rapid and complete iTCR internalization , thus making MAIT cells undetectable ( S10 Fig ) ; yet , both stimuli failed to induce IL-17 . We previously reported that SEB-exposed mouse and human iNKT cells similarly fail to down-regulate their Vα14-Jα18 and Vα24-Jα18 iTCR versions , respectively [9] . Furthermore , iNKT cells are indispensable for SEB-induced early IL-17 production [50] . Whether these findings are coincidental or mechanistically linked remains to be elucidated . We found that even in the absence of “SAg-responsive” TCR Vβ chains , human MAIT cells can be activated in an IL-12/IL-18-dependent , bystander fashion . MAIT cells expressed high levels of the receptors for these cytokines and were the main , if not the only , population of CD218ahighCD161high cells among PBMCs . A combination of rIL-12 and rIL-18 was able to mimic the SEB response in several experiments . However , unlike rIL-12 , rIL-18 alone induced little IFN-γ production , which is consistent with its role as a “co-factor” rather than a driver of Th1-type responses [52 , 53] . IL-12 stimulation can increase the expression of IL-18R on human T cells , leading to IFN-γ production [54] . On the other hand , IL-12 and IL-18 use signal transducer and activator of transcription ( STAT ) 4 and activator protein 1 ( AP-1 ) in their signaling pathways , respectively , and STAT4 potentiates AP-1-mediated IFN-γ promoter activation without directly binding to it [55] . This is significant because AP-1 can only weakly interact with an AP-1-binding region within the IFN-γ promotor , and IL-18 can only minimally induce an IFN-γ response on its own . Since MAIT cells express IL-18R constitutively , we postulate that IL-18 and IL-12 cooperate through the latter mechanism . Intriguingly , neutralization of IL-18 alone was more potent than that of IL-12 in multiple set-ups . This may be a unique feature of MAIT cell activation in response to SAgs because anti-IL-12 and anti-IL-18 were equally efficient in inhibiting the minute response mounted by Tconv cells against SEB ( S11 Fig ) . Stimulation with rIL-12 and/or IL-18 in our system does not involve iTCR ligation , and when endogenous IL-12 and IL-18 are neutralized , iTCR triggering by SEB is still present . This may reconcile , at least partially , our seemingly paradoxical observations on exogenous IL-18 stimulation versus endogenous IL-18 neutralization . In fact , IL-18 and TCR signaling have been recently demonstrated to work synergistically to induce IFN-γ secretion by human γδ T cells [56] , another type of innate-like T cells , which lends support to the above hypothesis . In addition to cytokine production , MAIT cells also reportedly exert direct cytotoxic effector functions [43] . In preliminary experiments , we have found that following SEB stimulation of human PBMCs , the frequencies of CD107a+GZM A+ , CD107a+GZM B+ , and GZM A+GZM B+ cells increase dramatically in the MAIT cell compartment but only minimally among Tconv cells ( S12A Fig ) , a response that was partially inhibited by IL-12/IL-18 co-neutralization ( S12B Fig ) . Therefore , SAgs can induce MAIT cell—mediated cytotoxicity with unclear outcomes at this point , a subject that we are currently investigating . SAgs can enter the systemic circulation in the absence of overt bacteremia , a condition that is perhaps best exemplified by menstrual TSS . It has also been suggested that SAgs liberated in the gastrointestinal tract by food-borne pathogens may cross the perturbed gut mucosa either directly or with the assistance of other bacterial toxins [57 , 58] and consequently access the liver via the portal vein [12] . The abundance of MAIT cells in the intestine and in the liver inevitably puts them in a unique position to respond to SAgs with deleterious outcomes . SAgs may also contribute to the pathogenesis of sepsis [59 , 60] . During polymicrobial sepsis , certain common bacteria ( e . g . , Staphylococcus spp . and Streptococcus spp . ) likely release the SAgs they harbor . However , predicting the net effect is not easy . First , the type and combination of microbes involved may vary in different patients . Second , how host responses to multiple SAgs secreted by multiple pathogens may cross-regulate each other is ill-defined . Third , SAg-induced responses are modulated by Toll-like receptor ( TLR ) ligands embedded in the cell wall of the very bacteria that secrete them , as we previously described [47] . Finally , we recently reported that SAgs promote bacterial colonization and infection [61] , although the significance of this phenomenon in the initial stages of sepsis is unclear . It will be important to assess MAIT cell functions in polymicrobial infections . A prospective study by Grimaldi et al . found an early and selective decline in MAIT cell blood counts of patients with severe sepsis and septic shock [62] . Interestingly , the cumulative incidence of intensive care unit ( ICU ) -acquired infections in patients inversely correlated with their peripheral blood MAIT cell numbers . We believe that pro-inflammatory cytokine ( especially IFN-γ ) production in excessive quantities by MAIT cells , in comparison with Tconv , iNKT and γδ T cells , is not the only mechanism by which MAIT cells may inflict harm . Our findings also implicate these cells in SAg-associated immunosuppression . Several mechanisms have been proposed to contribute to this phenomenon . Exposure to SAgs may delete or anergize many T cells [5 , 6] , thus physically or functionally depleting a fraction of T cells with antimicrobial specificities or properties . Regulatory or suppressor cell types may also take part in SAg-mediated immunosuppression . In vitro stimulation of human PBMCs with SEA , SpeA , and SpeK/L converts CD25- Tconv cells to IL-10–producing CD25+FoxP3+ regulatory T ( Treg ) cells [63] . However , Tilahun et al . demonstrated that the expansion of endogenous Treg cells or their adoptive transfer into HLA-DR3 transgenic mice fails to prevent SEB-provoked T cell proliferation or the organ damage sustained in these animals [64] . This calls into question the in vivo significance of Treg cells in SAg-mediated illnesses . Additionally , not all immunosuppressive mechanisms are detrimental to the host . We recently reported a profound and tissue-selective influx of granulocytic myeloid-derived suppressor cells ( MDSCs ) into the liver of DR4 tg mice shortly after an SEB challenge [49] . In this model , MDSCs attenuated SEB-induced T cell proliferation , which prompted us to propose that their local accumulation in the liver may benefit the host by ameliorating SAg-induced tissue damage . It needs to be emphasized that SAg-induced Tconv cell anergy has been documented in mouse models . It is not completely clear whether human T cells , especially the subsets that are poised to swiftly respond to pathogens , undergo anergy following exposure to SAgs . We report , herein , that human MAIT cell hyperactivation by SEB is accompanied by the acquisition of an anergic state that hinders their antimicrobial functions , for instance , against K . pneumoniae and E . coli . Accordingly , we propose a novel mechanism of immunosuppression in the human T cell compartment . In addition , using MAITlow and MAIThigh HLA-DR4 transgenic mice as well as humanized NSG mice , we have documented that MAIT cell anergy can occur in vivo . SEB-induced MAIT cell anergy was primarily accompanied by LAG-3 upregulation , which was evident not only in human PBMC cultures but also in humanized NSG mice . We hypothesize that LAG-3 upregulation on a large proportion of SEB-exposed MAIT cells confers upon them an anergic state that would prevent their optimal MR1-restricted responses against microbial pathogens . LAG-3 is known to interact with MHC class II molecules expressed by Ag-presenting cells [65 , 66] . It is tempting to speculate that LAG-3 upregulation by MAIT cells enables them to compete with CD4+ Tconv cells for access to MHC class II , thus potentially interfering with helper T cell functions , which would in turn cripple a critical arm of adaptive immunity to microbes . Several lines of evidence suggest that MAIT cell activation and anergy involve the same pathway . First , SEB-induced upregulation of CD69 and LAG-3 follow the same kinetics . Second , IFN-γ and LAG-3 induction both depend on p38 and MEK1/2 MAP kinases . Third , CD69 upregulation , pro-inflammatory cytokine production , p38 phosphorylation and LAG-3 induction are comparably inhibitable by IL-12/IL-18 co-neutralization . Fourth , all these phenotypic changes are inducible with strikingly similar magnitudes by SEB and IL-12/IL-18 stimulation . Based on the findings of the current investigation , we believe that MAIT cells may constitute attractive therapeutic targets in the context of SAg-mediated illnesses . First , they are naturally enriched at anatomical ports of entry for many SAg-producing microbes . Second , they mount massive pro-inflammatory cytokine responses almost immediately after their encounter with SAgs . Therefore , blocking their function may help mitigate the cytokine storm caused by these toxins in a timely fashion . Third , interfering with MAIT cell hyperactivation may also prevent their anergy and the suppression of certain antimicrobial defense mechanisms . Fourth , MR1 , the restriction element for MAIT cells , is monomorphic [16] . Therefore , MR1-restricted MAIT cell antagonists , similar to recently described MAIT cell ligands [17 , 44] , should potentially work in genetically diverse human populations .
Animal experiments were performed following an animal care protocol ( AUP# 2010–241 ) approved by the Animal Care Committee of Animal Care and Veterinary Services at Western University and in compliance with the Canadian Council on Animal Care guidelines . Human samples were collected after informed written consent was obtained and according to protocols approved by the Western University Research Ethics Board for Health Sciences Research Involving Human Subjects ( approval numbers: HSREB 5545 and HSREB 106937 ) . PBMCs were isolated from heparinized whole blood of healthy donors by density gradient centrifugation using low-endotoxin ( <0 . 12 EU/mL ) Ficoll-Paque PLUS ( GE Healthcare Life Sciences ) and 50-mL SepMate tubes ( Stemcell Technologies Inc . , Vancouver , BC ) , as per manufacturer’s instructions . HMNCs were immediately extracted from tumor-free liver tissues surgically removed from patients undergoing liver resection for colorectal carcinoma metastasis at the London Health Sciences Centre University Hospital ( London , ON ) . Tissue samples were pressed through a wire mesh , and the resulting homogenate was washed in 2% fetal calf serum ( FCS ) in cold PBS . Pelleted cells were washed again , placed in 33 . 75% low-endotoxin Percoll PLUS ( GE Healthcare Life Sciences ) , and spun at 700 × g for 12 min at room temperature . Pelleted cells were then treated with ACK lysis buffer for 2 min to lyse erythrocytes and washed before a nylon mesh strainer with 70-μm pores was used to remove clumps and debris . Adult , female C57BL/6 ( B6 ) mice were from Charles River Canada Inc . ( St . Constant , QC ) . B6 . CAST mice [51] and DR4 tg mice on a B6 background were housed and bred in a barrier facility at Western University . DR4 tg mice lack endogenous MHC II molecules and instead express a chimeric HLA molecule that is composed of HLA-DRA-IEα and HLA-DRB1*0401-IEβ [67] . CAST/EiJ mice and NSG mice were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . NSG mice were partially humanized via an i . p . injection of 1 × 107 human PBMCs . B6 , B6 . CAST and DR4 tg mice were sacrificed by cervical dislocation , and bone marrow was flushed , using 5-mL RPMI 1640 medium , out of femurs and tibias . Marrow cells were spun , exposed to ACK lysis buffer for 2 min to remove erythrocytes , washed in sterile PBS , and passed through a 70-μm nylon mesh strainer . Bone marrow cells from DR4 tg mice were mixed at a 1:1 ratio with either B6 . CAST or wild-type B6 marrow cells . Two million mixed cells were injected intravenously ( i . v . ) into wild-type B6 recipients , which were lethally irradiated at 1 , 100 cGy using a 137Cs γ-irradiator prior to adoptive transfer . Reconstituted recipients were provided with drinking water supplemented with 2 mg/mL neomycin sulfate to prevent infection . Eight weeks after reconstitution , chimeric mice were injected with PBS or SEB as indicated . Bone marrow cells from wild-type B6 or DR4 tg mice were prepared as described above . In a T75 flask , cells were seeded at a density of 1 × 106 cells/mL in RPMI 1640 medium containing 10% heat-inactivated FCS , GlutaMAX-I , 0 . 1 mM MEM nonessential amino acids , 1 mM sodium pyruvate , 100 U/mL penicillin , 100 μg/mL streptomycin , and 10 mM HEPES ( complete medium ) , which was further supplemented with 10 ng/mL mouse GM-CSF and IL-4 ( PeproTech Inc . , Rocky Hill , NJ ) . Cultures were replenished with fresh medium and cytokines every other day after discarding the floating cells . On day 7 , harvested cells were enriched for CD11c+ DCs using an EasySep Mouse CD11c Positive Selection Kit ( Stemcell Technologies ) . Mouse MAIT hybridoma lines 8D12 , 6C2 , and 17E6 [14 , 16] were grown in complete medium and maintained at 37°C in a humidified atmosphere containing 6% CO2 . The expression of TCR Vβ8 by these hybridomas , or lack thereof , was verified by flow cytometry after staining with a FITC-conjugated anti-Vβ8 . 1/Vβ8 . 2 mAb ( clone KJ16-133 ) or a rat IgG2a isotype control . Recombinant SAgs were made using an approved institutional biosafety protocol adhering to the Public Health Agency of Canada regulations . SEB was cloned from S . aureus ( strain COL ) , expressed in BL21 ( DE3 ) competent E . coli , and purified by nickel column chromatography [47] . SEA and TSST-1 were generated using a similar procedure . SpeA and SpeI were made and purified as previously described [31 , 68] . Using site-directed mutagenesis , we also generated a largely inactive form of SEB that carries an N→A point mutation at position 23 , which is essential for optimal binding to mouse TCR Vβ8 . 2 [30] . As in our past studies [9 , 48 , 49] , we used this mutant , which we refer to as SEBN23A , as a negative control . A frozen stock of a K . pneumoniae clinical isolate , Parkwood-18 , was a gift from Dr . Miguel Valvano ( Queen’s University Belfast , Belfast , United Kingdom ) . P . aeruginosa ( ATCC 27853 ) was generously provided by Dr . Lori Burrows ( McMaster University , Hamilton , Canada ) . We also generated lysate from E . coli strain DH5α and S . typhimurium strain LT2 ( ATCC 700720 ) . K . pneumoniae , E . coli , and S . typhimurium were grown in Luria broth , and P . aeruginosa was grown in Tryptic Soy Broth . Following overnight culture at 37°C , bacterial cells were washed 3 times in PBS , and the OD600 was adjusted to 2 . 0 or 6 . 5 ( in the case of K . pneumoniae ) . Klebsiella cells were subjected to pressure at 30 , 000 pounds per square inch ( PSI ) for 5 min to induce membrane rupture . For all other bacteria , lysates were prepared through repeated freeze—thawing of the cells . Lysates were stored at −80°C until use . Human bulk PBMCs , HMNCs , or fluorescence-activated cell sorting ( FACS ) -purified CD3+Vα7 . 2+CD161high cells or cell subsets were left untreated or stimulated with stated doses of indicated SAgs , with a 1:5 dilution of K . pneumoniae lysate or a 1:2 dilution of other bacterial lysates , with 5 ng/mL of rIL-12 ( PeproTech ) and/or 5 ng/mL of rIL-18 ( R&D Systems , Minneapolis , MN ) , with 100 ng/mL of LPS from E . coli 0111:B4 ( Sigma-Aldrich SKU # L4391 ) , or with a combination of 100 ng/mL of SEB and 100 ng/mL of LPS . Various experimental designs required the blockade of MR1 , HLA-DR , or LAG-3 by the addition of 5 μg/mL of a LEAF-purified anti-human/mouse/rat MR1 mAb ( clone 26 . 5 , BioLegend , San Diego , CA ) , 5 μg/mL of a mouse anti-HLA-DR mAb ( clone G46-6 , BD Biosciences , San Jose , CA ) , or 20 μg/mL of a mouse anti-human LAG-3 mAb ( clone 17B4 , Adipogen , San Diego , CA ) , respectively . IFN-γ , IL-12 , and/or IL-18 were neutralized using 5 μg/mL of NIB42 ( eBioscience , San Diego , CA ) , B-T21 ( eBioscience ) , and/or 125-2H ( R&D Systems ) mAbs , respectively . SB203580 ( a selective inhibitor of p38 MAP kinase ) and PD98059 [a MAPK/ERK kinase ( MEK ) 1/2 inhibitor] were purchased from Sigma-Aldrich , dissolved at 1 mg/mL DMSO , and used at a 20-μM final concentration in cultures . Finally , 200 ng/mL of the NFAT inhibitor CsA ( Focus Biomolecules , Plymouth Meeting , PA ) was present in some cultures . Freshly isolated , untreated , or stimulated cells were washed and stained at 4°C with fluorochrome-conjugated mAbs to cell surface CD3 , CD14 , CD69 , CD161 , CD212 ( IL-12Rβ1 ) , CD218a ( IL-18Rα ) , CD223 ( LAG-3 ) , CD279 ( PD-1 ) , CD366 ( TIM-3 ) , TCR Vα7 . 2 , TCR Vβ2 , TCR Vβ13 . 2 , and/or TCR γδ ( S1 Table ) , which were diluted in 2% FCS in cold PBS . After 30 min , cells were thoroughly washed , interrogated using a BD FACSCanto II flow cytometer , and analyzed by FlowJo software ( Tree Star , Ashland , OR ) . iNKT cells were identified through co-staining with an anti-CD3 mAb and allophycocyanin-conjugated , PBS-57-loaded human CD1d tetramers generously supplied by the NIH Tetramer Core Facility ( Atlanta , GA ) . For intracellular detection of IFN-γ , IL-2 , IL-17A , TNF-α , CD107a , granzymes ( GZM ) A and B , T-bet , RORγt , or phospho-p38 , a combination of 1 μM brefeldin A ( Sigma-Aldrich ) and 2 μM monensin ( eBioscience ) was added to the cells either at the beginning of the short-term cultures or during the final 5 h . When cell surface and intracellular staining needed to be combined , cells were first stained with mAbs to surface molecules , washed , resuspended in Intracellular Fixation & Permeabilization Buffer Set ( eBioscience ) , and kept in the dark for 20 min at room temperature . In the case of transcription factors , a FoxP3 Staining Buffer Set ( eBioscience ) was used . Cells were subsequently washed and stained with mAbs against indicated intracellular molecules ( S1 Table ) . To detect cell death and early apoptosis , cells were first stained with a 1:100 dilution of Fixable Viability Dye eFluor 780 ( eBioscience ) in PBS to allow for dead cell exclusion . Annexin V+ cells were then identified using an Apoptosis Detection kit from eBioscience . To confirm successful humanization of NSG mice , their splenic cells were stained with anti-human CD45 and anti-human CD3 mAbs ( S1 Table ) . For detection of mouse MAIT cells , MR1 tetramer reagents were assembled , labeled , and used [18 , 19] . In brief , Phycoerythrin ( PE ) Streptavidin ( BD Biosciences ) was added at 10-min intervals to biotinylated mouse MR1 monomers at room temperature . The resulting tetramerized MR1 molecules were stored at 4°C in the dark until use . Non-parenchymal lung MNCs were prepared after digestion of lung tissue homogenate with 0 . 5 mg/mL of collagenase type IV ( Sigma-Aldrich ) in RPMI medium for 1 h at 37°C . Cells were then gently forced through a 70-μm filter , washed , spun , erythrocyte-depleted , washed , and filtered again and incubated with a 1:400 dilution of 5-OP-RU-loaded MR1 tetramer in PBS containing 2% FCS for 30 min at room temperature . This step was followed by staining at 4°C with either a FITC-conjugated anti-mouse TCRβ mAb ( clone H57-597 ) or with an allophycocyanin-conjugated rat anti-mouse CD3ε mAb ( clone 17A2 ) , along with fluorochrome-labeled mAbs to mouse PD-1 ( clone J43 ) , LAG-3 ( clone eBioC9B7W ) , and TIM-3 ( clone RMT3-23 ) ( S1 Table ) . Finally , cells were stained with Fixable Viability Dye eFluor 780 ( eBioscience ) to allow for dead cell exclusion . Cells were subsequently washed and analyzed by flow cytometry . PE-conjugated , 6-FP-loaded MR1 tetramers served as a staining control in these experiments . In all flow cytometry experiments , isotype controls corresponding to fluorochrome-labeled mAb were used in parallel to allow for appropriate gating . Mouse MAIT hybridomas were seeded at 1 × 105 cells/250 μL complete medium/well of a U-bottom polystyrene microplate along with 2 × 104 B6 or DR4 tg BMDCs . Cultures were stimulated with indicated SAgs at a final concentration of 100 ng/mL , with 0 . 5 μg/mL of a hamster anti-mouse CD3ε mAb ( clone 145-2C11 from Cedarlane Labs , Burlington , ON ) , or with a 1:5 dilution of K . pneumoniae lysate . In several experiments , γ-irradiated ( 3 , 000 rad ) MAIT cells or BMDCs were utilized , and blockade of MR1 was achieved by the addition of 5 μg/mL of 26 . 5 ( BioLegend ) . The IL-2 content of culture supernatants was quantified after 24 h by an ELISA kit from eBioscience . Bulk human PBMCs were stimulated with 100 ng/mL of SEB for 2 h , 6 h , 12 h , and 24 h before culture supernatant samples were collected and analyzed by bead-based multiplexing ( Eve Technologies , Calgary , AB ) . Heat maps for indicated cytokines were generated using GraphPad Prism 7 software . CD3+Vα7 . 2+CD161high human MAIT cells and CD3+Vα7 . 2- Tconv cells were purified from human PBMCs using a BD FACSAria III sorter . In a limited number of experiments , concomitant staining for Vβ2 and Vβ13 . 2 was performed to enable sorting of MAIT cell subpopulations bearing these TCR Vβs . The purity of the sorted populations was always greater than 95% . In a microplate , 1 × 105 bulk MAIT cells or Tconv cells , or 4 × 104 Vβ2+ or Vβ13 . 2+ MAIT cells were co-cultured for up to 12 h with 2 × 104 FACS-purified , autologous CD3-CD14+ monocytes in the absence or presence of 100 ng/mL SEB . Human IFN-γ , IL-17A , IL-12p70 and IL-18 levels were then determined in culture supernatants as indicated . Human TNF-α was quantified by ELISA in supernatant samples harvested 24 h after THP-1 cells were stimulated with 100 ng/mL of LPS in the presence or absence of 100 μg/mL of polymyxin B . Seven days after NSG mice received human PBMCs , they were injected with 100 μg SEB i . p . Animals were bled 24 h later , and circulating levels of human IFN-γ were quantitated by ELISA .
Statistical assessments were made with the aid of GraphPad Prism software . Comparisons were performed using Student t-test or ANOVA , as appropriate , and differences with p < 0 . 05 were deemed significant . * , ** , *** , and **** denote p < 0 . 05 , p < 0 . 01 , p < 0 . 001 , and p < 0 . 0001 , respectively . Association analyses were conducted by the non-parametric Spearman’s rank correlation test . | Superantigens ( SAgs ) are toxins produced by Staphylococcus aureus and Streptococcus pyogenes , microbes that are responsible for a multitude of infectious diseases and conditions . Once released , SAgs activate many immune cells , resulting in a massive inflammatory response that is often followed by a state of immunosuppression , a state that favors opportunistic infections . Using primary human cells as well as wild-type and genetically altered mice , we have now identified a subset of unconventional , innate-like T lymphocytes , called mucosa-associated invariant T ( MAIT ) cells , as one of the most powerful and quick-acting sources of inflammatory mediators in the aftermath of systemic exposure to SAgs . We also demonstrate that robust activation of MAIT cells by SAgs quickly leads to their exhaustion , and this exhaustion interferes with their ability to participate in antimicrobial host defense and contributes to the immunosuppressive state . Our findings thus define a pathogenic role for MAIT cells during Gram-positive bacterial infections and also uncover a novel mechanism of SAg-mediated immunosuppression . Accordingly , we propose that MAIT cells can be targeted for efficacious treatment of SAg-mediated illnesses . | [
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"developmental",... | 2017 | MAIT cells launch a rapid, robust and distinct hyperinflammatory response to bacterial superantigens and quickly acquire an anergic phenotype that impedes their cognate antimicrobial function: Defining a novel mechanism of superantigen-induced immunopathology and immunosuppression |
HIV-associated neurocognitive disorders ( HAND ) represent a spectrum neurological syndrome that affects up to 25% of patients with HIV/AIDS . Multiple pathogenic mechanisms contribute to the development of HAND symptoms including chronic neuroinflammation and neurodegeneration . Among the factors linked to development of HAND is altered expression of host cell microRNAs ( miRNAs ) in brain . Here , we examined brain miRNA profiles among HIV/AIDS patients with and without HAND . Our analyses revealed differential expression of 17 miRNAs in brain tissue from HAND patients . A subset of the upregulated miRNAs ( miR-500a-5p , miR-34c-3p , miR-93-3p and miR-381-3p ) , are predicted to target peroxisome biogenesis factors ( PEX2 , PEX7 , PEX11B and PEX13 ) . Expression of these miRNAs in transfected cells significantly decreased levels of peroxisomal proteins and concomitantly decreased peroxisome numbers or affected their morphology . The levels of miR-500a-5p , miR-34c-3p , miR-93-3p and miR-381-3p were not only elevated in the brains of HAND patients , but were also upregulated during HIV infection of primary macrophages . Moreover , concomitant loss of peroxisomal proteins was observed in HIV-infected macrophages as well as in brain tissue from HIV-infected patients . HIV-induced loss of peroxisomes was abrogated by blocking the functions of the upregulated miRNAs . Overall , these findings point to previously unrecognized miRNA expression patterns in the brains of HIV patients . Targeting peroxisomes by up-regulating miRNAs that repress peroxisome biogenesis factors may represent a novel mechanism by which HIV-1 subverts innate immune responses and/or causes neurocognitive dysfunction .
Leukocytes infected by human immunodeficiency virus type 1 ( HIV-1 ) traverse the blood-brain barrier within days of primary infection resulting in subsequent infection of macrophage lineage cells ( microglia and perivascular macrophages ) and astrocytes in the central nervous system ( CNS ) [1 , 2] . As HIV/AIDS progresses , a subset of infected patients develop a neurological syndrome termed HIV-associated neurocognitive disorders ( HAND ) [3 , 4] . HAND affects approximately 25% of HIV-infected patients despite the availability of effective antiretroviral therapy [3 , 5–7] . Some of the proposed mechanisms that contribute to HAND include genetic host susceptibility factors , viral properties [8–11] and altered host immune responses [12 , 13] . Moreover , neurotoxic effects of some antiretroviral therapies have been implicated in HAND development ( reviewed in [14] ) . The collective actions of neurotoxic viral proteins and chronic neuroinflammation mediated by cytokines and free radicals culminate in synaptic injury and eventual neuronal death , leading to HAND . There are currently no specific therapies for HAND although antiretroviral therapy can alleviate some neurological defects . Among the factors suggested to contribute to the development of HAND is altered expression of host cell microRNAs ( miRNAs ) . These small noncoding RNAs can regulate both host and viral gene expression [15] and profiling miRNAs in different pathological conditions has yielded important insights into underlying disease mechanisms [16–18] . To this end , it was recently reported that miRNA profiles in the central nervous systems of HIV-infected patients with HAND , differs from nonHAND patients [19 , 20] . Similarly , the miRNA signatures in blood from HIV-infected elite controllers differ from those of viremic patients , HAND and nonHAND patients [21–23] . Importantly , altered expression of host miRNAs may not only contribute to the development of HAND but also could potentially be exploited as diagnostic and prognostic biomarkers for HAND [23] . To further investigate the link between host miRNA expression and HAND development as well as HIV-1 biology , brain miRNA profiles were examined in HIV/AIDS patients with and without HAND . We identified 17 miRNAs that had abnormal expression levels in the brains of HAND patients . Bioinformatic analyses revealed that four of the up-regulated miRNAs target key peroxisome biogenesis factors . Peroxisomes are ubiquitous and essential subcellular organelles responsible for the catabolism of fatty acids ( beta oxidation ) , amino acids , reduction of free radicals such as hydrogen peroxide and the synthesis of plasmalogens . The latter is critical for myelin formation and brain development [24] . Formation of peroxisomes requires multiple peroxin ( PEX ) -encoding genes and mutations result in devastating diseases that include defects in brain development ( reviewed in [25 , 26] ) . In addition to their roles in cellular lipid metabolism and brain development and function , peroxisomes serve as signaling platforms in antiviral defense [27] further underlying their importance in human health . Activation of peroxisomal-MAVS during RNA virus infections leads to the production of type III interferon ( IFN ) as well as IFN-stimulated genes ( ISGs ) [27 , 28] . Peroxisomes play a role in sensing the HIV-1 genomic RNA [29] and stimulation of peroxisome proliferator-activated receptor alpha by fenofibrate impairs replication of HIV-1 and flaviviruses in vivo [30 , 31] . Consistent with their roles in antiviral defense , a number of recently published reports revealed that during viral infection , peroxisome biogenesis and/or peroxisome-based signaling is disrupted [32–34] . In these cases , viral proteins directly interact with peroxisomes or biogenesis factors to interfere with peroxisome function or formation . Here , we show for the first time that peroxisomes are depleted during HIV-1 infection via a unique mechanism . While the PEX mRNA targeting miRNAs were initially discovered in the brains of HIV-infected patients with neurocognitive defects , subsequent analyses revealed that their upregulation is a fundamental aspect of HIV infection . Thus as well as potentially blunting the innate immune response during early stages of infection , HIV-1 induced loss of peroxisomes may play a role in development of neurological disorders in AIDS patients .
The development of HAND is dependent on multiple factors including aberrant expression of host-encoded miRNAs . To determine whether there were signature miRNA expression patterns common to HAND patients , we examined a well-defined patient cohort [35–37] , focusing on miRNA profiles in brain tissue from HIV/AIDS patients with HAND ( n = 20; with encephalitis , n = 10 and without encephalitis , n = 10 ) to HIV/AIDS patients without HAND or encephalitis ( n = 10 ) . To ensure there were sufficient patients in each group and because there were no significant differences in miRNA expression between the two HAND groups , the results from each HAND group were pooled . We found that expression of 17 miRNAs ( Fig 1 and Table 1 ) was consistently dysregulated in the HAND samples . Twelve of the miRNAs were upregulated and five were down-regulated at least 1 . 5 fold ( p < 0 . 05 ) . Three algorithms ( TargetScan , miRDB and DIANA ) were used to predict targets of each miRNA and high-ranking potential targets predicted by at least two out of three algorithms are shown . Notably , peroxisomal genes ( PEX2 , PEX7 , PEX11B and PEX13 ) that are the predicted targets of 4 up-regulated miRNAs ( miR-500a-5p , miR-34c-3p , miR-93-3p , and miR-381-3p ) are bolded and underlined . To understand the potential effects of the differentially expressed miRNAs in pathogenesis of HAND and/or HIV-1 biology , it was important to elucidate their cellular targets . Three bioinformatics algorithms ( miRDB , DIANA , and TargetScan ) were used to predict potential targets of the 17 differentially expressed miRNAs . We first focused on targets that were predicted by at least two of the three algorithms . In keeping with the notion that a single miRNA can affect expression of dozens of mRNAs , we identified hundreds of potential targets . Some of the highest-ranking candidates are listed in Table 1 . Interestingly , four of the up-regulated miRNAs ( miR-500a-5p , miR-34c-3p , miR-93-3p , and miR-381-3p ) are predicted to target mRNAs encoding the peroxins PEX2 , PEX7 , PEX11B and PEX13 . These proteins play different but critical roles in biogenesis of peroxisomes . Specifically , PEX2 and PEX13 are required for import of peroxisomal matrix proteins; PEX11B facilitates peroxisomal division and proliferation and PEX7 functions as a receptor for the import of peroxisomal matrix proteins with type 2 targeting motifs ( reviewed in [38] ) . Peroxisomes have only recently been shown to play roles in antiviral defense [24 , 25] but have long been linked to neuroinflammation ( reviewed in [39] ) . As such , we elected to determine if/how the HIV-induced miRNAs affect expression of peroxisomal proteins . In most cases , miRNAs negatively regulate gene expression at the post-transcriptional level through binding to the 3’untranslated regions ( UTRs ) of mRNAs . Therefore , we first determined whether miR-500a-5p , miR-34c-3p , miR-93-3p , or miR-381-3p affected expression of a reporter gene upstream from the 3’UTRs of PEX2 , 7 , 11B or 13 mRNAs ( Fig 2 ) . The pMIR-REPORT miRNA expression reporter system consists of a firefly luciferase reporter vector ( for 3’-UTR cloning ) and a β-gal reporter control plasmid ( for normalization based on potential differences in cell viability and transfection efficiency ) . Several controls were included for each experiment . For example , miR-344-3p targets the 3’UTR of KLF4 [40] and therefore , we used this miRNA as the positive control . As a negative control , cassettes encoding the 3’-UTRs for the PEX genes were cloned into the reporter vector in the opposite direction . Expression of luciferase activity under the control of PEX2 , PEX7 , PEX11B , or PEX13 UTRs was inhibited by 50–70% in cells transfected with miR-500a-5p , miR-34c-3p , miR-93-3p or miR-381-3p respectively ( Fig 2 ) . Conversely , these miRNAs did not affect luciferase activity when the orientations of PEX 3’UTRs were reversed . Together , these data indicate that four of the miRNAs upregulated in the brains of HAND patients efficiently suppress translation of PEX mRNAs . We next focused on determining whether expression of the PEX mRNA-targeting miRNAs reduced levels of peroxisomal proteins . Immunoblotting was used to quantify the relative levels of peroxisomal proteins in cells transfected with mimics of miR-500a-5p , miR-34c-3p , miR-93-3p , miR-381-3p or a non-silencing miRNA ( miR-NS ) . Data in Fig 3A show that compared to mock and miR-NS-transfected cells , over-expression of miR-500a-5p , miR-34c-3p , miR-93-3p and miR-381-3p resulted in significantly decreased levels of peroxisomal proteins albeit to different extents . Specifically , miR-500a-5p , which targets PEX2 mRNA ( Fig 2 ) , reduced levels of PEX2 protein by 35% . Interestingly , PEX7 and PEX11B protein levels were 70% and 69% lower respectively in cells transfected with miR-500a-5p . Similarly , the PEX13-targeting miR-381-3p decreased expression levels of four peroxisomal proteins including PMP70 ( a peroxisomal membrane protein ) , PEX7 , PEX13 , and PEX2 . Unexpectedly , transfection of cells with miR-34c-3p or miR-93-3p mimics did not significantly impact PEX7 or PEX11B protein levels respectively . However , miR-34c-3p expression resulted in loss of both PMP70 and PEX13 proteins . Expression of PEX13 was only slightly decreased by miR-93-3p . Finally , levels of catalase , a peroxisomal matrix protein , were unaffected by expression of the four miRNAs . There are a number of scenarios in which a single miRNA can affect expression of multiple Pex gene products . One possibility is that miR-500a-5p , miR-381-3p and/or miR-34c-3p inhibit translation of multiple mRNAs that encode PEX proteins . Indeed , miRNAs that target components of a cellular pathway can be synthesized as a common transcript that contains multiple primary miRNAs [41] . However , a search of the miRBase database indicated that genes encoding miR-500a-5p , miR-34c-3p , miR-93-3p , and miR-381-3p are located on different chromosomes . Moreover , the initial miRNA target search using miRDB , DIANA , and TargetScan did not indicate that multiple PEX mRNAs are targeted by miR-500a-5p , miR-34c-3p , miR-93-3p , or miR-381-3p . Nevertheless , we employed the luciferase-based reporter assay described above to experimentally determine if any of these miRNAs could target more than one PEX gene . Data in S1 Fig confirmed that the miRNAs only regulated expression of luciferase under the control of 3’UTRs from their predicted PEX mRNA targets . Specifically , miR-500a-5p , miR-34c-3p , miR-93-3p and miR-381-3p downregulated expression of luciferase under the control of the 3’UTRs from PEX2 , PEX7 , PEX11B and PEX13 mRNAs respectively . We also used siRNAs to determine if decreasing expression of PEX2 , PEX7 , PEX11B or PEX13 proteins affected steady state levels of one another . Unlike miRNAs , which are inherently degenerate with respect to mRNA targets , siRNAs are perfectly complementary to their mRNA targets . siRNAs against PEX2 , PEX7 , PEX11B or PEX13 were transfected into HEK293T cells and levels of proteins were determined by immunoblotting ( S2 Fig ) . These experiments showed that targeted knockdown of a single PEX protein can indeed result in concomitant loss of other PEX proteins . For example , siRNAs against PEX7 not only reduced the level of PEX7 protein , but PEX11B was also markedly lower . Similarly , a PEX13-specific siRNA reduced the levels of PEX13 and PEX7 proteins . Finally , we showed that downregulation of the multifunctional peroxisome biogenesis factor PEX19 using siRNA , effectively reduced levels of PEX19 , PEX7 , PEX11B and PEX13 proteins . Unfortunately , we were unable to achieve significant reduction of PEX2 protein with siRNAs , despite using at least three different siRNAs . Next we examined how overexpression of miR-500a-5p , miR-34c-3p , miR-93-3p , and miR-381-3p affected peroxisomes . Super-resolution microscopy was used to analyze the morphology , distribution and numbers of peroxisomes in miRNA-transfected cells . Peroxisomes were identified using an antibody to PMP70 , a peroxisomal membrane protein involved in membrane assembly [42] . Cells transfected with a non-silencing miRNA ( miR-NS ) contained hundreds of PMP70-positive puncta throughout the cytoplasm ( Fig 3B ) . While the number of peroxisomes was significantly reduced by expression of miR-500a-5p ( which targets PEX2 ) , most striking was the change in morphology and PMP70 staining of the peroxisomes . Specifically , miR-500a-5p over-expression resulted in enlargement and elongation of peroxisomes . Decreasing the intracellular level of PEX2 , an E3 ubiquitin ligase that targets PMP70 [43] could certainly explain the higher levels of PMP70 protein ( Fig 3A ) and increasing staining intensity of anti-PMP70 in miR-500a-5p over-expressing cells ( Fig 3B ) . It is important to point out that PEX11B is required for peroxisome fission ( reviewed in [38] ) and as such , the fact that miR-500a-5p expressing cells have lower levels of this protein could result in decreased fission of peroxisomes and concomitant lengthening and enlargement of these organelles . Unexpectedly , the effect of miR-93-3p ( which targets PEX11B ) on peroxisomes was minimal . Despite evidence showing that the 3’UTR of PEX11B is targeted by this miRNA ( Fig 2 ) , PEX11B protein levels were not significantly affected by over-expression of a miR-93-3p mimic ( Fig 3A ) . One possibility is that PEX11B protein is very stable and the cellular pool was not depleted within the time frame of our experiments . Finally , it can be seen that expression of miR-34c-3p and miR-381-3p reduce peroxisome numbers by 65% and 45% respectively ( Fig 3B ) . Notably , this is consistent with the immunoblot data in Fig 3A showing that levels of PMP70 protein were reduced by expression of miR-34c-3p and miR-381-3p . To determine if peroxisomes were affected by HIV-1 infection , immunofluorescence and immunoblot assays were conducted on infected Hela CD4+ cells and monocyte-derived macrophages respectively . Data in Fig 4A show that similar to what was observed in miRNA-transfected cells ( Fig 3B ) , HIV infection results in significant loss of peroxisomes in Hela CD4+ cells . These cells were used for the microscopy assays because their flat morphology is more conducive for peroxisome quantitation . Peroxisomes were identified using an antibody to the tripeptide Ser-Lys-Leu ( SKL ) , a targeting motif found at the carboxyl termini of many peroxisomal matrix proteins [44] ( Fig 4A ) . Quantification of SKL-positive structures showed that on average HIV-infected cells contained 40% less peroxisomes than mock-treated cells ( Fig 4A ) . Immunoblotting revealed that infection of primary macrophages , a physiologically relevant cell type in HIV patients , resulted in dramatic loss of PEX2 , PEX7 , PEX13 , and to a lesser extent , PEX11B ( Fig 4B ) . However , levels of catalase , a peroxisomal matrix protein were not affected by HIV infection . This indicates that the effects of HIV-1 protein expression on peroxisome-associated proteins are highly specific . Similar results were observed in infected Hela CD4+ cells ( S3 Fig ) . Next , we used immunoblotting to analyze peroxisomal protein levels and immunohistochemistry to assess peroxisome morphology in frontal lobe brain tissue from HIV/AIDS and uninfected patients . Data in Fig 5A show that PEX13 protein was virtually absent in HIV patients with or without encephalitis or HAND . Levels of PEX7 protein were also significantly ( 40% ) lower in the sample from an HIV patient without encephalitis or HAND , however in three HAND samples , steady state levels of PEX7 protein were lower than those seen in HIV patients without HAND as well as non-infected patients . Finally , levels of PEX2 and PEX11B proteins were reduced ( ~70–80% ) in brain tissue from all of the HIV patients assayed . As a secondary assay , brain tissue samples from uninfected and HIV/AIDS patients were examined by immunocytochemistry . Immunolabeling of frontal lobe sections showed that the intensity of PEX13 and thiolase immunostaining which was concentrated in astrocytes ( arrows ) , was consistently lower in HIV/AIDS tissue compared to that from uninfected patients ( Fig 5B ) . Although the data are from a small sample size , they suggest that HIV infection contributes to loss of peroxisomal material in brain tissue . Our data are consistent with a scenario in which the loss of peroxisomes during HIV-1 infection is caused by increased expression of miRNAs that target mRNAs encoding peroxisome biogenesis factors . To address this hypothesis , we first determined if miR-500a-5p , miR-34c-3p , miR-93-3p and/or miR-381-3p were upregulated in HIV-infected macrophages . Human primary macrophages were infected with HIV-1 ( MOI = 2 ) and after 5 days , relative levels of miRNAs were determined by RT-qPCR . Data in Fig 6A show that levels of miR-500a-5p and miR34c-3p were increased almost 2 . 5 fold whereas miR-93-3p and miR-381p were increased between 1 . 6 and 2 . 2 fold . In contrast , levels of miR-483-5p ( which does not target PEX mRNAs and was identified as a miRNA whose expression was decreased in brain tissue of HAND patients , Table 1 ) were slightly decreased in HIV-infected macrophages . To further investigate the mechanism underlying HIV-associated loss of peroxisomes , we used anti-miRs to block the functions of PEX mRNA-targeting miRNAs during HIV infection . As transfection of primary macrophages can be technically challenging [45] , we elected to employ CD4+ Hela cells for these experiments . Data in Fig 6B show that with the exception of miR-93-3p , expression of PEX-targeting miRNAs was significantly elevated in HIV-infected CD4+ Hela cells . Anti-miR-500a-5p had the most dramatic effect in that it completely prevented HIV-induced loss of PEX2 , PEX7 , PEX11B and PEX13 ( Fig 7A ) . Other miRNA inhibitors had intermediate effects . For example , anti-miR-34c-3p increased levels of PEX13; anti-miR-93-3p increased levels of PEX7 and PEX11B; and anti-miR-381-3p increased levels of PEX11B . Since miR-500a-5p had the greatest effect on peroxisomal protein expression , we questioned whether blocking the activity of this miRNA could prevent HIV-induced loss of peroxisomes . Results in Fig 7B show that anti-miR-500a-5p abrogated the effect of HIV-1 infection on peroxisomes . Specifically , the average number of peroxisomes in HIV-infected cells containing the inhibitor of miR-500a-5p was not statistically different from that of mock-treated cells . Because peroxisomes are now recognized to have important roles in antiviral signaling [27 , 28] , we questioned whether expression of miRNA mimics that target PEX mRNAs would affect innate immune genes . A549 cells were chosen for these experiments because they are human in origin and have been used extensively to study innate immune signaling . Interestingly , three of the miRNA mimics ( miR-500a-5p , miR-34c-3p and miR-93-3p ) significantly increased mRNA levels for five innate immune genes ( Fig 8 ) . MiR-93-3p had the most dramatic affect on expression of antiviral genes . Specifically , in cells transfected with miR-93-3p mimic , expression of IFI6 and viperin mRNAs were increased 14-fold and 50-fold respectively . MiR-500a-5p appeared to modestly increase expression of innate immune genes ( 2–4 fold ) whereas miR-381-3p did not significantly affect expression of viperin , IFI6 , IFIT2 , IRF1 or OAS1 ( Fig 8 ) .
A growing body of research has linked changes in miRNA expression to pathogenesis of neurodegenerative diseases including Alzheimer’s disease , Parkinson’s disease , Huntington’s disease and amyotrophic lateral sclerosis ( reviewed in [46] ) . The original goal of the present study was to identify miRNAs that are differentially expressed in the brains of HIV-infected patients with HAND . Of the 17 miRNAs whose expression levels were commonly deregulated in HAND patients , four ( miR-500a-5p , miR-34c-3p , miR-93-3p , and miR-381-3p ) were shown to regulate expression of the peroxisome biogenesis factors PEX2 , PEX7 , PEX11B and PEX13 . Subsequent analyses revealed that elevated expression of these miRNAs was not specific to HIV-HAND but rather , was a common feature of HIV infection . This is the first report to our knowledge demonstrating that viral infection leads to increased expression of mRNAs that downregulate peroxisomes , possibly as a mechanism to alter early antiviral signaling that emanates from these organelles . The present study also connects two divergent areas , HIV-1 biology and peroxisome dysfunction in the brain , providing previously unrecognized insights into the pathogenesis of a common neurological syndrome , HAND . Although larger samples sizes and further analyses are required to confirm our findings , in general , the relative loss of PEX proteins in brain tissue appears to be greater in HAND compared to non-HAND HIV . This scenario is consistent with our initial observation that miR-500a-5p , miR-34c-3p , miR-93-3p , and miR-381-3p were expressed at higher levels in brain tissue from HIV-HAND vs HIV-non-HAND patients . Moreover , a large number of studies indicate that peroxisomes are critical for brain function ( reviewed in [24] ) ; thus stressing the need for further investigation into peroxisomes and HIV pathogenesis . Peroxisome-based diseases in humans can be classified into two large groups; peroxisome biogenesis disorders and single peroxisome enzyme deficiencies . The first group includes Zellwegger syndrome spectrum disorders and rhizomelic chondrodysplasia punctata type 1 . In cells from patients with peroxisome biogenesis disorders , peroxisomes are absent due to mutations in one or more genes that encode critical biogenesis factors including PEX2 , PEX7 and PEX13 ( reviewed in [26] ) . Not surprisingly , these disorders often result in death at an early age and patients suffer from a wide variety of neurological abnormalities including leukodystrophy ( inflammatory degeneration of white matter ) , similar to that observed in advanced HAND , termed HIV-associated dementia [47] . Mutations in genes that encode peroxisomal enzymes also result in severe neurological deficits , again defined by inflammatory degeneration of white matter [48] . These studies underscore the fact that even partially diminished function of peroxisomes can lead to severe neurological disease . The function of peroxisomes in antiviral signaling is a relatively new discovery [27 , 28] . A pool of the mitochondrial antiviral signaling protein ( MAVS ) , an adaptor protein for retinoic acid-inducible gene 1 protein ( RIG-I ) , localizes to peroxisomes . Activation of MAVS-dependent signaling from peroxisomes by different RNA viruses leads to activation of type III interferon , a process that is thought to complement the type I interferon response induced from mitochondria , which occurs later . MAVS signaling from both peroxisomes and mitochondria is required for maximal anti-viral activity . The observation that viruses have evolved strategies to interfere with peroxisome-dependent anti-viral signaling illustrates the importance of this organelle in defending against pathogens . For example , the hepatitis C virus NS3-4A protease has been shown to cleave both peroxisomal and mitochondrial MAVS to suppress RIG-I signaling of immune defenses [33 , 49 , 50] . Another mechanism used by flaviviruses such as West Nile virus and Dengue virus , involves targeting of PEX19 , a critical peroxisome biogenesis factor , for degradation [32] . Flavivirus-infected cells contain significantly lower numbers of peroxisomes , an effect that is mediated in large part through binding of capsid protein to PEX19 . As a result of the reduced peroxisome pool , type III interferon expression is dramatically reduced . Finally , the observation that human cytomegalovirus HCMV protein vMIA , which inhibits signaling downstream from mitochondrial MAVS , also localizes to peroxisomes [34] , may indicate that peroxisomes play a role in defense against DNA viruses too . While vMIA interacts with peroxisomal MAVS and induces peroxisome fragmentation , disruption of peroxisomal morphology is not essential for this viral protein to inhibit antiviral signaling . Association of vMIA with peroxisomes may require interaction with PEX19 . Although it was not further investigated , it is intriguing to note that MAVS is also a predicted target of miR-93-3p ( Table 1 ) . Here we show that infection by HIV-1 , a lentivirus , negatively impacts peroxisomes by a novel mechanism . The fact that blocking miRNA function with anti-miRs abrogates HIV-induced loss of peroxisomes suggests that upregulation of miRNAs is the main mechanism by which the virus targets these critical organelles that function in antiviral defense and neuroprotection . Of note , three of the four PEX proteins ( PEX2 , PEX7 and PEX13 targeted by HIV-induced miRNAs are associated with peroxisome biogenesis disorders [26] . Interfering with peroxisome biogenesis/function by altering miRNA expression appears to be a very efficient mechanism because some of the HIV-induced miRNAs repress expression of multiple PEX proteins . For instance , miR-500a-5p , which targets PEX2 , also reduced expression of PEX7 and PEX11B proteins . Similarly , the PEX13-targeting miR-381-3p decreased expression of PMP70 , PEX7 , PEX13 , and PEX2 . This was not due to the miRNAs targeting multiple PEX mRNAs but rather , it seems that expression and/or stability of given PEX proteins is often dependent on other PEX proteins . For example , cells transfected with siRNAs against PEX7 not only reduced PEX7 protein but PEX11B was also markedly lower . Similarly , a PEX13-specific siRNA reduced the levels of PEX13 and PEX7 proteins . Consistent with these data , it has been reported that siRNAs against PEX7 also reduce levels of PMP70 protein [51] and knockout of the PEX2 gene in mice negatively impacts PEX14 , PEX3 , PEX16 and catalase [52] . Finally , PEX11B and PEX13 knockout mice express lower levels of PEX14 protein [53 , 54] . Inhibition of peroxisome biogenesis and/or function during virus infection is only a recently discovered phenomenon [32–34] . However , given the roles of these organelles in antiviral defense and nervous system function , understanding how viruses manipulate peroxisomes will undoubtedly reveal pathological mechanisms that underlie multiple viral diseases . In the case of HIV-1 , elevated expression of PEX gene-targeting miRNAs was initially detected among patients with HAND . However , the fact that these miRNAs are also upregulated during HIV-1 infection of macrophages and that loss of peroxisomal proteins was observed in the brains of HIV patients without HAND indicate that virus-induced loss of peroxisomes is a fundamental aspect of HIV biology . Of interest is whether the degree of PEX-specific miRNA expression correlates with disease severity . The higher levels of PEX-specific miRNA expression in HAND patients compared to HIV patients without HAND is consistent with this scenario . Moreover , in a small sample size , we observed that loss of certain PEX proteins ( PEX ) was more pronounced in brain tissue from HAND patients compared to HIV patients without HAND . However , further investigation is required to determine if this is a ubiquitous phenotype in HAND patients . It is important to point out that only a small subset of permissive brain cells exhibit detectable viral genome or protein expression . Thus , it is plausible that the miRNA changes as well as altered Pex gene expression in HAND brains might be due in part to effects on bystander cells such as astrocytes , which rarely exhibit in vivo productive infection but are the most populous cell type in the brain . Nonetheless , miRNAs are being explored as diagnostic and prognostic biomarkers for various neurological conditions including Alzheimers’s and Parkinson’s diseases as well as HAND ( reviewed in [55] ) . Presently , there is very little known about aberrant miRNA expression and peroxisomal biogenesis disorders . However , a large number of recent studies have focused on the relationship between miRNA expression and peroxisome proliferator-activated receptors [56–59] and some of the findings have implications for neurological disease . Future studies that further clarify how viruses manipulate miRNAs are likely to reveal novel roles for miRNAs in peroxisome-dependent anti-viral defense , lipid metabolism and neurodegenerative disorders .
Brain tissue from HAND and non-HAND patients was collected at autopsy with informed consent at different geographical locations ( Texas , New York , San Diego and Los Angeles ) by the National NeuroAIDS Tissue Consortium [36 , 37] . The use of autopsied brain tissues ( Protocol number 2291 ) is approved by the University of Alberta Human Research Ethics Board ( Biomedical ) and written informed consents from all participants were signed before or at the collection times . The protocols for obtaining post-mortem brain samples comply with all federal and institutional guidelines with special respect for the confidentiality of the donor's identity . Neocortical brain tissue samples from midfrontal gyrus were excised from fresh-frozen brain slices and shipped in dry ice to the Laboratory for Neurological Infection and Immunity Brain Bank at University of Alberta . Samples were stored at -80°C until total RNA extraction including microRNAs ( miRNAs ) was performed as follows . Briefly , ~100 mg/sample of autopsy-derived brain tissue was aseptically collected using sterile instruments into a 2 ml Lysing Matrix tube ( MP Biomedicals , Santa Ana , CA , USA ) . Tissue samples were homogenized in a FastPrep-24 tissue homogenizer ( MP Biomedicals , Santa Ana , CA , USA ) after adding 1 ml of Trizol reagent ( Invitrogen Carlsbad , CA , USA ) . Chloroform ( 200 μl ) was added to each homogenate which was then centrifuged 12 , 000 x g for 15 minutes at 4°C . The aqueous phase was collected and extraction followed as indicated in the manufacturer’s manual ( Qiagen , Catalog no . 217004 ) . Affymetrix miRNA 3 . 0 GeneChips were used for miRNA analyses . This microarray chip provides comprehensive coverage for mature human miRNAs ( 1733 probes ) and pre-miRNAs ( 1658 probes ) . The Affymetrix FlashTag Biotin highly sensitive and reproducible ( HSR ) RNA Labelling kit was used to label RNA samples for analysis . Equal concentrations of total RNA including microRNAs ( 800–1000 ng ) were poly-A tailed as specified by the manufacturer ( Affymetrix ) followed by biotin-HSR ligation . Next , samples were treated with T4 DNA ligase before they were hybridized to Affymetrix miRNA 3 . 0 GeneChip arrays at 48°C for 16 hours . Arrays were then stained and washed on an Affymetrix GeneChip Fluidics 450 following manufacturer’s protocol and then scanned with an Affymetrix GeneChip Scanner 3000 7G System . Genespring ( version 12 . 6 ) software ( Agilent Technologies ) was used to normalize the data and identify differentially expressed miRNAs . The normalization in this software is based on the Robust Multi-array Average ( RMA ) algorithm , in which data are background-corrected , log2 transformed and quartile normalized . To identify differentially expressed miRNAs , the median of each probe set in the HAND or nonHAND patients was calculated and the non-parametric test Mann-Whitney unpaired test was applied . To select for differentially expressed miRNAs in this analysis , a cut-off fold change ( ≥ 1 . 5 ) in relative miRNA abundance and a p value of <0 . 05 was considered statistically significant . Three different bioinformatics algorithms ( miRDB , http://mirdb . org/miRDB/index . html; Diana-microT-CDS;http://diana . imis . athena-innovation . gr/DianaTools/index . php ? r=microT_CDS/index; and TargetScanHuman v6 . 2 , http://www . targetscan . org/ ) were used to predict the potential targets of differentially expressed miRNAs . Only mRNA targets that were predicted by at least two of the three algorithms were investigated further . Complete EDTA-free protease inhibitor cocktail ( Roche Diagnostics ( Laval , Quebec , Canada ) ; ProLong Gold anti-fade reagent with 4 , 6-diamidino-2-phenylindole ( DAPI ) , SlowFade Gold reagent mounting media , cell culture media DMEM , RPMI 1640 , and fetal bovine serum ( FBS ) from Invitrogen ( Carlsbad , CA ) were purchased from the indicated suppliers . Lipofectamine 2000 and Lipofectamine RNAiMAX were purchased from Invitrogen ( Carlsbad , CA ) ; Per-Fectin transfection reagent was from Genlantis ( San Diego , CA ) . miRIDIAN microRNA mimics including human hsa-miR-500a-5p , hsa-miR-34c-3p , hsa-miR-93-3p , hsa-miR-381-3p; miRIDIAN microRNA Mimic Negative Control #1 and miRIDIAN microRNA mimic mouse mmu-miR-344-3p; miRIDIAN microRNA inhibitors including human hsa-miR-500a-5p-Hairpin Inhibitor , hsa-miR-34c-3p-Hairpin Inhibitor , hsa-miR-93-3p-Hairpin Inhibitor and hsa-miR-381-3p-Hairpin Inhibitor were purchased from GE Healthcare Dharmacon Inc . ( Lafayette , CO ) . MGC human PEX2 ( Clone ID: 3347824 ) , PEX7 ( Clone ID: 5176358 ) , PEX11B ( Clone ID: 3866690 ) , and PEX13 ( Clone ID: 6285875 ) sequence-verified full-length cDNA clones were purchased from GE Healthcare Dharmacon Inc . ( Lafayette , CO ) . Reagents for purification and quantitation of miRNAs including MiRNeasy Mini kit , miScript PCR Starter kit , miScript II RT kit , and miScript SYBR Green PCR kit were purchased from Qiagen ( Toronto , ON ) . Mouse monoclonal antibodies against the peroxisomal membrane protein PMP70 ( Sigma , St . Louis , MO ) , HIV-1 p24 ( Abcam , Cambridge , MA ) , and beta-actin ( Abcam , Cambridge , MA ) were purchased from indicated suppliers . Rabbit polyclonal antibodies to PEX7 , PEX11B , PEX13 , PEX19 and catalase were from Abcam ( Cambridge , MA ) ; Rabbit polyclonal antibody to PEX2 ( PXMP3 ) was purchased from Pierce ( Rockford , IL ) ; Rabbit polyclonal antibody to thiolase ( ACAA1 ) was from MyBioSource ( San Diego , CA ) ; Rabbit polyclonal antibody to the tri-peptide SKL were produced as previously described [60] . Donkey anti-mouse IgG conjugated to Alexa Fluor 680 , goat anti-rabbit IgG conjugated to Alexa Fluor 680 , donkey anti-mouse IgG conjugated to Alexa Fluor 488 , donkey anti-rabbit IgG conjugated to Alexa Fluor 488 , and donkey anti-mouse IgG conjugated to Alexa Fluor 546 were purchased from Invitrogen ( Carlsbad , CA ) . The buffy coats used for PBMC isolation were derived from healthy volunteer blood donors . Human monocytes were isolated using Histopaque ( Sigma-Aldrich ) . Briefly , the blood was diluted 1:1 with phosphate-buffered saline ( PBS ) , placed under a layer of Histopaque and centrifuged for 22 min at 1800 rpm in a clinical centrifuge . Cells from the interphase layer were harvested , washed twice with serum-free RPMI , and then resuspended in RPMI1640 with 15% FBS , 1% penicillin and streptomycin ( Invitrogen , Carlsbad , CA ) . The cells ( 2–4 million per well ) were then seeded in 6-well plates that were pre-coated with poly-L-ornithine ( Sigma , St . Louis , MO ) . After 4 hours , the cells were washed three times with warm RPMI medium before adding 2mL Differentiation medium ( 25 ng/mL macrophage colony-stimulatory factor ( M-CSF ) ( Sigma , St . Louis , MO ) in RPMI containing 2mM L-glutamine , 1% penicillin and streptomycin and 15% FBS ) to each well . Cells were incubated for 7 days in this media ( with media changes every 3 day ) to allow differentiation of MDMs . A549 and HEK293T cells from the American Type Culture Collection ( Manassas , VA ) were cultured in DMEM ( Invitrogen ) containing 10% heat-inactivated FBS , 4 . 5 g/liter D-glucose , 2 mM glutamine , 110 mg/liter sodium pyruvate at 37°C in a 5% CO2 atmosphere . Hela CD4+ ( clone 1022 ) cells ( NIH AIDS Reagent Program , Germantown , MD ) were cultured in RPMI 1640 supplemented with 10% FBS and 1 . 0 mg/ml G418 ( Geneticin , Gibco ) . A549 and HEK293T cells were transfected with the expression plasmids using Lipofectamine 2000 ( Invitrogen ) and PerFectin ( Genlantis ) respectively as described by the manufacturers . When using miRNA mimics or anti-miRs , cells were transfected with Lipofectamine RNAiMAX ( Invitrogen ) . HIV-1 infection in Hela CD4+ cells ( pYU2 , MOI = 10 ) or primary monocyte-derived macrophages ( pYU2 , MOI = 2 ) was performed under biosafety CL-3 conditions . To test whether miRNA mimics could silence predicted target genes , the entire 3’-untranslated regions ( UTRs ) of selected target genes were subcloned into the luciferase expression vector pMIR-REPORT-Luc ( Ambion ) . Plasmids were constructed using polymerase chain reaction ( PCR ) and standard subcloning techniques . Sequence-verified full-length cDNAs of each PEX gene were used as templates to amplify the 3’-UTRs by PCR with primers listed in Table 2 . The resulting PCR products were digested with HindIII and then subcloned immediately downstream of the luciferase cassette contained in the reporter plasmid pMIR-REPORT-Luc . The orientation of each 3’-UTR insert was determined by endonuclease digestion and all constructs were then verified by DNA sequencing . The pMIR-REPORT miRNA Expression Reporter System ( Ambion ) was used to validate miRNA targets and conduct quantitative evaluations of miRNA function . The assay employs an experimental firefly luciferase-based reporter vector and an associated β-gal reporter control plasmid ( pMIR-REPORT β-gal ) . The pMIR-REPORT Luciferase plasmid contains a firefly luciferase reporter gene upstream of a multiple cloning site for insertion 3’UTRs that contain predicted miRNA-binding sites in its 3’-UTR . By cloning a cDNA fragment with a miRNA target sequence into the pMIR-REPORT plasmid , expression of the luciferase reporter can be negatively regulated by miRNAs . β-galactosidase expression from the pMIR-REPORT β-gal was used to normalize variability due to differences in cell viability and/or transfection efficiency . After 48 hours , lysates prepared from HEK293T cells transfected with pMIR-REPORT-Luciferase containing 3’-UTRs from different PEX genes ( PEX2 , PEX7 , PEX11B or PEX13 ) , pMIR-REPORT β-gal together with miRNA mimics were subjected to luciferase and β-gal assays . Briefly , growth medium was removed and cells were rinsed once with PBS . A minimal volume of 1X Reporter Lysis Buffer ( RLB ) ( Promega ) was added to each well and then the plates were rocked for several times to ensure complete coverage of the cells with RLB . Cells scraped from the wells were transferred to microcentrifuge tubes and placed on ice for 10 minutes . The microcentrifuge tubes were vortexed for 10–15 seconds and then centrifuged at 12 , 000 x g for 2 minutes at 4°C . The supernatant/cell lysates were transferred to new tubes and used immediately for assays or stored at -70°C . For luciferase assays , 20 μl of cell lysate and 100 μl of Luciferase Assay Reagent ( Promega ) were mixed in microcentrifuge tubes and luminescence was measured using a model Synergy 4 Luminometer ( BioTek ) . For β-Galactosidase assays , 150 μl of cell lysate ( 2:1 dilution to 1X RLB ) was mixed with 150 μl of Assay 2X Buffer ( Promega ) and then incubated at 37°C for 30 minutes or until a faint yellow color had developed . The reactions were terminated with 1M sodium carbonate ( 500 μl ) after which the absorbances were read at 420 nm . The relative luciferase activity was expressed as a ratio of luciferase activity to β-gal activity . Transfected or HIV-infected cells grown in 6-well plates were washed twice with cold PBS on ice and then lysed with RIPA buffer ( 50 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1% Triton x-100 , 1% Sodium deoxycholate , 0 . 1% SDS , 1 mM EDTA ) containing a cocktail of protease inhibitors . Lysates were incubated on ice for 30 minutes and then centrifuged at 14 , 000 x g for 15 minutes at 4°C after which protein concentrations in the supernatants were quantified using a Pierce BCA protein assay kit ( Thermo Scientific ) . Equivalent amounts of total protein ( 20 μg ) were resolved by SDS-PAGE and then transferred to polyvinylidene difluoride membranes ( EMD Millipore ) membranes for immunoblotting . Membranes were blocked with 3% skim milk powder in PBS containing 0 . 1% Tween 20 ( PBS-T ) and then incubated overnight at 4°C or 3 hours at room temperature with appropriate primary antibodies diluted in 3% milk-PBS-T . After washing three times with PBS-T for 10 minutes each , fluorescent secondary antibodies ( donkey anti-mouse IgG conjugated to Alexa Fluor 680 or goat anti-rabbit IgG conjugated to Alexa Fluor 680 ) diluted in PBS-T were used to detect the primary antibodies . After 1-hour incubation with the secondary antibodies , membranes were washed three times with PBS-T for 10 minutes each . Detection and quantification of the protein signals in the immunoblots was performed using a Licor Odyssey Infrared Imaging System ( Lincoln , NE ) using the protocol posted at http://biosupport . licor . com . Relative levels of PMP70 , PEX2 , PEX7 , PEX11B , PEX13 , PEX19 , and catalase ( normalized to actin ) were determined using Odyssey Infrared Imaging System 1 . 2 Version software . Hela CD4+ and A549 cells grown on coverslips were processed respectively for confocal or super-resolution microscopy at 48h post-transfection or infection . Cells were washed in PBS containing 0 . 5 mM Ca2+ and 1 . 0 mM Mg2+ and then fixed with 3% paraformaldehyde ( for confocal imaging ) or 1 . 5% electron microscopy grade paraformaldehyde ( for super-resolution imaging ) for 30 min at room temperature . Samples were then quenched with 50mM NH4Cl in PBS for 5 minutes at room temperature , washed three times with PBS , and then permeabilized with 0 . 2% Triton-X-100 for 5 min . Incubations with primary antibodies diluted ( 1:500–1000 ) in blocking buffer ( 3% BSA in PBS ) were performed at room temperature for 2 hours followed by three washes in PBS containing 0 . 1% BSA . Samples were then incubated with secondary antibodies in blocking buffer for 1 hour at room temperature followed by three washes in PBS containing 0 . 1% BSA . Secondary antibodies were donkey anti-mouse/rabbit IgG conjugated to Alexa Fluor 488 and donkey anti-mouse IgG conjugated to Alexa Fluor 546 ( Invitrogen ) . For confocal microscopy , coverslips were mounted onto microscope slides using ProLong Gold antifade reagent with DAPI ( Invitrogen ) , and samples were examined using an Olympus 1x81 spinning disk confocal microscope equipped with a 60x/1 . 42 oil PlanApo N objective . Confocal images were acquired and processed using Volocity 6 . 2 . 1 software . For super-resolution microscopy , coverslips were mounted on slides pre-cleaned with acetone and ethanol using SlowFade Gold reagent mounting media ( Invitrogen ) . Images were acquired using a DeltaVision OMX V4 structured illumination microscope ( Applied Precision , GE ) equipped with a 60x 1 . 42 oil PSF ( PlanApo N ) objective and immersion oil N = 1 . 514~1 . 516 . Images were analyzed using Volocity 6 . 2 . 1 software . Z-stack images acquired using a confocal microscope were exported from Volocity 6 . 2 . 1 as an OEM . tiff file . The exported images were then processed using Imaris 7 . 2 . 3 software ( Bitplane ) . Peroxisomes within polygonal areas that excluded the nucleus were quantified ( quality and voxel ) . Within the selected regions , the absolute intensity/region volume of the peroxisomes were determined and then entered into a Microsoft Excel spreadsheet . The data were then analyzed using student’s t-test . Where indicated , 0 . 125 μm optical sections acquired using an Applied Precision OMX super resolution microscope ( with a 60X/1 . 42 Oil lens and three CMOS cameras ) were also analyzed . The raw data were processed using Deltavision OMX SI image reconstruction and registration software and the final images were imported into Volocity 6 . 2 . 1 software as . dv files for quantification . In each cell , peroxisomes were selected based on the absolute pixel intensity in the corresponding channel and their numbers and volumes were then determined . Only those SKL/PMP70-positive structures with volumes between 0 . 001 and 0 . 05 μm3 were included for measurement . Formalin-fixed paraffin-embedded human brain was processed and tissue sections ( 10 μm ) were prepared and labeled as described us previously [13 , 61 , 62] . Briefly , samples were deparaffinized by incubation for 1 hour at 60°C followed by one 10 min and 2 five min incubations in xylene baths through decreasing concentrations of ethanol to distilled water . Antigen retrieval was performed by boiling in 10mM sodium citrate ( pH 6 . 0 ) 1 hr . Slides were blocked with HHFH buffer ( 1 mM HEPES buffer , 2% ( v/v ) horse serum , 5% ( v/v ) FBS , 0 . 1% ( w/v ) sodium azide in Hank’s balanced salt solution ( HBSS ) ) for 4 hrs at room temperature . Slides were stained with hematoxylin and eosin ( H&E ) . In addition , serial brain sections were immuno-labelled with antibodies to host proteins . Immunocytochemistry was performed with rabbit anti-Iba-1 ( Wako Pure Chemical Industries Ltd . , Osaka Japan ) , anti-thiolase or anti-PEX13 with appropriate secondary antibodies . For immunofluorescence studies , slides were incubated with a cocktail of rabbit anti-GFAP ( DAKO , Carpenteria CA ) or anti-Iba-1 ( 1:400 ) and anti-PEX13 , overnight at 4°C . The primary antibodies were removed by three 5 min PBS washes and slides were incubated for three min in 0 . 22 micron filtered 1% ( w/v ) Sudan black in 70% ethanol and washed an additional 3 times in PBS . A cocktail of 1:500 Alexa 488 goat anti rabbit IgG , Alexa 568 goat anti mouse IgG for two hrs , washed 3 times in PBS stained with DAPI for 10 min , washed 3 times in PBS and mounted with Prolong gold antifade reagent . Slides were imaged with Wave FX spinning disc confocal microscope ( Zeiss ) . Total RNA including small RNA from HIV-infected Hela CD4+ cells or primary MDMs was purified using the miRNeasy Mini Kit ( Qiagen ) according to the manufacturer’s instructions . Mature miRNAs , certain small nucleolar RNAs and small nuclear RNAs ( snoRNAs and snRNAs ) were selectively reverse-transcribed into cDNA using miScript HiSpec buffer according to the instructions of miScript II RT Kit ( Qiagen ) . Mature miRNAs , which are polyadenylated , were reverse transcribed into cDNA using oligo-dT primers . The oligo-dT primers included a 3’ degenerate anchor and a universal tag sequence on the 5’ end , allowing amplification of mature miRNA during the real-time PCR step . The resulting cDNAs served as the template for real-time PCR analysis using miRNA-specific primers ( forward primers , from IDT ) and the miScript SYBR green PCR kit ( Qiagen ) , which contains the miScript universal primer ( reverse primer ) and QuantiTect SYBR green PCR master mix . The amplification cycles consisted of an initial activation step at 95°C for 15 min , followed by 40 cycles of 15s at 94°C , 30s at 55°C and 30 s at 70°C . Fluorescence data were collected during the 70°C extension step . The miRNA targets and primers that were used in this study are listed in Table 2 . As an internal control , levels of a small nuclear RNA RNU6B ( a miScript PCR control provided in the miScript PCR starter kit ( Qiagen ) ) were determined . Relative miRNA expression was normalized to RNU6B levels using the comparative cT ( ΔΔcT ) method . All miRNA expression studies were conducted using a Mx3005P ( Stratagen , LaJolla , CA ) thermocycler . Microarray data were deposited into the NCBI GEO database ( Accession number GSE97611 ) . | Host cells employ a myriad of antiviral defense systems but most viruses have developed effective countermeasures . Viruses such as HIV that cause lifelong infections are particularly successful in subverting the host antiviral response . While mitochondria have long been known to be critical hubs for antiviral signaling , it has only recently become apparent that peroxisomes are also important for this process . Peroxisomes are small and numerous structures that are best known for their roles in lipid metabolism . New evidence suggests that pathogenic viruses such as West Nile and Dengue viruses block the production of peroxisomes by sequestering and degradation a critical biogenesis factor . In the present study , we report that HIV significantly reduces the number of peroxisomes in infected cells via a completely novel mechanism . Specifically , HIV-infected cells express high levels of microRNAs that inhibit production of proteins required for peroxisome formation . Interestingly , levels of these microRNAs were elevated in the brains of patients with HIV-associated neurocognitive disorders . Thus , as well as affecting antiviral signaling , loss of peroxisomes during HIV infection may contribute to development of neurological disorders . Understanding how pathogenic viruses affect peroxisome biogenesis and cognate antiviral signaling may ultimately lead to novel therapeutic avenues and prevention of long-term sequelae . | [
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"viru... | 2017 | MicroRNAs upregulated during HIV infection target peroxisome biogenesis factors: Implications for virus biology, disease mechanisms and neuropathology |
Genome-wide association studies ( GWAS ) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives . GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms , using single SNP analyses and setting stringent type-I error rates . Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes . Here , we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes . We apply the method to three traits: coat colour , %CD8 cells , and mean cell haemoglobin , measured in a heterogeneous stock mouse population . We find that a model that contains both additive and dominance effects , estimated from genome-wide marker data , is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives . Correlations between predicted and actual phenotypes were in the range of 0 . 4 to 0 . 9 when half of the number of families was used to estimate effects and the other half for prediction . Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait . The prediction of phenotypes using large samples , high-density SNP data , and appropriate statistical methodology is feasible and can be applied in human medicine , forensics , or artificial selection programs .
Results from linkage analyses and , more recently , genome-wide association studies ( GWAS ) imply that a large number of loci underlie the genetic architecture of complex traits [1]–[15] . GWAS are usually multi-staged , have mostly focused on gene discovery and typically set very stringent type-I error rates in the first stage to avoid false positives . Analysis is most frequently performed one SNP at a time . Consequently , these studies may not properly capture all of the genetic variation that is present in the samples , The initial wave of GWAS has found many genetic variants that are robustly associated with disease or quantitative traits , but these variants typically explain only a small fraction of the genetic variance , and so the utility of predictions made using this information can be limited . An alternative to gene discovery is to focus on the prediction of phenotypes using all genotypic ( SNP ) information across the whole genome simultaneously . The prediction of phenotypes is useful in a range of fields , from artificial selection programs [16] to risk prediction in human medicine [17] and forensics . To predict phenotypes , identification or genotyping of causal variants is not necessary , as long as there are variants genotyped that are in linkage disequilibrium ( LD ) with the causal variants [16] , [17] . To predict phenotypes from genomic data , the relationship between genome-wide marker data and phenotypes needs to be modeled . The single SNP regression approach that is often applied in conjunction with stringent thresholds would be expected to inaccurately estimate the proportion of variance that can be explained from genotypic data . Instead , model selection approaches are required to find the set of SNPs that best explains and predicts variation in phenotype . Such approaches have already been proposed for mapping multiple quantitative trait loci ( QTL ) [18]–[23] and recently a method was suggested for the simultaneous analysis of all SNPs in a GWAS [24] . In this study , we use statistical modeling to fit multiple SNP effects from a GWAS and derive the best model with a Bayesian model selection approach termed Reversible Jump Markov Chain Monte Carlo ( RJMCMC ) [25] . We predict unobserved phenotypes for individuals based on genome-wide SNP data only , family information ( without genetic data ) only , or on a combination of the two .
Publicly available data including pedigree , genotypic and phenotypic information on heterogeneous stock mice were used ( [26]; http://gscan . well . ox . ac . uk/ ) . The total number of animals was 2 , 296 from 85 unrelated families . The available pedigree spanned four generations , generating complex relationships . In the last generation , there were 172 full sib families with an average size of ∼11 ( SD ∼8 ) . Genotypes were available for 12 , 112 SNPs on most animals in the pedigree , and we used the 11 , 730 SNPs on the autosomal chromosomes . Phenotypes were already adjusted for the environmental fixed effects , e . g . sex , age , year and season [26] , [27] . We chose three phenotypes , coat colour as a complex trait with a number of known causal loci ( estimated h2≈0 . 72 ) , and percentage of CD8+ cells ( %CD8 ) as a quantitative trait having high heritability ( estimated h2≈0 . 99 ) , and mean cellular haemoglobin ( MCH ) as a quantitative trait having moderate heritability ( estimated h2≈0 . 55 ) . Coat colour , as used here , is a measure of the darkness of the coat from white to black . For more detail about the data , see [26] , [27] . We fitted a range of linear mixed models , with multiple SNPs as fixed effects and , in some models , a polygenic effect to account for additive genetic effects not detected by the SNPs . These polygenic effects are estimated from the pedigree . The effect of a SNP genotype on the phenotype was modeled either by fitting the additive term of one of the alleles or by fitting both additive and dominance terms . In the additive genetic model , phenotypic observations are a linear function of fixed effects , a polygenic term representing the sum of unidentified additive genetic effects , the additive effects due to SNPs associated with QTL and residuals . The linear model can be written as , ( 1 ) where y is a vector of length Nr , with single trait phenotypes for all animals corrected for fixed environmental effects ( Nr = no . observations in Table 1 ) , nq is the number of SNPs associated with the QTL involved in phenotypic expression , μ is the overall mean , is a vector of Nr ones , u is a vector of N random polygenic effects for N animals ( N = 2296 ) , αi is the fixed effect of the ith SNP and e is a vector of residuals . Z is an incidence matrix for the random polygenic effects relating observations to individual animals , with dimensions Nr×N . Note that N>Nr as some animals have a polygenic effect estimated based upon phenotypic information from relatives without having a phenotypic observation themselves . Λi is a column vector of length Nr having coefficients 0 , 1 or 2 representing indicator variables of the genotype for each animal at the ith SNP . The variance structure of phenotypic observations is written as , where A is the numerator relationship matrix , I is a identity matrix , is polygenic additive genetic variance and is error variance . In the model containing additive and dominance effects , all terms are the same as the additive genetic model except that dominance effects due to SNPs are added . The model is written as , ( 2 ) where δi is the dominance effect of the ith SNP and Δi is a column vector having coefficients that are 1 for a heterozygous genotype and 0 for a homozygous genotype at the ith SNP . We predicted phenotypes of individuals by using information on relatives and/or the estimated effects of their SNP genotypes . Prediction of phenotypes was based on BLUP of polygenic values [29] using pedigree and phenotype information only ( i . e . ( 1 ) with the third term omitted ) , or on additional genomic information where the prediction model was based on additive effects only ( model A ) or on both additive and dominance effects ( model AD ) . Both A and AD models were fitted with and without the effects of additional polygenic factors from pedigree relationships . For the single SNP analyses , the prediction was performed from a multiple regression analysis using those SNPs that were selected previously from the single SNP analyses . As for the AD model , the single SNP analyses also fitted polygenic effects , plus the additive and dominance effect of the SNP . To assess how well we predicted unobserved phenotypes , we used one part of the data for estimation and the remaining part for prediction and validation . Approximately half of the phenotypic data for each trait were randomly selected . Using only half of the phenotypes and all genotypes , the other half of the phenotypes ( i . e . future , unobserved , phenotypes ) were predicted with the proposed genetic model using whole genome SNP data . We tested how well we could predict phenotypes from genetic data in two ways . The first prediction was within families , using phenotypic data from approximately half of the animals in each full sib family to predict the phenotypes of the other half of the animals ( intra-family comparison ) . The second prediction was across families , using phenotypic data from approximately half of the 85 unrelated families to predict the phenotypes of the animals in the other half of the families ( inter-family comparison ) . The latter prediction could also be used for data sets that lack pedigree information . When fitting the pedigree only , i . e . not using any marker data , there is no ability to predict the phenotypes of animals in other , unrelated families , so the accuracy of the inter-family prediction is zero . For each comparison , we correlated the predicted genotype of an animal in the prediction set with its phenotype ( which was not used in the estimation phase ) . We term the correlation between predicted phenotypes and actual phenotypes as the accuracy of prediction . To gauge the precision with which this correlation is estimated we performed 10 replicates . For each replicate , the estimation and prediction sets were sampled and analyzed . In addition to performing the model selection procedure and prediction from the entire autosomal SNP genotype set , we also investigated how well genotypic data from a single chromosome could predict phenotypes . For individual chromosome analyses , the AD model was used for the inter-family prediction with a single replicate per trait .
The total number of original phenotypes , the number of phenotypes used in the estimation analysis and the number of phenotypes to be predicted but not used in the estimation step are shown in Table 1 . For the prediction set , on average approximately 700 ( %CD8 ) to 950 ( coat colour ) observations were used . Table 2 shows the correlation between true and estimated phenotypes of the three different traits when using the intra- or inter-family prediction . It shows that the use of genomic information substantially increases the accuracy of predicting unobserved phenotypes , compared to BLUP ( fitting only the pedigree ) , and a substantial accuracy was achieved even with inter-family prediction , where genomic and phenotypes data in some families was used to predict phenotypes in other families . The accuracy of prediction is highest with intra-family prediction when using genomic information and phenotypic information from relatives to predict an individual phenotype . For example , for %CD8 and an additive model of gene action and fitting the pedigree , the correlation between predicted and observed phenotype is 0 . 71 whereas it is 0 . 64 when using only pedigree information . The accuracies of prediction with the model AD are generally greater than those with model A for intra- and inter-family prediction . The difference between the accuracies with and without considering dominance varies across the traits . For coat colour , the accuracy of prediction substantially increases in both intra-family ( 0 . 72 to 0 . 89 ) and inter-family ( 0 . 58 to 0 . 87 ) prediction . For %CD8 , the accuracy increases slightly for the intra-family prediction ( 0 . 71 to 0 . 73 ) . The increase due to inclusion of dominance is larger for the inter-family prediction ( 0 . 50 to 0 . 58 ) . For MCH , the accuracy slightly increases for both intra- and inter-family prediction . These results are consistent with a substantial amount of dominance variance for coat colour , some dominance variance for %CD8 and little dominance variance for MCH . When omitting polygenic terms in the genetic model and using whole genome marker information only , the correlations between predicted and actual phenotypes are generally decreased for the intra-family prediction , and practically unchanged in inter-family prediction except for coat colour ( Table 2 ) . The bottom two rows of Table 2 for the inter-family prediction show that phenotypes can be predicted from marker data and phenotypes observed in ‘unrelated’ families . For coat colour and the AD model , the prediction is very good ( correlation of 0 . 81 ) . The precision with which phenotypes can be predicted from genetic data is , of course , limited by how much of the variation between individuals is due to genetic factors . Prediction of unobserved phenotypes from genetic data will never be accurate for traits with a low heritability , even if the prediction of the genetic effect is 100% accurate . To quantify how much of the variation between individuals due to genetic effects we detected , we scaled the accuracy of predicting phenotypes by h , the square root of the heritability . This parameter represents the correlation between additive genetic value and phenotype , and is a key parameter in artificial selection programs [30] . The scaled accuracy is an estimate of the precision with which additive genetic values are predicted . When using an additive genetic model and whole genome information ( Model A ) , this estimated correlation between predicted and inferred genetic values for the intra-family prediction was 0 . 84 , 0 . 71 and 0 . 71 for coat colour , %CD8 and MCH , respectively , and 0 . 68 , 0 . 50 and 0 . 47 for the inter-family prediction ( Table 3 ) . When using an additive and dominance genetic model and whole genome information ( Model AD ) , the estimated correlation between predicted and inferred additive genetic values for the intra-family prediction was 1 . 05 , 0 . 73 and 0 . 75 for coat colour , %CD8 and MCH , respectively , and 1 . 02 , 0 . 59 and 0 . 48 for the inter-family prediction ( Table 3 ) . Therefore a large proportion of existing genetic variation was detected and exploited by our application . It should be noted that the values for model AD should be scaled by the square root of the broad-sense heritability which was , however , unknown . Instead , we scaled the values for the AD model by narrow-sense heritability , which may result in an overestimation of accuracy depending on the amount of dominance variance . Figures 1 , 2 and 3 show that the accuracy of prediction is higher when considering whole genome information compared with using information from one chromosome at a time . Even with coat colour , a single gene or a single chromosome does not determine all variation in phenotypic expression ( Figure 1 ) . Although the accuracy of prediction when considering chromosome 7 alone is high ( 0 . 79 ) , the accuracy can be improved when using whole genome information ( 0 . 88 ) . With %CD8 ( Figure 2 ) , the accuracy of prediction obtained by considering each chromosome at a time ranges from 0 . 05 to 0 . 50 , implying that most chromosomes contribute to variation in this complex phenotype . When considering the entire genome simultaneously , the accuracy of prediction increases to 0 . 63 . With MCH , the accuracy obtained from individual chromosomes varies up to 0 . 23 ( Figure 3 ) . However , again the accuracy of prediction is highest ( 0 . 40 ) when using whole genome information . The estimated negative correlations between actual phenotypes and predictions based upon a single chromosome ( e . g . , Figure 1 ) is most likely due to sampling error . Chromosomal analyses were done for a single replicate . The whole genome approach based on fitting multiple SNPs and using RJMCMC for model selection provides a posterior density of each SNP being associated with the phenotype . Therefore , the positions of trait loci can be estimated ( e . g . Figure 4A , C and E ) . For comparison , the method using regression on single SNPs that considers one position at a time was used . This method yields a likelihood ratio ( LR ) for each SNP which was plotted against genomic position ( Figure 4B , D and F ) . Averages of the posterior QTL density or LR from the 10 replicates are shown for the inter-family prediction . For coat colour , high posterior densities are shown for the regions around ∼159 Mb on chromosome 2 , ∼80 Mb on chromosome 4 and ∼80 Mb on chromosome 7 ( Figure 4 A , C and E ) . These regions agree very well with the positions of a number of known genes for variation in coat colour [31] ( diamonds in Figure 4 ) . Specifically , the non-agouti gene is at 154 Mb on chromosome 2 , tyrosinase-related protein is at 79 Mb on chromosome 4 , and the tyrosinase and Rab38 genes are at 81 Mb and 82 Mb , respectively , on chromosome 7 . The LR profiles from the single SNP method are similar to that from the multiple SNP method ( Figure 4B , D and F ) . However , correlated estimates due to linkage disequilibrium between the causal genes and multiple SNPs cause a broad confidence interval when using the single SNP method . For %CD8 , high posterior densities are shown for the regions around ∼170 Mb on chromosome 1 , ∼125 Mb on chromosome 2 and ∼30 Mb on chromosome 17 ( Figure 4A , C and E ) . Some of these estimated positions agree with putative QTL region previously reported by [26] ( also see http://gscan . well . ox . ac . uk/ ) ( diamonds in Figure 5 ) . The LR pattern from the single SNP method is similar to that from the multiple SNP method ( Figure 5B , D and F ) , but again the mapping resolution is lower . For MCH , high posterior densities are observed for the region near ∼155 Mb on chromosome 1 , ∼82 Mb on chromosome 8 and ∼65 Mb on chromosome 14 ( Figure 6A , C and E ) . Estimated positions agree well with putative QTL region previously reported [26] ( diamonds in Figure 6 ) . As with the other traits , the single SNP method has lower map resolution . Convergence of the parameter estimates was diagnosed from the pattern of the accuracy values after 100 , 1000 , 10000 and 100000 iterations when using intra-family prediction for a single replicate . The burn-in period was 10% of the total number of iterations . Figure 7 shows that the accuracy rapidly increases in early iteration rounds , and generally becomes a stable value after 10 , 000 iterations . A similar pattern was observed in the inter-family prediction , i . e . the accuracy reached a stable value after ∼10 , 000 iterations ( result not shown ) , indicating that only a moderate number of iterations are required to achieve the accuracies of predicted phenotype shown in the results . The pattern of convergence of the estimated parameters ( e . g . variances ) was similar to that of the accuracy ( result not shown ) , which was expected because accuracy was closely related to the estimated parameters . In this study , we used only a single starting value in order to save computing time due to many different situations to be tested with many analyses . However , for a single intensive analysis , it is always desirable to use multiple starting values to make sure that estimates reach apparent convergence .
We have proposed a method to simultaneously analyse whole genome SNP data for association with phenotypes , applied this method to three traits measured in a heterogeneous mouse stock and successfully predicted unobserved phenotypes . The precision of the prediction of unobserved phenotypes depends on the actual genetic architecture of the traits ( heritability , number of genes , distribution of effect sizes and mode of gene action ) , the marker density and experimental sample size . For the qualitative trait ( coat colour ) and the highly heritable quantitative trait ( %CD8 ) , the accuracies of predicting phenotypes were high , even when using genomic information from unrelated families in the same population . This is a valuable result with important applications in medicine , agriculture and forensics . Reversible jump theory is well established for solving model selection problem [20] , [25] , [32]–[34] . We found that RJMCMC in genomic selection was computationally efficient and gave reliable estimates . For the data set on mice ( ∼2200 individuals and ∼10 , 000 SNP ) , it took ∼15 minutes with a single CPU ( ∼2 GHz ) , which compares favourably to a number of other computing strategies on the same data set [35] . Assuming that computing time increases linearly with the number of individuals and markers , the method would run within one week even if the data set was large ( e . g . 10 , 000 individuals with 1 , 000 , 000 SNPs ) . More time may be required to adequately monitor convergence , however parallel computing strategies would be useful here , e . g . [36] . Therefore , the methods described in this study can scale up to much larger data sets . There are several approaches for whole genome association studies such as Bayesian random effect approaches [37] , ridge regression or shrinkage estimators [38] , [39] . However , most of these approaches are computationally intensive ( as reported by [37]–[39] , and some statistical properties are ill-defined ( as discussed in [38] ) . Data sets used in those studies [37]–[39] were much smaller than what we have used here . Nevertheless , we recognize that improvements to our model are possible , for example using random QTL effects , and that these may lead to even better results . Very recently , a fast analysis of all SNPs in a genome-wide association study was described using a method akin to a penalised likelihood approach [24] . This method was implemented to find a subset of SNPs that best explains case-control status in a disease study subject to a specified type-I error rate , but can also be used to select a subset for the prediction of phenotypes . When comparing results between different prediction strategies , the accuracies of the intra-family prediction were generally higher than those for inter-family prediction ( Table 2 ) . There are three possible explanations for this observation . Firstly , the prediction of phenotype within families can use both linkage ( family ) and linkage disequilibrium ( population ) information for detected gene effects , whereas the prediction across families can exploit only LD in the population . Secondly , there may be polygenic effects which were not captured by the SNPs but these can be captured when using the phenotypes of close relatives . Thirdly , in the data set that we used , effects due to the common environment shared by littermates are confounded with genetic effects . Therefore , if there are such non-genetic effects that cause resemblance between relatives ( in particular fullsibs ) , then these could be partially captured by the polygenic terms and even by SNP genotype effects . Importantly , such non-genetic common family effects do not affect the inter-family prediction . It was shown that the difference of the accuracy of prediction with and without polygenic terms based on pedigree information was large for the intra-family prediction whereas it was much smaller for the inter-family prediction for %CD8 and MCH ( Table 2 ) . This observation makes sense in that polygenic or common environmental effects can be informative for the prediction within families , but are not relevant for prediction across families . For coat colour , this pattern was not evident , presumably because the phenotypes are not affected by non-genetic family effects . Given phenotype and pedigree data , narrow- or broad-sense heritability ( h2 ) for the quantitative traits can be estimated in the classical genetic model [40] . However , since the data set used in this study consisted of full sib families with no replicates for maternal performance of dam , maternal environmental effects or family non-genetic effects may not be well-separated from genetic effects estimated in the classical model using pedigree information . Therefore , our estimate of heritability from the polygenic additive model may be biased upwards . We also tried to fit epistatic effects for pairs of SNP in addition to additive and dominance effects ( see [23] for more detail on the method used ) . However , the model including epistasis did not improve the accuracy of prediction for any trait ( results not shown ) . This was probably because the sample size was not sufficient to capture epistatic effects or , alternatively , because epistatic interactions do not contribute much to genetic variance in our data set [41] . We showed the strength of the multiple SNP method used in this study , compared to a set of SNPs obtained from the single SNP regression method , which is currently widely used in standard genome scans ( Figures 4 , 5 and 6 ) . Compared to the multiple SNP method , the single SNP analyses generate more apparently significant SNPs but our results suggest that it would be much more difficult to determine the number and location of causal variants . Both methods can provide SNP sets to predict unobserved phenotypes . The accuracies of prediction using SNPs obtained from single SNP regression were generally lower than those with the multiple SNP method ( Table 4 ) . This was probably due to the fact that the choice of SNPs was not optimum . For example , selecting only one significant SNP in a region might ignore the possibility of having two QTL in a region , or alternately that multiple SNPs are required to explain the variance due to a single QTL . In contrast , the multiple SNP method used Bayesian model selection which tested all possible models with a proper acceptance ratio according to the appropriate posterior distribution . We used a prior of Poisson distribution with mean μn = 1 for the number of QTL ( nq ) in the RJMCMC . This might be a conservative way of detecting QTL , avoiding false positives and reducing random noise if there was no apparent prior information about the number of QTL . We also tested the performance of the RJMCMC with a different prior which was Poisson distribution with mean μn = 14 . Note that the estimated number of QTL was ∼15 , ∼13 and ∼14 for coat colour , %CD8 and MCH , respectively ( Table 4 ) . Table 5 shows that the average number of SNP fitted simultaneously in each RJMCMC round was much larger with a prior mean of 14 than that with a prior mean of 1 . However , the accuracy ( correlation ) was not much different whether using a prior mean of 1 or 14 . Although the number of SNP simultaneously fitted in each RJMCMC round was smaller when using a prior mean of 1 , all or most of the significant SNPs were found and fitted in the model over many iterations . This is why the accuracy with a prior mean of 1 is very close to that when using a prior mean of 14 . This agrees with conclusions from previous studies [32] , [34] , [42] that estimation of QTL positions and effects are robust with respect to prior values . We also used a flat uniform prior for ρ ( assuming that there was no prior information for the QTL positions ) , and ML estimates for α and δ were obtained given nq and ρ ( also see online Supporting Information text S2 ) . If there is apparent and useful information about priors , the RJMCMC can implement the information , which may give better results . For our main RJMCMC analyses , we fixed the value of the polygenic heritability for computational reasons . We tested the sensitivity of this procedure on the accuracy of predicted phenotypes . For the polygenic heritability , three fixed values were compared , the previously used fixed value , half that value and a heritability of 0 . In addition , we estimated heritability in every MCMC round . Table 6 shows that the accuracies are not dramatically different between estimates although zero heritability , equivalent to no polygenic effect fitted , results in slightly lower accuracies . We tested intra-family prediction only as this may be affected by value of the polygenic heritability . Our results are based on ∼50% cross-validation . If more than 50% of the data are used for estimation then the accuracy of prediction may improve because estimates of marker effects will be more precise . We tested this by using 90% of the data for estimation stage and 10% for assessing the accuracy of prediction . Because families vary in size , it is not possible to select exactly 10% of each family . Therefore , we randomly divided the animals into 10 sets regardless of the family information . This generates a structure intermediate between inter- and intra-family prediction . We used 90% for estimation and 10% for validation and used 10 replicates without overlap in the validation sets . The correlation between true and predicted phenotypes and their SD were 0 . 91 ( 0 . 02 ) , 0 . 73 ( 0 . 04 ) and 0 . 61 ( 0 . 06 ) , for coat colour , %CD8 and MCH , respectively . The corresponding values for 50% cross-validation were 0 . 89 ( 0 . 03 ) , 0 . 73 ( 0 . 02 ) and 0 . 55 ( 0 . 02 ) . Hence , the accuracy for coat color and MCH are higher when using 10% cross-validation than when using 50% cross-validation , but the accuracy for CD8% is not much different . Standard deviation over 10 replicates tend to be larger with 10% cross-validation than that with 50% cross-validation . This is probably due to the fact that 90% discovery gives better estimation of marker effects and therefore we pick up a larger correlation , but that 10% validation gives larger sampling variance for the correlation . Our MCMC method used estimated rather than sampled values for some parameters , which is known as an empirical Bayesian approach [43] . For a given QTL model , based on sampled values for the number of QTL , their effects and their positions , we obtained ML estimates for the remaining model parameters . This differs from the full Bayesian approach where in the MCMC algorithm all model parameters are sampled conditional on data and other parameters . Hence , the posterior distribution for the model parameters could differ somewhat from those of a full Bayesian approach . The empirical Bayesian approach has a large computational advantage as for sampled values for QTL number , effects and positions , no time is wasted with evaluating all possible values of Θ but rather evaluation is at the most likely value . Estimates converge more quickly compared to the full Bayesian approach . It is unlikely that much information is lost in this empirical Bayesian approach because parameters in Θ have smooth distributions and it is not likely that critical information exists at values with lower probability density . Casella [43] discussed the empirical Bayesian procedure for a hierarchical model where in an iterative procedure ML estimates were obtained for hyper parameters and other parameters were sampled conditional on these ML estimates . He justified this procedure statistically by showing that it implies an Expectation Maximization algorithm . In our approach , ML estimates for Θ and the likelihood of the data given the model parameters are used in RJMCMC to get the posterior density of QTL parameters across model dimensions . The justification for our procedure is shown in [20] . The method used here for prediction of phenotypes would be useful in many situations but the accuracy achieved is expected to vary . The mouse population was formed from crossbreeding inbred lines and so LD is expected to exist over considerable distance . In species with much less LD , for example humans , more markers and more phenotypic records are needed to achieve the same level of accuracy . In conclusion , the prediction of unobserved phenotypes for complex traits from genome-wide marker data is feasible and can be accurate . Applications of our method are plentiful: in artificial selection programs it may lead to faster response to selection , by increasing the precision with which polygenic values are predicted [16] , in human medicine it can be used to identify individuals that are most at risk for disease [17] , and in forensics it can help to build a phenotypic profile from DNA evidence . | Results from recent genome-wide association studies indicate that for most complex traits , there are many loci that contribute to variation in observed phenotype and that the effect of a single variant ( single nucleotide polymorphism , SNP ) on a phenotype is small . Here , we propose a method that combines the effects of multiple SNPs to make a prediction of a phenotype that has not been observed . We apply the method to data on mice , using phenotypic and genomic data from some individuals to predict phenotypes in other , either related or unrelated , individuals . We find that correlations between predicted and actual phenotypes are in the range of 0 . 4 to 0 . 9 . The method also shows that the SNPs used in the prediction appear in regions that are known to contain genes associated with the traits studied . The prediction of unobserved phenotypes from high-density SNP data and appropriate statistical methodology is feasible and can be applied in human medicine , forensics , or artificial breeding programs . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"genetics",
"and",
"genomics/complex",
"traits",
"genetics",
"and",
"genomics/animal",
"genetics"
] | 2008 | Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data |
DNA damage response ( DDR ) and selective autophagy both can be activated by reactive oxygen/nitrogen species ( ROS/RNS ) , and both are of paramount importance in cancer development . The selective autophagy receptor and ubiquitin ( Ub ) sensor p62 plays a key role in their crosstalk . ROS production has been well documented in latent infection of oncogenic viruses including Epstein-Barr Virus ( EBV ) . However , p62-mediated selective autophagy and its interplay with DDR have not been investigated in these settings . In this study , we provide evidence that considerable levels of p62-mediated selective autophagy are spontaneously induced , and correlate with ROS-Keap1-NRF2 pathway activity , in virus-transformed cells . Inhibition of autophagy results in p62 accumulation in the nucleus , and promotes ROS-induced DNA damage and cell death , as well as downregulates the DNA repair proteins CHK1 and RAD51 . In contrast , MG132-mediated proteasome inhibition , which induces rigorous autophagy , promotes p62 degradation but accumulation of the DNA repair proteins CHK1 and RAD51 . However , pretreatment with an autophagy inhibitor offsets the effects of MG132 on CHK1 and RAD51 levels . These findings imply that p62 accumulation in the nucleus in response to autophagy inhibition promotes proteasome-mediated CHK1 and RAD51 protein instability . This claim is further supported by the findings that transient expression of a p62 mutant , which is constitutively localized in the nucleus , in B cell lines with low endogenous p62 levels recaptures the effects of autophagy inhibition on CHK1 and RAD51 protein stability . These results indicate that proteasomal degradation of RAD51 and CHK1 is dependent on p62 accumulation in the nucleus . However , small hairpin RNA ( shRNA ) -mediated p62 depletion in EBV-transformed lymphoblastic cell lines ( LCLs ) had no apparent effects on the protein levels of CHK1 and RAD51 , likely due to the constitutive localization of p62 in the cytoplasm and incomplete knockdown is insufficient to manifest its nuclear effects on these proteins . Rather , shRNA-mediated p62 depletion in EBV-transformed LCLs results in significant increases of endogenous RNF168-γH2AX damage foci and chromatin ubiquitination , indicative of activation of RNF168-mediated DNA repair mechanisms . Our results have unveiled a pivotal role for p62-mediated selective autophagy that governs DDR in the setting of oncogenic virus latent infection , and provide a novel insight into virus-mediated oncogenesis .
p62 ( also named EBIAP , ZIP3 , SQSTM1/Sequestosome-1 ) , a human homolog of mouse ZIPs ( Zeta PKC-interacting proteins ) , is well known as a selective autophagy receptor and a ubiquitn sensor , which controls myraid cellular processes , including redox homeostasis , DNA damage response ( DDR ) , cancer development , aging , inflammation and immunity , osteoclastogenesis , and obesity , with or without the involvement of autophagy [1–3] . Autophagy , with either non-selective ( random ) or selective fashion , is a unique intracellular process that engulfs damaged and even functional cellular constituents and delivers them to lysosomes for digestion and recycling in the cytosol under diverse stresses , such as nutrient deprivation , viral replication , cancer hypoxia , genotoxic stress , and replicative crisis . Autophagy is thereby a crucial cellular machinery conserved from yeast to higher eukaryotes that maintains organ metabolism , genome stability , and cell survival , and functions as either tumor suppressor at early stage or promotor at late stage [4–6] . Distinct from non-selective autophagy , selective autophagy sort specific substrates to lysosomes , and is mediated by an increasing pool of receptors , including p62 , NBR1 , TAX1BP1 , NDP52 , OPTN , TRIMs , and TOLLIP [3 , 7–10] . Reactive oxygen/nitrogen species ( ROS and RNS ) , the major cause of endogenous DNA damage , can be produced in chronic viral infections , in which viral replication is generally absent [11] . They can directly modify DNA and generate different levels of lesions , including double strand breaks ( DSBs ) [12 , 13] . Eukaryotic organisms have developed sophisticated strategies to repair DNA damage to ensure genomic integrity , with homologous recombination ( HR ) and nonhomologous end joining ( NHEJ ) being two non-redundant repair mechanisms for DSBs [14] . Unrepaired DSBs , however , incite chronic inflammation , resulting in genomic instability that promotes malignant transformation under certain conditions [15] . ROS/RNS also induce p62 expression through the Keap1-NRF2 pathway , licensing the induction of p62-mediated selective autophagy [16] . Mounting evidence indicates that DDR and selective autophagy closely crosstalk in response to oxidative stress , in which p62 plays a key role [17] . While p62 inhibits DNA damage repair , p62-mediated selective autophagy promotes DNA repair by targeting ubiquitinated substrates including p62 itself for degradation in cancer cells [18 , 19] , which usually harbor deficient traditional DNA repair mechanisms and heavily rely on autophagy as an alternative repair mechanism for survival [20 , 21] . In this sense , p62-mediated selective autophagy , which is activated upon DNA damage caused by various stresses such as conventional chemotherapeutic agents , allows these cancer cells to escape DNA damage-induced cell death [22 , 23] . ROS/RNS overproduction , deregulation of host DDR machinery , and chronic inflammation , are the most common features of viral persistent infections , and together with non-selective autophagy , have also been documented in latency of herpesviruses including Epstein-Barr Virus ( EBV ) [24–32] . Moreover , we and others have provided overwhelming evidence supporting that EBV latent infection reprograms the host ubiquitin machinery for its own benefits [33–35] , including the employment of linear ubiquitin chain assembly complex ( LUBAC ) -mediated ubiquitination to modulate LMP1 signal transduction [36] . However , as the major selective autophagy receptor and a ubiquitin sensor , p62 and its relationship with EBV latency and oncogenesis have never been investigated . In our recent publication , our findings have implied a role for the p62-autophagy interplay in ROS-elicited DDR in EBV latency [37] . In this study , we aimed to investigate the potential role of p62-mediated selective autophagy in regulating DDR in EBV latent infection . Our results show that p62-mediated selective autophagy is constitutively induced in virus-transformed cells , and correlates with ROS-Keap1-NRF2 pathway activity , and that a well-balanced basal level of p62-mediated selective autophagy is essential for maintaining genomic stability in this setting .
Our recent findings have shown that treatment of EBV+ cells with the calcium ionophore ionomycin , which raises the intracellular level of calcium ( Ca2+ ) essential for ROS production , elevates the protein levels of both p62 and LC3b-II ( the smaller cleavage product of LC3b , which generally represents a marker of autophagosomal activity in mammalian cells ) , and induces DNA damage; autophagy deficiency also elevates p62 protein levels [37] . Since both p62 and LC3b are targeted by autophagy for degradation , their turnover represents the autophagic flux ( autophagic degradation activity ) [38 , 39] . These results have implied that the p62-autophagy interplay may be involved in oxidative stress in EBV latent infection . We thus sought to evaluate the correlation between intracellular ROS , and p62 and autophagy levels in EBV latency programs . Results show that , although nearly 100% cells of each tested virus-associated cancer cell line produce ROS , their levels , as indicated by mean of fluorescence intensity ( MFI ) , are consistently higher in SavIII , JiJoye , and MT4 , compared to SavI , P3HR1 , and CEM , respectively ( Fig 1A ) . Correspondingly , the cell lines with higher ROS production remarkably express higher p62 consistently at both protein and mRNA levels ( Fig 1B and 1C ) . Furthermore , the basal levels of p62-mediated selective autophagy activity , as indicated by both the cleaved LC3b product LC3b-II and phosphorylation of p62 ( Ser403 ) , correspond to the endogenous ROS levels , and are readily detectable by immunoblotting in EBV type III latency and human T-cell leukemia virus-1 ( HTLV1 ) -transformed MT4 cell line ( Fig 1B ) . Phosphorylation of p62 ( Ser403 ) , which promotes p62-Ub binding , is crucial for activation of p62-selective autophagy [40] . Furthermore , interaction between selective autophagy receptors and Ub-like proteins ( UBLs ) , such as LC3b , is the molecular basis for selective autophagy [41 , 42] . Our IP results show that endogenous p62 and LC3b interact in virus-transformed cells ( Fig 1D ) . These results indicate that a basal level of p62-mediated selective autophagy is constitutively induced and correlates with the endogenous ROS level in viral latency . The antioxidant transcription factor NRF2 is spontaneously degraded by the ubiquitin ( Ub ) E3 ligase complex Keap1/Cul3/RBX1 under normoxia; ROS/oxidative stress triggers autophagic degradation of Keap1 , resulting in the accumulation and activation of NRF2 , which then induces p62 expression [43 , 44] . Thus , we evaluated the Keap1-NRF2 pathway activities , indicated by Keap1 and NRF2 expression levels , in these cell lines . As indicated in Fig 1B , the endogenous p62 levels positively correlate with the Keap-NRF2 pathway activities , strongly suggesting that the Keap1-NRF2 pathway induces p62 expression at least in part in viral latency . In support of our findings , Keap1-NRF2 pathway is also activated in KSHV latency [45 , 46] . Together , these results indicate that p62-mediated selective autophagy is constitutively induced in oncovirus latency , and correlates with the endogenous ROS-Keap1-NRF2 pathway activity . We next aimed to verify the induction of p62 by the ROS-Keap1-NRF2 pathway in viral latency . To this end , we first treated SavI and SavIII cells with the clinical topoisomerase II inhibitor doxorubicin ( Doxo ) , which generates the highest level of mitochondrial ROS causing DSBs [47] . Results show that Doxo augments the activities of the Keap1-NRF2 pathway in both cell lines in a time-dependent manner . Interestingly , p62 protein levels and p-p62 ( S403 ) are increased by Doxo treatment in SavI cells , but p62 protein levels are decreased in SavIII cells . Moreover , LC3b-II is increased in both cell lines but decreased at late stage in SavIII cells ( Fig 2A ) . These results are consistent with the notion that considerable levels of ROS are required for induction of p62 and mediated autophagy , but excess ROS and selective autophagy result in autophagic degradation of p62 and LC3b in that both are targets of p62-mediated selective autophagy [38 , 39] . We next used 3-amino-1 , 2 , 4-triazole ( 3-AT ) to inhibit the endogenous activities of catalase , an enzyme converting H2O2 to H2O+O2 , in cell lines with lower ROS levels to elevate their endogenous ROS levels . Then , we evaluated the Keap1-NRF2 pathway activity , and p62 , autophagy , and DNA damage levels . Results show that 3-AT treatment substantially elevates endogenous levels of the Keap1-NRF2 pathway activities and p62 expression at both protein and mRNA levels , and also induces p62 ( S403 ) phosphorylation , autophagy and the DNA damage hallmark γH2AX that are readily detectable ( Fig 2B ) . Accumulation of LC3-II does not necessarily reflect an increased autophagic activity; instead it may represent its decreased clearance due to the blockage of autophagic degradation . Thus , we further measured autophagy flux , as indicated by MFI , by flow cytometry ( Fig 2C ) . In contrast , quenching endogenous ROS with the ROS scavenger N-acetylcysteine amide ( NACA ) in indicated cell lines substantially dampens p62 levels and autophagy activities due to blockage of their endogenous Keap1-NRF2 pathway activities , as well as attenuates endogenous DNA damage ( Fig 2D ) . Furthermore , confocal microscopy ( Fig 2E ) and flow cytometry ( Fig 2F ) results show that treatment of IB4 cells with 3-AT or with the traditional oxidative DNA damage inducer H2O2 remarkably increases p62 expression , autophagosomes and autophagy flux , and that most p62 foci co-localize with autophagosomal bodies in the cytoplasm . Taken together , these results indicate that endogenous ROS , which correlate with Keap1-NRF2 pathway activity , are responsible for p62 expression and for induction of p62-mediated selective autophagy in viral latency . Since we have previously shown that mild ionomycin treatments induce p62 expression and profound autophagy in lymphoblastic cell lines ( LCLs ) , but stringent treatments promote p62 degradation due to induction of massive autophagy [37] , we used ionomycin treatment here to study the p62-autohagy interplay in regulating DDR in LCLs , which serve as a system crucial for genetic and functional study of carcinogen sensitivity and DNA repair [48] . We first used the lysosome-specific inhibitor bafilomycin A1 ( BafA1 ) , which inhibits lysosomal activity that occurs after LC3 processing , to inhibit autophagy activity induced by ionomycin in LCLs . Results show that both p62 and LC3b , which are both selectively degraded by p62-mediated autophagy [38 , 39] , are accumulated in ionomycin-treated cells due to impaired autophagy activities . As a consequence , the levels of γH2AX are remarkably augmented ( Fig 3A and 3B ) . To minimize the interference of potential “off-target” effects of BafA1 , we performed this experiment using another lysosome-specific inhibitor chloroquine , and obtained similar results ( Fig 3C ) . These results indicate that the autophagy-p62 interplay plays a role in DDR in EBV latency . We further show that ionomycin triggers profound ROS production and DNA damage in LCLs in a time-dependent manner ( Fig 3D and 3E ) , and the ROS scavenger NACA offsets the effects of ionomycin ( Fig 3E ) . Thus , these results indicate that ROS are responsible for ionomycin-induced autophagy and DNA damage in EBV latency . To confirm the requirement of ROS for induction of autophagy and DNA damage in EBV latency , we further used H2O2 to treat IB4 cells . Results show that H2O2 treatment induces profound DNA damage and reduces the endogenous p62 protein level in a dose-dependent manner , and blockage of autophagy activity with BafA1 potentiates the DNA damage that correlates with elevated p62 protein levels ( Fig 4A ) . Furthermore , autophagy inhibition by BafA1 promotes cell death ( as indicated by 7-AAD expression and Annexin-V binding , or caspase-3 activity ) induced by H2O2 ( Fig 4B and 4C ) or ionomycin ( Fig 3B ) , respectively . Importantly , confocal microscopy results further show that p62 translocates from the cytoplasm to the nucleus in response to autophagy inhibition ( Fig 4D ) . Taken together , these results ( Figs 3 and 4 ) indicate that autophagy inhibition exacerbates ROS-induced DNA damage by promoting p62 stabilization and nuclear translocation , further supporting that p62-mediated autophagy promotes DDR in EBV latency . It has been reported that autophagy inhibition or nuclear p62 accruing from autophagy deficiency promotes proteasomal degradation of HR DNA repair proteins such as RAD51 , CHK1 and FLNA [17 , 49] . Our results show that SavIII , JiJoye , MT4 , and IB4 , which have higher endogenous p62 and autophagy levels ( Fig 1B ) , have lower CHK1 and RAD51 protein levels as well as CHK1 activity ( as indicated by phosphorylation of CHK1 ( S345 ) ) , compared to SavI , P3HR1 , CEM , and BJAB , respectively ( Fig 5A ) . These results suggest that the p62-autophagy interplay may also regulate proteasome-dependent stability of CHK1 and RAD51 proteins and CHK1 activity in viral latency . It has also been reported that p62 promotes NHEJ by activating the Keap1-NRF2 pathway in a feedback loop , which consequently induces expression of NHEJ-specific repair proteins such as 53BP1 [50] . However , our results show that 53BP1 reversely correlates with p62 at the protein level in viral latency ( Fig 5A ) , indicating that NHEJ-mediated DNA repair activity is also compromised in virus-transformed cells . In addition , endogenous DNA damage ( as indicated by γH2AX expression ) is consistently lower in viral latency with higher p62-mediated autophagy levels , in which both HR and NHEJ pathways are deficient ( Fig 5A ) , supporting our hypothesis that p62-mediated autophagy functions as an alternative mechanism that enables these cells to resist to DNA damage . To check if autophagy has a role in regulation of the stability of these DNA repair proteins in virus-transformed cells , we inhibited endogenous autophagy activities with BafA1 . Results show that autophagy inhibition did not affect 53BP1 , but clearly decreases CHK1 and RAD51 protein levels that are associated with elevated endogenous p62 protein levels ( Fig 5B ) . To validate whether proteasome also mediates degradation of CHK1 and RAD51 in virus-transformed cells , we used the proteasome inhibitor MG132 to treat these cells that express high levels of endogenous p62 . As expected , our results show that MG132 treatment remarkably increases the protein levels of CHK1 and RAD51 ( Fig 5C , left panel ) , confirming that CHK1 and RAD51 protein stability is controlled in a proteasome-dependent manner in virus-transformed cells . In addition to its ability to inhibit proteasomal activity , MG132 is capable of inducing autophagy in various cancer cells [51] . Our results show that MG132 also increases the LC3b cleavage product LC3b-II in virus-transformed cells , and dramatically reduces p62 protein levels ( Fig 5C , left panel ) . To further validate the role of autophagy-p62 interplay in proteasomal degradation of CHK1 and RAD51 , we pre-treated the cells with BafA1 to inhibit autophagy before inhibition of proteasome activity . Results show that MG132 failed to cause CHK1 and RAD51 accumulation after p62 restoration by autophagy inhibition ( Fig 5C , right panel ) . These observations are in line with the previous report showing that CHK1 and RAD51 accumulation is attributable to p62 depletion resulting from robust autophagy induction [18] . Together , our results indicate that the CHK1 and RAD51 protein levels reversely correlate with the p62 levels in viral latency , implying a role of the autophagy-p62 interplay in negative regulation of their proteasome-mediated stability . However , the accumulation of CHK1 and RAD51 and decrease of p62 after MG132 treatment did not mitigate DNA damage; instead , DNA damage is strikingly increased ( Fig 5C ) . This observation can be explained by the fact that proteasome function is required for DNA damage repair [52] . Next , we sought to evaluate whether transient expression of p62 regulates DNA repair protein stability . To this end , we transfected the expression plasmids harboring HA-p62 or pcDNA4 vector control into BJAB , P3HR1 , and CEM cells , which express low levels of endogenous p62 . Then , we analyzed CHK1 , RAD51 , and 53BP1 . Surprisingly , results show that exogenic expression of p62 did not affect the protein levels of CHK1 and RAD51 , and did not induce LC3b cleavage either ( Fig 5D , left panel ) . By checking total p62 expression , we found that transfection of p62 expression plasmids did not evidently increase p62 levels , likely due to relatively low transfection efficiency of B cells . To address this issue , we transfected these cells with more p62 plasmids , and results show that a greater p62 expression is able to downregulate these proteins ( Fig 5D , right panel ) . We further employed two p62 mutants , with p62 ( 4A ) constitutively localizing in the cytoplasm and p62 ( 2A/1E ) mainly localizing in the nucleus , since a recent report shows that nuclear accumulation of p62 responding to autophagy inhibition is required for its degradation of CHK1 and RAD51 [18] . As expected , the nucleus-localizing mutant p62 ( 2A/1E ) , but not the cytoplasm-localizing mutant p62 ( 4A ) , remarkably decreases CHK1 and RAD51 protein levels ( Fig 5D , right panel ) . Together with Fig 5B and 5C , these results suggest that p62-mediated destabilization of DNA repair proteins is dependent on its accruing from autophagy inhibition , which we show results in p62 nuclear translocation ( Fig 4D ) . To further confirm the conditional role of p62 in downregulation of the DNA repair proteins , we depleted p62 expression in IB4 cells using shRNA-mediated gene knockdown . Results show that p62 depletion reduces autophagy activity , as shown by decreased autophagy flux ( Fig 5F ) , consistent with the notion that p62 is required for endogenous autophagy induction . However , the protein levels of CHK1 , RAD51 , and 53BP1 have no consistent and apparent changes in p62-depleted cells ( Fig 5E and 5G ) . Additional inhibition of the residual autophagy activities , or treatment with MG132 , which induces autophagy ( Fig 5C ) , also did not cause apparent difference on their levels in p62-depleted cells versus control cells ( Fig 5E ) . Considering that the majority of p62 is spontaneously located in the cytoplasm of virus-transformed cells ( Fig 4D ) , shRNA-mediated depletion might have minor effect on nuclear p62 . Thus , these results are indeed consistent with our findings and a recent report that nuclear localization of p62 is required for destabilization of DNA repair proteins [18] . Collectively , these results demonstrate that p62 accumulation in the nucleus in response to autophagy inhibition promotes proteasomal degradation of RAD51 and CHK1 in virus-transformed cells . These results also indicate that a fine balance of p62 and autophagy levels is required to confine endogenous p62 in the cytoplasm of virus-transformed cells . Further study is required to determine how autophagy inhibition causes p62 accumulation in the nucleus . It has been shown that p62 inhibits both HR and NHEJ [18] , through its physical interaction with RNF168 , which mediates histone ubiquitination that is prelude to activation of both HR and NHEJ DSB repair mechanisms [19] . Consistently , our confocal microscopy results show that RNF168 co-localizes with endogenous γH2AX DNA damage foci in IB4 cells . More importantly , shRNA-mediated p62 depletion significantly increases RNF168-γH2AX foci ( Fig 6A and 6B ) . Further , using the anti-ubiquitinated proteins antibody FK2 , we show that shRNA-mediated p62 depletion significantly increases ubiquitination and FK2-γH2AX foci in the nucleus ( Fig 6C and 6D ) . We further validated the interaction of endogenous p62 with RNF168 by immunoprecipitation ( IP ) in virus-transformed cells treated with BafA1 ( Fig 6E ) , and the increased histone H3 ubiquitination due to p62 depletion in cells treated with ionomycin ( Fig 6F ) . Our IP assays failed to detect definite p62-RNF168 interaction and H3 ubiquitination in these cells without treatments . In conclusion , these results indicate that p62 depletion promotes chromatin ubiquitination and RNF168-mediated DNA repair mechanisms .
In this study , we provide several lines of evidence that support a crucial role for p62-mediated autophagy in regulation of DDR in oncogenic virus latent infection . First , p62 is upregulated by ROS that correlates with the activity of the Keap1-NRF2 pathway , and considerable levels of p62-mediated selective autophagy are constitutively induced in this setting . Second , inhibition of autophagy in virus-transformed cells exacerbates ROS-induced DNA damage , and destabilizes the DNA repair proteins RAD51 and CHK1 in a manner depending on p62 accumulation in the nucleus; in contrast , excess autophagy induction promotes accumulation of the DNA repair proteins CHK1 and RAD51 that is associated with p62 degradation . Third , shRNA-mediated p62 depletion promotes RNF168-mediated chromatin ubiquitination and DNA repair in EBV latency . These findings have defined a crucial role for p62-mediated autophagy in regulation of DDR in viral latency ( Fig 7 ) . The p62-autophagy interplay is well balanced and controlled in diverse contexts , with cancer and aging being two representative systems [4–6 , 53] . Loss of this balance by exogenic or endogenous stresses may result in different impacts on DDR . Pharmaceutical inhibition of autophagy , or spontaneous autophagy deficiency during aging , chronic inflammation , or neurodegeneration , leads to p62 accumulation , consequently attenuating DNA repair that accounts for the etiology of age-related disorders [54 , 55] . In contrast , substantial enhancement of basal levels of autophagy in cancer cells by anticancer chemotherapeutic drugs or by radiation therapy promotes p62 degradation , and consequently confers these cells resistance to DNA damage-induced cell death [23 , 56 , 57] . Consistent with our findings , oncogenic viruses , including EBV , are known to inhibit ROS and autophagy at the early stage of lytic infection for optimal replication and oncogenic transformation [58–60] , but induce ROS-mediated autophagy in their latency to suppress replication and support oncogenic survival [26 , 27 , 32 , 38 , 61–67] . Moreover , our results indicate that the basal levels of p62-mediated autophagy are distinctly regulated in different EBV latency programs . ROS are produced separately by the EBV products LMP1 , EBNA1/2 , and EBERs , amongst which LMP1 induces predominant ROS [68–72] . In consistent , our results show that EBV type III latency produces a greater level of ROS compared with type I latency ( Fig 1A ) . Thus , cells with type III latency express higher endogenous levels of p62-mediated autophagy ( Fig 1B ) , which are required to overcome the higher risk of DNA damage in response to endogenous higher oxidative stress and replication stress to support their aggressive proliferation , and growth and survival demands . As such , a higher level of p62-mediated autophagy also confers these cells greater resistance to DNA damage in response to drug treatments . The type III latency cell line P3HR1 , which was derived from the parental JiJoye but lacks LMP1 expression , resembles type I latency cell lines in ROS production , and expression of p62 and autophagy ( Fig 1 ) , and DNA repair proteins ( Fig 5 ) , further supporting that LMP1 contributes the majority to these events . Surprisingly , our results consistently show that , in contrast to p62 elimination by massive autophagy , p62 depletion by shRNA potentiates , but not alleviates , DNA damage . There are a few possibilities to explain this paradox . First , p62 depletion impairs p62-mediated selective autophagy that is resident in the cytoplasm ( Fig 2E ) and required for maintaining the ability of the cell to resist to DNA damage ( Fig 5E and 5F ) ; second , other p62-mediated , autophagy-independent , DNA damage-protecting functions are abrogated after shRNA-mediated p62 depletion and consequently DNA damage is accelerated , given the fact that p62 is a multifunctional protein [3] . Investigation of these potential p62-mediated but autophagy-independent functions in viral latency and oncogenesis is underway . In fact , p62 depletion resulting in worsen DNA damage is coincident with its role as a tumor promoter , which is induced by Ras that accounts for at least 25% of human cancers [73] . p62 overexpression in hepatocellular carcinoma ( HCC ) predicts poor prognosis [74] . Based on the observations from us and other relevant studies , we propose that p62 plays a dichotomous role in DDR , depending on the presence or absence of autophagy that determines the p62 protein level and subcellular localization . Higher levels of nuclear p62 resulting from defective autophagy inhibit DNA repair and therefore perturb genomic instability that facilitates tumor initiation [75] . In line with our findings , it has been reported that p62 ablation decreases tumorigenesis in mouse models with defective autophagy [73] . A recent report has also shown that autophagy resulting from telomere shortening during replicative crisis protects genomic stability , and acts as a suppressor of tumor initiation [76] . In contrast , considerable levels of p62 in cancer cells promote DNA repair by mediating selective autophagy activation in the cytoplasm , and consequently confer these cancer cells resistance to DNA damage . p62 is upregulated at considerable levels in different cancer cells , including breast and prostate cancers , where it is required for induction of selective autophagy to support cancer cell metabolism and survival [74 , 77 , 78] . Regarding the mechanisms underneath deregulation of DDR by the p62-autophagy interplay , our results indicate that autophagy inhibition promotes proteasomal degradation of RAD51 and CHK1 in a manner depending on p62 accumulation in the nucleus ( Fig 5 ) , and that p62 depletion promotes RNF168-mediated DDR ( Fig 6 ) . Our results show that the majority of endogenous p62 is tethered to autophagosomes in the cytoplasm ( Fig 2E ) , but inhibition of DNA repair requires p62 in the nucleus [18] . Consistently , we show that autophagy inhibition promotes p62 nuclear translocation ( Fig 4D ) , although the mechanism remains to be disclosed . Thus , our results define a new role for p62-mediated autophagy in preventing DNA damage by confining p62 in the cytoplasm . In conclusion , p62-mediated selective autophagy not only confers invulnerability to DNA damage , but also at least partially contributes to the deficiency of traditional DDR mechanisms , in virus-transformed cells . In this regard , it is to our understanding that an oncogenic virus gains a dual benefit by invoking p62-mediated autophagy: one facet of p62-mediated autophagy endows its host cell with ability to resist DNA damage to support cell survival; the other facet hijacks the traditional DDR mechanisms in the host cell to facilitate genomic instability that promotes accumulation of oncogenic mutations . Although p62 was reported to promote NHEJ by inducing 53BP1 expression through the Keap1-NRF2 pathway that requests p62 S349 phosphorylation [50 , 79] , our results show that p62 reversely correlates with 53BP1 in viral latency ( Fig 5A ) , and that p62 accumulation failed to regulate 53BP1 levels ( Fig 5B ) . Thus , p62 does not regulate 53BP1 in our system , and how 53BP1 is downregulated in virus-transformed cells is worthy of further examination . Rather , p62 interacts with RNF168 in response to autophagy inhibition , and consequently inhibits RNF168-mediated chromatin ubiquitination in viral latency ( Fig 6 ) . In this regard , our findings are consistent with a recent study [18] , which has shown that p62 can impede both HR and NHEJ through its interaction with RNF168 , given that RNF168-mediated histone ubiquitination is prerequisite for activation of all DSB repair mechanisms [19] . Although these dsDNA repair pathways are compromised in EBV latency , we realize that they still have considerable levels of activity , which render successful CRISPR-mediated genome editing in these cells [80–83] . Viruses have evolved diverse strategies to hijack host traditional DDR machinery during their chronic infections to perturb genomic integrity , including their ability to deregulate the p62-autophagy balance , which we believe only makes partial contribution . In fact , our group has recently shown that traditional DDR mechanisms are also deficient in aging T cells in chronic HCV infection [84 , 85] , at least partially attributable to endogenous p62 accruing from deficient autophagy in these cells . p62 also inhibits DDR through other mechanisms that have not been fully elucidated . For example , nuclear p62 interacts with and inhibits PML nuclear bodies , which are involved in DNA repair [86 , 87] . Moreover , other autophagy mechanisms , such as chaperone-mediated autophagy [88] , also participate in DDR , by regulating stability of DDR-related proteins such as HP1α and CHK1 [89 , 90] , and by regulating p62-dependent or -independent cellular functions [91] . It is of great interest to investigate these potential mechanisms and their coupled cellular mechanisms in virus-mediated oncogenesis . Endogenous ROS/RNS trigger signal cascades that activate both DDR and autophagy programs . Unrepaired damaged DNA can serve as a major source of genomic instability particularly in cancer cells where traditional DDR and cell death pathways are compromised . Thus , cancer cells heavily rely on autophagy , not only to replenish their deficient DNA repair mechanisms , but also to corroborate their higher metabolic demand than normal cells do . Therefore , cancer cells are more vulnerable to autophagy inhibition , providing viable opportunities for therapeutic strategy by targeting autophagy , in particular in combination with another cellular mechanism that is specifically coupled with autophagy in a given cancer context for improving clinical efficacy and specificity [3 , 20 , 92] .
SavI , SavIII , P3HR1 and JiJoye are human B cell lines derived from EBV-positive Burkitt’s lymphoma ( BL ) patients . P3HR1 was derived from JiJoye but does not express LMP1 due to lacking the entire EBNA2 ORF in the viral genome [93] . BJAB is an EBV-negative BL line . The lymphoblastic cell line ( LCL ) IB4 was derived from umbilical cord B-lymphocytes latently infected with EBV in vitro . KR4 is a LCL with gamma irradiation resistance [94] . LCL45 is a newly established LCL by in vitro transforming primary B cells of a healthy adult peripheral blood with the EBV strain B95 . 8 . CEM is a HTLV1-negative , EBV-negative T cell line derived from acute leukemia , and MT4 is a HTLV1-transformed CD4+ T cell line derived from umbilical cord blood lymphocytes . B and T cell lines are cultured with RPMI1640 medium plus 10% FBS and antibiotics . All cell culture supplies were purchased from Life Technologies . p62 ( D-3 ) , LIMD1 ( H-4 ) , and histone H3 ( 1G1 ) mouse monoclonal antibodies were from Santa Cruz for immunoprecipitation or immunoblotting . p62-Alexa Fluor 488 mouse antibody for flow cytometry was from Millipore or R&D Systems . Phospho-p62 ( S403 ) , NRF2 ( D1Z9C ) mouse monoclonal antibody , and HRP-coupled secondary antibodies were from Cell Signaling Technologies . RNF168 rabbit polyclonal antibody and the FK2 mouse monoclonal antibody that recognizes K29- , K48- , and K63-linked polyubiquitin chains and monoubiquitin conjugation but not free ubiquitin for immunofluorescence were from Millipore . The RNF168 rabbit polyclonal antibody for IP was from Proteintech Group Inc . RNF168 sheep polyclonal , and LC3b and histone H3 rabbit polyclonal antibodies were from Invitrogen . RAD51 rabbit polyclonal and CHK1 ( G-4 ) mouse monoclonal antibodies were from Abcam and Santa Cruz , respectively . The γH2AX ( S139 ) mouse and rabbit antibodies were from BioLegend and Cell Signaling Technologies , respectively . Mouse HA ( clone HA-7 ) and Flag ( clone M2 ) antibodies were from Sigma . Secondary antibodies coupled with FITC , Alexa Fluor , APC , PE , Cy5 , or PerCP and human anti-CD19-PE were from BioLegend , BD Biosciences , Invitrogen , or eBioscience . HA-p62 cloned in pcDNA4 was a gift from Yu-Ying He [95] , and Flag-p62 mutants were gifts from Dr . Ying Zhao [18] . Flag-p62 ( R186A/K187A/K264A/R265A ) ( designated as p62 ( 4A ) ) cannot localize in the nucleus and Flag-p62 ( K7A/D69A/I314E ) ( designated as p62 ( 2A/1E ) ) mainly localizes in the nucleus [18] . CellRox Green , MG132 , chloroquine , and doxorubicin HCl , were purchased from Invitrogen , EMD Millipore , MP Biomedicals , and UBPBio , respectively . Ionomycin calcium salt , N-acetylcysteine amide , bafilomycin A1 , doxycycline , and mouse and rabbit IgG were purchased from Sigma . H2O2 was from Santa Cruz . A set of p62 shRNA cloned in pTRIPz/Puro comprising three individual p62 shRNA and a scramble control plasmids were purchased from Dharmacon . We selected two of them for this study and the targeting sequences on the human p62/SQSTM1 gene are: shRNA#1: 5’-TCTCTTTAATGTAGATTCG-3’ and shRNA#2: 5’- TCAGGAAATTCACACTCGG-3’ . Lentiviral packing , preparation , infection , and selection of stable cells by puromycin ( 0 . 5 µg/ml ) were performed as detailed in our previous publication [37] . shRNA expression was induced by 1 µg/ml doxycycline for 3 days , and then cells ( expressing red fluorescence ) were subjected to a second selection by FACS on a FACS/Aria Fusion Cell Sorter ( BD Biosciences ) . Stable transfectants were maintained in complete medium plus 1 . 0 µg/ml puromycin . For transfection of BJAB , P3HR1 , and CEM , GenePulser XCell ( Bio-Rad ) was used with optimal programs . These representative cell lines were chosen in that they are easier to be transfected with this technique , compared with other B cell lines . Cells were fixed in 2% paraformaldehyde ( PFA ) for 20 min , permeabilized with 0 . 3% Triton X-100 in phosphate-buffered saline ( PBS ) for 10 min , blocked with 5% bovine serum albumin ( BSA ) in PBS for 1 h , and then incubated with indicated primary antibodies at 4°C overnight . Cells were washed with PBS with 0 . 1% Tween-20 for three times , and then incubated with corresponding secondary antibodies coupled with FITC , Alexa Fluor , APC , PE , Cy5 , or PerCP , at room temperature for 1 h . Cells were then washed and mounted with DAPI Fluoromount-G ( SouthernBiotech , Birmingham , AL ) . Images were acquired with a confocal laser-scanning inverted microscope ( Leica Confocal , Model TCS sp8 , Germany ) . To assess endogenous p62-RNF168 interaction , 1X107 cells for each sample were lysed with NP40 lysis buffer ( 150 mM NaCl , 1% NP-40 , 50 mM Tris-pH 8 . 0 , plus protease inhibitors ) , and cell lysates were subjected to immunoprecipitation ( IP ) with 1 . 5 µg anti-RNF168 for overnight , and then incubated with 40 µl Protein A/G beads ( Santa Cruz ) for 1 h . After three washes , proteins on beads were denatured in 1% SDS before subjected to immunoblotting ( IB ) . IB was carried out with indicated antibodies and signals were detected with an enhanced chemiluminescence ( ECL ) kit following the manufacturer’s protocol ( Amersham Pharmacia Biotech ) . For H3 ubiquitination assay , endogenous H3 was pulled down with an H3 antibody in denaturing IP , as detailed in our publication [96] . Total RNA was isolated from tested cells using an RNeasy Mini kit according to the manufacturer's protocols ( Qiagen ) . The eluted RNA was subjected to reverse transcriptase reactions , which were performed with the use of GoScript RT kit following the manufacturer's instructions ( Promega ) . Quantitative real-time PCR ( qPCR ) was performed with the use of SYBR Green ( Applied Biosystems ) , on a CFX96 Real-time PCR Detection System ( Bio-Rad ) . All reactions were run in triplicates . Mean cycle threshold ( Ct ) values were normalized to 18s rRNA , yielding a normalized Ct ( ΔCt ) . ΔΔCt value was calculated by subtracting respective control from the ΔCt , and expression level was then calculated by 2 raised to the power of respective -ΔΔCt value . The averages of 2^ ( -ΔΔCt ) in the control samples were set to 1 or 100% . Results are the average ± standard error ( SE ) of triplicates for each sample . Primers for real-time qPCR are as follows: p62: F: 5'-CAGGCGCACTACCGCGATG-3' and R: 5'-ACACAAGTCGTAGTCTGGGCAGAC-3' . Keap1: F: 5’-CCATGGGCGAGAAGTGTGTCC-3’; R: 5'-ACAGGTTGAAGAACTCCTCTTGCTTG-3’ . 18s rRNA: F: 5’-GGCCCTGTAATTGGAATGAGTC-3’ and R: 5’-CCAAGATCCAACTACGAGCTT-3’ . Samples were fixed with 2% PFA for 20 min at RT , then wash with flow buffer ( eBioscience ) . Samples were then incubated with PE-conjugated anti-human CD19 antibody ( eBioscience ) or isotype controls for 20 min at RT , then wash with flow buffer , followed by incubation with p62-Alexa Fluor 488 antibody for 60 min at RT . Samples were then washed with flow buffer , and analyzed with BD C6 plus flow cytometer . For intracellular ROS measurement , 1X106 cells in 500 µl medium per well were seeded in 24-well plates , and cultured overnight . 1 µl CellROX Green Reagent ( Invitrogen ) was added to each well and incubated for 30 min . Cells were then washed 3 times with PBS , and fixed with 2% PFA for 20min at RT , followed by extensive washes and then incubated with PE-conjugated anti-human CD19 antibody ( eBioscience ) for 20min at RT , before subjected to flow cytometry . Apoptosis was quantified using flow cytometry as detailed in our previous publication [37] , for Annex V binding ( BD Biosciences ) and 7-Aminoactinomycin D ( 7-AAD ) expression ( eBioscience ) . Caspase 3 activity was evaluated by Western blotting . Unpaired , two-tailed student t tests were executed using GraphPad Prism ( version 6 ) to determine the differences between two data sets obtained from three independent experiments . p<0 . 05 ( * ) and p<0 . 01 ( ** ) , and p<0 . 001 ( *** ) were considered significant . Data are expressed as mean ± standard error ( SE ) of duplicate or triplicate samples , and representative results from at least three independent repeats with similar results are shown . | Reactive oxygen/nitrogen species ( ROS/RNS ) can induce both DNA damage response ( DDR ) and selective autophagy , which play crucial roles in cancer development . The selective autophagy receptor and ubiquitin ( Ub ) sensor p62 links their crosstalk . However , p62-mediated selective autophagy and its interplay with DDR have not been investigated in latent infection of oncogenic viruses including Epstein-Barr Virus ( EBV ) . In this study , we provide evidence that p62-mediated selective autophagy is constitutively induced in virus-transformed cells , and that its inhibition exacerbates ROS-induced DNA damage , and promotes proteasomal degradation of CHK1 and RAD51 in a manner depending on p62 accumulation in the nucleus . However , rigorous autophagy induction results in accumulation of DNA repair proteins CHK1 and RAD51 , and p62 degradation . Further , transient expression of a constitutive nucleus-localizing mutant of p62 recaptures the effects of autophagy inhibition on CHK1 and RAD51 protein stability . These findings support the claim that p62 accumulation in the nucleus in response to autophagy inhibition promotes proteasome-mediated CHK1 and RAD51 protein instability . However , small hairpin RNA ( shRNA ) -mediated p62 depletion did not affect CHK1 and RAD51 protein levels; rather , shRNA-mediated p62 depletion activates RNF168-dependent DNA repair mechanisms . Our results have unveiled a pivotal role for p62-mediated selective autophagy in regulation of DDR by overriding traditional DDR mechanisms in the setting of oncogenic virus latent infection , and provide a novel insight into the etiology of viral cancers . | [
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"organisms... | 2019 | p62-mediated Selective autophagy endows virus-transformed cells with insusceptibility to DNA damage under oxidative stress |
Hepatitis C virus ( HCV ) particles closely mimic human very-low-density lipoproteins ( VLDL ) to evade humoral immunity and to facilitate cell entry . However , the principles that govern HCV association with VLDL components are poorly defined . Using an siRNA screen , we identified ABHD5 ( α/β hydrolase domain containing protein 5 , also known as CGI-58 ) as a new host factor promoting both virus assembly and release . ABHD5 associated with lipid droplets and triggered their hydrolysis . Importantly , ABHD5 Chanarin-Dorfman syndrome mutants responsible for a rare lipid storage disorder in humans were mislocalised , and unable to consume lipid droplets or support HCV production . Additional ABHD5 mutagenesis revealed a novel tribasic motif that does not influence subcellular localization but determines both ABHD5 lipolytic and proviral properties . These results indicate that HCV taps into the lipid droplet triglyceride reservoir usurping ABHD5 lipase cofactor function . They also suggest that the resulting lipid flux , normally devoted to VLDL synthesis , also participates in the assembly and release of the HCV lipo-viro-particle . Altogether , our study provides the first association between the Chanarin-Dorfman syndrome protein and an infectious disease and sheds light on the hepatic manifestations of this rare genetic disorder as well as on HCV morphogenesis .
HCV chronically infects around 146 million people worldwide [1] and the associated cases of end-stage liver disease constitute a major indication for liver transplantation [2] . A hallmark of chronic hepatitis C is the dysregulation of the host lipid metabolism , notably with the occurrence of liver steatosis in 40% of chronically infected patients in absence of any other predisposition factor [3] . Curiously , the virion strikingly resembles very low density lipoproteins ( VLDL ) with its unusually low buoyant density , its association with apolipoproteins and peculiar lipid content [4–8] . This mimicry enables the use of lipid receptors in the virus entry process and facilitates the homing to liver cells as well as antibody escape [9] . It also suggested the involvement of the VLDL synthesis pathway in HCV morphogenesis . Indeed , in the course of virus assembly HCV modulates lipid droplets ( LD ) , the principal cellular lipid storage organelles , by deposition of viral components and manipulation of LD motility [10 , 11] . This process is probably initiated by the viral core protein , which uses DGAT1 to translocate from the ER membrane onto the lipid droplet ( LD ) surface [12] . Assembly complexes are then built in the vicinity of the replication complexes and straddle ER membrane and lipid droplets [10] . Importantly , the VLDL-associated apolipoprotein ApoE is a critical factor for HCV assembly , while other liver-specific components of the VLDL machinery might be involved but can be bypassed [13 , 14] . However , the mechanisms that mediate the loading of HCV particles with apolipoproteins and VLDL lipids are poorly understood . ABHD5 is the causative gene for the Chanarin-Dorfman syndrome ( CDS ) , a neutral lipid storage disorder associated with ichtyosis [15] . This very rare autosomal recessive disease affects multiple organs including the liver , with frequent cases of steatosis or hepatomegaly [16] . Despite its homology with other lipid hydrolases , ABHD5 lacks a direct lipase activity . However , two functions have been attributed to the protein: ( i ) a putative lysophosphatidic acid acyltransferase ( LPAAT ) activity , and ( ii ) a demonstrated lipase cofactor activity [15] . ABHD5 binds to lipid droplets and promotes the triglyceride mobilisation from their LD storage by activating hydrolysis in response to lipolytic stimulation [15] . Although mainly studied in adipocytes , ABHD5 expression and lipase cofactor activity seem to be ubiquitous . The fate of the mobilised lipids however depends on the tissue [17] . In hepatocytes , they may engage in the VLDL synthesis pathway: according to the current model , the hydrolyzed lipids are re-esterified at the ER membrane , forming luminal LDs that fuse with ApoB-positive VLDL precursors before secretion [18–20] . Using a rational siRNA screen , we identified ABHD5 as a new host factor participating in HCV morphogenesis . Moreover , we report that mutants of this lipase cofactor , responsible for the Chanarin-Dorfman syndrome , were unable to support virus production . We further expand on the molecular biology of ABHD5 with the description of a new tribasic motif that specifically mediates its lipid droplet consumption function . Finally , our data specifically link ABHD5 ability to trigger a lipid flux with its proviral effect on both HCV assembly and release .
To identify novel host factors involved in the loading of lipoproteins onto HCV particles and given the close relationship between the HCV replication cycle and host lipid metabolism we specifically screened host genes that have previously been implicated in modulating lipid droplet function/morphology and lipoprotein secretion . First of all , a fraction of our candidates were selected from published genome-wide siRNA screens identifying host factors involved in lipid droplet formation , maintenance and dynamics [21 , 22] . Among the extensive lists of genes reported by these authors , we eliminated housekeeping genes and shortlisted 11 genes ( PSCM3 , UBE2D3 , DYNLRB1 , YWHAE , PLA2G6 , ARF1 , PCYT1A , CHKA , FASN , SEC22B and BSCL2 ) representing all the different lipid droplet phenotypes observed by these authors upon gene knockdown . A second category of candidates ( RAB18 , CES1 , CES3 , ABHD5 , FABP1 , PLD1 , PLIN2 ( ADRP ) , PLIN3 ( TIP47 ) ) was selected upon their specific functional involvement in host lipid homeostasis or lipoprotein metabolism [23–27] . Among these , FABP1 is an abundant cytosolic lipid chaperone transporting fatty acids to diverse cell organelles , including the lipid droplets [26] . PLD1 has been involved in the formation of lipid droplets [28] and , in collaboration with ARF1 , of VLDL [29] . ADRP and TIP47 belong to the lipid droplet-associated PAT protein family ( perilipin , ADRP , TIP47 ) , which regulates the dynamics of lipid storage and release from these organelles [24] . Both ABHD5 and RAB18 have been involved in the recruitment of the triglycerides from cytosolic LDs: RAB18 by competing with ADRP and inducing the LD apposition to the ER [23] , and ABHD5 more directly by activating a lipase at the LD surface [30] . CES1 ( also called TGH , triglyceride hydrolase ) and CES3 are two ER-resident lipid hydrolases [31] . Interestingly , CES1 was found in the luminal LD proteome [32] and may participate in the second step of the VLDL synthesis [31] . The screen was controlled with inclusion of known HCV entry ( CD81 [33] ) , replication ( PI4KIIIα [34] ) and assembly factors ( APOE [35 , 36] ) . A functional view of our candidate selection is depicted in S1 Fig and the screening procedure is illustrated in S2 Fig Overall , 11 genes , including the PI4KIIIα and CD81 controls , behaved as dependency factors for HCV entry , translation or replication ( Fig 1a and S1 Table ) . Moreover , 4 genes acted as restriction factors for HCV assembly and release , while 7 others , including the APOE positive control , were potential cofactors for HCV production ( Fig 1b and S1 Table ) . Three primary hits were specifically contributing to HCV progeny virion production , i . e . their knockdown did not affect HCV entry and replication but significantly diminished virus assembly and release: FABP1 , PLA2G6 and ABHD5 . Because of its association with a human genetic disease and its interesting dual role in lipid droplet and lipoprotein metabolism , we focused here on ABHD5 . Individual siRNAs and shRNAs against ABHD5 confirmed that ABHD5 expression level regulated the efficiency of HCV production while having no effect on cell viability ( S2 Fig ) and no or minor effects on HCV entry or RNA replication ( Fig 1c–1g ) . Over 30 different mutations or deletions of ABHD5 [15 , 16] or its promoter [37] were described in 52 patients and associated with the Chanarin-Dorfmann syndrome , a lipid storage disorder [15] . Thus , we explored whether such variants would function as HCV assembly co-factors ( Fig 2 ) . The ABHD5 Q130P and E260K CDS mutants were chosen for their broader characterisation and minimal sequence alteration [17 , 38] . These mutations disrupt the capacity of the protein to bind lipid droplets and ADRP [39] as well as its lipase cofactor activity [30] located in the α/β hydrolase domain of ABHD5 ( Fig 2a ) . Western blot analyses confirmed the knockdown and the ectopic expression of wild-type or mutant ABHD5 proteins through the experiment ( Fig 2b ) . Consistently with Fig 1 , ABHD5 expression only had mild effects on HCV entry and replication ( Fig 2c ) . Importantly , expression of the shRNA-resistant wild-type ABHD5 protein , but not of the two Chanarin-Dorfman mutants restored virus production in ABHD5-silenced cells ( Fig 2d ) . Overexpression of the wild-type protein over the endogenous expression level promoted virus production , which was not the case for the mutants ( Fig 2d ) . Collectively these results validate ABHD5 as novel HCV assembly co-factor and indicate that two variants of the protein involved in the Chanarin-Dorfman syndrome do not support HCV assembly . We next aimed to specify the role of ABHD5 in HCV production . Upon manipulation of ABHD5 expression , secreted core amounts directly mirrored the released infectivity ( compare Fig 2d and 2e ) , indicating that the virion specific infectivity was unchanged . Secondly , we compared the intra- and extracellular infectivities upon ABHD5 up- or down-regulation ( Fig 2f ) . Knockdown of ApoE was used as a control as a specific assembly factor for HCV [13] . ABHD5 and ApoE knockdown resulted in similar reductions in intracellular infectivity , demonstrating the role of ABHD5 in virus assembly . However , contrary to ApoE , the extracellular infectivity after ABHD5 knockdown or overexpression was more affected than the intracellular infectivity , pointing at an additional role of ABHD5 in virion release . We next studied the localisation of ABHD5 and of the CDS mutants in our HCV permissive Huh-7-derived Lunet N hCD81 cell line by lentiviral transduction of HA-tagged ABHD5 constructs . Importantly , the HA-tagged protein shared a similar localisation pattern with the overexpressed untagged ABHD5 protein detected with an anti-ABHD5 antibody ( S3 Fig ) . We did not observe a colocalisation with the ER marker calnexin , nor with early endosomes or lysosomes ( Fig 3 ) . However , a fraction of ABHD5 concentrated at the surface of the lipid droplets ( marked with the Bodipy neutral lipid dye or the peripheral LD-associated protein ADRP ) , while another colocalised with the trans-Golgi marker p230 . As already reported [18] , the LD-associated ABHD5 population increased upon oleic acid treatment of the cells ( Fig 3 ) . In contrast to the wild-type protein , the CDS mutants showed a diffuse localisation through the cell , and a nuclear accumulation ( Fig 4 and S4 Fig ) . Indeed , their LD-association was strongly reduced , even upon oleic acid induction . Nevertheless , their Golgi accumulation was at least partially preserved . The defect in LD- and trans-Golgi-association of the CDS mutants was further evidenced by their decreased Pearson’s correlation coefficient with both lipid droplet markers ( Bodipy and ADRP , Fig 5a and 5b ) and with p230 ( Fig 5c ) , even when selectively the cytoplasmic fraction of ABHD5 was considered ( Fig 5a and 5c ) . To complement this analysis , we quantified ABHD5 subcellular distribution using an automated imaging-based cellular segmentation method as depicted in Fig 5d . This approach quantifies the total signal intensity of ABHD5 within the given compartments and revealed a ca . two-fold nuclear accumulation of both CDS mutants compared to the wild-type as well as a slight reduction of the trans-Golgi-associated population ( Fig 5e , right panel ) . ABHD5 abundance at the lipid droplet surface increased ca . 2 . 8-fold upon oleic acid treatment for the wild-type but only 1 . 3 to 1 . 5-fold for the CDS mutants . Consequently , the lipid droplet-associated population was reduced for the CDS mutants as compared to the wild-type in lipid-stimulated cells . Note that overexpression of wild-type ABHD5 but not the mutants reduced the size of the lipid droplet compartment ( see below and Fig 6 ) likely introducing a calculation bias that masked this effect in the basal conditions . To overcome this sampling bias , we calculated the concentration of the ABHD5 signal intensity in the different compartments as compared to the mean ABHD5 signal intensity in the whole cell ( Fig 5f ) . Importantly , only the wild-type protein was enriched ( 1 . 8–1 . 9 fold ) at the lipid droplet surface—both under basal and oleic acid treatment conditions . The lipid droplet and Golgi enrichment of ABHD5 is reminiscent of the sites for HCV assembly and release [10] . Consistently , ABHD5 colocalised with the HCV assembly machinery in HCV-infected cells ( S5–S7 Figs ) , in particular with core at the lipid droplet surface , with the host factor ApoE at the Golgi apparatus and with E2 and NS5A in the rest of the cytoplasm . This overlap was decreased or abolished for the CDS mutant Q130P . Finally , ABHD5 localisation and the aberrant distribution of the CDS mutants were confirmed in live cells ( S8 Fig and S1 and S2 Videos ) . Next , we tested the effect of ABHD5 expression on the intracellular triglyceride storage ( Fig 6 ) . Lipid droplet content was assessed by Bodipy staining and FACS ( Fig 6a , 6b and 6c ) or microscopy ( Fig 6d–6g ) . To circumvent the sample-to-sample staining variability , we implemented a mixed cell population assay: ABHD5-overexpressing or depleted cells were systematically mixed with mock-transduced cells and distinguished by ABHD5 staining with the HA epitope ( overexpression setup ) or by co-transduction of ABHD5 shRNA with an mRuby2-encoding construct ( knockdown setup ) . This experimental strategy is illustrated in S9 Fig FACS analysis readily distinguished the population of ABHD5-overexpressing cells from the mock-transduced cells ( HA+ vs . HA- cells , Fig 6a ) thus permitting a direct quantitative comparison of lipid droplet content ( Bodipy signal ) between these two cell populations . Overexpression of wild-type ABHD5 reduced the lipid droplet content of the cells by around 35% , while overexpression of the CDS mutants , even at similar expression levels , had no effect or a slight opposite effect on the lipid droplet content ( Fig 6a and 6b ) . Thus , wild-type but not mutant ABHD5 , seemed to consume lipid droplets . These results were reinforced by a significant increase in Bodipy staining upon ABHD5 knockdown ( Fig 6c , right panel ) . Of note , possible discrepancies between mRuby2 and shRNA expression ( transduced on separate vectors ) might underestimate the effects observed in this last assay . Fluorescence microscopy was used to explore ABHD5-mediated lipid droplet lipolysis at the single lipid droplet level . Each picture analysed comprised ABHD5-overexpressing cells ( detected with the HA epitope tag , highlighted with a yellow outline ) and mock-transduced cells ( internal control ) ( Fig 6d ) . The lipid droplet content of these two cell populations was quantified in an automated fashion . In line with the FACS results , cells overexpressing a functional ABHD5 protein showed a decrease in both the number ( Fig 6e ) and mean area ( Fig 6f ) of detectable lipid droplets , with a global shift of the lipid droplet pool towards small or undetectable lipid droplets ( Fig 6g ) . In contrast , overexpression of the Chanarin-Dorfman mutants had no effect on the cell lipid droplet content . To determine the requirements for ABHD5 LD consumption and its relevance for HCV assembly , we generated a panel of ABHD5 mutants targeting characterised or putative ABHD5 functional domains ( Fig 2a ) . These included ABHD5 predicted protein kinase A consensus sequence RKYSS [41] and C-terminal LPAAT motif . We further detected a potential nuclear localisation signal ( NLS ) corresponding to the ( P/R ) XXKR ( K/R ) motif ( see Methods ) and tested whether its mutation could prevent the nuclear accumulation and reverse the phenotype of the Q130P CDS mutant . Last , we reconstituted a typical lipase catalytic triad by restoring a serine residue in the lipase motif ( N153S ) . An alternative mutant ( NL153 , 154SF ) was designed to recreate the lipase motif of a plant homolog of ABHD5 , which is a weak hydrolase in vitro [17] . These latter mutants were constructed to assess whether a direct lipase activity of ABHD5 could be restored , bypass the requirement for the activation of a lipase to support LD hydrolysis and/or HCV production and therefore compensate for the CDS mutation . All variants were fused to a double HA tag as before . Expression of the mutants was confirmed by Western blot ( Fig 7a ) and their proviral function was tested in a knockdown complementation experiment ( Fig 7b ) . Briefly , reconstitution of a typical lipase motif ( N153S or NL153 , 154SF ) , single point mutation of the putative NLS ( K233A ) or targeting the putative phosphorylation sites ( SS237 , 238AA ) did not affect the properties of ABHD5 in this assay . More interestingly , the double mutation of the LPAAT motif abrogated ABHD5 proviral effect , while specific replacement of the histidine residue was tolerated to some extent . Triple mutation of the putative NLS did not restore the function of the CDS mutant ( Q130P ) . In fact , this mutation abrogated the proviral effect of parental ABHD5 suggesting that this tri-basic motif is critical for ABHD5´s role in HCV assembly and possibly also its natural function . Strikingly , the capacity of the ABHD5 mutants to support HCV production correlated perfectly with their lipolytic activity ( compare Fig 7b to 7c ) . In particular , restoration of the conserved lipase motif failed in reconstituting an active lipase . This correlation extended to the mutant subcellular localisation ( S10–S12 Figs ) : all functional mutants localised like wild-type ABHD5 , while all non-functional mutants spread as the CDS mutants and failed to associate with the lipid droplets . The single mutation of the LPAAT motif ( H327A ) showed an intermediate phenotype in HCV assembly , LD lipolysis and localisation pattern with partial nuclear accumulation ( Fig 7 and S10–S12 Figs ) thus further extending this direct correlation . The sole exception to this correlation was the KRK233-235AAA mutant of the tri-basic motif , a putative NLS , which did not support HCV assembly ( Fig 7b ) and LD lipolysis ( Fig 7c ) despite a subcellular localization comparable to parental ABHD5 ( Fig 7e and 7f ) . Note that this mutation of the basic stretch did not prevent the nuclear localisation of the CDS mutant ( Q130P ) ( S10–S12 Figs ) , arguing against its function as an NLS . This showed that lipid droplet accumulation was not sufficient for ABHD5 lipolytic or proviral activities . This is also the first description of an ABHD5 element that specifically determines the protein lipolytic activity without affecting the protein localisation . For this reason , we named this new motif the tribasic lipid droplet consumption motif ( TBLC ) . In summary , this panel of mutants suggested that the lipid droplet degradation triggered by ABHD5 was the supporting mechanism for ABHD5 proviral function and identified the TBLC motif as a crucial determinant in this process .
HCV usurps the host lipid metabolism at several steps of its replication cycle . Our rationale siRNA screen highlights and extends these intricate connections: out of the 19 gene candidates , 5 were specifically involved in HCV entry or replication , 7 in assembly or release and 3 in both early and late virus replication steps ( Fig 1 ) . To our knowledge , 9 of these factors have never been associated with HCV pro- or antiviral effects in cell culture ( ABHD5 , CES3 , FABP1 , PCYT1A , PLA2G6 , PLD1 , PSCM3 , SEC22B and YWHAE ) . Interestingly , the 3 hits specifically involved in HCV assembly and release ( PLA2G6 , ABHD5 and FABP1 ) gathered in the glycerolipid metabolism cluster in a bioinformatic analysis or , in the case of ABHD5 and FABP1 , have been reported to interact [42] ( S1 Fig ) . Of note , FABP1 is involved in the life cycle of Plasmodium falciparum [43] , a parasite sharing several other host factors with HCV ( e . g . CD81 and SR-BI ) . Moreover , PLA2G6 is a phospholipase related to PLA2G4 , which we previously reported as crucial for HCV assembly [44] . CHKA and PCYT1A were additional hits belonging to the same functional cluster and both take part in the synthesis pathway for phosphatidylcholine , a crucial constituent of membranes and VLDL particles [45] . This suggests that these factors might act cooperatively in a shared cellular and proviral function and that other HCV assembly cofactors might be found by further exploring this pathway . Collectively , these observations highlight the pivotal role of lipid remodelling processes for production of infectious HCV particles . One of the best hits of our screen was ABHD5 . ABHD5 knockdown reduced HCV production to levels similar to those achieved after downregulation of ApoE . This is quite remarkable since ApoE is a constituent of the virion involved both in virus assembly and entry , while ABHD5 likely does not have a structural role in the morphogenesis but rather acts via its lipase cofactor activity . ABHD5 concentrated together with HCV core protein at the lipid droplet surface ( Figs 3 and 5 ) . It promoted the lipid droplet consumption and HCV infectious virus production at the assembly and virus release steps . Interestingly , mutations of ABHD5 associated with the Chanarin-Dorfman syndrome prevented the protein association with lipid droplets ( Figs 4 , 5 and S3 ) , abrogated its lipolytic activity ( Fig 6 ) but also its proviral function ( Fig 2 ) . Consequently , the CDS patients might exhibit some resistance towards HCV infection; the rarity of the disease [37] however precludes epidemiological studies . Over our panel of mutants targeting different ABHD5 regions , the proviral and lipolytic functions of ABHD5 always correlated ( Fig 7 ) . Although the LPAAT motif was important in ABHD5 functions ( HCV assembly and LD-consumption ) , this is unlikely due to a genuine LPAAT activity as mutation of the putative LPAAT catalytic histidine residue only attenuated these functions ( Fig 7 ) . In hepatocytes , ABHD5-triggered lipolysis is thought to provide lipids for VLDL synthesis [46] . VLDL morphogenesis probably occurs in two stages [47]: ( i ) budding of a dense lipoprotein precursor by lipidation of ApoB at the ER membrane; and ( ii ) modification of this precursor by fusion with a luminal lipid droplet and acquisition of exchangeable apolipoproteins such as ApoE . The proposed role of ABHD5 in mobilising the LD triglycerides for the formation of the luminal lipid droplet [18] and the fact that HCV production does not require a complete VLDL synthesis pathway [13 , 14] but uses ABHD5 suggest that HCV hijacks the second step of the VLDL morphogenesis rather than the precursor biogenesis for assembly and release of infectious progeny [47] . HCV might bud together with luminal lipid droplets or interact ( and eventually fuse ) with them in the secretory pathway . This could be essential for assembly of infectious intracellular particles . The additional effect of ABHD5 on release efficiency could have several explanations: ( i ) ABHD5-mediated lipid loading of the virion might be a rate-limiting step and virions that complete this process too slowly might fail secretion , ( ii ) poorly infectious intracellular virions that have been incompletely loaded with lipids might be retained intracellularly due to strict quality controls , ( iii ) the extent of ABHD5-dependent virion lipid loading might regulate the virion scission event , which could be overcome artificially by the freeze and thaw procedure used to harvest intracellular viral particles; but one also cannot exclude that ( iv ) ABHD5 , possibly thanks to its Golgi fraction , plays an additional and independent role during the exit process . Overall , our results indicate that the key function of ABHD5 in HCV production is its lipase cofactor activity . The identity of the activated lipase however remains elusive . While ABHD5 was shown to activate ATGL in adipocytes [30] , this is still questioned in hepatocytes and a body of evidence suggests that ABHD5 cooperates with alternative or additional partners in the liver [16 , 17] . Importantly , we describe a new tribasic lipid droplet consumption motif ( TBLC ) that dissociates for the first time ABHD5 lipolytic function from its lipid droplet association [17 , 40] . Triple mutation of the TBLC motif ( KRK233-235AAA ) is likely to specifically abrogate ABHD5 interaction with his partner lipase and therefore this mutant will be a precious tool for the identification of ABHD5 effector lipase ( s ) in hepatocytes . In summary , our findings indicate that ABHD5 supports HCV production by triggering the mobilisation of the lipid droplet lipid stores for the assembly and release of infectious lipo-viro-particles . They shed light on host determinants of HCV and VLDL morphogenesis , on the role of ABHD5 in hepatocytes and the etiology of the liver dysfunctions observed in the Chanarin-Dorfman patients .
Lunet N hCD81 cells ( generated in our lab [49] ) ( hCD81 matches the primary sequence of human TAPA-1 , GenBank no . M33680 , except for one amino acid exchange ( K52M ) [48] ) have been described previously [49] . Lunet N hCD81 FLuc cells were derived from these Lunet N hCD81 cells by lentiviral gene transfer of a firefly luciferase ( FLuc ) gene using the pWPI-FLuc-BLR construct ( see below ) . These cells as well as Huh-7 . 5 [50] ( kindly provided by Charles Rice , Rockefeller University ) and HEK 293T cells [51] ( American Type Culture Collection , ATCC CRL-3612 ) were grown at 37°C and with 5% CO2 in Dulbecco’s modified Eagle’s medium ( DMEM; Invitrogen ) supplemented with 2 mM L-glutamine , nonessential amino acids , 100 U/ml of penicillin , 100 μg/ml of streptomycin , and 10% fetal calf serum . Blasticidine was further added at 5 μg/ml to maintain expression of the CD81 receptor and the FLuc reporter gene in the respective cell line . For the shRNA knockdown and rescue experiments , lentiviruses were produced as previously reported [52] . Note that independent lentiviral stocks were used in the biological replicates . Stocks of JcR-2a [34] and Jc1 [53] viruses were obtained by electroporation of the in vitro transcribed viral RNA into Huh-7 . 5 cells . Electroporation and in vitro transcription protocols have been described elsewhere [52] . Three shRNAs targeting ABHD5 were constructed in the pLenti3_U6_ECEP7 expression vector , a kind gift from T . Von Hahn and S . Ciesek ( Hannover Medical School ) . The seeding sequence of the first shRNA ( shABDH5 . 796 ) was derived from the best siRNA used in the initial screen ( siRNA b in Fig 2 ) and elongated with 2 nucleotides downstream ( seeding sequence starting at position 796 in ABHD5 coding sequence: GGCCTGATTTCAAACGAAAGT ) . The seeding sequences for the second and third shRNAs were the two best hits predicted by the Invitrogen design tool ( http://rnaidesigner . invitrogen . com/rnaiexpress/ ) : GCACCAACAGACCTGTCTATG ( position 406 ) and GCAGATCAACCAGAAGAATTC ( position 1101 ) . All 3 shRNAs were generated by annealing the complementary primers in a thermocycler ( 5 min at 90°C , ramp rate of 0 . 1°C /sec down to 37°C , 1h at 37°C , pause at 4°C ) and ligating the resulting fragment ( with overhangs ) into the pLenti3_U6_ECEP7 vector between the EcoRV and PstI restriction sites . Further details on the vector used and the cloning strategy have been previously reported [54] . The control constructs expressed CD81-specific [55] , ApoE-specific [44] or irrelevant shRNAs [56] in the same vector . The Ultimate ORF clone for ABHD5 ( Clone ID IOH10127 , accession NM_016006 . 4 ) was obtained from Invitrogen in the pENTR ( tm ) 221 vector . The ABHD5 sequence was subcloned into the pWPI vector with the simultaneous introduction of 4 silent mutations in the seeding sequence of shABHD5 . 796 ( construct named “ABHD5 WT” ) . This shRNA-resistant ABHD5 construct was then further used as a template to generate the Q130P and E260K mutants , in the same pWPI vector . In all cases , the cloning procedure relied on two-step fusion PCR performed with overlapping and mutated primers and introduction of the insert in the pWPI vector between the BamHI and SpeI restriction sites . The HA-tagged shRNA-resistant ABHD5 expression construct ( pWPI-ABHD5-shResist-L-HA-HA-BLR ) was cloned using the sequence- and ligation-independent cloning method as described by Li et al . [57] . Briefly , the untagged construct ( pWPI-ABHD5-shResist-BLR ) was digested with NdeI and SpeI and served as vector . The insert was PCR-amplified from a GBlock fragment ( IDT ) corresponding to the C-terminus of ABHD5 , the linker sequence GGGGSG and the double HA tag ( twice YPYDVPDYA ) , encased by the NdeI and SpeI restriction sites and 11 or 10 additional nucleotides respectively at the 5’ and 3’ ends . Both digested vector and insert were treated with the Taq DNA polymerase ( NEB #M073S used at 0 . 5 U/ml ) for 20 min at RT for its exonuclease activity to generate the compatible 5’ overhangs . Vector and insert were then mixed with an insert/vector molecular ratio of 2 and allowed to anneal at 37°C for 30 min . The mixture was finally transformed into DH5α . The HA-tagged ABHD5 mutant constructs were obtained by ligating the NotI–NdeI fragment cut from pWPI-ABHD5-Q130P-shResist-BLR or pWPI-ABHD5-E260K-shResist-BLR in the cut pWPI-ABHD5-shResist-L-HA-HA-BLR vector . Mutations described in Fig 7 were also introduced in this construct using two-step fusion PCR performed with overlapping and mutated primers and introduction of the insert in the pWPI vector between the BamHI and SpeI restriction sites . The mTurquoise2 [58]- , mCitrine [59]- and mRuby2 [60]-fusion constructs were generated in two steps . First of all , we constructed C-terminal fusion vectors harbouring the gene for the fluorescent protein preceded by a short linker and downstream a multiple cloning site . These constructs were generated in the pWPI-Puro backbone and using BamHI and SpeI as restriction sites . Note that the coding sequences for the 3 fluorescent proteins together with the linker were ordered as GBlocks ( IDT ) . Secondly , we transferred the ABHD5 sequence amplified by PCR from the pWPI-ABHD5-shResist-BLR or the pWPI-ABHD5-Q130P-shResist-BLR constructs into this cloning vector between the PmeI and AscI restriction sites and keeping the fluorescent protein coding sequence in frame . For the EGFP-tagged ABHD5 constructs , we PCR-amplified the NdeI-SpeI fragment covering the 5´end of ABHD5 ( including the codon corresponding to Q130/P130 but downstream the shRNA-targeting site ) , a linker and the EGFP from the pEGFP-hCGI-58 construct ( a kind gift of J . M . Brown ( Wake Forest University ) ) [18] . This PCR was digested and incorporated in the above described pWPI-ABHD5-shResist-BLR vector . The fusion was then transferred into the pWPI-Puro vector using the AscI and SpeI restriction sites . The pWPI-Nter-mRuby2-Puro plasmid used in Fig 6c was a N-terminal fusion vector were the mRuby2-coding sequence was cloned in the pWPI-Puro vector between the PmeI and AscI restriction sites . Finally , the pWPI-FLuc-BLR construct was generated by PCR amplifying the FLuc gene from the pFK-Luc-Jc1 plasmid [61] and cloning in the pWPI vector with the BamHI and MluI restriction sites . All newly generated constructs were controlled by multiple restriction analyses and by sequencing . Primer sequences and detailed instructions for the cloning procedures are available upon request . Silencer Select siRNAs were purchased at Ambion ( Life Technologies ) . The list and sequences of the siRNAs used in our screen are available upon request . Please note that the accession numbers of the genes targeted in our screen are given in S1 Table . SiRNAs targeting ABHD5 had the following sequences: ( a ) GGU UAA UCA UCU CAU UUU Att; ( b ) GGC CUG AUU UCA AAC GAA Att and ( c ) CGA CCA CAU UCA UAU GUG Att . Non-targeting control siRNAs 1 and 2 were also obtained from Ambion ( #4390843 , siRNA ID #s813 and #s814 ) and corresponded to the sequences UAA CGA CGC GAC GUA Att ( ctrl . siRNA 1 ) and UCG UAA GUA AGC GCA ACC Ctt ( ctrl . siRNA 2 ) . Mouse and rabbit anti-HA antibodies were purchased from Covance ( #MMS-101P ) and Sigma ( #H6908 ) , respectively . The anti-ABHD5 antibody was obtained from Tebu-bio ( Abnova ABHD5 mouse monoclonal antibody , clone 1F3 , #H00051099-M01 ) . The following antibodies were used to stain cellular organelles and proteins: rabbit anti-β-tubulin monoclonal antibody ( Thermo Scientific , clone E . 884 . 5 , #M45-15002 ) , mouse anti-calnexin ( ER marker , Abcam , ab31290 ) , mouse anti-p230 ( Golgi marker , BD #611281 ) , rabbit anti-ApoE ( Santa Cruz H223 ) , FITC-conjugated mouse anti-EEIA-FITC ( BD #612006 ) , mouse anti-Lamp1 ( Abcam #ab25630 ) . The sheep anti-ADRP antibody was a kind gift from J . McLauchlan [62] . Antibodies against HCV proteins consisted of the anti-core C7-50 antibody [63] , the anti-E2 CBH23 antibody [64] and the anti-NS5A 9E10 antibody [65] , which were kindly provided by D . Moradpour , S . K . Foung and C . M . Rice , respectively . Bodipy 495/503 was purchased from Invitrogen . The Alexa- and IRDye-conjugated secondary antibodies used for immunofluorescence and western blotting were from Life Technologies and LI-COR Biosciences , respectively . The screening procedure was derived from strategies previously published by us and others [34 , 66] ( S2 Fig ) . Briefly , Lunet N hCD81 FLuc cells were seeded at 104 cells/well in 96-well dishes , without antibiotics ( producer cells ) . Transfection of the siRNA pools was performed 5 h later and overnight with the Lipofectamine RNAiMAX transfection reagent ( Invitrogen ) and according to the manufacturer’s instructions . Per well , 0 . 5 pmol of each of the three gene-targerting siRNAs ( or 1 . 5 pmol of the negative siRNA control 1 or 2 ) and 0 . 2 μl of lipofectamine were used . The cell medium was replaced the next day . Forty-eight hours post-transfection , cells were infected overnight with the Jc-R2a virus and the medium changed . Two days later , the producer cells were lysed for Firefly luciferase ( Fluc , for cell viability ) and Renilla luciferase ( RLuc , for HCV entry and replication ) activity measurements . The producer cell supernatants were used to infect target Lunet-hCD81-FLuc cells seeded the day before at 3 x 103 cells/well in 96-well dishes . These target cells were lysed 72 h post-infection for RLuc activity readout ( whole replication cycle ) . The luciferase readouts were performed as previously described [67 , 68] . Lunet N hCD81 FLuc cells ( producer cells ) were electroporated with JcR-2a RNA as previously described [52] and seeded at 8 x 104 cells/well in 24-well dishes , without antibiotics . Transfection of single siRNAs was performed 5 h later and overnight as described above , but with 2 . 5 pmol siRNA and 0 . 8 μl lipofectamine RNAiMAX per well . Ninety-six hours post-transfection , the producer cells were lysed for FLuc and RLuc activity measurements . The producer cell supernatants were cleared by centrifugation ( 5 min , 13 , 000 g , RT ) and used to infect target Lunet-hCD81-FLuc cells seeded the day before at 2 x 104 cells/well in 24-well dishes . These target cells were lysed 72 h post-infection for RLuc activity readout . Lunet N hCD81 FLuc cells ( producer cells ) were seeded at 2 x 104 cells/well in 24-well dishes and transduced one day later for 4 h with shRNA-expressing lentiviruses , in duplicates . Three days post-transduction , twice one third of the cells were transferred into 12-well dishes . One day later ( 96h post-transduction ) , these cells ( 4 replicates for each condition ) were infected overnight with a JcR-2a virus stock and the medium was replaced . Three days post-infection , the producer cells were lysed for FLuc and RLuc measurements ( duplicates ) or for RNA extraction ( duplicates ) and determination of the ABHD5 mRNA level at the end of the infection period . The cell supernatants ( 4 replicates ) were used to infect target Lunet-hCD81-FLuc cells seeded the day before at 3 x 104 cells/well in 12-well dishes . These target cells were lysed 72 h post-infection for RLuc activity readout . The protocol for the rescue experiments was adapted from the transient shRNA knockdown experiment with the following modifications . Note that the time schedule for the rescue experiment was slightly shortened in Fig 7 and S8 Fig ( see indications in brackets in this paragraph ) as compared to Fig 2 , in an attempt to optimise the knockdown efficiency and to prevent virus spread from affecting the RNA replication readout ( see Fig 2c ) . Transduction was performed simultaneously with lentiviruses expressing the shRNA- and rescue constructs in triplicate wells . Three days post-transduction [2 days] , the cells were split as follows in 12-well dishes: ( i ) respectively 1/3 and 1/6 of the cells were seeded for the determination of ABHD5 expression by western blot at 96h post-transduction [72h] ( beginning of the infection period ) and 72 h post-infection [48h] ( or 7 days [5 days] post-transduction , end of the infection period ) ; ( ii ) 1/6 of the cells were seeded for JcR-2a infection and determination of the efficiency of the whole HCV replication cycle as above . An aliquot of the producer cell supernatant was kept for quantification of the released core amount by ELISA with a diagnostic kit ( Architect Anti-HCV; Abbott ) . For Western blot analysis of ABHD5 expression levels , trypsinised cell pellets were lysed in reducing Laemmli buffer , treated with benzonase for 15 min at 37°C and heated for 5 min at 95°C before loading on a 10% gel and separation by SDS-PAGE . Proteins were transferred with a semi-dry blotter on a PVDF membrane ( Millipore ) and probed with mouse anti-ABHD5 ( 1/1 , 000 ) antibodies and the rabbit anti-HA ( 1/1 , 000 , used in Fig 7 ) and anti-β-tubulin ( 1/1 , 000 ) antibodies followed by the secondary IRDye 800CW donkey anti-mouse IgG and IRDye 680RD donkey anti-rabbit IgG antibodies ( both at 1/15 , 000 , LI-COR ) . Signal intensities were read and quantified with the Odyssey CLx imager ( LI-COR ) . Lunet N hCD81 FLuc cells were electroporated with JcR-2a and once the cells have attached ( around 8 h post-electroporation ) ABHD5 expression was regulated by overnight lentiviral transduction of specific shRNA or expression construct . The regulation of APOE expression was used as a control . 72 h post-electroporation , intracellular virions were released from the cells by freeze-thaw as previously described [34 , 66] and their infectivity assessed and compared to the released extracellular infectivity by measuring the RLuc activities in infected target cells . For each sample , total RNA was extracted from the producer cells at 72 h post-infection with the total Nucleospin RNA II kit ( # 740955 , Macherey-Nagel , Düren , Germany ) . ABHD5-specific qRT-PCR was performed in duplex with the actin or GAPDH calibrator in the Lightcycler 480 instrument ( Roche , Mannheim , Germany ) , according to previously published instructions [52] . The sets of primers and probe were ordered from TIB MolBiol ( Berlin , Germany ) with the following sequences: F-ABHD5 , 5’-AGT TTG TGG AAT CCA TTG AAG AGT G-3’; R-ABHD5 , 5’-CTG CAA TCC TTA GGC CAG CTA-3’; ABHD5-TM probe , 5’-FAM-CGA GTA AGC CAA GAA TCC AC-BBQ-3’; F-actin , 5’-AGC CTC GCC TTT GCC GA-2’; R-actin , 5’-CTGGTGCCTGGGGCG-3’; actin-TM probe , 5’-YAK-CCG CCG CCC GTC CAC ACC CGC C-BBQ-3’; F-GAPDH , 5’-GAA GGT GAA GGT CGG AGT C-3’; R-GAPDH , 5’-GAA GAT GGT GAT GGG ATT TC-3’; GAPDH-TM probe: 5’-LCRed640-CAA GCT TCC CGT TCT CAG CCT-BBQ-3’ . Note that for the FAM/YAK probe combination , channel overlap was corrected by color compensation according to the instructions from the Roche Lightcycler manual . Lunet N hCD81 FLuc cells were seeded at 2 x 104 cells/well on coverslips in 24-well dishes and transduced overnight to express HA-tagged wild-type or mutant ABHD5 . For the oleic acid treatment , 30 μl BSA ( Gibco , #30036–578 , dissolved at 10 mg/ml ) were vortexed with 1 . 14 μl of oleic acid ( Sigma # O1008 ) and diluted in 10 ml medium . The mixture was added onto the cells at 48 h post-transduction and cells were fixed after an overnight incubation . For the colocalisation study between ABHD5 and HCV proteins , cells were seeded as above but first infected 24h post-seeding with Jc1 virus for 4 hours and then transduced overnight to express HA-tagged wild-type or mutant ABHD5 . Cells were fixed 72h post-Jc1 infection and after an overnight oleic acid treatment if applicable . Cell fixation and permeabilisation , antibody dilutions , immunofluorescence staining and confocal microscope observation were performed as previously described [69] but with the following modifications: antibodies were incubated for 1 h at RT , rabbit anti-HA antibody was used diluted 1/1 , 000 , mouse anti-ABHD5 1/1 , 000 , mouse anti-calnexin 1/2 , 000 , mouse anti-p230 1/100 , FITC-conjugated mouse anti-EEIA 1/500 , mouse anti-Lamp1 1/500 and rabbit anti-ApoE 1/200 . Note that coverslips were mounted with Fluoromount-G ( product 100–01; Southern Biotech , Birmingham , AL ) or ProLong Gold Antifade ( Invitrogen ) , but the latter was avoided for samples stained with Bodipy 495/503 , due to poor performance of the lipid dye in this mounting reagent . Pictures were taken either on an Olympus laser-scanning confocal microscope as described before [69] or on a Nikon Ti-E microscope equipped with a Perfect Focus System ( Nikon ) , a Yokogawa CSU-X1 spinning-disc and a cage incubator ( 37°C , 5% CO2; Okolab ) . A 100x magnification lens was used for all pictures except in Fig 6 where a 60x magnification lens was chosen to capture a broader field . IF quantifications were performed with Fiji and CellProfiler . Pipelines elaborated with CellProfiler were systematically tested and iteratively optimised by running them on a random subset of pictures representing each tested condition . Further details on the pipelines developed and used in this study are available upon request . The Pearson’s correlation coefficient was calculated with the Mander’s coefficient or the Coloc2 plugin in Fiji . For those analyses restricted to the cytoplasmic proteins ( Fig 5 ) , nuclear signals were excluded by using a mask created manually by thresholding using the Dapi channel . To quantify the colocalisation between ABHD5 and ApoE , regions of interests ( ROIs ) were manually drawn in each frame to select the HA-positive cells . For the colocalisation between ABHD5 and HCV proteins , double positive cells ( cells expressing both the HA-tagged ABHD5 construct and HCV proteins ) were selected in a similar way . The multichannel pictures corresponding to the Dapi / ABHD5 ( HA ) / Lipid droplet and/or Golgi costaining were loaded in CellProfiler . The ABHD5 picture was thresholded to remove very low intensity pixels . Nuclei , lipid droplets and Golgi stacks were identified automatically as objects using a constant manual intensity threshold and according to their size and shape . Since ABHD5 associates to the LD periphery , this area was also identified as rings by sequentially expanding and shrinking the size of the recognised lipid droplet objects . These objects ( nuclei , LD rings , Golgi ) were used to mask the thresholded ABHD5 pictures and segment the ABHD5 signal into its nuclear , cytoplasmic , LD- and Golgi-associated components . Mean and total ABHD5 signal intensities in the different cell compartments and in the whole thresholded picture were measured . For each picture , a mask encompassing the HA-positive cells was created manually in Fiji . These masks were loaded together with the original immunofluorescence pictures in CellProfiler . Using this program , nuclei and lipid droplets were automatically identified as objects using a constant manual intensity threshold and according to their size ( nuclei between 30–300 pixels diameter , lipid droplets between 2–40 pixels diameter ) and shape . Taking into account the previously created masks , it was possible to distinguish lipid droplets and nuclei belonging to HA-positive or HA-negative cells . The outputs included , for each frame , the total number of lipid droplets and their mean area , the size of each lipid droplet and the total nuclear area , for each cell population . Note that in Fig 5e , the number of lipid droplets in HA-negative and positive cell populations was normalised , in each frame , for the total nuclear area of the relevant cell population . This is to account for possible differences in the ratio of HA-negative and positive cells ( the nuclear rather than whole cell area was used because of the clear Dapi outlines allowing a robust and automated area calculation ) . In Fig 6g , for each frame and cell population , the number of lipid droplets in each size category was calculated as a percentage of the total number of lipid droplets . Data were then averaged over 10 frames ( each frame containing both HA-positive and HA-negative cells ) and finally over 3 independent experiments . Lunet N hCD81 cells were seeded at 2x105 cells per dish in 35mm-diameter IBIDI μ-dishes and lentivirally transduced overnight for the simultaneous expression of the mTurquoise2-tagged wild-type ABHD5 and the mCitrine-tagged Q130P mutant . When relevant , oleic acid combined with BSA ( see above ) was added for 4 hours prior observation . Before imaging , cells were washed and the medium replaced by complete DMEM without phenol red and with 10 mM Hepes . Cells were imaged with the Nikon microscope described above , using the Apo TIRF 100x objective and at 37°C with 5% CO2 . Acquisition was sequential with the 445nm laser and a CFPHQ emission filter on one hand ( mTurquoise2 detection ) and the 515 nm laser and YFP emission filter on the other hand ( mCitrine detection ) . Z-stacks were taken with the optimal step size according to the Nyquist criteria ( in this case , 200 nm ) and 3D reconstitutions performed with the Nikon NIS Elements software . Fixed cells were washed twice in FACS Wash buffer ( PBS-1% FCS ) and permeabilised in FACS Permeabilisation Buffer ( PBS-1% FCS-0 , 1% Saponin ) for 20 min on ice . The primary antibody ( mouse anti-HA ) was incubated in FACS Permeabilisation Buffer for 20 min on ice . Bodipy ( diluted 1/3000 ) and the secondary antibody ( AlexaA647-conjugated anti-mouse antibody , 1/1000 ) were added together and in the same buffer also for 20 min on ice . Between and after the antibody incubations the cells were washed twice in FACS Wash Buffer . Finally , cells were resuspended in FACS Fixation buffer and analysed with the BD Accuri C6 flow cytometer and corresponding software . Gating was used to eliminate cell debris from the analysis . The Bodipy/A647-stained samples were analysed with the FL-1 and FL-4 channels , which did not require color compensation . For the Bodipy/mRuby2 dye combination ( Fig 6c ) , the cell permeabilisation step was omitted and cells were stained with Bodipy only and in FACS Wash buffer . The fluorescence was read in the FL-1 and FL-3 channels and color compensation was applied according to the manufacturer´s instructions . Mean fluorescence intensities in the respective channels for the distinct cell populations were calculated by the BD Accuri C6 software . Note that the representative pseudocolor plots depicted in Fig 6a were however obtained by importing the raw Accuri C6 data into FlowJo and repeating the manual gating to eliminate cell debris . The presence of potential NLS was analyzed with cNLS Mapper ( http://nls-mapper . iab . keio . ac . jp/cgi-bin/NLS_Mapper_form . cgi ) . This program predicted a non-canonical importin-α-dependent monopartite NLS with the typical ( P/R ) XXKR ( K/R ) motif starting at position 229 and a score of 7 ( on a scale of 1 to 10 , knowing that NLS with scores of at least 8 induce the complete nuclear localisation of a GUS-GFP reporter protein ) [70 , 71] . Functional annotation clustering and enrichment scoring of the 20 candidates was performed using DAVID ( http://david . abcc . ncifcrf . gov ) version 6 . 7 , with ‘‘high” classification stringency settings [72] . DAVID analysis yielded 3 highly enriched clusters ( EASE score above 1 ) and 6 clusters in total . We next integrated STRING 9 . 1 database interactions ( http://www . string-db . org ) into the DAVID functional annotation clustering using the Matlab script previously described [73] . Unclustered proteins were placed in the network as inverted arrowheads . We added STRING interactions ( solid lines ) with a combined confidence score of 0 . 8 or higher between proteins of different clusters , and interactions of 0 . 4 or higher between proteins within the same functional annotation cluster . STRING interactions , which were exclusively based on textmining , were excluded from the network . Proteins were placed in their approximate cellular locations manually . For selected candidates ( TIP47 , Seipin , ADRP ) we used the annotation commonly used in the HCV field instead of the Uniprot ID . Statistical data analysis was performed in R ( http://www . r-project . org ) . siRNA screening data was analysed using the Bioconductor package RNAither [74] , using lowess and negcontrol normalisation , and using p<0 . 05 as threshold for hit selection . Individual follow-up experiments ( Figs 1c–1f and 2 ) were statistically analysed using Welch’s t-test or a paired t-test where applicable . For the rescue experiments in Fig 7 , the raw data were first pre-processed and paired t-tests were then performed . Pre-processing of the data consisted in ( i ) log-transformation of the raw luciferase measurements , ( ii ) calculation of the ratios between these log-transformed RLU in target vs . producer cells , and ( iii ) correction of each individual experiment by subtracting the mean ratio of the control from the other conditions . For the FACS-based experiments , the FL1 ratios of the two cell populations were compared to the 100% control with a t-test . In all experiments , p-values of <0 . 05 were considered statistically significant ( * ) , and p-values of <0 . 01 were considered highly significant ( ** ) . | HCV replication is linked to the host lipid metabolism , and virions are secreted as lipo-viro-particles whose density , size and biochemical content resemble VLDL . HCV assembles close to lipid droplets and is released via the secretory pathway , but it remains unclear how it accesses the VLDL assembly pathway . In this study , we identified ABHD5 as a new host factor supporting HCV assembly and release . ABHD5 is a lipid droplet-associated lipase cofactor . In hepatocytes , ABHD5 was proposed to promote the recruitment of triglycerides from cytosolic towards luminal lipid droplets by mediating a cycle of phospholipid hydrolysis/re-esterification . Our data suggest that this ABHD5-dependent lipid transfer is not only required for VLDL maturation , but also for HCV assembly and virion release , indicating that lipid remodelling impacts on both assembly and virus transport . Finally , ABHD5 is associated with the Chanarin-Dorfman syndrome , a rare human genetic lipid metabolism disorder . We found that the Chanarin-Dorfman syndrome mutants were unable to support HCV assembly , pointing at a new host polymorphism that could determine susceptibility to HCV infection . Altogether , our results establish a new link between HCV , VLDL assembly and lipid remodeling pathways and open new possibilities to study the etiology of the liver manifestations of the Chanarin-Dorfman syndrome . | [
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... | 2016 | ABHD5/CGI-58, the Chanarin-Dorfman Syndrome Protein, Mobilises Lipid Stores for Hepatitis C Virus Production |
Mosquitoes genetically engineered to be resistant to Plasmodium parasites represent a promising novel approach in the fight against malaria . The insect immune system itself is a source of anti-parasitic genes potentially exploitable for transgenic designs . The Anopheles gambiae thioester containing protein 1 ( TEP1 ) is a potent anti-parasitic protein . TEP1 is secreted and circulates in the mosquito hemolymph , where its activated cleaved form binds and eliminates malaria parasites . Here we investigated whether TEP1 can be used to create malaria resistant mosquitoes . Using a GFP reporter transgene , we determined that the fat body is the main site of TEP1 expression . We generated transgenic mosquitoes that express TEP1r , a potent refractory allele of TEP1 , in the fat body and examined the activity of the transgenic protein in wild-type or TEP1 mutant genetic backgrounds . Transgenic TEP1r rescued loss-of-function mutations , but did not increase parasite resistance in the presence of a wild-type susceptible allele . Consistent with previous reports , TEP1 protein expressed from the transgene in the fat body was taken up by hemocytes upon a challenge with injected bacteria . Furthermore , although maturation of transgenic TEP1 into the cleaved form was impaired in one of the TEP1 mutant lines , it was still sufficient to reduce parasite numbers and induce parasite melanization . We also report here the first use of Transcription Activator Like Effectors ( TALEs ) in Anopheles gambiae to stimulate expression of endogenous TEP1 . We found that artificial elevation of TEP1 expression remains moderate in vivo and that enhancement of endogenous TEP1 expression did not result in increased resistance to Plasmodium . Taken together , our results reveal the difficulty of artificially influencing TEP1-mediated Plasmodium resistance , and contribute to further our understanding of the molecular mechanisms underlying mosquito resistance to Plasmodium parasites .
Malaria is a devastating disease annually infecting over 200 million people worldwide , and is a leading cause of death in Sub-Saharan Africa [1] . The malaria-causing Plasmodium parasites are vectored by Anopheline mosquitoes , which constitute the obligatory primary host for Plasmodium . Out of 462 described species of Anopheles mosquitoes , about 34 are dominant vectors of human malaria [2 , 3] . A . gambiae is the major vector of the most deadly parasite , P . falciparum , in Sub-Saharan Africa . Thanks to vector control and the availability of anti-malarial combination therapies , estimated global malaria cases and mortality rates between 2000 and 2015 have declined by 37% and 60% , respectively , falling to approximately 440 , 000 deaths annually . Despite this downward trend , the fight against malaria is complicated by the spread of genetic resistance to insecticides in mosquitoes , and to anti-malarial drugs in Plasmodium . To prevent a reversal in the current malaria decline , new vector control strategies need to be developed . Vector control strategies based on genetically modified mosquitoes have been advocated for over 15 years to complement existing anti-malaria interventions ( reviewed by [4] and [5] ) . Interventions based on the sterile insect technique ( SIT ) [6 , 7] or its transgenic variants , such as the Release of Insects carrying a Dominant Lethal ( RIDL ) technique deployed in recent years against the dengue vector mosquito Aedes aegypti [8 , 9 , 10] , are unpractical on the vast geographical scales of malaria transmission . Gene drive-based strategies , whereby a desired genetic character ( female sterility for population suppression , malaria resistance for population replacement ) is inserted on a selfish genetic element designed to spread throughout a target population , have raised great interest [11 , 12 , 13] but their development has been limited by the paucity of molecular engineering tools . Today , these prospects are regaining momentum thanks to the advent of the CRISPR-Cas9 system , which provides a simple means to build a gene drive construct [14 , 15] . Major leaps towards establishing CRISPR-Cas9 gene drive designs for spreading malaria resistance and for population suppression have been accomplished in two recent studies [16 , 17] , moving these approaches a step closer to implementation . However , the capacity to modify , or even eradicate , an entire insect species poses serious ethical and environmental questions . While regional population suppression or eradication appears desirable for invasive insect species such as Aedes aegypti that is not part of the native ecosystem in most of its current range , the importance of the role that a “legitimate” species like A . gambiae in Sub-Saharan Africa plays in its ecosystem needs to be examined . In this case , gene drive strategies aiming at population replacement with malaria-refractory mosquitoes rather than population suppression may be preferred , at least as a first test of gene drive interventions within a malaria eradication program . Therefore , candidate genes that would confer mosquito resistance against malaria parasites need to be screened and validated for efficiency in transgenic mosquitoes . When Plasmodium ookinetes , the motile stage of the parasite that forms in the blood meal of female mosquitoes , traverse the mosquito midgut epithelium and reach the basal lamina facing the insect’s hemocoele , they are attacked by the mosquito’s innate immune system . This process has been mostly studied in A . gambiae mosquitoes in combination with the murine parasite P . berghei , which offers a convenient model to study Anopheles—Plasmodium interactions in the laboratory [18] . The killing of ookinetes at the basal lamina is associated with direct binding of mosquito Thioester protein 1 ( TEP1 ) to the ookinete surface [19] . TEP1 is a homolog of the mammalian C3 complement factor . Like C3 , which circulates in mammalian blood , TEP1 is a soluble hemolymph protein and its activity is dependent on an intact thioester bond thought to mediate covalent binding to bacteria and parasites [19 , 20] . TEP1 is secreted as a full-length protein ( TEP1-full ) and cleaved to produce activated TEP1 ( TEP1-cut ) in which the two cleaved parts of TEP1 remain connected by a disulfide bond [20 , 21 , 22] . TEP1-cut circulates in the hemolymph as a complex with two leucine rich repeat ( LRR ) proteins , LRIM1 and APL1C . Knocking down TEP1 itself or any of these LRR genes results in the disappearance of TEP1-cut from the hemolymph , and eliminates parasite killing [21 , 22] . However , TEP1 does not appear to directly lyse pathogens and the precise mechanism of killing remains unknown . TEP1 has also been shown to act in mosquito resistance against the human parasite P . falciparum , though parasite virulence factors such as the ookinete surface protein Pfs47 have evolved in some parasite strains to counteract the TEP1-dependent immune response [23 , 24] . In addition , several TEP1 alleles are present in mosquito populations . The abundant TEP1*S alleles ( hereinafter termed TEP1s ) are associated with susceptibility to P . berghei infection , supporting the production of large numbers of oocysts . The less frequent TEP1*R1 allele ( hereinafter TEP1r ) confers high resistance to P . berghei , and leads to parasite melanization [25] . Consistently , the laboratory L3-5 line that was selected based on the ability to kill P . cynomolgi [26] also kills all P . berghei parasites , is homozygous for TEP1r and exhibits high levels of parasite melanization . Furthermore , silencing TEP1 in mosquitoes before infection further increases parasite loads in susceptible mosquitoes and abolishes resistance and parasite melanization in refractory mosquitoes [19] . Recently obtained TEP1 mutants exhibit the same phenotype [27] . Melanization is a powerful anti-parasitic and anti-microbial defense response used by insects to isolate and kill invading pathogens . The biochemical process has been well characterized in Drosophila and shown to be tightly regulated by both positive and negative effectors [28] . Recognition of pathogens triggers activation of a serine protease cascade that culminates in the cleavage of the inactive circulating Prophenoloxidase ( PPO ) to its active Phenoloxidase ( PO ) form . PO then oxidizes quinones that polymerize to form melanin , which is in turn cross-linked with proteins and deposited on the pathogen . This chain reaction is localized in close proximity to the pathogen . High local levels of reactive oxygen species arising from quinone oxidation reactions also contribute to pathogen killing [29] . In mosquitoes , this serine protease cascade is negatively regulated by serpins and activated by several CLIP domain proteases [30 , 31] . In resistant mosquitoes , silencing either TEP1 , LRIM1 or APL1C abolishes melanization and allows parasite survival [19 , 21 , 22 , 32] . Furthermore , it is possible to disrupt melanization by knocking down genes that regulate actin polymerization without affecting the killing of parasites [33] . These results suggest that the role of TEP1r in the resistant L3-5 mosquitoes is first in killing the parasites , then in promoting their melanization . Here , we investigated the possibility of rendering mosquitoes resistant to P . berghei by transgenic expression of TEP1r . Together with insights regarding the potential of TEP1 transgenes as malaria control agents , our observations reveal new aspects of TEP1 biology , including expression pattern , promoter characterization , competition between TEP1 allelic forms , and anti-parasitic activity .
In order to over-express TEP1 in mosquitoes in the endogenous source tissue , we first sought to determine which cells express TEP1 mRNA and attempted to perform RNA in situ hybridization in both larva and adult mosquitoes . However , the fat body tissue did not withstand the procedure and was lost from the analysis . Thus , we constructed a transgenic reporter line expressing GFP under control of the TEP1 promoter . For this , we cloned a 3 . 1 kb genomic DNA fragment located 5’ of the TEP1s gene from the A . gambiae G3 strain , ending at the TEP1s initiator ATG codon . Transfection assays in Drosophila S2 cells confirmed that this cloned TEP1s 5’ region is responsive to bacterial challenge ( see below ) , as is the endogenous TEP1 gene [20] . Investigation of the expression pattern of GFP in the transgenic mosquitoes revealed strong expression in the fat body tissue at larval ( Fig 1a and 1b ) , pupal ( Fig 1c and 1d ) and adult stages ( Figs 1n and 2 ) . In addition , intense GFP expression was detected in cells of the midgut proventriculus ( cardia ) of adult mosquitoes , both males and females , regardless of blood feeding ( Fig 1i and 1j , and S1a and S1b Fig ) . Other tissues showing some GFP expression included the oviduct , and more variably , the Malpighian tubules ( S1c Fig ) . GFP was expressed in fat body loosely attached to the testis , but was not observed in the male reproductive organs themselves . As a control for fat body and hemocyte expression we used the Lipophorin ( Lp ) promoter-driven RFP reporter line constitutively expressing RFP in the fat body and the hemocyte-specific PP06-RFP line [34] . Comparing the expression of TEP1-GFP to the Lp-RFP pattern shows similar expression in the same cells in dissected carcass tissues ( Fig 2a ) . Mosquitoes carrying both reporter genes showed perfect GFP/RFP overlap . Upon crossing TEP1-GFP mosquitoes to the hemocyte reporter PPO6-RFP line , the observed RFP-positive hemocytes were never positive for GFP , indicating that the TEP1 reporter is not strongly expressed in the hemocytes that express PPO6 ( Fig 1e–1h ) . Although the reporter mosquitoes did not display obvious GFP-positive hemocytes , we do not rule out a possible expression of endogenous TEP1 in some hemocytes , as the cloned regulatory sequences may lack distant hemocyte-specific enhancers . To identify the expression pattern of other genes known to act in the TEP1 pathway , we also constructed transgenic reporter lines expressing GFP under the control of the promoters of the leucine-rich proteins LRIM1 and APL1C that associate with TEP1 in the hemolymph . Both reporter lines showed a GFP expression pattern similar to TEP1 , namely expression in the fat body at all developmental stages ( Fig 1k–1n ) . However , neither LRIM1-GFP nor APL1C-GFP exhibited GFP expression in the proventriculus . Of note , all these reporters were created using the same docking line , and are thus subjected to the same potential positional effects . Many other fluorescent reporter constructs inserted at the same locus exhibit different , promoter-specific patterns of expression [34] , indicating that the TEP1 , LRIM1 and APL1C promoters must contain sequences determining fat body-specific expression . In mosquitoes , fat body cells are challenging to manipulate . Dissection of mosquitoes in buffer results in immediate loss of large parts of the fat body . The fat body cells that remain attached to the carcass detach easily during fixation and disappear during permeabilization in buffers containing detergents . To verify the expression of TEP1 in the fat body cells that remain attached to dissected mosquito carcasses , we performed an antibody staining of fixed samples using a modified protocol ( see materials and methods ) . Indeed , careful examination showed that some fat body cells that express the fat body specific protein Lipophorin ( Lp ) also stain positive for TEP1 ( Fig 2b ) . In mosquitoes where TEP1 was silenced by RNAi , this fat body TEP1 staining was markedly reduced ( Fig 2c ) , indicating that the signal is TEP1-specific . Larvae and adults of the transgenic TEP1-GFP reporter line express GFP at strikingly variable levels ( S1d and S1e Fig ) . We followed the levels of GFP during larval development ( S2 Fig ) and found an increase in GFP levels in time , most fourth instar larvae having reached a maximum level of GFP expression , though striking differences from larva to larva persist until pupation . Newly eclosed adult mosquitoes also exhibit variable levels of GFP expression ( S1e Fig ) , probably reflecting a high variability in TEP1 expression that contrasts with the highly reproducible expression level of the Lp-RFP reporter . We hypothesized that the variability in GFP expression reflected TEP1 induction by immune pathways [35] upon encounter with various microorganisms , which may differ from larva to larva . We tested whether there is a correlation between the level of GFP in adult mosquitoes and intensity of P . berghei infection . Adult mosquitoes were scored for GFP expression prior to infectious blood feeding and grouped according to weak or strong reporter expression level . Mosquitoes were infected and oocysts were counted 7 days post infection . S1 Table summarizes the result of 5 independent experiments , showing no significant differences in oocysts loads between the high and low GFP expressing mosquitoes in 4 out of 5 experiments . Given the localization of GFP expression under control of the TEP1 promoter , we decided to express the TEP1 transgene in adult fat body tissue . To avoid unnaturally high TEP1 expression throughout development , we used the blood-meal inducible Vg promoter . Vg is induced at high levels after blood feeding [36] peaking 24h after blood meal , at the time when P . berghei ookinetes reach the basal lamina and encounter circulating TEP1 . This experimental design aimed at achieving high levels of TEP1 simultaneously to Plasmodium infection . Since the TEP1r allele is associated with refractoriness to P . berghei infection and shown to be more potent in Plasmodium killing , we based our construct on the TEP1r allele . TEP1r cDNA was cloned under the Vg promoter and injected into the TEP1s-containing X1 docking line [34] , to create the Vg-TEP1r , 3xP3-RFP transgenic line ( henceforth referred to as Vg-TEP1r ) . Analysis of TEP1r mRNA in the transgenic mosquitoes ( S3a Fig ) showed blood meal induction of TEP1r similar to induction of Vg [37] . Like Vg mRNA , TEP1r mRNA levels monitored with TEP1r-specific primers peaked 24h after blood meal and dropped gradually in the following days . However , Western blot analysis of the transgenically expressed TEP1r protein in the TEP1s background did not reveal a notable increase in TEP1 protein levels in the transgenic mosquitoes 24h after blood feeding ( S3b Fig ) , suggesting tight post-transcriptional control of hemolymph TEP1 protein levels . In order to verify expression from the Vg-TEP1r transgene , we introduced it into two different TEP1 null mutant lines [27] . The TEP1Δct2 mutant has a nonsense mutation causing a premature stop codon and loss of the entire C-terminus . The TEP1ΔT mutant has a deletion of a threonine located 16 amino acids before the thioester cysteine . Both mutants lack detectable TEP1 protein in the hemolymph at 24h after blood feeding , but low levels of mutated TEP1 become detectable in old TEP1ΔT mosquitoes only . Samples from TEP1Δct2; Vg-TEP1r mosquitoes analyzed by polyclonal anti TEP1 antibodies showed the presence of both forms of TEP1 , namely TEP1-full and TEP1-cut in the hemolymph expressed from the transgene ( Fig 3a ) . In contrast , hemolymph samples from TEP1ΔT; Vg-TEP1r showed only the TEP1-full form ( Fig 3b ) , suggesting that traces of the mutant TEP1ΔT protein inhibit maturation of wild-type TEP1 expressed from the transgene . In addition , TEP1ΔT ( but not TEP1Δct2 ) mosquitoes show reduced levels of LRIM1 and APL1C ( Fig 3a–3c and 3e ) . Unlike LRIM1 and APL1C depletion by RNAi silencing [21] , this decrease does not result in massive TEP1-cut deposition on tissue ( Fig 3d ) . Thus , traces of the mutant TEP1ΔT protein appear to downregulate both the endogenous LRIM1/APL1C complex and the cleavage of transgenic TEP1-full . It has been previously demonstrated that injection of bacteria into mosquitoes promotes TEP1 maturation and activates TEP1 transcription [20] . Furthermore , shortly after bacteria injection , TEP1-cut binds to bacterial surfaces and acts as a convertase to recruit the full-length protein [38 , 39] . The recruited TEP1-full is then cleaved close to bacterial surfaces and increases bacterial opsonization , thus marking bacteria for phagocytosis by hemocytes [40] . We tested whether injection of bacteria induced formation of the TEP1-cut form in the TEP1ΔT;Vg-TEP1r mosquitoes ( Fig 3e ) . While the positive control TEP1r mosquitoes showed an increase in both TEP1-full and TEP1-cut after bacterial injection , transgenic TEP1-full in the TEP1ΔT;Vg-TEP1r mosquitoes’ hemolymph still failed to be converted to TEP1-cut . In order to verify that the TEP1r transgene was functional , we infected TEP1 mutant mosquitoes complemented with the Vg-TEP1r transgene . The TEP1Δct2;Vg-TEP1r mosquitoes resemble the L3-5 line in that they express only the TEP1r allele . However , an important difference from the L3-5 line is that TEP1r is expressed exclusively in the fat body and only after a blood meal . In the TEP1ΔT;Vg-TEP1r line , only the full form of TEP1 is detectable in the hemolymph , which offers an opportunity to examine the activity of the TEP1-full form in the absence of detectable TEP1-cut . P . berghei infection of TEP1Δct2;Vg-TEP1r was strikingly reduced compared to mutant controls , with some live oocysts and mostly melanized parasites ( Fig 4a ) , indicating that the introduced TEP1r is fully functional and sufficient to kill parasites . Infection prevalence ( S2 Table ) in the transgenic is also significantly lower than in control Vg-GFP mosquitoes that express TEP1s only . P . berghei infection of TEP1ΔT;Vg-TEP1r , in which only the full form of TEP1 is detected , also resulted in a significant reduction in live oocyst numbers and in the concomitant appearance of high numbers of melanized parasites in some midguts compared to the non-rescued mutant ( Fig 4b ) . High levels of melanization are reminiscent of the L3-5 infection phenotype , where complete refractoriness is achieved by TEP1r-mediated parasite killing followed by parasite melanization [19 , 33] . However , contrary to L3-5 that are completely refractory to the parasite , the rescued mutant lines which derive from the susceptible G3 background are not fully refractory to P . berghei infection , with about 70% of infected midguts containing live oocysts 7 days after infection ( S2 Table ) and infection intensities comparable to wild-type control G3 mosquitoes . In fact , expression of TEP1r in mutant backgrounds rescued the mutant at least to the level of the wild-type TEP1s-carrying G3 line in terms of prevalence and intensity , and further conferred a gain-of-function phenotype ( melanization ) ( Fig 4a ) . In the TEP1ΔT;Vg-TEP1r line , it is striking that transgenic TEP1r appears to be functional in spite of its impaired maturation into the TEP1-cut form . These results imply either that TEP1 cleavage may be dispensable for some of its functions , or that low levels of TEP1-cut that we could not detect in the hemolymph ( Fig 3b and 3e ) or on parasites ( Fig 5 ) may be sufficient for function . The formation of TEP1-cut at foreign surfaces is dependent on a convertase complex controlled by SPCLIP1 [39] . To test if maturation of TEP1-full into TEP1-cut is required for the observed phenotype , we silenced SPCLIP1 by dsRNA injection in TEP1ΔT;Vg-TEP1r mosquitoes ( S1 File ) . This fully abolished melanization and tended to increase parasite numbers , suggesting that some TEP1r cleavage by the convertase complex , though undetectable , must be required for activity of the TEP1r transgene . Importantly , transgenic expression of TEP1r exclusively in the fat body using the Vg promoter rescued the TEP1 loss-of-function mutations ( Fig 4a and 4b ) , showing that TEP1 produced in the fat body and secreted into the hemolymph is sufficient for parasite killing . We tested whether expressing TEP1r in a wild type background where TEP1s is present could also reduce parasite loads after P . berghei infection . Oocyst counts revealed no difference in live oocyst numbers between the parental TEP1s line and the TEP1s;Vg-TEP1r transgenic line ( Fig 4c , left panel ) . However , the number of melanized parasites was significantly elevated in these mosquitoes ( Fig 4c , right panel ) . We verified that the increase in melanized parasites was attributable to the TEP1r transgene using allele specific RNAi . When transgenic TEP1r , but not endogenous TEP1s , was knocked down in the TEP1s;Vg-TEP1r transgenic line , the number of melanized parasites reverted to a level similar to control dsLacZ-injected mosquitoes ( Fig 4d ) . In the TEP1s;Vg-TEP1r line , transgenic TEP1r is induced to a peak level about 24 hours after a blood meal , possibly too late to efficiently fight parasite invasion . To induce TEP1 expression prior to the infectious blood meal and attempt to increase TEP1 concentration in the hemolymph , we offered a non-infectious blood meal and infected mosquitoes three days later , before the Vg promoter had returned to its off state . The results of such an experiment ( S4 Fig ) show no difference in infection levels between mosquitoes that were pre-induced by blood meal and naïve mosquitoes . We verified that transgenic TEP1r binds to parasites in the TEP1Δct2;Vg-TEP1r line ( S5 Fig ) and asked whether this was also the case in TEP1ΔT;Vg-TEP1r mosquitoes in which formation of the mature TEP1-cut form is inhibited . To this end , we performed immuno-histochemical staining of TEP1ΔT;Vg-TEP1r midguts 24h after infection using TEP1 specific antibodies ( Fig 5 ) . Results show that in the presence of TEP1ΔT , transgenic TEP1r protein is not detectable on ookinete surfaces , despite its observed , SPCLIP1 dependent , anti-parasitic and melanization activity . We tested whether the fat body-expressed , full length TEP1r in the TEP1ΔT;Vg-TEP1r line can enter hemocytes after bacterial injection . To this end we examined TEP1 staining in carcasses of blood fed mosquitoes , injected with heat killed E . coli , 24h after blood meal ( Fig 6 ) . Results show that in blood-fed TEP1ΔT;Vg-TEP1r mosquitoes , TEP1r artificially induced in the fat body by the blood meal accumulated in the hemocytes only when mosquitoes were injected with E . coli . Control wild type mosquitoes showed that TEP1 accumulation in hemocytes depended on bacterial injection , regardless of blood feeding . The TEP1ΔT mutant line , used here to control for the specificity of antibodies for TEP1 , showed no TEP1 staining in hemocytes under any condition . These results show that TEP1 detection in hemocytes can result from uptake of what is most likely TEP1-labeled bacteria . Since transgenic expression of TEP1r in a wild-type TEP1s background failed to produce refractory mosquitoes , we asked whether artificially elevating the levels of the endogenous TEP1s allele may lead to increased immunity against Plasmodium . To this aim , we stimulated expression of the endogenous TEP1 gene using synthetic transcription factors designed to bind specific promoter sequences . Transcription Activator Like Effectors ( TALEs ) are transcription-activating proteins derived from Xanthomonas bacteria , containing a DNA-binding domain composed of nucleotide-binding repeats that can be arranged in the desired order to bind chosen DNA sequences [41 , 42 , 43] . In order to trigger over-expression of the endogenous TEP1 gene , we designed TALE proteins that bind specific DNA sequences in the promoter region of TEP1 . To find the best potential binding position for a TALE , we first analyzed the 3 kb promoter region of TEP1 ( S6 Fig ) . Using a luciferase reporter assay in Drosophila S2 cells and nested promoter deletions , we found that a 250 bp fragment of the promoter was sufficient to induce reporter activity . The minimal promoter sequence , harboring a potential NF-κB binding site , responded to the addition of bacteria to the culture medium by increased luciferase activity ( S7 Fig ) . Mutating this NF-κB binding site abolished the reporter response to bacterial challenge ( S8 Fig ) . This suggests that the NF-κB pathway of S2 cells can activate the mosquito TEP1 promoter , in agreement with the known activation of native TEP1 transcription by the mosquito NF-κB pathway [35] . We used this minimal promoter to test several transcriptional activation domains for reporter activation by TALEs , namely those of yeast Gal4 , Drosophila Heat Shock Factor 1 and Herpes virus VP16 , and found that the VP16 activation domain fused to a TALE DNA-binding domain leads to highest reporter induction ( S9 Fig ) . The VP16 domain was thus chosen as the activation domain for all further designed TALEs . TALEs without an activation domain did not increase TEP1 expression and served as negative controls . We constructed five TALEs that bind at different positions along the TEP1 minimal promoter sequence ( Fig 7a and S6 Fig ) and tested their effect on reporter transcription in S2 cells ( Fig 7b ) . Results show that TAL3 binding the closest to the transcription start site had no effect on reporter activity , while the other TALEs had a modest effect , increasing activity by about 1 . 5–2 fold . However , TAL6 , binding the furthest from the transcriptional start site and just upstream of a sequence resembling a TATA box , triggered a strong , 20 fold increase in reporter activity . TAL6 was thus chosen to create transgenic mosquitoes with the aim of over-expressing endogenous TEP1 . In plants , natural and artificial TALEs induce target gene expression from alternative , TALE-specific transcription start sites [44 , 45] . We investigated whether TALEs triggered TEP1 expression through an alternative transcription site when transfected with the luciferase reporter in S2 cells . PCR products from the 5' RACE assay were sequenced and revealed that unlike in plants , both TAL0 and TAL6 preserved the normal transcriptional start site of the mRNA which remained located 43 bp upstream of the ATG ( S6 Fig ) . We generated several transgenic lines that express TALEs designed to bind and activate the TEP1 promoter . Our first choice of promoter for the expression of the synthetic transcription factor itself was the inducible fat body specific Vg promoter , in order to increase endogenous TEP1 levels after a blood meal , concomitant with Plasmodium parasite invasion . TAL6 fused to a VP16 ( TAL6vp16 ) activation domain was chosen due to its high activity in S2 cells . A control transgenic line was created with the same TAL6 lacking an activation domain ( TAL6ΔAD ) . Analysis of mRNA after blood feeding revealed that in both transgenic lines TALEs are induced by a blood meal , more efficiently in the case of Vg-TAL6vp16 ( Fig 7c ) . Furthermore , TAL6vp16 , but not TAL6ΔAD , induced a two-fold elevation in TEP1 mRNA levels . This elevation in the endogenous levels of TEP1 mRNA is modest compared to the 20-fold average elevation of the reporter in the cell line . Since the Vg promoter triggers a pulse of transcription after a blood meal , TALEs may activate TEP1 too transiently to achieve a significant effect . Therefore , we also tested if a constitutive fat-body promoter would durably induce high levels of endogenous TEP1 . For this , TAL6vp16 and TAL0vp16 were placed under the control of the Lp promoter , constitutively expressed in the fat body in larvae , pupae and adults . We included TAL0vp16 in addition to TAL6vp16 despite its modest effect in S2 cells ( ~1 . 5 fold increase , Fig 7b ) because of its unique binding position on the NF-κB binding site . TAL0vp16 was designed to bind and activate not only the promoter of TEP1 , but also similar sequences found in the promoters of APL1C and LRIM1 , two proteins that are known to bind TEP1 and contribute to its activity ( See Methods ) . Analysis of TEP1 mRNA in transgenic mosquitoes that constitutively express TAL6vp16 and/or TAL0vp16 ( Fig 7d ) revealed a maximum of 2-fold induction of endogenous TEP1 mRNA in mosquitoes that expressed both TALES simultaneously ( progeny of a cross between the two lines ) . Western blot analysis of TEP1 in these mosquitoes ( Fig 7e ) confirmed a 3–4 fold increase in TEP1 protein levels circulating in the hemolymph ( Fig 7f ) . We thus proceeded to test whether this elevation in TEP1 levels in the hemolymph had any effect on parasite loads after Plasmodium infection . Infection of the blood meal inducible Vg-TAL6vp16 transgenic line did not result in any significant change in parasite loads ( Fig 7g ) . Likewise , analysis of parasite loads after infection of the constitutively expressing Lp-TAL6vp16 and Lp-TAL0vp16 lines did not show any significant difference in oocyst numbers between transgenic and control lines ( Fig 7h ) .
TEP1 is a secreted protein that circulates in the hemolymph of mosquitoes at all developmental stages starting from larval stages [20] . Previous observations suggested that TEP1 is produced by hemocytes and secreted from these cells as a full-length protein which is then cleaved in the hemolymph [20] . Evidence to support this model included immuno-histochemical staining of mosquito tissues that showed TEP1 accumulation inside hemocytes , particularly after bacterial challenge and 24-48h after infection with P . berghei [22 , 35 , 46] . However , these results may also be explained by uptake of TEP1 protein , or TEP1-labeled particles , by hemocytes . In addition , analysis of RNA expressed by hemocytes using microarrays has shown that TEP1 transcript is present in these cells , but not enriched compared to whole female tissues , and is not further induced by infection [47] . TEP1 did not emerge as a hemocyte transcript in the hemocyte transcriptomics study of Pinto et al . [48] . Of note , it is technically challenging to acquire a pure population of hemocytes without contamination by fat body cells that detach into the hemolymph during sample collection [49] . Here we obtain evidence that TEP1 , LRIM1 and APL1C are expressed primarily in the fat body . This is similar to the expression pattern of many insect anti-microbial peptides [50 , 51] and to that of some Drosophila TEP family member genes as determined by in situ hybridization [52] . In mammals , complement proteins phylogenetically and functionally homologous to TEP1 are expressed and secreted from the liver ( [53] , reviewed in [54] ) . Interestingly , some of the functions of the liver are performed by the fat body in insects . We crossed the TEP1-GFP reporter mosquitoes to our hemocyte-specific PPO6-RFP line , and observed that the DsRed-positive hemocytes were GFP negative . We note that this result does not rule out that sub-populations of hemocytes not expressing the PPO6 promoter may also express TEP1; fine transcriptomic analyses will be required to quantitatively determine the contribution of each tissue to the global pool of circulating TEP1 . Our transgenic reporter lines also reveal that TEP1 , but not APL1 and LRIM1 , is also expressed in the proventriculus of adult mosquitoes . Interestingly , the proventriculus was found to be a site of immune gene expression in A . gambiae [55] and in other insects such as tsetse flies [56] . In A . gambiae , mRNA transcripts of antimicrobial peptides Defensin 1 , Cecropins , Gambicin , as well as TEP1 , are enriched in the proventriculus [55] . TEP1 is also expressed in the neighboring gastric caeca during larval stages [57] . Future studies may clarify whether proventriculus-expressed TEP1 has a function that bypasses the requirement for LRM1 and APL1-C , perhaps in the lumen of the digestive tract where TEP1 could interact with the gut microbiota . Variation in GFP levels among individual mosquitoes led us to test whether GFP fluorescence intensity correlates with infection outcome . We found no such correlation and concluded that GFP expression either did not reflect TEP1 levels in the mosquitoes at the time of P . berghei infection , or that TEP1 level variations do not correlate with resistance to the parasite . The lack of correlation between GFP levels and immune status may be due to the remarkable stability of GFP protein , which persists throughout mosquito life in the fat body after a blood feeding-induced pulse of expression in the Vg-GFP line [34] compared to the highly dynamic turnover of TEP1 protein , which disappears by 24h after RNAi silencing [37] . Lysis of ookinetes at the basal lamina is associated with binding of TEP1 directly to the ookinete surface [19] . However , TEP1 does not induce direct lysis of bound pathogens , and the molecular mechanism of killing remains unknown . It has been previously shown that TEP1-cut is the major form that binds ookinetes [21 , 22] . Binding of circulating TEP1-cut to pathogen surface triggers the recruitment of a convertase complex that requires the serine protease homologue SPCLIP1 and that cleaves circulating TEP1-full leading to further accumulation of TEP1-cut at the pathogen surface [38 , 39] . We expressed the TEP1r allele in two different TEP1 mutant backgrounds: TEP1ΔT carries a single threonine deletion that appears to destabilize the protein , as mutant TEP1ΔT protein becomes faintly detectable only in aged mosquitoes [27] . TEP1Δct2 lacks the C-terminal third of the protein and is likely to be a complete null . In these two mutant backgrounds , transgenic TEP1r produced in the fat body and secreted into the hemolymph was sufficient to rescue parasite killing , and to trigger parasite melanization . Interestingly , in the TEP1ΔT background , but not in the TEP1Δct2 background , maturation of transgenic TEP1r-full to TEP1-cut appeared to be inhibited as we never detected circulating TEP1-cut in the hemolymph , even after bacterial injection . This suggests that very low amounts of the non-functional TEP1ΔT protein inhibit maturation of TEP1-full , perhaps by blocking the enzyme responsible for TEP1 cleavage in the hemolymph . Possibly related to this , LRIM1/APL1C levels are reduced in the TEP1ΔT , but not in the TEP1Δct2 mutant . In the TEP1ΔT;VgTEP1r mosquitoes , TEP1 binding is not detectable by antibody staining on ookinete surfaces , consistent with the lack of detectable circulating TEP1-cut form . Yet , both mutants rescued with TEP1r showed an increase in parasite melanization . We initially hypothesized that contrary to current views , processing of TEP1-full to TEP1-cut by the SPCLIP1-dependent convertase and accumulation of TEP1-cut at the parasite surface may be dispensable for parasite melanization in some contexts . However , disabling the TEP1 convertase complex through silencing SPCLIP1 in TEP1ΔT;VgTEP1r mosquitoes abolishes melanization and , to some extent , increases parasite numbers . Therefore , conversion of TEP1-full to TEP1-cut seems to be a prerequisite for parasite killing and melanization even in TEP1ΔT;VgTEP1r mosquitoes , but a large accumulation of TEP1-cut at the surface of parasites may not be necessary to trigger killing and melanization . Another function of TEP1 in the hemolymph is the opsonization of bacteria [20 , 39 , 58 , 59] , marking them for phagocytosis by hemocytes . Shortly after the injection of bacteria , TEP1 protein can be detected inside the hemocytes that are attached to dissected mosquito carcasses . The appearance of TEP1 inside these cells may be due to induced expression of TEP1 within hemocytes , presumably aimed at replenishing TEP1 levels in the hemolymph , or due to phagocytosis of TEP1-covered bacteria by hemocytes . We observed that in a TEP1 null mutant background , TEP1r expressed from a transgene in the fat-body could enter hemocytes . Our results suggest that TEP1 detected in hemocytes after bacterial challenge comes at least in part from uptake of circulating TEP1 protein . TEP1r could be a candidate gene for creating malaria resistant mosquitoes by driving the gene into the wild mosquito population , for example using CRISPR-Cas9 gene drive [14 , 15] . This should not be achieved by replacement of endogenous TEP1s alleles with the more active TEP1r , as a few failed gene conversion events could result in Cas9-induced TEP1s loss-of-function mutations that would generate hyper-susceptible mosquitoes . Instead , the anti-parasitic factor should be driven into an independent neutral genomic locus . We thus tested whether over-expression of TEP1r in wild type background affects infection outcome . We found that transgenic TEP1r is indeed expressed in the presence of endogenous TEP1s , but fails to enhance parasite killing , while still able to stimulate melanization . Using allele specific knock-down we showed that the allelic difference between TEP1r and TEP1s is sufficient to explain the different intensities of the melanization reaction . In addition , silencing endogenous TEP1s in the same mosquitoes restored the ability of transgenic TEP1r to kill parasites and further elevated melanization ( Fig 4d ) , as observed when transgenic TEP1r was expressed in a mutant background . This indicates that although less efficient than TEP1r for parasite killing , endogenous TEP1s protein is able to outcompete transgenic TEP1r for the parasite killing function . This is reminiscent of the apparent recessivity of TEP1r in TEP1s x TEP1r genetic crosses [25] . Therefore , simple transgenic expression of TEP1r in a wild type background where TEP1s is the most frequent allele would not be sufficient to render a mosquito population refractory to Plasmodium infection . Since transgenic expression of TEP1r in a wild-type TEP1s background failed to produce refractory mosquitoes , we asked whether artificially elevating the levels of the endogenous TEP1s allele may lead to increased immunity against Plasmodium . To this aim , we stimulated expression of the endogenous TEP1 gene using synthetic transcription factors designed to bind specific sites in the TEP1 promoter . We designed a luciferase reporter for TEP1 promoter activity and tested it in Drosophila S2 cells . The promoter contained an NF-κB binding site , which mediated elevation of reporter activity when S2 cells were treated with heat-killed bacteria . Thus , the cloned TEP1 promoter fragment is activated by the S2 cell NF-κB pathway , consistent with TEP1 activation by the mosquito NF-κB pathway [35] . Once we determined the optimal TALE sequence for binding TEP1 promoter , we created several transgenic mosquitoes lines that expressed TALEs either under an inducible promoter ( Vg ) or a constitutive fat body promoter ( Lp ) . In all cases , the increase in endogenous TEP1 expression was very modest both at mRNA and protein levels , indicating that cell culture assays are poor predictors of TALE activity in vivo . TEP1 mRNA levels may be tightly regulated in vivo , or may depend on regulatory features that were absent or inactive in the S2 cells assays . In wild type mosquitoes , some parasites are bound by TEP1 and killed , while others evade TEP1 binding . If the quantity of circulating TEP1 were the rate-limiting factor in parasite binding , we expected that even a modest 2-fold increase in TEP1 expression would yield increased resistance to Plasmodium . However , infection assays with mosquitoes that expressed TALEs and showed a 2 to 3-fold elevation in TEP1 levels did not show any increase in resistance . This is similar to the lack of increased parasite killing in mosquitoes expressing transgenic TEP1r in addition to endogenous TEP1s , and suggests that the survival of some parasites is not due to a lack of TEP1 . The absence of effect of the TALE-mediated over-expression of TEP1s contrasts with the elevated melanization obtained by transgenic expression of TEP1r in mosquitoes carrying endogenous TEP1s , again confirming that enhancing melanization is an allelic property of TEP1r . To conclude , our results indicate that simply augmenting the level of a given anti-parasitic factor may not be sufficient to achieve greater resistance levels , and biotechnological approaches aiming at rendering the mosquitoes resistant to malaria will need to integrate the full complexity of molecular interactions between immune factors .
A . gambiae mosquitoes were maintained in standard conditions ( 28°C , 75–80% humidity , 12-hr/12-hr light/dark cycle ) . Larvae were raised in deionized water and fed finely ground TetraMin fish food . Adults were fed on 10% sucrose ad libitum and females were blood-fed on anaesthetized mice . For infection , mosquitoes were fed on CD1 mice infected with the P . berghei GFP-con 259cl2 clone ( ANKA strain ) that constitutively expresses GFP [60] . Mice infected with Plasmodium berghei were sacrificed before they developed malaria symptoms by cervical dislocation . Mice used to blood feed and infect mosquitoes were anesthetized with a mix of xylazine and ketamine . Infected mosquitoes were maintained at 21°C , unfed mosquitoes were removed 24h post feeding . Seven days after infection , live midguts were mounted on microscope slides in PBS , photographed under fluorescent light , and parasites were counted on the pictures . Statistical significance of differences in parasite counts was evaluated with a Mann-Whitney non-parametric test . Transgenic mosquitoes were created in the A . gambiae X1 attP docking line using the pDSA R/T/G/Y/P and pattB-RfB2 transgenesis vectors as described [34] . The following plasmids were constructed by Goldengate cloning to generate transgenic mosquitoes: pDSAP-LRIM1-GFP , pDSAT-APL1-GFP . In the case of the TEP1-GFP , 3xP3-RFP reporter and of the Vg-TEP1r , 3xP3-YFP constructs , plasmids were assembled using Gateway cloning into pattB-RfB2 [34] . Vg-TALE and Lp-TALE constructs were assembled by Goldengate cloning in pDSAR , pDSAT or pDSAG as described for TALEs [42] . Plasmid sequences , as well as promoter and TALE modules are provided in supplementary file S1 Text . In the case of TAL0 , we exploited the TAL repeat “NS” di-residue binding ambiguous bases to allow simultaneous targeting of the TEP1 , LRIM1 and APL1C promoters by TAL0 ( target sites indicated in S4 Table ) . Transgenic lines PPO6-RFP , 3xP3-YFP and Lp-RFP , 3xP3-GFP and Vg-GFP , 3xP3-RFP were previously described [34] . Promoters and genes were amplified from genomic DNA or cDNA by PCR with primers containing appropriate sites for Goldengate or Gateway cloning . All constructs were verified by sequencing . To introduce the TEP1r , 3xP3-YFP transgene in the background of the TEP1ΔT and TEP1Δct2 mutations [27] , the transgenic line was backcrossed three times to the mutant line . After each backcross , YFP-positive progeny larvae were purified with the COPAS flow cytometer [61] to retain the transgene . After the last backcross , one leg from 48 male and 48 female mosquitoes kept alive in individual tubes was analyzed by PCR as described [27] using the Phire Direct Tissue PCR kit ( Thermofisher ) . Mosquitoes showing homozygosity for the TEP1 mutation were pooled to establish a homozygous mutant and heterozygous transgenic line . For experiments , COPAS sorting of this line was used to generate cultures containing 50% non-transgenic mutant control and 50% transgenic mutant mosquitoes . In some cases , non-mutant control mosquitoes with RFP eye fluorescence ( such as Vg-GFP , 3xP3-RFP ) were added to the same culture . On the day of dissection , transgenic and non-transgenic mosquitoes were separated under a fluorescence microscope based on the eye fluorescence of the transgenesis markers . This process ensured identical exposures of all mosquito genotypes to the same growth and environmental conditions . Allele specific knockdown of TEP1 was performed by injection of dsRNA targeted specifically against TEP1r or TEP1s as previously described [25] . dsRNA was injected into the thorax of one day old female mosquitoes , three days prior to infection . Allele specific knockdown was verified by qRT-PCR . SPCLIP1 RNAi knockdown was performed as described [39][62] . Mosquitoes were dissected in PBS , fixed in paraformaldehyde for 20 min , permeabilized in PBS containing 0 . 1% triton ( PBST ) for 10 min , blocked in PBST containing 1%BSA ( PBSTB ) for one hour . Mosquito tissues were incubated with primary antibody ( rabbit anti TEP , mouse anti Lipophorin ) for at least two hours , washed 3 times and incubated with fluorescent secondary antibody ( Alexa488 , or Cy3 ) in PBSTB containing 1μg/μl DAPI . Tissues were washed three times in PBST and mounted on a slide with a drop of Aqua/poly mount ( Polysciences ) . For fat body analysis , mosquitoes were dissected and treated as previously described [63] . Images were collected under a Zeiss LSM 710 confocal microscope . RNA for RT PCR was extracted from groups of 5–10 mosquitoes . Mosquitoes were collected on ice into 300 μl RNAzol RT ( MRC ) , homogenized with a Precellys homogenizer , with 2 pulses of 20 seconds . RNA was purified on DirectZol RNA columns ( Zymo Research ) following manufacturer's instructions . RNA was treated with RNAse-free DNAse I ( ThermoFisher ) . Reverse transcription was performed using iScript cDNA Synthesis Kit ( Bio-Rad ) using 500 μg of RNA . SYBR Green PCR Master Mix , ( Applied Biosystems ) qPCR reactions were set up on 96 well plates with three technical and three biological repetitions and were run on 7500 Real-time PCR machine ( Applied Biosystems ) . Mosquito hemolymph from 10 mosquitoes was collected by proboscis clipping directly into 20 μl of Laemmli buffer . Samples were heated at 90°C for 2–5 minutes and 2 to 8 μl ( depending on the experiment ) were loaded on polyacrylamide gels . Rabbit polyclonal antibodies against TEP1 , APL1C , LRIM1 , PPO2 and Vg were used . Secondary HRP conjugated antibodies were used at 1:15000 dilution . Images were collected on Fusion FX7 scanner ( Vilber Lourmat ) . S2 cells were grown at 23°C in Schneider's medium ( Biowest ) supplemented with 10% FCS ( Invitrogen ) and 1% Penicillin/Streptomycin solution ( Sigma ) . Cells were seeded in 24 well plates ( Starsted ) at 50 , 000 cells/well one day prior to transfection . Constructs expressing both firefly luciferase and Renilla luciferase were used as reporters . TEP1 promoter was cloned upstream of firefly luciferase . Renilla luciferase under the constitutive actin5c promoter was used as transfection control . Cells were transfected using Effectene ( Qiagen ) according to manufacturer's instructions . Cells were collected three days after transfection for luciferase activity analysis . Luciferase assay was performed using the Dual-Luciferase Reporter Assay System ( Promega ) according to manufacturer's instructions . Luciferase activity is depicted as Firefly activity divided by Renilla activity , averaged on three wells transfected with a set of given plasmids . RNA from S2 cells was extracted using RNAzol RT ( MRC ) and purified on DirectZol RNA columns ( Zymo Research ) . 5' RACE was performed using First Choice RLM-Race kit ( Ambion ) according to manufacturer's instructions . Briefly , ligation of specific primers to 5' and 3' ends of the RNA allows the amplification of a PCR product that corresponds to the transcription start site of gene of interest . The resulting PCR product was cloned into CloneJet PCR Cloning Kit ( ThermoFisher Scientific ) followed by sequencing of several clones .
This project was approved by the French Ministry for Research ( agreement #555 . 01 ) upon review by its Ethics Committee for Animal Research CREMEAS #35 , which approved the research . Experiments were carried out in conformity with the 2010/63/UE European animal bioethics legislation . Our animal care facility received agreement #F67-482-2 from the veterinary services of the region Bas-Rhin ( Direction Départementale de la Protection des Populations ) . | We examined whether the natural anti-parasitic protein TEP1 can be harnessed to generate malaria resistant mosquitoes . We report the expression pattern of genes of the TEP1 immune pathway and the effects of both exogenous and enhanced endogenous expression of TEP1 on the development of Plasmodium in Anopheles gambiae . We found that exogenous expression of TEP1 increases resistance to Plasmodium in TEP1 mutants but not in a wild type background , and that enhancing expression of endogenous TEP1 does not increase resistance . We conclude that increased expression of TEP1 alone is not sufficient to render mosquitoes resistant to Plasmodium . | [
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... | 2017 | Transgenic Expression of the Anti-parasitic Factor TEP1 in the Malaria Mosquito Anopheles gambiae |
Bacterial populations co-ordinate gene expression collectively through quorum sensing ( QS ) , a cell-to-cell communication mechanism employing diffusible signal molecules . The LysR-type transcriptional regulator ( LTTR ) protein PqsR ( MvfR ) is a key component of alkyl-quinolone ( AQ ) -dependent QS in Pseudomonas aeruginosa . PqsR is activated by 2-alkyl-4-quinolones including the Pseudomonas quinolone signal ( PQS; 2-heptyl-3-hydroxy-4 ( 1H ) -quinolone ) , its precursor 2-heptyl-4-hydroxyquinoline ( HHQ ) and their C9 congeners , 2-nonyl-3-hydroxy-4 ( 1H ) -quinolone ( C9-PQS ) and 2-nonyl-4-hydroxyquinoline ( NHQ ) . These drive the autoinduction of AQ biosynthesis and the up-regulation of key virulence determinants as a function of bacterial population density . Consequently , PqsR constitutes a potential target for novel antibacterial agents which attenuate infection through the blockade of virulence . Here we present the crystal structures of the PqsR co-inducer binding domain ( CBD ) and a complex with the native agonist NHQ . We show that the structure of the PqsR CBD has an unusually large ligand-binding pocket in which a native AQ agonist is stabilized entirely by hydrophobic interactions . Through a ligand-based design strategy we synthesized and evaluated a series of 50 AQ and novel quinazolinone ( QZN ) analogues and measured the impact on AQ biosynthesis , virulence gene expression and biofilm development . The simple exchange of two isosteres ( OH for NH2 ) switches a QZN agonist to an antagonist with a concomitant impact on the induction of bacterial virulence factor production . We also determined the complex crystal structure of a QZN antagonist bound to PqsR revealing a similar orientation in the ligand binding pocket to the native agonist NHQ . This structure represents the first description of an LTTR-antagonist complex . Overall these studies present novel insights into LTTR ligand binding and ligand-based drug design and provide a chemical scaffold for further anti-P . aeruginosa virulence drug development by targeting the AQ receptor PqsR .
Bacterial cells communicate with each other through quorum sensing ( QS ) , a mechanism for co-ordinating gene expression at the population level via the release and detection of self-generated signalling molecules [1] . Once a critical threshold concentration of QS signal has been attained , a change in collective behavior ensues through the activation of a sensor or regulator protein . In general , QS facilitates the coordination of population behavior to enhance access to nutrients , provide collective defense against other competitor organisms or to encourage community escape where population survival is at risk [1] . QS signal molecules are chemically diverse and include both small peptides and organic molecules such as the N-acylhomoserine lactones ( AHLs ) and 2-alkyl-4 ( 1H ) -quinolones ( AQs ) . In addition , many bacteria possess several interacting QS modules organized into regulatory hierarchies employing multiple signal molecules from the same or different chemical classes . Such QS hierarchies regulate motility and biofilm development , secondary metabolite production , bioluminescence and virulence [1] . With respect to the latter , the global emergence of multi-antibiotic resistant bacteria and the paucity of new clinically effective antibiotics have renewed interest in the development of agents which control infection through the attenuation of bacterial virulence rather than inhibition of growth [2] , [3] . In this context , the QS-dependent regulation of virulence offers an attractive suite of potential targets which include the QS signal synthase , the response regulator and the QS signal molecule itself [4] , [3] . While there have been extensive attempts to unravel the molecular basis for AHL-dependent QS and to develop inhibitors directed against LuxR-type transcriptional regulators , there is relatively little structural information on the recognition and mechanism of action or inhibition of AQ-type QS signals . AQs are produced by pathogens such as Burkholderia pseudomallei and Pseudomonas aeruginosa [5] , [6] . P . aeruginosa thrives in diverse ecological niches and causes both acute and chronic infections in humans , animals , plants and insects . Multi-antibiotic resistant strains have emerged globally as a major cause of hospital-acquired infections for which current therapeutic options are very limited [7] . P . aeruginosa produces diverse exotoxin virulence determinants and secondary metabolites including cyanide , readily forms biofilms and is naturally resistant to many antimicrobial agents . Since many of these virulence genes are controlled by QS [8] , P . aeruginosa has emerged as a paradigm pathogen since it employs a sophisticated multi-signal QS system incorporating both AHL/LuxR type and AQ-dependent gene regulatory systems [8] ( Figure 1 ) . With respect to the AQs , P . aeruginosa produces over 50 different congeners which were originally identified via their antimicrobial properties but are now known to possess QS , immune modulatory , cytochrome inhibitory , metal chelating , membrane vesicle-stimulating and oxidant activities ( reviewed in [9] ) . 2-Heptyl-3-hydroxy-4 ( 1H ) -quinolone ( the ‘Pseudomonas Quinolone Signal’ , PQS ) and its immediate precursor , 2-heptyl-4-hydroxyquinoline ( HHQ ) are frequently considered to be the primary AQs involved in QS although other active AQ analogues , notably the C9 congeners , 2-nonyl-3-hydroxy-4 ( 1H ) -quinolone ( C9-PQS ) and 2-nonyl-4-hydroxyquinoline ( NHQ ) are produced by P . aeruginosa in similar concentrations [10] , [11] . The synthesis and action of PQS and HHQ and related congeners depends on the pqsABCDE operon , which is positively controlled by the transcriptional regulator PqsR ( MvfR ) [12] , [13] . The first four gene products of this operon are required for AQ biosynthesis [9] . HHQ is released into the extracellular milieu where it is internalized via adjacent cells [14] and oxidized to PQS via the action of the mono-oxygenase PqsH [5] , [13] , [15] . The function of the pqsE gene product , a putative metallohydrolase , is not currently understood . Although it does not contribute to AQ biosynthesis , it is required for swarming motility biofilm development and virulence and is involved in the negative regulation of the pqsABCDE operon [15] , [16] . Strains with mutations in pqsR and pqsA are severely attenuated in experimental animal infection models highlighting the important contribution made by AQ signalling to pathogenicity [16] , [12] . Furthermore the presence of AQs in the sputum and broncho-alveolar lavage fluid of cystic fibrosis patients chronically infected with P . aeruginosa provides evidence of their importance in human infection [17] , [18] . AQ synthesis and pqsE expression are subject to a positive feedback loop which involves the activation of PqsR by HHQ and PQS and their C9 congeners to drive the expression of the pqsABCDE operon [14] , [19] , [20] , [21] , [22] . In whole P . aeruginosa cell assays , HHQ and PQS exhibited EC50s in the low micromolar range for the PqsR-dependent activation of pqsA [23] . Activation of PqsR depends on the AQ alkyl chain length [23] , PQS congeners with C1 , C3 , C5 or C11 alkyl chains exhibit only weak activities compared with the C7 compounds . However , the C9 congeners , 3-hydroxy-2-nonyl-4 ( 1H ) -quinolone ( C9-PQS ) and 2-nonyl ( 1H ) -4-hydroxyquinoline ( NHQ ) are highly active [23] . Although both HHQ and PQS can activate PqsR , the PqsH-mediated introduction of a hydroxyl group at the 3 position of the quinolone ring confers additional physicochemical functionality to PQS over HHQ including iron chelation [14] and microvesicle formation [24] . Attempts to attenuate AQ signaling and the virulence of P . aeruginosa without perturbing bacterial growth have so far mainly focused on enzymes which inactivate PQS [25] and methylated or halogenated derivatives of the AQ precursor anthranilate such as 2-amino-4-chorobenzoic acid ( 4-CABA ) which inhibits AQ biosynthesis probably at the level of PqsA by competing with anthranilate for the enzyme active site [26] , [27] . This approach has recently shown promising results in limiting the systemic proliferation of P . aeruginosa infection in mice although the concentrations required to inhibit AQ production were high ( millimolar range; [27] ) and so unlikely to be clinically useful . More recently a number of PqsD inhibitors have been identified [28] . An alternative approach for blocking AQ biosynthesis and signalling and hence virulence would be to target the response regulator PqsR . Although the structures of the PqsR-activating ligands PQS and HHQ are known ( Figure 2 ) , there is no information on the PqsR ligand binding site . PqsR belongs to the LysR family of transcriptional regulators ( LTTRs ) which are widespread in bacteria [29] among which P . aeruginosa appears to have one of the largest repertoires [30] . LTTR proteins generally possess a highly conserved N-terminal helix-turn-helix ( HTH ) DNA-binding domain but a poorly conserved C-terminal ligand-binding domain usually termed the co-inducer binding domain ( CBD ) where the co-inducer is a low molecular weight ligand [29] . LTTRs function as either activators or repressors in feedback loops in which the co-inducer ligand is required for transcriptional control . LTTR proteins are generally thought to function as tetramers recognizing multiple binding sites within the promoter/operator region of the target gene ( s ) which include a regulatory binding site ( RBS ) incorporating the LTTR box ( general consensus T-N11-A ) and an activation binding site ( ABS ) . Occupation of these sites results in DNA bending and contact with RNA polymerase to initiate transcription [29] . Structural studies of LTTRs have been mostly restricted to the C-terminal CBD because of the insolubility associated with the HTH domain [29] . LTTR CBDs typically have two α/β sub-domains ( CBDI and CBDII ) connected by an anti-parallel β-sheet known as the hinge region [31] , [32] . Well characterised ligands for LTTRs include catechols and chlorinated aromatics which are co-inducers for CatM and BenM respectively binding into a pocket between the CBD subdomains [29] . With respect to PqsR , how this unusual LTTR [30] recognizes and responds to the larger hydrophobic ligands associated with AQ-dependent QS is not known . The structural basis for the recognition of AQs by PqsR has not been elucidated and there is consequently a lack of molecular detail to facilitate the development of PqsR inhibitors as novel therapeutics . Through a multidisciplinary effort we provide new insights into the structure of the PqsR co-inducer binding domain ( PqsRCBD ) in complex with both a native AQ agonist and a potent quinazolinone ( QZN ) antagonist . The QZN scaffold was evolved through ‘ligand based design’ and we show that in P . aeruginosa a very simple isosteric replacement is sufficient to switch a QZN from potent agonist to potent antagonist turning QS-dependent virulence gene expression on or off respectively . Although other LTTR agonist complex structures have been described this , to our knowledge , is the first description of an LTTR-antagonist complex crystal structure .
To investigate the molecular basis of PqsR ligand recognition we first sought to determine the crystal structure . The full length PqsR receptor containing both the DNA-binding domain and CBD was insoluble when expressed in Escherichia coli . We thus focused on a construct spanning C-terminal residues 94-332 incorporating the CBD ( Figure 3A ) . This was soluble and was utilised for initial crystallisations together with a truncated version which removed the 23 amino acid C-terminal tail . Crystals were obtained only in conditions where the precipitant , 2-methyl-2 , 4-pentanediol ( MPD ) was present and the structure was determined using SAD and SIR phases in spacegroup P6522 to 2 . 5 Å resolution ( Table 1 ) . Crystallisation of the PqsRCBD has been reported previously by two groups at 5 Å [33] and 3 . 25 Å resolution respectively [34] . The latter P6522 crystal form has similar cell dimensions to the crystal form reported here although the crystallisation conditions differ and MPD is not present . The construct reported in Xu et al . [34] has different domain boundaries spanning residues 91–319 compared to residues 94–294 and 94–332 reported here . The PqsRCBD structure has one molecule in the crystallographic asymmetric unit . Analytic gel filtration was performed with a calibrated Superdex 75 10/300 column revealing the recombinant PqsRCBD eluted close to the 43 kDa marker ( ovalbumin ) and above the 29 kDa marker ( carbonic anhydrase ) ( Figure S1A ) . As the calculated monomer molecular weight for PqsRCBD is 22 . 7 kDa this indicates a dimeric species is present in solution . This is in agreement with the gel filtration data for the PqsRCBD reported by Xu et al . [34] . The topology of the PqsRCBD structure is illustrated in Figure 3B showing two subdomains ( CBDs I and II ) connected by an antiparallel β-sheet termed the hinge region as observed in related LTTR structures of BenM and CatM [35] , [36] . Overall , CBDI is similar to other LTTRs with the exception of helix α2 which is shorter in PqsR . In CBDII a number of large changes are observed as a helix present in BenM between strands β5 and β6 is absent and replaced by an irregular loop structure ( L1 ) lying at the junction of the two subdomains . The hinge region also adopts a different conformation in PqsRCBD and the β4 , β8 antiparallel strands are repositioned closer to the C-terminal helix . These two changes of a helix removal and the repositioning of the hinge region effectively open up the CBDII structure to form a large hydrophobic pocket occupied by two MPD molecules ( Figures 3C and 3D ) . MPD molecule 1 ( MPD1 ) is highly buried , surrounded by aliphatic residues ( Leu , Ile , Ala ) from both sub-domains and a water mediated interaction with the carbonyl group of Ile236 at the bottom of the pocket ( termed the B pocket , Figure 3D ) . MPD2 is predisposed closer to the surface in CBDII interacting with the side chains of Tyr258 and Val211 ( the A pocket , Figures 3C and 3D ) . We examined the crystal lattice and a large dimer interface was observed with a buried surface area of 1232 Å2 ( topology shown in Figure 3E ) . A feature of the dimer is that it presents the two CBDII pockets on the same surface by aligning the two hinge regions which interact at either end of strands β4 and β8 forming contacts . At the centre , the β4 side chain Lys266 is fully extended forming a salt bridge to Glu259 close to the dimer axis . Further interfacial contacts occur between helices α3 and α6 at either end of the dimer ( Figure 3E ) . A second feature is the two interlocking loops which form a lid partially covering the MPD molecules ( lid loops L1 and L2 shown in red and blue respectively in Figure 3F ) . The pocket in each monomer connects across the dimer interface via a narrow channel shown as mesh in Figure 3E . A second crystallographic dimer interface occurs in the PqsR lattice which buries a smaller surface area ( 614 Å2 ) . This involves an antiparallel β-sheet formed by strand β2 as well as antiparallel packing of the two N-terminal α1 helices ( Figure S1B ) . One side of the interface consists of hydrogen bonds from the β-sheet and sidechain-sidechain interactions from symmetry related Ser123 side chains ( Figure S1 C and D ) . The residue Ser112 side chains from helix α1 form a similar side chain hydrogen bond close to the dimer axis . In the centre , hydrophobic contacts come from residues above and below the β-sheet ( Figure S1 C and D ) . We next sought to determine how the PqsRCBD recognises a naturally occurring AQ co-inducer . As the PqsRCBD lattice has a high solvent content ( 72% ) we performed crystal soaking experiments with the most active native ligands produced by P . aeruginosa namely HHQ , NHQ ( C9-HHQ ) , PQS and C9-PQS ( Figures 2 and 4A ) as well as shorter carbon chain length derivatives . For the majority of these experiments , a calculation of difference ( Fo-Fc ) maps resulted in the characteristic electron density for two MPD molecules occupying the PqsRCBD pocket and no evidence of a bound ligand ( Figure S2A ) . By contrast the soaking of NHQ for 24 h resulted in elongated and connected electron density spanning the two sub-pockets ( Figure S2B ) . The electron density in the deeper B pocket was observed to be planar in shape and model building allowed fitting of the quinolone moiety of NHQ in one unique orientation . The alkyl chain can be modelled extending into the remaining cigar shaped electron density to occupy the A pocket . The Tyr258 side chain pins the alkyl chain down against side chains from residues Ile186 , Val170 , Leu189 , Ile236 from the bottom and sides of the A pocket forming a comfortable fit ( Figures 4B and 4C ) . The bicyclic ring structure is enclosed on either side in the B pocket by contacts from Leu207 , Leu208 and Ile236 . From above , contacts come from Ile149 and Ala168 and from below the sidechain of Phe221 ( Figure 4B ) . The fit of the bicyclic ring into the B pocket is not precise and beneath the carbonyl and in front of the aliphatic ring two vacant sub-pockets are present . A surprising feature of the structure is that the interactions are all hydrophobic with the absence of any hydrogen bonds or electrostatic interactions to the NHQ carbonyl oxygen or NH group of the bicyclic ring ( Figure 4D ) . Although the interactions with NHQ are exclusively hydrophobic , modelling of the additional OH present in the alternative co-inducer , PQS , reveals this group could potentially form an additional contact through a hydrogen bonding interaction with the carbonyl group of Leu207 . Superposition of the PqsRCBD-NHQ with the PqsRCBD-MPD structure reveals subtle local conformational changes in the binding pocket ( Figure 4E ) . The Thr265 side-chain rotates by 180° to make a direct contact with NHQ and a concomitant 0 . 7 Å movement of the main chain affects a similar movement in the position of the adjacent residue Asp264 in the hinge region affecting strands β4 and β8 ( Figure 4E ) . To probe the contribution of the key PqsRCBD amino acid residues identified above ( Figure 4B ) to AQ binding , 13 site-specific substitutions were introduced into a 6His-tagged PqsR which retains activity in P . aeruginosa ( Figure 5A ) . PqsR mutant functionality was evaluated by ( i ) Western blot to confirm PqsR expression ( Figure 5C ) and ( ii ) the ability to restore pqsA expression in the P . aeruginosa ΔpqsR miniCTX::pqsA'-lux reporter strain ( Figure 5B ) . Each of the pqsR mutations altered pqsA promoter activity with mild reductions observed for three mutations Ile186Ala , Ile236Phe and Leu207Glu which exhibited 44 . 1% , 20 . 5% and 13 . 7% of the wild-type PqsR-6His , respectively ( Figure 5B ) . In the PqsRCBD structure , Ile186 is positioned at the far end of the A pocket and substitution by Ala removes the side chain atoms which contact the end of the alkyl chain; a loss of this contact would be predicted to reduce the interaction with the A pocket . Ile236 is positioned at the bottom of the A pocket lying at a boundary with the B pocket . It is fully buried by bound NHQ in the complex structure making contacts between its side chain atoms and the planar surface of the bicyclic ring . Mutation to Phe would be predicted to disrupt binding by introducing extra volume and changing the shape of the pocket . Leu207 is positioned on the side of the B pocket and interacts with NHQ at the junction between the alkyl chain and the bicyclic ring through its terminal side chain atom . The Leu207Ala and Leu207Glu mutations both result in a ∼10% response compared with wild type indicating that the altered size and charge similarly affect optimal ligand contacts . An almost complete loss of activity ( <2% of that of the wild type ) is observed for four mutations ( Ile149 , Phe221 , Tyr258 , Ile263 ) . As we are unable to purify a soluble form of the full length wild type or mutant PqsR variants we could not rule out whether these four mutations have an effect by disrupting protein folding rather than ligand binding . Furthermore , we also observed that the PqsRCBD protein precipitates in the presence of its highly hydrophobic ligands even at dilute protein concentrations ( a finding also noted by Xiao et al . , [21] ) making alternative biophysical approaches difficult . Using a P . aeruginosa ΔpqsA ΔpqsH ΔpqsR triple mutant which does not produce endogenous AQs and containing a chromosomally-integrated miniCTX::pqsA'-lux reporter , we examined the response of the PqsR-6His variants to PQS and HHQ . The data presented in Figure 5D shows that the variants exhibited reduced responses to PQS and HHQ , consistent with the data in Figure 5B . However , the degree of response was significantly different for the two co-inducers , with PQS giving a consistently higher response , e . g . the Leu207Glu mutation responded much more poorly to HHQ ( 5% ) than to PQS ( 39% ) . This observation is consistent with the notion that the additional 3-OH group of PQS can potentially form a hydrogen bond to the main chain carbonyl of Leu207 and thus it may not rely as heavily as HHQ/NHQ on the side chain interaction with Leu207 which is altered in this mutant . To conserve the steric requirements for optimal ligand/receptor interactions , antagonists have frequently been discovered through structural modification of native agonists . Hence we used the closely related 2-alkyl-4 ( 3H ) -quinazolinone ( QZN ) system as a template to probe structure-activity relationships ( SARs ) . We focused on QZN analogues with C7 or C9 alkyl side chains as AQ congeners with C5 or C11 have little activity ( [23] ) . A series of 42 variously substituted QZNs ( Figure 6 ) was synthesized and characterized as described in Text S1 and the corresponding EC50s and IC50s for each compound determined via dose–response curves generated using PqsR-dependent P . aeruginosa miniCTX::pqsA'-lux reporter gene fusion assays . For comparative purposes , Figure 2 summarizes the EC50 data obtained for PQS , HHQ and their corresponding C9 congeners , NHQ and C9-PQS in both P . aeruginosa ΔpqsA and P . aeruginosa ΔpqsAH mutant backgrounds since both HHQ and NHQ can be converted to the corresponding 3-hydroxy compound by the mono-oxygenase , PqsH [15] . Figure 2 shows that the 4 co-inducers have similar EC50s in P . aeruginosa . The SAR data for the QZNs is summarized in Figure 6 . C7-QZN and C9-QZN ( Figure 6 , 7 and 8 ) which are 3-aza analogues of HHQ and NHQ respectively , and 7F-C9-QZN ( Figure 6 , 8 ) , all of which lack a substitution in the 3-position , were devoid of agonist or antagonist activity . Hydroxylation at the 3-position of C7-QZN and C9-QZN gave compounds ( Figure 6 , 9 and 10 ) which were substantially weaker agonists than PQS . These agonist properties were substantially improved by the introduction of a halogen substituent at the 7-position of the carbocyclic ring as in 3-OH-7Cl-C9-QZN and 3-OH-7F-C9-QZN ( Figure 6 , 11 and 12 ) . However the 3-methoxy variants , 3-OMe-C7-QZN and 3-OMe-C9-QZN ( Figure 6 , 13 and 14 ) were inactive unless halogenated at C-7 ( Figure 6 , 15 and 16 ) . In this QZN series , 3-OMe-7F-C9-QZN ( Figure 6 , 16 ) was as potent an agonist as PQS . Introduction of a second fluorine to give 3-OMe-6F , 7F-C9-QZN reduced potency by ∼25-fold ( Figure 6 , 17 ) To explore the QZN SAR further and to identify an essential pharmacophore for antagonist activity , we replaced the 3-OH group with the isosteric 3-NH2 group in the above derivatives and synthesized 3NH2-C7-QZN ( Figure 6 , 19 ) and its alkyl chain altered variants ( Figure 6 , 20 , 21 , 29–32 ) . None of these compounds were agonists . This was particularly interesting given that replacement of the 3-OH in PQS with 3-NH2 ( Figure 2 , 1 and 5a ) results in a compound which is a more potent agonist than the natural ligand ( EC50 0 . 4±0 . 15 µM ) . QZNs with branched chains ( Figure 6 , 29 and 30 ) , unsaturation ( Figure 6 , 31 ) or with increased hydrophilicity ( Figure 6 , 32 ) were all inactive while compounds 19 , 20 and 21 ( Figure 6 ) were antagonists , the most potent being 3NH2-C7-QZN ( Figure 6 , 19; IC50 54±15 . 5 µM ) . An attempt to further improve the activity via the introduction of a 6-Cl substituent in 3NH2-C9-QZN to yield 3NH2-6Cl-C9-QZN ( Figure 6 , 22 ) resulted in the complete loss of antagonist activity but gratifyingly , potency was greatly increased by a 7-Cl substituent ( 3-NH2-7Cl-C9-QZN; Figure 6 , 23; IC50 5±1 . 6 µM ) . Fluorine substituted derivatives ( Figure 6 , 25 and 26 ) were also antagonists , the most potent compound being 3-NH2-6F , 7F-C9-QZN ( IC50 1 . 2±0 . 4 µM ) . Introduction of an electron withdrawing group CF3 at C-7 as in 3-NH2-7CF3-C9-QZN or 8-aza as in 3-NH2-8aza-C9-QZN ( Figure 6 , 27 and 47 respectively ) resulted in the complete loss of activity . The presence of electron donating methoxy substituents as in 3-NH2-6OMe , 7OMe-C9-QZN ( Figure 6 , 28 ) also rendered the compound inactive . Further modification of the 3-NH2 group of the C9-QZNs by acetylation , dimethylation , or 2-aminoethylation ( Figure 6 , 33 , 34 and 35 respectively ) yielded only inactive or weak antagonists , the potency of which could be increased as before by the presence of a Cl at the 6 or 7 position of the carbocyclic ring ( Figure 6 , 36 and 37 ) . The 3- ( 3-aminopropyl ) compound ( Figure 6 , 38 ) only showed marginal improvement in activity . However these compounds ( Figure 6 , 35–38 ) also exhibited growth inhibitory activity and were therefore excluded from further work given that a primary objective was to obtain PqsR inhibitors which attenuate P . aeruginosa virulence without inhibiting growth . Compounds 39–46 ( Figure 6 ) represent further attempts to increase antagonist potency through modification of the alkyl side chain by synthesising derivatives where the alkyl chain is terminally substituted with aryl ( Figure 6 , 39–42 ) , heteroaryl , biaryl or cyclohexyl groups ( Figure 6 compounds 43 , 44 , 45 and 46 ) respectively . These QZNs were all inactive apart from the phenyl substituted compounds 3-C2NH2-7Cl-PhC3-QZN and 3-NH2-7Cl-PhC3-QZN ( Figure 6 , 39 and 41 ) which were weak antagonists ( IC50s 79 . 2±2 . 7 µM and 39 . 6±11 µM respectively ) . To determine whether the QZN antagonists are competitive inhibitors which interact with the AQ-binding pocket , we first investigated whether inhibition of PqsR by 3-NH2-7Cl-C9-QZN could be overcome by increasing concentrations of PQS . The data are shown in Figure 7A which reveals that PQS above 25 µM competitively overcomes QZN-mediated PqsR inhibition . To investigate how the 3-NH2-7Cl-C9-QZN interacts with PqsRCBD crystals were soaked in a solution of the compound . The resulting structure revealed the quinazolinone moiety is buried in the B pocket with the alkyl chain extending into the A pocket . The QZN molecule forms very similar hydrophobic interactions with the pocket noted for NHQ ( Figure 7B ) . In addition , the Cl atom of the 3-NH2-7Cl-C9-QZN occupies the vacant sub pocket present in front of the aliphatic ring and forms a hydrogen bond with the side chain of Thr265 ( Figure 7C ) . The 3-NH2 substituent forms a hydrogen bond to the main chain carbonyl oxygen of Leu207 ( Figure 7 B and D ) . Superposition of this structure with the agonist NHQ-PqsRCBD complex reveals differences resulting from the additional contacts made by the QZN ( Figure 7D ) . This tilts the QZN bicyclic ring relative to NHQ affecting a subtle repositioning of the alkyl chain . The QZN interactions affect small changes in the main chain of the L1 loop and the 7-Cl atom induces a rotation of the Thr265 side chain rotamer by 90° to NHQ . In addition , the introduction of the 7Cl substituent into PQS to generate 7Cl-PQS resulted in an agonist which is ∼135 times more potent than PQS itself ( Figure 2 ) providing further confirmation of the importance of the vacant sub-pocket adjacent to the Thr265 residue . The QZN antagonists of PqsR were identified on the basis of their inhibition of pqsA transcription through the competitive antagonism of the AQ-dependent activation of PqsR . To determine whether the QZNs could also inhibit the expression of target virulence genes such as lecA ( which codes for the cytotoxic galactophilic lectin protein LecA , which also contributes to biofilm development; [37] ) and phzA1 ( which codes for an enzyme involved in the biosynthesis of the redox-reactive phenazine pigment pyocyanin; [38] ) , we constructed lecA-lux and phzA1-lux reporter gene fusions integrated in the chromosome of wild type P . aeruginosa PAO1 . Since lectin A and pyocyanin production depend on pqsE expression [16] , which in turn requires the PqsR-dependent activation of the pqsABCDE operon , inhibitors of PqsR should result in the down-regulation of lecA and phzA expression . Compared with the control , the expression of lecA is reduced by 3-NH2-7-Cl-C9-QZN ( Figure 8A ) . Similar results were obtained with the phzA1 promoter for which 12 . 5 µM 3-NH2-7Cl-C9-QZN reduced activity by ∼50% ( data not shown ) . In agreement with the findings for the phzA1 promoter , pyocyanin levels were also substantially reduced in P . aeruginosa cultures treated with the QZN ( Figure 8B ) . These data are consistent with a reduction in AQ levels and LC-MS/MS analysis of P . aeruginosa cultures grown in the presence of 3-NH2-7Cl-C9-QZN shows that the production of HHQ , NHQ , their corresponding N-oxides as well as PQS are reduced to very low levels ( Figure 8C ) . Since AQ-dependent QS also contributes to biofilm maturation we examined the impact of 3-NH2-7Cl-C9-QZN on biofilm development under flow conditions using a microfluidics device . Representative confocal microscope images of the green fluorescent protein ( GFP ) -tagged P . aeruginosa wild type grown in the presence or in the absence of 3-NH2-7Cl-C9-QZN , and of the P . aeruginosa ΔpqsA mutant strain are shown in Figure 8D . In common with the ΔpqsA mutant , 3-NH2-7Cl-C9-QZN-treated wild type biofilms exhibited reduced surface area coverage . This is consistent with previous reports on reduced biofilm formation in P . aeruginosa pqsA mutants , primarily as a result of a reduction in the release of extracellular DNA , an important constituent of the extracellular matrix which is released via a process that requires PQS [39] , [40] .
Understanding the molecular recognition of AQs has important implications for gaining insight into the molecular basis of the PqsR receptor ligand and inhibitor interactions . We determined the crystal structure complex of the PqsRCBD domain with native agonist and synthetic antagonist ligands . This revealed a core structure similar to that of other LTTR proteins incorporating two sub-domains ( CBDI and CBDII ) . Among LTTR proteins , the CBD domains have the same overall topology despite little sequence similarity [36] . However , in contrast to other LTTRs , where a small primary ligand-binding pocket is located in the cleft between the two sub-domains [35] , the PqsR ligand binding site is larger extending into CBDII as well as occupying a large B pocket between CBDI and CBDII ( Figure S3 ) . This pocket is partially covered by lid loops upon formation of a large dimer interface . A central antiparallel dimer organisation is common among LTTR crystal structures , however these are formed between the ‘cleft’ side of the monomer whereas the PqsRCBD dimer is formed from the hinge region β-strands . Crystal soaking experiments with NHQ revealed that this hydrophobic cavity within the PqsRCBD constitutes the AQ-ligand binding site where the quinolone moiety is buried within the B pocket , the alkyl chain extending into the surface crevice of the CBDII A pocket . The PqsRCBD-NHQ complex is stabilised entirely by hydrophobic interactions and no electrostatic interactions are involved . In P . aeruginosa reporter gene fusion assays , HHQ , PQS and their C9 congeners ( NHQ and C9-PQS ) each have similar EC50s ( Figure 2; [23] ) whereas the C11 congeners are inactive , consistent with the space constraints noted from the crystal structure . The importance of the amino acids observed to form the PqsRCBD ligand binding site in the crystal structure was investigated using site-directed mutagenesis . Of the 13 residues mutated , all resulted in a major reduction in activity except for the I186A mutation which is located at the edge of the A pocket and retained ∼44% activity . Interestingly the increased A pocket space available as a consequence of the I186A replacement did not increase the activity of the C9 ( i . e . NHQ ) or the C11 congeners of HHQ ( data not shown ) . PQS and its biosynthetic precursor HHQ ( as well as their C9 congeners ) can both act as activating PqsR co-inducers . Using a P . aeruginosa ΔpqsA ΔpqsH double mutant which cannot convert exogenously supplied HHQ to PQS , we observed a ∼3-fold higher induction of the pqsA promoter by PQS when compared with HHQ ( Figure 2 ) . This is much lower than the 100-fold higher induction reported by Xiao et al . [21] using a different P . aeruginosa strain and reporter assay . PQS has also been reported to potentiate the binding of recombinant PqsR in crude E . coli lysates to DNA more effectively than HHQ [20] , [21] . The greater efficiency of PQS over HHQ may be a consequence of the increased H-bonding opportunities with the Leu207 carbonyl afforded by the presence of the 3-OH substituent . In this context it is perhaps noteworthy that the L207A and L207E PqsR mutants are significantly more responsive to PQS than to HHQ . Although we were unable to obtain a structure for the PQS complex with PqsRCBD , it is anticipated that as both the QZN and NHQ PqsRCBD structures superpose accurately , that their similarity in chemical structure with PQS will result in a similar binding . Nevertheless , the primary advantage of introducing a 3-OH substituent in HHQ is probably not enhancement of PqsR activation but to confer additional functionalities since PQS , unlike HHQ , induces outer membrane microvesicle formation [24] and is a potent iron chelator [14] . LTTRs assemble into oligomers ( tetramers , and in one case an octamer ) which is the functional DNA-binding structure affecting DNA bending and the recruitment of RNA polymerase [41] , [35] , [29] . The degree of DNA bending is determined by the presence or absence of the co-inducer which causes a conformation change in the LTTR resulting in a relaxation of the degree of DNA bending [29] , [42] . In the redox switch LTTR protein OxyR , the reduced form is a tetramer and the activated , oxidised form undergoes a conformational change in CBDII affecting a CBDI interface within the tetramer . This is thought to position the DNA binding domains appropriately for interaction with DNA and the RNA polymerase [41] . The nature of the quaternary arrangement formed by PqsR in complex with DNA and co-inducer has yet to been elucidated . Progress towards this goal is hampered by the inability to prepare full-length recombinant PqsR receptor heterologously expressed in E . coli , an important goal for any future studies in this area . A comparison of the PqsRCBD structure with other LTTRs reveals that the L1 loop in CBDII occupies the same region of the topology as key residues required for co-inducer mediated conformational changes [41] , [35] , [32] . In OxyR , the region equivalent to the PqsR L1 loop switches conformation upon oxidation or reduction and this is linked to a re-organisation of the tetramer interface [41] . In TsaR and CatM , co-inducer interactions and conformational switching are mediated by an α-helix occupying the same position as the PqsR CBDII L1 loop [35] , [32] . Thus co-inducer affected changes in the region of the L1 loop may be the first steps on the pathway to activation of the receptor and hence gene expression . In our search for potent PqsR antagonists as novel therapeutics , we focused on the QZN system since sterically it is closely related to the natural AQ ligands . We systematically varied the nature and size of the substituents at the 2 and 3 positions in the heterocyclic ring as well as positions 6 and 7 in the carbocyclic ring of the QZN structure to deliver a range of analogues with pharmacophores that may have the desired stereo-electronic properties for antagonist activity . Thus a total of 46 QZNs ( Figure 6 ) were synthesised , characterised ( see supplemental Text S1 ) and assayed for their agonist and antagonist activities in a whole P . aeruginosa bacterial cell assay . The QZN analogues lacking substitution at C-3 ( Figure 6 , 5–7 ) were inactive although their corresponding AQs , HHQ and NHQ ( Figure 2 , 2 and 3 ) displayed strong agonist activity . Substitution at C-3 with OH gave analogues 8 and 9 ( Figure 6 ) which like the corresponding PQS and C9-PQS ( Figure 2 , 1 and 4 ) were partial agonists . The 3-methoxy derivatives 12 and 13 ( Figure 6 ) were weak antagonists but were totally devoid of agonist activity . Surprisingly , substitution with a halogen in the C-7 position in the carbocyclic ring reversed the activity and indeed 3-OMe-7F-C9-QZN ( Figure 6 , 15 ) had potent agonist activity ( EC50 2 . 2 µM ) comparable with that of the natural ligands . The introduction of a second halogen at C-6 has the opposite effect and consequently 3-OMe-7F-C9-QZN ( Figure 6 , 16 ) is a much weaker agonist . The QZN compounds synthesized fell in two distinct groups with 3-OH ( and OMe ) QZNs generally behaving as agonists and the 3-NH2 QZNs as competitive antagonists in whole bacterial cell assays of PqsR activity . Additionally , an alkyl chain of 9 carbons at C-2 in the heterocyclic ring and a halogen at C-7 in the carbocyclic ring are essential for optimum activity in both series . 3-NH2-7Cl-C9-QZN ( IC50 5 . 0 µM ) , 3-NH2-7F-C9-QZN ( IC50 4 . 3 µM ) and 3-NH2-6F , 7F-C9-QZN ( IC50 1 . 7 µM ) were the most potent antagonists discovered in our studies . Of these , 3-NH2-7Cl-C9-QZN was shown to be an effective inhibitor of AQ signaling by antagonizing AQ biosynthesis , virulence gene expression , pyocyanin production and biofilm development . Furthermore , the preference for a halogen at C-7 over C-6 is clearly apparent from the PqsRCBD/3-NH2-7Cl-C9-QZN complex structure ( Figure 7C ) where additional H-bonding opportunities for the 7Cl substituent are afforded by the pocket formed by the Thr265 . Thus it is probable that 3-NH2-7Cl-C9-QZN binds more strongly to the PqsRCBD than the native ligands via the strengthened electrostatic interactions between 3-NH2 substituent , the water molecule and Leu207 backbone carbonyl in conjunction with additional H-bonding between the 7-Cl and Thr265 . However this will require experimental verification . Remarkably , the simple replacement of the 3-OH with 3-NH2 in the 7Cl-substituted QZNs converts the compound from a potent agonist to a potent antagonist ( Figure 6 , 11 and 23 ) . The importance of this small but significant finding is that the replacement of the PQS 3-OH with 3-NH2 does not affect the switch from agonist to antagonist ( as both compounds are strong agonists with similar EC50s ( Figure 2; 1 . 9 µM compared with 0 . 4 µM ) . This indicates that for the QZNs compared with the AQs , the stereo-electronic consequences of the additional QZN ring nitrogen are profound in terms of PqsR activation . In addition to the preliminary SAR studies for PQS agonists [23] , [43] , PqsR antagonists have recently been described by Klein et al . [44] and by Lu et al . [45] . The former identified substituted benzamides lacking extended alkyl chains which bind to the PqsRCBD and exhibit relatively weak agonist or antagonist activities by using ( ± ) -trans-U50488 as a template for rational design since this κ-opioid receptor agonist was reported to stimulate pqsA transcription [46] . The biological evaluation of these compounds and those of Lu et al . [45] have mostly been undertaken using a heterologous E . coli-based PqsR-dependent transcriptional reporter which is more sensitive to PQS than P . aeruginosa [23] , [43] ) probably as a consequence of the numerous efflux pumps present in the latter . HHQ analogues with electron-withdrawing C6 substituents ( nitrile ( –CN ) , triofluoromethyl ( –CF3 ) and nitro ( –NO2 ) ) were potent antagonists in E . coli whole cell assays with EC50s in the nanomolar range [45] . However , at the concentrations tested , they failed to reduce PQS production in P . aeruginosa although pyocyanin levels were lower after treatment with the 6-CF3 analogue . Interestingly , the 7-CF3-substituted HHQ , in contrast to the 6-CF3 analogue , was devoid of antagonist activity and retained agonist activity at about 50% that of HHQ [45] . In the E . coli-based assay , PQS analogues with Cl substitutions at 5 , 6 , 7 or 8 all exhibited similar activities to PQS [43] . However , in P . aeruginosa , 7-Cl-PQS is ∼135× more potent than PQS ( Figure 2 ) , a finding consistent with the PqsRCBD/3-NH2-7Cl-C9-QZN complex structure which revealed that a 7-Cl substituent can occupy a pocket and form an H-bond with the side chain of Thr265 . Competitive PqsR antagonists such as 3-NH2-7Cl-C9-QZN bind within the PqsRCBD ligand binding pocket in the same orientation as agonists such as NHQ and form additional hydrogen bonds to the side chain OH of Thr265 and the main chain carbonyl of Leu207 . Since LTTR agonists stimulate transcription of target genes through changes in the orientation of the DNA and ligand-binding domains [32] this would suggest that the binding of 3-NH2-7Cl-C9-QZN , although likely to be tighter than an agonist , is not productive and either maintains the PqsR conformation in the same state as the unbound protein or drives the formation of a different , but inactive , conformation . The latter mechanism has been reported for an antagonist that binds in place of the native N-acylhomoserine lactone ligand to the LuxR family protein CviR and forces the transcriptional regulator to adopt a conformation incompatible with high affinity DNA operator binding [47] . Here we have found that superposition of the PqsRCBD-3-NH2-7Cl-C9-QZN and PqsRCBD-NHQ complexes results in subtle changes which tilt the bicyclic ring of the QZN relative to that of NHQ so the QZN interaction is not productive for PqsR activation . Taken together this work has increased our knowledge of the molecular recognition of ligands by PqsR , demonstrated how the simple and subtle exchange of two isosteres ( OH for NH2 ) within a co-inducer molecule can effectively switch virulence gene expression on or off and provided a template structure for the development of QZNs as novel therapeutics which control infection through attenuation of P . aeruginosa virulence .
The E . coli and P . aeruginosa strains used in this study ( Table S1 ) were grown in Lysogeny broth ( LB ) at 37°C . For AQ quantification , P . aeruginosa was grown in minimal medium [48] . When required for plasmid maintenance in E . coli ( pET28a derivatives ) or P . aeruginosa ( pME6032 derivatives ) , kanamycin ( 50 µg/ml ) or tetracycline ( 125 µg/ml ) were respectively added to the growth medium . The P . aeruginosa ΔpqsR in-frame deletion mutant and the triple ΔpqsA ΔpqsH ΔpqsR mutant were constructed in the PAO1 parent and ΔpqsA ΔpqsH mutant [14] respectively using the pDM4ΔpqsR plasmid . The upstream and downstream fragments of pqsR were amplified by PCR from PAO1 chromosomal DNA using the primers pairs FWpqsRUp-RVpqsRUp and FWpqsRDown-RVpqsRDown , respectively ( Table S2 ) , introduced into pDM4 [49] and the resulting ΔpqsR mutants obtained by allelic exchange [50] . The pqsA promoter fused to the luxCDABE reporter operon was introduced into both the ΔpqsR and ΔpqsA ΔpqsH ΔpqsR mutants using the miniCTXpqsA-lux plasmid as described previously [23] . For complementation assays , the pqsR gene with or without a C-terminus 6xHis coding sequence ( pqsR-6H ) was amplified by PCR from chromosomal DNA with primer pairs FWpqsR-RVpqsR or FWpqsR-RVpqsR-6H , respectively ( Table S2 ) , and cloned by EcoRI-SacI digestion into pME6032 [51] . Site-directed mutations were generated in pqsR-6H using the splicing by overlap extension PCR method [52] . Briefly , in the first step two distinct PCRs ( PCR-1 and PCR-2 ) were carried out for each pqsR-6his derivative mutant , using chromosomal P . aeruginosa DNA as a template . Each PCR-1 was performed with the forward primer FWpqsR and with a mutagenic reverse primer carrying the mutation in the desired codon ( Table S2 ) . Each PCR-2 was performed with a mutagenic forward primer complementary to the reverse primer utilized in the corresponding PCR-1 ( Table S2 ) and with primer RVpqsR-6H . In the second PCR step products obtained from PCR-1 and PCR-2 for each mutation were spliced together using the FWpqsR and RVpqsR-6H primers . The mutated pqsR-6H variants were cloned by EcoRI/SacI digestion in pME6032 and verified by DNA sequencing . The expression of each of the PqsR variants was confirmed by Western blot analysis using a mouse anti-6xHis antibody ( 1∶1 , 000; Sigma-Aldrich , St . Louis , MO , USA ) . For overexpression of the PqsRCBD , the regions corresponding to the PqsR C94-332 and PqsR C94-294 CBD were amplified by PCR and cloned into pET28a . The recombinant plasmids were introduced by transformation into E . coli Rosetta 2 ( DE3 ) and grown at 37°C to an OD600 0 . 8 prior to induction with IPTG ( 1 mM ) at 20°C for 16 h . After harvesting by centrifugation , the bacteria were lysed by sonication , centrifuged and filtered to remove cellular debris prior to nickel affinity column purification and elution with an imidazole gradient ( 0 to 1 M ) . The 6xHis tag which included the thrombin cleavage sequence was removed from the PqsRCBD proteins using thrombin ( Novagen; enzyme/substrate ratio 1∶1000 for 24 h ) . Final purification was achieved by gel filtration using a Superdex 75 16/60 gel filtration column , with a mobile phase consisting of 20 mM Tris-HCl , 150 mM NaCl , pH 7 . 4 . Both constructs yielded approximately 25 mg PqsRCBD/litre of culture as confirmed by SDS-PAGE . The same strategy was used to obtain the PqsR94-332 selenomethionine-labelled protein after transforming the E . coli methionine auxotroph strain B834 ( DE3 ) with pPqsR94-332 and growing the recombinant strain in selenomethionine medium . A protein concentration of 25 mg/ml was used for 96-well crystal screening ( Qiagen kits ) and crystals were obtained for PqsR94-332 and from several conditions using only the MPD suite ( Qiagen ) after 24 h at 19°C . Optimized conditions in 24-well sitting drop plates containing a reservoir of 100 mM trisodium citrate pH 6 . 0 , 200 mM ammonium acetate and 3% v/v MPD and identical crystals grew with the shortened PqsR94-309 construct . Analytical gel filtration was performed with a Superdex 75 10/300 column equilibrated with running buffer of 20 mM Tris-HCl , 150 mM NaCl , pH 7 . 4 . The standards used were ovalbumin ( 43 kDa ) , carbonic anhydrase ( 29 kDa ) and ribonuclease A ( 13 . 7 kDa ) . Crystals were transferred to a solution with cryoprotectant of 25% MPD and cryo-cooled for collection of diffraction data . The detailed method for PqsRCBD-MPD structure determination is outlined in Text S1 . Native and derivative datasets were collected at beamline IO4 of the Diamond synchrotron and data was processed using XDS and reduced with the CCP4 suite ( statistics are shown in Table 1 together with the description of the SIRAS structure determination for the PqsRCBD-MPD structure ) . PqsR ligands were dissolved in 100% MPD or in a 1∶1 mixture of MPD and isopropanol to give a concentration of 20 mM . When added to recombinant PqsRCBD even low concentrations of these compounds resulted in heavy precipitation . The soaking of PqsR94-309 crystals was carried out for 24–48 h with ligands at 5–10 mM . Soaking experiments with HHQ , NHQ , PQS , C9-PQS and shorter chain analogues were carried out and in each case crystals were transferred to a solution with cryoprotectant of 25% MPD and cryo-cooled for collection of diffraction data . Datasets were collected at beamline IO2 of the Diamond synchrotron and data were processed using XDS and reduced with the CCP4 suite ( Table 1 ) . Rigid body refinement ( REFMAC ) was carried out to adjust for small changes in cell dimension and 2Fo-Fc and Fo-Fc electron density maps were calculated using the CCP4 suite . Additional electron density was observed for NHQ and 3-NH2-7Cl-C9-QZN soaked crystals . Model building was carried out using COOT and refinement with REFMAC . The AQs and QZNs listed in Figures 2 and 6 were synthesised and characterised as described in the supplemental information provided in Text S1 . The impact of the AQs and QZNs on PqsR-dependent gene expression in P . aeruginosa was evaluated using lux-based pqsA , lecA and phzA1 promoter fusions ( Table S1 ) in 96-well microtiter plates as described before [14] , [23] . Bioluminescence and bacterial growth were quantified using a combined luminometer-spectrometer ( Tecan GENios Pro ) . Where required , AQs or QZNs were added to reporter strains and EC50 or IC50 values were extracted from the sigmoidal dose–response curves obtained using Prism2 ( Graphpad , San Diego , USA ) . The impact of 3-NH2-7Cl-C9-QZN on ( a ) AQ production was assayed by LC MS/MS after extracting bacterial cultures with acidified ethyl acetate [11]; ( b ) pyocyanin was quantified spectrophotometrically [16] and ( c ) biofilm development was examined using a Bioflux 200 microfluidics device ( Fluxion Biosciences; in conjunction with GFP-labelled P . aeruginosa strains , [53] ) . All assays were performed in triplicate at least twice . | Populations of bacterial cells collectively co-ordinate their activities through cell-to-cell communication via the production and sensing of signal molecules . This is called quorum sensing ( QS ) and in many bacteria , QS controls the expression of virulence genes , the products of which damage host tissues . Consequently , QS systems are potential targets for antimicrobial agents which do not kill bacteria but instead block their ability to cause disease . Pseudomonas aeruginosa causes a wide range of human infections and produces an armoury of virulence factors . Since many of these are controlled by alkylquinolone ( AQ ) -dependent QS , we determined the crystal structure of the AQ receptor ( PqsR ) in order to visualize the shape of the AQ-binding site and better design PqsR inhibitors which compete for the AQ binding site and so block QS . This work in conjunction with the chemical synthesis of AQ analogues resulted in the discovery of potent quinazolinone inhibitors of PqsR . These blocked AQ and virulence factor production in P . aeruginosa as well as biofilm development . Our studies present novel insights into the structure of PqsR and create further opportunities for target-based antibacterial drug development . | [
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"crystal... | 2013 | Structural Basis for Native Agonist and Synthetic Inhibitor Recognition by the Pseudomonas aeruginosa Quorum Sensing Regulator PqsR (MvfR) |
The functional contribution of CNV to human biology and disease pathophysiology has undergone limited exploration . Recent observations in humans indicate a tentative link between CNV and weight regulation . Smith-Magenis syndrome ( SMS ) , manifesting obesity and hypercholesterolemia , results from a deletion CNV at 17p11 . 2 , but is sometimes due to haploinsufficiency of a single gene , RAI1 . The reciprocal duplication in 17p11 . 2 causes Potocki-Lupski syndrome ( PTLS ) . We previously constructed mouse strains with a deletion , Df ( 11 ) 17 , or duplication , Dp ( 11 ) 17 , of the mouse genomic interval syntenic to the SMS/PTLS region . We demonstrate that Dp ( 11 ) 17 is obesity-opposing; it conveys a highly penetrant , strain-independent phenotype of reduced weight , leaner body composition , lower TC/LDL , and increased insulin sensitivity that is not due to alteration in food intake or activity level . When fed with a high-fat diet , Dp ( 11 ) 17/+ mice display much less weight gain and metabolic change than WT mice , demonstrating that the Dp ( 11 ) 17 CNV protects against metabolic syndrome . Reciprocally , Df ( 11 ) 17/+ mice with the deletion CNV have increased weight , higher fat content , decreased HDL , and reduced insulin sensitivity , manifesting a bona fide metabolic syndrome . These observations in the deficiency animal model are supported by human data from 76 SMS subjects . Further , studies on knockout/transgenic mice showed that the metabolic consequences of Dp ( 11 ) 17 and Df ( 11 ) 17 CNVs are not only due to dosage alterations of Rai1 , the predominant dosage-sensitive gene for SMS and likely also PTLS . Our experiments in chromosome-engineered mouse CNV models for human genomic disorders demonstrate that a CNV can be causative for weight/metabolic phenotypes . Furthermore , we explored the biology underlying the contribution of CNV to the physiology of weight control and energy metabolism . The high penetrance , strain independence , and resistance to dietary influences associated with the CNVs in this study are features distinct from most SNP–associated metabolic traits and further highlight the potential importance of CNV in the etiology of both obesity and MetS as well as in the protection from these traits .
The significance of copy number variation ( CNV ) in human genetic variation is now indisputable [1] , [2] . However , in contrast to the revolutionary progress achieved in the discovery of CNVs and delineating the mechanisms for their formation , our current knowledge of the downstream functional mechanisms by which CNVs contribute to trait manifestations is limited . Functional contributions of CNV to human biology have only been examined in a few physiological systems including the neuropsychiatric/behavioral fields [2] , [3] . About 400 million people worldwide are classified as obese [4] and are likely to suffer from premature mortality and obesity-associated morbidities , such as hyperglycemia , dyslipidemia , hypertension and metabolic syndrome ( MetS ) [5] . The etiologies for obesity include genetic contributions [4] , but the identities of the specific genetic factors remain largely unknown . Single nucleotide polymorphisms ( SNPs ) identified through linkage and genome-wide association studies ( GWAS ) explain only 1–2% of the variation in obesity phenotypes as measured by BMI [6] , [7] , [8] . Recent observations in humans indicate a tentative link between CNV and weight regulation . Deletions at 16p11 . 2 were associated with a highly penetrant form of obesity often found with hyperphagia and intellectual disabilities , whereas the reciprocal duplication conveys a 8 . 3 fold increased risk for being clinically underweight [9] , [10] . These comprehensive studies on patients added to the clinical observations of obesity associated with CNV that have been noted for several chromosomal syndromes and genomic disorders including Down [11] and Prader-Willi syndromes [12] . However , there is no experimental data that proves the causative role of the CNV in the abnormality in weight regulation , nor is there any study on the biology underlying this tentative link . Potocki-Lupski syndrome ( PTLS , MIM 610883 ) [13] , [14] is an intellectual disability and multiple congenital anomalies ( ID/MCA ) syndrome due to a heterozygous interstitial duplication CNV in chromosome 17p11 . 2 . Mildly lowered total cholesterol and LDL were noted for some PTLS patients [13] . The reciprocal deletion CNV of the same interval causes a distinct ID/MCA disorder known as Smith-Magenis syndrome ( SMS , MIM 182290 ) [15] , [16] , [17] . Obesity and hypercholesterolemia are phenotypes of SMS [16] , [18] , [19] . By chromosomal engineering , we previously constructed mouse models of PTLS and SMS carrying the duplication [Dp ( 11 ) 17] or deletion [Df ( 11 ) 17] of a 2 Mb chromosomal segment that includes the majority of the mouse region syntenic to the PTLS/SMS common recurrent CNV interval ( Figure S1 ) [20] . Both Dp ( 11 ) 17/+ and Df ( 11 ) 17/+ mice partially recapitulate the respective human phenotypes such as craniofacial abnormalities [21] , [22] , altered learning , memory and social interaction [20] , [23] , [24] , [25] , and display a transcriptome [25] that is distinct from their wild type ( WT ) littermates . In the context of exploring the biological link between CNV and weight control , we now utilize these mouse models to investigate the detailed metabolic consequences of the PTLS duplication CNV and the reciprocal SMS deletion CNV . To simplify data analyses , all experiments were performed with male animals .
First , we found that , similar to previous observations on several genetic backgrounds ( C57BL/6J/129S5 and N7 or N12 congenic C57BL/6J ) [20] , [25] , [26] , the Dp ( 11 ) 17/+ mice on an isogenic ( N>17 ) C57BL/6J background also display significantly reduced body weight compared to their WT littermates ( Figure 1A , 1B ) . In contrast , and again in accordance with earlier reports on different backgrounds ( C57BL/6J/129S5 and N7 or N12 congenic C57BL/6J ) [20] , [25] , the Df ( 11 ) 17/+ mice with the reciprocal deletion CNV on a pure ( N>10 ) 129S5 background are significantly heavier than their WT littermates after 15 weeks of age ( Figure 1C , 1D ) . Thus , the reciprocal duplication and deletion CNVs not only change body weight in opposing directions , but also , strikingly , do so in a highly penetrant manner that is independent of the genetic background . Highly penetrant weight change phenotypes were recently also observed in humans for two obesity-associated deletion CNVs on 16p11 . 2 and the reciprocal duplication of one of them associated with leanness [9] , [10] , [27] . The high penetrance differentiates CNV-associated obesity from SNP associated obesity in which , except for some very rare mutations in a few genes of the leptin/melanocortin pathway ( LEP , MC4R , etc . ) , almost all variants have low penetrance [28] , [29] . In addition to having reduced weight , adult Dp ( 11 ) 17/+ mice on an isogenic ( N>17 ) C57BL/6J background are also leaner than WT males , as measured via ECHO-MRI whole body scans ( Echo medical systems , Texas ) and manual dissection . Dp ( 11 ) 17/+ mice have a significantly lower percentage of both whole body fat mass and epididymal white adipose tissue ( EWAT ) ( Figure 2A , 2B ) , as well as a significantly higher percentage of lean mass ( Figure 2A ) . These findings are in accordance with our previous reports of reduced abdominal fat in Dp ( 11 ) 17/+ mice on different strain backgrounds ( N7 [26] and N12 C57BL/6J [25] ) , further demonstrating the strain-independent manifestation of the metabolic phenotypes caused by the duplication CNV . Moreover , adult Dp ( 11 ) 17/+ mice display significantly reduced fasting total serum cholesterol ( TC ) and LDL levels ( Figure 2C ) as well as a cardioprotective decrease of TC/HDL ratio ( Figure 2C ) . Interestingly , the change in TC and LDL resembles the clinical observations in PTLS patients [13] , despite the mechanistic differences in lipid metabolism between human and mouse [30] . Consistent with their lower adiposity [31] , the serum leptin concentration is decreased in Dp ( 11 ) 17/+ mice ( Figure 2D ) . Furthermore , the intraperitoneal glucose tolerance test ( IP-GTT ) demonstrates an overall improved glucose clearance in Dp ( 11 ) 17/+ mice compared to WT littermates; the difference in their serum glucose concentration becomes significant at 120 minutes ( Figure 3A ) . The plasma insulin levels during the GTT were significantly lower in the Dp ( 11 ) 17/+ animals throughout the test , suggesting that the improvement in glucose tolerance was not due to increased insulin production by the pancreas , but likely the result of improved insulin sensitivity ( Figure 3B ) . Indeed , in the insulin tolerance test ( ITT ) , insulin injection lowered blood glucose levels significantly faster in Dp ( 11 ) 17/+ than in WT mice , further corroborating their increased insulin sensitivity ( Figure 3C and 3D ) . Intriguingly , the circulating concentration of adiponectin is not changed in Dp ( 11 ) 17/+ mice ( Figure 2D ) , suggesting that adiponectin-independent pathways are involved in the alteration of their insulin sensitivity [32] . We found that the reduction in body weight of Dp ( 11 ) 17/+ mice is not simply due to an altered energy intake or increased activity since they consume identical amounts of food after four weeks of age ( Figure 4A ) and have similar activity levels to those of their WT littermates ( Figure 4B ) . Thus , intrinsic changes in energy expenditure likely explain the observed phenotypes . Indeed , as assayed by indirect calorimetry , Dp ( 11 ) 17/+ mice demonstrate an overall higher oxygen consumption ( VO2 ) per lean mass and higher respiratory exchange ratio ( RER ) than WT mice , indicating higher energy expenditure than their WT littermates ( Figure 4C–4F ) . Western blotting suggested an elevated expression level of protein uncoupled 1 ( UCP1 ) in brown adipose tissue ( BAT ) of the Dp ( 11 ) 17/+ mice ( Figure 4G , 4H ) , although there is considerable variability in UCP1 expression among different samples . UCP1 is a key component of thermogenesis in BAT [33] , this difference may partially explain the higher intrinsic energy expenditure of the Dp ( 11 ) 17/+ mice . Interestingly , Dp ( 11 ) 17/+ mice also appear to have a trend toward slightly higher body temperature ( 34 . 56±0 . 34°C ) than WT littermates ( 33 . 9±0 . 90°C ) . We did not observe differences in the expression levels of UCP2 and UCP3 in BAT between Dp ( 11 ) 17/+ and WT mice , nor did we observe differences in other signature metabolism genes ( Glut4 , AP2 in BAT , and Lpk , Fas , Acc1 , Srebp1c , Tnxip in the liver ) ( Figure S4A , S4B ) . Thus , while on a regular chow diet , Dp ( 11 ) 17/+ mice are leaner than their WT littermates , and they have lower serum TC/LDL levels and reduced leptin concentration . Dp ( 11 ) 17/+ mice also appear to be more insulin sensitive and have higher energy expenditure but display no difference in activity level in comparison to WT mice . These traits are reciprocal or antithetical to those of metabolic syndrome and most appear to manifest independent of the genetic background of the mouse strain . Importantly , except for the parameters related to energy metabolism , a comprehensive serum analysis did not observe any difference in other serum chemistry parameters between Dp ( 11 ) 17/+ and WT animals ( Table S1 ) . Also , under daily evaluation by veterinarian staff in our mouse facility , no overt illness was observed in Dp ( 11 ) 17/+ mice . Combined with the fact that Dp ( 11 ) 17/+ animals also have identical activity level and food intake to WT mice , the metabolic traits we observed in Dp ( 11 ) 17/+ mice are unlikely due to any illness related to the duplication CNV , but rather a direct effect from the CNV . Next , we investigated potential influences of the Dp ( 11 ) 17 CNV on the genetic susceptibility to diet induced obesity ( DIO ) . First , we placed Dp ( 11 ) 17/+ and WT mice on a HF ( 60% calories from fat ) diet for three weeks ( 19 to 22 ) after 19 weeks of a normal diet . After these three weeks , the WT mice had dramatically increased body weight compared to control WT mice of the same age that have been kept on a regular chow ( RC ) diet . The Dp ( 11 ) 17/+ mice , however , did not display significant weight gain compared to other Dp ( 11 ) 17/+ animals on RC ( Figure 5A ) . More specifically , the weight gain in WT mice is predominantly due to an increase in the amount of fat mass that is only observed in WT and not Dp ( 11 ) 17/+ mice ( Figure 5A ) . Indeed , while BAT and liver remain of similar sizes ( relative to body weight ) between the two genotypes after HF diet , the WAT tissues ( from four body locations: epididymal , mesenteric , retroperitoneal and inguinal WAT ) are much smaller in Dp ( 11 ) 17/+ than WT mice ( Figure 5B , 5C ) , accompanied by smaller-sized adipocytes ( Figure 5D ) . Furthermore , for WT mice , HF diet resulted in a marked decrease in glucose clearance during GTT but no change in blood insulin level; whereas the glucose clearance in Dp ( 11 ) 17/+ mice is much less affected by the HF diet ( Figure 6A , 6B ) . This difference in the extent of HF diet mediated insulin resistance between WT and Dp ( 11 ) 17/+ mice was further confirmed in ITT experiments , which demonstrate even more significant differences between the two genotypes after HF diet ( Figure 6C , 6D ) . Next , to explore the long-term impact of the Dp ( 11 ) 17 CNV in DIO , we examined Dp ( 11 ) 17/+ males along with their WT littermates on a 42% fat HF diet starting from week 3 for 20 weeks . While the high-fat diet causes massive weight gain in WT mice and literally “supersizes” these animals , it produces only a minimal increase in body weight in the Dp ( 11 ) 17/+ mice , confirming the salient resistance of the Dp ( 11 ) 17/+ genotype to diet-induced weight gain ( Figure 6E ) . In aggregate , these findings demonstrate the salubrious effect of this duplication CNV in that it provides protection against diet-induced obesity and insulin resistance . We next examined whether the metabolic traits conveyed by the duplication CNV are due to the copy number gain of a single gene . The typical CNV interval of PTLS/SMS encompasses over 40 human genes; one of them , retinoic acid induced 1 ( RAI1 ) , is considered the “predominant” causative gene in the deletion CNV interval mediating the majority of SMS clinical findings through haploinsufficiency [15] , [16] , [17] . Also , for PTLS , RAI1 is a major dosage sensitive gene contributing to the phenotype , as suggested by duplication mapping in humans [34] and the rescue of selected phenotypes after normalizing the gene dosage of Rai1 to n = 2 in Dp ( 11 ) 17/Rai1− animals [26] . To examine the contribution of the RAI1/Rai1 gene to the metabolic phenotypes of PTLS , we compared the metabolic profile of TgRai1 animals [35] that overexpress Rai1 but do not have copy number change of most of the surrounding genomic regions to that of Dp ( 11 ) 17/+ mice . Although the regulation of Rai1 expression in TgRai1 mice is mechanistically different from that in Dp ( 11 ) 17/+ mice , in which Rai1 is localized in a large genomic segment that has a well-defined duplication of the genome , expression studies demonstrated that the Rai1 “steady state” expression level is similar in TgRai1 [35] and Dp ( 11 ) 17/+ mice [25] ( 1 . 5 fold that of WT ) . Interestingly , TgRai1 animals display an initial growth retardation; however , they eventually normalize their body weight by 20 weeks of age [35] . This is distinct from the Dp ( 11 ) 17/+ mice , whose difference in body weight when compared to their WT littermates remains and even exacerbates as they age ( Figure 1 ) . Further , TgRai1 animals do not demonstrate the dramatically altered body composition and serum chemistry displayed by Dp ( 11 ) 17/+ mice ( Figure S2A , S2B ) . Finally , again in striking contrast to the remarkably improved insulin sensitivity and glucose clearance of Dp ( 11 ) 17/+ mice , TgRai1 animals demonstrate no significant differences in their blood glucose or plasma insulin during GTT when compared with WTs ( Figure S2C , S2D ) . We conclude that the dosage or steady state expression level of RAI1/Rai1 is unlikely the sole or major contributor to the obesity opposing and protective metabolic phenotypes observed in the PTLS mice . After studying the Dp ( 11 ) 17/+ mice , we sought to characterize the metabolic profile of the Df ( 11 ) 17/+ deletion mice on a fully congenic ( N>10 ) 129S5 background . In mirror image contrast to the observations in Dp ( 11 ) 17/+ mice , Df ( 11 ) 17/+ mice have not only increased body weight , but also significantly increased percentage of body fat and decreased percentage of lean mass ( Figure 7A ) . These findings are again in accordance with our previous reports of increased size of abdominal fat pad in Df ( 11 ) 17/+ mice on mixed [20] and congenic ( N>12 ) C57BL/6J [25] strain backgrounds . Intriguingly , Df ( 11 ) 17/+ mice also have reduced TC , similar to Dp ( 11 ) 17/+ mice ( Figure 7B ) . However , in contrast to the reduction of the atherogenic LDL in the case of Dp ( 11 ) 17/+ animals , the reduced TC of Df ( 11 ) 17/+ mice is the result of a reduced HDL , a cardioprotective species of plasma lipoprotein ( Figure 7B ) . The TC/HDL ratio appears higher in Df ( 11 ) 17/+ mice although the difference is not significant ( Figure 7B ) . During the GTT , Df ( 11 ) 17/+ mice display an impaired glucose tolerance phenotype ( Figure 7C ) accompanied by significantly higher plasma insulin levels during the test as compared with WT mice ( Figure 7D ) , suggesting that Df ( 11 ) 17/+ mice indeed have increased insulin resistance . This interpretation is bolstered by a blunted blood glucose decrement in response to insulin injection during an insulin tolerance test ( ITT ) in Df ( 11 ) 17/+ mice as compared to WT mice ( Figure 7E and 7F ) . The increased insulin resistance and impaired glucose tolerance in Df ( 11 ) 17/+ mice , along with the increased body weight , relative adiposity and reduced HDL further document that , metabolically , Df ( 11 ) 17/+ mice display endophenotypes that resemble a bona fide metabolic syndrome . We did not find significant differences in the level of UCP1 protein between Df ( 11 ) 17/+ and WT mice ( Figure S4C , S4D ) . From studies of human patients , a meta-analysis of 105 cases [16] including both children and adults concluded that 33 . 3% of the SMS patients are overweight ( BMI>24 ) . In a study of 49 SMS children ( 0 . 6 to 17 . 6 years ) , Smith et al . [19] observed that SMS boys had a significantly higher BMI than the published age-matched standards . To systematically address the potential obesity directly caused by the SMS deletion CNV in the context of the population norm , we compared 179 height and 216 weight measurements from 76 subjects with SMS aged between newborn and 46 years ( Figure S3 ) to the population mean of the same age/gender from the center for disease control and prevention ( http://www . cdc . gov/growthcharts/ ) . We found that both male and female SMS individuals of all age categories in this cohort are shorter than the general population ( Figure S3A , S3D , S3G ) . Male SMS subjects do not have significant weight abnormalities ( Figure S3B , S3H ) . Females below 11 years have weights below the population mean , whereas those older than 12 years appear heavier than the population mean , although the difference is not significant ( Figure S3E , S3H ) . Most importantly , male SMS subjects older than 2 years and female subjects older than 20 years have significantly higher BMI values than the general population ( Figure S3C , S3F , S3I ) . These observations are consistent with the interpretation that the SMS deletion CNV indeed causes a higher BMI in humans and thus conveys an increased risk for obesity . Smith et al [19] also found that the mean fasting TC of SMS patients in their cohort was significantly higher than published pediatric age-matched norms . The aggregate of weight gain and elevated TC in human SMS patients together with the weight gain and insulin resistance in the Df ( 11 ) 17/+ mice is consistent with MetS-like traits as part of the SMS endophenotypes . To explore the contribution of RAI1 copy number loss to the metabolic phenotypes of SMS , we also studied Rai1+/− mice [36] , [37] on the same 129S5 ( N>10 ) strain background as the Df ( 11 ) 17/+ mice . Similar to what we observed for Df ( 11 ) 17/+ mice , and in accordance with a previous study [18] conducted on a different genetic background , Rai1+/− males have both significantly increased body weight in adulthood ( Figure 8A ) and elevated overall proportion of body fat ( Figure 8B ) . Also similar to the Df ( 11 ) 17/+ mice , Rai1+/− animals display an unchanged TC/HDL ratio , although they have both elevated TC and HDL levels , opposite to the reduced TC and HDL levels in Df ( 11 ) 17/+ mice ( Figure 8C ) . Elevated cholesterol was also observed in Rai1+/− mice on C57BL/6J background in Burns et al [18] , although the difference was not statistically significant , which may reflect the mixing of male and female mice and a potential dilution of the difference in male animals . Similarly , Burns et al also noted the increased proportion of body fat in both males and females , although the difference in their assay is only significant for the females . The differences in our findings for the male mice may result from different experimental approaches; ECHO-MRI is less subject to variations introduced by the individual experimentalist and potentially more objectively detects subtle differences in body composition . In addition , the impairment of Rai1+/− in glucose clearance becomes significant at later time points during the GTT assay ( Figure 8D ) ; these animals also show higher plasma insulin at fasting and during the GTT ( Figure 8E ) , although there is no significant difference in blood glucose during ITT ( Figure 8F and 8G ) . Overall , Rai1 +/− mice are similar to Df ( 11 ) 17/+ mice in their increased body weight and total body fat percentage as well as hyperinsulinemia , impaired GTT and an unchanged TC/HDL ratio . Intriguingly , Edelman et al [16] observed a higher percentage of obesity in SMS patients with RAI1 point mutations ( 66 . 7% ) than those with 17p11 . 2 deletions ( 12 . 9% ) . Although the number of SMS patients due to RAI1 point mutation in that report is small ( n = 9 ) , these data nevertheless support a significant role for RAI1 copy number loss in the overall metabolic phenotype of SMS , and also suggest possible contributions from other genes/genetic elements in the SMS deletion interval or the deletion per se [25] .
In summary ( Figure 9 ) , our detailed analyses of mouse models and human patients demonstrate that the duplication CNV of PTLS conveys highly penetrant metabolic consequences that are antithetical to or “mirror” [9] those observed in MetS . At the same time , it confers protection against the development of diet induced obesity and insulin resistance . These phenotypes are not manifest in the transgenic TgRai1 mice with a similarly increased level of Rai1 expression but without the duplication CNV . In contrast , the reciprocal deletion CNV causes phenotypes that are opposite to those observed with the duplication CNV and that resemble a bona fide metabolic syndrome ( summarized in Figure 9 ) . The reciprocal/mirror phenotypes caused by the reciprocal CNV of Dp ( 11 ) 17 and Df ( 11 ) 17 is interesting . Reciprocal phenotypes associated with opposing gene/genome dosage alterations ( i . e . copy number loss versus copy number gain ) have been described for the complex neuropsychiatric traits of schizophrenia and autism , as well as microcephaly and macrocephaly , associated with , respectively , duplication/deletion CNV at 16p11 . 2 [38] , [39] , [40] and deletion/duplication of 1q21 . 1 [41] , [42] , [43] , [44] . Indeed , another pair of weight regulation associated duplication/deletion CNVs at 16p11 . 2 was also related to reciprocal changes in BMI and manifest leanness/obesity [9] , [10] , [27] . These reciprocal traits with opposing dosage alterations are consistent with the model of diametrically opposing phenotypes for genomic sister disorders postulated by Crespi et al [40] , [45] , [46] . Importantly , neither the duplication nor the deletion CNV associated phenotypes we describe herein in both human patients and mouse models can be attributed solely to the RAI1/Rai1 gene , although RAI1/Rai1 dosage loss does appear to partially contribute to the deletion phenotypes . Besides Rai1 , another gene Srebf1 ( coding for sterol regulatory element binding protein 1 , Srebp1 ) , that maps directly adjacent to RAI1/Rai1 in both human/mouse genome and functions as a key regulator in the biosynthesis of fatty acid and cholesterol , is the only gene in the SMS/PTLS interval known to be involved in energy metabolism . Overexpression of the active N-terminal portion of Srebp1 protein does not change the plasma lipid profile [47] and results in mild insulin resistance [48] . Both phenotypes are distinctly different from the metabolism phenotype we observed in Dp ( 11 ) 17/+ mice , rendering the copy number gain of Srebf1 unlikely the reason for the Dp ( 11 ) 17/+ metabolic phenotypes . Heterozygous Srebf1 knockout mice Srebf1+/− were described as “phenotypically normal” [49] . Srebf1−/− animals are 50–85% embryonic lethal , but the surviving mice display unchanged body weight and slightly reduced total cholesterol and triglycerides in plasma [49] . The copy number loss of Srebf1 in Df ( 11 ) 17/+ mice is thus also unlikely a major contributor to the observed metabolic phenotypes . Recently , a microRNA miR33b was found to be embedded in an intron of human SREBF1 [50] , [51] . Together with its paralogue miR33a ( embedded in the paralogue of SREBF1 , SREBF2 ) , miR33b targets the adenosine triphosphate-binding cassette transporter ( ABCA1 ) , decreases plasma HDL and boosts intracellular cholesterol levels in cooperation with SREBP proteins [50] , [51] , [52] . However , mouse Srebf1 does not contain mir33b [50] , [51]; it is thus not a candidate accounting for the metabolic phenotype observed in Dp ( 11 ) 17/+ and Df ( 11 ) 17/+ mice . The potential role of miR33b in human PTLS/SMS metabolic manifestation will have to be studied with different models , such as those that introduce a human miR33b into the mouse genome . Our current knowledge thus does not support a “single gene” contribution of the dosage change of Rai1 , Srebf1 or any other known genetic element to the metabolic phenotypes of SMS/PTLS . However , it is distinctly possible that the copy number change of RAI1 and SREBF1 in cis , with one of them exerting an epistatic effect on the other or functioning as a modifier , is required for manifestation of the complete metabolic phenotype of PTLS/SMS . Further , the potential “cis” effect could also possibly involve other genetic elements in addition to RAI1 and SREBF1 . These metabolic manifestations would then belong to the category of “contiguous gene syndromes” [53] or genomic disorders [54] , [55] that require multiple genes/genetic/genomic factors to work in concert , a concept referred to as cis-genetics and in contrast to the trans interactions of alleles at one locus formalized by Mendelism [56] . Similar mechanisms have been proposed for the craniofacial phenotypes of the SMS/Df ( 11 ) 17/+ mice , wherein the phenotypic penetrance is clearly modified by other genetic elements in the deletion interval although the copy loss of Rai1 appears to be responsible for most of the traits [21] , [22] . Further , a number of other mechanisms , including gene interruption and gene fusion due to CNV breakpoints , position effect , the unmasking of a recessive allele by a deletion , as well as potential effects of transvection can contribute to the functional consequence of a CNV [57] . None of them can be displayed by single nucleotide variations ( SNV ) . Recently , it has been demonstrated experimentally that a genomic structural change per se , as in a large CNV , can cause altered expression and functional perturbation of other loci/genes localized to the same chromosome , but outside of the CNV [25] . All these mechanisms can potentially contribute to the salient effect of the Dp ( 11 ) 17 CNV on weight regulation and energy metabolism that does not appear to be attributed to the dosage change of any single gene or genetic element ( s ) . Overall , we show that a duplication CNV can result in a lean body phenotype , metabolic phenotypes in mirror image contrast to those observed in metabolic syndrome , and protect from diet induced obesity . Moreover , we demonstrate that these phenotypes are fully penetrant , independent of genetic background and resistant to environmental influences . Furthermore , we provide evidence that the CNV effects are due to more than dosage alteration of a single gene , a finding that highlights distinct functional significance of CNV as compared to SNVs . These findings confirm that CNVs can be causative for weight regulation and energy metabolism phenotypes and suggest that CNVs could play a major role in the common complex diseases of human obesity .
All animal studies were approved by Baylor College of Medicine IRB and carried out in accordance with Baylor IACUC . Mice were housed 2–5 per cage in a 12-hour light/12-hour dark cycle with access to food and water ad libitum . Body composition was analyzed with the ECHO-MRI system ( Echo medical systems , Texas ) . Mouse serum was prepared from blood obtained through cardiac puncture and analyzed with the COBAS Integra 400 plus analyzer ( Roche ) . Plasma leptin , FFA , adiponectin and glycerol levels were measured by using a Mouse Leptin ELISA Kit ( Millipore ) , NEFA C Test Kit ( Wako ) , Mouse Adiponectin ELISA Kit ( Millipore ) and Serum/plasma Glycerol detection kit ( Sigma ) , respectively . Epididymal ( EWAT ) , mesenteric ( MWAT ) , retroperitoneal ( RWAT ) and inguinal ( IWAT ) white adipose tissues , as well as brown adipose tissues ( BAT ) and liver were dissected from mice deeply anesthetized with Isoflorane ( Butler ) . Tissues were weighed and fixed in 4% neutral buffered formaldehyde ( Fisher ) . Paraffin-embedded sections were stained with hematoxylin and eosin . Photomicrographs were captured by optic microscopy ( Zeiss Axiostar Plus ) . The locomotion activity assay was performed in home cages by using the VersaMax Animal Activity Monitoring System ( AccuScan Instruments ) . Mice were acclimated in the monitoring environments for at least 24 hours before the experiment . Energy expenditure was measured using the CLAMS System ( Columbus Instruments ) . Animals were allowed to acclimatize in the chambers for 72 hours , and measurements were taken subsequently for 72 hr during the light cycle and dark cycle while mice were freely allowed to access food and water . Oxygen consumption was normalized to lean tissue mass . For intraperitoneal GTT , 1 . 5 g of glucose/kg of body weight was injected after a 6-h fasting period . For ITT , an intraperitoneal injection of regular insulin ( Humulin R; 1 unit/kg of body weight ) was administered after a 4–6 h fasting . Blood glucose levels were measured using a glucometer ( Life Scan ) . Tissues were lysed in RIPA buffer with Complete Protease Inhibitor Cocktail ( Roche ) . Protein concentration was determined with BCA protein assay kit ( Pierce ) ; each sample was separated by SDS-PAGE and electro-transferred to nitrocellulose membrane for immunoblot analyses . Western blots for UCP1 protein were performed with antibody AB3036 ( Millipore ) , after which the same blot was normalized to actin using MAB1501 ( Millipore ) . The ImmunoCruz Western Blotting Luminol reagent ( SantaCruz ) was used as the substrate . RNA was isolated with Trizol ( Invitrogen ) , cDNA synthesized with SuperScript III System ( Invitrogen ) , and RT-PCR was performed on the Strategene MX3000 real time detection system using iQ SYBR Green PCR reagent kit ( Biorad ) . Results are expressed as mean ± s . e . m . Comparisons between two groups were made using either two-tailed Student's t-test ( EXCEL ) or ANOVA repeated measures ( SPSS ) , as appropriate . AUC analysis was performed using SigmaBlot . P<0 . 05 was considered to be statistically significant . | Genetic factors play a large role in obesity . However , despite recent technical progress in the search for genetic variants , the identities of causative and contributory genetic factors remain largely unknown . Whereas nucleotide sequence variation has been studied extensively with respect to its potential contribution to obesity , copy number variations ( CNV ) , in which genes exist in abnormal numbers of copies mostly due to duplication or deletion , have only more recently been observed to be associated with human obesity . In this report , we utilize chromosome engineered mouse strains harboring a deletion or duplication CNV to address the potential functional impact of CNVs on weight control and metabolism . We show that the duplication CNV leads to lower body weight; it is also metabolically advantageous and protects from diet-induced obesity and metabolic syndrome ( MetS ) . The deletion CNV causes a “mirror” phenotype with increased body weight and MetS–like phenotypes . Importantly , these effects manifest regardless of the genetic background and do not appear to be attributable to any single gene . These findings demonstrate experimentally that CNV can be causative for weight and metabolic phenotypes and highlight the potential relevance and importance of CNV in the etiology of obesity/MetS and the protection from these traits . | [
"Abstract",
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] | [
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] | 2012 | A Duplication CNV That Conveys Traits Reciprocal to Metabolic Syndrome and Protects against Diet-Induced Obesity in Mice and Men |
Severe dengue virus ( DENV ) disease is associated with extensive immune activation , characterized by a cytokine storm . Previously , elevated lipopolysaccharide ( LPS ) levels in dengue were found to correlate with clinical disease severity . In the present cross-sectional study we identified markers of microbial translocation and immune activation , which are associated with severe manifestations of DENV infection . Serum samples from DENV-infected patients were collected during the outbreak in 2010 in the State of São Paulo , Brazil . Levels of LPS , lipopolysaccharide binding protein ( LBP ) , soluble CD14 ( sCD14 ) and IgM and IgG endotoxin core antibodies were determined by ELISA . Thirty cytokines were quantified using a multiplex luminex system . Patients were classified according to the 2009 WHO classification and the occurrence of plasma leakage/shock and hemorrhage . Moreover , a ( non-supervised ) cluster analysis based on the expression of the quantified cytokines was applied to identify groups of patients with similar cytokine profiles . Markers of microbial translocation were linked to groups with similar clinical disease severity and clusters with similar cytokine profiles . Cluster analysis indicated that LPS levels were significantly increased in patients with a profound pro-inflammatory cytokine profile . LBP and sCD14 showed significantly increased levels in patients with severe disease in the clinical classification and in patients with severe inflammation in the cluster analysis . With both the clinical classification and the cluster analysis , levels of IL-6 , IL-8 , sIL-2R , MCP-1 , RANTES , HGF , G-CSF and EGF were associated with severe disease . The present study provides evidence that both microbial translocation and extensive immune activation occur during severe DENV infection and may play an important role in the pathogenesis .
Dengue virus ( DENV ) infection has been emerging in the American and Caribbean region in the past decade . During a DENV-2 outbreak in 2010 in the State of São Paulo , Brazil , more than 34 . 000 cases and 64 deaths were reported by the Health Department [1] . Symptoms of severe DENV infection range from shock and respiratory distress to major hemorrhagic manifestations and organ failure . The majority of these symptoms are manifest around the time of defervescence . In the early febrile phase , DENV infection is characterized by a high viral load and extensive activation of the Th1 response [2] . Around the time of defervescence , virus titres often decrease below the limit of detection . This critical phase is characterized by extensive immune activation and a so-called cytokine storm ( reviewed in [3] ) that is characterised by high levels of cytokines with mostly pro-inflammatory properties . The mechanism underlying this cytokine storm is still a matter of debate . Evidence points towards antibody-dependent enhancement in which cross-reactive non-neutralizing antibodies enhance the uptake of virus by monocytic cells ( reviewed in [4] ) . Moreover , it has been proposed that ‘original antigenic sin’ is important in the pathogenesis of dengue ( reviewed in [5] ) , postulating that low-avidity T cells are activated by the virus , but fail to clear it . During HIV infection , elevated levels of lipopolysaccharide ( LPS ) were detected in the circulation and correlated with immune activation [6] . There is evidence that a local pro-inflammatory environment in the gut causes disruption of the intestinal barrier , which may eventually result in microbial translocation ( MT ) ( reviewed in [7] ) . We recently showed that elevated LPS levels are present during DENV infection and correlate with disease severity [8] . DENV replicates in monocytes/macrophages , of which many reside in the gut-associated lymphoid tissue ( GALT ) . Therefore , we hypothesized that this may cause a local pro-inflammatory environment in the bowel , eventually affecting the integrity of the intestinal barrier and resulting in MT . Consequently , because LPS is known to be a potent immune stimulator , elevated LPS levels may contribute to the cytokine storm during severe DENV infection . In the present study , we studied markers of MT and immune activation in DENV infected patients . Using clinical classification and a cluster analysis we have identified cytokines that correlate with disease severity . Moreover , significantly increased LPS levels were found in a cluster of patients with a pronounced pro-inflammatory cytokine profile .
All procedures adopted in this study were performed according to the terms agreed by the Institutional Review Board from the Hospital das Clínicas , University of São Paulo ( CAPPesq - Research Projects Ethics Committee ) . This study was approved by CAPPesq under protocol 0652/09 . Written informed consent was obtained from all study volunteers . All included study participants were anonymized with a study number . This cohort has been described previously [1] . Briefly , during the 2010 outbreak samples were collected from patients with clinical suspected dengue fever presenting at the emergency department , department of internal medicine or the intensive care unit at the Ana Costa Hospital , Santos , State of São Paulo . Patients were diagnosed with DENV infection by detection of DENV NS1 antigen and/or IgM-specific antibodies using a commercially available rapid test ( Dengue duo test bioeasy , Standard Diagnostic Inc . 575-34 , Korea ) or by detection of DENV RNA by real time PCR ( RT-PCR ) . Details concerning the day of onset of fever ( day of fever ) , clinical signs and symptoms and the final diagnosis were recorded by the treating physician . Serum samples were withdrawn and stored at −80°C . Patients were classified according to the 2009 WHO classification [9] , [10] and the occurrence of hemorrhagic manifestations and the occurrence of plasma leakage and shock . Hemorrhagic manifestations were observed by the treating physician . The occurrence of plasma leakage was detected by ultrasound or X-ray examination . The diagnosis shock was made by the treating physician based on symptoms such as hypotension , narrow pulse pressure , tachycardia and cold extremities . Age-matched healthy volunteers with a similar socio-economic background were used as controls . The IgG avidity test was used to determine primary or secondary DENV infection [11] . Samples with low avidity IgG antibodies were classified as primary DENV infection , whereas samples with high avidity IgG antibodies were classified as secondary . Samples in which IgG antibodies were not detected could not be classified , although the majority was probably primary DENV infection . Viral load was determined by an “in-house” RT-PCR method and virus serotype was determined by a multiplex PCR . Both methods have been described in detail previously [12] . For both assays RNA was extracted from plasma using the Qiagen Viral RNA kit ( Qiagen , Germany ) . RT-PCRs were conducted in duplicate . For the viral load SuperScript III Platinum SYBR Green One-Step qRT-PCR kit with ROX ( Invitrogen , Inc . , EUA ) and for the dengue serotype multiplex PCR Platinum Taq polymerase ( Invitrogen , Brazil ) was used . In both RT-PCRs primers covering all four DENV serotypes were used [13] . Sequences of the primers were the following: D1 , 5′-TCA ATA TGC TGA AAC GCG CGA GAA ACC G; TS1 , 5′- CGT CTC AGT GAT CCG GGG G; TS2 , 5′- CGC CAC AAG GGC CAT GAA CAG; TS3 , 5′-TAA CAT CAT CAT GAG ACA GAG C; and DENV-4 , 5′-TGT TGT CTT AAA CAA GAG AGG TC . Samples were aliquoted and stored at −80°C . Repetitive freeze-thaw cycles were avoided . LPS was determined with a commercially available Limulus Amebocyte Lysate ( LAL ) assay ( Associates of Cape Cod Incorporated , USA ) . Samples were diluted 1∶20 with LAL Reagent Water and heat-inactivated at 60°C for 30 minutes . Depyrogenated glassware was used to prevent contamination ( Pyrotubes , Associates of Cape Cod Incorporated , USA ) . Hereafter , 50 µl of LAL was added and samples were incubated in the Pyros Kinetix Flex Machine ( Associates of Cape Cod Incorporated , USA ) . Escherichia coli endotoxin was used to prepare the standard curve . Soluble CD14 ( sCD14; ‘Quantikine’ ELISA , R&D Systems , UK ) , LPS binding protein ( LBP ELISA , Hycult Biotech , USA ) and IgM and IgG endotoxin core antibodies ( EndoCab ELISA , Hycult Biotech , USA ) were determined using commercially available assays . The assays were performed according to the manufacturer's instructions and every sample was measured in duplicate . One patient was excluded from the LPS analysis due to extremely high levels of LPS ( 56504 pg/ml ) and therefore a secondary bacteremia could not be excluded . Cytokines were measured using a multiplex immunoassay kit with spectrally encoded antibody-conjugated beads ( Human Cytokine 30-plex panel , Invitrogen , USA ) . The following cytokines were measured ( Sensitivity limits ( pg/ml ) : EGF ( <18 , 8 ) , Eotaxin ( <0 ) , FGF-basic ( <12 , 3 ) , G-CSF ( <38 , 5 ) , GM-CSF ( <40 ) , HGF ( <0 ) , IFN-α ( <116 ) , IFN-γ ( <34 ) , IL-1RA ( <116 ) , IL-1β ( <20 ) , IL-2 ( <33 ) , sIL-2R ( <40 ) , IL-4 ( <108 ) , IL-5 ( <40 ) , IL-6 ( <13 , 5 ) , IL-7 ( <60 ) , IL-8 ( <20 ) , IL-10 ( <47 ) , IL-12 ( p40/p70 ) ( <40 ) , IL-13 ( <60 ) , IL-15 ( <58 ) , IL-17 ( <80 ) , IP-10 ( <640 ) , MCP-1 ( <60 ) , MIG ( <20 ) , MIP-1α ( <17 ) , MIP-1β ( <18 ) , RANTES ( <20 ) , TNF-α ( <21 ) and VEGF ( <823 ) . Serum samples were diluted 1∶2 . The test was performed according to the manufacturer's instructions and was run on a Luminex 200 dual laser detection system . The cluster analysis procedure was adapted from van den Ham et al . [14] . Briefly , cytokine values were log-transformed and subjected to hierarchical correlation clustering ( i . e . , with distance measure 1 – pearson's pairwise correlation value ) using Ward's method that minimizes within-cluster variance . Both patients and cytokines were clustered to obtain a heatmap . Cytokines that had more than 5% of values missing ( FGF-basic , GM-CSF , IL-1β , IL-5 , IL-7 , IL-13 and IL-17 ) were excluded from the analyses . Three serum samples were excluded from the cytokine analysis , because their levels were out of range for most of the cytokines evaluated and therefore the quality of the sample was most likely compromised . Cluster analysis was performed in R 2 . 15 ( R Development Core Team [R Foundation for Statistical Computing] , 2012 , www . r-project . org ) . R scripts used to construct the trees and heatmaps are available upon request . The Kruskal-Wallis H test was used for comparison of more than two groups . Statistical significance between individual groups was determined with the Mann-Whitney U test . Using the Bonferroni correction a p-value cut-off of ≤0 . 0083 for cytokine analyses was applied . For testing the significance of LPS , LBP and sCD14 levels associated with the clinical classifications and the clusters a p-value cut-off ≤0 . 05 was used . Correlations were calculated using the Spearman's correlation coefficient . To calculate the association of severe disease with the three main clusters the Fisher's exact test was used . For this test we used a p-value cut-off ≤0 , 05 to reach significance .
During the 2010 outbreak serum samples were obtained from 811 patients with laboratory confirmed acute DENV infection . From this cohort , 99 patients with non-severe dengue were randomly selected based on the availability of samples and clinical data . Moreover , patients with severe co-morbidity were excluded . Eventually , 50 patients without warning signs ( WS− ) and 49 with warning signs ( WS+ ) were selected . Only 33 patients presented with severe dengue according to the 2009 WHO case classification [10] and they were all included in this analysis . Among patients with warning signs , 29/49 ( 59 . 2% ) showed plasma leakage diagnosed by ultrasound/X-rays ( pleural and peritoneal 12; peritoneal 12; pleural 5 ) , 23 ( 46 . 9% ) showed mucosal bleeding , 14 ( 28 . 6% ) persistent vomiting , 5 ( 10 , 2% ) abdominal pain and 3 ( 6 , 1% ) lethargy . Among patients with severe dengue , 27/33 ( 81 , 8% ) showed signs of severe plasma leakage ( 25 shock , 2 fluid accumulation leading to respiratory distress ) , 14 ( 42 , 4% ) showed severe bleeding and one ( 3 , 0% ) severe liver involvement ( AST and ALT>1000 ) . The clinical presentation and general characteristics of the cohort are described in Table 1 . Of the 132 patients included , three had a primary and 113 had a secondary infection based on the IgG avidity test [11] . In 16 patients IgG antibodies could not be detected . Viral RNA was detected in 120 of 132 samples . Significantly higher DENV RNA load was detected in samples collected 1–3 days after onset of fever compared to day 4–7 and day >7 ( Figure 1 ) . Moreover , DENV RNA levels were significantly higher in WS- patients compared to WS+ and severe patients , but this difference occurred most likely because they presented earlier after the onset of fever ( Figure 1 , Table 1 ) . Dengue serotype could be determined in 126/811 ( 15 . 5% ) patients with laboratory confirmed acute DENV infection during the 2010 Santos outbreak . From these , 118/126 ( 93 . 7% ) typed as DENV-2 , 4 ( 3 . 2% ) as DENV-1 and 4 ( 3 . 2% ) as DENV-3 . Among the 132 patients included in this study , DENV serotype could be determined in 20 ( 15 . 2% ) patients . 19 out of 20 patients were typed as DENV-2 and the remaining one as DENV-3 . Eotaxin , IL-2 , IL-4 , IL-1RA and IFN-γ were detected at very low levels in DENV infected patients and healthy controls and did not show any significant differences between groups when patients were classified according to the 2009 WHO classification or the occurrence of plasma leakage/shock and hemorrhage ( Table 2 ) . In the majority of samples MIP-1α and TNF-α also showed values below the detection limit . However , some healthy controls showed extremely elevated levels and therefore a significant difference between healthy controls and dengue patients was shown ( Table 2 ) . IFN-α , IL-10 , IL-12 IL-15 , IP-10 , MIG and MIP-1β were significantly increased or decreased in dengue patients compared to healthy controls if patients were classified according to the 2009 WHO classification ( Table 2 , Figure S1 ) . These cytokines did not show significant differences among the disease severity groups . Some cytokines showed significant differences in levels between dengue disease severity groups . Using the 2009 WHO dengue case classification , levels of RANTES and MCP-1 were significantly increased in WS− patients compared to patients with severe and WS+ dengue respectively . In contrast , levels of IL-6 , IL-8 , HGF and G-CSF were significantly increased in severe dengue compared to uncomplicated disease ( Table 2 , Figure 2 ) . When these cytokines were determined in patients classified according to the occurrence of plasma leakage and shock , levels of RANTES and EGF were significantly decreased in patients with shock compared to patients with uncomplicated dengue . Moreover , levels of IL-6 , HGF and G-CSF were significantly increased in shock patients compared to patients with uncomplicated disease ( Table 2 , Figure 3 ) . Patients were also classified according to the occurrence of hemorrhage . Levels of sIL-2R , IL-6 , IL-8 , IL-15 and G-CSF were significantly increased in patients with severe bleeding compared to patients with no bleeding ( Table 2 , Figure S2 ) . Nine out of 132 patients died within 14 days after the onset of fever . In these patients IL-6 , G-CSF and sIL-2R were significantly increased and RANTES significantly decreased in non-survivors compared to survivors ( Table 2 , Figure 4 ) . IFN-α , IL-12 , MCP-1 , MIG , MIP-1β showed a dynamic temporal pattern during the course of disease . They were significantly increased at day 1–3 after the onset of fever compared to day 4–7 and day>7 ( Table 2 , Figure S3 ) . This may explain why the levels of MCP-1 were significantly higher in uncomplicated than in more severe dengue in patients classified according to the 2009 WHO dengue case classification , since patients with non-severe dengue presented earlier in their course of disease ( Table 1 ) . Interestingly , the mediators IFN-α ( P = 0 . 001 ) , IL-12 ( P = 0 . 01 ) , MCP-1 P<0 . 0001 ) , MIG ( P = 0 , 01 ) and MIP-1β ( P<0 . 0001 ) showed to have a significant positive correlation with the viral load ( data not shown ) . The cluster analysis groups samples or cytokines based on cytokine levels only , and not based on clinical presentation ( non-supervised analysis ) . The sample and cytokine cluster analyses can be combined and visualized as a heatmap ( Figure 5 ) . A dendrogram shows the similarity between samples ( left side of figure 5 ) , where samples in the same branch are more similar regarding their cytokine profiles to each other than to samples in other branches . The sample dendrogram can be divided into three principle clusters that largely segregate healthy controls ( cluster A ) , mild to moderately ill DENV infected patients ( cluster B ) , and severely ill DENV infected patients ( cluster C ) . Clinical disease was more severe in cluster C than in clusters A and B , illustrated by a statistically significant higher incidence of severe disease ( P = 2 , 2×10−16 ) , shock ( 3 , 4×10−5 ) , severe hemorrhage ( P = 0 , 007 ) and death ( P = 0 , 03 ) in this cluster compared to cluster A and B ( Table 3 ) . A subgroup of ‘healthy control’ cluster A displayed elevated levels of inflammatory cytokines when compared to other control samples , including MIP-1α , MIP-1β , TNF-α , EGF and IL-8 . These controls are likely to have suffered from an underlying unidentified inflammatory condition ( Table 2 , Figure 5 ) . The majority of dengue cases were part of cluster B . In cluster B 40% of patients suffered from WS− , 39% from WS+ and 21% from severe dengue ( Table 3 ) . This distribution resembles the whole cohort , which consisted of 38% WS− , 37% WS+ and 25% severe dengue . The cytokine pattern in this cluster shows a rather diffuse pattern . A few patients show increased concomitant expression of IFN-γ , IFN-α , MCP-1 , MIG and IL-12 , which is indicative for an early antiviral response . Cluster C shows a strong pro-inflammatory cytokine pattern . RANTES and EGF are downregulated , whereas IL-6 , IL-8 , IL-10 , IL-15 , IL-1RA , sIL-2R , HGF , VEGF , G-CSF , MCP-1 , IP-10 , and MIG are upregulated compared to other clusters . Interestingly , IL-1RA , IL-10 , IL-15 , IP-10 , MIG and VEGF are associated with severe disease in the cluster analysis , but not with severe disease using the clinical classifications . Using for example the 2009 WHO classification IL-10 , IL-15 , IP-10 and MIG were significantly elevated in dengue patients compared to healthy controls , but levels were not significantly elevated in severe compared to uncomplicated dengue . Cluster C identifies a group of patients with an extensive pro-inflammatory cytokine profile , suggestive for a cytokine storm . Moreover , severe clinical symptoms occurred significantly more often in cluster C compared to the other clusters . Statistically significant elevated LPS levels were found in cluster C compared to ‘dengue’ cluster B and ‘healthy control’ cluster A ( Figure 6 ) . In the 2009 classification there proved to be a trend towards higher LPS levels in severe dengue , although these differences were not statistically significant . However , in the plasma leakage/shock classification LPS levels were significantly increased in patients with shock compared to patients with no plasma leakage . In the 2009 classification , LBP levels were significantly increased in dengue patients compared to healthy controls . Moreover , levels in severe patients were significantly increased compared to WS− patients . In the plasma leakage/shock classification levels were significantly increased in patients with shock and no plasma leakage compared to healthy controls . Moreover , levels in patients with shock were significantly increased compared to patients with plasma leakage . In the cluster analysis LBP levels in all three clusters differed significantly from each other . sCD14 levels were significantly increased in DENV infected patients compared to healthy controls in the 2009 and the plasma leakage/shock classification and in the ‘dengue’ clusters B and C compared to the ‘healthy control’ cluster A . Moreover , in the 2009 classification levels in WS+ patients were significantly increased compared to WS− patients . When patients were classified according to the occurrence of hemorrhagic manifestations , LPS levels were not significantly different , and sCD14 and LBP again showed to be significantly elevated in DENV infected patients compared to controls ( Data not shown ) . No significant differences in IgM- and IgG-specific endotoxin core antibodies were found among the groups classified according to the 2009 classification or the occurrence of plasma leakage and shock ( Data not shown ) .
In this study , we have examined a cohort of dengue patients and healthy controls to investigate the role of immune activation and MT in DENV pathogenesis . We found evidence for the occurrence of MT during DENV infection . Furthermore , in the cluster analysis , we showed that the cluster of patients with the highest LPS levels appeared to suffer from a cytokine storm . The two complementary analysis techniques applied in this study yielded similar results . However , the cluster analysis identified more markers associated with severe disease than the clinical classification system . The cluster analysis groups patients based on the occurrence of identical inflammatory processes , overcoming the potential clinical classification biases that may occur due to the fact that disease presentation of patients can be quite variable and the severity of disease is subject to clinical interpretation . In the cluster analysis , levels of cytokines determined the outcome of the clusters . Therefore this technique cannot be used to relate absolute values of cytokines to the clusters with patients . Altogether , the strength of our approach is the use of both clinical classification and cluster analysis in order to increase the sensitivity to find markers of disease severity . One limitation of this study is the cross-sectional study design . We have recorded the disease severity of the patient at the time of inclusion and at the same moment the samples for LPS and cytokine analysis were drawn , so the levels of LPS and cytokines were related to signs and symptoms that were present at that same time . Both in a previous [8] and in the present study , we have shown that elevated levels of LPS are associated with severe dengue . Moreover , MT was indirectly confirmed by increased levels of LBP and sCD14 as observed in sepsis patients [15] , [16] . In contrast to our previous study , the association between LPS levels and clinical disease severity was less strong . However , also in this study there proved to be a significant association in patients classified according to the occurrence of plasma leakage and shock . Moreover , LPS levels were significantly increased in the cluster with the highest incidence of shock ( cluster C ) and levels of LBP did also show a direct association with disease severity in the 2009 and the plasma leakage/shock classification . This cohort differed from our previous study in several ways: age of the population ( children vs . children and adults ) , the geographical location ( Indonesia vs . Brazil ) and the samples used ( plasma vs . serum ) . Whether age or different pathogen pressures at different geographical locations may account for the observed differences remain to be established . IgM and IgG endotoxin core antibodies were determined , but no strong association with disease severity was found . This is in agreement with studies in HIV and sepsis patients , which show conflicting results with regards to the association between endotoxin core antibodies and disease severity [6] , [17] , [18] . It has been hypothesized that severe DENV infection is caused by an exaggerated immune response , associated with a cytokine storm ( reviewed in [3] , [19] ) . The exact definition of a cytokine storm is still a matter of debate . In general it is assumed that a cytokine storm starts with an excessive release of pro-inflammatory cytokines ( e . g . TNF-α and IL-1β ) . These cytokines then induce other pro-inflammatory ( e . g . IL-6 ) , but also anti-inflammatory cytokines ( e . g . IL-10 ) . This augmented immune response could therefore be the result of a disturbed balance between pro- and anti-inflammatory cytokines . During severe DENV infections a cytokine storm has been proposed to be responsible for the increased vascular permeability and coagulation disturbances ( reviewed in [19] ) . Studies in patients with HIV and visceral leishmaniasis showed that MT may contribute to severe disease through excessive immune activation [6] , [20] . It is known that LPS stimulation can induce the production of IL-6 , IL-8 , TNF-α and IL-1β [21] and the growth factors VEGF and HGF [22] . Interestingly , in the present study high levels of four of these markers were found in the pro-inflammatory cluster C . This suggests that MT may play a role in the cytokine storm in severe dengue . Moreover , Bosisio et al . [23] showed that priming of mononuclear cells with IFN-γ increased the expression of the TLR4 receptor and subsequent LPS-induced cytokine production . This would suggest that DENV induced IFN-γ production could enhance the pro-inflammatory LPS signaling pathway . In addition , Chen et al . [24] , [25] showed that LPS could prolong DENV infection of monocytes and macrophages . A sustained DENV infection due to MT may also contribute to the cytokine storm during DENV infection . All these studies suggest that MT may play an important role in the initiation and perpetuation of the cytokine storm during severe DENV infection . However , in this study MT was associated with extensive immune activation , but to investigate whether there is a causal relationship between MT and the cytokine storm further studies are warranted . Our cohort confirms several known associations for dengue . In agreement with previous work , our study showed evidence of a strong Th1 response in the early phase of disease with peak levels of IFN-α [26] , [27] , IL-12 [28] , [29] , [30] , MCP-1 [31] , [32] , [33] , MIG and MIP-1β [31] . All these Th1 cytokines correlated significantly with viral load , suggesting that they are associated with a host response aiming at reducing the viral load . In the present study we have quantified pro- and anti-inflammatory mediators to provide evidence for a role of a cytokine storm in severe dengue patients . Levels of IL-10 , IL-15 , VEGF , G-CSF and IP-10 were increased in ‘severe dengue’ cluster C in the cluster analysis . High levels of IL-10 and VEGF have been described in severe dengue , especially at the day of defervescence [2] , [28] , [29] , [34] , [35] . Interestingly , patients in severe cluster C presented around this time ( day 3–5 after onset of fever ) . The high incidence of shock in cluster C could be partly explained by high levels of VEGF and MCP-1 , which are proposed to be important contributors to plasma leakage [33] , [35] . IL-15 and IP-10 [36] were reported to play an important role in the NK cell response , whereas G-CSF [37] stimulates neutrophil development and differentiation . High levels of IL-15 , IP-10 and G-CSF in cluster C suggest that extensive activation of the innate immune system may contribute to the cytokine storm in severe dengue . In contrast , high levels of IL-10 have an inhibitory effect on dendritic cells and macrophages ( reviewed in [38] ) . In both the clinical classifications and the cluster analysis , IL-6 , IL-8 , sIL-2R , RANTES , HGF and EGF were strongly associated with severe disease . High levels of IL-6 and IL-8 were reported in dengue cases with severe plasma leakage and shock [39] and in non-survivors [40] , [41] , [42] . IL-6 production is induced by TNF-α and IL-1β [37] . In our study no increased levels of TNF-α and IL-1β were found . This is in agreement with earlier reports [31] , [34] , [39] , [42] , [43] and can be explained by the observation that TNF-α and IL-1β are produced early after infection and are removed quickly from the circulation . In addition , sIL-2R has been associated with severe dengue [2] , [27] , [43] and is proposed to serve as a marker of immune activation ( reviewed in [44] ) . Thrombocytopenia is a hallmark of DENV infection and since thrombocytes are an important source of RANTES and EGF , severe thrombocytopenia may explain the depletion of these two markers . This has been described previously in severe dengue [28] and cerebral malaria [45] . In summary , we provide evidence that MT is associated with extensive immune activation during severe dengue . LPS may play an important role in the development of the cytokine storm . Besides the classical mediators ( e . g . IL-6 , IL-8 , IL-10 ) , we identified cytokines ( IL-1RA , sIL-2R ) , chemokines ( MCP-1 , IP-10 , MIG , RANTES ) and growth factors ( HGF , EGF , G-CSF , VEGF ) that may play an important role in the cytokine storm during severe DENV infection . | The pathogenesis of severe dengue virus ( DENV ) infection is still not fully understood . It is hypothesized that it is caused by a cytokine storm as is described in severe sepsis . In the sepsis field , the potent immunostimulator lipopolysaccharide ( LPS ) is proposed to play an important role in the development of a cytokine storm . In a previous study we have found elevated levels of LPS in children with severe DENV infection . In this study we have investigated if we could confirm that microbial translocation occurs in DENV-infected patients . Moreover , we have determined the levels of thirty cytokines to get more insight in the cytokine storm during DENV infections and we have investigated whether microbial translocation is associated with immune activation . The patients in this cohort were classified according to their clinical presentation . Furthermore , a cluster analysis based on the expression of the determined cytokines was applied to identify patients with similar cytokine profiles . With these two techniques , we identified cytokines that may contribute significantly to the cytokine storm , and we could relate elevated levels of LPS to patients with a pro-inflammatory cytokine profile . | [
"Abstract",
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"Materials",
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] | [
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] | 2013 | Microbial Translocation Is Associated with Extensive Immune Activation in Dengue Virus Infected Patients with Severe Disease |
Next-generation sequencing has made possible the detection of rare variant ( RV ) associations with quantitative traits ( QT ) . Due to high sequencing cost , many studies can only sequence a modest number of selected samples with extreme QT . Therefore association testing in individual studies can be underpowered . Besides the primary trait , many clinically important secondary traits are often measured . It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits . However , analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled . Some methods exist for analyzing secondary traits in selected samples , where some burden tests can be implemented . However p-values can only be evaluated analytically via asymptotic approximations , which may not be accurate . Additionally , potentially more powerful sequence kernel association tests , variable selection-based methods , and burden tests that require permutations cannot be incorporated . To overcome these limitations , we developed a unified method for analyzing secondary trait associations with RVs ( STAR ) in selected samples , incorporating all RV tests . Statistical significance can be evaluated either through permutations or analytically . STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations . It also enables jointly analyzing multiple cohorts ascertained under different study designs , which greatly boosts power . The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies . STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced . A significant association between LDLR and systolic blood pressure was identified , which is supported by pharmacogenetic studies . In summary , for sequencing studies , STAR is an important tool for detecting secondary-trait RV associations .
Next-generation sequencing has already revolutionized the study of complex traits , and made possible the detection of rare variant associations . Many sequence based association studies are currently being performed , some of which have already lead to the discovery of associations with clinically important traits , such as lipids levels [1] , age related macular degeneration [2] , inflammatory bowel disease [3] , blood pressure [4] , body mass index [5] , etc . In particular , there is increasing interest to detect associations with quantitative traits ( QT ) . It has been suggested that complex traits can be due to multiple variants with small effects , and are quantitative in nature [6] , [7] . Mapping multiple quantitative traits may help elucidate the etiology of complex traits , reducing sample heterogeneity [8] , dissecting gene pleiotropy and refine the definition of complex diseases [7] , [9] , [10] . For example , recent studies of type 2 diabetes have been focused on multiple related QTs , such as fasting glucose levels [11] , insulin resistance levels [11] , and c-reactive proteins [12] . Many quantitative traits are usually measured in different studies as secondary outcomes . It is of great interest to combine multiple studies for detecting associations with commonly measured primary or secondary traits . For example , the National Heart Lung and Blood Institute-Exome Sequencing Project ( NHLBI-ESP ) is studying a variety of different phenotypes and employed both extreme and random sampling . In order to improve power , all samples with the phenotype of interest measured are jointly analyzed . Specifically , the association analysis of low density lipoprotein cholesterol ( LDL ) levels is performed by combining several studies which include a well-phenotyped random population cohort , selected samples with extreme LDL levels as well as individuals with extreme body mass index ( BMI ) . Many methods have been developed for detecting rare variant associations [13]–[19] . These methods are all based upon aggregating multiple rare variants across a genetic region , which is usually a gene . Compared to analyzing each variant individually , these region based tests can be more powerful . However , rare variants that are involved in complex trait etiologies usually only have moderate effect sizes , and their aggregated frequencies across a genetic region can still be low . It is therefore necessary to sequence and analyze a large number of samples in order to have adequate power to detect associations . Although next generation sequencing is much more cost effective than Sanger sequencing , it is still expensive to generate high read depth data for large numbers of samples . Given cost constraints , in order to improve power , many studies sequence samples with extreme QT rather than the entire cohort . The selective sampling study design produces challenges for analyzing secondary traits . Without properly accounting for the sample ascertainment mechanisms , type I errors for detecting secondary trait associations may be inflated [20] , [21] . This is because due to the correlations between the primary and secondary traits , mean values for the secondary traits will be different between individuals with primary trait from opposite extreme tails . Additionally if the primary trait is associated with a gene region , the cumulative variant frequencies will also be different . Therefore spurious association can be created by ascertainment . The bias for the naïve analysis of secondary trait is demonstrated both theoretically and empirically in this article . Several methods exist for detecting secondary trait associations in selected samples . For example , a retrospective likelihood method was developed for mapping secondary phenotypes using regression models ( SPREG ) in case control studies [20] . It was subsequently extended in an empirical Bayesian framework [22] , which utilizes genotype information from cases for rare diseases and can be more powerful than the retrospective likelihood method . However , both methods are not directly applicable to the studies where the primary trait is quantitative and extreme sampling is implemented . We previously developed a method for detecting multiple ( secondary ) trait associations ( MTA ) in selected samples , which jointly models multiple phenotype associations and sampling ascertainment status [21] . MTA can be used to analyze data from studies with known sampling mechanisms , e . g . case control , and extreme sampling designs . It incorporates several rare variant association tests , whose statistical significance can be evaluated analytically e . g . the combined multivariate and collapsing ( CMC ) [16] , and Gene- or Region-based Analysis of Variants of Intermediate and Low frequency ( GRANVIL ) [18] . Weighted sum statistics [14] , [23] can also be incorporated , if the weights that are assigned to each variant site are not dependent on the trait . One major advantage of using MTA is that cohorts ascertained under different sampling schemes can be combined for detecting associations with commonly measured traits . These studies can be targeted at the same or different primary traits . By combining data from different studies , much larger sample sizes can be analyzed and the power to detect associations can be greatly improved [21] . However none of these methods for detecting secondary associations incorporate sequence kernel association test ( SKAT ) , a powerful variance component score test based method . This method can be more powerful when causal variants have bidirectional effects and/or a large proportion of the variants within gene region are non-causal . Standard permutation algorithms cannot be applied to obtain empirical p-value . This is because when the primary and secondary traits are correlated and the genetic region is associated with the primary trait , neither the secondary trait residuals nor the locus genotypes are interchangeable under the null hypothesis . Therefore , the statistical significance can only be evaluated via asymptotic approximations , which has several notable limitations: 1 . ) Due to the low frequency of rare variants , asymptotic approximation for some tests may be violated , which can lead to either inflated type I error or loss of power . 2 . ) For some rare variant association methods , the analytical distribution for the test statistics is unknown and therefore the statistical significance has to be evaluated empirically . These rare variant tests that require evaluating p-values via permutation are often more powerful than the methods implemented in MTA , e . g . CMC or GRANVIL . It is therefore desirable that these tests can be applied to analyze secondary traits . To overcome the limitations of existing methods , a unified model was developed to detect secondary trait associations using selected samples . In the samples with extreme primary quantitative traits , through re-parameterizing the likelihood functions , interchangeable residuals for the secondary traits can be obtained under the null hypothesis . The residuals are approximately independent , and normally distributed . We proved theoretically that the analysis of secondary trait associations can be equivalently implemented by analyzing the correlation between the secondary trait residuals and the gene/genetic region . Therefore , any rare variant association test that can analyze QT in random population based studies can be incorporated in STAR . In addition , multiple cohorts can be jointly analyzed through conventional mega-analysis methods that use individual participant data or meta-analysis methods that use summary level statistics . A variety of popular rare variant tests have been implemented in the STAR framework and the power to detect secondary trait associations was evaluated . Specifically , we considered the weighted sum statistic ( WSS ) [14] , [23] , sequence kernel association tests ( SKAT ) [17] , and variable threshold test ( VT ) [24] . Additionally the kernel based adaptive cluster test ( KBAC ) [15] , which was originally developed for analyzing binary disorders , was extended to analyze quantitative traits and incorporated in STAR for detecting associations with secondary phenotypes ( Text S1 ) . The performances for these methods were compared using extensive simulation studies . Genetic data were simulated under a realistic population genetic model as described by Kryukov et al [25] , which incorporates both demographic change and purifying selections . Phenotypes were simulated based upon parameters estimated from clinically important complex traits . It is demonstrated that under a broad variety of phenotype models , the power for detecting secondary trait associations can be greatly improved through the use of more powerful rare variant tests that are incorporated in STAR . There does not exist a method that is consistently the most powerful , and the power difference between top performing methods is generally small . When the effects of causal variants are unidirectional , the VT test outperforms other methods in most scenarios . When there are variants with effects in opposite directions or only a small proportion of the variants are causal , SKAT can be more powerful than alternative methods in many settings . The STAR method was also used to analyze a published sequence dataset from the SardiNIA project [1] , where nine genes were sequenced in 256 individuals with extreme LDL levels ( individuals taking lipid-lowering therapies were not considered for the analysis ) . In the original article by Sanna et al [1] , the authors focused on detecting associations with the primary trait LDL , and did not consider analyzing other metabolic and lipids traits . In this article , the analysis was extended to detect associations with other clinically important traits , which include high density lipoprotein cholesterol ( HDL ) , total cholesterol level ( TCL ) , triglyceride ( TG ) , insulin levels ( INSULIN ) , BMI , systolic and diastolic blood pressure ( SysBP and DiasBP ) . One association was identified between LDLR and SysBP , which is statistically significant after applying a Bonferroni correction for testing multiple genes and traits . This association has strong biological support from pharmacogenetics studies [26] . These findings provide new insight on the etiology for the LDLR gene , and established the importance of our method in sequence based association studies . An R-package , STARSEQ which implements the STAR method is available through the Comprehensive R Archive Network ( CRAN ) at http://cran . r-project . org/web/packages/STARSEQ/ . Additional companion softwares are deposited at http://code . google . com/p/starseq/ .
Under the null hypothesis of no gene/secondary trait associations , following the MTA framework , a multivariate generalized linear model can be implemented to estimate nuisance parameters [21] . The link functions for the mean parameters of the two traits satisfy ( 1 ) In the above model , and are covariates , such as age or sex . The residual terms for the secondary traits , i . e . are correlated with the primary trait , and not interchangeable under the null hypothesis , i . e . ( 2 ) It was previously shown via simulations that naïve inferences for secondary trait associations , which ignore sample ascertainment mechanisms , can be biased [20] , [21] . It can also be proved theoretically that due to extreme sampling on the primary trait , spurious associations can be created between the gene locus and secondary trait ( Text S2 , Figures S1 and S2 ) . Without adjusting for the sample ascertainment mechanisms , the biases in the secondary trait effects will increase linearly with respect to the trait residual correlation and approximately linearly with respect to the primary trait effects when the magnitude of primary trait effects is small . Using this theoretical framework , we also evaluated some standard adjustment methods . e . g . 1 . ) Separately analyzing individuals with high and low extreme primary traits , and then combining the results via meta-analysis . or 2 . ) Incorporating an indicator to denote whether an individual has a high or low extreme primary trait as a covariate , and perform linear regression analysis using the entire sample . We proved theoretically that these methods will not eliminate the bias in the association analysis of secondary traits , and type I error will still be inflated after the adjustment . In order to obtain unbiased results , sampling schemes have to be properly modeled . Ascertainment corrected likelihood can be used , which jointly models sample ascertainment status and genotype/phenotype association , i . e . ( 3 ) The likelihood model can be used for both trait dependent sampling and population based random sampling . We showed analytically that the secondary trait effects can be consistently inferred under the ascertainment corrected likelihood model . Details for the likelihood specification can be found in ( Text S3 ) . The likelihood function in equation ( 1 ) needs to be re-parameterized in order to facilitate deriving the SKAT statistics and performing permutations . It is clear that ( 4 ) The conditional probability satisfies ( 5 ) Instead of estimating the variance and correlation coefficients for , the following parameters are estimated , i . e . . As is shown in ( Text S3 ) , the Jacobian for the re-parameterization , i . e . is non-degenerate and the re-parameterization is one to one and invertible . Therefore , an equivalent mean model can be fitted under the null hypothesis , i . e . ( 6 ) Practical issues for fitting the model are discussed in ( Text S4 ) . In this model , the residual errors and for the primary and secondary traits are uncorrelated . In particular , the residual errors after re-parameterization are interchangeable under the null hypothesis . Burden tests , such as CMC and WSS , aggregate multiple rare variants across a genetic region and analyze them jointly . The following model can be used to obtain score statistics for a burden test: ( 7 ) In formula ( 7 ) , is the genotype coding for the locus multi-site genotypes . Examples include the weighted sum coding [14] , i . e . , where each variant site is assigned a weight and the weighted genotypes are aggregated . For some rare variant association tests such as KBAC [15] , the genotype coding can also depend on the QT , i . e . . Formula 7 can be used for detecting single variant associations as well , where is the coding for single variant genotype . Score tests can be formally constructed from the joint likelihood for testing the null hypothesis of no gene/secondary trait associations , i . e . . If the samples are ascertained based upon only the primary trait , score tests can be equivalently constructed from the conditional likelihood , i . e . . This is because the joint likelihood can be factorized , i . e . ( 8 ) When the samples are ascertained based upon the primary trait , the distribution of conditional on is independent of the ascertainment status , i . e . In addition , the term does not contain the parameter of interest . The score function thus takes the form ( 9 ) where , , and are maximum likelihood estimates under the null hypothesis . It is clear from formula ( 9 ) that is proportional to the covariance between the secondary trait residuals and the locus genotype coding . Given that , , and are consistent estimators under the null hypothesis , by Slutsky's theorem , the residuals for the secondary trait i . e . are approximately normally distributed and interchangeable under the null hypothesis . Therefore , the analysis of rare variant secondary trait associations can be implemented by analyzing the correlation between the corrected residuals and the locus genotype coding . Standard permutation algorithms can be implemented by shuffling the residuals under the null hypothesis . In our STARSEQ package , we also provide flexible tools for calculating the adjusted secondary trait residuals , which can be analyzed by any user specified rare variant association test . Using similar ideas , we show in ( Text S5 ) , that the extended SKAT statistic in STAR has the form ( 10 ) where is the kernel function used to compare two multi-site genotypes and , and is the estimated mean secondary trait value under the null model . P-values for the extended SKAT method can be obtained either analytically or via permutation . The KBAC test was previously developed for the analysis of binary trait associations [15] . It is extended to analyze rare variant QT associations in studies using randomly ascertained samples or samples with extreme traits . The extended KBAC method has also been incorporated in STAR for analyzing secondary trait associations . The details for the extensions are given in ( Text S1 ) . Type I error and power were evaluated for the following rare variant association tests that were extended in STAR , i . e . CMC , KBAC , WSS , SKAT and VT . Genetic data were generated according to a four parameter demographic model for Europeans [25] , [27] . In addition , purifying selection is also modeled , which influences the variant site frequency spectrum . Among the variants with selection coefficients >10−4 , 50% are randomly chosen to be causal for the primary trait , and another 50% of the variants are independently chosen to be causal for the secondary trait . The set of causal variants for the primary trait are denoted by and that for the secondary trait are denoted by . Variants belonging to the intersection of and modulate both the primary and secondary phenotypes . QTs were simulated according to the following bivariate normal distribution: ( 11 ) The magnitudes of the causal variant effects are assumed to be inversely correlated with the minor allele frequencies ( MAF ) , i . e . For a special case when , the magnitude for the effects of all rare causal variants is constant , i . e . . In the simulations , we considered models where 1 . ) 2 . ) and 3 . ) and . For each set of parameter values of and , we evaluated the power for different rare variant association tests when 1 . ) all causal variants have effects in the same direction or 2 . ) 80% of the variants increase the mean secondary trait value while the remaining 20% decrease the mean secondary trait value . In the simulations , the primary and secondary traits are assumed to be positively ( or negatively ) correlated with coefficients ( or ) . For the evaluation of type I errors , datasets were simulated with . Data for selective sampling studies are simulated , where for each dataset , a total of 5 , 000 individuals with extreme primary trait values are selected from a cohort of 100 , 000 individuals . Two-sided alternative hypothesis was tested for each method . Although p-values for CMC and SKAT can be obtained analytically , they can either be conservative or anti-conservative [23] . In order to calibrate the distribution of p-values , we evaluated the statistical significance of all methods empirically using 5 , 000 permutations . The power and type-I errors for each method were obtained using 10 , 000 replicates for a significance level of . As a comparison to STAR , type I error for linear regression analysis was also evaluated , where sample ascertainment mechanisms were ignored . In order to illustrate the application of STAR for combining multiple cohorts , a meta-analysis of three studies was simulated . The primary trait for each study is different and a common secondary trait is measured for all studies . In the first study , the gene region is associated with the primary trait , and causal variants have an effect of −0 . 5 . The correlation between the primary and secondary traits is 0 . 6 . In the second study , the primary trait is associated with the gene region , and causal variants have an effect of 0 . 25 . The primary and secondary traits are correlated with coefficient 0 . 4 . In the third study , the gene region is not associated with the primary trait , and the correlation between the primary and secondary traits is −0 . 2 . In each study , a different pool of 50 , 000 samples was simulated and 2 , 500 individuals with extreme primary trait were selected and analyzed for association . P-values for all rare variant tests in each study were obtained based upon 5 , 000 permutations . Meta-analysis is performed by combining Z-score statistics , which are transformed from p-values and weighted by the square root of the sample sizes in each study [28] . In order to evaluate type I errors , data were simulated under the assumption that the secondary trait effects for all variants were 0 . The empirical distribution of p-values was obtained using 10 , 000 replicates . For evaluating power , two scenarios were considered , i . e . ( A ) causal variants have an unidirectional effect of 0 . 5; ( B ) causal variants have bidirectional effects , where 80% of the causal variants have effect 0 . 5 and the other 20% of the causal variants have effect −0 . 5 . The power for analyzing each individual study and meta-analysis was evaluated using 10 , 000 replicates under a significance level of α = 0 . 05 . Association analyses were performed for the nine genes that were sequenced from the SardiNIA project [1] . First , coding regions of four genes , APOB , B3GA4 , LDLR and PCSK9 were tested for associations with the eight metabolic QTs . The genes APOC1 , APOC2 , APOE , B4GA4 contain no variants with MAF<1% , and SORT1 contains only 1 rare variant site . Gene-based association analysis was not performed for these five genes . Among the 256 individuals , 33 were taking blood pressure ( BP ) lowering medications; and their BP levels were adjusted by adding 10 mm Hg to their SysBP and 5 mm Hg to DiasBP levels [29] . Following the same strategy as the initial LDL analysis [1] , residuals for each trait were obtained and quantile-normalized after adjusting for age , age×age and sex in the entire SardiNIA cohort . The normalized residuals of the 256 samples were analyzed for associations with the four genes , i . e . APOB , B3GA4 , LDLR and PCSK9 . The five rare variant association tests incorporated in STAR were used to analyze the data . In addition to the secondary traits , the associations with the primary trait ( i . e . LDL levels ) were also analyzed . For the rare variant tests that use fixed MAF thresholds ( i . e . CMC , WSS , KBAC and SKAT ) , variants with MAF<1% were analyzed . For VT test , variants with MAF<5% were used in the analysis . The secondary traits were also analyzed using standard linear regression that ignores the ascertainment mechanism , as a comparison to the analysis using the STAR method .
Type I error for STAR was investigated when 1 . ) the gene region is neither associated with the primary trait nor the secondary trait . 2 . ) the gene region is associated only with the primary trait but not the secondary trait . The quantile-quantile plots of empirical p-values and their theoretical expectations are displayed for different rare variant tests . It can be seen that all tests incorporated in the STAR method have well controlled type I error . The p-values for the five tests are slightly conservative even when permutation is used to evaluate significance . This can occur when either the aggregate variant frequencies are low or the sample size is not sufficiently large . For example , when the primary trait effect is and residual correlation is , the type I errors for CMC , WSS , KBAC , VT and SKAT are respectively 0 . 048 , 0 . 046 , 0 . 042 , 0 . 045 and 0 . 047 ( Figure 1 ) . As a comparison , we also evaluated type I errors of linear regression analysis that ignores sample ascertainment mechanisms . When the gene region is not associated with the primary trait , type I errors for all rare variant tests are well controlled . However , if the gene region is also associated with the primary trait , the distribution of p-values under the null hypothesis is highly skewed and the type I errors for all tests are seriously inflated ( Figure S3 ) , which is concordant with our theoretical expectations . The power of detecting secondary trait associations was compared for a variety of rare variant tests ( Figure 2; Figure 3; and Figures S4 , S5 , S6 , S7 ) . Compared to the CMC method , the extended SKAT , WSS , KBAC and VT methods in STAR can be more powerful under a broad variety of models . For example , when the primary trait is associated with the gene region with effect and trait residual correlation is , if causal variants have fixed unidirectional secondary trait effect , i . e . , the power for WSS , KBAC and VT tests are respectively 73 . 5% , 74 . 1% and 78 . 1% , which all have greater power than the CMC ( 71 . 5% ) ( Figure 2 ) . If the secondary trait effects are bidirectional , the power for the VT ( 50 . 6% ) and SKAT ( 54 . 3% ) tests are much higher than that of the CMC ( 41 . 4% ) , and the power for KBAC ( 44 . 1% ) is also slightly greater than the power for the CMC ( Figure 3 ) . VT can be more powerful than methods that use fixed variant frequency threshold , when the secondary trait effects are unidirectional . This is because using a fixed variant frequency threshold may result in the inclusion of higher frequency non-causal variants or the exclusion of more frequent causal variants from the analyses . For example , when the primary trait effect is and trait residual correlation is , if the secondary trait effects are unidirectional and fixed with , the power for VT is 78 . 1% , which is considerably higher than the power for the CMC ( 71 . 5% ) ( Figure 2 ) . However , the difference in power between the best performing methods is small . For instance , when the primary trait effect is and trait residual correlation is , if the secondary trait effects are unidirectional and variable with and , the power for VT is 85 . 9% , which is only 0 . 3% and 2 . 6% higher than the power for the WSS and KBAC . The variance component score test SKAT is less powerful than burden tests when causal variant effects are unidirectional . For example , when , , and the causal variant effects are unidirectional with , the power for SKAT is 53 . 1% , which is 24 . 3% lower than VT and 21 . 6% lower than KBAC ( Figure 2 ) . However , when the causal variant secondary trait effects are bidirectional , SKAT is among the most powerful methods . For instance , if the magnitudes of the causal variant effects are inversely correlated with MAFs , when , and , the power for SKAT is 63 . 2% , which is much greater than the power for CMC ( 49 . 2% ) , WSS ( 51 . 0% ) , and KBAC ( 54 . 4% ) and slightly higher than the power for VT ( 60 . 3% ) ( Figure S5 ) . When the gene region is associated with both the primary and secondary traits , the power to detect secondary trait associations can be greater than when the gene region is only associated with the secondary trait . This is because variants with pleiotropic effects can be more enriched through extreme sampling . For example , when secondary trait effects are , and residual correlation is , if the gene region is not associated with the primary trait , the power for CMC , WSS , KBAC , VT and SKAT are respectively 61 . 9% , 61 . 4% , 64 . 7% , 67 . 7% and 49 . 7% ( Figure 2 ) . However , if the gene region is also associated with primary trait with effect , the power for the five tests increases to 65 . 3% , 63 . 3% , 67 . 7% , 70 . 1% and 53 . 1% respectively ( Figure 2 ) . Therefore , the power for detecting secondary trait associations can also be increased through sequencing samples with extreme primary trait values . The power and type I errors for STAR were evaluated for a simulated meta-analysis of three studies . As shown in ( Figure S8 ) , the empirical p-values and their theoretical expectations are well aligned on the quantile-quantile plot . Under a significance level of α = 0 . 05 , the type I errors for the five rare variant tests are CMC ( 0 . 051 ) , WSS ( 0 . 049 ) , KBAC ( 0 . 049 ) , VT ( 0 . 047 ) , SKAT ( 0 . 051 ) , which are well controlled . Due to the small sample size that is used , the type I errors for analyzing each individual study can still be slightly conservative . For example , in study 1 , where causal variant effect for the primary trait is −0 . 5 and the correlation between the primary and secondary traits is 0 . 6 , the type I errors for the five tests are respectively: CMC ( 0 . 046 ) WSS ( 0 . 044 ) , KBAC ( 0 . 046 ) , VT ( 0 . 047 ) and SKAT ( 0 . 045 ) . We also evaluated the power of the STAR method under the alternative hypothesis ( Figure S9 ) . It can be seen that the power for meta-analysis is always higher than the power for each individual study , which highlights the benefit of combining multiple studies to detect associations with commonly measured traits . Sequence data from the SardiNIA project were analyzed to detect associations with multiple lipids and metabolic traits . First , association analyses were carried-out for the primary trait LDL levels ( Table 1 ) . In the original article by Sanna et al [1] , extreme LDL values were dichotomized and association analyses were performed by comparing variant carrier frequencies between individuals sampled from opposite ends of the trait distribution . Only APOB was found to be nominally significantly associated with LDL ( p-value 0 . 03 ) . When QT values are analyzed instead of the dichotomized trait and more powerful association methods are used , the power to detect associations with the primary trait can be increased . For the association with APOB , the p-values for the five tests are , , , , , . Additionally a significant association with LDLR that was not previously detected was also observed ( , , , , ) . Among the tests that were used to analyze the association between LDLR and LDL , VT gives the smallest p-value . On the other hand , for the association with APOB , the score statistics from VT are maximized at the same MAF threshold as used by the other tests ( i . e . 1% ) . In this case , the CMC test gives the most significant p-value . Next we analyzed secondary trait associations with the four genes , i . e . APOB , B3GA4 , LDLR and PCSK9 ( Table 2 ) . One significant association , i . e . the association between LDLR and SysBP , is identified by CMC , WSS and KBAC after applying a Bonferroni correction for testing multiple genes and traits . The p-values for VT and SKAT are also nominally significant ( , , , , ) . The score statistics in VT are maximized at the MAF cutoff 1% . In this scenario , the p-value of VT is not as significant as that of CMC , WSS and KBAC , because the burden test statistics are not increased at alternative frequency thresholds and a penalty for multiple testing is paid . It is interesting to note that LDLR is associated with both the primary trait LDL and the secondary trait SysBP , which are correlated with a coefficient of 0 . 145 ( Table S1 ) . It is possible that a portion of the rare variants in LDLR have pleiotropic effects and are enriched in the dataset via selective sampling on the primary trait , which increase the power for detecting secondary trait associations . We also compared the analysis using STAR and standard linear regressions ( Table S2 ) . Due to the small sample sizes that are used , we did not observe an excess of false positive signals for the naïve linear regression analysis . However , we noted that for the association between LDLR and SysBP , the p-values from STAR are smaller . In addition , for the associations between LDLR , PCSK9 and TCL that were previously implicated in genome-wide association studies [30] , the p-values from STAR are also more significant .
In this article , we present a likelihood model which can be used to analyze secondary trait associations in selected samples . The method corrects for the bias in the distribution of the secondary traits induced by selective sampling . All rare variant association analysis methods can be extended within the STAR framework . STAR makes it possible to apply more powerful rare variant association tests for the analysis of secondary trait and allows jointly analyzing cohorts that were ascertained for different primary traits . The power for detecting associations with secondary traits can be greatly enhanced . In addition to performing gene-based association analysis , the STAR method and STARSEQ software can also be applied to detect single variant associations ( data not shown ) . Currently , many sequence based genetic studies are being performed to detect associations with complex traits . Due to the high cost of sequencing , the sample sizes for many of these studies are small . It was previously shown that in order to have sufficient power ( e . g . >80% ) to detect association with rare variants in an exome-wide study , in some cases it is necessary to sequence at least 10 , 000 samples with extreme traits from a cohort of 100 , 000 [25] . However , both the cohort size and the cost of sequencing exceed the capacity of most studies . Therefore to increase power it is important that multiple studies can be jointly analyzed . The STAR method is particularly useful , since cohorts that are ascertained for diverse primary traits using different study designs can be jointly analyzed . Previously CMC and GRANVIL tests were extended for analyzing secondary traits with p-value being evaluated analytically [21] . Many of the rare variant association methods implemented in STAR can be more powerful than CMC and GRANVIL . In fact , despite being computationally efficient , CMC and GRANVIL can be underpowered when a large portion of the variants in the gene region are non-causal or when the genetic effects of causal variants are bidirectional . Other methods such as SKAT may perform better in these scenarios . In addition , through assigning weights to different variant sites , variants that are potentially causal can be assigned higher weights , which can help to distinguish causal from non-causal variants . When variable selection based methods are used , the set of variants where the Z-score statistics are maximized can be selected and tested . These methods can be more robust against the inclusion of non-causal variant in the analysis , and also potentially be more powerful than CMC and GRANVIL methods even after adjusting for multiple comparisons . Permutation algorithm is often a necessary ingredient for rare variant association tests . Even if asymptotic approximations exist for some rare variant association tests such as SKAT and CMC , they may not be accurate and type I errors may be inflated or deflated [23] . This is because the asymptotic distribution for the test statistic can be affected by the number of rare variant sites and variant site frequency spectrums [17] , [23] . In practice , there can be considerable variation in the number of variant sites and frequencies within a gene region [31] . It is possible that an asymptotically valid test has inflated or deflated type I errors when genetic regions with only a few variant alleles are analyzed . Therefore , when a significant analytical p-value is obtained , it is necessary to empirically confirm the result using permutation . Under the STAR framework , we compared the power of several rare variant tests for analyzing secondary traits in selected samples . It is clear from our comparisons that when causal variants have unidirectional effects , burden tests perform better than SKAT . However , when variants with effects in opposite directions are present , SKAT can be more powerful than burden test based methods . Given that the goal of the article is to introduce a method for analyzing secondary traits in selected samples , rather than to compare different rare variant tests , our simulations are not as comprehensive as some existing reviews , such as Basu and Pan [32] and Ladouceur et al [33] . However , based upon the simulation scenarios that we considered , it is clear that the power for detecting associations can be greatly improved through the STAR model . In addition , our conclusions for comparing multiple rare variant tests are also compatible with the comprehensive reviews , in that there is not a consistently most powerful rare variant test and the difference in power between top performing methods is usually small . In addition to the simulation experiments , it is also important to examine and compare the performance of different methods in large scale sequencing studies , such as the NHLBI-ESP . In the analysis of the SardiNIA dataset , we adjusted the blood pressure for individuals undergoing antihypertensive therapy . The rank of sample blood pressure traits was only slightly changed after the adjustment . Given that we quantile-normalized the trait prior to the association analysis , the impact of the adjustment on the result is very minimal . In order to evaluate the robustness of the results , we also analyzed the associations with blood pressure when no adjustments were made , and the results are very similar ( data not shown ) . A significant association was identified between rare variants in LDLR and secondary trait SysBP , where carriers of rare variants in the LDLR gene tend to have lower SysBP levels . In fact , the LDLR gene has also been shown to be strongly associated with reductions of SysBP among the patients that receive atenolol , an antihypertensive drug [26] . These discoveries imply that variants in the LDLR gene may influence the etiology of SysBP . LDLR is potentially an important gene target for blood pressure treatment . In order to replicate signals [22] that were found in the SardiNIA cohort , many current large scale sequencing studies can be considered , such as the NHLBI-ESP etc . In addition to replicating associations , there is also great scientific interest in exploring whether rare causal variants identified in a founder population are identical to those from out-bred populations [34] . With the large scale application of next generation sequencing to study complex traits , samples from many existing cohorts will be sequenced . There can be insufficient power for analyzing associations in each individual study . It would be highly beneficial if samples from multiple cohorts can be combined for analyzing commonly measured traits . STAR is thus important and will greatly accelerate the process of identifying genes involved in complex trait etiology . | Next-generation sequencing has greatly expanded our ability to identify missing heritability due to rare variants . In order to increase the power to detect associations , one desirable study design is to combine samples from multiple cohorts for mapping commonly measured traits . However , many current studies sequence selected samples ( e . g . samples with extreme QT ) , which can bias the analysis of secondary traits , unless the sampling ascertainment mechanisms are properly adjusted . We developed a unified method for detecting secondary trait associations with rare variants ( STAR ) in selected and random samples , which can flexibly incorporate all rare variant association tests and allow joint analysis of multiple cohorts ascertained under different study designs . We demonstrate via simulations that STAR greatly boosts the power for detecting secondary trait associations . As an application of STAR , a dataset from the SardiNIA project was analyzed , where DNA samples from well-phenotyped individuals with extreme low-density lipoprotein levels were sequenced . LDLR was identified to be significantly associated with systolic blood pressure , which is supported by a previous pharmacogenetics study . In conclusion , STAR is an important tool for sequence-based association studies . | [
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] | 2012 | A Unified Method for Detecting Secondary Trait Associations with Rare Variants: Application to Sequence Data |
Temporal analysis of sound is fundamental to auditory processing throughout the animal kingdom . Echolocating bats are powerful models for investigating the underlying mechanisms of auditory temporal processing , as they show microsecond precision in discriminating the timing of acoustic events . However , the neural basis for microsecond auditory discrimination in bats has eluded researchers for decades . Combining extracellular recordings in the midbrain inferior colliculus ( IC ) and mathematical modeling , we show that microsecond precision in registering stimulus events emerges from synchronous neural firing , revealed through low-latency variability of stimulus-evoked extracellular field potentials ( EFPs , 200–600 Hz ) . The temporal precision of the EFP increases with the number of neurons firing in synchrony . Moreover , there is a functional relationship between the temporal precision of the EFP and the spectrotemporal features of the echolocation calls . In addition , EFP can measure the time difference of simulated echolocation call–echo pairs with microsecond precision . We propose that synchronous firing of populations of neurons operates in diverse species to support temporal analysis for auditory localization and complex sound processing .
Diverse groups of animals , such as electric fish , owls , and echolocating bats show remarkable temporal precision in the processing of sensory events . Specifically , weakly electric fish can detect a stimulus time disparity on the order of 0 . 4–1 μs [1 , 2] , barn owls can distinguish an interaural time difference of approximately 10 μs , and big brown bats ( Eptesicus fuscus ) can discriminate differences in echo arrival time on the order of 36–80 μs in target range discrimination tasks [3–5] and <1 μs ( and down to 10 ns ) in target range jitter discrimination tasks [6–8] . The neuronal basis of the microsecond temporal precision has been identified for both electric fish and barn owls [9–12] . Specifically , single neurons in the prepacemaker nucleus of the weakly electric fish were found to be sensitive to temporal disparity as small as 1 μs [11] . Similarly , the firing rate of many neurons in the midbrain of the barn owls can distinguish an interaural time difference smaller than the behavioral threshold [9] . By contrast , the neural basis for microsecond auditory resolution in echolocating bats remains unknown . Neurons hypothesized to function in bat sonar target distance measurement show facilitated responses to pairs of sounds , separated by a restricted range of delays , which mimic bat sonar calls and echoes , and this response property is referred to as echo delay tuning [13–15] . Although it has been hypothesized that echo delay–tuned neurons encode target range information in bats [16–19] , the tuning widths of echo delay–tuned neurons in echolocating bats are typically several milliseconds wide [13 , 14 , 17 , 20] , and this is far beyond the behavioral threshold . It has also been hypothesized that the variability in response latency of auditory neurons may contribute to the bat’s sonar range resolution [21–25] . In contrast to echo delay–tuned neurons with millisecond delay tuning widths , the response latency of many auditory neurons of echolocating bats varies by only a few hundred microseconds , a reduction in timing errors of up to a factor of 100 [24] . For example , about one-third of neurons in the midbrain inferior colliculus ( IC ) of the big brown bat show latency variability <1 ms [26] . The most precise neurons in the bat IC show latency variability between approximately 100 and 250 μs [23 , 25 , 26] . Of note , neurons of submillisecond latency precision are not exclusive to echolocating bats but have also been reported in other animal models , such as cats and mice [27 , 28] . How echolocating bats discriminate echo arrival time with microsecond resolution remains an unsolved problem [18 , 19 , 29–31] . Here , combining extracellular recordings and mathematical modeling , we show that synchronous neural firing can improve the precision of stimulus event timing an order of magnitude greater than the temporal precision of single neurons . Of note , in this study , we analyzed the stimulus-evoked extracellular field potential ( EFP , 20–600 Hz ) to characterize neural timing in a band up to 600 Hz , whereas local field potential ( LFP ) is generally analyzed in a lower band , up to 100–200 Hz . The most precise EFPs ( in the 200–600-Hz band ) showed a latency variability of 17 μs , based on extracellular recordings in the IC of seven awake big brown bats passively listening to simulated echolocation calls . Moreover , the spectrotemporal structure of the echolocation calls affected the latency variability of the EFP , which is consistent with predictions from sonar receiver models of ranging accuracy [32–34] .
In the first experiment , we took multichannel extracellular recordings with silicon probes in the IC of three head-fixed , awake big brown bats ( one male , two female ) passively listening to the broadcasts of simulated wideband echolocation calls of 3-ms duration and 70-dB sound pressure level ( SPL ) amplitude ( Fig 1A , top right ) ( referred to as the “standard call” in this study ) . Based on a threshold for detection of six times the background noise floor , corresponding to a signal-to-noise ratio ( SNR ) of 15 dB ( Fig 1A , grey dashed line ) , we recorded stimulus-evoked EFPs ( 20–600 Hz ) of large amplitude ( Fig 1A , blue trace ) in addition to the spikes from multiunit activity ( MUA ) ( Fig 1A , orange trace ) . Note that both EFP and MUA were derived from the same wideband neural recording with different cutoff frequencies of the digital filters . Over 20 presentations of the standard 3-ms FM stimulus , the response latency of the first EFP , measured as the time difference between the stimulus onset and the first negative peak of the EFP , was very stable , with a standard deviation ( SD ) of 150 μs in this example . Fig 1B shows data from another recording site that did not pick up isolated spikes but instead high SNR EFPs , with a latency SD of 59 μs . From both examples and S1 Movie , one can see that EFPs occurred directly after the presentation of the echolocation calls and typically occurred once per sound presentation . The high-amplitude , temporally precise EFPs have not been reported in neurophysiological studies of auditory processing in echolocating bats . To address the possibility that the EFPs were artifacts of electrical noise from our sound broadcast system , we made neural recordings from the IC of a bat listening to two spontaneously vocalizing conspecifics inside the sound booth . Further , this approach allowed us to study responses to variable , natural stimuli , which animals process in the real world . Fig 1C shows that EFPs were reliably evoked in response to the natural vocalizations of bats in the recording booth . Compared to the broadcasts of acoustic stimuli through an electrostatic loudspeaker , the natural bat vocalizations resulted in slightly longer response latencies and greater variability , which can be explained by differences in call parameters , particularly the amplitude and duration differences between sonar playbacks and natural sounds . In the loudspeaker broadcasts , the stimulus amplitude ( 70-dB SPL ) and duration ( 3 ms ) were fixed . By contrast , the calls produced by the spontaneously vocalizing bats varied in duration by approximately 2 . 2 ms ( 3 . 3–5 . 5 ms ) and amplitude by approximately 30 dB . The fact that EFPs could be evoked by vocalizations of live bats when the electrostatic loudspeaker was not powered eliminates the possibility that EFPs recorded in this study were a result of electrical artifacts . Moreover , EFPs showed selectivity for the fine spectrotemporal structure of the acoustic stimuli . Fig 1D shows EFPs from one recording site: EFPs were reliably detected in response to the top-row stimuli , including the standard call , −20 dB of the standard call , and the first harmonic of the standard call , but not in response to the bottom-row stimuli , which included a time-reversed version ( upward FM sweep ) of the standard call , white noise , and the second harmonic of the standard call . S1 Fig shows stimulus selectivity by the EFP for data from all three bats ( S1 Data ) . There were 202 recording sites , in which at least one of the six types of the acoustic stimuli evoked ≥5 EFP responses over 20 presentations ( i . e . , ≥25% response probability ) . We found that the EFP showed a selective response at 193 ( 95 . 5% ) and 124 ( 61 . 4% ) recording sites , based on a 25% and 50% response probability difference between at least two acoustic stimuli , respectively . Thus , the stimulus-evoked EFPs reflect robust responses to biologically relevant acoustic stimuli . How are the precise EFPs generated ? Synaptic inputs and spiking activity are two main sources of EFPs [35–38] . The contribution of synaptic inputs to EFPs declines rapidly above approximately 200 Hz . Spiking activity , on the other hand , contributes to both the 20–200-Hz band and the 200–600-Hz band [39 , 40] . Thus , the 20–200-Hz EFPs can arise from both synaptic inputs and spiking activity , while the 200–600-Hz EFPs reflect primarily spiking activity . To assess whether the observed EFPs arose specifically from spiking activity , we separated the 20–600-Hz band EFP into 20–200-Hz band and 200–600-Hz band and analyzed them separately . In total , the 200–600-Hz EFP was detected at 528 recording sites from three bats ( 154 sites from Bat 1 , 126 sites from Bat 2 , and 232 sites from Bat 3 ) . By contrast , the 20–200-Hz EFP and 20–600-Hz EFP were detected at 162 and 243 recording sites in total , respectively . The fact that the 200–600-Hz EFP was more prevalent in our recordings suggests that spiking activity contributed to the stimulus-evoked EFP , and all following EFP analyses were carried out on the 200–600-Hz EFP with 90% detection reliability . A 90% detection reliability criterion indicates that EFPs or spikes were detected in at least 72 out of 80 stimulus presentations . In addition to the EFP , we analyzed the first negative peak of multiunit activity ( MUA; 600–3 , 000 Hz ) . A comparison of the latency variability between the first negative peak of the EFP and MUA revealed that the EFP latency was more stable than the MUA ( Fig 2A; Wilcoxon rank sum test , P < 0 . 001 ) . At the 90% detection reliability , the median SD for the EFP and the MUA were 104 μs ( minimum SD: 53 μs ) and 425 μs ( minimum SD: 62 μs ) , respectively . A detailed examination of the data sets revealed that the MUA data contained many recording sites with greater latency variability than the EFP data , which might be the basis of the observed population differences . To test this idea , we excluded the sites of the MUA group whose SD were larger than the maximum SD of the EFP data but again found that the EFP response latencies were more stable than the MUA ( Fig 2B; Wilcoxon rank sum test , P < 0 . 001 ) . These results show that EFP latency is more precise in timing stimulus events than MUA . There is a topographical organization of the EFP across the IC with respect to the stability of response latency ( Fig 2C and 2D ) . Specifically , high-precision EFP latency was most often recorded in the ventral region of the IC , whereas higher variability in the EFP latency tended to appear in the dorsal region of the IC ( Fig 2D ) . Similarly , EFPs of shorter response latency were mainly found in the ventral region of the IC ( Fig 2C ) , which aligns closely with earlier observations of single neurons in the IC [41] . The EFP to the 3-ms standard FM stimulus showed a latency variability as low as 53 μs , which is more precise than the least variable single unit latencies of IC neurons reported in the literature , between 100 and 250 μs [23 , 25 , 26] . To test whether our recordings contained single neurons that were far more precise than those previously reported , we identified single spiking units of high SNR using the Wave_clus algorithms [42] and carefully evaluated the waveforms of the sorted single units ( see S1 Text for details ) . In total , we identified 59 single units in our recordings . The most precise single unit in our data set had an SD response latency of 140 μs ( with a population median of 1 . 36 ms ) , which is within the range of values reported in the literature . Our analyses revealed that the EFP showed the highest temporal precision in response latency , and single units showed the highest latency variability . This raises a fundamental question: how does the activity of single neurons contribute to EFPs of greater temporal precision ? Since action potential waveforms only contain a proportion of energy in the EFP frequency band ( <600 Hz ) , multiple overlapping spikes are required to produce a prominent EFP . We hypothesized that the number of firing neurons and neural synchrony influence the temporal precision of the EFP . To gain insights into the effect of the number of firing neurons on the properties of the EFP , we counted the number of negative peaks in the wideband neural signal , whose amplitudes were larger than the detection threshold ( 20–3 , 000 Hz , Fig 1A and 1B ) , within ±3 ms of the mean response latency of the EFP . We chose the ±3-ms time window to count the number of firing neurons , as it largely overlaps with the 5-ms period of the 200-Hz high-pass cutoff frequency of the EFP , aiming to obtain counts of neurons potentially contributing to the EFP . We counted the number of firing neurons from the wideband neural signal ( 20–3 , 000 Hz ) rather than from the 600–3 , 000-Hz band ( i . e . , spikes ) , since spikes from neurons at a distance that cannot be picked up by the electrode also contribute to the low frequencies of the EFP [35 , 36 , 39 , 43] . In other words , the 20–3 , 000-Hz wideband neural signal may contain more information than the MUA . We found that there was a weak negative correlation between the number of firing neurons and the temporal variability of the EFP ( Fig 2E ) . Moreover , the number of firing neurons correlated negatively with the response latency and positively with the peak amplitude of the EFP ( Fig 2F and 2G ) . These results suggest that the number of firing neurons could influence the properties of the EFP . The weak correlations between the number of firing neurons and the properties of the EFP also imply that the number of firing neurons is not the only factor influencing the properties of the EFP . To systematically investigate the effect of the number of neurons and neuronal firing synchrony on the EFP properties , we took a simulation approach that allowed us to examine quantitatively the effect of one factor at a time . We started with the simplest scenario in which five neurons with the same extracellular spike shape fired an action potential at a random latency between 17 . 5 and 22 . 5 ms , and thus each neuron had a response latency of 20 ± 1 . 5 ms ( mean ± SD , indicated by the red dash-dot line in Fig 3A ) . Of note , the 20-ms average response latency is arbitrarily chosen for illustrative purposes and does not affect the simulation results . Then , we band-pass filtered this virtual recording to generate simulated MUA and EFP and analyzed the first negative peak of the MUA and EFP for each simulated trial in the same way as for the experimental data . Fig 3A shows the results of 1 , 000 simulations . Both the MUA and EFP showed responses that were shorter in response latency and more precise in timing than the single neuron , supporting the experimental observations . Subsequently , we quantified the effect of the number of neurons on MUA and EFP for both high-synchrony scenarios in which all neurons fire randomly within a 1-ms time window and low-synchrony scenarios in which all neurons fire randomly within a 10-ms time window . Fig 3B shows that the MUA and EFP become more precise and greater in amplitude with increasing number of firing neurons but only under the high-synchrony simulation . For instance , the first EFP showed a precision of 75 μs and an amplitude of 2 . 5 mV when 50 neurons fired within a 1-ms time window ( Fig 3B ) , which is approximately four times more precise and approximately 32 times greater in amplitude than the single neuron . Moreover , the effect of increasing the number of firing neurons is stronger for the EFP than for MUA . Thus , this result is consistent with the experimental observation that the EFP can be more precise than MUA ( Fig 2A and 2B ) . Moreover , one can mathematically demonstrate that when multiple neurons fire within a short time window , about the width of the action potentials , EFPs become shorter in response latency , more precise in timing , and larger in amplitude ( Fig 3C , see details in S1 Text ) . This mathematical analysis leads to three predictions: ( 1 ) the peak amplitude of the EFP increases linearly with increasing number of the firing neurons; ( 2 ) the variability of EFP latency decreases as the number of firing neurons increases , following a power law relationship; ( 3 ) the decrease in response latency of the EFP is positively related to its variability . Importantly , all of these predictions agree with the experimental observations ( Fig 2E–2G ) and simulations ( Fig 3A and 3B ) . The simplified simulations and the mathematical model based on multiple copies of a single extracellular spike shape allowed us to directly examine the influence of the number of firing neurons and neuronal synchrony on the properties of the EFP , yet extracellular recordings from neurons naturally vary in spike waveform , which additionally depends on the position and geometry of the electrode . To consider a more realistic scenario , we performed biophysical simulations in which neurons produced varying spike waveforms at a virtual recording electrode . Fig 3D shows that the conclusions drawn from the simplified simulation approach also hold for simulated recordings with different extracellular potential waveforms . For example , the precision of the EFP latency measurement was higher than that of MUA , and the single neurons showed the highest latency variability . Particularly , the EFP ( 200–600 Hz ) measurement showed a precision of 45 μs with 100 neurons each firing with a 250-μs SD . Moreover , the magnitude of response latency reduction of both MUA and EFP latency measurements correlated positively with the variability of single neurons . One key component of bat echolocation is the dynamic adjustment in the spectrotemporal features of sonar signals with target distance [45–48] . For example , big brown bats initially use long duration calls ( up to 15 ms ) of narrow frequency bandwidth ( fundamental sweeps over 3–5 kHz ) to search for insects in open space and then progressively shorten the duration ( down to 1–2 ms ) and widen the bandwidth of the calls ( fundamental sweeps over 30 kHz ) while approaching the prey ( Fig 4A ) . It has been shown that bats achieve the highest precision in sonar ranging with short , wideband calls , which they produce when approaching prey [8 , 32 , 49] . Thus , we hypothesized that the precision of stimulus registration with the EFP changes with signal duration and signal bandwidth . Specifically , we predicted that wideband , short echolocation calls ( i . e . , produced by bats in the prey approach phase ) should generate EFPs with the highest temporal precision , and narrowband , long echolocation calls ( i . e . , produced by bats in the prey search phase ) should generate EFPs of the lowest precision . To test the hypothesis that stimulus bandwidth and duration influence the temporal stability of the EFP latency , we conducted a second experiment by taking extracellular recordings from the IC of four awake big brown bats ( all females ) passively listening to simulated echolocation calls of varying bandwidth and durations . We found that measurements of EFPs at a single site showed the highest precision in registering the timing of acoustic events ( a minimum SD of 17 μs at a recording depth of 1 , 060 μm ) when the bats were listening to echolocation calls of the shortest duration ( 1 ms ) ( Fig 4C , S2 Fig ) . The average value of the 10 most precise EFPs evoked by the 1-ms broadband echolocation calls was 21 ± 2 μs . By contrast , EFPs showed the largest variability in registering the timing of acoustic events ( a minimum SD of 168 μs at a recording depth of 720 μm ) when the bats were listening to calls of longest duration ( 12 ms ) and narrowest bandwidth ( 5-kHz bandwidth of the first harmonic ) . The average value of the 10 most precise EFPs evoked by the 12-ms narrowband echolocation calls was 200 ± 20 μs . Overall , the precision of EFP timing with respect to stimulus events became progressively poorer with increasing call duration ( two-way ANOVA , F = 158 . 85 , df = 3 , P < 0 . 001 , all Padj < 0 . 001 for pair-wise comparisons ) . Similarly , the response latency of EFPs became progressively longer with increasing call duration ( Fig 4B ) ( two-way ANOVA , F = 218 . 9 , df = 3 , P < 0 . 001 , all Padj < 0 . 001 for pair-wise comparisons ) . EFPs to narrowband calls showed the greatest variability in the temporal registration of stimulus events ( two-way ANOVA , F = 65 . 63 , df = 2 , P < 0 . 001 , all Padj < 0 . 001 for pair-wise comparisons ) and the longest response latency ( two-way ANOVA , F = 85 . 21 , df = 2 , P < 0 . 001 , all Padj < 0 . 001 for pair-wise comparisons ) , except for the shortest duration of 1 ms ( Wilcoxon rank sum test , all Padj > 0 . 05 ) . The systematic change in EFP latency and precision with stimulus duration and bandwidth suggests that EFP latency itself could serve to code different stimulus parameters . To understand how much information is potentially encoded by the response latency of EFPs , we applied information theory and calculated the sample size–corrected Shannon mutual information [50] . First , we calculated the mutual information for each stimulus bandwidth , namely the wideband ( 25–55 kHz for the first harmonic ) , the midband ( 25–40 kHz for the first harmonic ) , and the narrowband ( 25–30 kHz for the first harmonic ) . Fig 5A shows an example with a relatively high mutual information , and Fig 5B shows an example with a relatively low mutual information . Of note , the theoretical maximum of the mutual information calculated for four stimulus types within each bandwidth category is 2 bits . Fig 5C–5F show the mutual information for all recording sites from all four bats . A maximum mutual information of 2 . 57 bits suggests that the response latency of EFPs from a single recording site can maximally distinguish six out of the 12 total stimulus types ( Fig 5F ) . Nevertheless , within each bandwidth category , the response latency of EFPs from 185 ( 22 . 2% ) , 143 ( 18 . 2% ) , and 15 ( 3 . 6% ) recording sites can distinguish at least three out of four stimulus types with a mutual information larger than 1 . 58 ( red dashed line ) . Moreover , the wideband chirp featured the highest mutual information , followed by the midband chirp , and the narrowband chirp had the smallest mutual information ( Wilcoxon rank sum test , all P < 0 . 001 ) . These data suggested that the response latency of EFPs encodes more information about the duration of the stimulus type with a wider frequency bandwidth . Echolocating bats use the time delay between an emitted call and returning echo to estimate the distance or range of objects in the environment [5 , 17 , 19] . For the big brown bat , the operating range of echolocation for prey detection is up to a few meters , which corresponds to echo delays of a few tens of milliseconds . To test whether the high-precision EFP evoked by single sounds can estimate the time difference of a pair of sounds that simulate the call–echo pairs used in echolocation tasks , we took neural recordings from the IC of three big brown bats ( the same bats in Experiment 1 ) passively listening to simulated call–echo pairs . The calls were the “standard call” ( Fig 6A ) presented at an amplitude of 75-dB SPL . The echoes were attenuated versions of the standard call and were presented at either 25- , 45- , or 65-dB SPL and at delays between 2 and 30 ms following the call . Fig 6A shows an example of EFP responses to a call–echo pair over 20 presentations . The simulated echo delay was 28 ms . The echo was 10 dB weaker than the call , i . e . , 65-dB SPL . After each presentation of the call–echo pair , there were often two EFPs . The first ( black dots ) and the second ( blue dots ) EFP responses occurred at a latency of 7 . 8 ± 0 . 075 ms and 35 . 8 ± 0 . 082 ms , respectively . The estimated echo delay ( red dots ) from the EFPs was 27 . 99 ± 0 . 08 ms , which is not only very precise across stimulus presentation but also aligns with the actual 28-ms echo delay . Fig 6B shows that across the 684 tested stimulus conditions from all three bats , the time difference estimated by EFPs showed a submillisecond precision in 411 ( 60% ) stimulus conditions and showed a precision greater than 100 μs in 155 stimulus conditions ( 15% ) . Fig 6C and 6D shows that the precision of the stimulus time difference estimation by EFPs was largely constrained by the precision of the EFP response to the weaker echo ( Fig 6D ) , not by the precision of the first EFP response to the more intense call ( Fig 6C ) . In response to simulated call–echo pairs , the first EFP responses to the calls , with a median SD of 0 . 1 ms , were more precise in timing than the second EFP responses to the echoes , with a median SD of 0 . 29 ms ( Wilcoxon signed-rank paired test , P < 0 . 001 ) . Fig 7 shows the EFP responses at a single recording site to simulated call–echo pairs at three different echo amplitudes ( 25- , 45- , and 65-dB SPL ) and at varying echo delays between 2 and 30 ms . These data show that at each echo amplitude , the estimated time difference of EFPs increases with the echo delay of the simulated call–echo pairs ( from the bottom to the top ) and achieves a precision greater than 100 μs for many stimulus conditions .
In this study , we have shown that the response latency of the EFP evoked by simulated echolocation calls shows high precision in the temporal registration of stimulus events , with latency variability as low as 17 μs . The EFPs , recorded in the IC of the awake , passively listening big brown bat , were generated from the synchronous firing of a population of neurons , and both the number of firing neurons and the tightness of neuronal synchrony influenced the EFP properties of response latency , precision ( variability ) , and amplitude . We also provide evidence that the precision of the EFP depends on the spectrotemporal features of bat echolocation calls , and EFPs evoked by simulated call–echo pairs can precisely estimate echo delay . There is a general consensus that spiking activity represents an important source of LFPs at frequencies higher than approximately 100 Hz , at least in the hippocampus [35 , 38 , 43 , 51 , 52] . Yet sources other than spiking activity , such as synaptic events , can also contribute to the LFP at higher frequencies [43 , 53] . Since the term LFP has been traditionally used to refer to EFPs at frequencies below 100–200 Hz , we adopted the term EFP [54] in this study to describe neural activity in a band between 200 and 600 Hz . Combining extracellular recording and mathematical modeling , we provide evidence that the EFPs in our IC recordings are generated by a population of neurons firing synchronously . Although the neurons that generate the EFP are likely located within the IC , one recent study raised the possibility that the EFP can be generated via axon bundles projecting from one brain region to another [54] . Specifically , the authors found that EFPs could be reliably recorded from axon bundles , and the properties of the EFPs were affected by the properties of the axons . This work implies that the source of EFPs recorded in the IC of bats in our study could be either from the local IC neurons , from axons passing through the IC , or a combination of the two . One intriguing feature of the EFP is its remarkable precision in registering the timing of stimulus events . The most precise EFP evoked by the same acoustic stimulus over 30 trials showed an SD of 17 μs in response latency . Here , the reported 17-μs temporal precision might be an underestimation of the highest temporal precision achievable by EFPs in the IC of the big brown bat . Since the temporal precision of the EFP is determined by both neural synchrony and the number of firing neurons , based on Equation 2 in Fig 3C , one can predict a temporal precision of 3 . 2 μs from 1 , 000 neurons that each has 100-μs variability in response latency . Single neurons showing 100-μs temporal variability in response latency have been reported for the IC of the big brown bat [26] . Note that all the measured and predicted temporal precision reported above was for single EFP sites . Considering the possibility that the nervous system can combine information across multiple brain sites [28] , submicrosecond temporal precision could be achieved , although the exact mechanisms of such population coding have yet to be identified . Our study revealed two potential mechanisms that the nervous system can exploit to represent the timing of stimulus events with high precision: ( 1 ) many neurons responding to a given stimulus and ( 2 ) high synchrony of firing among these neurons . Increasing the tightness of neuronal synchrony is a general computational principle for modulating nervous system functions . At the single neuron level , precise spike timing can propagate or be maintained across synapses when multiple neurons fired in high synchrony [55–57] . At the synaptic level , highly synchronized presynaptic inputs are more effective at driving a neuron to fire [58] and generating spikes with higher temporal precision [59 , 60] . There is also evidence that neuronal synchrony is functionally linked to the behavioral performance of animals [61–63] . For example , Gutierrez and colleagues ( 2010 ) found that within the taste–reward circuit of rats , neurons that fired in synchrony with licking behavior exhibited greater cue discrimination than nonsynchronized neurons and that the magnitude of this effect increased with learning . On the other hand , the potential importance of a larger population of neurons for specific brain functions is a matter of debate [64] . Here , we identified one potential advantage of an increased neuronal population size in improving the temporal precision of the EFP . Nevertheless , it is worth noting that without neural synchrony , the population size of firing neurons itself contributes very little to improving the temporal precision of the EFP ( Fig 3B , the low-synchrony scenario ) . One crucial finding of this study is the functional relationship between the precision of registering the timing of stimulus events in the EFP and the spectrotemporal features of the echolocation calls of bats . During a foraging task , big brown bats use echolocations of long duration and narrowband frequency range to search for prey . A main function of the search-phase echolocation calls is prey detection , and long duration narrowband signals are well suited for this task [32 , 65] . Once a prey item is detected , insectivorous bats rapidly change the echolocation call structure by decreasing the call duration and increasing the frequency bandwidth while approaching the prey . One important function of the approach-phase echolocation calls is to track the position of the prey , which thus requires estimating target distance , i . e . , measuring echo arrival time , with great precision . Moreover , there is extensive evidence showing that short broadband echolocation calls are well suited for precise measurements of echo arrival time [5 , 8 , 32 , 49] . Here , we found that the EFP evoked by echolocation calls of short duration and wideband spectrum showed the most precise registration of stimulus events , and by contrast , the EFP evoked by long-duration , narrowband echolocation calls showed the poorest temporal precision . Moreover , we have shown that the precise EFPs evoked by single sounds can also be used to estimate the time difference of simulated call–echo pairs with a precision as great as 45 μs , representing a feasible neural substrate for the 36–80-μs behavioral echo delay discrimination by echolocating bats [4] . The precision of time difference estimation of the simulated call–echo pairs was largely constrained by the second EFP responses to the weaker echoes , which were less precise than the first EFP responses to the more intense calls . This finding suggests that echo amplitude might play a role in influencing the precision of target ranging and points to the potential importance of stabilizing echo amplitude for target ranging . Interestingly , there is an accumulating body of evidence that echolocating bats maintain a relatively constant echo amplitude while approaching a target [66 , 4 , 67 , 68] . Thus , the temporal precision of the EFP , as a marker for acoustic events , provides a window to neural computational principles that might underlie the accurate echo delay discrimination behavior of echolocation tasks . The findings reported here raise several important questions that can be answered by further research . One critical question concerns the mechanisms by which the brain makes use of the neural events underlying the precise EFPs . At this time , knowledge is lacking about the contribution of the biophysical properties of single neurons and/or biochemical environments to the EFP . Because EFPs are generated by synchronous firing in a population of neurons , the high-precision EFP may be a proxy for population coding across single neurons . Thus , the question of how the brain might extract and use information carried by EFPs could be approached through population analysis of stimulus-evoked activity in pools of single neurons . We also conjecture that inhibition may play an important role in generating the precise EFPs , which is known to underlie precise interaural time difference estimation in mammals [69] . Specifically , many properties of IC neurons , such as selectivity for interaural intensity difference , sound frequency , and sound duration , are generated by convergent inhibitory and excitatory inputs , as revealed by the intracellular recording of postsynaptic potentials [70–72] . Both intracellular recording and application of inhibitory neurotransmitter antagonists in the IC could provide a first step to investigate the role of inhibition in shaping EFP properties . Going beyond the implications for understanding mechanisms of precise target ranging performance in echolocating bats , the high-precision EFP bears general relevance for understanding stimulus coding in the brain across species . Precise timing of neural signals could encode rich information that is relevant to a variety of brain functions [73] . For example , the temporal precision of neural events may be a key property in categorizing sound features by the auditory system [74 , 75] . Indeed , the information theoretic analysis showed that EFPs from many recording sites in the bat IC could unambiguously differentiate three out of four simulated echolocation calls of different durations . By extension , EFP analysis could generate insights into mechanisms supporting other auditory behaviors , such as acoustic communication , scene analysis , and spectrotemporal discrimination [76] . Although specialized neural mechanisms for temporal processing have been revealed in species that use stimulus timing in natural behaviors , such as barn owls [9] and electric fish [11] , the extent to which diverse species share common mechanisms to achieve high temporal precision is an important open question . We propose that the computational principle of synchronized firing across a population of neurons represents a general mechanism for the nervous system to register precisely the timing of sensory events .
Big brown bats , Eptesicus fuscus , collected in the state of Maryland under a permit issued by the Department of Natural Resources were used as subjects ( permit number 55440 ) . The Johns Hopkins University’s Institutional Animal Care and Use Committee approved all the procedures used for this study ( protocol number BA14A111 ) . The protocols are in compliance with the Animal Welfare Act regulations and Public Health Service Policy . The university maintains accreditation by the Association for the Assessment and Accreditation of Laboratory Animal Care International . Extracellular recordings were made from auditory neurons in the IC of seven adult big brown bats ( one male , six females ) in an acoustic booth . In the big brown bat , the IC sits on the dorsal surface of the brain and is up to approximately 2 mm in length both anterior-posteriorly and dorsoventrally . On the day of neural recording , the bat was placed into a custom-made bat holder , its head was immobilized via a headpost , and a craniotomy of <1-mm2 size above the central IC was made with a scalpel under a microscope . Multiple penetrations were made to the IC of the same bat in different recording days and bats were recorded between a few days and more than a month . Within a single penetration , the probe was systematically advanced dorsoventrally from the surface down to approximately 1 , 800 μm using a hydraulic Microdrive ( FHC ) . Extracellular potentials were recorded by a silicon probe from Neuronexus that had the 1 × 16 arrangement of recording sites , with an intersite separation of 50 μm . The probe thickness was 25 μm and the site area of the probe was either 177 μm2 or 703 μm2 . On the day of the headpost surgery , under isoflurane inhalation anesthesia , part of the skin and the temporal muscles overlying the IC were removed , and a custom headpost was attached to the bone at the midline using cyanoacrylate gel . During neural recording , the awake bat was passively listening to the broadcasts of simulated echolocation calls or call–echo pairs from a custom electrostatic loudspeaker ( 1-cm diaphragm ) placed 60 cm away from the contralateral ear of the recording IC at an angle of approximately 30° from the middle line . Digital echolocation calls were generated from customized LabVIEW scripts and played at a sampling rate of 1 MHz using the data acquisition card from National Instrument ( PXIe 6358 ) . We achieved a flat frequency response of the playback system for the frequency range of 20–100 kHz ( ±1 dB ) by digitally compensating for the uneven frequency response with its compensatory impulse response [77 , 78] . The compensatory impulse response was computed using the Maximum Length Sequence method based on 5 seconds white noise recordings with a one-fourth–inch measurement microphone ( Model 7016 , ACO , United States ) . Acoustic stimuli were played at an amplitude of 70-dB SPL ( root mean square ) unless otherwise stated . Each acoustic stimulus was repeated at least 20 times . Specifically , the standard 3-ms echolocation call ( Fig 1A , first harmonic sweeps down from 55 kHz to 25 kHz , and the second harmonic from 110 kHz to 50 kHz ) was repeated 80 times in the first experiment , and all other stimuli , such as white noise or time-inversed echolocation calls shown in Fig 1D , were repeated 20 times . In the second experiment that tested the functional significance of the EFPs , each stimulus was repeated 30 times . The call–echo pairs in the third experiment were repeated 20 times . The time interval between stimulus presentations was 300 ms , and the order of stimulus presentation was randomized for each experiment . The neural recording was digitized at a sampling rate of 40 kHz using a Plexon system of 64 analog channels . The original neural signal was amplified by a factor of 20 times before digitization but later was restored to the correct scale during data analysis . Neural recording and sound broadcasting were synchronized via a transistor–transistor logic ( TTL ) signal outputted from a second analog output channel of the National Instrument card each time when an acoustic stimulus was broadcast , and the TTL signal was directly recorded by an analog input channel of the Plexon system . More details on the animal surgical preparation and neural recording can be found in Macias and colleagues ( 2018 ) [79] . All neural recordings were batch processed in Matlab ( R2015a , Mathworks ) . The general steps of data processing include ( 1 ) band-pass filtering the original recording with Elliptic filters from the Wave_clus algorithms [42]; ( 2 ) an adaptive threshold , six times of the background noise level , based on the same equation from Quiroga and colleagues ( 2004 ) , was used to identify spikes and the peaks of EFPs; and ( 3 ) the onset time of the acoustic stimuli was identified based on the TTL signal , then the response latency of the MUA or the EFP was computed from the first negative peak . Negative spikes were the dominant observations across our recording . We quantified the precision or variability of the peaks of the EFPs with the SD . Data points were considered as outliers and excluded from analysis if their values were greater than q3 + w × ( q3 − q1 ) or less than q1 − w × ( q3 − q1 ) , where w was set to 1 . 2 and q1 and q3 were the 25th and 75th percentiles of the sample data , respectively . We applied the outlier exclusion criterion , as we are particularly interested in how precise in timing the EFPs can potentially achieve . Moreover , outliers can greatly bias the estimations of the mean and the SD , which are the principal measures in the current study . Although outlier exclusion results in less variation in the measured EFP latencies and the corresponding SD of the EFPs , it is important to note that the reported EFPs were based on a 90% detection reliability criterion ( see Results above ) , which limited the outliers in <10% of the data points . Note that although a sampling rate of 40 kHz results in a temporal resolution of 25 μs , the SD of a 25-μs resolution sampling system can be as small as approximately 5 μs [80] . Thus , the reported best temporal precision of 17 μs is well within the resolution of our data sampling system . A cell model , L4_SS_cADpyr230_1 , was downloaded from the Human Brain Project's Neocortical Microcircuitry ( https://bbp . epfl . ch/nmc-portal/welcome ) [44] . We constructed cell populations consisting of 10–1 , 000 cells , by randomly rotating and distributing instances of the same cell model with a uniform cell density of 200 , 000 neurons per mm3 , within a disc of 100-μm thickness . Because of the fixed cell density , the radius of the disc was dependent on the population size . Each cell in the population received 100 conductance-based excitatory synaptic inputs randomly distributed across the cell membrane with a uniform area weighted density . The synapses were implemented using a double exponential function ( Exp2Syn in NEURON ) with the rise and decay time constants of 1 ms and 3 ms , respectively , and a reversal potential of 0 mV . The synaptic weights were normally distributed around 0 . 2 nS with a standard deviation of 0 . 04 nS . The arrival of the 100 synaptic inputs was normally distributed with a mean value of T = 230 ms after stimulation onset , with a standard deviation of 0 . 25 ms . This evoked an action potential in the cell model , and after the extracellular action potential was calculated , a time window of ±15 ms around the maximum value of the somatic membrane potential was extracted from the extracellular action potential and saved to file . The time resolution of the simulation was 1/32 ms . Extracellular potentials were calculated using LFPy2 . 0 ( http://EFPy . github . io/ ) [81] , which runs on the NEURON simulator [82] . For the calculation , each cell compartment was treated as a line source , except for the somatic compartments , which were treated as a point source . The extracellular conductivity was set to 0 . 3 S/m [83] . The extracellular potential was calculated in the center of the cell population . Each of the calculated extracellular action potentials was randomly jittered in time , with different SDs for the jitter , and summed to produce the population signal . For each of the different tested SDs for the jitter , 100 trials of the population signal were calculated . The signals were filtered with elliptic filters ( scipy . signal . ellip ) to obtain the EFP and MUA . The first peak of the filtered signal was identified as the first negative crossing below a threshold of six times the SD of the signal . All code used for this project is available from https://github . com/torbjone/sharp_wave_ripples/ . Details of the mathematical model shown in Fig 3 , spike sorting , and the information analysis were presented in the S1 Text . | We routinely rely on a stopwatch to precisely measure the time it takes for an athlete to reach the finish line . Without the assistance of such a timing device , our measurement of elapsed time becomes imprecise . By contrast , some animals , such as echolocating bats , naturally perform timing tasks with remarkable precision . Behavioral research has shown that echolocating bats can estimate the elapsed time between sonar cries and echo returns with a precision in the range of microseconds . However , the neural basis for such microsecond precision has remained a puzzle to scientists . Combining extracellular recordings in the bat’s inferior colliculus ( IC ) —a midbrain nucleus of the auditory pathway—and mathematical modeling , we show that microsecond precision in registering stimulus events emerges from synchronous neural firing . Our recordings revealed a low-latency variability of stimulus-evoked extracellular field potentials ( EFPs ) , which , according to our mathematical modeling , was determined by the number of firing neurons and their synchrony . Moreover , the acoustic features of echolocation calls , such as signal duration and bandwidth , which the bat dynamically modulates during prey capture , also modulate the precision of EFPs . These findings have broad implications for understanding temporal analysis of acoustic signals in a wide range of auditory behaviors across the animal kingdom . | [
"Abstract",
"Introduction",
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"an... | 2018 | Neural timing of stimulus events with microsecond precision |
To evaluate the relative effectiveness of tsetse control methods , their costs need to be analysed alongside their impact on tsetse populations . Very little has been published on the costs of methods specifically targeting human African trypanosomiasis In northern Uganda , a 250 km2 field trial was undertaken using small ( 0 . 5 X 0 . 25 m ) insecticide-treated targets ( “tiny targets” ) . Detailed cost recording accompanied every phase of the work . Costs were calculated for this operation as if managed by the Ugandan vector control services: removing purely research components of the work and applying local salaries . This calculation assumed that all resources are fully used , with no spare capacity . The full cost of the operation was assessed at USD 85 . 4 per km2 , of which USD 55 . 7 or 65 . 2% were field costs , made up of three component activities ( target deployment: 34 . 5% , trap monitoring: 10 . 6% and target maintenance: 20 . 1% ) . The remaining USD 29 . 7 or 34 . 8% of the costs were for preliminary studies and administration ( tsetse surveys: 6 . 0% , sensitisation of local populations: 18 . 6% and office support: 10 . 2% ) . Targets accounted for only 12 . 9% of the total cost , other important cost components were labour ( 24 . 1% ) and transport ( 34 . 6% ) . Comparison with the updated cost of historical HAT vector control projects and recent estimates indicates that this work represents a major reduction in cost levels . This is attributed not just to the low unit cost of tiny targets but also to the organisation of delivery , using local labour with bicycles or motorcycles . Sensitivity analyses were undertaken , investigating key prices and assumptions . It is believed that these costs are generalizable to other HAT foci , although in more remote areas , with denser vegetation and fewer people , costs would increase , as would be the case for other tsetse control techniques .
Tsetse control technologies and their mode of delivery are evolving all the time . A major purpose of this evolution is to develop approaches that can reduce the incidence of human and animal African trypanosomiasis ( HAT and AAT ) more cheaply and/or more effectively . Measuring cost-effectiveness accurately , and in such a way that different operations and approaches are fully costed and can be validly compared , is essential to underpin decision-making in this field [1] . This paper analyses the costs of an actual field operation using the new technology of tiny targets undertaken in Arua District , Uganda in 2012/2013 whose ultimate aim was to reduce transmission of HAT by controlling Glossina fuscipes fuscipes [2] . In contrast to analyses of tsetse control operations primarily undertaken to control AAT , costs of such operations undertaken in HAT foci have only been intermittently reported on in the entomological literature [3] . With the development of lower cost devices a major concern , these reports have focussed on their unit cost and related this cost to the km2 and the human population ‘protected’ . The term ‘protected’ was introduced to indicate the area and the people within that area who benefited from tsetse control , as against the much more restricted area of tsetse habitat where traps or targets were actually deployed . Thus , excluding manpower , the newly developed Vavoua trap was reported as costing about half as much as the standard biconical and pyramidal traps [4] . Four projects using traps and screens in HAT foci published cost-effectiveness estimates for Côte d’Ivoire [5] , Congo [6] , Equatorial Guinea [7] and Uganda [8] . Coincidently these all relate to the 5-year period 1986–1990 . In Uganda , the project area’s population was around 320 , 000; the other three all worked in HAT foci containing about 25 , 000 people . All cite trap costs , which can be compared to levels today by converting from local currencies to United States dollars ( USD ) at the historical rates applicable at the time , and then updated to current ( 2014 ) prices by applying the USD inflation rate ( http://inflationdata . com/inflation/Inflation_Rate/HistoricalInflation . aspx historical data ) which for this period yields a factor ranging from 2 . 16 to 1 . 88 , thus roughly doubling all prices . Thus , at 2014 prices in Côte d’Ivoire screens cost USD 6 . 6 and Vavoua traps USD 13 . 6; in Equatorial Guinea [7] pyramidal traps costing an estimated USD 9 [9] were used . Meanwhile , in the Congo [6] , pyramidal traps costs USD19 and villagers were supplied with a repair kit for the traps costing USD 6 , giving an average annual cost per trap of USD10 , all at 2014 prices . In Uganda [8] pyramidal traps costing USD 6 were being used on a large scale , and the newly developed mono-screen trap [10] cost USD 8 . 8 at 2014 prices . In 2012/13 pyramidal traps were bought for use in Uganda at a cost of USD 10—indicating that their relative price has remained surprisingly stable over time . In order to evaluate the relative cost-effectiveness of traps and targets/screens , these monetary costs needed to be assessed alongside measures of effectiveness against tsetse populations . For traps , catches can be compared ( e . g . [10] ) . However , in order to compare traps and screens a more sophisticated metric is required because targets do not retain the flies killed . The tsetse control operation analysed here follows on from a decade’s research into increasing the ‘cost-effectiveness’ of the targets themselves , measured in terms of tsetse caught or killed per m2 of cloth for G . f . fuscipes [2 , 11] and G . palpalis palpalis [12] . This standard metric has made it possible to compare the effectiveness of the classic targets or biconical / pyramidal traps , in use since the 1980s as described above , with much smaller devices . The amount of fabric required gives a clear and measurable indicator of trap/target cost which can be compared over time and across countries and currencies . For G . p . palpalis , the killing efficiency of a medium-sized horizontal target design 0 . 5 m2 was shown to be 6 times greater than of the classic 1 m2 target [11] . The adoption of the ‘tiny’ 0 . 125m2 ( 0 . 5 X 0 . 25 m ) target for use in this trial follows directly from these studies [11] showing the killing efficiency of G . f . fuscipes per m2 to be between 5 . 5 and 15 higher than for 1 m targets and up to 8 . 6–37 . 5 greater than for biconical traps . Similar results were recently obtained by [13] for G . p . palpalis , showing 0 . 25 m2 targets to be promising as cost-effective devices , but using relative catches as a metric . The cost-effectiveness of different traps for G . f . fuscipes has also been studied using catch per linear m of fabric as a metric [14] . The four historic projects cited above went on to calculate trap costs per person protected , which at 2014 , prices came out to USD 11 , USD 11 , USD 0 . 5 and USD 1 for Côte d’Ivoire , Equatorial Guinea , Congo and Uganda respectively . For Côte d’Ivoire , adding the deployment costs for fuel and vehicle maintenance plus trap replacement and reimpregnation with insecticide increased the cost to USD 13 per person protected . Although labour and staff costs were not costed [5] provided a detailed inventory of all inputs , including people’s time alongside full instructions for estimating the costs of operations . In Uganda , adding cost for staff , labour and transport increased the cost per person protected to USD 2 . Costs were also given per km2 . These costs reflect very different population densities in the HAT foci from 17 per km2 in Côte d’Ivoire to 100 in Uganda . Devices were also placed at very different densities with 25 per km2 protected in Côte d’Ivoire , 10 to 15 per km2 in Uganda . Costs per km2 in Uganda , at 2014 prices , worked out at USD 85 , rising to USD 179 if staff , labour and transport were included; in Côte d’Ivoire the cost per km2 protected was higher at USD 217 . These costs refer to the first year of deployment . For all the projects , it was thought that costs would fall in the second year of operation , with trap life sometimes extending beyond one year and deployment sites having been selected and prepared . However , a full analysis of the cost-effectiveness also has to include all delivery costs . It has long been known that the tsetse control techniques described as “expensive” and” high-tech” and usually deployed on a larger scale , such as aerial spraying and the sterile male technique , used on their own , require less expensive ground level support and thus have apparently lower delivery costs than targets and traps , since flying time is usually included in the ‘core’ cost of the technology . As far back as the late 1970s it could be shown that the differential between the apparently high cost of helicopter spraying and ground spraying was greatly eroded when the full delivery costs for ground spraying were included [15] and the same was true for targets and aerial spraying [16] . More recent comparisons [1] also indicate that while total costs of bait technologies ( whether stationary: traps/targets or live: insecticide-treated cattle ) can be substantially lower , in relative terms , their delivery costs are substantially higher in relation to their core costs ( insecticides and traps/targets ) than is the case for than aerial spraying or the sterile male technique ( core costs of insecticide , sterile males and flying time ) . The need to reduce delivery costs for the bait technologies was part of the reason why many projects have tried to involve local communities , not just by informing them about the objectives and benefits of using traps and targets , but also in terms of contributing labour and ensuring traps /targets remained in place and in working order [5] . However , community involvement has had mixed success [17] , with the needs of communities often being treated as secondary to the entomological objectives . Although not part of a project budget , inputs by community members impose an economic cost on that community , so that an economic analysis should value these inputs . Lastly , whereas the costs of traps and targets or insecticide can be reduced , delivery costs do not necessarily decline proportionately . For this reason , simply multiplying the trap or insecticide cost by a constant to estimate the delivery cost is unlikely to be reliable . To date , apart from the meticulous detailed information recorded by and the preliminary estimates made for the use of insecticide-treated cattle in southeastern Uganda and reported in [1] there is no published accurate assessment of the delivery costs of such an operation in a HAT focus . Accordingly , alongside the entomological monitoring of the control operation in Arua reported by [18] an important component of the project’s work was the detailed recording and pricing of all inputs .
The study focussed on a control operation using tiny targets which covered 250 km2 . Work began in June 2012 with a sensitisation operation , and continued to the end of June 2013 , thus covering a period of 13 months . It was split into six sub-activities , spread over that period as given in Table 1 and illustrated in Fig . 1 . To monitor costs for each activity , a data sheet was kept , recording the number of days spent in the field ( Table 1 ) , staff deployment , labour hired , vehicles used and kilometres travelled , use of other capital items such as global positioning sets ( GPS ) , laptop computers , specialist items ( traps , targets and extension materials ) and all other running costs ( e . g . , fuel and oil , vehicle maintenance , hired transport , stationery , GPS batteries , protective clothing for staff , assorted minor consumables ) . In this way all field and non-field costs were recorded and both variable and fixed costs were fully accounted for . Office overheads were also monitored . The information thus collected provides a full set of data for an actual field operation . The operational area on which these costs are based consisted of the area surrounding five blocks , each of 7 x 7 km , which were the subject of a control operation initiated in 2011 ( Fig . 2 ) . At the beginning of December 2012 , the control area was enlarged from ~250 km2 ( 5 x ~50 km2 ) to 500km2 . The work done in the ‘new’ areas was carefully logged and separated from that done in the five original ‘old’ blocks ( Fig . 2 ) . These areas are contiguous and there was little additional travel between locations . Accordingly , all costs were divided by 250 to produce a cost per km2 controlled . The costing methodology adopted was the ‘full costing’ approach described in [1] . By clearly itemising cost components , the calculations undertaken here are presented so as to enable effective comparison with those presented for other operations . The overall objective was to produce a replicable costing at current prices for a field tsetse control operation run by local staff in the Ugandan context . Thus costs were adjusted so as to remove the purely research components . The organisation and supervision of the work was undertaken by an academic research team composed of an anthropologist and three entomologists . Supervisory staff inputs were costed at the salaries and travel allowances paid to a Ugandan senior entomologist , for the time spent in the field and on administrative duties . This included a proportionate allowance for weekends and holidays . Each district in Uganda has an entomologist responsible for vector control . Two categories of preparatory work were costed: sensitising local populations and a preliminary tsetse survey . Two costings for the survey are presented , one as actually incurred , the other for a streamlined operation with no research component . The sensitisation programme was treated as a ‘capital’ investment—which would be valid for at least three years before follow up activities were needed . Within each component activity , the ‘full costs’ for this operation were calculated as follows . Depreciation was estimated for all capital items which outlasted the 13-month project duration . The relevant items and the assumptions used are listed in Table 2 . For non-durable items ( fuel and oil for vehicles , labour , travel allowances and/or per diems for staff , protective clothing for staff , backpacks , slashers , pangas , stationery , GPS batteries , extension materials and refreshments for villagers during village meetings ) the actual recorded expenditure was used . Although the life of traps can exceed one year , they were classified along with targets as ‘specialised equipment’ and their working life was conservatively estimated at 150 days deployment . However , for a project undertaken within an existing government structure , some capital and recurrent costs would be spread over several activities , of which tsetse control in a specific area would only be one . In the case of this project , as explained above , in addition to the parallel control work undertaken in the ‘old’ project’s five 50 km2 squares , a substantial proportion of the time was allocated to research . Thus only 25% of the office overheads were allocated to the project being costed . Also , for this reason only a share of basic salaries , reflecting the time spent on the project , was included for entomologists . Similarly , for some items ( e . g . , motorbikes , GPS sets , laptops , traps ) an appropriate share of their annual use ( based on kilometres travelled or days used , as recorded in the data sheets ) was attributed to the project . However , for the 4x4 pickup truck , the cost was based on the total recorded distance travelled which was virtually all for this 250 km2 control project . Prices were all converted to US dollars ( USD ) mainly from 2012/13 Uganda Shillings ( UGX ) ( and occasionally other currencies ) . The average rate for the project period was 2615 UGX = 1 USD , ranging from 2416 to 2700 ( http://www . oanda . com/currency/historical-rates/ ) . The rate of 2615 UGX = 1 USD was applied throughout . Other currencies used were the British pound ( GBP ) and the Euro ( EUR ) , whose conversion rates to 1 USD for the period were 0 . 6382 GBP and 0 . 7755 EUR . Total costs are rounded to the nearest USD without removing the effects of rounding , thus in some cases the totals will appear not to add up , but all individual figures are accurately rounded . Costs per km2 are rounded to the nearest USD 0 . 1 . All costs are at market prices applicable at the time and place where they were incurred and include the cost of shipping to Uganda as relevant .
Before undertaking tsetse control in the new enlarged area , a preliminary entomological survey was undertaken . The objective of this was to identify suitable sites for locating traps . Traps were deployed and visited daily for 3 days , 1 to deploy and 2 to monitor . The total number of field days was 20 , deploying 8 traps ( Table 3 ) . The work was undertaken by a senior entomologist and driver , using the 4x4 pickup truck . Total travel was just over 2000 km , accounting for 40% of the vehicle’s mileage during the study period . Some use was also made of the office motorbike . Pangas and slashers were used to cut down the vegetation around the traps sites . The costs are summarised in Table 3 . Total costs came to USD 5901 , which works out at USD 23 . 60 per km2 . The costs were dominated by cost of depreciation ( 44% ) and maintenance ( 25% ) of the pickup truck , reflecting its low annual travel of some 5000 km and relatively high maintenance costs , which were only to some extent offset by its low depreciation , both reflecting the fact that it was 15 years old . Staff salaries and allowances accounted for a further 23% of costs , and fuel 6% . During the course of the project , the reliance on the pick-up truck was gradually reduced , and teams of trap and target attendants were trained . They accessed the project area either by motorbike or bicycle , sometimes using public transport , transporting traps and targets in backpacks . To investigate the impact of this technological and logistical improvement , the preliminary monitoring costs were recalculated ( Table 4 ) based on the timings achieved in activity D ( monitoring the actual target deployment ) and allowing for more intensive monitoring ( 12 traps and 25 field days , monitoring a total of 100 trap sites , or 4 per 10 x 10 km square ) . This intensity is equivalent to the monitoring undertaken in study zone in 2010 when work first began in Arua area . The work would be done by a team of two trap attendants using a motorbike . This approach would allow for considerable cost savings , reducing the total cost to USD 1281 , or USD 5 . 12 per km2 . Before undertaking the sensitisation campaign preparatory research activities were [19] undertaken . This highlighted people’s wariness , and in some cases fear , of the targets and traps , and underlined the need for an effective public awareness initiative . The campaign proceeded in several steps . Three different sensitization materials were developed in English and translated into Lugbara: letters for communities , information leaflets and flip-charts used for training and for house-to-house sensitisation . Preliminary meetings with sub-county authorities were carried out and they were briefed about the project activities and sensitization campaigns planned in their area . All the villages in the new control area were identified and mapped , using geographical positioning systems ( GPS ) . This work was initiated by the research team , led by an anthropologist , and then taken over by the target attendants . In total 130 villages were involved in the campaign . The costs ( Table 5 ) are based on what was actually experienced , although the speed of work varied , with the initial research team of 4 managing to walk 12 km a day and identify 18 villages a day ( 4 . 5 per person per day ) . When this was taken over by the target attendants , whose task included this work and then later placing and renewing targets , the rate fell to 1 . 2 villages per person per day . Eight training meetings were organised for the village health teams ( VHTs ) at the sub-county headquarters at which refreshments were provided and travel costs paid . VHTs , consisting of two volunteers chosen by each village , are part of the health delivery system in Uganda . VHTs were paid two daily lunch allowances , which corresponded to the usual national VHT rates . Out of the 260 VHT’s , 257 came for training and then returned to their villages to carry out two days of house-to-house sensitization using picture-based flip charts and samples of targets . They were requested to place information sheets on the usual village notice boards and trees . VHTs were moving on foot or using their own bicycles , so no extra transport cost was included in this exercise . On the third day the research team recollected sensitisation materials and the sensitization forms on which the number of people who received the message in house-to-house campaign was recorded . On this occasion VHT members received their lunch allowances and transport fees . This was followed by training meeting for VHTs from another sub-county , where flip charts and targets were reused . In total 8 , 713 households were visited and 56 , 983 people received the message . The campaign took place over six months and its costs are summarised in Table 5 . The total cost came to USD 11 , 984 . Transport accounted for 43% of costs , labour and staff a further 47% , with only 5% required for the extension materials . It is considered that at least three years would elapse before further sensitisation activities would be needed . Accordingly a third of the costs were allocated to the one-year tsetse control operation , coming to USD 15 . 86 per km2 . In addition , during the course of the project , trap and target attendants undertook sensitization regularly on an informal basis as part of their daily routines: talking to people washing by the rivers , or working in fields . This had no cost implication , as the full cost of their work is included in the activities described below . The target deployment activity began in early December 2012 , and has been described in detail [18] . Targets were deployed at about 6 per km2 when averaged over the whole area , but at a higher density in the riverine habitat as shown in Fig . 2 ) . Because they are so close to each other , the targets in the ‘new’ project area costed here show up as a green dotted line , whereas those in the ‘old’ project area form a red dotted [18] . A total of 1 , 536 were deployed , and 1 , 551 provided for in the cost estimate , thus allowing for a slight excess . Targets were manufactured by Vestergaard-Frandsen ( Lausanne Switzerland ) and shipped from Vietnam . The cost per target was USD 1 . Effective target life was assumed to be more than six months and less than a year ( see activity E ) so targets were treated as recurrent rather than capital cost items . The cost of shipping and insurance varied greatly . In storage tiny targets have a long shelf life—estimated to be about two years . A rapid air consignment cost USD 0 . 40 per target , lower cost air transport was quoted at USD 0 . 17 for 10 , 000 targets and USD 0 . 126 for 50 , 000 . Larger quantities could be sent by sea , at an estimated cost of USD 0 . 045 for 100 , 000 and USD 0 . 012 for 500 , 000 . If larger scale target deployment were coordinated by the Ugandan government , the sea route would be preferable . In these cost calculations , a cost of USD 0 . 10 was used , on the assumption that targets would mostly be transported by sea . In order to deploy the cloth targets , wooden supports had to be prepared and glued , a task under taken by the ‘target fixer’ . The deployment was done by teams with bicycles . Targets and supports were transported in backpacks . For the more distant sites , the target teams used public transport ( ‘boda-boda’ taxi motorbikes and small pick-up trucks ) to reach the deployment zone and were paid transport allowances to fund this . The total cost for the activity came to USD 7 , 370 , or USD 29 . 5 per km2 ( Table 6 ) . The single largest cost item was the targets , accounting for 27 . 5% of costs , followed by labour at 25% . Once a substantial proportion of the targets were deployed ( end of December , 2012 ) the monitoring activity began ( Fig . 1 ) . This continued until the end of the evaluation period . Traps were deployed in the new control zone at 12 sites every twice a month for 3 days , and monitored , as illustrated in Fig . 2 . Some of the new trap sites were outside the project area , in order to monitor the impact of the control operation on the boundaries of the treated area . This work was done by the trap attendants , who were supplied with motorbikes . The payment modalities gradually evolved—from allowances to reclaiming actual costs . In the cost calculations ( Table 7 ) all the costs were based on the fuel required for the actual mileage and the associated maintenance costs plus depreciation . The total cost for this activity came to USD 2 , 250 which worked out at USD 9 . 00 per km2 . Vehicle running and depreciation accounted for 46% of this cost , and labour a further 29% . The target maintenance operation began at the end of March 2013 . All target sites were visited and targets were repaired or replaced as required , the vegetation around them cleared , etc . By the end of the evaluation period 950 targets had been replaced . The modalities were the same as for the initial deployment , with trap attendants using bicycles or hiring local transport to access the intervention areas . However , the time required was much less , since the sites for the targets had already been determined and only some vegetation clearance was required . The costs are summarised in Table 8 . The total costs came to USD 4 , 290 or USD 17 . 16 per km2 . The main cost item was vehicle running ( 34% ) followed by targets ( 29% ) and labour ( 24% ) . Lastly , it is important in field-based projects such as this not to neglect the costs of administering and organising the work . The costs of each field activity include non-field days ( itemised in Table 1 ) , mainly for supervisory and research staff . Over and above this it was necessary to run an office , allowing internet access , other communications and processing of data for research purposes as well as routine administration and organisation . The cost components are itemised in Table 9 . The total cost of running the office for the evaluation period came to USD 8 , 724 . The office served the two 250 km2 control operations . About half the time was taken up with research . Accordingly , 25% of the total cost was attributed to the ‘new area’ control operation costed in this paper . This worked out at USD 8 . 72 per km2 . Combining the costs from the six component activities produced the results given in Table 10 . The overall total for the control work in the ‘new area’ was USD 21 , 337 , coming out at USD 85 . 4 per km2 or USD 13 . 8 per target deployed . Of this over half ( 55% ) was for deploying and maintaining targets . The cost of the targets themselves came to 13% of total project costs . By expenditure category , the single largest cost component was transport ( 35% ) followed by labour ( 24% ) . In order to test the robustness of the cost estimates , a range of sensitivity analyses was undertaken ( Table 11 ) . These looked first at the impact on overall costs of cost increases of a third in crucial components: targets , traps , labour , senior staff and fuel and public transport costs . The most sensitive items were labour ( 8 . 0% increase ) and fuel and public transport costs ( 6 . 1% ) , reflecting their relative share in total costs . Secondly , the implications of varying some of the key assumptions made in the cost estimation were examined . This showed that while varying the share of office overheads allocated to the tsetse control operation had only a limited impact , if the sensitisation programme had to be repeated every two rather than every three years , costs would increase by 9 . 3% . Using the preliminary survey as an example showed that if just this single activity were undertaken using senior staff and a vehicle rather than local staff with bicycles and motorbikes , the overall cost would increase by over 20% .
It is important to set these results in context . As stated in [18] , a fully inclusive cost of USD 85 . 4 per km2 involves a marked reduction on earlier estimates . If office overheads , sensitisation and preliminary studies ( which account for 35% of costs ) , are excluded the figure comes out as a ‘field cost’ of USD 55 . 7 per km2 , which is the figure that should be compared to the costs published in much of the literature . Referring back to Fig . 2 and the methods section , it should be noted that cost are given for the whole area ‘protected’ rather than per mile of riverine habitat treated . In historic terms this cost is well below the USD 179 ( at 2014 prices ) noted by [8] also for Uganda with 10–15 traps per km2 . In terms of contemporary estimates it is also far lower than the USD 556 estimated by [1] for 10 traps per km2 . The human population density in the control zone was estimated at 500 per km2 , based on [20] together with gridded data obtained from http://www . afripop . org/ . Thus the cost per person ‘protected’ is thus very low , at USD 0 . 17 . The results of the sensitivity analysis ( Table 11 ) help to explain why the operation was so-cost effective , and to underpin a discussion of the factors which might limit this cost-effectiveness . Looking first at the cost of targets , one of the reasons this type of operation is much less expensive than those undertaken in the late 1980s , as reported on above , is that targets now come made of fabric which has been pre-impregnated with insecticide , so repeat impregnations are not required . Also , when comparing to operations targeting other Glossina species , especially those of the morsitans group , it should be noted odours are not required as bait here . However , while the target cost was fixed at USD 1 by the supplier , the costs of shipping from Vietnam , where the targets were made , fluctuated a lot . A simple 33% increase in the cost of targets would take the overall cost of the operation up by 4 . 3% . As explained above , based on the quotes received and in the hope that shipments could be by seas , the cost used in this study was USD 0 . 10 per target , a figure which seems a reasonable compromise . If the transport cost were doubled to USD 0 . 20 per target , the cost of the project would rise to USD 86 . 5 per km2 . Shipping by sea is only feasible for large quantities , so , in order to keep costs low an in-country project would need to be implemented in several sites of the type studied and to buy in bulk . The only other specialist cost items—traps and extension materials—account for 0 . 1% and 1 . 0% of total costs , so cost increases in these items would have little overall impact on the project . Looking at the other items for which price increases were costed in Table 11 , the most significant is labour . Labour accounts for 24 . 1% of all costs , so increasing it by a third would result in an 8 . 0% increase in project costs . The high reliance on labour , whether government employees , locally sourced or provided by the community is a characteristic of the bait technologies . In this project in particular , the low overall cost was achieved by first training target/trap attendants who did much of the work and second by providing them with motorcycles , bicycles or hired local transport , thus reducing dependence on large project-owned vehicles . This was only possible because the small targets can be easily transported in a backpack , so that one person can carry up to 30 tiny targets . The impact of this is nicely demonstrated by comparing the two costings for preliminary surveys ( Tables 3 and 4 ) . The cost reduction from USD 23 . 6 to USD 5 . 1 per km2 is achieved by limiting senior staff involvement to supervision and by replacing the use of a 4x4 project vehicle with a good quality office motorbike and cheaper motorbikes used by the target attendants . The more expensive option would involve 21 . 6% higher costs ( Table 11 ) . Another unknown in relation to the preliminary survey is how extensive an area needs to be covered . In this project , some knowledge of the basic area already existed . If a new project was targeting a completely unknown area , or a region where previous tsetse surveys were out of date , the preliminary survey may need to be more extensive . A 50% increase in the area surveyed would increase overall costs by 6 . 4% ( Table 11 ) and larger increases would have a linear impact on cost increases , if done at the same intensity . However , this survey was intended to help site targets , and it could be argued that a survey aimed simply at identifying future intervention sites might cover a larger area , but at a lower trapping intensity . Ensuring that local populations want , support and understand the tsetse control measures is key to success [5 , 8 , 17] . Pre-intervention attitudes and knowledge in local populations highlighted that effective sensitisation would be a vital part of this project [21] . One unknown in the costings was , for a longer control operation , after how long and to what extent it would be necessary to visit communities again and reinforce the information provided at the start of the operation . As explained above , the village representatives continued to remind their communities of the purpose of the control activities , as did the trap and target attendants in the course of their work . For the latter , easy contact with local people was facilitated by their mode of travel using bicycles or motorcycles . Accordingly a compromise figure of three years was decided on—so that the sensitisation activity was effectively ‘depreciated’ over a 3 year period . If it were deemed necessary to repeat the operation in full after two years , the cost of the operation would increase by 9% . On the other hand , if nothing more had to be done for four years , the cost of the operation would be reduced by 4 . 6% ( Table 11 ) , which is a more likely scenario . Attributing a share of the office overheads , for what was in many respects more a research than a control operation was also somewhat subjective . Sensitivity analysis indicated that varying the 25% proportion initially allocated from 20% to 33% had only a small impact on total costs ( Table 11 ) . Again , as was the case for the preliminary survey assumptions , the differences between the full cost of the office overheads , and those attributable to the control operation illustrates how costs are reduced if the research components are removed . Thus , looking at individual components of the costs which might change in value or whose underlying assumptions might need changing , it is clear that foreseeable changes in the cost or quantities of a single item are unlikely to have a major effect on the costs . Of course , if several changes occur together , then a cumulative effect would be more significant . But overall , the costs can be described as robust . However , it is important to state that these costs apply to a specific tsetse control operation , and thus they only include what was done as part of that operation . Wider studies—such as surveys of trypanosomiasis in human and livestock populations were not undertaken as part of this tsetse control operation , although livestock were sampled as part of the separate research activities . The area was well known as a focus of HAT and controlling the disease in livestock was not a driver for the work . There were no separate training courses . The entomologists were already qualified in tsetse control . The target and trap attendants received their training in GPS use , trap placement and monitoring and target placement and repairs in the field , under supervision from the entomologists . On the sensitisation side , project staff were similarly trained by the anthropologist . The existence of the VHT’s within the Ugandan health service meant that at the village level , a support network for this type of work already existed . Long term monitoring will also provide more detail on target life and replacement rates . There may be some cost reductions in a second deployment as deployment costs in year two could fall because sites have already been identified and trap and target attendants are implementing well practiced routines . Lastly , it is important to ask—how applicable will these costings be to similar work undertaken in other parts of Africa ? Differences are likely to be experienced at three levels . The first one involves different prices , salary structures and a different organisational set up at the level of government services . Secondly , there are also important issues around economies of scale and shared resources to consider . For example , these costs assume the presence of a district entomologist who could allocate a costed share of his time to a particular vector control activity; shipping costs for targets are very dependent on scale , etc . These costings apply to a relatively small area—but should be regarded as ‘lean’ , in the sense that almost all resources are fully used and no spare capacity is included . If even smaller areas were targeted , some extra travel from area to area might be required . Also , by spreading the cost of sensitisation over three years , the programme is implicitly assumed to go on for that long . Both of these levels are country and project specific . The third level is ecological , integrating a number of factors . Although the maintenance of T . b . gambiense HAT foci relies on the presence of a human reservoir , population densities can vary greatly: from under 20 per km2 as discussed above for the forest zone of Côte d’Ivoire [5] and the 500 per km2 estimated for the north-western Uganda study zone costed here . In the T . b . rhodesiense focus of south-eastern Uganda [8] , where cattle have been shown to be the major reservoir [22] of the disease , the human population density at the time was 100 per km2 . In areas with lower human population densities , tsetse habitat is likely to be more dense , access more difficult , terrain could be rugged , overnight stays or camping may be required and local labour be less easy to recruit . All of these factors will drive cost per km2 upwards and in areas of low human population density the cost per person protected will rise steeply . Ultimately , in some isolated or rugged areas it might not be possible to rely on all of the lower cost forms of transport , but the benefit of using cheaper and more portable targets will be maintained . All the considerations discussed above ( sensitivity to changes in price and assumptions , price and organisational differences , ability to harness economies of scale , accessibility and its links to human and livestock population density and tsetse habitat ) would apply equally to any ground-based tsetse control technology , and to some extent to all vector control methods . Thus while the actual cost levels achieved in this exercise may not be replicable in every situation , the principles on which the cost savings are based will be: low cost delivery using motorbikes or bicycles and local labour together with a cheap and highly portable target with a high killing efficiency . | Sleeping sickness remains a serious threat in Sub-Saharan Africa . The disease is normally controlled by medical screening of the human population and treatment of individuals found to be infected . The disease is transmitted by tsetse flies but vector control is rarely used for control . A major reason given is that is too expensive in resource poor settings . We have developed a novel technology based on insecticide treated screens ( = tiny targets ) to control flies more cost-effectively . A 250 km2 field trial of tiny targets has been performed in Northern Uganda and we made use of this to undertake a full costing analysis of tiny target technology . The cost of the operation was costed at USD 85 . 4 per km2 . This represents a major reduction in the cost of tsetse control . The reductions are largely due to the low costs of tiny targets and to the ease with which they can be deployed . | [
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] | [] | 2015 | Costs Of Using “Tiny Targets” to Control Glossina fuscipes fuscipes, a Vector of Gambiense Sleeping Sickness in Arua District of Uganda |
A genetic interaction ( GI ) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene . Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network . This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults , and also into the link existing between genotype and phenotype . While a significant conservation of GIs and GI network structure has been reported between distant yeast species , such a conservation is not clear between unicellular and multicellular organisms . Structural and functional characterization of a GI network in these latter organisms is consequently of high interest . In this study , we present an in-depth characterization of ~1 . 5K GIs in the nematode Caenorhabditis elegans . We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network , including co-expression , phenotypical manifestations , relationship with protein-protein interaction dense subnetworks ( PDS ) and pathways , molecular and biological functions , gene essentiality and pleiotropy . Our study shows that GI classes link genes within pathways and display distinctive properties , specifically towards PDS . It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors . Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism . It also suggests a model to understand better how GIs control system robustness and evolution .
The behaviour of biological systems and their adaptation to environmental changes depend on many factors on the path from genomic structure , through gene expression , molecular and functional interactions , to phenotypic manifestations . To simplify studies of these different levels of information , systems biologists may build a theoretical framework where biological systems are decomposed into six abstraction levels [1]: the genome structure ( level I ) , the gene expression ( level II ) , the physical interaction between systems elements ( protein , DNA , RNA , etc . level III ) , the functional relationship between these elements ( level IV ) , their biological and molecular function ( level V ) and the phenotypical manifestations ( level VI ) . Within this framework , genetic interactions ( GIs ) are located at the level IV together with signaling and metabolic pathways [1] . The identification of a GI between two genes reveals that a mutation on the first one alters the biological consequences ( the phenotype ) associated to a mutation on the second one . Mapping GIs represents an important approach in understanding the link between genotype and phenotype . It is also a critical step to understand the robustness of biological systems–i . e . how the system compensates for the alteration of a function . Mapping GIs in human also recently emerged as a necessity to identify biomarkers from Genome-wide association studies ( GWAS ) and consequently , move the medical field towards a more personalized practice [2] . To date , only few ( primarily unicellular ) organisms have been amenable to experimental genome-wide screening approaches for mapping GIs . Thus , most of our information on the structure and the function of GI networks has been restricted to yeast ( reviewed in [1] and [3] ) . Extensive studies on GIs in these systems showed clear relationships between GI networks and networks located at other abstraction levels . These studies revealed the relationship of GIs with signaling and metabolic pathways [4–7] , between co-expressed genes , and between genes coding for interacting proteins [8 , 9] . They also identified the relationship between GIs , bioprocesses and phenotypes [10–12] . They characterized the degree of connectivity of genes within GI networks and assessed their enrichment in genes with high connectivity ( GI-Hubs ) as well as in multifunctional and essential genes [4 , 6 , 10 , 13] . Importantly , these studies identified dense subnetworks within the GI network ( GDS ) [10] . They showed that GDS tend to lay between molecular machines , that we will define in this study as dense subnetworks of protein-protein and protein-DNA interactions [6 , 8 , 9] . They also showed that GDS are monochromatic , i . e . they are composed of either positive ( suppressive/alleviating ) or negative ( synergistic/aggravating ) GIs [14–17] , and are functionally biased [6 , 18] . These studies connect four abstractions levels ( levels II to V ) , showing that GI networks coordinate molecular machines within bioprocesses . These studies using yeast as a model brought also precious information on the role played by GIs in genomic robustness and evolutionary processes [7 , 19 , 20] . For example , these studies identified two separate groups of duplicated genes within distinct GDS: a group composed of redundant genes playing an important role on genomic robustness of systems and a group of redundant genes with divergent biological functions and with expected reduced impact on robustness [7] . In addition , they revealed that positioning of GIs within or between PPI-dense subnetworks ( PDS ) had an impact on their evolutionary conservation: GIs within PDS being more conserved than GIs between PDS [19 , 20] . To date , the structure and the function of GI networks are still largely unknown in multicellular organisms . Characterizing these networks is therefore required to better understand the genomic robustness of these systems and also how functional relationships between alleles influence phenotypical outcomes and evolutionary processes in multicellular contexts . To address this problem , we provide the first deep characterization of a network composed of ~600 GIs in a multicellular organism , the nematode Caenorhabditis elegans . This study aims to identify functional properties associated with GIs and groups of GIs and to understand better how the structural and functional organization of a GI network links molecular machines ( abstraction level III ) to bioprocesses , phenotypes and diseases ( abstraction levels V and VI ) in a multicellular context . Our results indicate that GIs form a heterogeneous group of entities when considering biological data located at different abstraction levels in C . elegans . We describe the specific characteristics of GI classes with respect to the connectivity degree within the GI- and the PPI-networks , their relationship with protein-protein interaction dense subnetworks ( PDS ) , signaling and metabolic pathways and with phenotypic manifestations ( essentiality , pleiotropy ) . We also discuss the impact of this structure on C . elegans genome robustness and evolution .
Considering that the function and the structure of genetic interaction networks are mainly unknown for multicellular organisms while being of increasing interest , we characterized a network composed of ~1 , 500 GIs of the nematode C . elegans . To do so , we first investigated whether GIs constitute a heterogeneous group of entities in this organism and consequently , whether we could identify several GI classes with distinctive biological properties in this network . We retrieved 1 , 514 genetic interactions ( GIs ) from Wormbase , Biogrid and the literature as described in the Methods section and in S1 Table . This set of GIs , called GIs-all , is composed of 750 ( 49 . 5% ) interactions identified as experimentally validated GIs by either Wormbase and Biogrid curation systems ( see Methods ) or manually curated from the literature in our laboratory [21] . The remaining 764 GIs ( 50 . 46% ) were identified using Textpresso , an automatic text mining system [22] . To test the false-positive rate in this later GI set , we manually curated 261 of these interactions and found that 252 of them ( 96 . 55% ) were true-positives ( experimentally validated GIs; S1 Table ) . Overall , GIs-all is predicted to contain at least 98% of validated GIs . Statistical attributes using expression , protein-protein interaction ( PPI ) and phenotypic data were previously described as powerful tools to segregate GIs-all from a set of gene-pairs randomly selected from the genome [21] . GIs being shown as rare events [10] , we expect the latter set of gene-pairs to be mostly composed of “true” negative examples of GIs ( see Methods ) . Attributes used in this study capture the level of co-expression between interacting genes ( Exp , Fig 1A and 1B ) , the enrichment of shared phenotypes ( Ph , Fig 1A and 1B ) , their ability to encode proteins that interact physically ( I , Fig 1A and 1B ) and/or have more common interacting-partners than expected by chance alone ( CI , Fig 1A and 1B ) . Two attributes were also designed to identify GIs within functional modules ( N and NPh , Fig 1A and 1B ) . The attribute called "Neighborhood" ( N ) identifies the enrichment of phenotypes in the neighborhoods of gene-pairs within a network where genes are linked when they are significantly co-expressed , or code for interacting proteins ( see Methods for details; [21] ) . The attribute "Neighborhood with Phenotype" ( NPh ) identifies pairs of genes with a positive value for the attribute N and the association of both genes with the phenotype enriched in their neighborhood ( see Methods for details; [21] ) . We used these attributes to assess whether GIs display distinctive dependency towards data located at different abstraction levels . To do so , attribute values were used to cluster GIs-all together with 1 , 500 gene-pairs randomly chosen in the C . elegans genome . If GIs were forming a homogeneous group of entities with unique distinctive properties when considering negative sets of examples , we would expect them to cluster as a group from these negative examples . However , GIs-all tend to cluster into groups of GIs ( using Euclidean distance-based dissimilarities; see Methods ) with some dispersion among the negative examples ( Fig 1A ) . This suggests that GIs-all can be subdivided into GIs groups with potential distinctive biological properties . In an attempt to characterize the function of these GI groups , we used a cluster selection algorithm to identify GI classes from GIs-all ( Fig 1B ) , while controlling the robustness of this classification ( see S1 Text and S1 Fig ) . This study identified ten GI classes , six of them ( C1-C6 ) being significantly different from gene-pairs randomly selected from the genome ( see S1 Text ) . Analysis of the proportion of missing data supporting each class revealed that missing expression , phenotype and/or PPI data could not explain the classification by itself . We conclude from this analysis that GI classes with similar combination of attribute values would still be identified from a GI set exempt of missing data S2 Fig . Two main classes of GIs have been identified in the yeast genome: positive ( alleviating interactions ) and negative ( synergistic interactions ) [8 , 12 , 23–25] . Monochromaticity of GI dense subnetworks ( GDS ) , enriched in either positive or negative interactions , has also been identified in yeast [7 , 18] . These monochromatic GDS were described as being functionally biased—i . e . they tend to be associated with specific biological functions [7 , 18] . This characterization of GI networks in yeast was critical to understand better the role of GIs in coordinating gene function within biological processes . To characterize the potential distinctive biological properties of GI classes and their ability to control different bioprocesses , we first assessed whether GIs tend to form dense subnetworks ( GDS ) in C . elegans . We also tested whether these GDS may be monochromatic or multichromatic—if they were enriched in one or several GI classes . Lastly , we assessed whether these GI classes are functionally biased . This analysis revealed that GIs from GIs-all form few GDS ( see S1 Text and S3A Fig ) . However , while the number of GDS formed is too small to assess their enrichment in unique class or combination of classes as done in yeast , clustering of classes based upon the GDS composition , revealed that GI classes tend to form GDS in a biased manner: C1 with C2 , C4 with C5 and C3 with C6 ( see S1 Text and S3A Fig ) . Analysis of the gene composition of GI classes also revealed that C4 GIs share more genes with C5 than with the other classes ( see S1 Text and S4 Fig ) supporting the hypothesis that GI classes may assemble into GDS in a biased way . Enrichment of GI classes in Gene Ontology annotations ( GO , [26] ) was also measured . GO annotations were found enriched for all GI classes except C2 ( Fig 2A and S2 Table and S5–S11 Figs ) . This analysis was done first by considering gene repetition within classes ( some genes being involved in more than one GI in a given class ) and secondly , without considering gene repetition ( asterisk indicates enrichment observed without considering gene repetitions; Fig 2A and S2 Table ) . Similar results were obtained for both analyses , suggesting that GI classes are functionally biased as detailed below . C1 interactions were enriched in genes involved in cell division ( P = 0 . 01 , Fisher’s exact test; Fig 2A and S2 Table ) . For example , C1 GIs were identified between spd-2 , spd-5 and dhc-1 controlling centrosome assembly and maturation [27] ( S5 Fig ) . We expect genes involved in cell division to be essential . To test a potential enrichment of essential genes in C1 , we measured the enrichment of two gene sets of 294 and 1 , 259 essential genes identified using systematic RNA silencing by Kamath et al . [28] and Sonnichsen et al . [29] in GI classes when compared to GIs-all using a hypergeometric test ( bars above the red line indicate a significant enrichment; P < 0 . 05; Fig 2B ) . These data showed that C1 and C2 display the highest enrichment level in essential genes when compared to GIs-all and other classes . These data support the functional bias of C1 towards cell division and also associate GDS enriched in C1 and C2 interactions to essential biological functions . Enrichment analysis of GO terms within GI classes also revealed that C4 and C5 were enriched in genes coding for kinases ( P < 0 . 0001 , Fisher’s exact test; Fig 2A and S2 Table ) . A kinase-centric study of the GI network in yeast showed that genes coding for kinases are often redundant and are involved in either kinase-kinase ( K-K ) or kinase-substrate ( K-S ) GIs [18] . We assessed whether GI classes were enriched in these two kinds of interactions when compared to GIs-all . In this study , kinases were identified based on their GO annotation ( S2 Table ) and substrates were identified as non-kinases . This analysis revealed that C4 is significantly enriched in K-K ( P = 0 . 004 , Fisher’s exact test ) and K-S ( P = 0 . 03 , Fisher’s exact test ) . Considering that K-K interactions were primarily observed between redundant kinases [18] , we assessed whether C4 was also enriched in GIs between redundant genes . We thus measured the enrichment in GI classes of 306 genes identified as redundant ( linked by a synthetic sick or lethal interaction ) and evolutionarily conserved genes [30] . This analysis revealed that both C4 and C5 were enriched in evolutionarily conserved redundant genes ( Bars above the red line indicate significant enrichments; P < 0 . 05; Fig 2C ) . In order to assess whether C4 and C5 were enriched in interactions involving only redundant kinases or redundant genes at large , we measured this enrichment after removing kinases from both the Tischler et al . list of conserved redundant genes and from GI classes . This analysis revealed that only C4 was enriched in GIs involving non-kinase redundant genes ( Fisher’s exact test; P = 9 x 10−6 ) . This suggests that C5 GIs involve mainly conserved redundant kinases while C4 GIs involve conserved redundant genes coding for kinases or not . Examples of C4 GIs between conserved genes coding for redundant kinases and non-kinases are the interactions between the type I and type II TGF-beta receptor coding genes daf-1 and daf-4 [31–33]; and between the three Rho GTPases ced-10 , mig-2 and rac-2 shown to control cell migration [34] ( S5 Fig ) . Moreover , GO annotation enrichments revealed that C3 was enriched in genes involved in cell signaling ( P = 0 . 01 , Fisher’s exact test; Fig 3A ) and C6 in small GTPase signaling ( P = 0 . 001 , Fisher’s exact test; Fig 3A ) . Altogether , these data suggest that GI classes may assemble in GDS in a “class-biased” manner and also display a functional bias . Genes interacting through C1 and C2 GIs tend to be essential genes , more particularly involved in cell division . Genes interacting through C4 or C5 GIs tend to code for evolutionarily conserved redundant kinases and non-kinases . This study also suggests a potential function for genes interacting through C3 and C6 GIs in cell signaling . The coordination of molecular machines by GIs has been intensively investigated in yeast through characterization of the relationship existing between GIs and protein-protein interaction ( PPI ) networks [5 , 8 , 9] . Four out of the six attributes used in this study are built , even partially , on PPI data ( N , NPh , CI , I; see Methods ) . GI classes , displaying positive values for different combinations of attributes ( Fig 1B ) , may exhibit distinct relationship with the PPI network . To assess the relationship between GI classes and molecular machines we measured the average PPI-degree of proteins coded by genes within GI classes and GIs-all ( Fig 3A ) . This revealed that C4 and C5 GIs involve genes with a median PPI-degree significantly higher than the other classes and GIs-all ( P < 10−3 and P < 10−6 respectively , Wilcoxon rank-sum test; Fig 3A ) . We then assessed whether this was due to a significant enrichment of genes coding for PPI-Hubs—defined as the 20% proteins with the highest PPI-degree within the PPI network ( Fig 3B and S12 Fig ) . This revealed that C4 and C5 were indeed significantly enriched in PPI-Hubs when compared to GIs-all ( P < 10−4 and P < 10−11 respectively , Fisher’s exact test; Fig 3B ) . The structure of the PPI networks has been extensively studied in unicellular and multicellular organisms [35] . As shown for GIs , PPI tend to assemble into PPI-dense subnetworks ( PDS ) orchestrated around nodes . These nodes can either be Hubs or non-Hubs , with distinct structural functions within the PPI network [35 , 36] . The betweenness centrality metric was previously defined to identify network nodes playing a critical role in the PPI network organization ( called “bottleneck nodes” ) [36] . These nodes were characterized by their positioning in many shortest paths within the PPI network ( [36] , Fig 3C ) . Measurement of the betweenness centrality of PPI-non-Hubs and PPI-Hubs in the different GI classes revealed that C5 non-Hub proteins tend to be bottlenecks with a significantly higher betweenness centrality than GIs-all ( P < 10−9 , Wilcoxon rank-sum test; Fig 3D ) . The betweenness centrality of PPI-Hubs was not significantly different between GI classes and GIs-all ( Fig 3E ) . Altogether , these data clearly show that GI classes have distinctive properties with respect to the PPI network and may consequently coordinate differently molecular machines in biological processes . They identify C4 and C5 interactions as enriched in genes coding for PPI-Hubs that tend to be non-bottlenecks ( green node; Fig 3C ) , suggesting that these Hubs may be embedded within PDS rather than at their periphery . C5 also appear to be enriched in non-Hub Bottleneck nodes ( Blue nodes , Fig 3C ) thought to play a critical role in PDS coordination [36] . Therefore , these data suggest , C4 and C5 interactions may play a critical role in controlling the assembly and the coordination of PDS . The data presented above suggest that C4 and C5 GIs link genes coding for proteins within or between PDS while C1 , C2 , C3 and C6 may link genes coding for proteins outside PDS . To test this hypothesis we used the Cytoscape “MINE” plugin [37] to identify PDS within the PPI-network as previously done [8 , 9] ( see Methods ) . This approach identified 106 PDS containing at least four proteins . We then assessed whether GIs-all and GI classes were enriched in genes coding for proteins: ( i ) in the same PDS ( GI within-PDS; red lines / bars; Fig 4A and 4B ) ; or ( ii ) between PDS ( pairs of proteins located in different PDS; GI between-PDS; blue lines / bars; Fig 4A and 4B ) . To evaluate this enrichment level , we compared the frequencies obtained for the different scenarios ( within-PDS and between-PDS ) with those for randomized GI networks of similar size and structure , and computed log-ratio transform ( LR ) scores ( see Methods , Fig 4B ) . Consistently with previous studies [38] , GIs-all was enriched in within-PDS connections while being depleted in between-PDS connections ( Fig 4B ) , confirming that GIs in C . elegans are more frequently observed within-PDS than between-PDS . Interestingly , C2 and C3 GIs tended to occur between-PDS , and C4 and C5 GIs occur both within- and between-PDS , while GIs in C1 and C6 appear to be independent from PDS ( Fig 4B ) . We assessed whether this PDS-independency results from a dependency of C1 and C6 towards connected 3-protein triangles , called bistable motifs , which are not considered as PDS in our study ( see Methods ) . However , no bistable motif was found in GIs-all suggesting that C1 and C6 GIs are independent from PDS . These data confirm our hypothesis that C4 and C5 GIs are enriched in within- and between-PDS . We will consequently , characterize them as PDS-centric interactions , and C1 and C6 GIs as PDS-independent . Moreover , C2 and C3 GIs appeared to be enriched in between-PDS connections suggesting some kind of functional relationship with the PDS-centric interactions . Taken together these data suggest that GI classes display distinct relationships towards PDS and consequently , may coordinate differently molecular machines within biological processes . In the yeast S . cerevisiae , subgroups of GIs have been identified and characterized based on their relationship with physical interaction networks–mainly protein-protein ( PPI ) and protein-DNA ( PDI ) interactions [5 , 8 , 9] . In these studies , a " pathway " was defined as a dense subnetwork or a connected graph module of PPIs ( defined as PDS in our study ) and PDIs . These studies showed that GIs tend to occur rather between- than within-pathways [5 , 8 , 9] . Interestingly , a similar study was done on GI networks in C . elegans and showed the opposite–GIs occur more frequently within- than between-pathways [38 , 39] . These results are in agreement with data obtained in this study for GIs-all ( Fig 4B ) . The pathway described above is quite different from the definition used by developmental geneticists for whom a pathway consists in a group of genes functioning together to control a given biological process [40] . Characterization of the relationship between PDS and pathways is consequently required to clarify the relative positioning of GIs and pathways ( both located at the abstraction level IV ) between molecular machines ( level III ) and biological processes ( level V ) . To do so , 61 pathways were retrieved from the KEGG database [41] along with 33 pathways controlling the embryonic and larval development of C . elegans that we manually curated from the literature ( S3 Table; see Methods ) . We then assessed whether gene-pairs involved in a given pathway ( within-pathway interactions ) tend to code for proteins , which are part of the same PDS ( within-PDS; orange lines / bars; Fig 5A and 5B ) or of two distinct PDSs ( between-PDS; green lines / bars; Fig 5A and 5B ) . We compared the frequencies obtained for these scenarios with those resulting from randomization of pathways ( see Methods , Fig 5B ) . Interestingly , proteins coded by gene-pairs in the same KEGG or pathways from the literature ( KEGG and Lit . respectively; Fig 5B ) are enriched in within- and between-PDS interactions ( orange and green bars respectively; Fig 5B ) . We also confirmed that this enrichment of within- and between-PDS observed within-pathways did not depend on the topology of the PDS network but instead depend upon the interactions themselves ( see S1 Text and S14 Fig ) . These data suggest that pathways and PDSs are distinct functional modules . They also suggest that pathways are composed of several PDS and that proteins involved in a given pathway may be part of the same PDS or of different PDSs . We confirmed these assumptions through a close examination of genes/proteins involved in pathways and PDSs as shown in S4 Table and detailed in supplementary information ( S1 Text ) . Considering that pathways and PDS are distinct functional modules and that GI classes have distinct relationships towards PDS , we were interested to test whether this would also be the case for pathways . We thus investigated if GI classes and GIs-all were enriched in within-pathways ( pairs of genes functioning in at least one common pathway , see Methods ) and/or between-pathways ( pairs of genes involved in at least one pathway but not involved in any common pathway , see Methods; Fig 6A ) . As detailed for PDS in the previous sections , we compared the frequencies obtained for the different scenarios ( within-pathways and between-pathways ) with those for randomized GI networks ( Fig 6B and 6C , see Methods ) . GI classes and GIs-all were enriched in within-pathway interactions for both pathways retrieved from the KEGG database and from the literature ( Fig 6B and 6C respectively ) . Remarkably , GI classes were depleted in between-pathway interactions while GIs-all was enriched in this kind of interactions ( Fig 6B and 6C ) . These results suggest that non-selected clusters , C7 to C10 ( Fig 1B ) , are enriched in between-pathway interactions . Characterization of these later GI classes revealed that it was indeed the case: except for C10 that was enriched only in within-pathway interactions , C7 , C8 and C9 GIs were enriched in both within- and between-pathways interactions ( S13 Fig ) . Together , these data show that C1/C6 PDS-independent , C4/C5 PDS-centric and C2 , C3 GIs are enriched in within-pathways interactions . This implies that pathways involve both functional interactions that are organized around PDS and other that are independent from PDS . Highly connected genes called connectors , or GI-Hubs , are genes whose alteration impacts on a large number of genes . Their function is consequently expected to be central within pathways and bioprocesses [5 , 6 , 39] . GI-Hubs are defined here as the 20% genes with the highest GI degree within the GI-network ( S16A Fig ) . In order to understand better the function of GI classes within pathways , we characterized the distribution of GI-Hubs/connectors within GIs-all and their potential enrichment in GI classes . Therefore , we computed the average GI degree of interacting genes in GI classes and GIs-all ( Fig 7A ) , and also measured their enrichment in GI-Hubs ( Fig 7B ) . This analysis revealed that C3 and C6 showed a median GI-degree significantly higher than GIs-all and are enriched in GI-Hubs ( Fig 7A and 7B ) . These results suggest C3 and C6 are enriched in connectors that may play a critical role in the coordination of gene functions within pathways . Such a function for C3 and C6 GIs is consistent with their expected involvement in cell signaling ( Fig 2A ) . Our study supports a model in which four out of the six classes of GIs coordinate the function of genes either in a PDS-centric or in a PDS-independent manner . They also identify two additional classes of GIs with central coordination function through the involvement of connectors/GI-Hubs . C . elegans being a multicellular organism , one may ask whether some parts of this organization may be organ or process specific while others may be ubiquitous . To answer this question , we assessed whether GI classes are enriched in pleiotropic genes–i . e . genes whose genetic alteration is associated to multiple phenotypic expressions and consequently , whose function is required in several organs and/or at different developmental stages of the animal . To do so , we divided the C . elegans phenotype ontology into 22 groups of phenotypes ( S15 Fig ) . We computed a pleiotropic index ( PI ) for each gene as the number of phenotype groups containing at least one phenotype expressed upon genetic alteration of the gene of interest ( see Methods ) . The PI median genome-wide ( 2 ± 1 . 48 MAD ) is smaller than in GIs-all ( 5 ± 2 . 97 MAD; S16B Fig ) , and its distribution revealed that a large number of genes displayed a low PI while a small number of genes have a high PI ( S16B Fig ) . Using this distribution of PI , we defined a class of highly pleiotropic genes ( High-PI ) as the 20% genes displaying the highest PI genome-wide ( S16B Fig ) . We measured the PI of genes involved in GI classes and found that only genes involved in C1 , C2 and C6 GIs displayed significantly higher median PIs than GIs-all ( P < 10−4 , Wilcoxon rank-sum test; dark grey boxes; Fig 7C ) and are significantly enriched in High-PI genes ( P < 10−5; Fisher’s exact test; Fig 7D ) . We further characterized the distribution of GI classes at different PI ranges and the ability of these classes to link genes between ranges ( see S1 Text and S17 Fig ) . This study revealed that while C5 connects genes only within an average PI range , other classes connect genes across PI ranges , especially C3 and C6 that tend to link genes within an average PI range to either genes with low ( C3 class ) or high PI ( C6 class ) . These data suggest that PDS-independent GIs tend to involve pleiotropic genes while PDS-centric GIs involve genes with an average to low pleiotropy . They also further characterized the properties of connectors involved in C3 and C6 GIs , linking genes within different pleiotropic ranges: C6 and C3 GIs link genes from an average to a high-pleiotropy and from a low to an average pleiotropy respectively . These data show that the organization of the characterized GI network within pathways is more complex when considering the multicellular nature of C . elegans . They imply that genetic mutations within a given pathway may either lead to pleiotropic or tissue/developmental stage-specific phenotypic manifestations . This also implies that such mutation may be compensated by a mutation of a genes involved in the same pathways but with pleiotropic or non-pleiotropic effect . PDS-centric GIs ( C4 and C5 ) are GI classes displaying positive values for I and CI attributes ( Fig 1B ) , which are attributes mainly built on PPI network . We , consequently , wondered whether these two classes of GIs could have been identified considering PPI data alone . In order to test the value of the data integration strategy used in this study to identify the GI classes , we assessed whether functional characteristics observed for each class may depend on one or a combination of the attributes used for the classification ( Fig 1A and 1B ) . To answer this question , we defined a threshold value for each attribute ( S6 Table ) , allowing us to identify groups of GIs associated to a positive or a negative value per attribute . We subsequently assessed the enrichment in GI-Hubs , PPI-Hubs , redundant genes , essential genes , genes with high PI ( High-PI; Figs 8A and S18 ) , within- and between-PDS ( W-PDS and B-PDS; Figs 8B and S19 ) as well as within- and between-pathway interactions ( W-Path and B-Path respectively , Fig 8B ) in these GI groups . We clustered GI groups and GI classes ( C1-C6 ) based on these enrichment levels using Euclidean distances ( Fig 8A and 8B ) . This analysis revealed that the enrichment of a given GI property is not associated to a positive or a negative value for a single attribute but for several of them ( Fig 8A and 8B ) . For example , enrichment of genes with High-PI was observed in groups of GIs with positive or negative values for Ph ( enrichment of phenotypic manifestations ) , with negative value for CI ( high number of common partners within the PPI network ) , and positive value for NPh ( enrichment of a phenotype associated to interacting genes in their respective neighborhoods ) . This suggests that integration of a set of attributes defines the biological functions of identified GIs . This analysis also revealed that the biological characteristics observed for GI-classes are not found in GI groups associated to either a positive or a negative value for an attribute . For instance , when comparing C4 and C5 with the GI group positive for CI ( interacting genes coding for proteins sharing a significantly high number of common PPI partners ) , these GI groups/classes displayed similar enrichment of redundant genes , PPI-Hubs and essential genes ( blue square; Fig 8A ) but have different enrichment profiles when considering their relative positioning towards pathways and PDS ( blue square; Fig 8B ) . This suggests that the enrichment of essential genes , PPI-Hubs and redundant genes in C4 and C5 may be significantly influenced by a positive value for the CI attribute ( Fig 1B ) . Positive value for this attribute does not , however , explain the depletion of C4 and C5 in GIs between-pathways and their enrichment in GI between-PDS ( Fig 8B ) . Altogether , this study demonstrates that the biological characteristics identified for GI classes depend on combination of attributes identified through data integration strategy . Importantly , it indicates which combination of attributes is appropriate in integration to identify classes of GI with specific biological functions .
We showed in this study that PDS and pathways are different structural/functional modules , consistent with their respective positions at abstraction levels III ( physical interactions ) and IV ( functional interactions ) [1] . Furthermore , we showed that both PDS-centric and PDS-independent GIs contribute to pathways . While , the function of PDS and protein complexes within pathways has been extensively studied ( reviewed in [42] ) , the function of PDS-independent GIs is quite unexplored . The apparent independence of C1 interactions towards PDS is intriguing . It may be explained by the possibility that physical interactions between protein products of genes involved in C1 GIs have not been identified yet . Interestingly , physical interactions between proteins controlling early embryogenesis , including that controlling cell division have been the subject to special attention [43] , suggesting that this portion of the PPI network may be less prone to missing data than other parts . This supports the idea that cell division-associated interactions found in C1 define true PDS-independent GIs . PDS-independence of C1 GIs may also result from a potential enrichment of transcriptional regulatory elements in this class ( and consequently , protein-DNA interactions instead of PPI ) . As an example of this possibility , C1 GIs include interactions between the chromatin modifying enzymes coding genes mes-4 , mes-2/3/6 and mep-1 , which were shown to differentially modify histones and consequently , to bind to DNA and not physically to each other [44] ( S6 Fig ) . Our study also revealed that C3 and C6 GI classes are enriched in GI-Hubs and may play a critical role in coordinating molecular machines within pathways . GI-Hubs have been previously proposed to constitute connectors with high modifier potential–i . e . the ability to modify the expression of phenotypes resulting from genetic alterations of multiple genes–both in C . elegans and in yeast [39] . Our study also characterized C6 GIs as involving pleiotropic connectors ( PC , Fig 9 ) and C3 GIs as involving non-pleiotropic connectors ( NPC , Fig 9 ) . The involvement of PC in the coordination of molecular machines within pathways is consistent with a study of pleiotropic genes in C . elegans suggesting that genes involved in early embryogenesis are organized into partially overlapping functional modules and that pleiotropic genes represent connectors between these modules [45] . Our data , while supporting this model , suggest that non-pleiotropic genes may also have an organizational role within pathways through C3 interactions . Intriguingly , genes interacting using C3 and C6 GIs , while being associated individually to several phenotypes did not present any significant correlation in their phenotypic profiles . This is the major distinctive property of C3 and C6 GIs , when compared to other classes . A study investigating the tissue specificity of PPI-network identified both housekeeping/pleiotropic and "local" PPI-Hubs [46] . It also documented the way housekeeping/pleiotropic Hubs interact with tissue-specific proteins , being consequently involved in different biological processes than other housekeeping Hubs [46] . Such a scenario transposed to GI-networks may partially explain how a connector may be associated to a panel of phenotypes which is significantly different than that of its partners . Overall , our study establishes an organizational model for pathways suggesting that they are built around both pleiotropic PDS-independent and non-pleiotropic PDS-centric GIs coordinating molecular machines together with both pleiotropic and non-pleiotropic connectors . Such an organization suggests that mutations in genes of a given pathway may lead to either pleiotropic or non-pleiotropic effect . These effects may then be modulated at the organism or at the tissue level , through selection of compensative mutations [47] or through buffered pleiotropy effect [48] . Further characterization of the GI-network structure within and between pathways will thus certainly provide a better understanding of the abundant pleiotropy effect found in human complex diseases and traits [49] . C5 and C4 GIs involve genes with an average to a low pleiotropy . This is consistent with their enrichment in genes coding for kinases ( Fig 2A ) and also evolutionarily conserved redundant genes that are mostly kinases in C5 and either kinases or non-kinases in C4 . While these genes may be involved in more than one bioprocess , their redundancy may highly limit the number of phenotypes expressed upon perturbation , thus reducing their apparent pleiotropy . While compensatory functions between duplicated genes were shown to be evolutionarily unstable for most of the duplicated gene-pairs [50] , a small fraction of them are submitted to natural selection that stabilizes their functional overlap [45] . These selected pairs tend to have a high propensity of clustering into the same protein complexes , and share common interaction partners [45] . Protein complexes were identified form PPI networks as dense subnetworks with methods similar to that used in this study to identify PDS [51] . Altogether , these data are in agreement with C4 and C5 GIs displaying positive values for the CI attribute ( gene pairs coding for proteins with a significant high number of common PPI partners; Fig 1B ) . They are also consistent with C4 being enriched in evolutionarily conserved redundant genes . Gene duplication , when associated to functional redundancy , was associated to genomic robustness–i . e . increased resistance of the system to genetic alterations [52] . The PDS-centric within-pathway GIs involving redundant and conserved genes within- or between-PDS may then contribute to the robustness of C . elegans genome . The present study shows that C1 to C6 GIs classes are enriched in within-pathway interactions while C7 to C9 classes are enriched in both within- and between-pathways GIs . Interestingly , these later GI classes could not efficiently be dissociated from gene pairs randomly picked from the genome based on the six attributes used for the classification . While attribute values may be highly influenced by missing data for these GI classes , it is intriguing to observe that the vast majority of between-pathways interactions lay in these classes . It was shown in yeast that both positive and negative GIs within functional modules ( protein complexes , gene belonging to the same biological process ) are significantly more conserved between S . cerevisiae and S . pombe , than wiring between these modules [19 , 20 , 53] . Considering that a pathway is a functional module , it would then be interesting to assess whether C1 to C6 GIs would be more evolutionarily conserved than C7 to C9 . Similarly , it would be interesting to assess whether interactions within-PDS would be more evolutionarily conserved than between or outside PDS interactions . The organization of the within-pathway GI-network described here is consistent with the evolution theory model called selection , pleiotropy and compensation ( SPC ) recently built from quantitative genetics studies ( reviewed in [47] ) . This model predicts that adaptive change in one character ( through functional alterations of a pleiotropic gene ) is associated with deleterious pleiotropy in others and subsequent selections to compensate for these pleiotropic effects . This compensation involves the genetic alteration of non-pleiotropic/“private” gene ( s ) . This model relies upon the existence of functional interaction between pleiotropic and non-pleiotropic genes within pathways targeted by evolution . Interestingly , such interactions have been identified in tissue-specific PPI-networks [46] and are also consistent with the model proposed here , in which pleiotropic Hubs are connected to genes displaying average pleiotropy through C6 GIs within pathways . We showed that integration of attributes based on data located at different abstraction levels ( in this study , levels II , III and VI ) , identified GI classes with distinctive biological properties from a GI-network . Similar classification strategy applied to gene-pairs found within-pathways of systems inappropriate to a genome-wide experimental mapping of GIs would be interesting to interrogate the evolutionary conservation of functional interactions in multicellular organisms . Integrative genomics approaches using a selected subset of statistical attributes may also be used to improve predictors for specific classes of GIs such as connectors/GI-Hubs , which are of high interest for health-oriented research . Our study provides the first deep structural and functional characterization of a GI-network enriched in within-pathways interactions in a multicellular organism . It proposes a model in which PDS-centric and PDS-independent interactions coordinate molecular machines within pathways together with pleiotropic and non-pleiotropic connectors . This study demonstrates the value of integrative genomics approaches , using data from several abstraction levels to characterize genetic interaction networks , their relationship with networks located at different abstraction levels and to study the systems basis of complex biological phenomena , including genomic robustness , pleiotropic effects and adaptive evolution .
The complete set of genetic interactions ( GIs-all ) consists of 1 , 514 GIs retrieved from Wormbase , Biogrid and/or curated manually from the literature ( [54] and this study S1 Table ) . Five GIs were curated using the curation and blind re-curation procedure of BIOGRID [55]; 689 GIs ( 45% ) were curated by an author-based curation approach used by Wormbase to insure the accuracy of their data [56]; 56 and 261 GIs were manually curated in our laboratory in the context of [54] and this study respectively ( S1 Table ) . The 261 GIs curated in this study cover GIs identified from the literature by Textpresso [22] and found in GI classes ( C1 to C6 ) ( S1 Table ) . References to an experimental validation were found in the literature for 252 of these GIs , leaving nine of them without experimental evidences ( 3 . 57% ) . Less than 3 . 3% of GIs lay in that category of non-validated interactions per class . GIs-all was used to build a GI network analyzed and visualized using Cytoscape v2 . 8 . 2 [57] . GI degree was computed for each gene using the network statistic tool . Attributes and data used to compute them have been described previously [54] . Briefly , The co-expression attribute Exp ( A , B ) is the P-value derived for the Pearson correlation of genes A and B across 514 microarray experiments ( retrieved from [58] ) relative to the empirically estimated probability distribution of correlation for all gene pairs ( i . e . a fitted normal ) . The co-phenotype attribute Ph ( A , B ) uses 107 phenotypes ( retrieved from WormBase release WS141 ) and measures the statistical significance of the number of shared phenotypes between the two genes ( A and B ) via a standard Fisher’s exact test ( N = the number of phenotypes observed for at least two genes ) . The multispecies PPI network was taken from [54] . Briefly , PPIs were obtained from all C . elegans , Saccharomyces cervisiae , Drosophila melanogaster , and Homo sapiens yeast two-hybrid datasets stored in BioGRID v2 . 0 . 37 ( http://www . thebiogrid . org/ ) and from two additional yeast two-hybrid datasets [59 , 60] . To create a multi-species PPI network , we used the orthology mappings generated by InParanoid v1 . 35 [61] ( non-default parameters: score cutoff 10 , in-paralog confidence cutoff 0 . 025 , sequence overlap cutoff 0 . 2 ) . The interaction attribute I ( A , B ) indicates whether the proteins encoded by A and B exhibit a PPI in the multi-species PPI network as defined in [54] . Similarly , the common interactors attribute , CI ( A , B ) , considers the statistical significance of the observed number of common physical interactors of the proteins encoded by A and B , in the multi-species PPI network . The attribute CI is then assigned a P-value derived from a one-tailed Fisher’s exact test ( N = the number of genes encoding proteins that are in the multi-species PPI network ) . For the other attributes , a biological network called the PhEP was created where two genes A and B are connected by an edge if the Pearson correlation coefficient of their gene expression exceeds 0 . 35 or if their gene products exhibit a PPI in the multi-species PPI network . For both A and B , we measured how surprising it is to witness the observed number of their neighbors ( i . e . genes connected to it by one edge ) in the PhEP network labeled with a specific phenotype identified by RNAi in C . elegans . This was measured using a one-tailed Fisher’s exact test ( N = the number of genes with some assigned phenotype ) . If the derived P-value is less than or equal to 0 . 05 for A and B for at least one phenotype , we assign a value of 1 to a categorical variable N ( A , B ) , and 0 otherwise . Similarly , if A and B exhibit a phenotype that is also enriched in both their neighborhoods in the PhEP network , a value of 1 is assigned to a categorical variable NPh ( A , B ) , and 0 otherwise . Missing values for any of the derived attributes ( due to missing values in the underlying datasets ) were replaced with the expected value ( i . e . the sample mean ) of the attribute before training . See [54] for additional information regarding the attributes and datasets used to derive them . Unless otherwise specified , heatmaps were the result of hierarchical clustering using Ward’s agglomerative method with a distance metric between x and y based on Euclidean distance and is given by: d=∑i ( xi−yi ) 2 The Canberra distance is given by: dx , y=∑i|xi−yi||xi|−|yi| where dx , y is the Canberra distance between two GI classes x and y , xi and yi is the number of genes that occur in the classes . The binary distance is defined as the fraction of the n genes that is present in only one of classes x and y . GIs-all was hierarchically clustered , and uncertainty in the resulting clusters was judged with approximately unbiased ( AU ) P-values acquired after a multiscale bootstrap resampling of the data ( 10 , 000 resamples ) using the R package “pvclust” v1 . 2–2 with the default parameters [62] . The dendrogram was cut at each height with the R function cutree . Clusters with AU >0 . 95 were retained for further analysis . For each remaining cluster of genetic GIs ( positive set ) , a logistic regression model was tested using leave-one-out cross-validation ( LOOCV ) against a negative set ( randomly selected gene pairs ) of equal size with the requirement that one of the genes found in a negative pair was included in the cluster of genetic interactions . For all clusters , the six attributes were used in the regression models . True and false positive rates were computed at 20 equally spaced model score cutoffs in [0 , 1] , resulting in a receiver operating characteristic ( ROC ) curve for each model . The area under the ROC curve ( AUC ) was used as an indicator of how well a classifier model could discriminate GIs found in our positive training sets from negative examples . Gene Ontology ( GO ) term , essential gene , redundant genes , PPI-Hubs , GI-Hubs and High-PI genes enrichments were evaluated using a one-tailed Fisher’s exact test . Measurement of enrichment in groups of GIs defined by positive or negative values for a single attribute required the identification of a threshold above which the attribute value is considered as positive . These thresholds are indicated in the S6 Table as well as the number of GIs within GIs-all associated to a positive value to the attribute . For GO term enrichments , the reference universe N was constituted of all terms associated to genes ( with or without repetition ) found in genetic interactions of GIs-all ( see Dataset of genetic interactions section ) . Note that certain genes are involved in more than one GI within GI classes . As indicated in the result section , GO enrichment was done considering the gene frequency within classes ( each repetition being considered as an independent gene ) or without considering the gene frequency ( each gene is used only once per class and in GIs-all to calculate the enrichment ) . The universe N for the other enrichment tests contained all genes ( with repetition ) found in GIs-all . To evaluate the monochromatic index of each GI class , we first partitioned the GIs-all network into several dense subnetworks using the Cytoscape plugin “MINE” v1 . 5 [37] with the default parameter values . The resulting network contained a total of 42 GIs subnetworks ( GDS ) ( S3A Fig ) . To assess the proportion of interactions from a GI class within a particular GDS , we calculated a monochromatic score ( MS ) in a similar way than described previously [6] . Let BR represent the ratio of GIs from a given class within GIs-all and MR , the ratio of GIs from the same GI class within a GDS . The monochromatic scores of a GI class and for a GDS is given by: ifMR>BR , MS= ( MR−BR ) ( 1−BR ) ifMR=BR , MS=0ifMR<BR , MS= ( MR−BR ) BR PPI-Hubs were identified as the 20% proteins with the highest PPI-degree ( k ≥ 22 ) as previously done [36] . The degree and the betweenness centrality were assessed for all genes , Hubs and non-Hubs using the network statistic tool in Cytoscape v2 . 8 . 2 [57] . Distribution of interaction degrees and betweenness centralities was computed for all the genes ( considering the frequency of involvement for each gene in GIs of the class ) in a given set of GIs . The modular partitioning of PPI-networks was done using the Cytoscape plugin “MINE” v1 . 5 [37] with the default parameter values . Significant PPI dense subnetworks ( PDS ) were selected by taking all complexes with a score ( Density * Number of Proteins ) ≥ 4 , giving a total of 106 PDS covering 1 , 760 proteins connected by 17 , 430 edges . The size distribution of all PDS is given in S14A Fig . Significant GI-dense modules ( GI-modules ) were selected by taking all complexes with a score ( Density * Number of Proteins ) ≥ 3 . KEGG pathways were retrieved from the Kyoto Encyclopedia of Genes and Genomes database 61 . 1 release [41] . Pathways from the literature were manually curated from [63] ( S3 Table ) . To measure the enrichment of within-PDS/pathway and between-PDS/pathway within classes of GIs and pathways , we defined several networks . Networks UCi are composed of genes and interactions found in GI classes Ci . A network WPa is composed of genes found in pathways , which are linked by an edge if these genes are found in at least one common pathway . We also defined a network WPDS , which is composed of proteins and PPI found within PDS . The networks BPa and BPDS are composed of nodes found in WPa and WPDS respectively and all possible edges between these nodes from which were removed the edges found respectively in WPa and WPDS . ( note that BPDS do not overlap with the PPI-network ) . We then computed the frequency F in these networks with respect to the mean frequency calculated for a random network V as follows: FX , V=∑ ( TX∩TY ) ∑TX/∑ ( TV∩TY ) ∑TV where TX , TY and TV represent all edges in network X , Y and V respectively . To compute the frequency of within-PDS and between-PDS interactions found in pathways , we defined the following: X = WPa , Y = WPDS and BPDS respectively , and V is a random network with the same structure as WPa ( detailed in the Network randomization section below ) . To compute the frequency of within-PDS and between-PDS found in GI classes , we defined the following: X = UCi . Y = WPDS and BPDS respectively , and V is a random network with the same structure as UCi . To compute the frequency of within-pathways and between-pathways found in GI classes , we defined the following: X = UCi , Y = WPa and BPa respectively and V is a random network with the same structure as UCi . We then computed the log10-ratio ( LR ) transform score of the frequency for network X relative to randomized network V . To avoid the log-ratio of a zero value , we used a simple transform that took care of any undetermined possibilities as follows: LR ( FX , V ) =|log10ifFX , FV>0−1ifFX>0&FV=00ifFX=0&FV=0 The following randomization procedure was used to randomize GI networks ( Figs 4 and 6 and S13 Fig ) and pathways ( Fig 5 ) . To do so , for each network being randomized , all connected gene-pairs were split in two groups . The order of genes and the number of edges in the first group were kept unchanged . Genes in the second group were randomly reordered . This aims to preserve the degree of connectivity for any gene present in the network and its randomized version . Restriction was applied to make sure that a pair of gene could not be composed of twice the same gene . The number of randomized networks generated was determined giving Hoeffding’s inequality [64] . Basically , by increasing the number ( n ) of random networks , we minimize the relative error ρ and get a better estimation of p , the real mean frequency of edges in within- or between-pathways/PDS . Since the calculated mean μ is an estimation of the real mean p and ε=ln2−ln ( 1−c ) 2n , let μ− = max{0 , μ−ε} and μ+ = max{1 , μ+ε} and with probability c = 0 . 99 , μ- < p < μ+ . And if μ- > 0 , we get with probability c , the maximum value of ρ: ρ=|μ−p|p≤max{|μ−μ−| , |μ−μ+|}μ− For all networks being randomized , 100 , 000 randomizations were sufficient to obtain a reasonably small value of the relative error ρ with a probability of 0 . 99 . As a consequence , error bars cannot be seen ( because they are too small ) and are not indicated on bar graphs in Figs 4–6 . The second randomization method , used to validate within- and between-PDS relationships ( S14 Fig ) , aimed at randomizing all the PDS node labels to create new PDS ( Random; S14D Fig ) with the exact same topology than that extracted from the PPI network ( Original; S14C Fig ) , but with different node labels . In short , for a given PDS , we permuted the node labels by randomly selecting labels from a list of nodes present in another PDS and not already been reassigned . The procedure was done iteratively until more than 90% of labels in a given PDS were permuted . Edges were unchanged to preserve the degree distribution , PDS size , within- and between-PDS connectivity ( as seen in S14C and S14D Fig ) . After the randomization process , the resulting network contained less than 3% overlapping edges with the original PDS newtork . Note that all PPI used in our study have been generated using yeast two-hybrid systems which use protein bait to identify preys . However , the bait/prey orientation of PPIs was not considered in this study . IDs of observed phenotypes for every gene found in the C . elegans genome , and their hierarchical relationships , were downloaded from WormBase ( release WS220-bugfix ) . Relationships between phenotypes were visualized in Cytoscape , where a node represents a phenotype , and an edge between two nodes , the hierarchical relationship between two phenotypes . Groups of phenotype corresponding to the 22 most general phenotypes , covering in 1 step the entire network , were identified ( S15 Fig ) . The pleiotropic index ( PI ) of a gene was computed as the number of these 22 classes containing at least one phenotype associated with the gene . This strategy was used to ensure that we identify the involvement of a gene in different tissues and at different developmental stages without being biased by the extensive characterization of certain developmental stages and/or biological processes . As seen in S15 Fig , some groups of phenotypes , for example , "Developmental variant" and "morphology variant" are associated to a much larger number of specific phenotypes than other groups . Several specific phenotypes of highly populated groups may be attributed to individual genes . Our strategy avoids having those genes being pleiotropic if not associated to other phenotypic groups . Each distribution of PIs was computed from a given set of genes , e . g . all C . elegans genes , or genes in a given set of GIs . Odds ratios ( OR ) , used to measure the enrichment of GIs between genes within or across certain PI ranges , are defined as: log10 ( OR ) =log10[nx , i/nall , iNx/Nall] where nx , i = number of class x GIs ( e . g . , C1 GIs ) in subnetwork i ( i being for example a subnetwork of GIs between genes with pleiotropic index >8 ) ; nall-i = total number of GIs in subnetwork i; Nx = total number of class x GIs ( e . g . C1 GIs ) and Nall = total number of GIs in the genetic interactome . Significant enrichments of GI classes in each subnetwork were estimated using a one-tailed Fisher’s exact test . Only OR associated with a P-value <0 . 05 were represented in S17 Fig . Wilcoxon rank-sum test was done according to Hollander and Wolfe ( 1972 ) using the wilcoxon . test function in R . This test is used when the population cannot be assumed to be normally distributed . All computed P-values were adjusted using the Benjamini and Hochberg ( 1995 ) method for controlling the false discovery rate ( specifically , with the p . adjust function in R ) . | Network biology has focused for years on protein-protein interaction ( PPI ) networks , identifying nodes with central structural functions and modules associated to bioprocesses , phenotypes and diseases . Network biology field moved to a higher level of abstraction , and started characterizing a less intuitive kind of interactions , called genetic interactions ( GIs ) or epistasis . Mostly due to technical challenges associated to the genome-wide mapping of GIs , these studies primarily focused on unicellular organisms . They uncovered modules embedded within the structure of these networks and started characterizing their relationship with PPI-network and biological functions . We provide here the first in-depth characterization of a network composed of ~600 GIs within signaling and metabolic pathways of a multicellular organism , the nematode Caenorhabditis elegans . We characterize the structure of this network , and the function of GI classes found in this network . We also discuss how these GI classes contribute to the genomic robustness and the adaptive evolution of multicellular organisms . | [
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"analysis",... | 2016 | Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways |
Many bacteria build biofilm matrices using a conserved exopolysaccharide named PGA or PNAG ( poly-β-1 , 6-N-acetyl-D-glucosamine ) . Interestingly , while E . coli and other members of the family Enterobacteriaceae encode the pgaABCD operon responsible for PGA synthesis , Salmonella lacks it . The evolutionary force driving this difference remains to be determined . Here , we report that Salmonella lost the pgaABCD operon after the divergence of Salmonella and Citrobacter clades , and previous to the diversification of the currently sequenced Salmonella strains . Reconstitution of the PGA machinery endows Salmonella with the capacity to produce PGA in a cyclic dimeric GMP ( c-di-GMP ) dependent manner . Outside the host , the PGA polysaccharide does not seem to provide any significant benefit to Salmonella: resistance against chlorine treatment , ultraviolet light irradiation , heavy metal stress and phage infection remained the same as in a strain producing cellulose , the main biofilm exopolysaccharide naturally produced by Salmonella . In contrast , PGA production proved to be deleterious to Salmonella survival inside the host , since it increased susceptibility to bile salts and oxidative stress , and hindered the capacity of S . Enteritidis to survive inside macrophages and to colonize extraintestinal organs , including the gallbladder . Altogether , our observations indicate that PGA is an antivirulence factor whose loss may have been a necessary event during Salmonella speciation to permit survival inside the host .
Escherichia coli and Salmonella enterica are the two core species of the family Enterobacteriaceae , that constitutes a diverse group of bacteria that generally inhabit the gastrointestinal tract of animals . Although these two species are closely related , E . coli comprises commensal bacteria that do not normally cause disease , with the exception of certain pathogenic strains , whereas all members of S . enterica are considered pathogenic . Hence , an intriguing issue regarding bacterial evolution is the identification of determinants that make Salmonella able to establish parasitic interactions but enable E . coli to establish beneficial interactions with the human host . In this regard , it is believed that a combination of different genetic factors accounts for such a difference in virulence: first , Salmonella harbor virulence genes that are not present in E . coli; second , Salmonella may have lost genes from the ancestral core genome that if present , would diminish its pathogenic potential; third , E . coli may carry a virulence suppressor gene ( s ) that interferes with the synthesis and/or stability of a virulence protein ( s ) ; and fourth , Salmonella and E . coli may differ in the regulation of cellular factors important for survival in the host [1–3] . An intriguing difference between Salmonella and E . coli that might account for their distinctive lifestyles as regards the human host is the exopolysaccharide that each species uses to build the biofilm matrix . Bacteria spend most of their lives inside a biofilm surrounded by a highly hydrated layer that provides protection against desiccation , diffusion of antibiotics , toxic metal ions and other compounds , predation by protozoans and the host immune system , amongst others [4 , 5] . Diversity in biofilm exopolysaccharides composition is high , with some bacterial species being able to produce different types depending on the environmental conditions [6 , 7] . In parallel to such high diversity and for reasons that remain unknown , a wide range of phylogenetically distant bacteria make use of the same exopolysaccharide to embed themselves inside a biofilm . One example of a “universal” exopolysaccharide is cellulose , composed of β ( 1–4 ) -linked D-glucose units , used by a wide variety of bacteria , including both E . coli and Salmonella [8–10] , as a significant biofilm matrix component . Another example corresponds to a homopolysaccharide composed of N-acetylglucosamine with β ( 1–6 ) glycosidic linkage [11] . Production of this exopolysaccharide was firstly described in Staphylococcus epidermidis and S . aureus where it was referred to as PIA/PNAG [12–14] . Later on , the synthesis of a similar exopolysaccharide was also reported in E . coli where it was named as PGA [15] , and also in Acinetobacter baumannii [16] , Klebsiella pneumoniae [17] , Bordetella bronchiseptica and B . pertussis [18 , 19] , Actinobacillus pleuropneumoniae [20] , Yersinia pestis [21] , Burkholderia species [22] and Bacillus subtilis [23] . In these bacteria , several functions have been ascribed to PGA such as surface attachment , intercellular adhesion , biofilm formation , epithelial cell attachment , and resistance to antibiotics , antimicrobial peptides and human PMNs [16 , 19 , 22 , 24–29] . In E . coli , the production , modification , and export of PGA requires the machinery encoded by the pgaABCD operon [15] . PgaA and PgaB are needed for poly-GlcNAc export and PgaC and PgaD are necessary for poly-GlcNAc synthesis [30–33] . As it generally occurs for bacterial exopolysaccharides , PGA synthesis is allosterically activated by the second messenger bis- ( 3’-5’ ) -cyclic dimeric GMP ( c-di-GMP ) [30 , 34 , 35] . Strikingly , Salmonella lacks the pgaABCD operon and any identifiable genetic loci similar to pga required for PGA synthesis . Here , we pursue the reasons that explain why E . coli and Salmonella differ in their capacity to produce the PGA exopolysaccharide . We provide evidence that production of PGA reduces Salmonella resistance against bile salts and its capacity to survive inside macrophages , completely impairing the infection cycle and rendering Salmonella avirulent . Together , these observations highlight the relevance of gene loss in the adaptation to novel pathogenic niches and define the loss of the PGA exopolysaccharide as a landmark event during Salmonella speciation .
To investigate whether the presence of the pgaABCD operon in Escherichia and its absence in Salmonella is due to a lineage-specific acquisition in Escherichia or to a loss in Salmonella , we performed different comparative and phylogenetic analyses ( see Materials and Methods ) . Analysis of the genomic context of E . coli PgaA protein in the STRING database [36] correctly identified the presence of four genes in the pgaABCD operon as significantly associated using exclusively gene neighborhood and gene co-occurrence information . The gene cluster , often only presenting the three upstream genes , is widespread among Enterobacteriaceae , being present in 22 species of the 83 available in the database . Besides Escherichia , the genera with the cluster include Klebsiella , Pectobacterium , Yersinia , Citrobacter , and Enterobacter , among others . Analyses of the presence/absence of the genes revealed a similar pattern , confirming the absence of the genes in Salmonella species and other genera . Importantly , both analyses revealed a patchy presence/absence pattern , including many recent apparent losses within some genera such as Citrobacter or Escherichia . We then reconstructed individual phylogenies in each of the genes in the cluster by aligning the top 500 hits of a blastP search in NCBI nr database , after setting a filter to exclude sequences assigned to E . coli . All the top hits belonged to related species of Enterobacteriaceae excluding the possibility of recent , independent transfers of the cluster from a non-Enterobacteriaceae species . Maximum likelihood phylogenies of the four genes produced roughly similar topological arrangements of the included taxa ( schematically depicted in Fig 1 ) . We performed a similar analysis with phoH , the gene located in the vicinity of the cluster , encoding a protein with a nucleoside triphosphate hydrolase domain . This gene has a broader distribution , present in 68 of the 83 taxa , a pattern suggesting a vertical inheritance with few independent losses . Importantly , however , for the shared species , the phylogenies of phoH and that of the four genes in the pgaABCD cluster showed an overall similarity ( Fig 1 , S1 Fig and S1 Dataset ) . This indicates that the five genes followed a similar evolutionary history , with the exception of differential loss of genes in alternative lineages . This topology was largely congruent with the species tree for Enterobacteriaceae provided in the PATRIC database , which is based on the analysis of several shared genes [37 , 38] , with the notable exception of the position of Yersinia or Serratia strains . Previous studies have shown that losses are more frequent than lateral transfer in the evolution of prokaryotic genomes [39] , and lateral transfer would generate discordance between gene trees [40] . Hence , our results point to an overall dominance of vertical inheritance and differential gene loss in the evolution of this gene cluster within Enterobacteriaceae . Considering this scenario , the pgaABCD cluster was lost somewhere after the divergence of Salmonella and Citrobacter clades , and previous to the diversification of the currently sequenced Salmonella strains . If Salmonella inability to synthesize PGA is exclusively due to loss of the pgaABCD operon , complementation with pgaABCD should be sufficient to restore PGA production . To test this hypothesis , we transformed a S . Enteritidis wild type strain with plasmid pJET::pga carrying the pgaABCD operon of E . coli MG1655 under the control of its own promoter , and analyzed PGA synthesis upon growth under Salmonella biofilm forming conditions ( incubation in LB broth , at room temperature , without shaking ) using a dot blot assay and an anti-PIA/PNAG antiserum . As expected , PGA was not detected in cell extracts of the wild type strain whereas WT pJET::pga produced PGA and accumulated it throughout the incubation time ( Fig 2A and S2A Fig ) . We next examined if , as it happens in E . coli [30] , PGA production in Salmonella is also dependent on c-di-GMP . To do so , we firstly complemented S . Enteritidis ΔXII with the pgaABCD operon . S . Enteritidis ΔXII is a multiple mutant , derivative of the wild type strain , carrying mutations in all twelve genes encoding GGDEF domain proteins ( putative c-di-GMP synthases ) and thus incapable of synthesizing c-di-GMP [41 , 42] . The dot-blot assay showed that ΔXII pJET::pga was unable to produce PGA , confirming that c-di-GMP is indeed essential for PGA production in Salmonella ( Fig 2A ) . Secondly , we constructed a strain in which the adrA gene of Salmonella , which encodes a c-di-GMP synthase , is under the control of a constitutive promoter . This strain ( WT PcL::adrA ) constitutively produces high levels of c-di-GMP . Upon transformation with pJET::pga , this strain produced higher PGA levels than the wild type strain ( Fig 2A and S2A Fig ) , showing that heterologous PGA synthesis in Salmonella is commensurate to cellular c-di-GMP levels . Finally , and in order to identify the source of c-di-GMP in WT pJET::pga that triggers PGA production , we used a collection of twelve strains , derivatives of ΔXII , each of which contained the chromosomal copy of a single gene encoding a GGDEF domain protein in the original wild type genomic location [41 , 42] . The analysis of cell extracts of each strain complemented with pJET::pga showed that five GGDEF domain proteins , namely AdrA , YedQ , YegE , YfiN and SEN4316 , when individually present in the chromosome of the cell , were able to elicit c-di-GMP dependent PGA synthesis ( S2B Fig ) . Overall , these results showed that heterologous pgaABCD expression is sufficient to restore Salmonella capacity to synthesize PGA and that this synthesis is dependent on c-di-GMP levels that are provided as a pool by different Salmonella c-di-GMP synthases . In staphylococcal cells , production of PGA can be visualized as a ring of cells adhered to the glass wall at the air–liquid interface , when bacteria are incubated in a glass tube under shaking conditions [43] . To investigate whether Salmonella is likewise able to build a PGA mediated biofilm , we analyzed biofilm formation by WT pJET::pga and WT PcL::adrA pJET::pga after incubation in LB broth , at 28°C for 16 hours under shaking conditions . Only the second strain , which produces constitutive and high levels of c-di-GMP , produced a visible ring of bacteria adhered to the glass wall ( Fig 2B ) . Structure of this PGA based biofilm was then compared with the natural cellulose based biofilm formed by Salmonella using scanning electron microscopy ( Fig 2C ) . To do so , we used a cellulose overexpressing strain ( WT PcL::adrA ) , a PGA positive and cellulose minus strain ( ΔbcsA PcL::adrA pJET::pga ) and a control strain that produces neither cellulose nor PGA ( ΔbcsA PcL::adrA ) . In the case of the PGA dependent biofilm , cells were tangled up in an abundant extracellular matrix mesh that interconnected the bacteria . Furthermore , spherical , knob-like structures were evident on the bacterial cell surface . These knob-like structures have already been described in PGA ( PIA/PNAG ) related biofilms of E . coli , Yersinia pestis and Staphylococcus epidermidis [44–46] . On the other hand , bacteria inside a cellulose based biofilm were covered by a sheet-like material [47] that totally encased bacteria and that appeared more compact and structured than the PGA biofilm . To further investigate the differences between both types of biofilms , macrocolony biofilms were grown on LB agar plates ( S3A Fig ) and a water-droplet analysis of colony hydrophobicity was performed [48] . Results showed that a cellulose mediated biofilm is highly hydrophobic , whereas a PGA based biofilm exhibits intermediate hydrophobicity compared with the non-biofilm producing strain , ΔbcsA PcL::adrA ( S3B Fig ) . Collectively , these findings showed that heterologous PGA expression alongside high c-di-GMP levels enable Salmonella to build a PGA mediated biofilm that greatly differs at the structural level from the natural cellulose based biofilm . Biofilm exopolysaccharides provide protection from the external environment . Thus , a consequence of PGA loss might be a reduction in Salmonella resistance to environmental threats , unless another compound assumed such a function . To test this hypothesis , we compared the resistance provided by PGA and cellulose to several environmental stresses . Since it has already been described that cellulose mediates chlorine survival of Salmonella and other bacteria [10 , 49 , 50] , we first analyzed the susceptibility of macrocolony biofilms formed by the cellulose-positive strain ( WT PcL::adrA ) and the PGA-positive cellulose-negative strain ( ΔbcsA PcL::adrA pJET::pga ) to chlorine . The non-biofilm producing strain , ΔbcsA PcL::adrA , was used as a control . A 40 min exposure to sodium hypochlorite ( 200 p . p . m . ) caused a decrease of ~5 . 5 logs in the number of control bacteria , compared to samples treated with only PBS ( Fig 3A ) . Conversely , the same sodium hypochlorite treatment caused a reduction of ~1 log in the number of bacteria inside a cellulose or a PGA based biofilm ( Fig 3A ) . These results determined that the protection against chlorine conferred by PGA is equivalent to that provided by cellulose . Next , we tested the resistance that PGA and cellulose confer to five minutes of UV light irradiation . Although both exopolysaccharide overproducing strains survived better than the control strain that produces neither polysaccharide , the cellulose-positive cells showed a significantly higher survival rate than the PGA-positive cellulose-negative strain ( Fig 3B ) . Thus , under our experimental conditions , cellulose provides better protection against ultraviolet radiation than PGA . Microbial biofilm formation and production of extracellular polymeric substances are generally associated with metal resistance and tolerance [51] . To evaluate the protection conferred by cellulose and PGA on Salmonella against heavy metal stress , we treated macrocolony biofilms with 0 . 5 mM cadmium chloride ( CdCl2 ) . Results indicated that cellulose and PGA confer equal resistance to metal toxicity ( Fig 3C ) . Phages are found in abundance in environmental settings and bacteria have developed sophisticated mechanisms , including biofilm formation , to limit phage reproduction . To address the impact of cellulose and PGA biofilm extracellular matrices on phage infection , we infected bacteria that had been grown on membrane filters under biofilm forming conditions with a P22 phage lysate and analyzed the transduction frequency of a streptomycin resistance cassette . Results showed that , under our experimental conditions , neither exopolysaccharide protected Salmonella from phage infection ( Fig 3D ) . Overall , these findings suggested that PGA provides , at the most , similar benefits to those conferred by cellulose against environmental threats , at least under the conditions tested . Since both polysaccharides seem to have redundant roles in environmental survival , our results support the idea that during speciation the PGA pathway was lost without affecting survival outside the host during the Salmonella cyclic lifestyle . During infection , the ability of Salmonella to survive and replicate in the vacuole within host phagocytic cells is essential for systemic disease [52] . To investigate the consequences of PGA production in Salmonella intramacrophage replication , we tested the ability of a PGA producing strain to replicate in RAW264 . 7 murine macrophages and compared it with that of a cellulose producing strain . To guarantee the synthesis of PGA or cellulose inside macrophages , we created Salmonella strains displaying high c-di-GMP levels inside these cells through the use of the macrophage activated phoP promoter fused to the adrA gene [53] . We firstly constructed WT PphoP::adrA and confirmed that it produced a cellulose based biofilm in response to the low Mg2+ signal activating the phoP promoter ( S4 Fig ) . Then , we engineered ΔbcsA PphoP::adrA PcL::pga , a cellulose mutant that constitutively expresses the PGA synthesis machinery from the chromosome but that synthesizes PGA in a phoP dependent fashion ( S4 Fig ) . As a control , we constructed WT ΔbcsA PphoP::adrA producing neither cellulose nor PGA . The three strains were phagocytosed at similar rates and as it has already been described , the cellulose overproducing strain was defective for replication inside macrophages [53] , showing an ~50% intramacrophage survival relative to the control strain ΔbcsA PphoP::adrA ( Fig 4A ) . Remarkably , the PGA producing strain was significantly more attenuated than the cellulose overproducing strain , showing a 7% intramacrophage survival relative to the control strain ( Fig 4A ) . Salmonella contained within the phagosomal environment encounter a diversity of antimicrobial factors including cationic antimicrobial peptides ( CAMP ) and reactive oxygen species ( ROS ) [54] . To investigate the cause ( s ) behind the low intramacrophage survival phenotype related to PGA production , we firstly performed one-hour polymyxin susceptibility assays [55] of bacterial cells previously grown under low Mg2+ levels , a condition that promotes polymyxin resistance through activation of the PhoP regulon [55 , 56] . The presence of either polysaccharide , cellulose or PGA , did not have an effect on Salmonella polymyxin resistance ( Fig 4B ) . Then , we investigated whether reduced intracellular replication was linked to increased sensitivity to ROS production by assessing the ability to grow in the presence of 1mM H2O2 ( Fig 4C ) . When wild type Salmonella were inoculated into 1 mM peroxide-containing medium at 107 CFU/ml , there was no increase in cell numbers for the first 3 h of incubation , followed by fast recovery [57] . Growth of the cellulose overproducing and control strains were indistinguishable from that of the wild type , whilst the PGA overproducing strain showed a significant viability loss throughout the incubation time ( Fig 4C ) . Taken together , these results indicated that PGA production has a detrimental effect on Salmonella intramacrophage survival and that such survival decrease may be partially explained by the fact that PGA makes Salmonella more sensitive to oxidative stress . Since heterologous expression of PGA makes Salmonella less capable to survive inside macrophages , we hypothesized that PGA production might result in virulence attenuation upon infection by the natural oral route of BALB/c mice , which are susceptible to systemic infection with Salmonella . Taking into account that c-di-GMP is involved in modulating the innate immune response [58 , 59] , we constructed a Salmonella strain that constitutively produced PGA from the chromosome , without altering natural c-di-GMP levels . As expected , levels of PGA production by this strain , WT PcL::pga , were lower than those produced by WT pJET::pga ( S5 Fig ) . Additionally , the bcsA gene was mutated in this strain , resulting in ΔbcsA PcL::pga , which produced PGA but not cellulose . Thus , virulence assays were carried out by comparing the pathogenic behavior of the control strain , ΔbcsA , which produces neither cellulose nor PGA , with that of either the PGA producing strain ΔbcsA PcL::pga or the wild type strain , which produces natural levels of cellulose during infection . These two strains did not show any discernable fitness cost compared to ΔbcsA when grown in LB broth at 37°C ( S6 Fig ) . Firstly , the impact of PGA and cellulose synthesis on the capacity of Salmonella to adhere and invade the intestinal epithelium was analyzed by carrying out a competitive index analysis in an ileal loop coinfection experiment ( Fig 5A ) . Both the wild type and ΔbcsA PcL::pga strains showed reduced capacity to adhere and invade the intestinal epithelium compared with the control strain , ΔbcsA . Secondly , we assessed the level of organ colonization following oral co-inoculation of the control strain , ΔbcsA , and either the wild type or ΔbcsA PcL::pga strain . In the case of mice co-infected with the wild type and ΔbcsA strains , the bacterial burden of the wild type was slightly higher than that of ΔbcsA in all organs analyzed ( livers , spleens and gallbladders ) . Conversely , the PGA producing strain showed to be extremely attenuated , since no ΔbcsA PcL::pga bacteria were recovered from the organs examined after co-infection with the control strain ( Fig 5B ) . To exclude the possibility that the control strain outcompetes the PGA producing strain when coinfection experiments are performed , we next compared the virulence of ΔbcsA and ΔbcsA PcL::pga strains by carrying out single infection experiments . Results confirmed that the PGA producing strain was highly attenuated , since mice inoculated with ΔbcsA PcL::pga did not show any disease symptom and most of them presented bacterial counts under the detection limit in livers , spleens and gallbladders ( Fig 5C ) . It is important to note that in the case of gallbladders , the entire organ was plated and that six out of seven gallbladders from mice inoculated with ΔbcsA PcL::pga were free from infection . Thus , these findings reflected the PGA impact on Salmonella intramacrophage survival and supported the view that heterologous PGA production impairs Salmonella survival in orally infected mice . Bile resistance is indispensable for Salmonella to colonize the hepatobiliary tract during systemic infection and persist in the gall bladder during chronic infection [60 , 61] and again , this characteristic represents a major difference between Enterobacteriaceae species . Thus , to further examine the consequences of PGA production in the Salmonella infection process , we analyzed the ability of Salmonella PGA producing cells to cope with the presence of bile . Dilutions from cultures of the wild type , the PGA producing strain , ΔbcsA PcL::pga , and their corresponding exopolysaccharide minus strain , ΔbcsA , were spread on LB plates supplemented with 24% bile bovine . Exposure to bile caused a decrease of ~3 logs in the number of cfu of both the wild type and ΔbcsA strain , whereas it provoked a reduction of ~5 logs in the case of the PGA producing strain ( Fig 6A ) . Remarkably , PGA production in E . coli was also very detrimental for bile survival , since an E . coli strain producing PGA showed a ~2 . 5 logs reduction in bile sensitivity compared either with the wild type or with a pgaC mutant ( Fig 6B and S7 Fig ) . To determine if the observed bile sensitivity mediated by PGA was common to other membrane active agents , we tested the sensitivity of Salmonella and E . coli PGA producing strains to the anionic detergent SDS . The minimal inhibitory concentration ( MIC ) of SDS for the Salmonella wild type and ΔbcsA strains was 17% , whilst it decreased to 15% in the case of ΔbcsA PcL::pga . On the other hand , the MIC for E . coli MG1655 and ΔpgaC strains was found to be 15% , compared with 7% for the PGA producing strain MG1655 PcL::pga . Altogether , these results indicate that PGA causes a significant reduction in bile resistance both in Salmonella and E . coli and suggest that this negative effect on resistance might be generalizable to other detergents .
Acquisition of new genes is considered to be a mechanism to enhance an organism’s ability to colonize a new environment , resist a specific antimicrobial or evade the immune system [40 , 62] . However , genomic data reveal that gene loss is also a widespread strategy to enhance bacterial fitness [63–68] . There are at least two reasons why bacteria may loss genes during evolution . A gene product or pathway may become superfluous in the new environment . In the absence of purifying selection , the gene accumulates neutral mutations , generating pseudogenes that may be finally removed from the bacterial genome . Alternatively , the product of the gene may be detrimental , triggering selection to optimize bacterial fitness in the new environment . It is well established that Salmonella evolution towards virulence has , at least , involved the acquisition by horizontal gene transfer ( HGT ) of a virulence plasmid and several pathogenicity islands that contain the genes necessary for invasion of intestinal epithelial cells and the systemic phase of infection [69 , 70] . However , the possibility that adaptation of Salmonella to the intracellular environment has occurred through gene loss has rarely been considered [65 , 71] . McClelland et al . proposed that gene deletion has contributed to genome degradation in S . Paratyphi A and Typhi serovars as they specialized to be human restricted variants . Nevertheless , these authors pointed out that the contribution of gene deletion to this evolution is less obvious than that of point mutations ( pseudogenes ) since the existence of a deletion is sometimes hard to determine [72] . Our work provides evidence that acquisition by Salmonella of an arsenal of virulence factors might have been useless in a strain producing PGA , supporting the idea that gene transfer and gene loss are inter-related processes , and that both contribute to the ongoing evolution of pathogenicity [73] . All bacterial species adapted to the mammalian intestine are resistant to the antibacterial activity of bile salts . However , the resistance of Salmonella enterica is especially remarkable . During systemic infection , Salmonella is able to transit from the liver into the gallbladder , where it can either induce inflammation and acute infection or persist chronically , creating a carrier state [74–76] . Several cell components and mechanisms have been related with Salmonella resistance to bile [77 , 78] . On one hand , different efflux pumps transport bile salts outside the cell decreasing their intracellular concentration [79 , 80] . On the other , diverse strategies that involve membrane reorganization and provide barriers to reduce bile salts uptake have been described , such as remodeling the lipopolysaccharide ( both lipid A and O-antigen ) , changing the length of the enterobacterial common antigen and reducing the content of the Braun lipoprotein bound to the peptidoglycan , the levels of muropeptides cross-linked by 3–3 peptide bridges and the amount of porins sensitive to bile [81–86] . Our finding that constitutive production of PGA causes bile sensitivity in S . Enteritidis suggests an alternative strategy: the removal of compounds ( PGA ) that render the bacteria susceptible to bile . How PGA causes this effect is presently unclear . PGA represents an unusual bacterial exopolysaccharide , as some GlcNAc residues become deacetylated by the PgaB protein during secretion , providing a positive net charge to the polymer [15 , 32] . Thus , the presence of PGA may favor the accumulation of anionic bile salts on the bacterial surface . We showed that constitutive expression of PGA also causes bile sensitivity in E . coli . These results raise the broader question of why E . coli , which displays a fair level of bile resistance necessary to grow in the small intestine , still produces PGA . Bile salts are maintained at high concentrations in the duodenum , jejunum , and proximal ileum . In the distal ileum , bile salts are absorbed into the blood-stream , and the majority of bile is recycled back into the small intestine and does not enter the colon [78] . E . coli resides in the microbiota found in the cecum and colon of humans . Thus , the presence of PGA might be compatible with the bile concentration in the small intestine and not with the concentration in the gallbladder . Alternatively , it is also possible that E . coli has developed regulatory systems to prevent PGA expression in the small intestine . The second step of the infection process that is negatively affected by the presence of PGA is the survival and replication in the vacuole within host phagocytic cells . During systemic infection , Salmonella survives and replicates in vacuoles within host phagocytic cells where it must overcome the reactive oxygen species produced by macrophages [87] . It has been reported that Salmonella needs to repress cellulose production inside the vacuole through the activation of MgtC , which prevents a rise in c-di-GMP [53] . Increased levels of cellulose interfere with replication inside the vacuole and impair virulence in mice . The mechanisms underlying the antivirulence trait of cellulose has not been determined . We have now found that PGA production also hinders Salmonella division inside macrophages . Regarding this phenotype , we showed that production of PGA increases the susceptibility to H2O2 treatment , thus providing a potential mechanism for this attenuation . The notion that PGA is detrimental during infection of mammal cells is supported by studies with Y . pestis [28] . Y . pestis forms PGA mediated biofilms below 30°C in the blood-feeding fleas favoring the transmission and invasiveness of the bacteria from fleas to mammals [88] . However , PGA production has to be inhibited in the mammal host over 30°C to allow the development of a lethal infection . This temperature dependent regulation of PGA depends on the tight regulation of the c-di-GMP secondary messenger . Salmonella is an ubiquitous bacterium with a dual intracellular/extracellular lifestyle . Its extracellular life involves survival in the environment , a scenario in which exopolysaccharide-mediated biofilms play an important role , protecting bacteria against environmental threats . Our results indicate that PGA loss provides a fitness advantage when Salmonella colonizes the liver , gallbladder or resides inside the macrophages . However , loss of PGA might have negative consequences for survival in the environment unless another compound off the cell wall was able to compensate for PGA absence . Comparative phenotypic analysis between the protection conferred by PGA and cellulose against environmental threats revealed that PGA confers at the most similar benefits than cellulose , indicating that cellulose is sufficient to provide Salmonella with protection against environmental stresses and compensate for the loss of PGA function . Our findings provide a plausible explanation for PGA loss from the Salmonella genome during evolution . They also enhance our understanding of the benefits and burdens of a widely used exopolysaccharide to form the bacterial biofilm matrix , highlighting the necessity of additional studies to depict the exact role of PGA at each step of the life cycle . Finally , our study may also encourage microbiologists to turn more attention towards gene loss research as an approach to obtain information about how pathogenic bacteria have adapted to the host .
Protein sequences from E . coli PgaABCD and PhoH were used in a Blastp search against the NCBI non-redundant database accessed in July 2016 , using an e-value threshold of 10−5 and excluding from the results hits taxonomically assigned to E . coli . The sequences from the top 500 hits were retrieved for each search and aligned using MUSCLE v 3 . 8 [89] and then trimmed using trimAl v1 . 4 [90] ( gap-score cut-off 0 . 9 ) . A Maximum Likelihood phylogenetic reconstruction was performed using phyML v3 . 0 [91] with the JTT model , setting the number of rate categories to four , and inferring the number of invariant positions and the parameters of the gamma distribution from the data . Branch support was computed using an aLRT ( approximate likelihood ratio test ) based on a chi-square distribution . Animal studies were performed in accordance with the European Community guiding in the care and use of animals ( Directive 2010/63/EU ) . Protocols were approved by the ethics committee of the Public University of Navarra ( Comité de Ética , Experimentación Animal y Bioseguridad of the Universidad Pública de Navarra ) ( approved protocol PI-004/11 ) . Work was carried out in the animal facility of the Instituto de Agrobiotecnología , Universidad Pública de Navarra . Animals were housed under controlled environmental conditions with food and water ad libitum . Mice were euthanized by CO2 inhalation followed by cervical dislocation and all efforts were made to minimize suffering . The strains and plasmids used in this work are described in S1 Table . Escherichia coli and S . enterica subsp . enterica serovar Enteritidis ( S . Enteritidis ) cells were grown in LB broth and on LB agar ( Pronadisa ) with appropriate antibiotics at the following concentrations: kanamycin ( Km ) , 50 μg ml-1; ampicillin ( Am ) , 100 μg ml-1; carbenicillin ( Cb ) , 50 μg ml-1; chloramphenicol ( Cm ) , 20 μg ml-1; and streptomycin ( Sm ) 500 μg ml-1 . Routine DNA manipulations were performed using standard procedures unless otherwise indicated . Plasmid DNA from E . coli was purified using a Quantum Prep plasmid kit ( BioRad ) . Plasmids were transformed into E . coli and S . Enteritidis by electroporation . Transformants carrying Red helper plasmids were made electro-competent as described [10 , 92] . Restriction enzymes were purchased from ThermoFisher Scientific and used according to the manufacturer’s instructions . Oligonucleotides were synthesized by StabVida ( Caparica—Portugal ) and are listed in S2 Table . Phage P22 HT105/1 int-201 [93] was used to carry out transductions between strains according to recommended protocols [94] . S . Enteritidis 3934 ΔXII is a multiple mutant carrying mutations in all genes encoding GGDEF domain proteins [42] . Derivatives of ΔXII containing the following single GGDEF protein encoding gene , namely adrA , yeaJ , sen1023 , yciR , yegE , yfiN , yhdA , sen3222 , and yhjK were constructed as described [41] . In the case of ΔXII+sen2484 , ΔXII+yfeA and ΔXII+sen4316 strains , DNA fragments corresponding to the coding sequences of sen2484 , yfeA and sen4316 genes were amplified with primer pairs A and D and chromosomal DNA from S . Enteritidis 3934 as a template . Amplified fragments were sequenced and cloned into the pKO3blue plasmid that was electroporated into ΔXII . Integration and excision of the plasmid was performed as described [41] in order to obtain the corresponding restored strains . To express adrA under the PcL constitutive promoter in S . Enteritidis 3934 , a PCR generated linear DNA fragment was used as described [95] with some modifications . The Red helper plasmid pKD46 was transformed into S . Enteritidis 3934 , and transformants were selected on LB agar Am after incubation at 30°C for 24 h . One transformant carrying pKD46 was made electrocompetent as described [10] . A DNA fragment containing a kanamycin resistance gene , the PcL promoter and the RBS sequence of the PcL cassette was generated by PCR using primers adrA Km PcL rbs Fw and adra Km PcL rbs Rv and chromosomal DNA from strain MG1655 Km PcL-λATT-GFP as template [96] . Electroporation ( 25 mF , 200 W , 2 . 5kV ) was carried out according to the manufacturer’s instructions ( Bio-Rad ) using 50 μl of cells and 1 to 5 μg of purified and dialysed ( 0 . 025 μm nitrocellulose filters; Millipore ) PCR product . Shocked cells were added to 1 ml of LB broth , incubated for 1 h at 28°C and then spread on LB Km agar to select KmR transformants after incubation at 37°C for 24 h . Transformants were then grown on LB Km broth at 44°C for 24 h and incubated overnight on LB Am agar at 28°C to test for loss of the helper plasmid . To place the adrA gene under the control of the phoP promoter , a protocol described previously was carried out with some modifications [92] . In a first step , primers Km SceI PphoP adrA Fw and Km SceI PphoP adrA Rv , with 60-bp homology extensions , were used to amplify a kanamycin resistance cassette and an I-SceI recognition site from plasmid pWRG717 . This DNA was integrated upstream the adrA gene via λ Red-mediated recombination using plasmid pWRG730 , a temperature-sensitive plasmid for independent inducible expression of the λ Red recombinase and I-SceI endonuclease . After confirming proper insertion of the resistance cassette by colony PCR with primers 01-E and Km SceI PphoP adrA Rv , a DNA fragment generated by PCR and derived from oligonucleotides PphoP adrA Fw and PphoP adrA Rv and S . Enteritidis 3934 chromosomal DNA as template , was electroporated into the mutant strain still containing the pWRG730 plasmid . This DNA fragment included the phoP promoter and homology regions used for its upstream adrA integration . After 1 h of incubation at 28°C , 100 μl of a 10−2 dilution was plated on LB agar plates containing 500 ng ml-1 anhydrotetracycline , which induced expression of I-SceI endonuclease . After overnight incubation at 28°C , single colonies were purified and successful recombination was checked by monitoring absence of antibiotic resistance , colony PCR with oligonucleotides 01-E and PphoP adrA Rv , and sequencing of the resulting fragment . Finally , pWRG730 was cured by incubating selected colonies at 44°C . To insert the pgaABCD genes from E . coli K-12 MG1655 into the S . Enteritidis 3934 chromosome , the T64B prophage site was chosen [97] . Two DNA fragments , sb13 AB and sb13 CD , of ∼500 bp length of the S . Enteritidis sb13 gene , were amplified with primer pairs SmaI sb13 AB Fw/SphI sb13 AB Rv and SphI sb13 CD Fw/SalI sb13 CD Rv , respectively . The PCR products were cloned into the pJET 1 . 2 vector ( ThermoFisher Scientific ) and resulting plasmids were digested with SmaI and SphI enzymes in the case of the AB fragment and SphI and SalI enzymes in the case of the CD fragment . AB and CD fragments were ligated in the same ligation mixture with the pKO3 vector [98] digested with SmaI and SalI enzymes , resulting in plasmid pKO3::sb13AD . The pJET::pga plasmid constructed in this study was digested with SphI to obtain a DNA fragment containing the pga promoter and pgaABCD genes . Ppga::pgaABCD was ligated with pKO3::sb13AD digested with SphI , resulting in pKO3::sb13AD-Ppga::pgaABCD plasmid . Integration and excision of the plasmid was used as described [98] to obtain WT Ppga::pgaABCD strain . Insertion of Ppga::pgaABCD into the sb13 gene was confirmed by PCR using primers sb13 OK Fw and pgaA comp Rv . The ability of this strain to produce PGA was not detectable by Dot Blot , probably because heterologous chromosomal expression of the pgaABCD operon under its own promoter was not sufficient to produce evident PGA levels . Thus , a second Salmonella strain was generated in order to express pgaABCD under the PcL constitutive promoter and in the chromosome . To do so , a 427 bp DNA fragment , namely sb13 AB2 , of the S . Enteritidis sb13 gene was amplified with primers BglII sb13 AB Fw and BamHI sb13 AB Rv , using S . Enteritidis 3934 chromosomal DNA as template , and cloned into the pJET 1 . 2 vector ( ThermoFisher Scientific ) . A second DNA fragment containing the PcLrbs promoter [96] and the first 543 bp of the pgaA gene coding sequence was constructed by overlapping PCR , using two separate PCR products . Primers BamHI PcLrbs Fw and sb13 PcLpga Rv were used to amplify the PcLrbs promoter , using E . coli MG1655 Km PcL-λATT-GFP chromosomal DNA as template [96] . Primers PcL pgaA Fw and PstI PcL pgaA Rv were used to amplify 543 bp of the pgaA gene , using E . coli MG1655 chromosomal DNA as template . These two purified PCR products were mixed , and a second PCR using BamHI PcL rbs Fw and PstI PcL pgaA Rv primers was performed to obtain a single DNA fragment , PcLrbs::pgaA , that was cloned into the pJET 1 . 2 vector ( ThermoFisher Scientific ) . Plasmids pJET::sb13AB2 and pJET:: PcLrbs::pgaA were digested with BglII /BamHI and BamHI/PstI enzymes , respectively , and digestion products were ligated in the same ligation mixture with the pKO3Blue vector [41] digested with BglII and PstI enzymes , resulting in plasmid pKO3Blue::sb13AB2-PcLrbs::pgaA that was electroporated in WT Ppga::pgaABCD strain . Integration and excision of the plasmid was used as described [41] to generate Wt PcLrbs::pgaABCD . Insertion of PcLrbs::pgaABCD into the sb13 gene was confirmed by PCR using primers sb13 OK Fw and sb13 PcL pgaA Rv . Finally , a bcsA mutation was transduced from ΔbcsA strain to generate ΔbcsA::CmR PcLrbs::pgaABCD , which is hereafter abbreviated as ΔbcsA PcL::pga . To express the pgaABCD operon under the PcL promoter in E . coli MG1655 , a PCR generated linear DNA fragment and the Red helper plasmid pKD46 were used as described above . Primers used to generate the DNA fragment containing a kanamycin resistance gene , the PcL promoter and the RBS sequence of the PcL cassette were Km PcL rbs pga Fw and Km Pcl rbs pga Rv . To delete a 500 bp fragment of the pgaC gene in E . coli MG1655 , and as a consequence suppress PGA production in E . coli [15] , a protocol described previously was carried out with some modifications [92] . First , primers pgaC Km SceI Fw and pgaC Km SceI Rv , with 60-bp homology extensions , were used to amplify a kanamycin resistance cassette and an I-SceI recognition site from plasmid pWRG717 . This DNA was integrated in the pgaC gene using plasmid pWRG730 plasmid and integration was confirmed by colony PCR with primers pgaC Km SceI Fw and pgaD Rv . Phosphorylated 80-mer double-stranded DNA derived from oligonucleotides ΔpgaC Fw and ΔpgaC Rv was electroporated into the mutant strain still containing the pWRG730 plasmid . After 1 h of incubation at 28°C , 100 μl of a 10−2 dilution was plated on LB agar plates containing 500 ng ml-1 anhydrotetracycline , which induced expression of I-SceI endonuclease . After overnight incubation at 28°C , single colonies were purified , and successful recombination was checked by monitoring absence of antibiotic resistance and colony PCR with oligonucleotides ΔpgaC Fw and pgaD Rv . Finally , pWRG730 was cured by incubating selected colonies at 44°C . PGA exopolysaccharide levels were quantified as previously described [14] with minor modifications . Briefly , cultures in 5 ml LB or LB Cb broth of the strains tested were adjusted to the same number of cells and centrifuged at 18 , 000 x g for 5 min . Pellets were resuspended in 50 μl of 0 . 5 M EDTA ( pH 8 . 0 ) and suspensions were incubated for 5 min at 100°C and centrifuged at 18 , 000 x g for 5 min . Each supernatant ( 40 μl ) was incubated with 10 μl of proteinase K ( 20 mg ml-1 ) ( Sigma ) for 30 min at 37°C . After the addition of 10 μl of Tris-buffered saline ( 20 mM Tris-HCl , 150 mM NaCl [pH 7 . 4] ) containing 0 . 01% bromophenol blue , 5 μl were spotted on a nitrocellulose membrane using a Bio-Dot microfiltration apparatus ( Bio-Rad ) . The membrane was blocked overnight with 5% skimmed milk in phosphate-buffered saline ( PBS ) with 0 . 1% Tween 20 , and incubated for 2 h with specific anti-PNAG antibodies diluted 1:10 , 000 [25] . Bound antibodies were detected with peroxidase-conjugated goat anti-rabbit immunoglobulin G antibodies ( Jackson ImmunoResearch Laboratories , Inc . , West- grove , PA ) diluted 1:10 , 000 and developed using the SuperSignal West Pico Chemiluminescent Substrate ( ThermoFisher Scientific ) . All extracts assayed in a particular experiment were analyzed on the same membrane . Images obtained in a GBox Chemi HR16 system ( Syngene ) were cut and put together to assemble horizontal figures showing PGA quantification . The cellulose mediated biofilm formed in glass tubes on standing rich cultures was examined visually after growth in 5 ml of LB broth at room temperature for 72 h [10] . The PGA mediated biofilm was visualized after growth in LB broth at 28°C in an orbital shaker ( 250 r . p . m ) for 16 h [43] . Macrocolony biofilms on the surface of LB agar plates were formed after spotting 50 μl drops of overnight liquid cultures and incubating at 28°C for 48 hours [99] . For scanning electron microscopy bacterial strains were grown under biofilm forming conditions . Growth medium was removed and bacterial cells were fixed by adding a fixation solution ( 1 . 3% glutaraldehyde , 0 . 07M cacodylate buffer and 0 . 05% rhutenium red ) . Samples were then washed in and post-fixed by incubation with 2% osmium tetroxide for 1 h . Bacteria were then fully dehydrated in a graded series of ethanol solutions and dried in hexamethyldisilazane ( HMDS , Sigma ) . Finally , samples were coated with 40 Å platinum , using a GATAN PECS 682 apparatus ( Pleasanton , CA ) , before observation under a Zeiss Ultra plus FEG-SEM scanning electron microscope ( Oberkochen , Germany ) ( Laboratoire de Biologie Cellulaire et Microscopie Electronique , UFR Médecine ( Tours , France ) ) . Macrocolony biofilms were formed on the surface of LB or LB Cb agar plates as described above , and a 10 μl water droplet stained with red food colouring was placed on the biofilm to show the hydrophobicity exhibited by the structure [48] . To perform sodium hypochlorite survival analyses , a protocol described previously was carried out with some modifications [10] . Macrocolony biofilms were formed on sterile polymer membrane filters ( diameter 47 mm; Millipore ) resting on LB agar or LB agar Cb media for 48h at 28°C . Filters were then transferred to an empty petri dish and macrocolonies were treated with 10 ml PBS containing 200 p . p . m . sodium hypochlorite for 40 min at 37°C . Control samples were incubated with 10 ml of PBS . Macrocolonies were harvested with a bent tip and bacteria were washed in PBS three times and suspended in 5 ml of PBS . After vortexing and sonicating ( 30 sec; potency 3; Branson sonifier 250; microtip ) , bacteria were enumerated by viable plate counts . Bacterial strains were grown in LB Cb broth at 28°C in an orbital shaker ( 200 r . p . m ) for 16 h . After sonication ( 30 sec; potency 3; Branson sonifier 250; microtip ) , the OD600nm was adjusted to 1 and serial dilutions were plated on four plates of N media agar supplemented with Cb [53] . After 24h of growth at 28°C , two plates were irradiated with UV light for 5 min . All plates were then incubated at 28°C for 48h and the numbers of surviving bacteria were counted . Results are shown as % survival relative to non-irradiated samples . Experiments were conducted in triplicate . In order to incubate all strains on the same plates , strains ΔbcsA PcL::adrA and WT PcL::adrA were transformed with a pJET empty plasmid . Macrocolony biofilms on sterile polymer membrane filters ( diameter 47 mm; Millipore ) resting on LB agar or LB agar Cb media were formed after spotting 5 μl drops of overnight liquid cultures and incubating at 28°C for 48 hours . Filters were then transferred to an empty petri dish and macrocolonies were treated with 10 ml of 0 . 5 mM CdCl2 for 3 h at 28°C . Control samples were incubated with 10 ml of PBS . Macrocolonies were harvested with a bent tip and bacteria were washed in water three times and suspended in 5 ml of PBS . After vortexing and sonicating ( 30 sec; potency 3; Branson sonifier 250; microtip ) , bacteria were enumerated by viable plate counts . Overnight cultures in LB or LB Cb broth were sonicated ( 30 sec; potency of 3; Branson sonifier 250; microtip ) and the OD600nm was adjusted to 1 . Sterile polymer membrane filters ( diameter 47 mm; Millipore ) were placed on LB or LB Cb agar plates and seeded with a 50 μl drop of each bacterial suspension . Plates were inverted and incubated at 28°C for 48 h to allow macrocolony biofilm formation on top of the filters , that were then transferred to an empty petri dish and treated with a P22 phage lysate generated from the streptomycin resistant strain S . Typhimurium SL1344 . After 1h of incubation at 37°C , the entire content of the plates was collected , washed in PBS and plated on LB Sm agar . The number of streptomycin resistant cfu were indicative of transduction efficiency . Experiments were conducted in triplicate . Macrophage survival assay was conducted essentially as described [53] with some modifications . The murine macrophage cell line RAW 264 . 7 was propagated in Dulbecco’s modified Eagle’s medium ( DMEM ) ( Gibco ) supplemented with 10% fetal bovine serum ( Invitrogen ) and 1% Penicillin/Streptomycin/Glutamine ( Gibco ) . Macrophages were seeded at a density of 2 x 105 cells per well in 24-well plates 24 h prior to infection . Salmonella overnight cultures grown at 37°C in LB broth were sonicated ( 30 sec; potency 3; Branson sonifier 250; microtip ) and diluted 1:100 in LB broth . Cultures were incubated at 37°C in an orbital shaker ( 200 r . p . m ) to an OD600nm of 1 and the suspension was sonicated again and washed twice with DMEM deprived of serum . Macrophages were then infected with Salmonella strains at a multiplicity of infection of approximately 10:1 and plates were centrifuged at 1000 r . p . m . for 10 minutes at room temperature . After 20 min of phagocytosis , monolayers were washed twice with PBS and treated with gentamicin ( 100 μg ml-1 ) for 1 h . To estimate phagocytosed bacteria , samples were then washed three times with sterile PBS and macrophages were lyzed with 1% ( vol/vol ) Triton X-100-PBS to release intracellular bacteria that were counted by plating 25 μl of serial dilutions onto LB plates . To assess bacterial survival , medium was replaced by DMEM supplemented with 10% FBS and 12 μg ml-1 gentamycin and the cells were incubated at 37°C . After 18 h of infection , wells were washed twice with PBS and were treated with Triton X-100 as indicated above . The percentage survival was obtained by dividing the number of bacteria recovered after 18 h by the number of phagocytosed bacteria and multiplying by 100 . At each stage when infected cells were lysed , the number of viable cells in duplicate monolayers infected with each strain was assessed by 0 . 4% trypan blue exclusion and counting viable cells . No difference in viability was noted between cells infected with the different strains . Experiments were done in triplicate on three independent occasions . One-hour polymyxin susceptibility assays were performed as described [55] . Polymyxin B Sulfate ( Sigma ) was used at a final concentration of 2 . 5 μg ml-1 . Data are presented as survival percentage relative to samples incubated in LB without polymyxin . Experiments were conducted in triplicate . Sensitivity to hydrogen peroxide was tested as previously described [57] with minor modifications . Briefly , overnight cultures were subcultured at 1/100 in 5 ml LB containing either no or 1 mM H2O2 ( Merk ) . Replica cultures were used for each time point . Cultures were grown at 37°C with aeration and collected hourly . LB broth contains ∼30–40 μM Mg2+ [101] , which activates the phoP promoter , thus , leading to adrA expression . After sonication ( 30 sec; potency 3; Branson sonifier 250; microtip ) the number of surviving bacteria were counted by plating serial dilutions onto LB plates . Experiments were performed on three separate occasions . In order to differentiate strains in all mice competitive infections performed , the wild type and ΔbcsA PcL::pga strains were made streptomycin ( Sm ) resistant through P22 phage transduction of the aadA gene from the natural streptomycin resistant strain S . Typhimurium SL1344 [102] . To compare the in vivo interaction of Salmonella strains with murine intestinal epithelial cells , the ligated ileal loop co-infection model was used as described previously [10 , 100] . Strains were incubated on LB agar for 48 hours at room temperature , suspended in PBS and sonicated ( 30 sec; potency 3; Branson sonifier 250; microtip ) prior to infection . Competitive index ( CI ) was defined as the log10 of the ratio of the exopolysaccharide producing strain to control strain recovered ( Output ) divided by the ratio of the exopolysaccharide producing strain to control strain present in the inoculum ( Input ) . A CI > 0 indicates the exopolysaccharide producing strain with a colonization advantage compared to the control and a CI < 0 indicates the exopolysaccharide producing strain with a colonization disadvantage over the control . Colonization experiments were carried out with 8-week-old female BALB/c mice ( Charles River Laboratories ) . Mice were acclimated for 7 days after arrival before the experiments were started in the animal facility of the Instituto de Agrobiotecnología , Universidad Pública de Navarra . Food and water were removed , twelve and two hours respectively , before the administration of bacterial suspension . Mice were prefed with 20 μl of 10% sodium bicarbonate 30 min before bacterial inoculation . Water and food were again supplied right after inoculation . Strains were incubated on LB agar for 48 hours at room temperature , suspended in PBS and sonicated ( 30 sec; potency 3; Branson sonifier 250; microtip ) prior to infection . Mice were inoculated intragastrically with 100 μl of bacterial suspensions . In the case of coinfection experiments , the total bacteria inoculum was 2 x 108 cfu of combined polysaccharide producing strain and ΔbcsA strain at a ratio of 1:1 . In the case of individual infections , inoculum was 1 x 108 cfu of the strain analysed . The cfu of each strain in the inoculum ( input ) were quantified by plating dilution series on LB agar supplemented with chloramphenicol and LB agar supplemented with streptomycin to distinguish between strains . Over the course of infection , mice were examined twice per day and a final disease score was given to each mouse according to clinical signs observed as follows . No clinical signs ( 0 ) ; mild clinical signs: ruffled fur ( 1 ) ; moderate clinical signs: ruffled fur plus , lethargy , hunched posture and decreased activity ( 2 ) ; severe clinical signs: paresis , paralysis , tremor , shivers , ataxia , rigidity ( 3 ) . When evident signs of disease ( score 2 to 3 ) were observed , mice were euthanized by CO2 inhalation followed by cervical dislocation . Then , dilution series of liver , spleen and gallbladder lysates were plated on LB agar for enumeration of cfu ( output ) , using antibiotic resistance to differentiate strains . Values for CI were calculated as described above . Bile bovine sensitivity assay was performed as described [82] with minor modifications . Bacterial strains were grown in LB broth at 28°C for 48 h in an orbital shaker ( 200 r . p . m ) . Two microliter portions of serial dilutions were incubated for 24 h at 37°C in LB agar plates containing either no or bile bovine ( Sigma ) . The bile concentration used was 24% or 13% when assessing Salmonella and E . coli bile sensitivity , respectively . To carry out the SDS MIC analysis , bacterial strains were grown in LB broth at 28°C for 48 h in an orbital shaker ( 200 r . p . m ) and diluted such that samples of 2x103 CFU/ml were subjected to various concentrations of SDS in polypropylene microtiter plates ( ThermoFisher Scientific ) . The plates were incubated overnight at 37°C under nonaerated conditions and the wells of the plate were visually analyzed to determine the MICs . All statistical analyses were performed in GraphPad Prism 5 . 01 . Sodium hypochlorite survival , UV light irradiation data , heavy metals resistance , susceptibility of biofilms to phage infection , macrophage survival and Polymyxin B resistance analyses were analysed by the Mann-Whitney U test . A two-way analysis of variance combined with the Bonferroni test was used to analyse statistical significance in hydrogen peroxide sensitivity assays . A nonparametric Mann-Whitney U test and an unpaired Student’s t test were used to assess significant differences in individual colonization or coinfection experiments , respectively . | During bacterial evolution , specific traits that optimize the organism’s fitness are selected . The production of exopolysaccharides is widespread among bacteria in which they play a protective shielding role as main constituents of biofilms . In contrast to closely related siblings , Salmonella has lost the capacity to produce the exopolysaccharide PGA . Our study reveals that Salmonella lost pga genes , and that the driving force for such a loss may have been the detrimental impact that PGA has during Salmonella invasion of internal organs where it augments the susceptibility to bile salts and oxygen radicals , reducing bacterial survival inside macrophages and rendering Salmonella avirulent . These results suggest that gene-loss has played an important role during Salmonella evolution . | [
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"salmon... | 2017 | Lack of the PGA exopolysaccharide in Salmonella as an adaptive trait for survival in the host |
A main determinant of prolonged Trypanosoma brucei infection and transmission and success of the parasite is the interplay between host acquired immunity and antigenic variation of the parasite variant surface glycoprotein ( VSG ) coat . About 0 . 1% of trypanosome divisions produce a switch to a different VSG through differential expression of an archive of hundreds of silent VSG genes and pseudogenes , but the patterns and extent of the trypanosome diversity phenotype , particularly in chronic infection , are unclear . We applied longitudinal VSG cDNA sequencing to estimate variant richness and test whether pseudogenes contribute to antigenic variation . We show that individual growth peaks can contain at least 15 distinct variants , are estimated computationally to comprise many more , and that antigenically distinct ‘mosaic’ VSGs arise from segmental gene conversion between donor VSG genes or pseudogenes . The potential for trypanosome antigenic variation is probably much greater than VSG archive size; mosaic VSGs are core to antigenic variation and chronic infection .
A survival strategy common to many bacterial , viral and eukaryotic pathogens , comprising the most rapidly evolving arms race between pathogen and host , is antigenic variation [1] . Over the course of infection , the host mounts specific immune responses against a major pathogen surface antigen , but these are unable to eradicate the entire pathogen population , as some individuals have already switched to express different variants of the antigen . As the survivors proliferate , the process reiterates , resulting in chronic infection that favours transmission . Antigenic variation is powered by diversity in expressed antigens across the pathogen population over the course of infection , and reinfection of partially-immune or already-infected hosts , commonplace in many field situations , is also favoured by expressed antigen diversity [2] . African trypanosomes—parasites of humans and animals in sub-Saharan Africa—have perhaps the most comprehensive system of antigenic variation described [3] . Bloodstream trypanosomes are enshrouded in a dense , highly immunogenic coat of variant surface glycoprotein ( VSG ) homodimers that conceals invariant surface molecules of the parasite and is the major target of the host immune response [4] . Each VSG monomer consists of a membrane-proximal C-terminal domain ( CTD ) that is inaccessible to antibodies [5] , and an exposed N-terminal domain ( NTD ) that contains the biologically relevant epitopes [6] . Only one VSG is transcribed at a time , but spontaneously , and at high frequency ( 0 . 1–1% switch/parasite/generation ) , the expressed VSG is changed , usually through its replacement with a different VSG ‘donor’ gene via gene conversion [7] . The Trypanosoma brucei genome accommodates an archive of thousands of VSGs , located mainly in subtelomeric arrays on conventional chromosomes [8] and in the subtelomeres of a pool of approximately 100 minichromosomes [9] . The archive of the T . brucei reference strain ( TREU927/4 ) is well annotated , but is likely to remain somewhat incomplete , due to poor coverage of the minichromosomes and the fact that often only one of each pair of homologous chromosomes is represented . Bringing the genomically encoded diversity present in the archive to bear on a host would favour prolonged infection [10] . However , most annotated archive genes are pseudogenic , with only an estimated 5% of the array VSGs predicted to be fully functional [11] . Furthermore , infections tend to be dominated by the non-switching , quiescent , ‘stumpy’ trypanosome transmission form , which could further limit expressed antigenic diversity [12] . Many pathogens , including Anaplasma spp . [13] , Borrelia burgdorferi [14] , Neisseria gonorrhoeae [15] , Treponema pallidum [16] , Mycoplasma spp . [17] and Babesia bovis [18] , undergo a process of segmental gene conversion ( SGC ) that introduces variation in the expressed antigen . In this process , conversion occurs within the open reading frame , producing a gene that contains segments from two or more donors . By varying only the immunodominant region of an antigen , SGC can make efficient use of a small genome , and can potentially generate vast combinatorial diversity from a limited ‘archive’ of antigen genes [19] . VSGs can also undergo SGC . In its simplest form , VSG SGC replaces just the NTD-encoding part of the gene , retaining all or part of the previously expressed CTD-encoding region [20] , [21] . In other cases , SGC occurs throughout the VSG , producing ‘mosaic’ genes [22]–[24] , which tend not to appear early in infection and may be selected by immune responses as infections progress [25] . It has been hypothesized that ‘strings’ of related mosaic VSGs , produced stochastically by the accumulation of SGC events in a sublineage , could produce novel variants , facilitating both prolonged infection and superinfection of partially-immune hosts [26] . However , as most previous work has focussed on the early stage of infection , the patterns , extent and function of VSG expression in the chronic stages are still unclear . How is VSG switching mediated in chronic infection , what is the extent of expressed antigenic diversity , and to what degree does mosaicism contribute to the diversity phenotype ? We have sequenced and analysed hundreds of cDNAs harvested longitudinally from 11 chronic infections to identify the prevalence and patterns of mosaicism , and have subjected a ‘string’ of expressed mosaics to serological analyses . Our results show far greater richness in VSG expression than previously thought , and demonstrate that mosaicism is a major contributor to chronic infection .
To follow changes in VSG expression , RNA was purified from blood samples collected longitudinally from 11 mice infected with T . brucei TREU927/4 GUTat 10 . 1 . VSG sequences were retrieved by VSG-specific cDNA amplification , cloning and sequencing , rather than via next-generation RNA sequencing , the short read-lengths of which would have complicated unambiguous assembly , especially in a background of expression of related VSG . In total , 756 full-length and 8 partial VSG sequences were obtained , and each sequence was assigned a three-part name XX-YYcZZ , where XX was the infection number , YY was the sampling time in days , and ZZ was a numerical identifier . These data were supplemented with data obtained from similar infections [11] , to give 801 sequences . Putative donor genes were identified by comparing sequences with a database of genomic VSG sequences ( based on www . vsgdb . net , [27] , see Materials and Methods ) using BLAST [28] . SGC was inferred when two or more donors appeared to contribute to the expressed VSG sequence in a segmental fashion , and no other sequences were a more parsimonious match . An example is given in Figure 1A . Expressed VSG sequences were also compared with one another . Based on similarities between NTD-encoding regions , the 801 sequences grouped into 93 distinct ‘sets’ , each of which was likely to have been founded on a particular primary donor , or group of donors . SGC within a set was inferred when set members were >2 . 5% divergent from one another in a nucleotide alignment , differences were grouped in one or more clusters ( five or more differences over 30 nt ) , and distinct clusters of differences were observed in different clones . Donors contributing to a set were given a shorthand name xx-y , where xx was the set number , and y a single letter identifier A–D ( Table S1 ) . Donor sequences combined in various ways , generating an additional layer of diversity amongst expressed VSGs , as can be seen in Figures 1B and 1C . SGC occurred in two broad patterns: ‘3′ donation’ , in which variation from the primary donor occurred in the predicted CTD-encoding region and utilized donors with low overall identity [20] , [21] , and ‘mosaicism’ , which occurred in the NTD and/or CTD-encoding regions of the VSG and utilized highly sequence-related donors [11] , [22]–[24] . Mosaicism and 3′ donation were often detected in the same clone sequence . Comparison with donors identified mosaicism in 187/629 ( 30% ) unique sequences , and identified 3′ donation in 358/629 ( 57% ) unique sequences . The extent of 3′ donation varied , and in 90 cases ( 25% of all 3′ donations ) the boundary of conversion occurred merely in the region predicted to encode the GPI-anchor signal sequence . For these analyses , 172 sequences were removed as they were incomplete or duplicates of other sequences from the same sample . Comparison between clone sequences identified patterns of variation corresponding with mosaicism in 24/93 sets ( 26% ) , and variation at the 3′ end in 32/93 sets ( 34% ) . Two possible sources of error were that inferred SGC events occurred artifactually by template switching during in vitro amplification by RT-PCR , or that inferred SGC events represented the straightforward expression of unannotated VSGs in the genome . Two experimental approaches were taken to test these possibilities . First , pairs of primers were designed to bind specifically to one donor or the other , either side of a sequenced SGC event , and PCR reactions using different combinations of these primers were applied to genomic DNA samples , including samples obtained from primary clonal infections . PCR using primers that were both directed against the same donor were able to amplify product from pre-infection , early and terminal genomic DNA ( gDNA ) samples , but PCR using primers that were directed against different donors—i . e . corresponding with the SGC event—were able to amplify product only from terminal gDNA samples ( Figure 2A and 2C ) . These results are consistent with the SGC event appearing in the gDNA of the parasite population over the course of infection . Second , to test whether inferred SGC could be better explained by unannotated VSG present in the genome at the start of infection , restriction endonuclease-digested pre-infection gDNA was analysed by Southern hybridization with a probe corresponding to a donor for the SGC event in question . Identified genomic copies could account for all detected hybridization events ( Figure 2B and 2D ) . Six examples of SGC events were tested by each approach ( Figure 2 and data not shown ) ; the results were consistent with neither type of error having arisen . Together these results show that VSG SGC occurs frequently over the course of a 4–5 week infection . The properties of the putative donors were then investigated . The NTD-encoding regions were considered separately from CTD-encoding regions , due to the frequent occurrence of 3′ donation ( for the latter see below ) . BLAST searching and pairwise alignments between clone and donor sequences identified 103 donor genes that had contributed to generate the expressed VSG NTDs; these are shown in Table S1 . The involvement of a further 29 donors , whose sequences were unavailable , could be inferred by comparison between clone sequences . Identified donors were either annotated copies located in the subtelomeric arrays ( ‘array donors’ ) or partial sequences or assemblies of read sequences ( see Supplemental Experimental Procedures for full details ) . Eleven read sequences represent putative minichromosomal VSGs . It is possible that the 101 sequences ( 23 sets ) for which no donor could be found also represent minichromosomal VSGs , which are underrepresented in the assemblies . Donors for 3′ donation were more difficult to identify unambiguously , perhaps due to ( i ) the likelihood of their being telomere-proximal [29] , and hence underrepresented amongst annotated VSGs; ( ii ) the possibility that successive 3′ donation events accumulating at an expression site produce a sequence with a composite structure that cannot be dissected [29]; and ( iii ) the general similarity between VSG CTDs [11] . Donors were therefore sought only when at least 80 bp of 3′ donation was apparent . Seventeen 3′ donation donors could be found , with five additional donors inferred by identifying identical 3′ regions in otherwise unrelated clones . Half of all 3′ donation donors ( 11/22 , 50% ) corresponded with minichromosomal reads and/or VSGs expressed at an early point in infection , and in five cases , indicated in Table S1 , there was sufficient downstream sequence to identify ‘TTAGGG-like’ repeats that occur 3′ of telomere-proximal VSGs [30] . These findings are consistent with a model in which 3′ donation exchanges the NTD of the expressed VSG , whilst retaining at least part of the previously-expressed CTD sequence . Many ( 43/103 , 42% ) of the putative NTD donors were pseudogenes , summarized in Figure 3 . Their ( partial ) expression was achieved by mosaicism , 3′ donation , or both . SGC was thereby able to release genomically-encoded antigenic diversity that otherwise would have been inaccessible . ‘Richness’—the total number of different variants present in a population—is a principal aspect of diversity [31] . Two infections sequenced to ≥20 clones/sample showed upwards of ten different VSG sets in 5/10 samples analysed , as many as 15 VSG sets in a sample at one time , and 30–31 different VSG sets in total between days 21 and 31 . An example from day 27 of infection 05 is given in Figure 4 , and further details are given in Table S2 . Moreover , the fact that several VSG sets were represented by ‘singletons’ ( sequence recovered only once ) suggests this estimate understates total antigenic richness expressed by the trypanosome population . Rarefaction calculations [32] based on these data project that there could be as many as 95 different VSG sets co-present ( infection 05 , day 27 , measured richness 14 sets , Chao1 estimated richness 32 . 0 sets , 95% confidence upper boundary 95 . 3 sets ) . Members of different sets shared less than 59% NTD-encoding nucleic acid identity lending confidence to the premise that richness is immunologically relevant . The relatively small size of many samples makes accurate estimation of total sample antigenic diversity difficult , but it is clear that African trypanosome antigenic variation comprises , rather than homogeneous waves of individual variants , richly diverse populations . Despite overall diversity , VSG expression followed a loose hierarchy , with the incidence of mosaic and array VSGs increasing as infection proceeded ( as seen in Figure 5 ) , consistent with evidence from previous studies [7] , [11] , [33] . Prior to day 21 of infection , only 10/163 ( 6% ) sample-unique sequences ( 4/24 sets , 17% ) were mosaics , compared with 177/466 ( 38% ) of sequences ( 30/78 sets , 38% ) from day 21 onwards . This result held when the more abundant , post-day 20 data were randomly subsampled to 163 sequences without replacement , to control for the differences in number of sequences . Mosaicism is therefore a feature of chronic , rather than acute , infection . However , it is interesting to note that two non-mosaic VSGs detected prior to day 21 were closely related to variants that had accumulated segmental conversions in samples obtained from later timepoints ( 10-07c01 matched mosaics from days 27–30 in infections 01 , 06 , 10 and 12; 09-03-04 matched mosaics from days 27–32 in infections 01 , 05 and 06 ) , indicating that early-expressed VSGs can be modified by SGC as an infection progresses . For a given expressed VSG , SGC donors shared homology with each other . Mosaic donors had at least 73 . 2% nt identity ( Table S1 ) , but there was apparently no demand for strict sequence identity: in 49 out of 496 mosaic SGCs analysed , the boundary of conversion occurred in a region with less than 4 bp perfect identity between donors , and in three cases ( SGCs in 04-21c40 , 04-21c04 and 04-27c03 ) there was 0 bp perfect identity at the boundary . For this analysis , identical SGCs present in different VSGs obtained from the same infection were counted only once . Where they could be identified , 3′ donors showed local homology at the boundary of 3′ donation ( >85% nucleotide identity over 13–143 bp , median 57 bp ) , although full-length nt identity was as low as 33% . Diversity generated by SGC was abundant , even within a single sample . Day 27 in infection 05 , for example , saw five related Set_10 mosaic variants , three related Set_14 mosaic variants , and two related Set_64 mosaic variants , shown in Figure 4 . A total of seven related Set_22 mosaics were found in infection 04 at day 21 . Related mosaics , formed from the same set of donors , had as low as 78 . 1% amino acid identity ( 04-23c07 and 04-27c21 ) , although in all cases related mosaics formed from the same set of donors were more similar to one another than their donors were to one another ( data not shown ) . Progressive mosaicism , in which serial SGC events are proposed to accumulate gradually in an expressed VSG , generating an increasingly complex ‘string’ of mosaics , could be inferred in Set_04 , Set_14 , Set_40 and Set_84 . However , predecessors for many complex mosaics , for example 05-27c28 , 11-17c01 and 01-27c08 , ( each constructed from >10 segments ) were not identified . Such predecessors may not have been recovered by the process of cDNA cloning and sequencing due their relatively low abundance in a rich population of VSG variants , although one would expect a large pool of predecessors to be necessary ( and hence readily detected ) were each segment being added at maximally the ‘full-length’ VSG switching rate of approximately 10−3 events/cell/generation [34] . These patterns indicate a role for mosaicism in combining families of related donors—whose members may or may not be intact genes—to generate rapidly an additional layer of combinatorial diversity amongst expressed VSGs . Because of the homology between mosaic donors and their products , we selected a string of related mosaic VSGs isolated from a single infection ( Set_14 from infection 04 ) to test whether diversity introduced by mosaicism could contribute directly to antigenic variation . These variants had as low as 79 . 1% amino acid identity between mature NTDs , and each could be explained by the segmental combination of up to four donor genes , 14-A ( Tb927 . 11 . 20570/Tb11 . 09 . 0005 ) , 14-B ( Tb927 . 11 . 19190/Tb11 . 13 . 0003 ) , 14-C ( identified in an assembly of read sequences , cloned and sequenced from gDNA and given GenBank accession number KC434956 ) and 14-D ( Tb10 . v4 . 0009 ) , and up to eight independent point mutations . In one case ( variant 04-27c44 ) , the expressed VSG had also undergone a 3′ donation event . Three of the four donors were pseudogenes; the fourth had an atypical GPI anchor signal sequence with uncertain functionality , as denoted in VSGdb ( this signal sequence did not appear in any Set_14 clones ) . Five of the Set_14 VSGs from infection 04 , shown in Figure 6A , were expressed transgenically under drug selection as intact surface coats in Lister 427 trypanosomes , as described in Materials and Methods and Figure S1 . VSG 427-4 , a known functional VSG absent from TREU 927/4 , was expressed in a similar manner as a negative control . A standard infection-and-cure protocol was used to raise polyclonal antibody responses in mice . At least two different antiplasma were obtained for four of the five variants , as well as for parasites expressing 427-4 and unmodified parasites expressing VSG 427-2 ( antiplasma could not be obtained for variant 04-21c04 as this transgenic parasite line exhibited inadequate virulence , data not shown ) . Monoclonal antibodies ( mAbs ) were also generated for two of the variants , 04-23c07 and 04-29c06 . To test the antigenic relatedness of the Set_14 mosaics , antibodies were applied in three assays on live cells: indirect immunofluorescence , complement-mediated lysis ( CML ) , and agglutination . The results are shown in Figure 6B . With polyclonal antisera , four of the five related mosaics cross-reacted in all assays , reciprocally , but one variant , 04-29c06 , which had arisen later than the others in infection 04 , was antigenically distinct . Likewise , neither of the anti-04-29c06 mAbs bound to the other four mosaics . One mAb raised against 04-23c07 bound two other Set_14 mosaics , and the other bound only a hidden epitope on 04-23c07 , as revealed by acetone fixation; neither bound to 04-29c06 . To investigate the regions of variant 04-29c06 that contribute to its antigenic distinctness , the amino acid sequences of the cross-reacting variants were compared with the sequence of variant 04-29c06 . At twenty-six positions in the NTD , shown in Figure 6C , variant 04-29c06 differed from all of the earlier-occurring variants . Predictions of the three-dimensional structure of variant 04-29c06 using I-TASSER [35] and PHYRE2 [36] , shown in Figure 6D , suggested that 22 of these residues occurred in the region likely to form loops at the membrane-distal end of the NTD , a region which , on another VSG , correlated with B-cell epitopes [6] . SGC can therefore contribute directly to antigenic variation during infection , by generating related , but antigenically distinct , mosaics .
Antigenic variation is a survival strategy driven by the expression of antigenic diversity by the pathogen population . With their huge archive , rapid switch rate , and large population size within a host it is perhaps not surprising that T . brucei infections display great antigenic richness: here we show that many variants , numbering at least 15 in some cases , and estimated to comprise many more , may be expressed across the parasite population at one time . Hosts larger than mice—in which trypanosome antigenic variation likely evolved—are capable of sustaining a greater parasite burden , precipitating even more switch events [37] , and thus even greater richness . MacGregor et al . ( 2011 ) have predicted that , for trypanosomes , the high prevalence of the non-switching stumpy form during chronic infection might limit the expression of different VSGs . Our observation of great richness suggests that any such limitation is unlikely to be of significant impact . Conversely , stumpy form prevalence might actually enhance persistence of minor variant subpopulations , by suppressing their numbers below the threshold required for induction of a specific immune response [10] , [38] . Trypanosome antigenic variation should be viewed more as stochastic , continuous onslaught by many variants , rather than fastidious and tightly regulated expression of few variants , although it is interesting to note that of the ∼1000 VSGs that constitute the annotated archive [11] , <10% were identified as contributing to the expressed VSGs studied here . By underpinning chronicity of infection , expressed VSG diversity likely goes hand-in-hand with the dynamics of differentiation , enhancing opportunities for successful transmission and facilitating the persistence of the trypanosome in its ecosystem [39] . Richness in expressed surface antigen variants may be a feature common to many pathogens , pre-empting host immune responses and memory: antigen sequences cloned from infections by the bacterium Borrelia burgdorferi showed non-saturating richness [14] ( although some sequences varied only in single nucleotides ) , and in Plasmodium it is likely that the whole archive of ∼60 genes has appeared by day 11 of infection [40] . How does SGC serve the T . brucei diversity phenotype ? Following the initial phase of infection , associated primarily with non-SGC activation of minichromosomal VSGs and distinct peaks of parasitaemia [33] , segmentally-converted VSGs become abundant . Two broad patterns of VSG SGC were observed . One , termed 3′ donation , involves retention of at least part of the previously expressed CTD . Swapping just the antigenically important NTD allows the expression of VSGs with damaged CTD-encoding regions—in this way it is analogous to the patterns of variable cassette exchange seen in the variable surface antigens of other pathogens [41]—but it seems unlikely that any combinatorial diversity introduced by 3′ donation can itself contribute to antigenic variation because the boundaries of conversion occur within the buried CTD [5] . The other pattern , mosaicism , can likewise utilise damaged VSGs , and also introduces diversity into the antigenically important NTD , generating sets of related mosaics within an infection , and generating infection-unique variants that could potentially contribute to superinfection [26] . Evidence for progressive mosaicism—the stepwise increase in complexity of an expressed mosaic ‘string’ within an infection , similar to that in Anaplasma marginale [42]—was limited: the sheer number of different variants present may have prevented detection of intermediate mosaics , but it is also possible that mosaicism is a rapid process , with multiple segments accumulating in a short period . A useful , novel mosaic is a VSG able both to form a functional coat and escape circulating immune responses: rapid SGC , allied with efficient selection—the death of individual trypanosomes that have activated a dysfunctional mosaic VSG or perhaps even a form of VSG quality control [43]—would enable a sublineage to efficiently explore the space of potential mosaics , favouring production of a variant that fulfils these criteria . In more natural hosts , where the greater number of switches arising from greater population size accelerates the kinetics of antigenic variation , the infection is likely to progress to this phase sooner , as the easily-activated VSGs are neutralised and unique variants remain to drive prolonged infection [25] . T . brucei homologous recombination depends on substrate length and homology [44] , a pattern reflected in mosaic VSG construction: donors shared high identity ( >73 . 7% identity ) , and thus their associated mosaics were similar to one another . If only similar sequences combine , how efficient can this process be at introducing antigenic dissimilarity ? Multiple segments , and the accommodation of non-identity at their flanks , both of which were observed in mosaic VSGs , may compensate for overall similarity . N . gonorrhoeae and B . burgdorferi , both of which rely on SGC for generating and expressing antigenic diversity , show similar patterns: short conversion events with little or no identity at their flanks [14] , [15] . Previous analyses of mosaic VSGs found that although their products could escape individual mAbs , they were insufficiently distinct to evade polyclonal antibody responses [24] , and a study of related VSGs found antigenic divergence between two variants sharing 70% amino acid identity but cross-reaction between a mosaic and its donor , with which it shared 88% amino acid identity , mostly in the NTD [45] . Here , we found that mosaicism could contribute directly to antigenic variation: polyclonal antibodies raised against the earlier-detected VSGs could bind other earlier-detected variants , whereas the variant detected at the later timepoint , sharing between 79 . 1–87 . 5% NTD amino acid identity with the earlier variants , was completely antigenically distinct . The capacity of T . brucei antigenic variation may therefore be greater than predicted from the genome sequence . Yet given that four donors were required for the assembly of this mosaic VSG set , the yield of merely two distinct variants would appear not to be an efficient use of the archive , nor an effective way to introduce antigenic novelty in and of itself . It is possible that testing of further related mosaics would reveal additional antigenically variant forms , and natural infections , with a more extensive chronic phase , may see longer strings of more distinct mosaics . Severe immunosuppression occurring in the mouse model [46] may have also biased against identifying antigenically distinct variants . On the other hand , incomplete variation through SGC might be sufficient in the context of a complex natural infection , where antibody clearance might operate [47] , differentiation and incomplete cross-reaction suppress variants below the levels required to induce potent , specific responses [38] , and host immunity is suppressed [48] . Perhaps , for trypanosomes , the true value of VSG SGC comes from its ability to accommodate and exploit longer-scale changes arising during evolution of the VSG archive . Like many other multi-gene families [49] , the archive probably evolves through a process of birth-and-death evolution , in which genes ‘born’ by duplication diversify by accumulating mutations and short gene conversions [50]: while some genes persist intact , others acquire disruptive mutations and ‘die’ . Their subtelomeric location promotes rapid mutation of archive VSGs [51] while the only occasional expression of each VSG results in weak selective pressure per allele [52] . As they diverge , it is likely that intact archive VSGs become pseudogenic , and damaged archive genes continue to diversify ( L . Plenderleith , pers . comm . ) . Archive diversification is favourable as it facilitates reinfection , likely to be important in isolated foci where most hosts have already been infected . In these circumstances , second order selection—in which the mechanisms responsible for the evolution and maintenance of a gene family are under stronger selection than the individual family members [53]—would favour an expression mechanism that can cope with the pseudogeneity that would inevitably arise following a protocol of archive hypermutation . The ability to express diverging—and possibly damaged—VSGs using SGC expands the effective archive size , increasing the total number of antigenically different variants that the parasite population can muster . Might other species of African trypanosome , such as T . vivax and T . congolense , similarly rely on SGC for antigenic variation ? T . vivax and T . congolense have a lower degree of archive pseudogenicity than T . brucei [54] . Perhaps tighter bottlenecks in the life cycle of T . brucei [55] , compared with T . vivax and T . congolense [56] , favour deleterious genetic drift , promoting rapid fixation of pseudogenizing mutations . If the generation of mosaic VSGs was a specific adaptation in response to this challenge , the occurrence of mosaic VSGs in T . vivax or T . congolense infections might be much less frequent . On the other hand , mosaic VSGs might be prevalent in T . vivax and T . congolense infections if SGC has evolved primarily as a generator of combinatorial antigenic diversity . SGC is clearly important in underpinning the diversity phenotype in T . brucei chronic infection . Further experiments would identify the contribution of SGC to the kinetics of antigenic variation in natural hosts and the role that mosaicism plays in superinfection , particularly in a field setting , and to establish how combinatorial variations generated by SGC can yield antigenically unique variants . In addition , data from T . vivax and T . congolense infections are required to unravel the selective pressures that have favoured the development of such a comprehensive diversity phenotype .
All studies involving animals were conducted in compliance with the UK Animals ( Scientific Procedures ) Act 1986 ( ASPA ) and under the auspices of Licence 60/3760 which was approved by the University of Glasgow ASPA Ethical Review Committee . Trypanosoma brucei TREU 927/4 GUTat 10 . 1 parasites were used to infect 6–8 week old female Balb/c ( infections 01–09 ) or MF1 ( infections 10–12 ) mice . Two infections , 08 and 09 , were primary clonal infections , initiated with single parasites , the others were initiated with 40 , 000 ( infections 01–06 ) or 10 , 000 ( infections 10–12 ) parasites . Blood samples were taken into CBSS containing 5% w/v sodium citrate and RNA produced using the RNeasy kit ( QIAGEN ) . Reverse transcription was performed using the SuperScript III First-Strand synthesis kit ( Invitrogen ) using oligo[dT] as the primer . cDNA was column purified ( QIAGEN ) before PCR amplification using Herculase II Fusion ( Stratagene ) using primers directed against the spliced leader and conserved 16-mer ( sequences in Table S3 ) . Amplicons were purified and subcloning was carried out using the TOPO-TA subcloning kit as described previously [11] . For each reaction , a control was performed to ensure that the RNA sample was not contaminated with genomic DNA . The VSG coding sequence was assembled from overlapping sequence reads produced from primers corresponding to sequences in the vector . In cases where these reads were insufficiently long to obtain a good quality full-length sequence , reactions were performed to cover the central region of the gene . Genomic DNA ( gDNA ) was prepared by phenol:chloroform extraction and ethanol precipitation , according to a standard molecular biology protocol . PCRs to test for mosaic VSGs were performed using Taq polymerase according to a standard amplification protocol; primer sequences are listed in Table S3 . Sequences were assembled , visualised , compared and analysed using CLC Genomics Workbench ( CLC Bio , Aarhus , Denmark ) , eBioX ( available at www . ebioinformatics . org/ebiox ) and custom Ruby scripts . Scripts are available on request . Intact sequences presented in this study are contained in GenBank entries KC434459–KC434954 , and full details are available from the Dryad digital data repository ( doi:10 . 5061/dryad . 7pc00 ) . Richness estimator was ‘bias-corrected Chao1’ performed using EstimateS ( Version 7 . 5 , R . K . Colwell , http://purl . oclc . org/estimates ) . The ‘genomic VSG database’ was obtained by collecting all available VSG sequences from TriTrypDB ( www . tritrypdb . org using the text search query ‘vsg’ and applying a filter to TREU 927/4 genes ) and from VSGdb ( www . vsgdb . net , all entries ) . The list was made non-redundant by removing duplicate sequences . The assembly of read sequences is described in Table S4 . Transgenic VSGs were expressed under drug selection in Lister 427 trypanosomes , which have an extremely low rate of switching [57] , by first inserting the exogenous VSG into the active expression site [58] , and then removing the endogenous VSG 427-2 . VSG expression plasmids , a kind gift from G . Rudenko , were manipulated using enzymes provided by NEB according to the manufacturer's instructions . VSGs amplified from the subcloning plasmid were cloned into a variant of p221_PUR117VSG_UTR [58] , in which VSG117 had been replaced with an SbfI site . The insert was sequenced to ensure fidelity in cloning . T . brucei Lister 427 13-90 parasites were maintained in HMI-9 medium supplemented with 20% FBS and passaged regularly to avoid overgrowth . Transfections were carried out using an AMAXA protocol ( T-cell nucleofection buffer , programme X-001 ) . After the first round of transfection media were supplemented with 2 . 5 µg . ml−1 puromycin for drug selection , following the second round of transfection with plasmid pBS_VSG221KO ( G . Rudenko , manuscript in preparation ) media was further supplemented with 1 µg . ml−1 blasticidin . To test VSG expression , PCR reactions were performed on cDNA using primers directed against VSG 427-2 , the Set_14 VSGs , VSG 427-4 , and two other Lister 427 VSGs , VSG 427-6 and VSG 427-9 , according to a standard Taq polymerase amplification protocol ( primers are listed in Table S3 ) . In each case , parasites were found to be expressing only the exogenous VSG under consideration , as shown in Figure S1A . To test for the presence of other VSG mRNA , total VSG cDNA was amplified and digested with a restriction endonuclease for which a recognition site was present in the Set_14 VSGs and not at a similar position in other expression-site-occupying VSGs . The digest yielded products of the expected sizes , leaving little or no residual uncut product ( data not shown ) . Amplified VSGs , subcloned and sequenced , were found to match the specific variant under consideration . To test whether VSG mRNA was being translated , crude cell lysate from 2 . 5×106 cell equivalents was analysed by SDS-PAGE ( NuPage system , Invitrogen ) . The size of the variant band corresponded with the predicted size of the exogenous VSG , as can be seen in Figure S1B . For two variants ( Set_14 variants 04-23c07 and 04-29c06 ) , the variant band was excised from a gel and subjected to mass spectrometry . In both cases , peptides corresponding with the Set_14 variants were identified by at least one significant match and were the only VSGs identified ( data not shown ) . Together , these analyses indicated that the transgenic parasite lines were expressing the VSGs under consideration . Furthermore , the survival of the parasites in complement-competent plasma , as can be seen in Figure 6B , indicated that the transgenic surface coat was functional . To avoid prolonged in vitro passaging , stabilates of clones were prepared for subsequent experiments; thawed stabilates were maintained in culture for a maximum of two weeks . To generate antibodies against a transgenic surface coat , 1×106 parasites were injected intraperitoneally into a Balb/c mouse , which was treated with 20 mg . kg−1 cymelarsen ( Rhône-Merieux ) when the parasitaemia exceeded 107 . 2 parasites . ml−1 . Five to eight days after cure , plasma was retrieved from terminal blood samples by collecting the supernatant from a 10 min centrifugation at 14 , 000 g . Monoclonal antibody-producing hybridomas were obtained by preparing splenocytes from these infections according to a standard polyethylene glycol ( PEG ) fusion protocol . Hybridoma lines were cloned by limiting dilution at least twice to ensure a pure population of mAb . For indirect immunofluorescence , all reactions were carried out on ice . 1×106 cells were incubated in primary antibody solution ( 1∶25 dilution of antiplasma in trypanosome dilution buffer [47] or undiluted hybridoma culture supernatant ) for 10 min . Cells were fixed in the presence of primary antibody to minimise clearance [47] by the addition of 1 vol 8% w/v paraformaldehyde in PBS and incubating for 10 mins . For each reaction a negative control was included to test for non-specific antibody fixation . Cells were washed twice with PBS , resuspended in secondary antibody solution ( Alexa-488 labelled goat anti-mouse IgG , provided by Invitrogen , 1∶500 dilution in PBS+1% w/v BSA ) , incubated for 15 minutes , washed twice in PBS , mounted on a glass slide and examined using a Zeiss Axioscop 2 microscope . For each reaction , minimally 200 trypanosomes were examined . Indirect immunofluorescence on acetone-fixed trypanosomes was performed as described previously [34] . For complement-mediated lysis , complement-competent plasma , obtained from guinea pig blood by centrifugation , was used to dilute antibodies and trypanosomes . Trypanosomes were at a final concentration of 0 . 5×107 parasites . ml−1 , and the reaction was incubated at room temperature for 1 hr before scoring for cell death . For the agglutination assay , parasites were at a final concentration of 1×107 parasites . ml−1 , antibodies and trypanosomes were diluted in TDB , and scoring took place after 30 min at room temperature . | Trypanosoma brucei—a deadly parasite of humans and animals—owes its success to its ability to cope with host immunity , and the mechanism it uses to do so is a remarkable example of biological variation . Immune responses that develop against the parasite surface coat are only partially effective against the parasite population; some individual parasites will have already switched to a different variant of the coat antigen , and thus survive to prolong infection . Little is known about how the pattern of antigen variation unfolds , particularly after the early stage of infection . Here , we examined different antigen variants that appeared over the course of infection , to estimate their diversity and to see whether the parasites are able to generate new antigen variants by combination . We found antigen diversity was much greater than expected , and that ‘mosaic’ variants—produced by combining bits of more than one antigen gene—played a central role in the later stages of infection . These results provide important evidence for the robustness of this key survival strategy , provide clues about its evolution , and allow us to identify patterns in common with other antigenically variable pathogens . | [
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"micr... | 2013 | Mosaic VSGs and the Scale of Trypanosoma brucei Antigenic Variation |
The identification of T cell epitopes and their HLA ( human leukocyte antigen ) restrictions is important for applications such as the design of cellular vaccines for HIV . Traditional methods for such identification are costly and time-consuming . Recently , a more expeditious laboratory technique using ELISpot assays has been developed that allows for rapid screening of specific responses . However , this assay does not directly provide information concerning the HLA restriction of a response , a critical piece of information for vaccine design . Thus , we introduce , apply , and validate a statistical model for identifying HLA-restricted epitopes from ELISpot data . By looking at patterns across a broad range of donors , in conjunction with our statistical model , we can determine ( probabilistically ) which of the HLA alleles are likely to be responsible for the observed reactivities . Additionally , we can provide a good estimate of the number of false positives generated by our analysis ( i . e . , the false discovery rate ) . This model allows us to learn about new HLA-restricted epitopes from ELISpot data in an efficient , cost-effective , and high-throughput manner . We applied our approach to data from donors infected with HIV and identified many potential new HLA restrictions . Among 134 such predictions , six were confirmed in the lab and the remainder could not be ruled as invalid . These results shed light on the extent of HLA class I promiscuity , which has significant implications for the understanding of HLA class I antigen presentation and vaccine development .
The human adaptive immune response is composed of two core elements: antibody-mediated response ( sometimes called humoral response ) , and T cell–mediated response ( sometimes called cellular response ) . Research on HIV vaccines initially focused on the antibody-mediated response but more recently has included the cellular response [1 , 2] , which is the focus of our application . At the core of the cellular response is the ability of certain antigen-presenting cells to digest viral proteins into smaller peptides , and then to present these peptides at the surface of the cell . Presentation of a peptide depends on the peptide first forming a complex with an HLA ( human leukocyte antigen ) molecule . If a peptide is presented , it can then be recognized by ( naive ) T cells , allowing activation of these T cells so that they may subsequently recognize and attack virally infected cells displaying the same complex . Any peptide that is able to generate such an immune response in the context of a given HLA allele is called an epitope , and , in particular , an epitope restricted by that allele . Only certain HLA alleles can form a complex with any given peptide , and hence the compatibility of these two elements is essential for the adaptive immune response just described . Several types of T cells exist , each playing its own , though interdependent , role . In ongoing HIV vaccine research , the elicitation of a CD8+ T cell response has shown promise . Since CD8+ T cells recognize only HLA class I bound epitopes , our data , and hence our paper , focus on epitopes recognized in the context of these particular molecules , although the statistical framework is not tailored or limited to this domain and could be immediately applied to HLA class II epitopes , for example . Humans have up to six HLA class I alleles arising from the A , B , and C loci . Currently , there are hundreds of possible alleles at each of these loci , with more being discovered every year [3] . A crucial task in HIV vaccine development is the identification of epitopes and the alleles that restrict them , since it is thought that a good vaccine will comprise a robust set of epitopes [4–6] . By robust , we mean a set which broadly covers regions that are essential for viral fitness in a given human population characterized by a particular distribution of HLA alleles . Also , note that beyond vaccine design , epitope identification may have important applications such as predicting infectious disease susceptibility and transplantation success . Traditional methods for identifying epitopes involve time-consuming , technically demanding , and expensive culturing of T cells . Recently , a more expeditious laboratory technique using ELISpot assays has been developed [7] . Unfortunately , the ELISpot assay gives only information about which individual donors generated an immune response to a particular peptide , but does not provide any information about which of a donor's HLA alleles are restricting this reaction; it is this HLA specificity that is crucial and in which we are most interested . However , by leveraging information contained in ELISpot reactivity across a large set of donors with known HLA types , in conjunction with the statistical model presented in this paper , we can determine ( probabilistically ) which HLA alleles are likely to be responsible for the observed reactivities . Thus we are able to learn about new HLA-restricted epitopes in an efficient , cost-effective , and high-throughput manner . A related , though distinct problem from our problem of epitope identification is that of epitope prediction ( e . g . , [8–11] ) , in which new epitopes are predicted in silico , on the basis of amino acid sequence and other information , but not on the basis of assays that directly measure binding energies or other measures such as the ELISpot assay . The work presented here focuses strictly on the identification of restricting ( i . e . , epitope presenting ) HLA class I alleles from ELISpot data , although newly identified epitopes can aid the task of epitope prediction by providing more known examples to learn from .
For a set of J epitopes ( more precisely , each peptide under examination may contain one , or several , epitope ( s ) , but for simplicity of presentation , we refer to the peptides as epitopes ) and K donors , we have a set of measured binary ELISpot reactivities ( actual laboratory assays provide real values which are thought by the laboratory scientists to convey mostly binary information [14] ) , which are used as input to our model . We are also given the six HLA class I alleles for each donor . Let hi = 1 denote that a donor has HLA allele i , and hi = 0 denote that the donor does not have that allele . Let yj be the observed , binary reactivity for epitope j in a donor ( as measured by the ELISpot assay ) . An important assumption in our model is the following: whether an epitope is restricted by a particular HLA allele is independent of whether that epitope is also restricted by any other HLA allele . This assumption is commonly referred to as an assumption of causal independence [15] . From this assumption , it follows that the probability of not observing a reaction to a particular epitope , in a given donor , is the probability that none of that donor's HLA alleles cause a reaction . Because of the independence assumption , this is simply the product over the probability of each HLA ( that the donor has ) not causing a reaction . Formally , if epitope j is restricted by HLA i , then we let qij be the probability that we observe a reaction in a donor with HLA i and no other HLAs restricted by epitope j . Also , let lj be the probability that a reaction is observed to epitope j when a donor has none of the restricting HLAs for epitope j—a so-called leak term ( corresponding to unrepresented causes such as reactivity due to HLA E molecules ) . Given settings for these parameters , qij and lj , our model stipulates that the probability that a donor does not react to epitope j , p ( yj = 0 | {qij} , lj ) , or does react , p ( yj = 1 | {qij} , lj ) , is given by Such a model is sometimes referred to as a noisy-OR model . It can be viewed as a probabilistic version of the common ( deterministic ) logical OR , and has been shown to be useful in a number of settings [16] . The model can be represented in graphical form as shown in Figure 1 . Here , nodes represent the variables {hi} and {yj} and an arc is drawn from hi to yj if epitope j is restricted by HLA i ( i . e . , if qij > 0 ) . The characteristics of how the probability of an observed reaction changes with an increasing number of restricting alleles depends on the values of {qij} . For example , if qij ≡ qj ≈ 1 , then for a given donor with M restricting alleles , each additional restricting allele beyond one allele would do little to increase the probability of a reaction to epitope j ( as with a deterministic logical OR ) . Alternatively , if qij ≡ qj ≈ 0 , then according to the Taylor series expansion , the probability of reactivity to epitope j would increase roughly linearly with M . The likelihood of the ELISpot data under this model is simply the product of likelihood terms for the reaction in each patient k , to each peptide ( given the HLA types for each patient ) : Given the model just described , and experimental ELISpot and donor HLA data , we wish to infer which epitopes are restricted by which HLA alleles . That is , we wish to know which qij should be included in the model ( which arcs should appear in the graphical model ) . This is a problem of model selection . Note that this problem breaks down into J separate problems , one for each epitope under consideration , since under our model , qij and qi′j′ are independent from one another when j ≠ j′ . To tackle this problem of inferring HLA-restricted epitopes from our data and model , one might consider simply learning a maximum likelihood value for all possible qij simultaneously , and concluding that those for which qij > 0 are those which support the hypothesis of an HLA-restricted epitope . However , in practice , with finite and noisy-data , almost all qij > 0 , and this approach would lead to a huge number of false epitope hypotheses . Instead , we need a more robust way of deciding which qij to include in the model . There are a variety of standard approaches to this problem , most centered on some form of model selection score , such as the Akaike Information Criterion ( AIC ) [17] , the BIC ( Bayesian Information Criterion ) [18] , or the MDL ( Minimum Description Length ) [19]—all of which are forms of penalized likelihood scores . These scores are but three commonly used model selection scores , and many variations of these exist as well . However , all of these scores have an intuitive interpretation of balancing the fit of the data to the model , with model complexity ( controlling the model complexity so that overfitting does not occur ) . The fit of the data to the model is usually assessed by the maximum likelihood of the data under the model in question , while the model complexity is usually controlled by penalizing for the number of free parameters in the model—hence the term penalized likelihood . For example , the AIC of a model , M , is given by , where is the maximum likelihood , and Q is the number of independently adjusted parameters in the model . Given a model selection score , one then chooses a search procedure to select qij ( arcs ) for inclusion in the model . The ideal way to do so is to try every subset of arcs and choose the subset which gives the highest model score ( for example ) . However , with n possible arcs per epitope there are 2n subsets , and this approach is not feasible for most problems . Thus , in practice , it is common for some form of greedy , stepwise procedure to be used , such as greedily adding arcs to the model , or greedily adding/deleting arcs , terminating when the model score can no longer be increased . Then the final model built in the greedy sequence of models is taken as the model to be used and/or interpreted . Commonly , the search is started with the empty model ( no arcs ) . In synthetic experiments with our model , we found that a greedy add/delete procedure , starting from the empty set , worked well ( see Results for details ) , and thus we use such a procedure to identify specific HLA alleles restricting given epitopes . It may at first seem counterintuitive that deleting an arc could increase the score when in a previous step adding that same arc had increased the score . However , when one considers that different variables can explain the same data to differing degrees , then it becomes clear how this can arise . Suppose one arc most explains some part of the data , followed next by , say , two other arcs , each of which explains that part of the data less well than the first arc , but which together explain the data better than the first arc by itself . In this case , after addition of the first arc , followed by addition of the next two arcs , the first arc would become redundant in light of the other arcs , and so removing it can increase the model selection score ( it will not improve the likelihood , but will incur a parameter penalty ) . In practice , for our problem and data , the delete operator was used only occasionally . Different model selection scores used in a given search procedure lead to different recovered models . In particular , AIC is known to be generally less conservative ( allowing more arcs ) as compared with , say , BIC and MDL . Note that if one were to use an add-only procedure ( where deletion of an arc is not allowed ) for noisy-OR based models , then the AIC , BIC , MDL , and the Likelihood Ratio Test ( LRT ) [20] would each add arcs in the same greedy order , though with each score stopping at a different point in the search ( except for BIC and MDL which are equivalent ) . So the fundamental difference between these scores is not so much which arc to add next , but when to stop adding arcs . Rather than dogmatically choosing one score with which to find restricting HLA alleles , we develop a novel approach in which we use a parameterized family of model scores . Then , for any chosen model score parameter setting , we are able to estimate the FDR of the resulting model ( that is , we are able to estimate the proportion of recovered qij which are not truly HLA-restricted epitopes ) . Then we choose a model score parameter setting which produces an FDR that we find reasonable for our purposes ( i . e . , one producing an FDR that gives us enough epitope hypotheses to pursue , but not too many false leads ) . This approach to model selection confers two advantages over the more traditional approach described: ( 1 ) we do not depend in a fundamental way on the choice of a single model selection score , and ( 2 ) regardless of which model selection score we use ( within the parameterized family ) , we are able to estimate the FDR of our selected arcs , providing us with a good sense of what ( interpretable ) features the model has actually recovered , rather than , say , far less interpretable measures of quality such as the maximum likelihood of the data under the recovered model compared with that under some baseline model . We call the parameterized family of model scores XIC , ( to denote that it encompasses various Information Criterion such as AIC and BIC ) . The XIC for model M is parameterized by f and is given by where is the maximum likelihood of model M ( M represents , for example , a model consisting of a particular subset of {qij} ) , Q is the number of independently adjusted parameters in the model , and f parameterizes the family of scores represented by XIC . When f = 1 , the XIC behaves identically to the ( negative ) AIC during search , because it is directly proportional to it . When f = ½ log N , where N is the sample size of the data , then the XIC is identical to the BIC . When f = 0 , the XIC is the maximum likelihood . Thus by varying f , the XIC spans a range of model selection scores , from very liberal ones for low values of f , to increasingly conservative ones for higher values of f . Leaving aside the issue of estimating the FDR for the moment , our model selection procedure is the following: 1 . Select a value for the XIC parameter , f = f* . 2 . Start with the empty set of arcs under consideration ( that is , no qij are in the initial model ) , but include all of the leak terms , lj . Compute the XIC of this “leak-only” base model , M0 . 3 . For every qij under consideration , compute the XIC of the model which is the same as M0 but also includes qij . If none of these models has a higher XIC than M0 , stop the search . Otherwise , add the qij whose corresponding XIC was largest , and call the resulting model , M1 . 4 . Repeat the previous step , except using M1 in place of M0 , and also allowing arc deletions: for all qij in M1 , compute the XIC of the model which is the same as M1 , except that it does not contain qij . Among all the possible arc additions and deletions , choose the operation which most increases the XIC , and call the resulting model M2 . 5 . If possible , continue greedily adding/deleting arcs , stopping when the XIC can no longer be increased . Then we use the last model in the sequence as our final model from which to infer HLA-restricted epitopes . That is , for all qij included in the final model , we will call the hypothesis that epitope j is restricted by HLA i , true . The smaller f* is , the more qij will be included in the final model . Next we show how to estimate the number of qij recovered using this procedure that we expect to be spurious ( i . e . , arising from chance alone , rather than from true HLA restrictions ) . For any specified value of the model selection parameter , f , we want to know how many qij in the recovered model are likely to be true ( rather than spuriously generated ) . That is , we want some sort of statistical significance measure for the epitope hypotheses we have generated . We compute such a measure using a method that we have recently developed [21] . Next we provide some background to this area of research , followed by presentation of our approach . When inferring whether a single hypothesis is true or not , statisticians have traditionally relied on the p-value , which controls the number of false positives ( type I errors ) . However , when testing hundreds or thousands of hypotheses simultaneously , the p-value needs to be corrected to help avoid making conclusions based on chance alone ( known as the problem of multiple hypothesis testing ) . A widely used , though conservative correction , is the Bonferroni correction , which controls the Family Wise Error Rate ( FWER ) . The FWER is a compound measure of error , defined as the probability of seeing at least one false positive among all hypotheses tested . In light of the conservative nature of methods which control the FWER , the statistics community now places great emphasis on estimating and controlling a different compound measure of error , the false discovery rate ( FDR ) [12 , 13] . In a typical computation of FDR , we are given a set of hypotheses where each hypothesis , i , is assigned a score , si ( traditionally , a test statistic , or the p-value resulting from such a test statistic ) . The FDR is computed as a function of a threshold , t , on these scores , FDR = FDR ( t ) . For threshold t , all hypotheses with si ≥ t are said to be significant ( assuming , without loss of generality , that the higher a score , the more we believe a hypothesis ) . The FDR at threshold t is then given by where S ( t ) is the number of hypotheses deemed significant at threshold t and F ( t ) is the number of those hypotheses which are false , and where expectation is taken with respect to datasets of the same sample size as the observed data drawn from the true joint distribution of the variables . When the number of hypotheses is large , as is usually the case , one can take the expectation of the numerator and denominator separately: Furthermore , it is often sufficient to use the observed S ( t ) as an approximation for E[S ( t ) ] . Thus , the computation of FDR ( t ) boils down to the computation of E[F ( t ) ] . One approximation for this quantity which can be reasonable is E[F ( t ) ] ≅ E0[F ( t ) ] , where E0 denotes expectation with respect to the null distribution ( the distribution of scores obtained when no hypotheses are truly significant ) , and it is this approach that we take . ( For traditional applications of FDR , Storey and Tibshirani offer a clever method to compute E[F ( t ) ] which is less conservative than using E[F ( t ) ] ≅ E0[F ( t ) ] [13] . However , this approach is not appropriate in the present context . ) Applying this approach to estimating the number of true qij recovered by our model selection procedure ( i . e . , the number of true HLA-restricted epitopes found by our model ) , we generalize S ( · ) and F ( · ) to be functions of f , the XIC parameter in Equation 4 . In particular , S ( f ) is the number of qij found by our model selection procedure when the XIC is used with parameter setting f and F ( f ) is the number of those qij which do not truly correspond to HLA-restricted epitopes ( i . e . , false positives ) . As in the standard FDR approach , we use the approximation E ( S ( f ) ) ≅ Q ( D , f ) , where Q ( D , f ) is the number of qij found by applying our model selection procedure with XIC parameter f to the observed data D ( in our application , D ≡ {yj} ) . In addition , we estimate E0 ( F ( f ) ) to be N ( Dr , f ) averaged over multiple datasets Dr , r = 1 , … , R , drawn from a null distribution . That is , we estimate the FDR of our HLA-restricted epitopes using the following: The addition of 1 to the numerator smoothes the estimate of E0[F ( f ) ] so as to take into account the number of random permutations performed . Without this smoothing , if one performed too few random permutations such that ∑fQ ( Dr , f ) = 0 due to sampling error , then the estimate of E0[F ( f ) ] and hence FDR ( f ) would also be 0 . We prefer our more conservative estimate , especially as the bias it induces diminishes as the number of permutations increases . We sample Dr from a null distribution for each epitope by permuting the ELISpot data for that epitope , but leaving the HLA types of the donors intact . This permutation guarantees that any qij recovered from the model selection procedure on this data are only spuriously recovered . Also note that although the parameters qij are independent for different epitopes , j , and thus the model selection procedure , can operate independently on each epitope , for the purposes of estimating the FDR , we pool all of the epitopes together , so that the approximations we make in computing the FDR are more reasonable . As shown in the Results section , by way of synthetic experiments , we find that these approximations for estimating the FDR work quite well in practice . There is , however , one concern about the use of the null distribution described , for which we refer the reader to [21] , but which , to our knowledge , does not affect our use of this methodology in this paper . By construction , the emphasis of our FDR approach is on the accuracy of the estimate of the number of false positives , and does not examine the number of false negatives . Whereas this emphasis may seem undesirable , it is common for experimenters to be more interested in how many hypothesized interactions are real , rather than how many were missed , because experimenters will in most cases be using resources to pursue the positive hypotheses , not the negative ones . A similar line of reasoning is mentioned in [13 , 22] . The problem of finding a meaningful ranking of the individual HLA-restricted epitope hypotheses does not immediately fall out of the FDR framework . However , we can naturally construct a ranking algorithm for the epitope hypotheses by using a Likelihood Ratio statistic . Let M denote the model that we learn with our model selection procedure ( regardless of the value of f used ) . Then we rank our hypotheses using a likelihood ratio statistic , vij , which is the log of the ratio of the likelihood of the final model , to that of the final model without the qij we are evaluating . Specifically , our ranking algorithm is: For each qij included in M , do the following: construct a model , M′ij defined to be model M , but without qij , and then compute the likelihood ratio: Assign a rank to each qij equal to the rank of vij in the set {vij} . This ranking assesses each qij based on how much it contributes to the likelihood of the data in the model , M , in the context of all qij recovered from the model selection procedure . ( The likelihood ratio , vij , viewed from a Bayesian perspective , is a quantity proportional to a BIC approximation to the Bayes factor [18] , which , under the assumption of a uniform prior over arc sets , amounts to the posterior probability of qij being included in the model , given the remaining arcs in the model . )
The synthetic model used to generate data was our epitope model , as described earlier , fitted to the real HIV ELISpot data by using our model selection procedure . We used an XIC setting for f that resulted in an estimated FDR ≅ 0 . 3 ( f = 2 . 9 ) . This produced 165 qij in our synthetic model . Additionally , we retained the learned maximum likelihood values for these qij ( and the leaks , lj ) , so as to be able to generate data from the model . To generate synthetic data from this fitted model , donor HLA data was left as it appeared in the real data , and then Equations 1 and 2 were used to compute the probability that a particular donor would react to a particular epitope , pj , conditioned on the learned values of {qij} and {lj} . Then samples for each donor , sj , were drawn from a uniform distribution on ( 0 , 1] and the reactivities , yj , were set to yj = ( sj ≤ pj ) . Three synthetic datasets ( each consisting of 102 donors and 140 epitopes ) were generated in this manner , all from the same synthetic , generative model . Plots of actual versus expected FDR for the three datasets are shown in Figure 2A . Estimates of FDR are quite accurate at the lower end , which is the region of interest for our problem and also most other problems of interest ( where not too many spurious hypotheses are included ) . That the FDR becomes increasingly conservative ( i . e . , it peels away from the idealized line ) can likely be explained by the approximation we make in generating a null distribution . Further discussion of this issue , and a suggested resolution , can be found in [21] . For the XIC parameter , f , we used the range [1 . 97 , 3 . 46] , with f = 3 . 46 producing actual and estimated FDRs around 0 . 02 , and f = 1 . 97 producing actual FDRs around 0 . 67 and estimated FDRs around 0 . 95 . Note that BIC corresponds to the cluster of points that have estimated FDRs around 0 . 7 ( f = 2 . 3 ) . AIC ( f = 1 ) corresponds to something even less conservative than anything shown ( even higher FDRs ) . Not only do we want to know that our FDR estimate is accurate , but we also want to know that our model selection procedure is a reasonable one . We therefore examine how many ground truth qij were recovered , and at what cost in false negatives . This information is displayed in Figure 2B . Note that because we created a synthetic model with what were presumed to be 30% spurious qij , many of these qij are likely quite small ( signifying weak associations ) , and therefore would be more difficult to recover in synthetic experiments using data generated from this model . Such difficulties are also likely to arise with real data in real applications . The points in Figure 2B that have about 50 false positives correspond to an estimated/actual FDR of around 0 . 3 . The points which have about 150 false positives are those corresponding to XIC = BIC ( for which FDR ≅ 0 . 7 ) . Overall , the tradeoff between the number of false positives and false negatives is very reasonable . Using the real HIV data , we found 134 HLA-restrictions at FDR ≅ 0 . 2 among the possible 140 × 70 possible HLA restrictions . To validate our predictions on the real HIV dataset , we performed in vitro assays that specifically measured particular HLA restrictions [26] . Ideally , all 134 pairs should have been evaluated , but this was too expensive and work-intensive . Consequently , six pairs for which the HLA-peptide association is biologically interesting ( i . e . , unlikely based on current understanding of peptide–HLA binding ) were evaluated . All six relationships were confirmed [26] . Prior to this study ( partially reported in [26] ) , it was thought that HLA class I epitopes were restricted mainly by a single HLA allele , or if by more than one allele , then only a few that were structurally highly related and commonly fell into the same HLA supertype [27] ( supertypes group together HLA alleles with similar amino acid binding motifs ) . However , our analysis suggests that a single epitope is frequently restricted by numerous HLA alleles . Additionally , when viewed through the traditional lens of supertypes , we found restrictions across supertypes . For example , IYQEPFKNLK was previously known to be restricted by A11 , and we found that it is also restricted by A24 ( confirmed experimentally ) , where A11 and A24 belong to two different supertypes . Table 1 shows a summary of the number of previously known HIV epitopes restricted by one HLA allele , and up to four HLA alleles ( none were known to be restricted by more than four alleles ) [14] . After adding our newly statistically identified HLA-restricted epitopes , these numbers change dramatically , as shown in Table 2 . These tables suggest that HLA class I epitopes are far more “promiscuous” than originally thought , a notion that has significant implications for the understanding of HLA class I antigen presentation and vaccine development . We refer the reader to [26] for a more detailed account of the biological findings . ( Note that there are a few differences between the results reported in [26] and the current presentation of results . In [26] , the previously known HLA-restrictions were “fixed” to be present in the model before model selection was used to search for new HLA restrictions . We thought it would be of interest to see the results when this a priori information was not used . Additionally , the number of HIV “optimal” epitopes tested was reported as 162 in [26] , whereas we report 140—this is due to the fact that epitope–HLA pairs were counted in the former , while here we count only unique epitopes—of which some were repeated across HLA restrictions . The raw data are , however , identical . ) Table S1A lists all epitopes identified by our statistical analysis , sorted by rank from most to least important , along with their learned qij values , and noting which epitopes were previously known , which were confirmed , and what other HLA alleles were previously known to restrict each epitope . Of the 134 identified epitopes we identified , 46 were previously known ( eight of our top ten ranked epitopes were known ) .
We have introduced , implemented , and examined use of a statistical approach for identifying epitope-restricting HLA alleles from ELISpot data . This approach provides a high-throughput , efficient , and cost-effective method for the screening of novel HLA-restricted epitopes . Additionally , our methodology introduces a new approach to the model selection problem , wherein a parameterized family of model selection scores can be explored , by estimating the FDR resulting from the use of each score , and choosing one which suits the needs of the user . In other words , we are able to customize the tradeoff between high discovery rates , and false leads , rather than relying on a single model selection criterion . Several improvements to the model are possible . ( 1 ) Some donors tend to have a higher overall reaction level , thus it may be fruitful to include a latent variable which models this donor-specific bias . ( 2 ) A confounding factor in our analysis is the existence of false negatives due to a failed chemical reaction in the ELISpot assay . One could add an observation component to model this type of experimental noise . ( 3 ) We stated that the ELISpot data are real-valued , but thought to be informative at a mostly binary level . However , it might be possible to extract more information by using the actual real-valued measurements . Lastly , by applying our methodology to real HIV data , we have helped to shed light on the extent to which HLA class I epitopes are promiscuous . This has significant implications for the understanding of HLA class I antigen presentation and vaccine development . | At the core of the human adaptive immune response is the train-to-kill mechanism in which specialized immune cells are sensitized to recognize small peptides from foreign pathogens ( e . g . , HIV virus ) . Following this sensitization , these cells are then activated to kill other cells that display this same peptide ( and that are infected by this same pathogen ) . However , for sensitization and killing to occur , the pathogen peptide must be “paired up” with one of the infected person's other specialized immune molecules—an HLA ( human leukocyte antigen ) molecule . The way in which pathogen peptides interact with these HLA molecules defines if and how an immune response will be generated , which has implications for vaccine design where one may artificially introduce select peptides to pre-train the immune system . Furthermore , there is a huge repertoire of such HLA molecules , with almost no two people having the same set . We introduce a statistical approach for identifying which HLA molecules interact with which pathogen peptides , given a particular kind of laboratory data . Our approach takes as input , data that tells us only which pathogen peptides generate a response , but not which HLA molecules support the response . Our statistical approach fills in this missing information . | [
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] | 2007 | A Statistical Framework for Modeling HLA-Dependent T Cell Response Data |
Hard ticks subvert the immune responses of their vertebrate hosts in order to feed for much longer periods than other blood-feeding ectoparasites; this may be one reason why they transmit perhaps the greatest diversity of pathogens of any arthropod vector . Tick-induced immunomodulation is mediated by salivary components , some of which neutralise elements of innate immunity or inhibit the development of adaptive immunity . As dendritic cells ( DC ) trigger and help to regulate adaptive immunity , they are an ideal target for immunomodulation . However , previously described immunoactive components of tick saliva are either highly promiscuous in their cellular and molecular targets or have limited effects on DC . Here we address the question of whether the largest and globally most important group of ticks ( the ixodid metastriates ) produce salivary molecules that specifically modulate DC activity . We used chromatography to isolate a salivary gland protein ( Japanin ) from Rhipicephalus appendiculatus ticks . Japanin was cloned , and recombinant protein was produced in a baculoviral expression system . We found that Japanin specifically reprogrammes DC responses to a wide variety of stimuli in vitro , radically altering their expression of co-stimulatory and co-inhibitory transmembrane molecules ( measured by flow cytometry ) and their secretion of pro-inflammatory , anti-inflammatory and T cell polarising cytokines ( assessed by Luminex multiplex assays ) ; it also inhibits the differentiation of DC from monocytes . Sequence alignments and enzymatic deglycosylation revealed Japanin to be a 17 . 7 kDa , N-glycosylated lipocalin . Using molecular cloning and database searches , we have identified a group of homologous proteins in R . appendiculatus and related species , three of which we have expressed and shown to possess DC-modulatory activity . All data were obtained using DC generated from at least four human blood donors , with rigorous statistical analysis . Our results suggest a previously unknown mechanism for parasite-induced subversion of adaptive immunity , one which may also facilitate pathogen transmission .
Hard ticks ( Ixodidae ) adopt a feeding strategy in which they cut into the skin of their hosts to insert their mouthparts , then remain attached for extended periods ( in the case of adult females , a week or more ) taking a single , large blood meal . This makes them unique amongst blood-feeding arthropods ( such as mosquitoes and sand flies ) which otherwise feed little and often , with each meal lasting just minutes [1] , [2] . In order to feed successfully , hard ticks must somehow overcome not only haemostasis and the rapidly-responding components of innate immunity , but also the slower-developing adaptive immune response of their vertebrate hosts . Their apparent ability to overcome these challenges and to subvert host immunity may help explain why they transmit possibly the greatest diversity of pathogens of any arthropod vector . For example , Rhipicephalus appendiculatus ( the brown ear tick ) transmits the protozoan Theileria parva , the causative agent of the devastating cattle disease East Coast fever , and Nairobi sheep disease virus which causes severe disease in sheep and goats; R . sanguineus ( the brown dog tick ) transmits the bacterium Rickettsia conori , causing Mediterranean spotted fever in humans; R . ( Boophilus ) microplus ( the cattle tick ) is globally the most important tick parasite of livestock , transmitting babesiosis and anaplasmosis infections; and Dermacentor andersonii ( the Rocky Mountain wood tick ) transmits the bacterium causing Rocky Mountain spotted fever , the most lethal form of rickettsial illness in humans . Innate immunity is triggered primarily by evolutionary-conserved features of pathogen-derived molecules , or by the molecular signatures of tissue damage or stress [3] . These are typically detected by pattern recognition receptors ( PRRs ) on tissue-resident cells , such as mast cells and macrophages , as well as soluble PRRs in the tissue fluids . The former include Toll-like receptors ( TLRs ) , while the latter include components that activate the complement cascade . A major outcome of both TLR and complement activation is the initiation of an inflammatory response . Locally , this results both in increased vascular permeability , with the movement of soluble effector molecules to the site of insult and , importantly , in further recruitment of innate effector cells such as neutrophils and monocytes into the tissue . Hard ticks appear to protect themselves from the effector molecules of innate immunity , in part by producing a diversity of proteins that bind to and neutralise soluble mediators , such as mast cell-derived histamine and complement components [4]–[7] . They also possess mechanisms to limit the development of the inflammatory response in the shape of “evasins” which bind to and neutralise chemokines , the primary mediators of leucocyte recruitment [8] , as well as proteins that appear to inhibit neutrophil function [9] . In contrast , adaptive immunity is mediated by two types of lymphocyte , T cells and B cells , with the former being broadly divided into CD4+ and CD8+ T cells . CD4+ T cells orchestrate the immune response by recruiting , activating , and regulating other effector cells ( including those of innate immunity ) , whereas CD8+ T cells develop into cytotoxic T cells which eliminate cells with intracellular infections , and B cells differentiate into antibody-secreting plasma cells . To counter these responses , components of tick saliva and salivary gland extracts ( SGEs ) from hard ticks can inhibit adaptive immunity by inhibiting lymphocyte function or by binding and neutralising antibodies [10]–[13] . Dendritic cells ( DC ) bridge innate and adaptive immunity . DC are the key initiators and modulators of T cell responses , and so play pivotal roles in the initiation and regulation of adaptive immunity as a whole [14]–[16] . They are resident within most peripheral tissues , including the skin , and act as immune “sentinels” which sample antigens from their surroundings for subsequent recognition by T cells ( antigen presentation ) , whilst also detecting “danger” , in the shape of pathogens or tissue damage , through their expression of PRRs [14] , [15] . In response to such stimuli , DC undergo a programme of phenotypic and functional changes termed maturation , during which they also migrate from the periphery into secondary lymphoid tissues . Here , they activate naïve antigen-specific T cells [16] . DC are uniquely effective in doing so through their capacities both to degrade protein antigens to peptides for loading onto MHC Class I or II molecules ( for recognition by CD8+ and CD4+ T cells , respectively ) and to express the specialised “co-stimulatory” molecules which are required for T cell activation . Their influence on the T cell response is not however simply limited to its initiation . Following activation , CD4+ T cells may differentiate into different subclasses of effector cells ( notably Th17 , Th1 and Th2 cells , each of which drives the appropriate immune response for the elimination of a different class of threat ) , or into regulatory T cells ( Treg ) , which can contribute to a state of antigen-specific immunological non-responsiveness or tolerance . DC direct this differentiation , through factors which include their profile of co-stimulatory molecule expression and their secretion of T cell-polarising cytokines [15] , [17] . It is clear that manipulation of DC provides a mechanism by which a parasite or pathogen could profoundly affect the adaptive immune response , either by inhibiting the response completely ( for example , by preventing DC activation of T cells entirely , or by driving Treg differentiation ) or misdirecting it , thus resulting in the generation of a type of adaptive response that is ineffectual at repelling the invader . Given this , it is no surprise that many parasites ( including viruses , bacteria , protozoa , and metazoa ) have evolved strategies to modulate DC function [18]–[22] . The same also seems true of hard ticks , which are classified into two main groups , the metastriates and prostriates , with the former representing the majority of known species [23] . Both groups elaborate broad-spectrum immune modulators which also have effects on DC: prostriate ticks produce prostaglandin E2 ( PGE2 ) , while metastriate ticks produce PGE2 , adenosine , and Sialostatin L [24]–[26] . However , only in prostriates has a modulatory protein been identified which acts on DC with any degree of specificity . This protein , Salp15 , inhibits pro-inflammatory cytokine secretion by DC whilst additionally modulating CD4+ T cell function [27] , [28] . To our knowledge , no such molecule has been reported in any metastriate species . We therefore hypothesised that metastriate ticks may have evolved distinctive DC-modulatory proteins . Here we report the identification and characterisation of a unique class of proteins specific to metastriate ticks . These molecules selectively and profoundly modulate the maturation of DC in response to diverse stimuli , and prevent their development from precursors .
To search for DC modulators produced by metastriate ticks we designed a simple screen , based on the capacity of tick salivary gland extracts ( SGE ) to modulate DC maturation in response to bacterial lipopolysaccharide ( LPS ) ; LPS was employed as it is by far the best studied DC maturation stimulus . We initially focused our efforts on studying SGE from Rhipicephalus appendiculatus , the vector of Theileria parva . Cattle are the preferred hosts of R . appendiculatus at all life stages , but collections have also been taken from other large mammals , including humans . Rodents , however , do not appear to be good hosts for any life stage [29] . We employed human DC throughout this study . To screen for the presence of DC modulators , we first treated monocyte-derived DC with SGE from adult R . appendiculatus , then added LPS as a model stimulus . LPS acts as an agonist for TLR4 on DC and triggers their maturation , the extent of which can be assessed by determining surface levels of the co-stimulatory molecule CD86 , which is reliably increased ( “upregulated” ) during normal DC maturation . DC-modulatory activity in SGE was measured as a reduction in CD86 upregulation in response to LPS . Such an activity was indeed observed following incubation with SGE from 3 day-fed females , but not after incubation with SGE from unfed or 6-day fed females , nor with SGE from any males ( figure S1 ) . SGE from 3 day-fed females ( SGE-3F ) was therefore selected for further study . Treatment of SGE-3F with Proteinase K abrogated its DC-modulatory activity , while mock-treatment had no effect , showing that a proteinaceous SGE component was sufficient for DC modulation in this assay ( figure S2 ) . We cannot entirely exclude contributions by PGE2 or adenosine , both previously described as non-protein immunomodulatory components of tick saliva ( see above ) , but the failure of mock-treatment ( using the same buffers and temperatures ) to substantially reduce activity shows that any such contribution must relatively small compared with the effects of the active protein component ( s ) . Furthermore , the complete abrogation of activity in Proteinase K samples suggests that any non-protein active components are labile under the treatment conditions ( i . e . due to thermal instability ) . Prostaglandin E2 is unstable in aqueous solution , particularly at high temperatures [30] and may have been destroyed by treatment . Adenosine , however , would be expected to survive heat treatment , so our results suggest that it is not present in significant quantities in SGE-3F . Previous studies have described adenosine in saliva from 5–7 day fed R . sanguineus [26] , but we are not aware of any reports of its presence in saliva , or in SGE , after 3 days of feeding . Its presumed absence from our samples may , therefore , be attributable to the length of feeding , or to the use of SGE rather than saliva . Multiple rounds of chromatography were then used to isolate the active protein . SGE-3F was first passed through a Q column at pH7 . 0 , removing many SGE components but leaving the DC-modulatory activity intact . The Q column flow-through was then fractionated using size exclusion chromatography , and a fraction with DC-modulatory activity was further fractionated using HPLC . Activity was detected in a group of consecutive fractions , centred around a single peak on the HPLC trace , from which Edman sequencing generated a 16 residue N-terminal sequence: TPSMPAINTQTLYLAR . We used the above N-terminal sequence to design degenerate primers for polymerase chain reaction ( PCR ) cloning of a 460 bp sequence from R . appendiculatus salivary gland cDNA ( data not shown ) . This sequence ( representing the 3′ region of the candidate mRNA ) was , in turn , employed to design primers for amplification of the 5′ region using 5′RACE . Finally , we performed PCR cloning of the complete coding sequence of the putative DC-modulatory protein , which we named “Japanin” ( Genbank accession KC412662 ) . Its 531 bp coding sequence encodes a 176 amino acid peptide . Analysis with SignalP 4 . 0 [31] suggests that it is a secretory protein , lacking a transmembrane domain , and comprising a 24 residue cleavable signal peptide followed by a 152 residue mature peptide of predicted 17 . 7 kDa molecular weight , the N-terminal sequence of which is consistent with that obtained by Edman degradation . Inspection of the primary amino acid sequence of Japanin indicated that it is a member of the lipocalin family ( see below ) . To facilitate detection and purification of recombinant Japanin , we constructed a polyhistidine-tagged version using PCR . This “Japanin-his” DNA sequence ( comprising a Kozak consensus sequence for initiation of translation [32] , the full-length Japanin coding sequence , and a tag sequence encoding a diglycine linker and six histidine residues ) was subcloned into bacterial and mammalian expression vectors ( pET52b and pCDNA3 . 1 ) , and into a transfer vector ( pBacPAK8 ) for the generation of recombinant baculovirus ( see materials and methods ) . The polyhistidine tag enabled the detection of recombinant Japanin in Western blots with an anti-polyhistidine antibody . We used this to show that Sf9 cells infected with Japanin-His-baculovirus secreted recombinant Japanin , as did pcDNA3 . 1-Japanin-His transfected HEK293T and CHO cells . We were not , however , able to recover intact recombinant Japanin from bacterial expression cultures ( data not shown ) . Since it seemed more appropriate to use an arthropod , rather than a mammalian , system for expression of a tick protein , Sf9-derived Japanin was used in subsequent experiments . It was isolated from the supernatant of Sf9 expression cultures by binding to Talon resin , and further purified with a gel filtration polishing step ( see Materials and Methods ) . In order to confirm that we had indeed identified a protein with DC-modulatory properties , we employed the same assay previously used to screen SGE , assessing the ability of Japanin to inhibit DC upregulation of CD86 in response to LPS . To establish whether any effect was dose-dependent , we tested the effect of Japanin at a range of concentrations . As can be seen from the results of a representative experiment in figure 1 , Japanin had a potent and dose-dependent effect on DC maturation , although responses to any given dose differed between donors ( not shown ) ; figure 2a ( see TLR4 ) shows analysis of the data from 20 independent experiments in which CD86 upregulation was reduced by an average of around 50% by 500 ng/ml Japanin . That Japanin was apparently produced only by three day-fed female ticks amongst the cohorts examined is not entirely surprising , as temporal regulation and gender differences in tick saliva proteins are well-described phenomena [33] . Hard tick feeding occurs in two stages: slow and rapid [34] . In adult females , slow feeding lasts 6 to 7 days or more with a 10-fold weight gain; rapid feeding lasts only a further 12–24 hours during which body weight increases a further 10-fold [35] . These distinct feeding stages may explain why Japanin is present in 3 day-fed female SGE ( from the slow-feeding stage ) but apparently not in 6 day-fed SGE ( from the rapid-feeding stage ) . Likewise , the initiation of feeding is required for de novo production of many factors in tick saliva , so the absence of DC modulators in SGE from unfed ticks was unsurprising . The absence of DC modulatory activity in male tick SGE at all time-points may be related to the fact that they take a smaller blood meal than females [1] and so may have less need to modulate DC function and , potentially , the host's adaptive immune response . In fact , there are numerous reports of differences between conspecific male and female ticks in SGE activities , for example in immunoglobulin-binding proteins [36] , histamine binding proteins [4] , and chemokine binding proteins [37] . One possible reason is that females focus on imbibing an enormous blood meal to produce thousands of eggs , increasing in size a hundred-fold , whereas male R . appendiculatus demonstrate ‘mate guarding’ by secreting male-specific immunomodulatory proteins that help their mate to feed [36] . Having shown that Japanin has DC-modulatory properties , we next assessed whether or not it binds , and potentially acts , specifically on DC . We labelled recombinant Japanin with a fluorochrome , and measured its binding to monocyte-derived DC and to peripheral blood mononuclear cell subsets ( PBMC ) by flow cytometry . Fluorochrome-labelled OmCI ( a tick-derived lipocalin with no known effect on DC [38] ) was used as a control for non-specific binding . Japanin bound strongly to monocyte-derived DC ( figure 3a ) and at a low level to CD1c+ DC ( figure 3c ) but there was no appreciable binding to any major populations in PBMC ( figure 3b ) , including monocytes ( defined as lin−CD14+ cells ) , B cells ( lin+HLA-DR+CD14− ) , T cells or NK cells ( lin+HLA-DR− cells ) . Nor was there appreciable binding to other blood DC subsets ( figure 3c ) , or to activated T cells that had been stimulated for 4 days with anti-CD3/CD28 beads ( figure S3 ) . Gating strategies are shown in figure S4 . These data clearly show Japanin to be a highly-specific DC modulator , the first described from a tick . Notably , it does not bind to T cells , even after their activation , suggesting that it could potentially influence adaptive immunity solely by acting on DC . The results distinguish Japanin from the prostriate tick-derived Salp15 , which binds to both DC through DC-SIGN , and T cells through CD4 , directly modulating the activity of both cell types [28] . DC maturation is a complex process which can endow the cells with both stimulatory and inhibitory functions . Classically , it involves upregulation of co-stimulatory molecules , such as CD86 , which help to initiate adaptive responses , along with the secretion of pro-inflammatory cytokines and T cell-polarising cytokines . DC may also upregulate co-inhibitory molecules , such as CD274 ( B7H1; PD-L1 ) , which help to suppress adaptive responses , and secrete anti-inflammatory cytokines or express alternate T cell-polarising molecules . The balance between these various responses is determined in part by the nature and duration of the maturation stimulus , including any intrinsic host-derived DC-modulating factors , and these in turn help to determine the strength and nature of the subsequent T cell response and the overall type of immunity that results . Being a prokaryotic product , LPS is not produced by ticks . We reasoned that Japanin was therefore not likely to have evolved as a specific regulator of LPS-induced maturation , and hypothesised that it may also modulate DC maturation in response to other stimuli . We found that Japanin was capable of inhibiting CD86 upregulation in response to a wide range of stimuli including the TLR3 agonist poly ( I:C ) and the TLR7/8 agonist CL097 , as well as the cytokines IFN-α2 and IFN-γ which signal through entirely distinct intracellular pathways ( figure 2a ) . Preliminary studies further suggested that Japanin inhibits CD86 upregulation in response to the TNF-family member CD154 ( CD40L ligand ) which is crucial for cross-talk between DC and activated T cells ( data not shown ) . In fact , the only stimulus tested for which Japanin did not induce a clear inhibition of CD86 upregulation was TNF-α ( figure 2a ) . Furthermore , the modulatory activity of Japanin is not based on interruption of receptor-proximal signalling components , as these are not shared between TLR and IFN-receptor signalling pathways [39] . Given that the role of DC in adaptive immunity is not limited to activation , but extends to educating and directing the T cell response , we speculated that the tick might benefit more from subverting DC maturation than from simply inhibiting it; for example this might result in the development of a type of immunity that is harmless to the tick , or even in the induction of tolerance . To investigate whether Japanin had effects more sophisticated than a simple inhibition of CD86 expression , we extended our studies to the expression of other membrane molecules associated with DC maturation ( using flow cytometry ) , and to the secretion of a wide variety of cytokines ( using multiplex analysis of culture supernatants ) . In these experiments , we added Japanin at the same time as LPS , rather than as a pre-treatment , as trial experiments showed that this reduced intra-experimental variance in cytokine concentration readings ( data not shown ) . We found that the effects of Japanin extend to a marked reduction in the upregulation of not just CD86 but also the maturation marker CD83 ( figure 4a ) and to a dramatic reduction in the secretion of a range of cytokines . The latter included pro-inflammatory [IL-1-β , IL-6 , TNF-α] and/or Th17-polarising [IL-1-β , IL-6] and Th1-polarising [IL-12p70 , IFN-γ] molecules ( figure 4b ) . However , this was not due to a complete inhibition of DC maturation , as Japanin enhanced expression both of the co-inhibitory molecule CD274 ( figure 4a ) and of the anti-inflammatory cytokine IL-10 ( figure 4b ) . Moreover , Japanin had no significant effect on expression of MHC Class II molecules ( HLA-DR ) or the co-stimulatory molecule CD40 ( figure 4a ) , which are respectively required for antigen presentation to , and cross-talk with , T cells . Nor did it have a significant effect on LPS-induced secretion of IL-7 , IL-8 or CCL11 ( figure S5 ) . Interestingly , Japanin was also active in the absence of LPS , reducing CD86 expression , increasing CD274 ( as well as CD40 ) expression , and enhancing IL-10 secretion ( figures 2b , 4a , 4b ) during the ‘spontaneous’ DC maturation that occurred slowly while in culture . Collectively , the above results show that Japanin acts through a sophisticated reprogramming of the DC maturation process , rather than by simply blocking it . Such a complex spectrum of effects has never previously been reported for a DC modulator ( see discussion ) suggesting that Japanin affects a distinct and novel transcriptional programme in DC . Following maturation in response to injury or other stimuli , skin-resident DC typically migrate out of the skin into the lymphatics and move to the draining lymph nodes . This can happen extremely rapidly , and involve the large majority of skin DC , potentially resulting in a situation where an exodus of pre-existing DC could leave the skin effectively DC-free and “unguarded” [40] , [41] . In order to avert this , monocytes are recruited from the blood into sites of inflammation where they are capable of differentiating into DC , thus replenishing the DC population and restoring immune monitoring [42] , [43] . It would seem to be advantageous to the tick to be able to prevent such replenishment , and so we looked at the ability of Japanin to affect the differentiation of DC from monocytes in vitro . To do this , we employed the same system which we used previously for the generation of DC from monocytes in the presence of GM-CSF and IL-4 , but this time added Japanin to the differentiation cultures from the start . In the absence of Japanin , these conditions typically promoted the development of CD14high CD1a− monocytes into CD14low CD1a+ DC . However we found that when Japanin was added ∼50% of cells retained the CD14highCD1a− monocyte-like phenotype , even after 5 days in culture ( figure 5 ) . The above results appear to conflict with the finding that Japanin does not bind monocytes . However , we found that binding of Japanin to monocytes increases during culture with GM-CSF and IL-4 ( figure S6 ) , suggesting that it could act during the early stages of differentiation of monocytes into DC . Japanin therefore seems to impose a powerful block on this differentiation process . Given the influx of monocytes into the bite site in response to local tissue damage [44] , the effect of Japanin on differentiating monocytes may be as important to ticks as its effects on DC , preventing the re-establishment of immune surveillance following initial DC exodus . Further study of the Japanin-treated cells will be required to elucidate whether they truly resemble “arrested” monocytes , or whether they have differentiated along an alternative pathway , but what is clear is that DC differentiation is blocked or greatly altered . Lipocalins are a family of small ( ∼20 kDa ) proteins characterised by an eight-stranded antiparallel β-barrel fold with a repeated +1 topology , typically preceded by a short N-terminal 310-helix and followed by a C-terminal α-helix . They frequently have one or more binding pocket ( s ) for small molecule ligand ( s ) [45] , [46] . Inspection of the primary amino acid sequence of Japanin indicated that it is a lipocalin , a conclusion supported by comparisons with other tick-derived lipocalins ( figure 6 ) . Japanin conserves residue properties at the key positions described by Adam and colleagues [47] to a similar extent as tick proteins with resolved lipocalin structures ( figure 6a ) , and shares the position of cysteine residues and the presence of a conserved motif ( ( Y/C ) -hydrophilic- ( L/M ) -W-hydrophobic ) with these and with other tick proteins thought to be lipocalins ( figure 6b ) . This provisional description of Japanin as a lipocalin has recently been confirmed by the resolution of its crystal structure , details of which are currently being prepared for publication ( personal communication , Susan Lea ) . Tick genomes frequently encode several isoforms or homologues of a protein , apparently as a result of frequent gene duplication events during tick evolution [48] . We therefore investigated whether R . appendiculatus expresses any Japanin homologues , extending our studies to the closely related Rhipicephalus sanguineus species . We designed degenerate primers using the Japanin coding sequence , and used these to clone three sequences with similarity to Japanin: Japanin like-RS ( JL-RS ) from R . sanguineus , and Japanin like-RA1 and -RA2 ( JL-RA1 and JL-RA2 ) from R . appendiculatus ( Genbank accessions KC412663 , KC412664 , and KC412665; see materials and methods for cloning procedure ) . Each encodes a 175–177 residue peptide , comprising a 24 residue signal sequence ( according to SignalP prediction ) and a 151–153 residue mature peptide . Alignment of the predicted mature peptides with Japanin shows that JL-RS is 82% identical and 91% similar to Japanin , JL-RA1 is 50% identical and 74% similar , and JL-RA2 is 54% identical and 76% similar; similarity was calculated using a PAM250 matrix . The high levels of sequence homology between Japanin and the above homologues suggested shared function . To investigate this , we transfected HEK293T cells with pCDNA3 . 1 expression constructs encoding either polyhistidine-tagged Japanin , JL-RS , -RA1 or -RA2 . The presence of recombinant protein within the supernatants was confirmed using anti-polyhistidine Western blot ( figure 7a ) , and the transfectant supernatants were then assessed for their ability to modulate DC maturation , both spontaneously and in response to LPS . Each of the three homologues was found to modulate DC maturation overall ( p<0 . 05 ) , albeit with some individual differences in the extent of effect on each of these responses , and on CD86 vs . CD274 expression ( figures 7b , 7c ) . This suggests that their sequence similarity reflects a shared DC-modulatory function; we are currently evaluating whether or not other modulatory effects are similar to those of Japanin . Finally , in order to search for further Japanin-related sequences in public databases , tblastn searches were performed against the mature peptide sequence of Japanin ( see materials and methods ) . The NCBI est database returned a cDNA derived from the metastriate tick Dermacentor andersoni with homology [32% identical , 53% similar] to Japanin ( Genbank accession EG363153 . 1 , labelled as DA_E1224_06G04 in figures 8 and 9 ) . Furthermore , the Whole-genome shotgun contig database returned regions of Rhipicephalus ( Boophilus ) microplus genomic DNA whose translations are highly similar to Japanin [40–58% identical , 55–74% similar] ( Genbank accessions ADMZ01222530 . 1; ADMZ01123695 . 1; ADMZ01066468 . 1; ADMZ01299354 . 1 ) . All the identified homologues conserve Japanin's cysteine residues , which may play a structural role through disulphide bond formation ( figure 8 ) . Tick lipocalins have conserved intron positions and phase [49]; this means that the identification of intron boundaries may be guided by intron-exon structure in addition to the more usual prediction of likely splicing site sequences [50]–[52] . This allowed us to predict intron boundaries with greater confidence than would otherwise be possible , and enables us to tentatively ascribe the three sequences with greatest similarity to Japanin ( ADMZ01222530 . 1 , ADMZ01123695 . 1 , ADMZ01066468 . 1 ) to exons 2 , 4 and 5 of a single gene ( figure S7 ) , which we provisionally entitle Japanin-like RM ( JL-RM ) . The fourth sequence ( ADMZ01299354 . 1 ) , had the lowest similarity to Japanin , and overlaps with one of the other sequences . It may represent part of a second R . microplus Japanin homologue . Of course , in the absence of mRNA/cDNA sequences , it is possible that the three “JL-RM” sequences actually comprise parts of two or three distinct Japanin homologues . Further studies will be needed to determine whether these D . andersoni and R . microplus homologues also have DC-modulatory activity . It is noteworthy that despite the availability of extensive genome and transcriptome data from prostriate ticks [53] , clear examples of Japanin-related molecules were identified only from metastriates , suggesting that Japanin and its homologues are unique to metastriates . The conserved number and positioning of cysteine residues , the conservation of key motifs , and the sequence homology to Japanin , allow us confidently to describe all of the above Japanin homologues as tick lipocalins . In order to estimate their evolutionary relationship to Japanin and to other tick lipocalins , we performed phylogenetic analysis , building a distance dendrogram using maximum-likelihood methods ( see materials and methods ) . We compared the sequences of Japanin and its five identified homologues to 236 complete sequences derived from hard ticks , as well as 3 soft tick proteins with resolved structures . This analysis clearly shows that these molecules form a distinct clade within hard tick lipocalins , grouping in complete isolation from any previously identified proteins or putative proteins and with strong boot-strap support ( figure 9 ) . Note that for reasons of clarity , only selected proteins are named in figure 9 , and the full tree is provided in Newick ( . nwk ) format as supplementary data in dataset S1 .
Manipulation of dendritic cell function is a survival strategy adopted by a wide range of pathogens , from viruses and bacteria to protozoan and metazoan parasites [54] . Here we describe a novel tick-derived protein , Japanin , which combines the ability to extensively reprogramme DC maturation with a profound inhibitory effect on DC differentiation . Japanin appears to be one member of a unique family of highly specific DC-targeting proteins that seem to be produced only by metastriate ixodid ticks . To our knowledge , previously described molecules derived from blood-feeding arthropods are either highly promiscuous in their cellular and molecular targets , or have limited effects on DC . For example two proteins , Maxadilan and LJM111 , have been identified in the saliva of Lutzomyia sand flies that can alter the balance of cytokine secretion and costimulatory molecules by DC . The former apparently acts to favour the development of a Th2 response [55] although it was initially characterised as an exceptionally potent vasodilator [56] and is now known to have a widely-expressed receptor ( PAC1 ) [57]; it is currently unclear whether the activity of LJM111 is in any way DC-specific [58] , [59] . Sialostatin L , from Ixodes scalpularis ticks , alters DC cytokine secretion and costimulatory molecule expression in response to LPS [24] , but it also alters T cell polarisation in the absence of DC [24] , and inhibits proliferation of a T cell line [60] . As well as inhibiting cathepsin S , which plays a key role in MHC Class II molecule processing , and hence in antigen presentation [61] , Sialostatin L also inhibits cathepsin L1 , and so may play a role in limiting neutrophil activity ( given the role of cathepsin L1 in IL-8 activation [62] ) and/or in controlling tissue remodelling [63] . Salp15 ( with its homologues ) , from Ixodes spp . ticks , is the only unambiguous example of a substantially DC-specific modulatory protein from a blood-feeding ectoparasite [27] but even this molecule also acts directly on CD4+ T cells [28] . Moreover , the effects of Salp15 on DC appear limited to a reduction in the secretion of certain pro-inflammatory cytokines by DC; unlike Japanin , it has no effect on membrane molecule expression or anti-inflammatory cytokine secretion . Rather than simply inhibiting DC maturation , Japanin appears to hijack the normal maturation process and to redirect it in a totally different direction . It blocks LPS-induced secretion of pro-inflammatory and Th17- and Th1-promoting cytokines , and reduces expression of a key co-stimulatory molecule ( CD86 ) required for T cell activation . Meanwhile , it also promotes secretion of the anti-inflammatory cytokine IL-10 and increases expression of CD274 ( PD-L1 ) , both of which are involved in the suppression of T cell immunity and the induction of antigen-specific tolerance [64]–[66] . Moreover , Japanin appears to modulate DC maturation in response to multiple “danger” signals . We have studied responses to bacterial LPS , a TLR4 agonist , in most detail , but its modulatory effects appear to extend to responses stimulated by TLR3 agonists ( the natural ligand for which is viral double-stranded RNA ) , and by interferons , which are produced in response to tissue damage and infections . To our knowledge , no molecule has been previously reported to combine such a wide spectrum of potent and specific effects on DC maturation with the ability to modulate responses to a wide range of stimuli . The ability of Japanin to modulate DC responses to a broad range of stimuli makes sense given that ixodid ticks might otherwise trigger DC maturation and T cell responses in a number of ways: ( i ) during attachment they cause tissue damage at the skin feeding site; and ( ii ) their saliva carries tick-borne pathogens . Hence tick feeding is likely to provide both endogenous ( i . e . tissue damage-related ) and exogenous ( for example through TLRs ) triggers for DC activation . Moreover , ( iii ) their saliva contains multiple bioactive proteins and peptides that help blood-feeding but which could potentially be recognised as foreign antigens by the host . Presumably to subvert such defences , prostriate ( Ixodes spp . ) ticks elaborate Salp15-like proteins which modulate both DC and T cell functions . The current study shows that metastriate ticks produce Japanin-like molecules which appear to modulate DC in a highly specific manner . We have been unable to detect binding of Japanin to T cells or B cells , or to any other major cell population in human blood , although we have not excluded a modulatory effect on macrophages , as reported recently for unfractionated Rhipicephalus ( B . ) microplus saliva [67] . In principle , Japanin may act directly on tissue-resident DC at the bite site and , being a comparatively small ( ∼20 kDa ) molecule , it may also be carried in the lymphatics to influence lymph node-resident DC . Furthermore , the capacity of Japanin to modulate the differentiation of monocytes into DC in culture suggests that , in vivo , it may also act locally ( and perhaps even within regional lymphoid tissues ) to subvert the development of DC from their precursors . Although the Japanin family of DC modulators appears to be restricted to metastriate tick genera , such as Rhipicephalus and Dermacentor , the lipocalin structure of Japanin is widely represented in the salivary gland transcriptome of blood-feeding arthropods [68] , [69] . Lipocalins are found throughout the plant and animal kingdoms as well as bacteria , reflecting the robust and versatile nature of their β-barrelled structure . Typically , they are extracellular proteins that transport small hydrophobic ligands , although there are notable exceptions such as the tick lipocalins that bind small hydrophilic ligands [4] , [47] . Examples of tick lipocalins that subvert host defences are the histamine- and complement-binding proteins [40] , [48] , and several mammalian lipocalins ( dubbed “immunocalins” ) have modulatory effects in immunity [70] . Japanin and its homologues appear to be the first examples of the lipocalin molecular architecture being employed to target DC . In haematophagous ectoparasites , DC modulators have presumably evolved to suppress host immunity in order to facilitate blood-feeding . For those species that are vectors of pathogens , such molecules could also create a permissive environment for pathogen transmission . Although saliva and SGE from several species of mosquitoes , sand flies and ticks has been shown to both affect DC activity and enhance pathogen transmission , the relationship between DC modulation and pathogen transmission has not been resolved [71]–[73] . For example , Salp15 facilitates transmission of the Lyme disease spirochete from the tick vector to the host [74] . However , it is unclear whether this is due to the binding of Salp15 to: ( i ) DC-SIGN on DCs , thus inhibiting the spirochete-induced production of pro-inflammatory cytokines by DCs and so modulating DC-induced T cell activation [27]; ( ii ) CD4 , thereby inhibiting T cell activation [28] , [75] , [76]; and/or ( iii ) OspC , an outer surface protein on the spirochete , hence protecting the spirochete from antibody-mediated killing [77] . Likewise , Maxadilan promotes transmission of Leishmania parasites although the relative contribution of DC modulation to enhanced transmission is unresolved [78] . Metastriate ticks are important vectors of human and animal pathogens , so could Japanin facilitate tick-borne transmission ? In vivo experimental studies showed that an unidentified protein in SGE of R . appendiculatus promotes transmission of Thogoto virus and tick-borne encephalitis virus ( TBE virus ) , and that TBE virus infects Langerhans cells [72] , [79] . The effect of saliva components on Theileria parva , the cause of the devastating African cattle disease , East Coast fever , is unknown . Interestingly , tick-borne transmission of this protozoan pathogen commences about 3 days after initiation of R . appendiculatus feeding , coinciding with the production of Japanin [80] . The existence of a Japanin homologue in Dermacentor andersonii , a major vector of Rocky Mountain spotted fever , is also of note . Further studies are needed to determine whether Japanin and its homologues play a role in the transmission of tick-borne pathogens; one possible approach would be through their RNA-mediated knockdown [81] . Our findings describe an entirely new and highly specific class of DC modulators , potentially providing a novel mechanism for the control of adaptive immunity . We anticipate that further work will reveal the mechanism by which Japanin exerts its effects on DC , besides revealing its effects on development of T cell responses and the adaptive response as a whole . Ultimately , these DC modulators in saliva of metastriate ticks may help enable ectoparasites to feed successfully on their hosts without provoking effective immune responses , while at the same time creating a permissive environment for pathogen transmission to their hosts .
Lipopolysaccharide ( LPS ) was from E . coli 055:B5 , and purchased from Sigma ( catalogue #L4005 ) . Polyinosinic:polycytidylic acid ( poly I:C ) and CL097 were from Invivogen . Human IFNα2 , IFNγ , TNFα , IL-4 and soluble CD40 ligand were from Peprotech . Human GM-CSF was from Gentaur . Recombinant OmCI was produced as previously described [40] . Foetal calf serum ( FCS ) was from Invitrogen . Plasmids were from Invitrogen ( pCR2 . 1-TA , pCR-Blunt II-TOPO & pCDNA3 . 1 ) , Clontech ( pBacPAK8 ) and Novagen ( pET52b ) . Unless otherwise noted , PCR was carried out using Phusion Hot Start DNA polymerase ( NEB ) . Phosphate buffered saline ( PBS ) and Hanks Balanced Salt Solution were from PAA . Mammalian cells were cultured at 37°C/5% CO2 in complete RPMI ( C-RPMI ) , consisting of RPMI 1640 ( PAA ) supplemented with 10% FCS , 100 U/ml penicillin ( PAA ) , 100 µg/ml streptomycin ( PAA ) . Tissue culture plastics were from Corning . Sf9 insect cells were cultured at 28°C in Sf900III serum-free medium ( Invitrogen ) . Sf9 liquid culture was in Erlenmeyer flasks with shaking at 110 rpm . Standard E . coli strains and techniques were used to produce plasmids during molecular cloning . Human blood products were from anonymous healthy donors , and supplied by the National Blood Service ( England & Wales ) . Human monocyte-derived DC were generated using a protocol derived from the method of Sallusto and Lanzavecchia [82] . Peripheral blood mononuclear cells ( PBMC ) were isolated from Buffy coats and leucocyte cones using gradient centrifugation with Lymphoprep ( Axis Shield ) . Monocytes were isolated from PBMC by negative selection using the EasySep Human Monocyte Enrichment Kit ( Stemcell ) as per manufacturer's instructions , then cultured at 5×105/ml in C-RPMI supplemented with 1000 U/ml human GM-CSF and 100 ng/ml human IL-4 . Cultures were fed after three days by replacing one third of the medium with fresh C-RPMI supplemented with 3000 U/ml GM-CSF and 300 ng/ml IL-4 , and cells were harvested for use in assays after 5 or 6 days of culture . Prior to some assays , DC were frozen in Voluven ( Fresenius Kabi ) supplemented with DMSO ( Hybrimax grade , Sigma ) and FCS to give final concentrations of 5 . 5% hydroxyethyl starch 130/0 . 4 , 4 . 8% DMSO and 3 . 8% FCS in isotonic saline . Freezing was carried out at 1°C/minute . DC were cultured at 1×106 cells/ml in flat-bottomed 96-well tissue culture plates in C-RPMI supplemented with 1000 U/ml GM-CSF and 100 ng/ml IL-4 . Japanin was added to 500 ng/ml ( unless otherwise stated ) , and a maturation stimuli was either added immediately or after 24 hours in culture . Cells were then cultured for 18–22 hours , and analysed by flow cytometry . In some experiments , multiplex measurement of supernatant cytokine concentrations was also performed . In the experiments shown in figures S1 and S2 , sufficient SGE was added to give a final SGE-derived protein concentration of 50 µg/ml PBMC were obtained from leucocyte cones as described above , and T cells were isolated by negative selection using the Easysep human T cell enrichment kit ( Stemcell ) as per manufacturer's instructions . They were then stimulated by culture with Human T-activator CD3/CD28 Dynabeads ( Life Technologies ) for four days , as per manufacturer's instructions . R . appendiculatus ticks were reared according to Jones et al . [83] Salivary glands were dissected under a microscope and rinsed briefly in cold PBS . Salivary gland extract ( SGE ) was prepared by disruption of freshly-prepared salivary glands in PBS with a 1 ml Dounce homogenizer . The SGE was clarified by centrifugation ( >10000 g for 3 min ) and stored at −20°C . SGE from 350 salivary glands was diluted in 50 mM Na2HPO4/50 mM NaCl ( pH7 . 0 ) and passed through a 1 ml Hi-Trap Q sepharose anion exchange column ( GE ) . Unbound material ( Q column flowthrough ) was concentrated to a final volume of 500 µl using a 5000MWCO Vivaspin 6 centrifugal concentrator ( GE Healthcare ) which had been pre-treated with γ-globulin to prevent non-specific absorbance of proteins . The Q column flowthrough was then fractionated by gel filtration over a Superdex 75 HR10/30 column ( GE Healthcare ) using 50 mM Hepes ( pH7 . 6 ) , 150 mM NaCl as running buffer , and each fraction assayed for DC modulatory activity . Consecutive active fractions were pooled , dialysed against 50 mM HEPES ( pH 8 . 3 ) , and fractionated by High Performance Liquid Chromatography ( HPLC ) on a C4 column , with elution using a 0–100% gradient of acetonitrile . HPLC fractions were freeze-dried under vacuum , redissolved in PBS , and assayed for DC modulatory activity . The fraction with maximal activity was used for Edman degradation sequencing . The template for Japanin cloning was cDNA generated from 1 day-fed female R . appendiculatus salivary glands . RNA was isolated from 30 salivary glands using Trizol reagent ( Invitrogen ) , and cDNA generated using ImPromII reverse transcriptase ( Promega ) . Initial cloning of Japanin sequence was performed using Taq DNA polymerase ( NEB ) in nested PCR with degenerate primers designed against the N-terminal peptide sequence . A ∼600 bp product was gel purified using the QIAquick gel extraction kit ( Qiagen ) and ligated into the pCR2 . 1-TA cloning vector . ) Sequencing of this construct revealed the 3′ region of the Japanin coding sequence; this was used to design primers for the amplification of the 5′ region using 5′ RACE System for Rapid Amplification of cDNA Ends ( Invitrogen ) in conjunction with Japanin-specific primers . Amplified DNA was gel purified and sequenced , providing the 5′ region of the coding sequence . The 5′ and 3′ sequences obtained thus far were then used to design primers for the amplification of full-length Japanin coding sequence using two rounds nested PCR , with the second round using primers which added a 5′ BamHI restriction site and a 3′ NotI restriction site . The second round product was digested with BamHI and NotI ( NEB ) , and ligated into similarly digested pBacPAK8 to generate pBacPAK8-Japanin . In order to obtain a polyhistidine-tagged version of Japanin , nested PCR was performed with Phusion DNA polymerase using pBacPAK8-Japanin as a template , employing reverse primers designed so as to replace the 3′ stop sequence and NotI-site with DNA encoding two glycine residues ( to serve as a flexible linker ) and six histidine residues ( the “polyhistidine tag” ) , followed by a stop sequence , and then finally a NotI site . The product from this PCR was digested with BamHI and NotI , and ligated into similarly digested pBacPAK8 ( to produce pBacPAK8-Japanin-his ) , pCDNA3 . 1 ( pCDNA3 . 1-Japanin-his ) and pET52b ( pET52b-Japanin-his ) . Partial sequences of JL-RA1 , JL-RA2 and JL-RS were obtained from Rhipicephalus appendiculatus ( JL-RA1/2 ) or R . sanguineus ( JL-RS ) cDNA expression libraries which had been previously generated in the Lambda Zap II vector ( Stratagene ) . PCR was performed using a degenerate , Japanin-derived forward primer ( ACMSAKACYCTYTACCTYGYG ) in combination with either a vector specific reverse primer ( TTATGCTGAGTGATACCC ) , in the case of JL-RA2 , or a Japanin-specific reverse primer ( ATATGCGGCCGCTTATGGATAGCACCTCTCGT ) , in the case of JL-RA1 and -RS . PCR products were cloned into pCR-Blunt II-TOPO and sequenced , providing sequences for the 3′ region of each DNA . Sequences of the 5′ region of each were then obtained from the same libraries by PCR using a vector-specific forward primer ( CGCAATTAACCCTCACTAAAGGGAAC ) with gene-specific reverse primers ( CGTTAGTTTCAGTGAACGTGAGTGTCC for JL-RA1; CGTTTGGTATCTTCATTTTAGATGAGTATCC for JL-RA2; CATGAGAACAGCTTCGATGAATATGC for JL-RS ) , and products cloned into pCR-Blunt II-TOPO and sequenced . Full-length cDNAs , each with the addition of sequence encoding a C-terminal diglycine linker and a polyhistidine tag ( GGHHHHHH ) were obtained as synthetic genes ( from DNA2 . 0 ) and subcloned into pBacPAK8 using standard techniques . Recombinant JL-RA1 , -RA2 and -RS was produced and purified as described below . Proteinase K treatment of SGE-3F was performed by incubation with 150 µg/ml Proteinase K ( Sigma ) for 2 hours at 50°C , followed by heating to 98°C for 10 minutes to inactivate the enzyme . Recombinant baculovirus was obtained using the approach of Possee et al . [84] Briefly , Sf9 cell monolayer was co-transfected with flashBac Gold baculovirus ( Oxford Expression ) and pBacPAK8 transfer vector ( described above ) , using Cellfectin ( Invitrogen ) as per manufacturer's instructions . Recombinant virus was amplified by infection of Sf9 cells in liquid culture at a low multiplicity-of-infection ( moi ) , and the amplified virus used to infect Sf9 liquid cultures at moi = 2 for protein expression . Viral titre was assessed by plaque assay . The medium was cleared by centrifugation ( 2000 g , 10 min ) 72 hours after infection , and proteins were precipitated by adding polyethylene glycol ( PEG4000 , Sigma; 18 g/100 ml ) . The precipitate was dissolved in HBSS ( pH 7 . 4 ) , loaded on to a 1 ml Talon column ( Clontech ) , and eluted using 150 mM imidazole . The protein-containing eluate fractions were pooled , concentrated using a 9K MWCO Pierce Protein Concentrator ( Thermo Scientific ) , then further purified by size exclusion chromatography with a Superdex 75 HR10/30 column ( GE Healthcare ) using PBS ( pH7 . 4 ) as running buffer . Concentration of purified protein was measured by its absorbance at 280 nm using extinction coefficients reported by the ProtParam tool ( http://web . expasy . org/protparam/ ) . Purity was confirmed by silver stain of SDS-PAGE gels . SDS-PAGE was performed using precast Precise Tris-HEPES gels ( Thermo Scientific ) as per manufacturer's instructions . Proteins were wet transferred to PVDF membrane ( Thermo Scientific ) using 30 V for 1 hour in Towbin buffer ( 25 mM Tris , 192 mM glycine ) with 20% methanol . Membranes were blocked with StartingBlock T20-PBS ( Thermo Scientific ) , then stained first with biotinylated anti-His tag antibody ( Penta-His , Qiagen ) , and then with streptavidin-HRP ( Jackson ImmunoResearch ) . All staining steps , and extensive washing , was in PBS/0 . 05% Tween 20 . Bands were visualised by luminescent substrate ( ECL , Thermo Scientific ) with X-ray film ( CL-XPosure , Thermo Scientific ) . Cells were stained in PBS/2% FCS and analysed with a FACSCanto flow cytometer ( Becton Dickinson ) . The following antibodies were used: 5C3 ( anti-CD40 , APC-conjugated ) ; HB15e ( anti-CD83 , FITC-conjugated ) ; GL1 ( anti-CD86 , PE-conjugated ) ; MIH1 ( anti-CD274 , PE-Cy7-conjugated ) ; LN3 ( anti-HLA-DR , APC-conjugated ) . All were from eBioscience . Isotype control antibodies showed negligible binding to DC . Cells were gated according to FSC/SSC , and , in some experiments , according to exclusion of 7AAD ( Sigma ) . Japanin did not increase the frequency of 7AAD-staining cells . Clarified tissue culture supernatants were diluted with 1 volume of PBS and stored at −20°C . They were analysed using Milliplex MAP Luminex beads ( Millipore ) as per manufacturer's instructions . Recombinant Japanin and OmCI were labelled with DyLight 649 using DyLight 649 Amine-Reactive Dye ( Thermo Scientific ) as per manufacturer's instructions . In the experiment shown in figure S6 , examining the binding of Japanin at different points during the differentiation of DC from monocytes , proteins were instead labelled with Alexa Fluor 488 , using the Alexa Fluor 488 Microscale Protein Labelling Kit ( Life Technologies ) as per manufacturer's instructions . Cells were incubated with 100 ng/ml labelled Japanin or 340 ng/ml labelled OmCI for 1 hour on ice in HBSS ( containing 1 . 3 mM Ca2+ 0 . 8 mM Mg2+ ) /2% FCS , washed extensively , and analysed by flow cytometry . For the determination of Japanin binding to PBMC subsets , PBMC were incubated with Fc block ( Miltenyi Biotec ) for 15 minutes on ice , washed , then incubated with DyLight 649-labelled Japanin or OmCI as described above , but with the addition of the following antibodies: anti-CD1c-Brilliant Violet 421 ( clone L161 , Biolegend ) ; CD3-biotin ( clone OKT3 , Biolegend ) ; CD7-biotin ( clone 124-1D1 , eBioscience ) ; CD14-Brilliant Violet 650 ( clone M5E2 , Biolegend ) ; CD11c-PE-Texas Red ( clone B-ly6 , BD Biosciences ) ; CD19-biotin ( clone HIB19 , eBioscience ) ; CD20-biotin ( clone2H7 , eBioscience ) ; CD45-eFluor 605NC ( clone HI30 , eBioscience ) ; CD56-biotin ( clone HCD56 , Biolegend ) ; CD123-PerCP-Cy5 . 5 ( clone6H6 , Biolegend ) ; CD141-PE ( clone AD5-14H12 , Miltenyi Biotec ) ; HLA-DR-V500 ( clone G46-6 , BD Biosciences ) . The cells were washed , then incubated with streptavidin-Alexa Fluor 700 ( Life Technologies ) , and washed again prior to analysis . Dead cells were excluded by using Fixable Viability Dye eFluor 780 ( eBioscience ) in the first staining step . The biotinylated antibody panel visualised with streptavidin-Alexa Fluor 700 ( CD3/CD7/CD19/CD20/CD56 ) is referred to in the text and figures as “lineage” ( or “lin” ) . Translated BLAST ( Basic Local Alignment Search Tool [85] searches were performed with the mature Japanin peptide sequence as the query , using the NCBI online interface ( http://blast . ncbi . nlm . nih . gov/ ) . Similarity scores were obtained with blastp or tblastn , as appropriate , using the same interface , and with a PAM250 matrix . An initial group of 4 tick lipocalins with published structures ( FS-HBP2 [4] , Am182 [86] , Monomine [86] and OmCI [87] ) were aligned using ClustalX [88] , and this seed alignment used to construct a gap penalty mask . This mask was then employed in the alignment of an additional 242 hard tick lipocalins using ClustalX . Sequences for alignment were taken from: ( i ) the table provided as supplementary data by Francischetti and colleagues [89] , from which all complete sequences identified as hard tick lipocalins , were used , with the exception of those described as group VIII , which we do not believe to be lipocalins ( based on the absence of conserved sequence features ) ; ( ii ) LIR2 and LIR7 [90]; ( iii ) Ir-LBP [91]; ( iv ) the sequences described in this paper . Sequences are named according to their published abbreviation , Genbank accession number , or as referred to by Francischetti and colleagues . This alignment was manually refined to align key conserved sequence features , and MUSCLE [92] used to realign subsections of the alignment between conserved features . The edges were trimmed manually to leave a conserved core . Evolutionary history was then inferred using the maximum-likelihood method . After model selection according to AICc and BIC criteria , the Whelan and Goldman + Freq . model [93] was used , with initial tree ( s ) for the heuristic search generated by applying the Neighbour-Joining method to a matrix of pairwise distances estimated using a JTT model . A discrete Gamma distribution was used to model evolutionary rate differences among sites ( 5 categories ( +G , parameter = 7 . 3058 ) ) . The bootstrap consensus tree inferred from 50 replicates is taken to represent the evolutionary history of the taxa analysed . All positions with less than 90% site coverage were eliminated . That is , no more than 10% alignment gaps , missing data , or ambiguous bases were allowed at any position . There were a total of 112 positions in the final dataset . An alternative analysis where all positions with less than 95% site coverage were supported the conclusion that Japanin-like proteins form a clade , as did an analysis using the neighbour-joining method ( with evolutionary distances computed using the JTT matrix-based method ) . For analysis of cytokine secretion and flow cytometry data , it was necessary to fit a two-level model in order to take into account within-donor correlations . Accordingly , a linear mixed effects model with donor as a random effect was employed , with p values estimated using Markov chain Monte Carlo sampling ( MCMC ) . Normality and stability of variance were also required; this was achieved by means of a log transformation . The inverse ( exponential ) transformation to arrive at the model values involves Jensen's inequality bias: this is a 2nd order effect which varies according to the reciprocal of the sample size , and in this case was negligible for practical purposes . In some cases , above-scale values necessitated putting data into ordered categories , after which a two-level ordinal regression model , with donor as a random effect , was fitted successfully . Statistical analyses were performed with R [94] , using the lme4 [95] and ordinal [96] packages for modelling , and the languageR package [97] for MCMC sampling . Line and bar charts were produced with R , using the ggplot2 [98] , plotrix [99] and Cairo [100] packages . Flow cytometry data was collected using FACSDiva ( BD Biosciences ) , and analysed and plotted with FlowJo ( Tree Star ) . Sequence alignments were viewed and edited using UGENE [101] , and formatted for publication with Jalview . Phylogenetic analyses were conducted using MEGA version 5 . 1 [102] , and trees were formatted for publication with FigTree 1 . 4 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . UniProt accession numbers for proteins mentioned in the text can be found in Table S1 . | Dendritic cells ( DC ) are specialised cells of the vertebrate immune system . DC can sense different types of infectious agents and parasites , and both trigger and help regulate the specific types of immunity needed to eliminate them . We have discovered that the largest and globally most important group of hard ticks produce a unique family of proteins in their saliva that selectively targets DC , radically altering functions that would otherwise induce robust immune responses; these proteins also prevent DC developing from precursor cells . The production of these salivary molecules may help to explain two highly unusual features of these hard ticks compared with other blood-feeding parasites: their ability to feed continuously on their vertebrate hosts for considerable lengths of time ( 7 days or more ) without eliciting potentially damaging immune responses , and their capacity to transmit possibly the greatest variety of pathogens of any type of invertebrate . | [
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"immunoregulat... | 2013 | Novel Immunomodulators from Hard Ticks Selectively Reprogramme Human Dendritic Cell Responses |
Interactions between protein domains and lipid molecules play key roles in controlling cell membrane signalling and trafficking . The pleckstrin homology ( PH ) domain is one of the most widespread , binding specifically to phosphatidylinositol phosphates ( PIPs ) in cell membranes . PH domains must locate specific PIPs in the presence of a background of approximately 20% anionic lipids within the cytoplasmic leaflet of the plasma membrane . We investigate the mechanism of such recognition via a multiscale procedure combining Brownian dynamics ( BD ) and molecular dynamics ( MD ) simulations of the GRP1 PH domain interacting with phosphatidylinositol ( 3 , 4 , 5 ) -trisphosphate ( PI ( 3 , 4 , 5 ) P3 ) . The interaction of GRP1-PH with PI ( 3 , 4 , 5 ) P3 in a zwitterionic bilayer is compared with the interaction in bilayers containing different levels of anionic ‘decoy’ lipids . BD simulations reveal both translational and orientational electrostatic steering of the PH domain towards the PI ( 3 , 4 , 5 ) P3-containing anionic bilayer surface . There is a payoff between non-PIP anionic lipids attracting the PH domain to the bilayer surface in a favourable orientation and their role as ‘decoys’ , disrupting the interaction of GRP1-PH with the PI ( 3 , 4 , 5 ) P3 molecule . Significantly , approximately 20% anionic lipid in the cytoplasmic leaflet of the bilayer is nearly optimal to both enhance orientational steering and to localise GRP1-PH proximal to the surface of the membrane without sacrificing its ability to locate PI ( 3 , 4 , 5 ) P3 within the bilayer plane . Subsequent MD simulations reveal binding to PI ( 3 , 4 , 5 ) P3 , forming protein-phosphate contacts comparable to those in X-ray structures . These studies demonstrate a computational framework which addresses lipid recognition within a cell membrane environment , offering a link between structural and cell biological characterisation .
The association of peripheral proteins with the cytoplasmic leaflet of the plasma membrane is an important step in a diverse array of cellular processes , from cell signalling to membrane trafficking [1] . The cytoplasmic leaflet of the eukaryotic cell membrane carries a net negative surface charge owing to the presence of anionic lipids [2] , and recruitment of cytosolic proteins to the membrane is often achieved with the aid of these lipids [3] . Anionic lipids are thought to constitute between 10–15% [1] , [4] , [5] , [6] , [7] of the total lipids in the plasma membrane , and to be largely present in the inner ( i . e . cytoplasmic ) leaflet of the bilayer . The bulk of these anionic lipids , for example phosphatidylserine ( PS ) , are monovalent and participate in cell signalling by helping to recruit signalling proteins to the plasma membrane through electrostatic interactions [8] . Polyvalent lipids such as phosphoinositides ( PIs ) also exist , albeit at lower abundance . For example phosphatidylinositol ( 4 , 5 ) -bisphosphate ( PI ( 4 , 5 ) P2 ) typically makes up around 1% of the lipids in the cytoplasmic leaflet of the plasma membrane [5] . Though comparatively rare , PIs are involved in the regulation of several cell signalling pathways . From the PI framework structure , it is possible to generate seven physiological phosphatidylinositol phosphates ( PIPs ) , which are differentiated by the number of substituent phosphate groups and pattern of phosphorylation around the inositol ring . The net negative charge is dependent on the phosphorylation motif [9] , and so each PIP can act as a distinct target for a given class of proteins . The well-defined distribution of PIP molecules between the cytosolic membranes aids spatial regulation of protein recruitment . For example PI ( 4 , 5 ) P2 and phosphatidylinositol ( 3 , 4 , 5 ) -trisphosphate ( PI ( 3 , 4 , 5 ) P3 ) are predominantly found in the plasma membrane , whereas PI ( 4 ) P is mainly restricted to the Golgi apparatus [10] . A number of protein domains are involved in membrane recognition by signalling and trafficking proteins [1] , [10] , with the pleckstrin homology ( PH ) domain one of the most widespread . This is a structurally conserved domain of approximately 100 amino acid residues [11] , [12] which is found in many signalling proteins , and in many cases is thought to play a role in targeting proteins to the surface of the plasma membrane by recognising specific phospholipids , in particular the PIPs [1] , [13] . One well studied example of this family is the PH domain within the general receptor for phosphoinositides isoform 1 ( GRP1; Figure 1 ) . GRP1 is a member of the cytohesin family of proteins , and is responsible for catalysing GDP/GTP exchange on ADP-ribosylation factor ( ARF ) GTPases at the membrane surface . The anionic lipid PI ( 3 , 4 , 5 ) P3 acts to recruit GRP1 to the plasma membrane through electrostatic interactions , and the GRP1 PH domain reversibly binds PI ( 3 , 4 , 5 ) P3 with high affinity [14] . The importance of electrostatic interactions in binding of GRP1-PH [15] and related PH domains [16] to membranes has been demonstrated . It has been suggested that GRP1-PH first interacts with the membrane via weak nonspecific interactions with background anionic lipids , thus increasing the residence time at the membrane surface , and facilitating subsequent two dimensional diffusion to allow the protein to locate its target PI ( 3 , 4 , 5 ) P3 molecule [15] . It is therefore of interest to explore electrostatic steering of the GRP1 PH domain to the inner leaflet of the plasma membrane not only by interactions between the target PIP lipid and the PH domain , but also between the more general anionic lipid background and the PH domain . In particular , we wish to know to what extent the anionic background aids steering as opposed to acting as a ‘decoy’ luring the PH domain away from its target PIP molecule . This is a specific example of a more general problem of encounter and recognition within the crowded environment of the interior of the cell [17] , [18] , [19] , [20] . Such problems may be addressed by computer simulation methods , including Brownian dynamics ( BD ) simulations which have previously been used to model processes ranging from enzyme-substrate encounters [21] to protein folding within the crowded environment presented by bacterial cytoplasm [22] . BD simulations have been used extensively to model protein-protein encounters in aqueous solution [23] , and also those involving membrane proteins [24] , [25] , [26] . This suggests BD simulations are well suited to explore long range interactions governing PH/PIP encounters . Models of protein-protein association in solution typically incorporate two distinct steps . The diffusing partner molecules first interact through electrostatic interactions over a long range , approaching closely and then forming an initial encounter complex . The second step involves the relaxation and conformational rearrangement of the two partners within the encounter complex to form the final bound complex [27] . Thus one might anticipate protein-membrane interactions to also involve two or more comparable stages . Guided by these considerations , we have conducted a multiscale simulation study in which we employ BD to model the initial encounter between the protein and the membrane , subsequently switching to atomistic molecular dynamics ( MD ) simulations to model the formation of the membrane-bound PH-PIP complex . Using this approach we demonstrate electrostatic competition between target ( PIP ) and decoy ( anionic ) lipids for the PH domain . Significantly , we show that the experimentally observed lipid composition of the cytoplasmic leaflet is optimal for electrostatic steering of the PH domain to the PIP target .
All simulations started with the protein randomly positioned and oriented relative to a lipid bilayer membrane . The protein centre lay on a hemispherical open surface of radius 100 Å and with z>60 Å , ensuring that the protein was always at least d = 40 Å away from and perpendicular to the cytoplasmic surface of the bilayer at the start of the simulation ( see Methods and Figure 2 ) . We carried out an ensemble of 5000 BD simulations for each system ( Figure 1C shows a snapshot from one of these BD simulations ) . We then examined the distributions of the position and orientation of the PH domain relative to the target PI ( 3 , 4 , 5 ) P3 headgroup as specified by the three coordinates r , d and θ ( Figure 2 ) , evaluated across the time courses of these ensembles of simulations . We also recorded the coordinates of the PH domain ( r , d , θ ) upon first encounter of the protein and bilayer for each simulation . Our model for a PI ( 3 , 4 , 5 ) P3-containing membrane was taken from previous atomistic MD simulations [28] , replicated in the x , y-plane to generate a square bilayer patch of approximately 1560 POPC lipids with a single PI ( 3 , 4 , 5 ) P3 molecule in its centre . The final dimensions of the bilayer were approximately 220 Å×220 Å×40 Å . In the first instance , we performed BD simulations with an uncharged , zwitterionic POPC membrane , with only the PI ( 3 , 4 , 5 ) P3 molecule carrying a net negative charge ( Figure 3A ) . To mimic the presence of anionic lipids in BD simulations of charged membranes , we assigned negative charges to the phosphatidylcholine lipid headgroup . Initially , we assigned identical , fractional negative charges to all of the nitrogen atoms to generate an even charge distribution across the surface ( Figure 3B ) . In this case , the diffusing protein effectively interacts with the average charge distribution of the lipid bilayer . The topography of the electrostatic potential at the membrane surface is dependent upon the distribution of the lipids . The lipid bilayer is dynamic and lipids undergo two-dimensional lateral diffusion in the plane of the membrane , and so the charge distribution is likely to fluctuate over time . The lateral diffusion constant of POPC lipids at 300 K is 1 . 7×10−8 cm2 s−1 [29] . However , the calculated diffusion constant of GRP1-PH at 300 K is 1 . 0×10−6 cm2 s−1 ( see Methods ) , almost two orders of magnitude larger . Although care must be taken when attempting to directly compare two- and three-dimensional diffusion constants ( see e . g . [30] , [31] ) , this indicates that the protein may be more mobile than the lipids , with timescales of approximately 40 ns and 30 µs respectively for a 50 Å diffusional motion . This suggests that we should examine further the consequences of assuming the interaction of the protein with the average charge distribution of the lipid bilayer . To explore this , we also applied an alternative approach in which we assigned a single negative charge to subset of the headgroups selected randomly ( Figure 3C ) , thus generating a less even distribution of decoy negative charges . In this case , the protein interacts with a discrete distribution of charges , which is more in keeping with faster diffusion of the protein relative to the lipids . The fractional charges explored in the first set of simulations ranged from −0 . 2 e to −1 . 0 e; the number of lipids randomly assigned a charge of −1 . 0 e ranged from 20% to 100% in the second approach . Another alternative method for exploring slower lipid diffusion is to generate lipid configurations via coarse grained molecular dynamics ( CGMD ) simulations of a lipid bilayer and then extract representative snapshots for use in the BD simulations . This has an advantage over the simple random assignment described above in that it is able to account for more complex lipid bilayer phenomena such as lipid demixing . Initially , we were interested to see how easily GRP1-PH could locate its target lipid PI ( 3 , 4 , 5 ) P3 when the surrounding membrane contained increasing numbers of negatively charged lipids . It might be expected that increasing the ( negative ) surface charge density would disrupt GRP1-PH targeting by masking the position of the negatively charged PI ( 3 , 4 , 5 ) P3 . With only PI ( 3 , 4 , 5 ) P3 present in a POPC bilayer ( 0 . 0 e ) GRP1-PH spends the majority of the trajectory diffusing close to its target lipid , evidenced by the large peak at small values of r ( Figure 4A ) . However , when the surface charge is increased ( −0 . 2 to −1 . 0 e ) the peak height diminishes and the maximum shifts to larger values of r , which appears to suggest a reduction in positional steering with the protein much less likely to closely approach the PI ( 3 , 4 , 5 ) P3 molecule . To assess the degree of targeting , we extracted the peak half-width at half maximum ( Figure 4A , inset ) , with low values corresponding to narrow distributions of r and efficient PI ( 3 , 4 , 5 ) P3 positional steering , while higher values correspond to wide distributions of r and comparatively poor PI ( 3 , 4 , 5 ) P3 positional steering . It is also of interest to investigate how the distribution of d positions ( i . e . along the bilayer normal ) of the protein changes depending upon the membrane charge . As anticipated , as the membrane negative charge increases , GRP1-PH spends more time closer to the surface of the bilayer ( Figure 4B ) . It is noteworthy that a −0 . 2 e surface charge on the cytoplasmic leaflet ( corresponding to an overall bilayer composition of 10% anionic lipids , close to that observed experimentally [7] ) results in the PH domain spending the majority of its time close to or at the bilayer surface ( smaller values of d along z ) without significant diffusion away from ( on r ) the target PI ( 3 , 4 , 5 ) P3 molecule . We repeated the simulations with integer negative charges located on individual lipids rather than fractional charges evenly distributed across all lipid headgroups . Initially , the lipids carrying negative charges were selected randomly ( Figure 5A ) . In these simulations the same overall trends were observed but with some variations reflecting the fixed ‘snapshot’ of the mixed lipid bilayer used in the BD simulations . Thus the exact extent of the positional steering behaviour observed is dependent upon the instantaneous distribution of monovalent negatively charged lipids used in the BD simulation setup . We therefore speculate that lipid clustering might modulate positional steering of PH domains to PI ( 3 , 4 , 5 ) P3 . To test this , we performed a 0 . 5 µs CGMD simulation of a mixed lipid bilayer containing 20% anionic lipids ( see Methods ) and extracted configurations at intervals of 100 ns ( Figure S1 ) . These five configurations were then used as an input for a set of BD simulations , to probe the sensitivity of GRP1-PH targeting to the distribution of anionic lipids . Despite the fact that the anionic lipid concentration is the same in each snapshot , we see variations in the positional steering for the different lipid configurations ( Figure 5B ) , again suggesting that steering is influenced not only by the concentration of anionic lipids but also by the distribution of these lipids over the surface . Interestingly , if we take the average of the r distributions over these five sets of BD simulations , we generate a profile similar to that observed for the single set of BD simulations using the fractional charge distribution where each lipid is assigned a charge of −0 . 2 e ( Figure 6 ) . This suggests that the fractional charge distribution is a reasonably good model of the time-averaged behaviour of the system , allowing for lipid dynamics on the sub-microsecond timescale . As well as the effect of bilayer surface charge on the positional steering of GRP1-PH to the membrane , we also wished to investigate how surface charge might influence orientational steering of the protein as it approaches the surface , as this may be anticipated to influence the formation of a ‘productive’ GRP1-PH/PI ( 3 , 4 , 5 ) P3 complex upon encounter . GRP1-PH carries a dipole moment , and the vector is directed towards the binding cavity of the protein ( Figure 1 ) . In order to monitor the orientation of the protein over the BD trajectories , we calculated the angle , θ , made by a vector from the PH domain to the target PI ( 3 , 4 , 5 ) P3 and the z axis ( see Supporting Information ) , with θ = 0° corresponding to the protein orientation seen in the docked GRP1-PH/PI ( 3 , 4 , 5 ) P3 complex observed in previous structural and MD simulation studies [28] . The distribution of θ as a function of negative surface charge shows a clear effect of a surface charge of −0 . 2 e or more on orienting the PH domain relative to the bilayer ( Figure 7A ) . Thus the distribution of orientations shifts towards values of θ corresponding to alignment of the GRP1-PH dipole moment ( which lies at an angle of 56° to the reference vector ) with the membrane normal ( Figure 7 ) . The membrane charge therefore appears to influence not only the position of the protein but also its orientation Although increasing surface charge aids orientational steering of GRP1-PH , this seems to come at the cost of some loss of positional steering . It seems that a level of negative surface charge ( −0 . 2 e ) close to that reported [7] in the cytoplasmic leaflet of mammalian plasma membranes may be optimal in achieving both forms of steering , as can be seen in a two-dimensional distribution of r and θ values adopted during a simulation with a −0 . 2 e bilayer ( Figure 5B ) . To explore this further we analysed the distribution of first-encounter positions between the bilayer and the PH domain ( Figure 7C ) . At −0 . 2 e there was clear positional steering of the PH domain to the PI ( 3 , 4 , 5 ) P3 molecule . Increasing the surface charge to −0 . 4 e or more resulted in almost complete loss of positional steering . We performed BD simulations of two mutants of GRP1-PH ( R284A and K279A ) which have previously been shown to reduce binding of the protein to soluble inositol phosphates [32] . It was therefore of interest to see whether these mutations also influenced steering of the PH domain to PI ( 3 , 4 , 5 ) P3 in a lipid bilayer . The mutant R284A has been shown experimentally to almost completely abolish binding in solution whereas the K279A mutation has a smaller effect . Both mutants show a modest reduction in positional steering ( Figure S2 ) , and in the fraction of time spent close to the bilayer , with smaller values of d along z . This effect was more pronounced in the case of the R284A mutant , which correlates with the experimental results . This reduction in steering could be a contributory factor to the experimentally observed lower binding affinity , but clearly other effects such as conformational changes and sidechain-specific protein-lipid interactions may also be important . In order to implement a multiscale approach to simulating membrane binding of PH domains we combined BD simulations with subsequent MD simulations . Similar combined approaches have been successful in studying DNA-enzyme interactions [33] . Optimal encounter complexes from the BD simulations , i . e . in which both positional and orientational steering were observed , were used as initial configurations for atomistic MD simulations to explore the conformational changes involved in complex formation . As seen above a suitable configuration for binding is likely to be one with small values of r , d and θ simultaneously , with GRP1-PH in close proximity to PI ( 3 , 4 , 5 ) P3 with its binding cavity and also its dipole moment oriented towards the ligand . We therefore performed a simple search of the trajectories in order to locate a configuration satisfying these requirements . One such optimal configuration ( r = 9 Å , d = 18 Å , θ = 27° ) was extracted and we performed two MD simulations to test whether the protein was able to bind to its target lipid from this position ( Figure 8 ) . In both MD simulations GRP1-PH approaches the PI ( 3 , 4 , 5 ) P3 molecule in the lipid bilayer , with the separation between the centre of mass of the protein and that of the IP ( 1 , 3 , 4 , 5 ) P4 headgroup falling to 15 Å in both cases . This is in good agreement with the centre-to-centre separation of 13 Å found in the ligand-bound crystal structure ( PDB 1FGY [34] ) . To investigate the geometry of the complex , we mapped the minimum distance between each amino acid residue of the protein and each of the phosphorus atoms of the I ( 1 , 3 , 4 , 5 ) P4 headgroup in the crystal structure . This revealed a characteristic protein-ligand interaction ‘fingerprint’ which agreed well with that seen in both of the simulations ( Figure 8 ) . After a 100 ns MD simulation the protein locates the membrane-bound PI ( 3 , 4 , 5 ) P3 within the first 20 ns of simulation , and binds via a set of amino acid residues similar to that found in the crystal structure . This set of interactions is preserved throughout the 100 ns simulation .
We have used a multiscale simulation approach , combining BD and MD simulations , to characterise in atomic detail the association of GRP1-PH with a PI ( 3 , 4 , 5 ) P3-containing lipid bilayer . The BD simulations reveal how long range electrostatic interactions steer the PH domain , both positionally and orientationally , towards the PI ( 3 , 4 , 5 ) P3-containing anionic bilayer surface . There appears to be a payoff between non-PI ( 3 , 4 , 5 ) P3 anionic lipids attracting the PH domain to the bilayer surface in a favourable orientation , and their acting as ‘decoys’ for interaction of PH with the PI ( 3 , 4 , 5 ) P3 molecule . This provides a refinement of an earlier model of the role of background anionic lipids in PH domain binding [15] . It is notable that the dipole moment vector of GRP1-PH points from the centre of mass of the protein towards the location of the bound PI headgroup . This , coupled with the observation that increasing surface charge leads to enhanced alignment of the dipole moment with the membrane normal , suggests that the orientation of the molecular dipole moment may play an important role in successful PH domain targeting . Evaluation of the molecular dipole moments for a variety of other PH domains suggests that this orientation is a conserved feature of PH domains ( Figure S3 ) . Thus , it is likely that the GRP1-PH targeting behaviour observed here may be conserved across the PH domain family . Significantly , a typical level of anionic lipid in the bilayer ( approximately 20% in the cytoplasmic leaflet ) seems to be optimal to both enhance orientational steering and to localise GRP1-PH proximal to the surface of the bilayer without sacrificing its ability to locate PI ( 3 , 4 , 5 ) P3 within the bilayer plane . Thus the protein is steered into the correct orientation for binding by the higher surface charge density as compared with a zwitterionic membrane , but the charge density is not yet high enough to mask the position of PI ( 3 , 4 , 5 ) P3 , thereby allowing for efficient positional steering . Finally , we demonstrate that using appropriate encounter complexes from the BD simulations as initial configurations for atomistically detailed MD simulations , which include explicit solvent molecules and intramolecular motions , leads to formation of a GRP1-PI ( 3 , 4 , 5 ) P3 complex at the membrane surface that accurately reproduces the geometry of the bound complex from the crystal structure . This combined BD-MD technique therefore provides a means to model the membrane binding modes of lipid-recognition proteins , a class of proteins which play a number of key roles in membrane function [1] and disease [35] . With respect to PH/PI ( 3 , 4 , 5 ) P3 recognition we arrive at an overall model which combines electrostatic steering directly to the target PI ( 3 , 4 , 5 ) P3 , possibly with an element of non-specific ( electrostatic ) bilayer association , followed by 2D diffusion at the surface until the PH/PI ( 3 , 4 , 5 ) P3 encounter occurs . This process is likely to involve further complexities related to multiple membrane targeting domains binding to more than one target lipid [36] . Our findings contribute to a more general consideration of lipid bilayer composition and recognition by protein domains ( see e . g . [1] , [5] , [37] ) . The test system used in this study , with a PI ( 3 , 4 , 5 ) P3/lipid ratio of approximately 1∶1000 is likely to be ( globally ) representative of mammalian cell plasma membranes . While it is difficult to estimate the physiological concentration of PI ( 3 , 4 , 5 ) P3 , which varies according to the level of cell stimulation , PI ( 3 , 4 , 5 ) P3 is generated from PI ( 4 , 5 ) P2 , the concentration of which is around 1% of cell membrane lipids [5] . Thus , if we assume that even at the peak of cell stimulation the concentration of PI ( 3 , 4 , 5 ) P3 will be less than 1% , then the PI ( 3 , 4 , 5 ) P3 concentration present in our simulations is of the correct order of magnitude . Of course , this is something of a simplification given the importance of localisation and gradients of PI ( 3 , 4 , 5 ) P3 in cell signalling and dynamics [38] and also possible larger scale differences in PIP composition in plasma membranes between the apical and basal regions ( with higher concentrations of PI ( 4 , 5 ) P2 and PI ( 3 , 4 , 5 ) P3 respectively ) in epithelial cells [39] . These studies indicate that it is essential to the function of a cell that domains such as GRP1-PH not only bind in a stable fashion to their cognate PIPs but are able to locate them in complex ‘mixed’ systems similar to those present in vivo . It is important to consider the limitations of the current model . Our BD simulations treat the bilayer as a static entity lacking any internal dynamics . This is likely to be sufficient to capture longer range steering interactions , but a more dynamic model may be needed if this approach is to be applied to larger , more complex membrane systems . One option would be to combine BD for longer range protein/membrane interactions with a CGMD [40] approach to generate and update configurations of a mixed lipid bilayer . In particular such an approach should enable one to capture effects whereby cationic proteins interacting with a membrane surface may result in redistribution of anionic lipids within the membrane [41] , [42] , [43] , [44] . It has been suggested that this can result in correlated diffusion of lipids and protein [45] and in enhancement of the binding affinity of a protein by charged lipids having a higher local concentration in its vicinity [46] , [47] . The BD simulations presented here also neglect any effect of hydrodynamic interactions on the association process [48] , [49] , [50] , [51] . This has been suggested to lead to potential problems in simulations of protein/protein association in solution , and should be explored for any effects on a protein diffusing close to a membrane surface . In our analysis of the results of the BD simulations we distinguish between positional and orientational aspects of electrostatic steering . We make this distinction as it is necessary for both types of steering ( positional and orientational ) combine favourably to yield a ‘productive’ encounter complex . In contrast , one could imagine a scenario whereby the PH domain closely approaches the PI ( 3 , 4 , 5 ) P3 ligand ( i . e . good positional steering ) but with its molecular dipole in the wrong orientation for binding ( i . e . poor orientational steering ) . From a biological perspective the main limitation is that our simulations mimic in vitro biophysical studies , with a simplified bilayer lipid composition . Current lipidomics studies [7] are revealing the spatial and temporal complexities of membrane lipid composition within living cells . Furthermore , recent studies of syntaxin-1A/PIP2 interactions [52] indicate that electrostatic interactions between PIP2 and the basic residues in the juxtamembrane region of syntaxin-1A result in formation of approximately 75 nm diameter PIP2-rich microdomains in the inner leaflet of PC12 cell plasma membranes . Therefore , it seems likely that our studies have only scratched the surface in terms of understanding how the GRP1-PH domain locates and binds to a PI ( 3 , 4 , 5 ) P3 molecule within a cell membrane . However , by combining previous approaches using electrostatics calculations [5] and detailed MD simulations [28] , they provide a computational framework to enable us to begin to address the more complex cell membrane environment , thus offering a link from membrane protein structure and biophysics through to cell biology of membranes .
Our model for a PI ( 3 , 4 , 5 ) P3-containing phospholipid bilayer was taken from previous MD simulations [28] , replicated in the x and y directions to generate a square bilayer patch comprising approximately 1560 POPC lipids with a single PI ( 3 , 4 , 5 ) P3 molecule in the centre . The final dimensions of the bilayer were approximately 220 Å×220 Å×40 Å . Atomic partial charges on the lipids were identical to those used previously [28] . In the first instance , we carried out BD simulations with an uncharged , zwitterionic POPC membrane , with only the PI ( 3 , 4 , 5 ) P3 molecule carrying a net negative charge . To mimic the presence of anionic lipids in BD simulations of charged membranes , we assigned negative charges to the nitrogen atoms of the phosphatidylcholine headgroups of the POPC lipids . As discussed above we either assigned identical , fractional negative charges to all of the nitrogen atoms to generate a relatively even charge distribution across the surface or assigned a single negative charge to a random subset of the nitrogen atoms , to generate an uneven distribution of negative charge . Finite difference Poisson-Boltzmann calculations were carried out using the APBS software [53] . The Poisson-Boltzmann equation was solved at a temperature of 300 K and an ionic strength of 0 . 1 M using cubic grids of dimensions 385×385×385 for the bilayer and 129×129×129 for the protein , each with a 1 Å spacing . Grids were centred on the centre of mass of the bilayer and of the protein respectively . Brownian dynamics simulations were performed using SDA version 5 . 01 [54] . While the specifics of the SDA software are documented in detail elsewhere , for completeness we briefly review the method here . The diffusion equation is solved using the algorithm developed by Ermak and McCammon [55] , and the translational Brownian motion of the protein is simulated as the displacement Δr of the relative separation vector r during a time step Δt according to the relation:where F is the force on the protein , R is a random vector that satisfies and and the prefactor D/kBT represents the solvent friction . Rotational motions are treated in an analogous fashion:where Tij is the torque on the protein and W is a random rotation angle that satisfiesThe forces between the diffusing protein and the target bilayer are computed as finite-difference derivatives of the free energy of interaction between the protein and the bilayer . The unfavourable electrostatic desolvation term is given by:This is approximated by:As in previous studies , the scaling factor α was set to 1 . 67 . Electrostatic desolvation grids were calculated according to the protocol developed by Elcock et al . [56] . HYDROPRO [57] was used to estimate the translational and rotational diffusion constants for the protein giving values of D = 1 . 042×10−6 cm2 s−1 and DR = 1 . 656×107 rad s−1 respectively . We used the effective charge method [58] to assign partial charges to the protein . We modified the SDA source code to truncate the b-sphere , such that all trajectories began on the hemispherical open surface given by r2 = x2+y2+z2 , z>60 Å . As the width of one leaflet of the bilayer is around 20 Å , this ensured that the protein always lay at least d = 40 Å distant from the surface of the bilayer at the start of the trajectory ( Figure 2 ) . Rotational diffusion of the lipid bilayer was switched off . The Debye length of the system at an ionic strength of 0 . 1 M is approximately 10 Å , and accordingly we chose the radius of the b-surface to be 100 Å . Our bilayer patch was approximately 220 Å wide , and so we elected to set the q-surface to 105 Å . This resulted in the termination of any trajectory that came close to the edge of the bilayer , thus limiting edge effects . We carried out 5000 BD simulations for each system . We note that our truncation of the b-sphere coupled with our choice of the q-surface is likely to invalidate the Northrup-Allison-McCammon ( NAM ) method for computing reaction rates [59] , since the ensemble reactive flux is no longer spherically symmetric . However the focus of this study was to investigate the behaviour of the protein as it explored the bilayer surface , rather than attempt to calculate reaction rates explicitly . One problem when attempting to plot histograms of r = x2+y2 is that the bin width between r and r + dr is not constant , and so the area of the bin is proportional to r . This has the effect of overrepresenting the larger distances in the histogram . One way of remedying this is to reweight the data by some appropriate factor to compensate for this effect [60] . In plane polar coordinates the Jacobian factor is 2πr , and so we rescale the data in each of the bins of our histogram:The same issue occurs when constructing histograms of the orientation of the protein , which can be represented by a unit vector rotating in space . Plotting histograms of the distribution of angles that this vector makes with the z axis is problematic since the areas of the spherical segments between θ and θ + dθ are not equal , which again distorts the distribution . In spherical polar coordinates the Jacobian factor is r2sin ( θ ) , and so we rescale our histograms of the protein orientation using the following: MD simulations were carried out with GROMACS version 4 . 0 . 5 [61] using the GROMOS96 43a1 forcefield [62] . Simulations were run at 300 K with temperature kept constant by coupling to a Berendsen thermostat [63] with a coupling constant of τT = 0 . 1 ps . Pressure was maintained at 1 atm using a Parrinello-Rahman barostat [64] , [65] and semi-isotropic pressure coupling , with τp = 1 . 0 ps and a compressibility of 4 . 6×10−5 bar−1 . The SPC water model [66] was used , and the system was energy minimised for up to 1000 steps using the steepest descent algorithm prior to the production runs . Simulations were carried out using a timestep of Δt = 2 fs , and bond lengths and angles were constrained using the LINCS algorithm [67] . Independent simulations were initiated from the same system configuration but with a different set of initial velocities . The neighbour list was updated every 10 steps and atomic positions were written every 10 ps . Electrostatic interactions were treated with the particle mesh Ewald ( PME ) approach [68] with a short-range direct space cut-off of 10 Å . CGMD simulations were carried out with the MARTINI forcefield [69] , using a timestep of 10 fs . In this CG model , zwitterionic lipids such as POPC are approximated by a positively charged particle ( choline ) , a negatively charged particle ( phosphate ) , two polar particles ( glycerol ) and two acyl chains made up of four and five hydrophobic particles respectively . We denote these lipids as CG4/5 . Negatively charged lipids such as POPS are treated in a similar fashion , except that the positively charged particle is now replaced with a polar particle to represent the switch from choline to serine . These negatively charged CG lipids are therefore denoted as CG4/5– . The CGMD simulation comprised a mixture of approximately 1560 lipids in total , with CG4/5∶CG4/5– lipids in a ratio of 80∶20 in the PI ( 3 , 4 , 5 ) P3-containing upper leaflet of the lipid bilayer and pure CG4/5 lipids in the lower leaflet of the lipid bilayer . No evidence of lipid flip-flop between bilayer leaflets for the CG4/5– lipids was observed over the simulation . | Cell signalling pathways are crucial for many biological processes including cell proliferation and survival . Signalling is governed by a complex network of interactions within the cell , and disruption of signalling can lead to a variety of human diseases . Often , a key event in the signalling cascade is the reversible recruitment of peripheral membrane proteins to the surface of the cell membrane , where they then bind to a specific lipid in order to perform their function . However , it is not clear how these proteins locate their target lipid in the complex multi-lipid environment of the plasma membrane . Here , we have used a combination of computational techniques to simulate the association of a signalling protein with the surface of the cell membrane . We demonstrate that the mechanism of membrane binding is dependent upon the lipid composition of the lipid bilayer , and the results show that orientational and positional steering of the protein is optimised when the anionic lipid content of our model membrane matches the physiological composition observed in cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biology",
"biophysics",
"simulations",
"biophysics"
] | 2012 | Finding a Needle in a Haystack: The Role of Electrostatics in Target Lipid Recognition by PH Domains |
It is estimated that in Latin America and the Caribbean ( LAC ) at least 13 . 9 million preschool age and 35 . 4 million school age children are at risk of infections by soil-transmitted helminths ( STH ) : Ascaris lumbricoides , Trichuris trichiura and hookworms ( Necator americanus and Ancylostoma duodenale ) . Although infections caused by this group of parasites are associated with chronic deleterious effects on nutrition and growth , iron and vitamin A status and cognitive development in children , few countries in the LAC Region have implemented nationwide surveys on prevalence and intensity of infection . The aim of this study was to identify gaps on the mapping of prevalence and intensity of STH infections based on data published between 2000 and 2010 in LAC , and to call for including mapping as part of action plans against these infections . A total of 335 published data points for STH prevalence were found for 18 countries ( 11 . 9% data points for preschool age children , 56 . 7% for school age children and 31 . 3% for children from 1 to 14 years of age ) . We found that 62 . 7% of data points showed prevalence levels above 20% . Data on the intensity of infection were found for seven countries . The analysis also highlights that there is still an important lack of data on prevalence and intensity of infection to determine the burden of disease based on epidemiological surveys , particularly among preschool age children . This situation is a challenge for LAC given that adequate planning of interventions such as deworming requires information on prevalence to determine the frequency of needed anthelmintic drug administration and to conduct monitoring and evaluation of progress in drug coverage .
Helminth infections impose a great and often silent burden of morbidity and mortality on poor populations in developing countries . The most common helminth infections are caused by soil-transmitted helminths ( STH ) : roundworms ( Ascaris lumbricoides ) , whipworms ( Trichuris trichiura ) , and hookworms ( Necator americanus and Ancylostoma duodenale ) . Worldwide estimates suggest that A . lumbricoides infects 1 . 221 billion people , T . trichiura , 795 million , and hookworms , 740 million . Infections occur most frequently in the Americas , China and East Asia , and Sub-Saharan Africa [1] . STH are amongst the most prevalent pathogenic organisms on the planet , estimated to infect almost one-sixth of the global population with the highest rates among school-age children ( SAC ) who are frequently infected with two or more species at a time [2] . Stunting usually occurs between 6 months and 2 years of age , overlapping with the period in which STH begin to emerge [3] . STH infection primarily affects physical and cognitive development [4] . A . lumbricoides can cause malnutrition; hookworms damage the intestinal mucosa leading to bleeding , loss of iron and anemia , and infections by T . trichiura cause chronic reduction of food intake [5] . During pregnancy , mild or severe infections by hookworms can cause anemia in the mother and damage to the fetus , leading to low birth weight [6] . In areas where helminths are common , deworming activities can be done once or twice a year among the population at risk ( those with no access to improved sanitation facilities ) , including deworming for pregnant women after the first trimester . Deworming during pregnancy reduces severe maternal anemia , increases birth weight and reduces infant mortality [7] . Thus , regular treatment against helminth infections produces both immediate and long-term benefits , contributing significantly to improving the growth and cognitive development of affected individuals , especially children . In 2001 , the World Health Assembly adopted Resolution WHA/54 . 19 [8] urging all Member States where STH are endemic to attain “a minimum target of regular administration of chemotherapy to at least 75% and up to 100% of all SAC at risk of morbidity by 2010” . On October 2009 , the Directing Council of the Pan American Health Organization ( PAHO ) approved Resolution CD49 . R19 [9] stating the commitment of PAHO's Member States to eliminate or reduce neglected diseases , among them STH , to levels such that they are no longer considered public health problems by the year 2015 , and hence help to achieve the Millennium Development Goals . In PAHO's Resolution , STH and schistosomiasis were classified as diseases whose prevalence can be drastically reduced with available cost-effective interventions . Regarding STH , the following goal to be reached by 2015 was defined: reducing prevalence among SAC in high risk areas ( prevalence >50% ) to less than 20% as measured by quantitative egg count in feces . The Resolution also mentioned several interventions to reach the STH control goal , especially those related with improved access to safe water and basic sanitation , preventive chemotherapy and health education through inter-sectoral collaboration . According to WHO/PAHO estimates , in LAC there were 13 . 9 million preschool-age children ( PSAC ) and 35 . 4 million SAC in need of preventive chemotherapy for STH in 2012 . These estimates have been calculated based mainly on the percentage of people without access to improved sanitation facilities , differentiated by rural and urban areas , due to the fact that STH prevalence and intensity of infection in LAC are not well mapped [10] . The purpose of this paper is to present the status of the mapping of prevalence and intensity of STH infection and to identify information gaps in LAC for the 2000–2010 period based on a literature search . This study is a call for action in LAC to address the existing information gaps in order to prioritize integrated interventions for STH control based on solid evidence , increase efforts towards reduction of the morbidity caused by these parasites , and reach the targets in the WHO and PAHO resolutions .
A wide literature search was conducted to collect data on STH prevalence and intensity of infection ( Ascaris lumbricoides , Trichuris trichiura and hookworms ) among preschool ( 1–4 years of age ) and school-age ( 5–14 years of age ) children for the 2000–2010 period at the lowest subnational administrative levels in LAC countries ( Checklist S1 ) . The decision to include studies published between 2000 and 2010 was taken arbitrarily by the authors considering this 10-year period to have sufficient updated information on the status of STH mapping in the Region to establish the current information availability , reflecting the level of interest on this issue in the Region . A database was built including information from 236 studies , of which 120 met the inclusion criteria [11]–[130] . There are 45 countries and territories in LAC with at least 13 , 591 units at second subnational level that may be districts , municipalities or provinces depending on the geopolitical structure of each country or territory . Once the information was collected , a preliminary report was published in PAHO's website [131] . That document is the main information source for the analysis presented here . The scientific literature search was done through the online databases of PubMed ( including MEDLINE ) , LILACS ( including SciELO ) , BIREME and Cochrane . Additionally , we carried out a search of information published in the websites of health ministries , NGOs and FBOs reporting data on deworming activities to PAHO and WHO from 2005 to 2010 , as well as information published on the websites of PAHO country offices in LAC . The online database search was done using MeSH terms to facilitate an ample retrieval of published information on STH prevalence and intensity of infection in LAC . The following MeSH terms and subheadings were used for searches on PubMed: ( ( “Helminthiasis”[Mesh] OR ( “Helminthiasis/epidemiology”[Mesh] OR “Helminthiasis/parasitology”[Mesh] OR “Helminthiasis/statistics and numerical data”[Mesh] ) ) ) AND ( “Child”[Mesh] OR ( “Child/epidemiology”[Mesh] OR “Child/statistics and numerical data”[Mesh] ) ) . The following terms were also used for searches on PubMed to recover more papers: 1 ) ‘Prevalence intestinal parasites child’ restricted by country , sub-regions in LAC ( Central American Isthmus , Latin Caribbean , Andean area , Southern Cone , Non-Latin Caribbean ) and publication year of study; 2 ) ‘Soil transmitted helminths prevalence’ restricted by country , sub-regions in LAC ( Central American Isthmus , Latin Caribbean , Andean area , Southern Cone , Non-Latin Caribbean ) and publication year of study; and 3 ) ‘Ascaris lumbricoides or Trichuris trichiura or hookworms or Necator americanus or Ancylostoma duodenale’ by country , sub-regions in LAC ( Central American Isthmus , Latin Caribbean , Andean area , Southern Cone , Non-Latin Caribbean ) and publication year of study . Additionally , for the search of papers in BIREME and LILACS databases , the following terms were used: 1 ) “Helminthiasis” AND “prevalence” by country and LAC; and 2 ) “Intestinal parasites” AND “prevalence” by country and LAC . The following were the inclusion criteria: 1 ) studies with data on prevalence and intensity of infection published from 2000 to 2010 including the geographical location of the study so as to enable the identification of the municipal or local level; and 2 ) studies including STH prevalence data disaggregated by age groups . The following were the exclusion criteria: 1 ) studies undertaken before 1995; 2 ) reports with data on intensity of infection not using WHO classification parameters ( mild , moderate and high ) , and 3 ) studies with no data on their geographical location or including data on prevalence and intensity of infection of limited use for the present analysis ( e . g . studies reporting prevalence data only for adults , or studies in children with eosinophilia ) . Given the limited number of data published on STH prevalence and intensity of infection in the Region , no restriction was established regarding sample size , type of study ( community-based or among school children , base line or intervention monitoring ) or laboratory diagnostic methods used , as the aim was not to verify the existence of accurate data for the two indicators in the Region , but to explore existing information and gaps on mapping ( Figure S1 ) . After reviewing the articles and reports , information was extracted from those that met the inclusion criteria and then a database was built with the following variables: name of country; name of geographical location of study; name of geographical locations within the first and second subnational levels corresponding to the geographical location of the study ( in those cases where no information on this regard appeared , the publication year was recorded ) , sample size , STH prevalence , prevalence of infection by species , intensity of infection and age group of study subjects . Each value of prevalence and intensity of infection registered on the database was denominated a data point . In those studies that included data for several geographical locations at the lowest subnational level ( e . g . , municipalities ) , data were registered for each of these locals and , therefore , we were able to extract more than one prevalence or intensity of infection data point from several studies . A descriptive analysis of the number of studies including data on prevalence and intensity of infection published from 2000 to 2010 was done by country , age group and prevalence and intensity of infection range . Although the search was restricted to studies published from 2000 to 2010 , some authors included results from surveys conducted before the study publication date , and for this reason our analysis included data only from studies carried out from 1995 onwards . Besides the analysis of frequency and proportion distributions , the geographic locations of prevalence and intensity of infection data points for preschool and school age children were mapped , as this was useful to visualize gaps in data publishing . The database was made with MS Excel 2010 and the analysis with Tableau 7 . 0 .
A total of 236 publications were found , of which 120 met the selection criteria established for the study; the publications corresponded to 18 countries: Brazil ( 39 publications , 32 . 5% ) , Argentina ( 11 , 9 . 2% ) , Colombia ( 10 , 8 . 3% ) , Venezuela , Mexico , Ecuador ( 9 , 7 . 5% each ) , Peru ( 7 , 5 . 8% ) , Cuba ( 5 , 4 . 2% ) , Honduras , Bolivia ( 4 , 3 . 3% each ) , Guatemala ( 3 , 2 . 5% ) , Haiti , Costa Rica , Belize ( 2 , 1 . 7% in each country ) , Saint Lucia , Paraguay , Nicaragua and Guyana ( 1 , 0 . 8% each ) . All publications were articles published in scientific journals . Although some documents were found in the websites of health ministries , NGOs and FBOs , none of them included information meeting the inclusion criteria . The studies recovered by the Cochrane database had no information on their geographic location and , therefore , they were not considered . A total of 335 data points on STH prevalence were registered and analyzed for 18 countries out of which 12 . 0% were for preschool-age children ( 40 prevalence data points ) , 56 . 7% for school-age children ( 190 prevalence data points ) and the remaining 31 . 3% ( 105 prevalence data points ) were registered for children under 15 years of age because authors did not differentiate by age groups , although they did state in the methodology that the study had been undertaken among children under 15 years of age . Another 687 data points on prevalence by STH species were also extracted , of which 40 . 2% corresponded to A . lumbricoides , 35 . 5% to T . trichiura , 19 . 5% to hookworms ( undifferentiated by species ) , 2 . 9% to A . duodenale , and 1 . 9% to N . americanus . Additionally , 151 data points were extracted for intensity of infection . The main characteristics of the 335 data points for STH infection prevalence were the following: 1 ) 27 . 8% of prevalence data points showed values over 50% , 34 . 9% between >20 and 50% , and 37 . 3% below 20%; 2 ) 63% of prevalence data points corresponded to data published by four countries ( Argentina , Brazil , Honduras and Mexico ) ; 3 ) 35% or more of STH prevalence data from Belize , Ecuador , Guatemala , Honduras , Mexico and Venezuela showed values over 50% . Prevalence ranges found in each country are shown in Table 1 . The geographic locations of STH prevalence data points for PSAC and SAC by prevalence ranges are shown in Figures 1 and 2 . A total of 151 infection intensity data points from seven countries ( Argentina , Bolivia , Brazil , Colombia , Ecuador , Honduras and Venezuela ) were registered and analyzed . The following were their main characteristics: 1 ) The largest number of data points was found for Honduras ( 41 . 7% ) , Bolivia and Brazil ( 15 . 9% each ) ; 2 ) 56 . 9% of infection intensity data corresponded to SAC , 17 . 8% to PSAC and 25 . 1% to children under 15 years of age; 3 ) 37 . 1% showed light , 34 . 4% , moderate and 28 . 5% heavy intensity infections ( Table 2 ) ; 4 ) 65 . 1% of the data points on heavy intensity infections corresponded to A . lumbricoides ( 28 data points ) , and 5 ) no data on intensity of infection published after 2005 were found . The most recent year in which prevalence data points were found varied among countries . Regarding prevalence data points for SAC , and taking as the most recent data those from studies conducted after 2005 , we observed that 10 countries ( out of 16 with data points ) had studies conducted from 2005 to 2010 . As for preschool age children , six countries ( out of 11 with data points ) had studies , while nine countries had studies for children under 15 years of age ( out of 16 with data points ) .
It is necessary and urgent to update the mapping of STH prevalence and intensity of infection in several LAC countries in order to make better evidence-based decisions regarding deworming activities . The data available for PSAC are insufficient to know the real situation of STH prevalence and intensity of infection in many countries in the LAC region , and although more data for SAC population were found , these data are only from a limited number of countries and some second administrative levels . It is also necessary to prioritize the operational research agenda within governments and interested groups in order to develop STH mapping activities . Mapping is necessary to know where are the populations at risk and which age groups are at risk of STH infection , as well as the municipalities where authorities need to focus their efforts ( if these data are available ) , and also for monitoring and evaluation purposes to know the impact of interventions , not only deworming , but also nutrition , education , and environmental interventions and integrated actions to reduce child morbidity and mortality and improve child development . Without enough accurate and specific data about STH prevalence and intensity of infection by age group ( PSAC and SAC ) in LAC , it will be difficult to identify with certainty the main needs , resources to be assigned and places where integrated actions , including deworming , must be focused in order to reach the regional and global deworming goals for children at risk and reduce the prevalence and intensity of infections by STH . | Soil-transmitted helminths ( STH ) are part of the group of neglected infectious diseases ( NID ) in Latin America and the Caribbean ( LAC ) , and are associated with several adverse chronic effects on child health . Although control interventions such as periodic administration of anthelmintic drugs , health education , improved access to safe water and sanitation , among others , are acknowledged to be an important means to reduce morbidity and to achieve control , epidemiological information on prevalence status is lacking at the lowest sub-national administrative levels ( municipalities , districts or provinces ) in many countries thus hindering decision making regarding not only the treatment , but also the monitoring of progress in deworming coverage , the assessment of epidemiological impact on parasite prevalence and load and , therefore , the achievement of the overall public health goals . Epidemiological surveys can be expensive and require time and effort for their implementation , which could explain the low number of studies published with data on prevalence and intensity of infection in the Americas . The use of alternative methodologies , for instance those based on geographical information systems and remote sensing technologies , or of sentinel surveillance in schools may help countries in the task of collecting information and support the implementation of integrated control programs against STH . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Update on the Mapping of Prevalence and Intensity of Infection for Soil-Transmitted Helminth Infections in Latin America and the Caribbean: A Call for Action |
Pregnancy-associated malaria is caused by Plasmodium falciparum malaria parasites binding specifically to chondroitin sulfate A in the placenta . This sequestration of parasites is a major cause of low birth weight in infants and anemia in the mothers . VAR2CSA , a polymorphic multi-domain protein of the PfEMP1 family , is the main parasite ligand for CSA binding , and identification of protective antibody epitopes is essential for VAR2CSA vaccine development . Attempts to determine the crystallographic structures of VAR2CSA or its domains have not been successful yet . In this study , we propose 3D models for each of the VAR2CSA DBL domains and we show that regions in the fold of VAR2CSA inter-domain 2 and a PfEMP1 CIDR domain seem to be homologous to the EBA-175 and Pkα-DBL fold . This suggests that ID2 could be a functional domain . We also identify regions of VAR2CSA present on the surface of native VAR2CSA by comparing reactivity of plasma containing anti-VAR2CSA antibodies in peptide array experiments before and after incubation with native VAR2CSA . By this method we identify conserved VAR2CSA regions targeted by antibodies that react with the native molecule expressed on infected erythrocytes . By mapping the data onto the DBL models we present evidence suggesting that the S1+S2 DBL sub-domains are generally surface-exposed in most domains , whereas the S3 sub-domains are less exposed in native VAR2CSA . These results comprise an important step towards understanding the structure of VAR2CSA on the surface of CSA-binding infected erythrocytes .
Adhesion of Plasmodium falciparum parasite-infected erythrocytes ( IE ) to the vascular bed is mediated by P . falciparum erythrocyte membrane protein 1 ( PfEMP1 ) , which interacts specifically with receptors on the vascular endothelium or placenta [1 , 2] . The adhesion mechanism is thought to be developed by the parasite to avoid filtering through the spleen , where erythrocytes infected with late stage asexual parasites are removed from the circulation [3] . Antibodies that target PfEMP1 and abrogate binding are believed to be important mediators of acquired malaria immunity ( reviewed in [4] ) . Pregnancy-associated malaria ( PAM ) is caused by P . falciparum sequestering in the placenta by binding to chondroitin sulfate A ( CSA ) , which is a type of glycosaminoglycan attached on the surface of syncytiotrophoblasts [5] . Women suffering from PAM develop antibodies which protect them and their offspring during subsequent pregnancies [6] . These protective antibodies are thought to recognize a relatively conserved antigen as plasma and parasites from pregnant women from different malaria endemic areas cross-react [7] . The PfEMP1 variant mediating placental binding was recently discovered and named VAR2CSA [2 , 8] . The extracellular part of VAR2CSA consists of six Duffy-binding-like ( DBL ) domains , a large inter-domain ( ID2 ) and a C-terminal region predicted to be cytoplasmic . Most PfEMP1 molecules , but not VAR2CSA , contain two cysteine-rich interdomain regions ( CIDR domains ) [9 , 10] . Some CIDR domains bind to CD36 [11] and they have been described as degenerated DBL domains [12] despite a very low sequence homology between DBL and CIDR domains . The invasion of erythrocytes and the subsequent adhesion of IE to vascular endothelium or placenta are key events in the asexual life cycle of P . falciparum and thus of major importance for the virulence of this parasite . Erythrocyte invasion is mediated by proteins belonging to the erythrocyte binding ligand family ( EBL ) and in P . falciparum the erythrocyte binding antigen ( EBA ) -175 is the best described EBL protein . EBA-175 has some similarity to VAR2CSA: Firstly , EBA-175 contains two DBL domains ( called F1 and F2 ) . Secondly , the EBA-175 DBL domains bind glycans on the sialylated glycophorin A on the erythrocyte surface [13] . The monomeric structure of EBA-175 has been determined by X-ray crystallography and the primary features of the two DBL domains were found to be α-helices and an anti-parallel β-hairpin [14] . EBA-175 also crystallized as a dimer , and the structure of this complex showed that the DBL domains of EBA-175 interacted in a reverse handshake orientation [14] . The simian malaria parasite , Plasmodium knowlesi invades erythrocytes through the host receptor “Duffy antigen receptor for chemokines” ( DARC ) [15] . This interaction is also mediated by a parasite-encoded DBL-containing protein , Pkα-DBL , and the crystal structure of Pkα-DBL has been shown to be very similar to PfEBA-175 despite extensive sequence variation [16] . Based on the structure of Pkα-DBL , the DBL domain could be divided into three sub-domains named S1–S3 which are connected by short linkers . Both the glycan binding site of PfEBA-175 and the DARC binding site of Pkα-DBL are predominantly located in S1 and S2 [14 , 16] . With the aim of making a vaccine that can reverse or inhibit parasite binding in the placenta , considerable effort has been put into defining the specific part/parts of VAR2CSA that bind to CSA ( reviewed in [17] ) . The best way of determining this interaction would be to produce the extracellular part of VAR2CSA and co-crystallize this multidomain protein with CSA . However , it is very difficult to express such a large protein and previous attempts to crystallize even single VAR2CSA DBL domains have failed . Thus , novel methods are required to generate testable models and hypotheses on the overall 3D structure of PfEMP1 molecules . The DBL domains of PfEMP1 are often illustrated as “pearls on a string” and vaccine development strategies are focusing on the VAR2CSA DBL domains as single entities in a larger protein . We have recently published data showing a structural model of VAR2CSA DBL3X and mapped areas of DBL3X that are surface-exposed and reactive to naturally acquired antibodies on the native protein [18] . In this previous study , we showed that most variable regions of DBL3X are located in flexible loops or surface-exposed parts of the model . These findings were supported by an analysis of 106 VAR2CSA sequences which was published recently [19] , and showed that polymorphic sites in general are situated in flexible loop regions or other surface exposed areas of VAR2CSA DBL structure models . In this study , we modeled the remaining five 3D7 VAR2CSA DBL domains , the VAR2CSA inter-domain 2 ( ID2 ) and a number of CIDR domains from different PfEMP1 molecules to get insight into the location of epitopes in the whole VAR2CSA molecule . The models indicate that DBL domains contain features that are structurally conserved . Furthermore it appears that there is homology between the ID2 , CIDR and part of the resolved structures of the DBL fold . By absorbing antibodies on native VAR2CSA on the surface of IE and comparing antibody reactivity on a VAR2CSA peptide array before and after absorption , we define areas of the VAR2CSA molecule which may be accessible to antibodies in the native protein . For all domains we find that relatively invariant parts are recognized and surface-exposed in the native VAR2CSA . The surface-exposed epitopes on the six VAR2CSA domains are largely found within S1 and S2 , whereas S3 appears to be hidden in the complete VAR2CSA structure . This finding is interesting because it leads to the first suggestions about the overall structure of VAR2CSA; based on these data we discuss the domain architecture of VAR2CSA and suggest a model where the protein is surface-exposed as a globular or multimerized structured protein stabilized by long α-helices in the S3 region .
The 3-D structures of the 3D7 VAR2CSA DBL domains ( Figure 1 ) were modeled using the HHpred server [20] developed for low-homology modeling . HHpred template searches confirmed that the determined structures of the P . falciparum EBA-175 DBL domains F1 and F2 and the P . knowlesi DBL domain Pkα-DBL [14 , 16] could be used as templates for modeling ( HHpred probability scores were all 100% ) , although the sequence identity between the VAR2CSA DBL domains and templates was between 16–20% ( see modeling details in Table S1 ) . The determined structures of the two EBA-175 DBL domains each contain a region where structural information is missing and the structure of Pkα-DBL has four such regions ( Table S1 and Figure 2 ) . In crystallographic experiments , the local occurrence of missing structural information indicates regions of flexibility and loosely defined secondary structure . For Pkα-DBL , the regions of missing structural information were used to divide the DBL fold into the subdomains S1–S3 [14] and we adapted a similar classification for the VAR2CSA DBL domains ( Figures 1 and 2 ) . VAR2CSA residues corresponding to regions of missing structural information in the templates were modeled as insertions relative to the template structure . The structure of such inserted regions is difficult to predict correctly [21] . Secondary structure predictions using the PSIPRED method [22] is part of the HHpred modeling protocol . The prediction results are divided into helix , strand and coil , where the coil class consists of secondary structure types mostly found in loops . Interestingly , the template regions of missing structural information aligned with VAR2CSA DBL sequences predicted to have coil secondary structure . In general , the VAR2CSA sequence variation is high within these regions ( Figure 2 ) . Taken together , this suggests that most of the variable regions in all VAR2CSA DBL domains form a variety of flexible loops with different conformations . These results support the findings reported by Bockhorst et al . , who recently reported a similar tendency in structural models of VAR2CSA DBL2X and DBL3X [19] . From a structural perspective these findings makes biological sense because the overall DBL fold could be preserved while surface-exposed parts and loop areas which are possibly less important for stabilization of the fold , would have more freedom to mutate . The quality of a structure model obviously has a pronounced effect on the information that can be deduced from the model . We used the automated structure analysis tool ANOLEA [23] for evaluation , and Z-scores ranging from 5 . 00 to 9 . 61 with 52%–68% high-energy residues were obtained . The results indicated that the quality was lowest in the loop regions . Analysis using Verify3d [24] resulted in a similar conclusion ( data not shown ) . Since these results did not convince us that the models were correct , we decided to further investigate the quality of the models by inspecting them for conserved residues stabilizing the determined structures of EBA-175 and Pkα-DBL domains . Structural alignment of the EBA-175 and Pkα-DBL domains identified a number of positions , which can be assumed to be important for the stabilization of the DBL fold ( Table S2 ) . The positions of these residues were distributed throughout the domains in blocks . We then made a multiple structural alignment including the six VAR2CSA DBL models and the EBA-175 and Pkα-DBL structure to identify VAR2CSA residues at the positions corresponding to the positions identified as conserved and stabilizing EBA-175 and Pkα-DBL ( Figure 2 ) . This analysis showed that a high number of hydrophobic positions forming a hydrophobic core in the DBL structure were conserved in the models , together with positions of helix capping and positions of interacting buried polar residues ( Table S2 and Figure 2 ) . This conservation of stabilizing positions indicates that the alignments of the model sequences to the template sequences are correct in regions surrounding these positions . Additionally , we found that most template-stabilizing positions are located in semi-conserved blocks reported in an analysis of 106 VAR2CSA sequences [19] ( data not shown ) , which suggests that these stabilizing positions in the templates are conserved in VAR2CSA to stabilize the folding of the VAR2CSA DBL domains in general . The structural alignment and sequence alignments used by HHpred for modeling were analyzed for conservation of cysteines forming disulfide bonds in the template DBL structures . The models of the VAR2CSA DBL domains all contain conserved cysteine positions likely to form disulfide bonds ( Figures 1 and 2 ) . The disulfide bonds were numbered according to the occurrence of the cysteines in the sequence . Cysteines of disulfide bond 1 are conserved in all models except DBL6 . Likewise , the cysteines of disulfide bond 5 are conserved in all models except DBL1 . A number of cysteines in the models are in close proximity to a disulfide bond-forming template cysteine . This suggests that the local alignments used for the modeling are sub-optimal or that alternative disulfide bonds are formed in the VAR2CSA DBL domains . An interesting example is the disulfide bond 2 ( Figure 1 , number 2 and Figure 2 , positions 38 and 64 ) . The disulfide bond is proximal to a region containing glycan-binding amino acids in the EBA-175 F1 and F2 domains ( Figure 2 , positions 44 , 48 , and 50–53 ) and it may play a role for the function of these domains . Among the VAR2CSA DBL domains , only DBL3 has both cysteines conserved . None of the other VAR2CSA DBL sequences have two cysteines in the proximity , and it is thus unlikely that the apparent variation stems from incorrect alignments . The lack of the disulfide bonds in some regions of the VAR2CSA DBL domains may suggest higher flexibility and a more dynamic structure than in DBL domains stabilized by a higher number of disulfide bonds . The analysis of different types of stabilizing characteristics shows that these to a large extent are conserved between the template and the VAR2CSA models . Since our aim was to map experimental data onto the DBL models , rather than to analyze the structural conformations in detail , we concluded that the models were of sufficient quality for mapping of these data . The extracellular part of VAR2CSA consists of six DBL domains and a sequence stretch consisting of 337 amino acids named inter-domain 2 ( ID2 ) [9] . This part of the molecule has attracted little attention and has been viewed as an inter-domain spacer sequence . We analyzed the ID2 sequence ( PFL0030c positions 879-1216 ) for homology to other proteins using the HHpred search and alignment tool . Interestingly , the EBA-175 and Pkα-DBL domains were all identified as homologous to the ID2 domain with very high HHpred probability scores ( probabilities between 98 . 3% to 99 . 9% ) . The similarity was pronounced in a region of 100 residues ( PFL0030c positions 1017–1116 ) , which aligned to the first two α-helices in S3 with a sequence identity of 19 . 8% . Using the EBA-175 F2 DBL domain as template , we modeled the structure of ID2 positions 1017–1116 ( Figure 3A and 3B ) . The secondary structure of the whole ID2 domain was then predicted using PSIPRED ( data not shown ) . The topology of predicted secondary structure elements in the ID2 domain suggested that the C-terminal part of ID2 has a similar fold to DBL S3 , but did not support the notion that the N-terminal part of ID2 has a fold similar to DBL S1 and S2 . In most PfEMP1 molecules the first DBL domains are separated by a cysteine-rich inter-domain region ( CIDR1 ) on which there is no structural data available . Using the HHpred server homology between CIDRs and EBA-175 was identified . To identify the significance of our results , and to make a more general analysis , we used a number of CIDR domains for the analysis . Since VAR2CSA ID2 and CIDR1 are placed either after the first or the second DBL domain and can be divided into sub-groups , we examined CIDR1 sequences representative for the three subgroups CIDR-alpha , beta and gamma . Similarly to the ID2 domain , the homology was detected in the first two helices of the DBL S3 and a structure model was made using the EBA-175 F2 DBL as template structure ( Figure 3B ) . The homologous region was part of the CIDR M2 region defined by Smith et al . [10] . Similarly to our results of ID2 , an analysis of topology in the predicted secondary structure suggested that C-terminal structures of CIDR domains are similar to the known structures of DBL S3 domains , but that the structure of the N-terminal may vary from DBL domains . These predictions suggest that like ID2 , the C-terminal part of CIDR1 seems to form a structure similar to that of the DBL S3 . In rational PAM vaccine design , it is important to establish which parts of native VAR2CSA are accessible to antibodies acquired by women who have developed immunity to pregnancy-associated malaria . Additionally , the cross-reactivity of these antibodies is an important issue to investigate because of the variability in VAR2CSA sequences . The latter has been addressed in several studies which have been investigating the cross-reactivity of naturally acquired human antibodies between different P . falciparum lines . Both cross-reactivity [25 , 26] and isolate-specific recognition of antibodies have been reported [26 , 27] . These studies were based on antibody reactivity to IE and the specificity as well as the target of the cross-reactive and isolate-specific antibodies are thus not known . In our study , we have instead used VAR2CSA peptide arrays measuring binding to shorter peptides . Anti-VAR2CSA IgG from pools of plasma was absorbed using VAR2CSA-expressing IE and the antibody reactivity of the pools in a VAR2CSA peptide array compared before and after absorption [18] . Measuring the antibody reactivity in the peptide assays allowed qualitatively mapping of surface-exposed regions of the VAR2CSA DBL domains . Plasma samples from 180 Tanzanian women , sampled at the time of delivery , were tested for reactivity towards CSA binding parasites ( 3D7CSA and FCR3CSA ) in flow cytometry and in ELISA towards recombinant VAR2CSA protein . Two pools with high levels of antibodies towards IE and high levels of anti-VARCSA IgG were made using plasma from 32 and 10 Tanzanian pregnant women , respectively . One of the plasma pools ( Human plasma pool 1 ) was depleted using erythrocytes infected with 3D7CSA parasites . From the other pool ( Human plasma pool 2 ) , two depleted plasma pools were generated by depleting one part with erythrocytes infected with 3D7CSA , and depleting the other part on erythrocytes infected with the FCR3CSA parasite line . As a control , we depleted a pool containing anti-VAR2CSA IgG ( Human plasma pool 3 ) on a 3D7 parasite expressing a non-VAR2CSA PfEMP1 variant ( VAR4 ) . The antibody assays were performed on an array containing 442 overlapping 31mer peptides corresponding to the extracellular part of VAR2CSA based on the sequence of 3D7 . Before absorption , we observed a number of intense peaks for most of the domains ( Figures S1–S6 ) . The intensity varied between domains , for instance , the peptide reactivity in DBL1 resulted in highly intense peaks , whereas the peptide activity of DBL2 resulted in less intense peaks . The antibody reactivity towards the majority of peptides was not affected by the depletion; nevertheless depletion on native VAR2CSA consistently removed antibody reactivity against some peptides in all domains ( Figures S1–S6 ) . In general depletion on the non-CSA binding parasite did not reduce anti-VAR2CSA reactivity in the peptide array , but a small reduction of peptide-specific reactivity was detected in regions for DBL2 and DBL4 . For DBL4 there was an overlap in the depletion on the non-CSA binding parasite and the CSA-binding parasite , indicating that some of the surface-exposed parts identified in DBL4 could be due to non-specific absorption . In addition to the human plasma pool , a pool of sera from six rabbits each immunized with a different VAR2CSA DBL domain was absorbed and tested . The pattern of reactivity in the peptide array with this pool before absorption was slightly different from the reactivity obtained with the human plasma pools and this difference was also reflected in the absorption experiments . Taken together these results indicated that all domains including the N-terminal segment contained continuous peptide sequences accessible to antibodies when the VAR2CSA protein was expressed on the surface of CSA-binding IE . It was difficult however , to detect a pattern for this reactivity between the domains when depicting the reactivity on a string of residues . We therefore went on to visualize the reactivity on the DBL models . To facilitate the mapping of depleted regions from the peptide array data we calculated depletion values ( DV ) by subtracting the depleted reactivity from the non-depleted reactivity . DV were calculated for each of the four depletion experiments ( human pool 1 versus 3D7 or FCR3 , human pool 2 versus 3D7 , rabbit pool versus 3D7 ) and mapped onto the six structural DBL models ( Figures S7–S11 ) . Figure 4 shows the results for DBL6 and it is apparent that the depletion experiments using the two human plasma pools identified essentially the same regions as target for surface reactive antibodies ( Figure 4A versus 4B ) . The results obtained in the absorption experiment using FCR3-infected erythrocytes gave essentially the same results as the experiment using 3D7-infected cells ( Figure 4B versus 4C ) . Since the peptide array was based on the 3D7 sequence this indicates that the surface-exposed epitopes on the 3D7 and FCR3 versions of VAR2CSA are cross-reactive or target-conserved epitopes . The absorption experiment using the rabbit plasma pool only showed depletion of antibodies targeting peptides residing in S1 and S2 ( Figure 4D ) . There was no indication of depletion of antibodies targeting S3 in any of the experiments . To facilitate a comparison between the DBL domains , and to reduce the risk of false positive DV , we created consensus DVs for each domain by calculating a sum of normalized DV from each of the four absorption experiments and scoring the residue as positive if the value was above a fixed threshold ( Figure 5 ) . Overall there was agreement between the models based on the individual absorption experiments and the consensus DV ( Figure 4A and 4B versus Figure 5 , DBL6 ) . The results for DBL3 were also in agreement with the surface-exposed epitopes identified previously [18] . When evaluating the consensus models it should be kept in mind that surface-exposed VAR2CSA regions can only be detected if the plasma pool contains antibodies against these regions and if the antibodies can be detected in the peptide array assay . Thus , regions which are not targeted by antibodies , or regions targeted by antibodies that cannot be detected in the peptide array experiment ( because they either target non-linear sequences or polymorphic sequences not represented in 3D7 VAR2CSA , ) will not be scored as surface reactive . As a consequence this method will only map a certain proportion of epitopes . Likewise , residues buried in the native molecule but residing close to a region representing a surface-exposed epitope on the peptides will score as positive . When comparing the consensus models for the six DBL domains , it was evident that the pattern of reactivity was comparable in DBL domains 1 , 2 , 3 , 5 , and 6 , whereas the pattern in DBL4 was unique ( Figure 5 ) . For the former domains the targets of surface-reactive antibodies were mainly located in S1 and S2 , whereas little reactivity was found in S3 , which is located on the lower left side of the models in Figure 5 . In S1 and S2 both loops and α-helices were targets of surface reactive antibodies . The loop between S1 and S2 ( Figure 2 positions 81–87 and appearing most prominently in the upper left corner of the DBL2 model on Figure 5 ) is flexible in the Pkα-DBL structure and we observe a high sequence variation between the DBL domains in the region , suggesting that the corresponding loops in the VAR2CSA DBL domains are flexible , and reinforcing the possibility that VAR2CSA domains can be divided into sub-domains . In DBL domains 2 , 3 , 5 , and 6 , a loop in S2 ( appearing most prominently in the lower right corner of DBL3 on Figure 5 ) was also targeted by surface reactive antibodies . A loop region in S1 ( Figure 2 , positions 44–52 , appearing most prominently in the center of DBL6 on Figure 5 ) was recognized in DBL domains 1 , 2 , 3 , and 6 . Interestingly , the corresponding regions in EBA-175 contain glycan-binding residues . The S2 α-helix appearing in the upper right on all models was recognized in all domains but DBL4 , whereas some of the other α-helices in S1 and S2 were recognized to a varying degree . The regions of DBL4 targeted by surface-reactive antibodies differed markedly from the other domains . Relatively more reactivity was detected against S3 and the reactivity against S2 was mainly against regions on opposite side of the domain compared to the other domains ( Figures 5 and 6 ) . The possibility that DBL4 is positioned different from the other domains in the quaternary VAR2CSA structure is in agreement with the finding that the DBL4-specific rabbit antibodies are not reacting with the native VAR2CSA on IE ( [28] and unpublished data ) . The different pattern of DBL4 recognition could also be explained by the fact that some antibodies targeting DBL4 peptides were non-specifically absorbed on IE ( Figure S4 , lower panel ) . There is no evidence that S3 is undergoing less diversifying selection , which might be expected considering the present experimental data . To address whether epitopes in S3 in particular are conformationally arranged compared to S1+S2 and thus showing a bias in the results , we affinity purified rabbit antibodies on monomeric recombinant DBL2 and assessed these antibodies on the peptide array . We found that epitopes in both S2 and S3 could be detected by the peptide array analysis ( data not shown ) , indicating that the system was functioning for epitopes in S3 as well . Using a multiple sequence alignment of seven full-length VAR2CSA sequences , residues were classified as conserved if they were all identical and as polymorphic if any of the sequences showed variation in the particular position . It is apparent from Figures 5 and 6 that all domains contained both conserved and polymorphic regions targeted by surface reactive antibodies , but the conserved regions were most prominent in DBL3 and DBL5 . This is in agreement with data showing that antibodies raised against recombinant proteins representing DBL3 and DBL5 are more likely to cross-react with heterologous parasites , than antibodies raised against the other domains [28] . Furthermore , these findings also support data showing that antibodies raised against one DBL3 or DBL5 variant are highly cross-reactive with a large panel of placental isolates ( Magistrado et al . , submitted ) . Finally , the data is also in agreement with the reactivity of human monoclonal antibodies produced by immortalized B cells from malaria-exposed pregnant women which are directed predominantly against these two domains [29] . To further investigate the presence of conserved immunogenic epitopes in VAR2CSA domains , we performed competition ELISA using a large panel of recombinant VAR2CSA DBL3 domains derived from placental isolates [18] . One variant of VAR2CSA DBL3 was coated in ELISA plates and the antibody reactivity of a high-titered VAR2CSA plasma pool was compared before and after pre-incubation with a competing VAR2CSA DBL3 variant . By this method it was possible to quantify the relative level of variant-specific IgG towards individual DBL3 variants . As positive and negative controls we incubated the plasma pool with homologous VAR2CSA DBL domain or VAR2CSA DBL5 domain as competing antigen ( Figure 7 ) . Pre-incubation with non-homologous DBL3 domain reduced the reactivity by 25%–100% . No competition/absorption was seen with the control proteins . These data strongly suggest that the insect-cell produced DBL3 domains share common and cross-reactive motifs . To determine whether some of these conserved linear regions were exposed on the surface of the native parasite protein , we synthesized peptides corresponding to two regions in DBL3 and DBL5 suggested by the peptide array data to be surface-exposed ( Figure 5 ) . The first peptide P62 , corresponded to aa position 1350–1370 in DBL3 . This region is highly conserved having only one variant aa ( based on alignment of 43 VAR2CSA DBL3 sequences ) . The second peptide , P63 , corresponds to aa 2045–2061 in DBL5 and is also relatively conserved having 2 variant aminoacids out of 17 ( based on alignment of 15 VAR2CSA DBL5 sequences ) . P62 and P63 were tested in ELISA for reactivity with Tanzanian male and female plasma and both showed significantly higher reactivity with female plasma as compared to male plasma ( Mann-Whitney rank sum test , p < 0 . 05; data not shown ) . The two peptides were screened in ELISA for reactivity against plasma from rabbits immunized with recombinant DBL3 or DBL5 protein and peptide-reactive rabbit sera was used to affinity purify antibodies on the peptides to create peptide-specific IgG reagents . The affinity-purified antibodies were subsequently assayed in flow cytometry for reactivity with native VAR2CSA expressed on erythrocytes infected with CSA-binding 3D7 or FCR3 strain ( Figure 8 ) . Both the DBL3 and DBL5 peptide purified antibodies reacted strongly with FCR3CSA strain and to lesser extend with the 3D7CSA parasite . The DBL5 peptide antibodies were affinity-purified from a rabbit immunized with a FCR3 DBL5 domain , and the difference of two amino acids in the peptide sequence could explain the lower reactivity with 3D7CSA compared with FCR3CSA . The DBL3 peptide-specific antibodies were affinity purified from a rabbit immunized with a placental variant of DBL3 protein and there is only one amino acid differing between this placental sequence and the FCR3 and 3D7 sequences . It was intriguing to find that the affinity-purified antibodies recognized the parasites to varying degree . One reason for this could be a difference in levels of VAR2CSA protein expressed on 3D7 and FCR3 , or the observed difference could be accounted for by the single polymorphisms in the peptide sequences . A third explanation could be that polymorphic flexible loop regions , flanking the conserved surface exposed parts , influence the accessibility of the surface-exposed regions to varying degrees between 3D7 and FCR3 . Conserved surface-exposed epitopes appear to be attractive vaccine targets . However , protective immunity is acquired through successive pregnancies [6 , 30] , and is a function of transmission intensity [31] . Naturally acquired protection could therefore depend on the ability to recognize several polymorphic VAR2CSA variants . This is consistent with the finding that some targets of naturally acquired antibodies are under diversifying selection [18] . The levels of antibodies against pregnancy-associated parasite-encoded antigens on the surface of the erythrocyte increase with the number of pregnancies , and are correlated to the adhesion inhibitory capacity [7 , 32] . Therefore , our identification of antibody binding parts in more conserved regions of VAR2CSA may seem like a paradox . However , there could be several explanations for this finding: Firstly , the identified regions may have importance for the function of VAR2CSA , and this could lead to conservation . Second , the conserved regions may be less immuno-dominant than other more variable regions of VAR2CSA . This would make the development of antibodies directed against these regions less frequent , and result in a delay in the development of immunity . Finally , whole antibody binding surfaces may be composed of both conserved and variable regions , where the variance of a few residues would be enough to disrupt the binding . Most antibody binding epitopes of globular proteins have been estimated to be discontinuous in nature [33] , and additionally it has been shown that most discontinuous epitopes are comprised by 14–19 amino acids , including a linear segment of 4–7 amino acids [34] . Therefore , the complete binding surface of antibodies recognizing P62 or P63 could very likely be discontinuous , and comprised by both polymorphic and more conserved regions . We have identified conserved VAR2CSA regions targeted by antibodies recognizing the surface of IE . Furthermore we have been able to induce rabbit antibodies against these regions by immunization with recombinant DBL domains . This is a promising finding for vaccine development and it will be important to establish whether antibodies against conserved surface-exposed VAR2CSA regions can inhibit the binding of parasites to CSA . So far , little is known about the overall structure of VAR2CSA or any other PfEMP1 . It has been suggested that the PfEMP1 protein architecture is comprised by a compact semi-conserved head structure which largely defines the binding affinity and a number of variable C terminal domains [12] . Previous data have indicated that the general DBL fold is relatively conserved [14 , 16 , 18 , 35] and that DBL domains can interact with each other as building blocks to form binding sites [14] . The data presented here indicate that DBL S3 is less surface-exposed than S1 and S2 . Sub-domain 3 contains two long α-helices which are conserved in the template structures . A number of multimeric protein complexes have been reported to be stabilized by interactions between long α-helices; a well-studied example is the trimer of influenza virus hemagglutinin [36] . The data presented here does not lend support to the idea of a compact conserved head structure in VAR2CSA . Rather it is tempting to propose models of VAR2CSA where α-helices of different DBL domains interact to bury a considerable part of S3 in the interface between the domains . The interaction could be formed between DBL domains of a single VAR2CSA molecule to form a more globular shape of VAR2CSA . Another possibility is that several VAR2CSA molecules form dimers or multimers with S3 buried in the middle . These models do not exclude the possibility that the flexible loop regions , also present in S3 , protrude from the compact core structure of the protein [29] , however we were not able to measure antibodies to these regions using the peptide array . The hypothesis that VAR2CSA is present as a globular protein opens up for the possibility that the CSA binding site is comprised of regions from different DBL domains , like the glycan binding sites of EBA-175 [14] . Further exploration of the quaternary structure of VAR2CSA is therefore of the utmost importance .
Structures of the CIDR domains and the 3D7 VAR2CSA DBL and ID2 domains were modeled using the HHpred server with default settings [20] . The HHpred method is based on comparisons and alignments of hidden Markov models ( HMMs ) , which include gaps and insertion probabilities . The modeling of the DBL3 domain was done as described previously [18] . VAR2CSA domains were modeled separately by splitting the PFL0030c sequence into separate domains ( DBL1 aa 57–400 , DBL2 aa 531–879 , DBL4 aa 1575–1911 , DBL5 aa 1999–2283 , DBL6 aa 2340–2633 , ID2 aa 879-1216 ) . All HMM databases available in web-server were used for template structure search , including the Protein Data Bank ( PDB ) . For all the VAR2CSA DBL domains described , the structures of the EBA-175 DBL domains [14] and the Pkα-DBL domain [16] had HHpred probability scores significantly higher than other structures detected . The DBL structure with the highest sequence and secondary structure alignment scores were chosen as template for each domain . Template alignments proposed by the HHpred method were used to generate 3D models by using a HHpred server toolkit protocol for MODELLER [37] . Models were evaluated using Verify3d [24] and ANOLEA [23] , available in the HHpred server toolkit . Superimpositions of EBA-175 F1 , F2 , Pkα-DBL and VAR2CSA domains were made using the MAMMOTH multi-alignment server [38] . NACCESS version 2 . 1 . 1 [39] was used for analysis of relative accessible surface areas ( RSAs ) in the template structures . Residues with RSA < 30% were considered buried . Separate secondary structure predictions of CIDR and ID2 were made using the PSIPRED [22] . All structural visualizations were produced using PyMol [40] . Plasma samples sampled at the time of delivery from Tanzanian woman were tested in flow cytometry for the presence of antibodies against CSA binding parasites . Positive samples were selected for further analysis in ELISA; plasma levels of DBL1-DBL6 VAR2CSA specific IgG were measured in standard ELISA assays [41] . Plasma samples positive against more than two domains were used to make two pools of plasma samples . No single plasma sample was used in both pools . Plasma originated from women with different parities ranging from one to seven . The control human plasma pool 3 was a hyperimmune pool made from 7 Tanzanian women and 3 male . For testing the recognition pattern of the two synthetic peptides P62 and P63 we used a panel of Tanzanian female plasma which was selected on the basis of having high levels of antibodies towards CSA binding parasites and as controls we used Tanzanian male plasma . In addition we tested this panel of male and female plasma for reactivity to GLURP protein to ensure that we were not merely measuring different levels of malaria exposure . Rabbits were immunized with DBL1–DBL6 and ID2 recombinant protein as described [42] and the seven serum samples were pooled to create the VAR2CSA rabbit pool . 3D7 and FCR3 parasites were panned on BeWo cells to create CSA adhering parasite lines . Using real time PCR and flow cytometry with VAR2CSA specific reagents as well as plasma from Tanzania , we verified that the parasites were gender-specifically recognized and expressing high levels of VAR2CSA on the surface of the infected erythrocyte [43] . Ethical clearance for collection of plasma samples was given by the Tanzanian health authorities . For the parasite IgG depletion 40 μl of the plasma pool were incubated with 2 . 0 × 108 MACS purified intact late stage trophozoite- and schizont-IE for 20 min at 4 °C . Hereafter , the cells were centrifuged at 800g for 8 min , and the supernatant used to suspend a new pellet of 2 . 0 × 108 infected erythrocytes . This procedure was repeated four times . Depletion on parasites was performed sequentially until iRBC reactivity could not be reduced further as determined by flow cytometry ( see Figure S12 ) . A Pepscan array containing 442 31mer peptides corresponding to the extracellular part of 3D7 VAR2CSA was used for antibody binding studies . The sequences of the peptides had an overlap of six residues and the purity of the peptides was expected to be 70% or higher . All Pepscan data were analyzed for short motifs to derive the activity of single residues as described previously [18] . The use of the motif analysis ensured that reactivities of single residues were derived from several peptides in order to take effects of possible impurities into account . As part of the motif analysis , the data from each individual experiment were normalized based on the average activity measured for the total array . This was done to avoid experimental errors caused by inter-assay variation . However , this procedure caused peaks remaining after depletion to gain intensity , because the depletion of other peaks lowered the general average of the data . Care was taken to avoid mis-interpretations of data caused by a gain in intensity upon depletion . For each of the four depletion studies , DVs were calculated by subtracting depleted Pepscan data points from non-depleted points . Peaks that have a diminished intensity after depletion lead to positive DVs . Therefore only positive DVs of each individual experiment were split into five equally sized intervals ranging from the highest to the lowest DVs and with increasing color intensity . The five intervals were then plotted on the models . For consensus DVs , the DVs for each depletion study on individual DBL domain were first normalized by subtracting the mean and dividing with the standard deviation . Subsequently , the consensus DVs was calculated by summing the normalized values for each position in the DBL domains . Consensus DVs > 1 were plotted on the models as this threshold ensured that only the peaks with the highest consensus DVs were included . All VAR2CSA DBL3 constructs derived from placental isolates were cloned and sequenced as described elsewhere [18] and the DBL5δ domain of PFD1235w was cloned as described in [44] . The DBL5 domain of VAR2CSA was amplified from genomic FCR3 DNA with following primers: 5′ CC CCC GGG AGA TGT TTT GAT GAT CAG ACA and 3′ ATT TGC GGC CGC CAT TAC CTT TAT CAT ACT C and cloned into the pAcGP67-A vector as described for the DBL3 constructs . Recombinant Baculovirus particles were generated in Sf9 insect cells by co-transfecting with pAcGP67-A and linearized Bakpak6 Baculovirus DNA ( BD Biosciences ) . Recombinant constructs were purified on a HIS-Select Nickel Affinity Gel ( H8286 , SIGMA ) as secreted histidine-tagged proteins from the supernatant of virus-infected High-Five insect cells . Prior to competition ELISA , all DBL3 constructs and the DBL5ɛ of VAR2CSA were tested in indirect ELISA for antibody reactivity by the female plasma pool ( diluted 1:100 ) of 10 Tanzanian women ( Korogwe , mixture of primi- and multigravidae , selected based on their high anti-VAR2CSA titers in flow cytometry ) and OD490 values ranged from 1 . 3 to 3 . 2 . MaxiSorp microtiter plates ( Nunc ) were coated with antigen ( 1 . 5 μg/ml in PBS ) overnight at 4 °C . The female plasma pool ( diluted 1:100 ) was pre-absorbed with competing antigen ( 1 μg/ml ) for 2 h at room temperature ( RT ) . After incubating the plates with blocking buffer ( PBS , 0 . 5 M NaCl , 1% Triton X-100 , 1% BSA ) for 1 h at RT , the pre-absorbed pool was added to the antigen-coated wells in triplicate and incubated for 1 h at RT . In addition to the pre-absorbed plasma pool ( pP ) , a non-absorbed pool ( P ) was included for each coating antigen . Following washing of the plates four times with washing buffer ( PBS , 0 . 5 M NaCl , 1% Triton X-100 , pH 7 . 4 ) , the secondary antibody ( rabbit anti-human IgG HRP , P0214 , Dako , Denmark ) diluted 1:3000 in blocking buffer was added and incubated for 1 h at RT . Plates were again washed four times and antibody reactivity visualized by the addition of o-phenylenediamine substrate . Color reactions were stopped by the addition of 2 . 5 M H2SO4 and OD was measured at 490 nm . The percent reduction in antibody reactivity was calculated as follows: 100*[1-ODpP/ODP] . Peptide synthesis was done at Sigma Genosys at at X > 70% purity . Two conserved , surface-exposed regions were synthezised: P62-DBL3 YKNMILGTSVNIYEYIGKLQ residing at aa position 1350–1370 in the 3D7 sequence and P63-DBL5 RIVRGPANLRNLKEFKE residing at aa position 2045–2061 . Affinity purification of antibodies was done using HiTrap NHS-activated HP columns ( GE Healthcare , http://www . gehealthcare . com/ ) according to the manufacturer's instructions . In brief , 1 . 5 mg of synthetic peptide ( Sigma-Genosys ) was dissolved in 0 . 2 M NaHCO3 , 0 . 5 M NaCl ( pH 8 . 3 ) , and applied to the 1 ml column that had been equilibrated with 3 × 2 ml 4 °C 1 mM HCl . After coupling , the columns were washed alternating 0 . 5 M ethanolamine , 0 . 5 M NaCl ( pH 8 . 3 ) and 0 . 1 M acetate , 0 . 5 M NaCl ( pH 4 ) , followed by a final wash with PBS ( pH 7 . 4 ) . Before affinity purification , the peptides were tested for reactivity against a panel of rabbit sera immunized with different recombinant DBL domains . A rabbit plasma sample reacting with one peptide was chosen to affinity purify antibodies specific for that peptide . Serum from a rabbit immunized with a placental DBL3 variant ( accession number ABK91125 ) was used to affinity purify on P62 and serum from a rabbit immunized with DBL5 FCR3 was used to affinity purify antibodies on P63 . Rabbits immunized with the 3D7 VAR2CSA DBL3 and DBL5 protein did not have antibodies against the peptides . 1 ml of rabbit plasma was diluted in PBS ( 1:2 ) , filtered through a 0 . 45-μm filter and applied to the column at a flow rate of 0 . 5 ml / min . After washing the column in 5 ml PBS , affinity-bound antibodies were eluted in fractions with a total volume of 3 . 2 ml of 0 . 1 M glycine-HCl ( pH 2 . 8 ) and neutralized in 1 M Hepes ( pH 8 ) . The specificity of the purified antibodies was tested in ELISA against ( 1 ) the peptide used for affinity purification ( 2 ) the recombinant DBL domain used to immunize the rabbit that provided the plasma ( 3 ) another VAR2CSA domain . | Individuals living in areas with high Plasmodium falciparum transmission acquire immunity to malaria over time and adults have markedly reduced risk of getting severe disease . However , pregnant women constitute an important exception , and they become more susceptible to malaria during pregnancy . This so called pregnancy-associated malaria ( PAM ) has severe consequences for both mother and child , and a vaccine would save hundreds of thousands of lives each year . PAM is caused by P . falciparum–infected red blood cells that bind to receptors in the placenta . By binding to the placental tissue , the parasites avoid being filtered though the spleen where they would have been killed . The protein mediating this placental binding is a very large multidomain and variant protein named VAR2CSA . Using structural modeling of VAR2CSA and antibody reagents from women who have had PAM , we show that antibodies tend to bind in similar regions , on one side of the individual VAR2CSA domains . In addition , we show that highly conserved parts of this variant protein are accessible for antibodies . This finding correlates with epidemiological data showing that woman acquire immunity towards PAM relatively fast , and the identification of these epitopes is thus a major step towards a protective vaccine . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"immunology",
"plasmodium",
"microbiology",
"computational",
"biology"
] | 2008 | Structural Insight into Epitopes in the Pregnancy-Associated Malaria Protein VAR2CSA |
Babesia bovis , is a tick borne apicomplexan parasite responsible for important cattle losses globally . Babesia parasites have a complex life cycle including asexual replication in the mammalian host and sexual reproduction in the tick vector . Novel control strategies aimed at limiting transmission of the parasite are needed , but transmission blocking vaccine candidates remain undefined . Expression of HAP2 has been recognized as critical for the fertilization of parasites in the Babesia-related Plasmodium , and is a leading candidate for a transmission blocking vaccine against malaria . Hereby we identified the B . bovis hap2 gene and demonstrated that it is widely conserved and differentially transcribed during development within the tick midgut , but not by blood stage parasites . The hap2 gene was disrupted by transfecting B . bovis with a plasmid containing the flanking regions of the hap2 gene and the GPF-BSD gene under the control of the ef-1α-B promoter . Comparison of in vitro growth between a hap2-KO B . bovis clonal line and its parental wild type strain showed that HAP2 is not required for the development of B . bovis in erythrocytes . However , xanthurenic acid-in vitro induction experiments of sexual stages of parasites recovered after tick transmission resulted in surface expression of HAP2 exclusively in sexual stage induced parasites . In addition , hap2-KO parasites were not able to develop such sexual stages as defined both by morphology and by expression of the B . bovis sexual marker genes 6-Cys A and B . Together , the data strongly suggests that tick midgut stage differential expression of hap2 is associated with the development of B . bovis sexual forms . Overall these studies are consistent with a role of HAP2 in tick stages of the parasite and suggest that HAP2 is a potential candidate for a transmission blocking vaccine against bovine babesiosis .
Bovine babesiosis is a tick-borne disease that limits food production in tropical and subtropical regions worldwide . The disease is mainly caused by Babesia bovis , B . bigemina , and B . divergens and is endemic in large parts of Australia , Africa , Asia , Europe , and Latin America [1] . Parasites of the genera Babesia are transmitted by ixodid ticks including Rhipicephalus spp [2–4] . Animals that survive acute infection remain persistently infected and are reservoirs for tick transmission [5 , 6] . Bovine babesiosis control strategies have been met with limited success in some countries . However , these strategies , including acaricide treatment and live , attenuated vaccines [1 , 7–9] , are restricted due to increasing acaricide-resistant tick populations and by practical constraints of the live Babesia vaccines , such as possible reversion to virulence and the risk of tick transmission [7 , 10 , 11] . Despite safety concerns , some countries in endemic regions still use live vaccines to mitigate acute infection and prevent mortality . To complete its life cycle , Babesia may require strict regulation of gene expression to develop , invade , replicate and survive in distinct and diverse hosts and tick vectors . Babesia parasites have a complex life cycle including asexual replication of haploid stages in the mammalian hosts and sexual reproduction of diploid stages in the tick vector [12] . The initial phenotypic differentiation of Babesia into sexual stages that occurs in the tick midgut lumen may require the expression of a subset of proteins necessary for fusion and formation of diploid zygotes [13] . Zygotes selectively infect tick midgut epithelial cells and subsequently develop into kinetes [14] . Mature kinetes are released into the tick hemocoel and invade various tick organs including salivary glands and ovaries . Eventually , the parasite is vertically transmitted to the next tick generation where another morphological change occurs as the parasite transforms into sporozoites in larval salivary gland acinar cells [12] . In a closely related human pathogen , Plasmodium specific proteins have been identified with important functions for parasite development within mosquitos . Plasmodium expresses a protein known as HAPLESS2/GCS1 ( HAP2 ) and it is exclusively expressed on the surface of microgametes that occur in the mosquito gut lumen [15] . This protein is critical for the fertilization of parasites prior to development of the stage that infects mosquito gut epithelial cells . In Plasmodium , HAP2 is a candidate for a transmission blocking vaccine . Similar to Plasmodium , fertilization of B . bovis gametes within the vector tick midgut lumen is an obligate step for the parasite to perpetuate its life cycle . Disruption of B . bovis fertilization during parasite development in tick midgut would prevent transmission via tick vectors . Recent research described additional members of the B . bovis 6-Cys genes and defined the 6-Cys A and B genes as markers for midgut stages [16] . However , little else is known regarding the expression of additional sexual stage proteins by B . bovis , or the events leading to sexual reproduction of the parasite during its development in the midgut . In silico analysis demonstrated the presence of a gene in B . bovis genome that is orthologous to the Plasmodium hap2 gene . The pattern of expression , localization and biological significance of HAP2 in B . bovis remains unknown . In this study , we demonstrate that hap2 is transcribed exclusively during B . bovis development within the tick midgut , and not by blood stage parasites . We also demonstrated that deletion of hap2 does not affect the growth of B . bovis blood stages in cultures , and that the expression of HAP2 is associated to sexual stage development in in vitro sexual stage induction experiments . Collectively , the data indicates that similar to Plasmodium [17] , B . bovis HAP2 is a potential candidate antigen for developing transmission blocking vaccines that might elicit a host immune response able to disrupt the development of a B . bovis stage infectious for tick midgut epithelial cells .
To examine the pattern of hap2 expression in B . bovis infected tick midgut , approximately 20 , 000 Rhipicephalus microplus larvae , La Minita strain , were placed under a cloth patch on a splenectomized calf as previously described [18 , 19] . When approximately 1% of the nymphs molted to adults , the calf was inoculated intravenously with B . bovis Texas strain stabilate contained 107 infected erythrocytes [20] to synchronize peak parasitemia with female tick repletion . Replete female ticks were collected , washed in tap water , dried and incubated at 26°C in 93% relative humidity . During development of B . bovis within tick midgut , five engorged ticks from the incubator were dissected daily for 6 consecutive days . Individual midgut was placed into 1 ml of Trizol ( Thermo Fisher Scientific , Waltham , MA ) and stored at -80°C . To evaluate hap2 expression in B . bovis blood stages , infected defibrinated blood was collected and the erythrocytes washed five times with Puck’s saline G to remove white blood cells . Parasites were pelleted by centrifugation of infected blood and suspended in Trizol . To extract RNA from in vitro sexual stages induced culture , parasites were isolated by differential centrifugation of 400 xg to 2 , 000 xg to pellet the extracellular stages . Total RNA extracted using the Trizol according to the manufacturer’s protocol . The samples were treated with DNase I , quantified by Nanodrop ( Thermo Fisher Scientific ) , and 150 ng of total RNA utilized for synthesizing cDNA ( Thermo Fisher Scientific ) . Primer sets for hap2 were designed to amplify a 165 base pair fragment ( Table 1 ) . PCR cycling conditions consisted of 95°C for 3 min followed by 35 cycles of 95°C for 30 sec , 55°C for 30 sec and 72°C for 30 sec , with a final extension of 72°C for 5 min . PCR products were visualized by 2% agarose gel electrophoresis . The PCR amplicon was cloned into PCR 2 . 1-TOPO ( Thermo Fisher Scientific ) and submitted for sequencing ( Eurofins MWG Operon , Louisville , KY ) . Full length B . bovis hap2 cDNA synthesized from infected female tick midgut RNA was used to compare to the complete annotated B . bovis genome sequence [5] and other apicomplexan genomes using Multiple Sequence Alignment by CLUSTALW ( http://www . genome . jp/tools/clustalw/ ) . Domain prediction of hap2 gene was performed using the Simple Modular Architecture Research Tool ( SMART ) ( http://smart . embl-heidelberg . de ) . Trans-membrane domains and signal peptides in the HAP2 protein were predicted using the Transmembrane Hidden Markov Model package 2 ( TMHMM2 ) ( http://www . cbs . dtu . dk/services/TMHMM-2 . 0 ) . The detection of glycosylphosphatidylinositol ( GPI ) anchor was predicted using an online GPI prediction server ( http://mendel . imp . ac . at/gpi/gpi_server . html ) . The complete gDNA sequence for hap2 gene was compared among four geographically distinct B . bovis strains including Texas strain , Mo7 , Argentina L17 virulent and Argentina L17 attenuated . Strain-specific single nucleotide polymorphisms ( SNPs ) were then estimated in order to calculate the ratio of synonymous to non-synonymous changes . To estimate ω ( dN/dS ratio ) , “SNAP” was used ( http://hcv . lanl . gov/content/sequence/SNAP/SNAP . html ) . The parameters were set up as follows: ω >1 indicated positive selection , as the selection had caused some amino acid substitution; ω<1 indicated occurrence of purifying selection and a high degree of sequence conservation [21] . Nucleotide substitutions were calculated manually . B . bovis Texas strain parasites were maintained in long-term microaerophilous stationary-phase ( MASP ) cultures as previously described [22 , 23] . Cultured blood parasites were used as to knockout the hap2 gene . Briefly , a recombinant plasmid containing a fusion luciferase-GPF-BSD ( LUC-GFP-BSD ) gene under the control of the ef-1α-B promoter flanked by portions of the hap2 gene , 660 bp in 3’ and 950 bp in 5’ , was constructed ( Fig 1A and 1B , GenBank accession number: KX234096 ) . E . coli were transformed , 5 colonies selected , and grown overnight in 5 ml of LB broth . The phap2-luc-gfp-bsd plasmids were extracted and submitted for sequencing . Recombinant plasmids were purified using EndoFree Plasmid Maxi Kit ( Qiagen , Santa Clarita , CA ) according to the manufacturer’s protocol ( EndoFree Plasmid Maxi Kit , cat # 12362 ) . Twenty μg of plasmids phap2-luc-gfp-bsd or pBlueScript ( pBS ) as a control were diluted into 25 μl Cytomix and electroporated with 75 μl of 20% B . bovis infected erythrocytes as previously described [24] . Six hours after transfection , blasticidin was added to culture medium to a final concentration of 4 μg/ml for parasite selection . Parasite growth was determined by counting the parasitemia using Giemsa stained blood smears . One week after transfection , the expression of GFP was examined by fluorescent microscopy as previously described [25] . The B . bovis transfected-hap2KO-gfp-bsd ( Tf-hap2KO-gfp-bsd ) line was cloned by fluorescence activated cell sorting using 96-well plates [26] . Genomic DNA was isolated from Tf-hap2KO-gfp-bsd clonal line ( cln ) and B . bovis wildtype Texas strain . Briefly , B . bovis Tf-hap2KO-gfp-bsd was expanded to 25% parasitemia . The erythrocytes were pelleted and washed with phosphate-buffered saline . Erythrocytes were lysed with red blood cell lysis solution ( Qiagen , Hilden , Germany ) incubated for 5 min at room temperature . Parasites were lysed using cell lysis solution ( Qiagen , Hilden , Germany ) with 20 μg/ml of Proteinase K and incubated at 56°C for 30 min . Proteins were removed and DNA isopropanol precipitated , washed with 70% ethanol , and suspended in 100 μl of DNA hydration solution ( Qiagen , Hilden , Germany ) . A PacBio library was constructed using the SMRTbell Template Prep Kit v1 . 0 . Genomic DNA was sheared using the Covaris G-Tube at 1350G for 20 min , cleaned and size selected using Ampure XP beads ( Beckman Coulter , Indianapolis , IN ) . Standard sequencing was performed on the PacBio RSII using P6/C4 sequencing chemistry and MagBead loading . Genome sequences were assembled de novo with the Hierarchical Genome Assembly Process ( HGAP ) v 2 . 0 that is integrated into the SMRT analysis package . Single contig containing transfection specific sequences in the genome of the transfected clonal line were identified using BLAST utilizing all portions of the donor plasmid as queries . Sexual forms were induced essentially as previously described [27] , with few modifications . The parasites used in these experiments were derived either from stabilates generated from blood of an infected splenectomized calf , or from hap2-KO culture , and maintained in in vitro cultures for one week before induction . These in vitro cultured B . bovis infected erythrocytes were suspended in an induction medium consisting of 0 . 465 ml final volume of culture ( from a 100 ml stock solution containing 58 ml HL-1 culture medium ( Lonza , Rockland , ME , USA ) , 40 ml bovine serum , 0 . 01 M TAPSO , 1 ml of 100X antibiotic-antimytotic solution ( Invitrogen , CA , USA ) , and 100 uM xanthurenic acid ( Sigma , St . Louis , MO , USA ) , with 10% bovine red blood cells ( 0 . 0465 ml of packed blood ) . Cultures were incubated at 26°C in air for up to 20h . Cultures were also incubated with the induction medium at 37°C for 20h in a MASP as previously reported [28] , or in induction medium without xanthurenic acid at 26°C for 20 h . Three synthetic peptides from the extracellular region of HAP2 were manufactured by BioSynthesis , Inc . ( Texas , USA ) . Peptide 1: DGPEKRFRQRKGFFVC ( 15-mer , amino acids 2 to 17 ) , peptide 2: KTPKGGAKKKKQKLDSSEWEHK ( 21-mer , amino acids 454 to 475 ) and Peptide 3: ERKREQESRERQAEHER ( 17-mer , amino acids 726 to 743 ) . The peptides were conjugated to keyhole limpet hemocyanin ( KLH ) and used to immunize rabbits ( BioSynthesis , Lewisville , TX , USA ) . Rabbits were inoculated with 0 . 5ml of conjugated peptides ( conc . at 1 . 43 mg/ml ) mixed in complete Freund’s adjuvant for the initial inoculation and in incomplete Freund’s adjuvant for all the booster injections . The adjuvants were mixed with the antigens at a ratio of 1:1 . Inoculations were performed subcutaneously along the back , and intramuscularly in the hind limbs . All injections ( less than 0 . 2ml/site ) were done at multiple sites regardless of the route . The resulting immune sera were titrated by ELISA and used in subsequent immunoblot assays . The specificity of the anti HAP2 polyclonal antibody was further tested in immunoblots using non-purified recombinant HAP2 expressed in prokaryotic expression system pBAD/thio-TOPO ( Invitrogen , CA , USA ) ( S1 Fig ) . The full size hap2 gene was amplified from B . bovis cDNA by PCR amplification , using primers hap2 full length ( Table 1 ) . The resulting amplicons were cloned into the pBAD/thio-TOPO vector . Plasmid DNA extracted from E coli positive clones were sequenced to confirm their identity and the correct orientation of the hap2 insert . One positive clone was selected for expression of HAP2 in E . coli-transformed cultures ( 125 ml ) using expression induction with 0 . 2% arabinose for 3 h at 37°C . Bacterial pellets were suspended and homogenized in lysis buffer-Nonidet-P40 ( NP-40 ) ( 150 mM sodium chloride 1 . 0% NP-40–50 mMTris , pH 8 . 0 ) and protease inhibitor ( 1 μg/ml ) . Total protein from cell lysate used for the immunoblots . The polyclonal antibody for Bbo 6-Cys A was described and obtained in a previous study [16] . The antigens used in the immunoblots were prepared from B . bovis-infected erythrocyte culture or xanthurenic acid induced culture . Sexual stages were pelleted from induced culture by differential centrifugation of 400xg to 2 , 000xg to pellet the sexual stages . Parasites were suspended and homogenized in lysis buffer and 1 μg/ml protease inhibitor ( Roche Diagnostics , Indianapolis , IN , USA ) . Total protein was quantified by Micro BCA Protein Assay ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) , 5 μg of total protein were mixed with 5x SDS-PAGE sample buffer ( GenScript , Fl , USA ) , boiled for 5 min and then sonicated for 2 min with 20 sec intervals , and separated into 4–20% Mini-PROTEAN TGX Precast Gels ( BioRad Laboratories , Hercules , CA , USA ) . Proteins were transferred to a nitrocellulose membrane ( Whatman , Dassel , Germany ) for 1 h at 100 V . The membranes were blocked with 5% skim milk in TBS ( Tris-buffered saline: 50 mM Tris-HCl/ 150 mM NaCl , pH7 . 6 ) for 1 h at room temperature , washed three times in TBS and incubated for 1 h with 1:100 dilution of primary antibody against B . bovis 6-Cys A and HAP2 . Monoclonal RAP-1 antibodies were used to detect RAP-1 protein during in vitro cultured B . bovis [29] as well as pre-immune rabbit serum as positive and negative controls , respectively . After three washes in TBS , The membranes were incubated for 30 min with 1:5000 dilution of HRP conjugated goat anti-rabbit IgG ( H+L ) or anti-mouse IgG ( H+L ) antibodies ( KPL , Gaithersburg , Maryland , USA ) , and washed again three times with TBS . Antibody reactivity was visualized using chemiluminescent HRP antibody detection reagents ( KPL , Gaithersburg , Maryland , USA ) . Sexual stages were enriched from in vitro cultures induced by 20h using differential centrifugation as described above . Parasites were washed in 3% normal goat serum in PBS . A portion of the cells were then incubated for 1h with 1:50 anti-HAP2 , or anti-6-Cys A primary antibodies diluted with 10% normal goat serum in PBS . The cells were then washed twice in the PBS by 400 xg centrifugation and incubated for 30 min with 1:1000 goat anti-rabbit Alexa Fluor 555 secondary antibodies ( Thermo Fisher Scientific ) diluted with 10% normal goat serum . The cells were again washed twice with PBS , and air dried on slides , and nuclei were stained with 4 , 6-Diamidino-2-phenylindole dihydrochloride ( Thermo Fisher Scientific ) . Identically produced negative controls were performed using pre-immune ( PI ) rabbit serum instead of the primary antibodies . All samples were independently visualized by fluorescent microscopy and images were processed as described below . Slides were viewed and digitally photographed using an Axio Imager , M1 microscope ( Carl Zeiss Imaging , Inc . , Phoenix , AZ , USA ) . The microscope is equipped with an X-Cite 120 Fl illuminating system ( EXFO Photonic Solutions ) . Digital images were captured using an AxioCam MRm digital camera connected to a desktop computer running the AxioVision ( version 4 . 8 . 1 . 0 ) program . Image stacks were obtained using optimal z-axis spacing [250 nm z-step , Plan-Apochromat 63x/1 . 4 oil M27 objective ( Carl Zeiss Imaging , Inc . , Phoenix , AZ , USA ) ] . Z-stack image files were imported for processing into the ImageJ-based open source processing package Fiji ( version 1 . 48b; http://pacific . mpi-cbg . de/ ) [30] . Surface exposure of HAP2 in in vitro Xanthurenic acid induced parasites was confirmed by analyzing parasites in IFA after trypsinization [31] as follows . Sexual stages of B . bovis induced in in vitro cultures were washed twice in PBS by 400 xg centrifugation . Cells were trypsinized for 30 minutes at 37o C with 0 . 05% trypsin-EDTA ( Gibco BRL/Invitrogen , Carlsbad , CA , USA ) , trypsinization was terminated with the addition of trypsin inhibitor ( Sigma-Aldrich , St Louis , MO , USA ) for 10 min at 37°C . Parasites were then washed in 3% normal goat serum in PBS . A portion of the cells were then incubated for 1h with 1:50 anti-HAP2 primary antibodies diluted with 10% normal goat serum in PBS , and washed twice in the PBS by 400 xg centrifugation and incubated for 30 min with 1:1000 goat anti-rabbit Alexa Fluor 555 secondary antibodies ( Thermo Fisher Scientific , CA , USA ) diluted with 10% normal goat serum . The cells were again washed twice with PBS . To estimate cell viability , cells were suspended in PBS and mixed with equal volume of 6-Carboxyfluorescein Diacetate ( 6-CFDA [31] , final concentration in PBS . 10 μg/ml: Calbiochem-Behring , La Jolla , CA , USA ) , and Incubated at room temperature for 15 minutes . The cells were then washed once with PBS and incubated with nucleic acid stain Hoechst 33342 ( Thermo Fisher Scientific , CA , USA ) for 30 minutes . Finally , cells were washed twice with PBS , and air dried on slides . All samples were independently visualized by fluorescent microscopy as described above . This study was approved by the Institutional Animal Care and Use Protocol Committees of the University of Idaho , Moscow , Idaho ( protocol #2016–20 ) in accordance with institutional guidelines based on the U . S . National Institutes of Health ( NIH ) Guide for the Care and Use of Laboratory Animals . The rabbit antibodies were generated according to the approved animal care protocol D16-00398 ( OLAW ) by BioSynthesis , and to USDA Research license number 23-R-0089 .
A single copy B . bovis hap2 gene is located in between 1 , 452 , 162 bp and 1 , 454 , 808 bp of chromosome 3 , containing 8 introns and 9 exons . This multi-intron structure is usually conserved in the hap2 genes among apicomplexan parasites [32–35] . The annotated hap2 mRNA [GenBank XM_001611756] revealed an orf of 2 , 271 bp , coding for a 79 . 53 kDa protein containing 723 amino acids . B . bovis HAP2 protein contains a single HAP2 domain Similar to Plasmodium falciparum 7G8 HAP2 ( XP_001347424 ) . This domain is functionally involved in a highly conserved sperm protein that is essential for gamete fusion . The HAP2 domain is located between amino acids 348 and 394 , suggesting a similar conserved function for this gene among these parasites . The HAP2 domain is predicted to be located in the extracellular region of the protein , which is likely exposed on the surface of the parasites . Overall , B . bovis HAP2 deduced amino acid sequence appears relatively well conserved when compared with its homologues in other species . In silico predictions suggests that the B . bovis HAP2 protein lacks a glycosyl phosphatidylinositol ( GPI ) anchor . In addition , HAP2 is also predicted to contain a signal peptide between amino acids 1–33 , a hydrophobic transmembrane domain located between amino acids 683–705 , and a predicted coiled coil domain between amino acids 721–753 , located near the C-terminus . The coiled coil domain is also present in an identical location in many viral fusion proteins , consistent with possible role in the membrane fusion reaction . Collectively , all these features are suggestive of the possible trafficking of HAP2 to the external surface of the parasite , and its possible role as a fusogenic protein . We examined the occurrence of sequence variation and single nucleotide polymorphisms ( SNPs ) among the hap2 gene among distinct B . bovis strains . The hap2 gene is highly conserved among the distinct strains ( 99% to 99 . 9% aa identity ) . The analysis was performed using a sequence database including hap2 gene derived from B . bovis Texas strain , Mo7 , Argentina L17 virulent and Argentina L17 attenuated . The calculated synonymous and non-synonymous S/N ratios ( Table 2 ) with the parameter , ω , ( ω = dN/dS ) , as an indicator of potential selection pressures . In all cases , ω of less than 1 was obtained and revealed no evidence for positive selection for the hap2 gene , suggesting that B . bovis hap2 gene is under no diversifying immune selection . The data also indicates a low likelihood of selective forces such as immune pressure of the host acting on the evolutionary history of this gene , consistent with low or lack of exposure of the protein to the immune system of the host during infection , which suggests no expression of hap2 in blood stages of the parasite . The pattern of hap2 transcript expression was investigated by RT-PCR analysis performed on RNA extracted from B . bovis infected erythrocytes and tick midguts . Previous transcriptome and RNA sequence analysis using short-term cultured merozoites from strains differing in origin and virulence phenotypes show that the hap2 gene is transcribed at very low or undetectable levels compared to constitutively expressed rap1 in the blood stages ( S2 Fig ) [36] . Consistently , only rap-1 , but not hap2 transcripts were detected in B . bovis blood stages by RT-PCR . In contrast , hap2 transcripts were transiently detected at days 2 , 3 and 4 , but not at days 0 , 1 , 5 and 6 post-repletion during the development of B . bovis in the tick midgut ( Fig 1 ) . Sequence analysis of the RT-PCR derived 165 bp amplicon ( Fig 1 ) demonstrated identity to the hap2 sequence from the annotated B . bovis genome [5] . Overall , the results indicate that the hap2 gene is differentially transcribed during B . bovis development within tick midgut , but not during development of parasites within the mammalian host . Interestingly , the differential intensity of the RT-PCR bands ( Fig 1 ) suggests that expression of hap2 peaks at day 2 post repletion , but this observation needs to be confirmed using a quantitative assay . The hap2 gene was disrupted using the transfection plasmid phap2-lucgfpbsd . The structure of the B . bovis hap2 locus and the experimental design for the disruption of hap2 are represented in Fig 2 . Selection with blasticidin resulted in the emergence of the transfected and green fluorescent Tf-hap2KO-gfp-bsd cell line . In contrast , parasites electroporated with the control pTf-pBS plasmid did not survive upon blasticidin selection ( Fig 3A ) . Clonal cell lines were generated from the mixed parasite line Tf-hap2KO-gfp-bsd by flow cell sorting [37 , 38] . Clonal lines were evaluated by expression of GFP ( Fig 3B ) . The clonal line termed Tf-hap2KO-gfp-bsd-cln was selected for further analysis . Growth curve analysis demonstrated that both B bovis Tf-hap2KO-gfp-bsd-cln and its wild type parental strain had similar replication kinetics ( Fig 3C ) . The clonal line Tf-hap2KO-gfp-bsd-cln was fully sequenced . Analysis of the full genomic DNA sequence of the Tf-hap2KO-gfp-bsd-cln line revealed an output of polished assembly of 609 contigs with the largest contig being 87kb . BLAST of the hap2 gene ( BBOV_III006770 ) against assembly contigs revealed a single hit at contig 1592 . The area covered by contig 1592 was roughly 1 , 438 , 762 to 1 , 455 , 743 of chromosome 3 , which contained a ~6 , 448 bp insertion ( GenBank accession number: KX234097 ) . Full sequencing of the genome of the Tf-hap2KO-gfp-bsd-cln confirmed replacement of the hap2 gene by the 5’ hap2 ( 935 bp ) , EF promoter ( 761 bp ) , luciferase ( 1 , 651 bp ) , gfp-bsd ( 1 , 100 bp ) , 3’ rap1 ( 1 , 288 bp ) , and 3’ hap2 ( 656 bp ) fragments , present in the transfection plasmid . Thus , analysis of the structure of the hap2-KO gene in the clonal line Tf-hap2KO-gfp-bsd-cln indicates that these sequences were inserted by homologous recombination . It was the only foreign DNA insert detected by whole genome sequencing . The rest of the B . bovis Tf-hap2KO-gfp-bsd-cln genomic sequence was essentially identical to the wildtype B . bovis genome sequence [5] . Collectively , these data confirmed a successful insertion of the transfected genes disrupting the targeted hap2 locus of B . bovis , and suggests that disruption of the hap2 locus did not affect the pattern of growth of the parasite in in vitro cultures . B . bovis sexual stages were induced in vitro by decreasing the temperature to 26°C and the addition of xanthurenic acid to the culture media . Microscopic inspection of Giemsa stained cells from induced cultures showed the presence of extra-erythrocytic parasites with long projections and large round parasite stages , indicative of parasite sexual stage development ( Fig 4A ) . No such sexual stages forms were found upon similar microscopic inspection of Tf-hap2KO-gfp-bsd-cln parasites ( Fig 4B ) , and fluorescent microscopy inspection of Tf-hap2KO-gfp-bsd-cln live parasites ( S3 Fig ) developing in in vitro cultures with induction medium xanthurenic acid ( XA ) at 26°C . Previous comparative studies performed in B . bovis parasites from in vitro cultures and in tick midgut , defined the expression of the 6-Cys A and B genes as markers of sexual stage parasites [16] . A similar comparative transcript analysis using RT-PCR and sequencing confirmed expression of 6-Cys A and B and hap2 genes in parasites emerging upon in vitro sexual stage induction . In contrast non-induced parasites failed to produce hap2 and 6-cys A and B transcripts ( Fig 5A ) . In addition , Tf-hap2KO-gfp-bsd-cln parasites , produced rap-1 transcript , but failed to produce 6-cys A and B transcripts ( Fig 5B ) upon xanthurenic acid induction . In addition , anti-6-Cys A and anti-HAP2 antibodies react with wild type B . bovis antigens of ~60 kDa and~80 kDa respectively in induced cultures but did not recognize any native protein in non-induced B . bovis culture in immunoblots ( Fig 6 ) . In contrast , the control 60 kDa RAP-1 is recognized in lysates from both , induced and non-induced parasites with comparable signal intensities ( Fig 6 ) . Tf-hap2KO-gfp-bsd-cln Induced parasites didn’t show reactivity against anti-HAP2 antibodies ( S4 Fig ) . The size of the antigens recognized by all antibodies matches the predicted sizes of the RAP-1 , 6-Cys A and anti-HAP2 proteins . Thus , the data is consistent with co-expression of the sexual stage marker 6-Cys A and HAP2 proteins in induced wild type B . bovis cultures , but not in non-induced cultures . In addition , live immunofluorescence assays ( Live IFA ) confirmed expression of the 6-Cys A and HAP2 proteins on the surface of sexual stage induced B . bovis T3B strain , but not in the non-induced parasites ( Fig 7A–7D ) . Overall the data is consistent with the notion that expression of the hap2 gene is associated with the development of B . bovis sexual stage forms induced in vitro . We confirmed surface exposure of HAP2 by performing live immunofluorescence analysis on trypsin-treated B . bovis induced cells ( Fig 8 ) . Parasites treated with trypsin are no longer recognized by anti-HAP2 antibodies in live immunofluorescence assays ( Fig 8 AF 568 ) . In addition , the trypsin treatment did not alter the membrane integrity and the viability of the treated parasites , as they are still stained with the vital 6-CFDA stain in a pattern that is similar to non-trypsin-treated parasites ( Fig 8 . AF 488 ) .
The hap2 gene products in B . bovis related apicomplexan parasites have been consistently associated with differential expression and the formation of sexual forms . In this study , we demonstrated transcription of hap2 in parasites residing in the midgut of replete R . microplus female tick fed on a bovine infected with B . bovis , but not in in vitro cultured blood stages . A requirement of B . bovis to perpetuate its life cycle is the ability to develop sexual stages within the lumen of the Rhiphicephalus tick midgut . The fusion of gametes results in a stage infectious to tick midgut epithelial cells . Within midgut cells , B . bovis transforms into kinetes . This stage egresses from the midgut into the hemolymph to further infect ovaries [12] . Previous work indicated the differential expression of members of the 6-Cys family in tick stages [16] , suggested that specific B . bovis proteins are necessary for parasite development within the tick vector . Interestingly , transcription of hap2 is limited to days 2 to 4 after dropping . This pattern of transcription may be required for synchronized sexual stages formation , or be related to the timing of gamete fusion inside the tick midgut . These observations are consistent with Plasmodium where HAP2 is expressed only in gametocytes and gametes [15] . Differential expression of HAP2 is indispensable for fertilization of Plasmodium parasites , with a demonstrated specific fusion function during gamete interaction [15] . Importantly , genome sequence analysis among B . bovis isolates demonstrated that HAP2 is highly conserved with an identity of 99% to 99 . 9% . The high degree of HAP2 sequence conservation among strains also supports the usefulness of HAP2 as a potential antigen for vaccine development aimed to block B . bovis transmission . The synonymous and non-synonymous ratios ( Table 2 ) revealed no evidence for positive selection for the hap2 gene , consistent with a low frequency of single nucleotide polymorphisms in hap2 from different B . bovis isolates . These results suggest that B . bovis HAP2 is under no diversifying selection , a property shared with current transmission-blocking vaccine candidates in Plasmodium [39] . The data also suggest the occurrence of functional restrictions to sequence variations for this gene , which enhances its potential as a vaccine candidate . We also examined if knocking out the hap2 gene affected the growth fitness of the parasite in in vitro cultures . The in vitro growth fitness of the hap2 KO parasites was similar to the wildtype strain indicating that the gene is not critical for B . bovis development within erythrocytes . Importantly , full sequence of hap2 knocked out parasites demonstrated the insertion of a single copy of the transfected selectable marker/reporter genes disrupting the hap2 locus , and thus such transfected parasites are ideally suited for exploring the functional significance of the hap2 gene in B . bovis . Furthermore , the remainder of the genome of the transfected B . bovis genome was unaltered , and no other insertions derived from the transfection plasmid were found in the genome of the KO strain confirming the high specificity and efficiency of the homologous recombination mechanisms operating in B . bovis . These data also confirm the usefulness of transfection as an approach to study gene function by disrupting gene expression in different B . bovis stages . In this study we were able to induce B . bovis sexual forms using xanthurenic acid in in vitro cultured parasites for the first time . Xanthurenic acid is a metabolic intermediate derived from the metabolism of tryptophan which is present in the gut of the Anopheles mosquito where it is known to induce gametogenesis of Plasmodium falciparum [40 , 41] . It remains unknown whether this metabolite is also present in the tick midguts , or if gametogenesis in Babesia parasites also requires a xanthurenic acid depending mechanism . However , similar to previous observations in B . bigemina [27] , we were also able to induce changes in B . bovis morphology using particular culture incubation settings in the presence of xanthurenic acid . The induced parasites present several distinct morphology and shapes , consistent with previous similar inductions on B . bigemina and with forms found in the midgut of ticks engorged on Babesia infected cattle . Importantly , because expression of the 6-cys A and B genes are known to be markers of B . bovis sexual stages , the molecular data on expression of these two genes upon induction included in our study validated for the first time the observation that the addition of xanthurenic acid concomitant with decreased incubation temperatures , results in the induction of sexual stages , as visualized before just by changes in the morphology . In addition , and consistent with morphology changes , induction result in their progression into a life stage that is morphologically and molecularly different than the life stage forms that typical in blood parasites cultured under standard ( non-induced ) culture conditions . Indeed the detection of expression of the 6-cys A and B genes in these induced forms is fully consistent with the formation of sexual forms normally induced in the midgut of R . microplus ticks feeding in Babesia infected animals [16] . Interestingly , our data show a correlation among the inability of Tf-hap2KO-gfp-bsd-cln parasites to change morphology , and to generate sexual stage specific expression products such as the members of the 6-Cys gene family . In contrast , these mutant parasites Tf-hap2KO-gfp-bsd-cln are fully able to develop and grow in vitro in erythrocytes , supporting the concept that an intact copy of the hap2 gene is required for sexual stage induction but irrelevant for blood stage development . The results can be compared with similar previous findings in malaria parasites , where sexual stage fusion was found dependent on the expression of the hap2 gene [42–44] . Importantly , two distinct lines of evidence , direct live immunofluorescence and loss of recognition of surface exposed HAP2 upon trypsinization , supports that B . bovis induced parasites express HAP2 in their surface . Therefore , it is likely that the B . bovis HAP2 indeed also functions as an ancestral gamete fusogen in this parasite , since highly diverse eukaryotic gametes carrying loss-of-function mutations in HAP2 also fail to fuse [45] . In summary , HAP2 is differentially expressed by B . bovis during its development within R . microplus and the in vitro induction data suggests that surface exposed expression of this protein might be connected to the completion of the B . bovis life cycle during parasite development in the tick midgut . The absence of detectable hap2 transcripts by B . bovis blood stages suggested that hap2 is unnecessary for parasite development during infection of mammalian host . In contrast , the data supports that expression of HAP2 occurs in concurrence with the development of sexual stages upon induction with xanthurenic acid under in vitro culture conditions . Overall , these findings strongly suggest a role of hap2 during tick stages of the parasite , probably including sexual reproduction and supports HAP2 as a leading candidate for a transmission blocking vaccine against bovine babesiosis . Further in vivo studies are necessary to determine if disrupting hap2 interferes with the development of B . bovis within tick midgut and beyond . | Babesia bovis , is a tick borne apicomplexan parasite responsible for important cattle losses globally . Babesia parasites have a complex life cycle including asexual replication in the mammalian host and sexual reproduction in the tick vector . Novel control strategies aimed at limiting transmission of the parasite are needed , but transmission blocking vaccine candidates remain undefined . In this study we analyze the conservation and role of the hap2 gene in the erythrocyte stage of the life cycle of the parasite and found that expression of the gene is not required for the development of the parasite in erythrocytic stages , using a hap2 mutated parasite line . In addition , we developed an in vitro system for the induction of sexual forms of B . bovis and found expression of the hap2 gene and surface localization of the protein . However , hap2-KO parasites are unable to develop sexual stages . We concluded that HAP2 is a leading candidate for a transmission blocking vaccine against bovine babesiosis due of the high level of conservation , surface exposure , and specific expression in tick stage and in in vitro induced sexual stages parasites . | [
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"protozoans"... | 2017 | The Babesia bovis hap2 gene is not required for blood stage replication, but expressed upon in vitro sexual stage induction |
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