Over the past years, infectious disease has caused enormous economic loss in pig industry. Among the pathogens, gram negative bacteria not only cause inflammation, but also cause different diseases and make the pigs m...Over the past years, infectious disease has caused enormous economic loss in pig industry. Among the pathogens, gram negative bacteria not only cause inflammation, but also cause different diseases and make the pigs more susceptible to virus infection. Vaccination, medication and elimination of sick pigs are major strategies of controlling disease. Genetic methods, such as selection of disease resistance in the pig, have not been widely used. Recently, the completion of the porcine whole genome sequencing has provided powerful tools to identify the genome regions that harboring genes controlling disease or immunity. Immunogenornics, which combines DNA variations, transcriptorne, immune response, and QTL mapping data to illustrate the interactions between pathogen and host immune system, will be an effective genomics tool for identification of disease resistance genes in pigs. These genes will be potential targets for disease resistance in breeding programs. This paper reviewed the progress of disease resistance study in the pig focusing on Gram-negative bacilli. Major porcine Gram-negative bacilli and diseases, suggested candidate genes/pathways against porcine Gram-negative bacilli, and distributions of QTLs for immune capacity on pig chromosomes were summarized. Some tools for immunogenomics research were described. We conclude that integration of sequencing, whole genome associations, functional genomics studies, and immune response information is necessary to illustrate molecular mechanisms and key genes in disease resistance.展开更多
Genetic variation is a key factor influencing cytokine production capacity,but which genetic loci regulate cytokine production before and after vaccination,particularly in African population is unknown.Here,we aimed t...Genetic variation is a key factor influencing cytokine production capacity,but which genetic loci regulate cytokine production before and after vaccination,particularly in African population is unknown.Here,we aimed to identify single-nucleotide polymorphisms(SNPs)controlling cytokine responses after microbial stimulation in infants of West-African ancestry,comprising of low-birth-weight neonates randomized to bacillus Calmette-Gue rin(BCG)vaccine-at-birth or to the usual delayed BCG.Genome-wide cytokine cytokine quantitative trait loci(cQTL)mapping revealed 12 independent loci,of which the LINC01082-LINC00917 locus influenced more than half of the cytokine-stimulation pairs assessed.Furthermore,nine distinct cQTLs were found among infants randomized to BCG.Functional validation confirmed that several complement genes affect cytokine response after BCG vaccination.We observed a limited overlap of common cQTLs between the West-African infants and cohorts of Western European individuals.These data reveal strong population-specific genetic effects on cytokine production and may indicate new opportunities for therapeutic intervention and vaccine development in African populations.展开更多
B cells express B-cell receptors(BCRs) which recognize antigen to trigger signaling cascades for B-cell activation and subsequent antibody production. BCR activation has a crucial influence on B-cell fate. How BCR is ...B cells express B-cell receptors(BCRs) which recognize antigen to trigger signaling cascades for B-cell activation and subsequent antibody production. BCR activation has a crucial influence on B-cell fate. How BCR is activated upon encountering antigen remains to be solved, although tremendous progresses have been achieved in the past few years. Here, we summarize the models that have been proposed to explain BCR activation, including the cross-linking model, the conformation-induced oligomerization model, the dissociation activation model, and the conformational change model. Especially, we elucidate the partially resolved structures of antibodies and/or BCRs by far and discusse how these current structural and further immunogenomic messages and more importantly the future studies may shed light on the explanation of BCR activation and the relevant diseases in the case of dysregulation.展开更多
Background:The tumor microenvironment(TME)performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma(ccRCC).However,a lack of recognized pre-clinical TME-based ...Background:The tumor microenvironment(TME)performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma(ccRCC).However,a lack of recognized pre-clinical TME-based risk models poses a great challenge to investigating the risk factors correlated with prognosis and treatment responses for patients with ccRCC.Methods:Stromal and immune contexture were assessed to calculate the TMErisk score of a large sample of patients with ccRCC from public and real-world cohorts using machine-learning algorithms.Next,analyses for prognostic efficacy,correlations with clinicopathological features,functional enrichment,immune cell distribu-tions,DNA variations,immune response,and heterogeneity were performed and validated.Results:Clinical hub genes,including INAFM2,SRPX,DPYSL3,VSIG4,APLNR,FHL5,A2M,SLFN11,ADAMTS4,IFITM1,NOD2,CCR4,HLA-DQB2,and PLAUR,were identified and incorporated to develop the TMErisk signature.Patients in the TME high risk group(category)exhibited a considerably grim prognosis,and the TMErisk model was shown to independently function as a risk indicator for the overall survival(OS)of ccRCC patients.Expression levels of immune checkpoint genes were substantially increased in TME high risk group,while those of the human leukocyte antigen(HLA)family genes were prominently decreased.In addition,tumors in the TME high group showed significantly high infiltration levels of tumor-infiltrated lymphocytes,including M2 macrophages,CD8+T cells,B cells,and CD4+T cells.In heterogeneity analysis,more frequent somatic mutations,including pro-tumorigenic BAP1 and PBRM1,were observed in the TME high group.Importantly,19.3%of patients receiving immunotherapies in the TME high group achieved complete or partial response compared with those with immune tolerance in the TME low group,suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC.Conclusions:We first established the TMErisk score of ccRCC using machine-learning algorithms based on a large-scale population.The TMErisk score can be utilized as an innovative independent prognosis predictive marker with high sensitivity and accuracy.Our discovery also predicted the efficacy of immunotherapy in ccRCC patients,indicating the intimate link between tumor immune microenvironment and intratumoral heterogeneity.展开更多
基金supported by National Natural Science Foundation of China(30901021)863the Key Programs for Science and Technology Development of Hubei Province
文摘Over the past years, infectious disease has caused enormous economic loss in pig industry. Among the pathogens, gram negative bacteria not only cause inflammation, but also cause different diseases and make the pigs more susceptible to virus infection. Vaccination, medication and elimination of sick pigs are major strategies of controlling disease. Genetic methods, such as selection of disease resistance in the pig, have not been widely used. Recently, the completion of the porcine whole genome sequencing has provided powerful tools to identify the genome regions that harboring genes controlling disease or immunity. Immunogenornics, which combines DNA variations, transcriptorne, immune response, and QTL mapping data to illustrate the interactions between pathogen and host immune system, will be an effective genomics tool for identification of disease resistance genes in pigs. These genes will be potential targets for disease resistance in breeding programs. This paper reviewed the progress of disease resistance study in the pig focusing on Gram-negative bacilli. Major porcine Gram-negative bacilli and diseases, suggested candidate genes/pathways against porcine Gram-negative bacilli, and distributions of QTLs for immune capacity on pig chromosomes were summarized. Some tools for immunogenomics research were described. We conclude that integration of sequencing, whole genome associations, functional genomics studies, and immune response information is necessary to illustrate molecular mechanisms and key genes in disease resistance.
基金supported by the Spinoza grant of the Netherlands Organization for Scientific Research and an ERC Advanced Grant(grant number833247)supported by the European Research Council(starting grant ERC-2009-StG-243149)+3 种基金the Novo Nordisk Foundation(research professorship grant to P.A.)the Danish National Research Foundation(grant DNRF108)the DANIDA,European Union FP7,and OPTIMUNISE(grant Health-F3-2011-261375 to the Bandim Health Project)supported by the Hypathia tenure track grant Radboud UMC。
文摘Genetic variation is a key factor influencing cytokine production capacity,but which genetic loci regulate cytokine production before and after vaccination,particularly in African population is unknown.Here,we aimed to identify single-nucleotide polymorphisms(SNPs)controlling cytokine responses after microbial stimulation in infants of West-African ancestry,comprising of low-birth-weight neonates randomized to bacillus Calmette-Gue rin(BCG)vaccine-at-birth or to the usual delayed BCG.Genome-wide cytokine cytokine quantitative trait loci(cQTL)mapping revealed 12 independent loci,of which the LINC01082-LINC00917 locus influenced more than half of the cytokine-stimulation pairs assessed.Furthermore,nine distinct cQTLs were found among infants randomized to BCG.Functional validation confirmed that several complement genes affect cytokine response after BCG vaccination.We observed a limited overlap of common cQTLs between the West-African infants and cohorts of Western European individuals.These data reveal strong population-specific genetic effects on cytokine production and may indicate new opportunities for therapeutic intervention and vaccine development in African populations.
基金grants from the National Natural Science Foundation of China(81825010,81730043,81621002,31811540397 AND 81961130394)。
文摘B cells express B-cell receptors(BCRs) which recognize antigen to trigger signaling cascades for B-cell activation and subsequent antibody production. BCR activation has a crucial influence on B-cell fate. How BCR is activated upon encountering antigen remains to be solved, although tremendous progresses have been achieved in the past few years. Here, we summarize the models that have been proposed to explain BCR activation, including the cross-linking model, the conformation-induced oligomerization model, the dissociation activation model, and the conformational change model. Especially, we elucidate the partially resolved structures of antibodies and/or BCRs by far and discusse how these current structural and further immunogenomic messages and more importantly the future studies may shed light on the explanation of BCR activation and the relevant diseases in the case of dysregulation.
基金supported by grants from the National Natural Science Foundation of China(grant numbers:81802525 and 82172817)the Natural Science Foundation of Shanghai(grant number:20ZR1413100)+3 种基金Beijing Xisike Clinical Oncology Research Foundation(grant number:Y-HR2020MS-0948)the National Key Research and Development Project(grant number:2019YFC1316005)the Shanghai“Science and Technology Innovation Action Plan”Medical Innovation Research Project(grant number:22Y11905100)the Shanghai Anti-Cancer Association Eyas Project(grant numbers:SACA-CY21A06 and SACA-CY21B01).
文摘Background:The tumor microenvironment(TME)performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma(ccRCC).However,a lack of recognized pre-clinical TME-based risk models poses a great challenge to investigating the risk factors correlated with prognosis and treatment responses for patients with ccRCC.Methods:Stromal and immune contexture were assessed to calculate the TMErisk score of a large sample of patients with ccRCC from public and real-world cohorts using machine-learning algorithms.Next,analyses for prognostic efficacy,correlations with clinicopathological features,functional enrichment,immune cell distribu-tions,DNA variations,immune response,and heterogeneity were performed and validated.Results:Clinical hub genes,including INAFM2,SRPX,DPYSL3,VSIG4,APLNR,FHL5,A2M,SLFN11,ADAMTS4,IFITM1,NOD2,CCR4,HLA-DQB2,and PLAUR,were identified and incorporated to develop the TMErisk signature.Patients in the TME high risk group(category)exhibited a considerably grim prognosis,and the TMErisk model was shown to independently function as a risk indicator for the overall survival(OS)of ccRCC patients.Expression levels of immune checkpoint genes were substantially increased in TME high risk group,while those of the human leukocyte antigen(HLA)family genes were prominently decreased.In addition,tumors in the TME high group showed significantly high infiltration levels of tumor-infiltrated lymphocytes,including M2 macrophages,CD8+T cells,B cells,and CD4+T cells.In heterogeneity analysis,more frequent somatic mutations,including pro-tumorigenic BAP1 and PBRM1,were observed in the TME high group.Importantly,19.3%of patients receiving immunotherapies in the TME high group achieved complete or partial response compared with those with immune tolerance in the TME low group,suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC.Conclusions:We first established the TMErisk score of ccRCC using machine-learning algorithms based on a large-scale population.The TMErisk score can be utilized as an innovative independent prognosis predictive marker with high sensitivity and accuracy.Our discovery also predicted the efficacy of immunotherapy in ccRCC patients,indicating the intimate link between tumor immune microenvironment and intratumoral heterogeneity.
