CTGCAAQuantity, splicing patterns and stability(A) RNA-sequencing(A) RNA-sequencing: expression QTL (eQTL): expression QTL (eQTL))LnToQitha(c iLfiTdQom e neontostihsi H:gnicneuqestior (C(BATOD)AAp HDOCT-ASp-HeQCen chQnST-T L-cLshmeroqmuaenn qauen nscitatcnsuitnagtusgs) : : -PIhC )D(ti : DNA methylation QTL (meQTL)(F) Bisulfite-sequencing: Transcription factor binding QTL ion factor binding DNA methylationquencing(E) ChIP-sequencing)TF binding sitesquE) ChIP-seq nscriptioptio)(bQTL) ssee71Figure: Integration of genetic information into immune functions: The eQTL projectFigure: Network formation of various parameters in insulin-sensi-tive and -resistant individualsCorrelations of fecal microbiota (light blue), fecal metabolites (green), plasma metabolites (light brown), plasma inflammatory cytokines (yellow), and PBMC gene expression (light purple) in insulin-sensitive (left) and -resistant (right) individuals are shown. The size of each symbol represents the amount/numbers of the parameters. Red and gray lines reflect positive and negative correlations, respectively, and the thickness of the lines indicates the strength of correlation. Fecal saccharides show a strong correlation with plasma inflammatory cyto-kines and inflammatory/metabolic genes in PBMCs and these parameters are higher in insulin-resistant individuals compared to insulin-sensitive individuals.genome wide association study (GWAS). Germline genet-ic variations provide us with evidence into the causal relationship of an observed phenomenon and its pathogenesis. In this regard, the majority of GWAS risk variants have been reported to locate in the non-coding regions on the chromosome and function as an expression-quantitative trait locus (e-QTL), regulating the expression levels of genes. Therefore, by integrating genomic in-formation, qualitative and quantitative analyses of transcriptomes together with cell-specific epigenomes, we will better understand the causal pathogenic components of immune cells in various immune-mediated diseases.We are now setting up a system to identify various subtypes of leukocytes from peripheral blood mononuclear cells (PBMC) of heathy individuals. We expect to obtain the utmost unbiased relationship between genotypes and gene expression from healthy donors. Cell separation is performed by fluorescence-activated cell sorting into about 30 different subsets. Cells are then ana-Japan and worldwide, with changes in lifestyle and social en-vironment. It is estimated that about 1 in 5 Japanese suffer from diabetic or prediabetic (medically defined as glucose intolerance) conditions. Thus, prevention of T2D and control of its progres-sion are important and urgent needs - medically, socially, as well as economically. We therefore set this issue as one of the center projects to try to identify gut microbial T2D-predictive biomark-ers or factors. In collaboration with the University of Tokyo Hos-pital, we recruited volunteers taking a complete medical checkup with the following criteria: 1) no obesity or glucose intolerance (control), 2) obesity (BMI ≥ 25) and 3) glucose intolerance (fasting blood glucose ≥ 110 mg/dl or HbA1c ≥ 6.0%). In addition to the thorough clinical examination data from the medical checkup, lyzed in the steady state or in further stimulated conditions, such as with combinations of cytokines and cell surface receptor ago-nists to capture the dynamic responses of gene regulation. Firstly, genotyping as well as RNA-seq are performed. With this data, we will obtain eQTL as well as splicing QTL information. Cap analy-sis of gene expression (CAGE), assay for transposase-accessible chromatin using sequencing (ATAC-seq) and several histone mark analyses for each subset are powerful tools to be used for identifying the causal relationship between genetic variation and gene expression.the following were collected in RIKEN: fecal metagenomic and metabolomic data, plasma metabolomic data, and CAGE-based RNAseq data of peripheral blood mononuclear cells (PBMCs). We also collected nutritional and physical activity data using a brief self-administered diet history questionnaire (BDHQ) and accelerometry, respectively.We found that insulin resistance and metabolic syndrome were significantly associated with fecal monosaccharides and sugar derivatives. We further revealed that these fecal monosaccharides and sugar derivatives strongly associated with host inflammatory gene expression in PBMCs and with plasma cytokine levels (Fig-ure). Representative microbes associated with insulin resistance showed distinct carbohydrate metabolism and host metabolic phenotypes. These results are now submitted for publication.Enhancer(H) Captured HiC-sequencing/MicroC-sequencingDNA methylationH3K4me3H3K9acH3K4me1TranscriptionfactorsPolymerase ⅡRNAPromoter(G) CAGE-sequencing:Promoter usage QTL (puQTL)H3K27acDNase IH3K27me3Tn5E-lncRNA(G) CAGE-sequencing:Enhancer activity QTL (eaQTL)E-lncRNAGenetic variantsGeneCoding RNACdiP-lncRNAeQTL project: Integration of genetic information into immune functionsMany disease susceptibility variants have been identified by Search for gut microbiota-associated biomarkers involved in the pathogenesis of Type 2 diabetes mellitusType 2 diabetes mellitus (T2D) is increasing rapidly, both in
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