üJapanese, age 20-75üNo treatment for diabetesüNo treatment for intestinal diseasesüNo prior use of antibiotics in two weeksElectronic Medical RecordSkin imagesBlood samplesSkin samplesNew drug development supported by RIKEN DMP (S1 theme)Microbiome dataSkin barrier data(Lab for Developmental Genetics)in collaboration with IMS and Keio University School of Medicine68Analysis of human samples and clinical data to realize personalized preventive medicine for atopic dermatitisAnalysis of disease models towards understanding underlying mechanisms of atopic dermatitisSOCS3 cKO mice(Lab for Developmental Genetics)(Lab for Cytokine Regulation)Animal model DBHuman atopic dermatitis DBFigure: Schematic summary of this projectFigure: Study workflow: Data driven research for Atopic DermatitisSpade mice(Lab for Developmental Genetics)(Lab for Tissue Dynamics)Tmem79 KO mice(Lab for Skin homeostasis)(Lab for Gut homeostasis)Stat3 cKO mice(Lab for Cytokine Regulation)Hydro metabolitesTranscriptomeClinical data16S (Species)41Lipid metabolites63510014,614CytokinesHydro metabolites10Study participants(N= 306)ClinicalphenotypesüInsulin resistance (IR)üMetabolic syndrome (MetS)482Predicted genes6,458,217Lipid metabolites2,654195HostMicrobiotabolic disease both in Japan and worldwide. It is estimated that about 1 in 5 Japanese suffer from diabetic or prediabetic (medically defined as glucose intolerance) conditions. Thus, pre-vention of T2D is an urgent need - medically, socially as well as economically. As a center project, various teams in IMS have been engaged in identifying gut microbial T2D-preventive biomarkers or factors involved in the pathogenesis of T2D. To this end, they have been collaborating with the University of Tokyo Hospital to recruit volunteers with the following criteria among those taking a complete medical checkup: 1) no obesity or glucose intolerance (control), 2) obesity (BMI ≥ 25), and 3) glucose intolerance (fast-ing blood glucose ≥ 110 mg/dl or HbA1c ≥ 6.0%). In addition to the thorough clinical examination data as part of the medical checkup, collected in RIKEN were: fecal and saliva metagenomic and metabolomic data, plasma and urine metabolomic data, CAGE-based RNAseq data of peripheral blood mononuclear cells, and whole genome sequencing data (Figure). Also nutri-tional and physical activity data have been collected using a brief self-administered diet history questionnaire (BDHQ) and acceler-ometry, respectively.disorder. Although it has been suggested that an individual approach to each patient is crucial for understanding AD, suitable methods have not been established yet. The purpose of this study, therefore, is to establish a method for disease clustering into sub-groups and to develop novel predictive treatment algorithms in each sub-group. To achieve this, we perform integrated analysis of genome and transcriptome data and multimodal clinical informa-tion from AD patients.In keeping with the above approach, IMS-MIH collaborative research project has been collecting large numbers of high-quality clinical samples in collaboration with Keio University Hospital and have established an integrated data analysis and repository infrastructure called Medical Data Integration Assistant (MeDIA). They have acquired more than 1000 transcriptome datasets [mRNA-seq of skin tissue and peripheral blood mononuclear cells (PBMC)] and are performing data analysis by using super-vised and unsupervised machine learning with other annotating data such as clinical data and serum cytokine profiles. Notably, they revealed that PBMC transcriptome analysis classified the disease phenotypes of AD patients into five clusters characterized by different blood cells. In addition, skin transcriptome analysis They found that insulin resistance and metabolic syndrome were significantly associated with fecal monosaccharides and sugar derivatives. Furthermore, these fecal monosaccharides and sugar derivatives strongly associated with host inflammatory gene expression and cytokine levels. Representative microbes associ-ated with insulin resistance showed distinct carbohydrate me-tabolism and host metabolic phenotypes. These results are now submitted for publication.addresses the issue of characterizing a population of patients re-sponding to treatment with biologics. The project also advances the verification of therapeutic molecular targets and understand-ing of pathological conditions by guiding the analytical findings in humans to animal model research.The project team is highly focused on the integration of tech-nology and knowledge possessed by experts in various fields, and is also working on collaborations with various companies and open science, with an awareness of social implementation. Their approach will not only pave the way toward realization of personalized medicine for AD, but also for development of new technologies in data-driven medical research, and therefore will have a considerable impact on society.Search for new biomarkers involved in the pathogenesis of Type 2 diabetes mellitusType 2 diabetes mellitus (T2D) is a highly prevalent meta-Medical Sciences Innovation Hub Program (MIH)Atopic dermatitis (AD) is a heterogeneous and multifactorial
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