Context-specific and combinatorial genetic regulatory grammars in diabetes

糖尿病的上下文特定和组合遗传调控语法

基本信息

批准号：
10172891
负责人：
Stephen CJ Parker
金额：
$ 41.2万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10172891
关键词：
Affect Alleles American Amino Acid Sequence Amino Acids Animal Model Beta Cell Binding Sites Biological Biological Assay Biological Process Blood Vessels CRISPR/Cas technology Cell physiology Chromatin Chromosomes Code Complex Computer Analysis Coupled DNA DNA Binding DNA Binding Domain DNA Sequence Development Diabetes Mellitus Diagnosis Disease Drug Screening Enhancers Environment Environmental Risk Factor Exons Foundations Functional disorder Genes Genetic Genetic Code Genetic Transcription Genetic Variation Genome Genomic approach Glucose Goals Human In Situ Insulin Islet Cell Islets of Langerhans Knowledge Lead Maps Measures Mediating Medical Care Costs Molecular Molecular Profiling Monitor Morbidity - disease rate Mutation Neurologic Non-Insulin-Dependent Diabetes Mellitus Nucleotides Phenotype Physiological Positioning Attribute Prediabetes syndrome Proteins RFX regulatory factor RNA Regulation Regulator Genes Regulatory Element Reporter Reporting Research Resolution Risk Rodent Single Nucleotide Polymorphism Site Stretching Surveys Syndrome System Testing Time Transcriptional Regulation Translating Untranslated RNA Variant Wages Work combinatorial cost diabetes mellitus genetics diabetic epigenome experimental analysis fasting glucose functional genomics genetic signature genetic variant genome editing genome wide association study islet neonatal diabetes mellitus novel precision drugs precision genetics promoter response risk variant screening trait transcription factor transcriptome translational approach

Affect Alleles American Amino Acid Sequence Amino Acids Animal Model Beta Cell Binding Sites Biological Biological Assay Biological Process Blood Vessels CRISPR/Cas technology Cell physiology Chromatin Chromosomes Code Complex Computer Analysis Coupled DNA DNA Binding DNA Binding Domain DNA Sequence Development Diabetes Mellitus Diagnosis Disease Drug Screening Enhancers Environment Environmental Risk Factor Exons Foundations Functional disorder Genes Genetic Genetic Code Genetic Transcription Genetic Variation Genome Genomic approach Glucose Goals Human In Situ Insulin Islet Cell Islets of Langerhans Knowledge Lead Maps Measures Mediating Medical Care Costs Molecular Molecular Profiling Monitor Morbidity - disease rate Mutation Neurologic Non-Insulin-Dependent Diabetes Mellitus Nucleotides Phenotype Physiological Positioning Attribute Prediabetes syndrome Proteins RFX regulatory factor RNA Regulation Regulator Genes Regulatory Element Reporter Reporting Research Resolution Risk Rodent Single Nucleotide Polymorphism Site Stretching Surveys Syndrome System Testing Time Transcriptional Regulation Translating Untranslated RNA Variant Wages Work combinatorial cost diabetes mellitus genetics diabetic epigenome experimental analysis fasting glucose functional genomics genetic signature genetic variant genome editing genome wide association study islet neonatal diabetes mellitus novel precision drugs precision genetics promoter response risk variant screening trait transcription factor transcriptome translational approach

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项目摘要

Over 29 million Americans are diagnosed with diabetes and another 86 million have prediabetes, resulting in an estimated $245 billion in annual medical costs and lost work and wages (https://www.cdc.gov/features/diabetesfactsheet/). Diabetes is a complex disease that results from the combined effects of genetic and environmental factors over time. Both common and rare genetic forms of diabetes share transcriptional dysregulation of insulin-producing beta cells in pancreatic islets as a hallmark. For example, the most common form of diabetes, type 2 diabetes (T2D), has been genetically dissected with multiple genome wide association studies (GWAS) that have collectively revealed >100 independent disease and related-trait associated single nucleotide polymorphisms (SNPs). Most of these loci localize to non-coding regions and have relatively small effect sizes. Using functional genomics approaches, we and others have shown these SNPs are highly significantly enriched to overlap important transcriptional regulatory elements like stretch enhancers (SE) or enhancer clusters that are specific to pancreatic islets. More recently, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprint motifs. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Importantly, rare autosomal recessive mutations that alter DNA-contacting amino acids in the DNA binding domain of RFX6 result in Mitchell–Riley syndrome, which is characterized by neonatal diabetes. Our findings could represent a connection between rare coding variation in the islet master TF RFX6 and common noncoding variations in multiple target sites for this TF. The impact of these variations mirror the expected physiological effect, with coding variants that result in neonatal diabetes and noncoding variants that result in later-onset T2D. However, it is presently unknown how these different classes of genetic variants might interact. To help close these major gaps in knowledge, we will build mechanistic understanding of genetic variant effects on transcriptional regulation and the impact these effects could have on diabetes. We will accomplish this through integrative computational analyses of experimental measures of genome, epigenome, and transcriptome profile variation across cellular states and species coupled with novel high-throughput reporter assays to test the functional relevance of targeted genetic perturbations. The resulting increase in understanding of diabetes genetic regulatory grammars will provide a foundation for interpreting disease-relevant genetic variation and providing more precise disease predictions.