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：
10434740
负责人：
Stephen CJ Parker
金额：
$ 40.5万
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 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10434740
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 Persons 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

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.

超过2900万美国人被诊断出患有糖尿病，另有8600万人患有糖尿病，导致估计每年2450亿美元的医疗费用以及损失的工作和工资（https://www.cdc.gov/features/diabetesfactsheet/）。糖尿病是一种复杂的疾病，由随着时间的推移，遗传和环境因素的结合影响。普通和稀有遗传形式的形式糖尿病具有胰岛胰岛中产生胰岛素β细胞的转录失调作为标志。例如，最常见的糖尿病形式是2型糖尿病（T2D），已与遗传解剖多个基因组宽结合研究（GWAS）统称> 100个独立疾病和相关特征相关的单核苷酸多态性（SNP）。其中大多数本地化为非编码区域，效应大小相对较小。使用功能基因组学方法，我们和其他人都有显示这些SNP高度富集以重叠重要的转录调节元素，例如特定于胰岛的拉伸增强剂（SE）或增强子簇。最近，我们发现 T2D GWAS基因座被特别富集在胰岛调节因子X（RFX）足迹基序中。值得注意的是，在独立局部内外，T2D的风险等位基因与RFX足迹重叠在高信息含量位置下破坏RFX图案。重要的是，罕见的常染色体隐性突变改变了RFX6的DNA结合结构域中的接触DNA接触氨基酸会导致Mitchell-Riley综合征，以新生儿糖尿病为特征。我们的发现可能代表稀有编码之间的联系胰岛主TF RFX6的变化以及该TF多个目标位点中的常见非编码变化。这这些变化的影响反映了预期的物理效应，并带有导致新生儿的编码变体糖尿病和非编码变体导致后来发作的T2D。但是，这是未知的不同类别的遗传变异可能相互作用。为了帮助缩小知识的主要差距，我们将建立对遗传变异对转录调控的影响的机械理解以及这些影响的影响可能患有糖尿病。我们将通过实验的集成计算分析来实现这一目标基因组，表观基因组和跨细胞状态和物种的转录组轮廓变化的度量再加上新型的高通量记者测定法，以测试目标通用的功能相关性扰动。仅遗传调节语法的糖尿病的了解的增加将提供解释与疾病相关的遗传变异并提供更精确的疾病预测的基础。