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

• 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
• 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

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 万美国人被诊断患有糖尿病，另外 8600 万美国人患有糖尿病前期， 导致每年估计 2,450 亿美元的医疗费用以及工作和工资损失 （https://www.cdc.gov/features/diabetesfactsheet/）。糖尿病是一种复杂的疾病，其原因是 随着时间的推移，遗传和环境因素的综合影响。常见和罕见的遗传形式 糖尿病的共同特点是胰岛中产生胰岛素的β细胞的转录失调。 例如，最常见的糖尿病形式，2 型糖尿病 (T2D)，已通过基因剖析 多项全基因组关联研究 (GWAS) 共同揭示了超过 100 种独立疾病 以及相关性状相关的单核苷酸多态性（SNP）。大多数这些位点定位于非编码 区域并具有相对较小的效应量。使用功能基因组学方法，我们和其他人已经 显示这些 SNP 高度显着富集，与重要的转录调控元件重叠，例如 胰岛特异性的拉伸增强子 (SE) 或增强子簇。最近，我们发现 T2D GWAS 位点在胰岛调节因子 X (RFX) 足迹基序中显着富集。 值得注意的是，在独立基因座之内和之间，T2D 风险等位基因与 RFX 足迹一致重叠 破坏高信息内容位置的 RFX 主题。重要的是，罕见的常染色体隐性突变 改变 RFX6 DNA 结合域中 DNA 接触氨基酸会导致 Mitchell-Riley 综合征， 其特点是新生儿糖尿病。我们的发现可能代表了稀有编码之间的联系 胰岛主 TF RFX6 中的变异以及该 TF 的多个目标位点中的常见非编码变异。这 这些变异的影响反映了预期的生理效应，编码变异导致新生儿 糖尿病和导致迟发性 T2D 的非编码变异。但目前尚不清楚这些 不同类别的遗传变异可能会相互作用。为了帮助缩小这些主要的知识差距，我们将建立 遗传变异对转录调控影响的机制理解以及这些影响的影响 可能对糖尿病有影响。我们将通过对实验的综合计算分析来实现这一目标 测量跨细胞状态和物种的基因组、表观基因组和转录组谱变化 结合新颖的高通量报告分析来测试目标遗传的功能相关性 扰动。由此增加对糖尿病基因调控语法的理解将提供 为解释与疾病相关的遗传变异和提供更精确的疾病预测奠定了基础。