Context-specific and combinatorial genetic regulatory grammars in diabetes

超过2900万美国人被诊断为糖尿病，另有8600万人患有糖尿病前期， 造成每年约2,450亿美元的医疗费用、工作和工资损失 (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，遗传和环境因素的综合影响。既有常见的也有罕见的遗传形式 糖尿病的特征是胰岛中产生胰岛素的β细胞转录失调。 例如，最常见的糖尿病形式，2型糖尿病(T2D)，已经被基因解剖为 共同揭示了&gt；100独立疾病的多基因组范围关联研究(GWAS) 和相关性状相关单核苷酸多态性(SNPs)。大多数这些基因座定位于非编码 区域，并且具有相对较小的效果大小。使用功能基因组学方法，我们和其他人已经 显示这些SNP被高度显著地浓缩以重叠重要的转录调控元件，如 胰岛特异的拉伸增强剂(SE)或增强子簇。最近，我们发现 T2D Gwas基因座显著富含胰岛调节因子X(RFX)足迹基序。 值得注意的是，在独立的基因座内和独立的基因座之间，与RFX足迹重叠的T2D风险等位基因一致 在信息含量较高的位置扰乱RFX主题。重要的是，罕见的常染色体隐性突变 改变RFX6 DNA结合域中与DNA接触的氨基酸导致Mitchell-Riley综合征， 以新生儿糖尿病为特征。我们的发现可能代表了罕见的编码之间的联系 胰岛主因子RFX6的变异和该因子在多个靶点的常见非编码变异。这个 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 糖尿病和导致较晚发病的T2D的非编码变异。然而，目前还不清楚这些是如何 不同类别的基因变异可能会相互作用。为了帮助弥合这些重大的知识差距，我们将建立 基因变异对转录调控的影响及其影响的机制研究 可能对糖尿病有影响。我们将通过实验的综合计算分析来实现这一点 不同细胞状态和物种间基因组、表观基因组和转录组图谱差异的测量 结合新的高通量报告分析来测试靶向基因的功能相关性 微扰。由此增加对糖尿病基因调控语法的理解将提供一种 为解释与疾病相关的基因变异和提供更准确的疾病预测奠定了基础。

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.