Context-specific and combinatorial genetic regulatory grammars in diabetes

超过2900万美国人被诊断患有糖尿病，另有8600万人患有前驱糖尿病， 导致每年约2450亿美元的医疗费用和工作和工资损失 （https：//www.cdc.gov/features/diabetesfactsheet/）。糖尿病是一种复杂的疾病， 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， 糖尿病共有胰岛中产生胰岛素的β细胞的转录失调作为标志。 例如，最常见的糖尿病形式，2型糖尿病（T2 D），已经被遗传解剖， 多个全基因组关联研究（GWAS）共同揭示了>100种独立的疾病 和相关性状相关的单核苷酸多态性（SNP）。这些基因座大多定位于非编码区 区域和具有相对较小的效应大小。使用功能基因组学方法，我们和其他人已经 显示这些SNP高度显著地富集以重叠重要的转录调控元件， 延伸增强子（SE）或增强子簇，其对胰岛具有特异性。最近，我们发现， T2 D GWAS基因座在胰岛调节因子X（RFX）足迹基序中显著且特异性地富集。 值得注意的是，在独立基因座内和跨独立基因座，与RFX足迹一致重叠的T2 D风险等位基因 在高信息含量位置破坏RFX基序。重要的是，罕见的常染色体隐性突变 改变RFX 6的DNA结合结构域中与DNA接触的氨基酸导致Mitchell-Riley综合征， 其特征是新生儿糖尿病。我们的发现可能代表了罕见编码 胰岛主TF RFX 6中的变异和该TF的多个靶位点中的常见非编码变异。的 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T2 D的非编码变体。然而，目前尚不清楚这些 不同种类的遗传变异可能会相互作用。为了帮助填补这些知识上的重大空白，我们将建立 理解遗传变异对转录调控的影响及其影响 对糖尿病的影响我们将通过实验的综合计算分析来实现这一点。 跨细胞状态和物种的基因组、表观基因组和转录组谱变异的测量 结合新的高通量报告基因测定，以测试靶向遗传修饰的功能相关性， 扰动由此产生的对糖尿病遗传调控语法的理解的增加将提供一个 这是解释疾病相关遗传变异和提供更精确疾病预测的基础。

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.