Diabetes risk variants affecting transcription factor-regulated cellular networks

影响转录因子调节细胞网络的糖尿病风险变异

• 批准号: 10395986
• 负责人: Kyle Jeffrie Gaulton
• 金额: $ 38.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395986
• 关键词: ATAC-seq; Affect; Allelic Imbalance; American; Binding; Binding Sites; Biological Assay; Cells; ChIP-seq; Communities; Complex; Data; Development; Diabetes Mellitus; Disease; Fasting; Functional disorder; GTP-Binding Protein alpha Subunits, Gs; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genomics; Histones; Human; Human Genetics; Islets of Langerhans; Maps; Molecular; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Pathogenesis; Pathway interactions; Process; Quantitative Trait Loci; Regulation; Regulator Genes; Research; Resources; Risk; Sampling; Signal Transduction; Statistical Methods; TF gene; Tissues; Translating; Untranslated RNA; Variant; Work; causal variant; cell type; diabetes pathogenesis; diabetes risk; epigenome; epigenomics; gene network; gene regulatory network; genetic association; genetic risk factor; genome-wide; high throughput screening; in silico; insight; islet; novel strategies; novel therapeutics; risk variant; transcription factor

Type 2 diabetes is a complex disease that affects 1 in 10 Americans and is influenced by many common genetic risk factors. Genetic association studies have identified over 100 loci that influence T2D risk, although how these loci mechanistically contribute to diabetes pathogenesis is largely unknown. The majority of these risk loci map to non-coding sequence, and likely alter gene regulatory processes in specific cell-types. Translating this breadth of diabetes regulatory variation into their molecular mechanisms can thus profoundly inform on diabetes pathophysiology, although remains challenging. In this study we propose a novel approach to identify T2D-relevant transcription factors and gene networks regulated by these factors by combining statistical human genetics, epigenomics, high-throughput assay and quantitative trait locus (QTL) mapping. In this approach we identify T2D risk variants that affect the cell-type expression of a transcription factor gene, characterize the genomic binding sites and target gene network regulated by these transcription factors, and broadly determine the effects of variants disrupting transcription factor-regulated networks on diabetes risk. In preliminary findings we have identified several diabetes risk variants that affect the cell-type expression level of a transcription factor gene, almost none of which have known involvement in diabetes- relevant pathways. In Aim 1 we will combine genetic fine-mapping with epigenomic annotation and eQTL data from diabetes-relevant cells to identify diabetes risk variants that affect the cell- type expression of a transcription factor. In Aim 2 we will perform ChIP-seq assays of five transcription factors in pancreatic islet samples combined with eQTL data to map the trans network of target genes affected by transcription factor regulatory variants. In Aim 3 we will combine allelic imbalance mapping and in silico motif prediction of islet ChIP-seq data to quantify the genome-wide effects of variants disrupting transcription factor-regulated networks on diabetes risk. The results of these studies will reveal specific transcription factors that are regulated by diabetes risk variants, and the gene networks regulated by these factors that in turn impact diabetes pathophysiology. Together these studies will provide insight into critical transcription factors and gene networks involved in diabetes pathogenesis.

2型糖尿病是一种复杂的疾病，每10个美国人中就有1个受到影响， 常见的遗传风险因素遗传关联研究已经确定了100多个基因座， 影响T2 D风险，尽管这些基因座如何机械地促进糖尿病发病机制 基本上是未知的。这些风险基因座中的大多数映射到非编码序列，并且可能改变 在特定的细胞类型的基因调控过程。翻译如此广泛的糖尿病监管 因此，它们的分子机制的变化可以深刻地告知糖尿病 病理生理学，虽然仍然具有挑战性。在这项研究中，我们提出了一种新的方法， 鉴定T2 D相关转录因子和由这些因子调节的基因网络， 结合统计人类遗传学、表观基因组学、高通量分析和数量性状 基因座（QTL）作图。在这种方法中，我们确定了影响细胞类型的T2 D风险变体， 转录因子基因的表达，表征基因组结合位点和靶点， 这些转录因子调控的基因网络，并广泛地决定了 变异破坏转录因子调节网络对糖尿病风险的影响。初步 我们已经确定了几种影响细胞类型表达的糖尿病风险变体， 转录因子基因的水平，几乎没有一个已知的参与糖尿病- 相关路径。在目标1中，我们将联合收割机遗传精细作图与表观基因组注释相结合 和来自糖尿病相关细胞的eQTL数据，以鉴定影响细胞的糖尿病风险变异， 转录因子的类型表达。在目标2中，我们将进行五个基因的ChIP-seq测定， 胰岛样本中的转录因子与eQTL数据相结合， 受转录因子调控变体影响的靶基因网络。在目标3中， 将胰岛ChIP-seq数据的等位基因不平衡作图和计算机模拟基序预测联合收割机组合， 量化变异破坏转录因子调控网络的全基因组效应 糖尿病风险。这些研究的结果将揭示特定的转录因子， 由糖尿病风险变异体调节，以及由这些因素调节的基因网络， 进而影响糖尿病的病理生理。这些研究将为关键的 转录因子和基因网络参与糖尿病发病机制。

项目成果

Glucocorticoid signaling in pancreatic islets modulates gene regulatory programs and genetic risk of type 2 diabetes.

• DOI: 10.1371/journal.pgen.1009531
• 发表时间: 2021-05
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Aylward A;Okino ML;Benaglio P;Chiou J;Beebe E;Padilla JA;Diep S;Gaulton KJ
• 通讯作者: Gaulton KJ

Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes.

将跨疾病状态的单细胞多组学测量与遗传学相结合，确定了 2 型糖尿病中 β 细胞功能障碍的机制。

• DOI: 10.1101/2022.12.31.522386
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Wang,Gaowei;Chiou,Joshua;Zeng,Chun;Miller,Michael;Matta,Ileana;Han,JeeYun;Kadakia,Nikita;Okino,Mei-Lin;Beebe,Elisha;Mallick,Medhavi;Camunas-Soler,Joan;DosSantos,Theodore;Dai,Xiao-Qing;Ellis,Cara;Hang,Yan;Kim,SeungK;MacDo
• 通讯作者: MacDo