The impact of genomic variation on environment-induced changes in pancreatic beta cell states

项目概要/摘要 胰腺β细胞分泌胰岛素以维持血糖稳态。胰岛素分泌紧密 受葡萄糖调节并受许多环境信号调节，包括其他营养素、激素、 和炎症细胞因子。 β细胞暴露于环境信号会影响基因调控程序 数小时内，这些信号依赖性变化有助于使胰岛素分泌适应机体的变化 州。与胰岛素分泌测量相关的遗传变异在基因组元件中非常丰富 在β细胞中活跃，其中许多变异也与糖尿病风险相关。因此β细胞 具有使其成为研究信号依赖性基因调控的理想细胞模型的特性 与人类健康和疾病相关的过程。然而，驱动信号的特定基因组程序 β细胞诱导的状态变化仍不清楚。人类发展的最新进展 我们和其他人的多能干细胞 (hPSC) 衍生的多细胞胰岛类器官模型提供了遗传 用于将基因组活性与细胞表型联系起来的易于处理的β细胞模型。我们的团队进一步开创了 开发多种单细胞检测方法，包括染色质可及性、超高通量配对 染色质可及性和基因表达，以及配对的 3D 染色质相互作用和 DNA 甲基化； 我们已成功应用于原代人胰岛和 hPSC 胰岛类器官的方法。我们有 进一步开发了机器学习和基于网络的变异解释方法，包括来自 单细胞 RNA 和表观遗传数据。在本提案中，我们将开发新型基因调控网络（GRN） 预测基因组元素、基因和表型之间网络水平关系的模型 单细胞多组图谱绘制了 hPSC 胰岛类器官中信号和时间依赖性变化。分节 B 和 C 我们将测量 hPSC 胰岛中的基因组元件活性、基因表达和胰岛素分泌 使用配对单核在四个时间点将类器官暴露于十种不同的分泌信号 可访问的染色质和基因表达以及配对的单细胞 DNA 甲基化和 3D 染色质结构 化验。在 D 节中，我们将从这些数据生成 GRN，使用机器学习来推断元素基因并 元素-表型关系并使用经过训练的模型来完善 GRN。根据生成的 GRN，我们将 预测特定基因组元件中的遗传变异对目标基因表达、基因网络的影响 活性和细胞表型。在 E 节中，我们将使用中等规模的 CRISPR 验证和完善模型 单独和组合基因组元件的干扰以及等位基因特异性基因编辑 选择 hPSC 胰岛类器官中的葡萄糖相关变异并测量顺式基因表达变化 和反式。该项目的结果、数据和方法一起使用了细胞类型的模型， 对环境信号快速响应并具有可量化的表型输出将广泛应用于 研究基因组调控动态的社区。

PROJECT SUMMARY/ABSTRACT Pancreatic beta cells secrete insulin in order to maintain blood glucose homeostasis. Insulin secretion is tightly regulated by glucose and modulated by numerous environmental signals, including other nutrients, hormones, and inflammatory cytokines. Exposure of beta cells to environmental signals affects gene regulatory programs within hours, and these signal-dependent changes serve to adapt insulin secretion to changes in organismal states. Genetic variants associated with measures of insulin secretion are strongly enriched in genomic elements active in beta cells, and many of these variants are also associated with risk of diabetes. Beta cells therefore possess characteristics that make them an ideal cellular model for studying signal-dependent gene regulatory processes relevant to human health and disease. However, the specific genomic programs that drive signal- induced state changes in beta cells remain poorly characterized. Recent advances in the development of human pluripotent stem cell (hPSC)-derived multi-cellular islet organoid models by us and others provide a genetically tractable beta cell model for linking genomic activity to cellular phenotypes. Our group has further pioneered the development of numerous single cell assays, including chromatin accessibility, ultra-high-throughput paired chromatin accessibility and gene expression, and paired 3D chromatin interactions and DNA methylation; methods that we have successfully applied to both primary human islets and hPSC-islet organoids. We have further developed machine learning and network-based approaches for variant interpretation including from single cell RNA and epigenetic data. In this proposal we will develop novel gene regulatory network (GRN) models to predict network-level relationships among genomic elements, genes, and phenotypes derived from single cell multiomic maps charting signal- and time-dependent changes in hPSC-islet organoids. In Sections B and C we will measure genomic element activity, gene expression, and insulin secretion in hPSC-islet organoids exposed to ten different secretory signals each across four time points using paired single nucleus accessible chromatin and gene expression and paired single cell DNA methylation and 3D chromatin architecture assays. In Section D we will generate a GRN from these data, use machine learning to infer element-gene and element-phenotype relationships and use the trained models to refine the GRN. From the resulting GRN we will predict the effects of genetic variants in specific genomic elements on target gene expression, gene network activity, and cellular phenotype. In Section E we will validate and refine models by using medium-scale CRISPR interference of genomic elements individually and in combination as well as allele-specific gene editing of selected glucose-associated variants in hPSC-islet organoids and measuring gene expression changes in cis and trans. Together, the results, data, and methods from this project using a model of a cell type which both rapidly responds to environmental signals and has a quantifiable phenotypic output will be widely applicable to the community studying the dynamics of genomic regulation.