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

项目摘要/摘要 胰岛β细胞分泌胰岛素以维持血糖动态平衡。胰岛素分泌紧密 由葡萄糖调节，并受许多环境信号的调节，包括其他营养物质、激素、 和炎性细胞因子。β细胞暴露于环境信号会影响基因调控程序 在几个小时内，这些信号依赖的变化有助于使胰岛素分泌适应生物体的变化 各州。与测量胰岛素分泌相关的遗传变异强烈地富含基因组元素 在β细胞中活跃，这些变异中的许多也与糖尿病的风险有关。因此，贝塔细胞 具有使其成为研究信号依赖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- 贝塔细胞的诱导状态变化仍然没有得到充分的描述。人类发展的最新进展 多能干细胞(HPSC)来源的多细胞胰岛类器官模型提供了一种遗传学上的 将基因组活性与细胞表型联系起来的易处理的β细胞模型。我们集团进一步开创了 多种单细胞分析的发展，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； 方法我们已经成功地应用于原代人胰岛和hPSC-胰岛有机体。我们有 进一步开发机器学习和基于网络的变式口译方法，包括 单细胞RNA和表观遗传学数据。在这项计划中，我们将开发新的基因调控网络(Grn)。 预测基因组元素、基因和表型之间网络水平关系的模型 单细胞多组图绘制了hPSC-胰岛类器官中信号和时间依赖的变化。以节为单位 B和C我们将测量hPSC-胰岛的基因组元件活性、基因表达和胰岛素分泌 利用配对单核在四个时间点分别暴露于十种不同分泌信号的有机化合物 可及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部分中，我们将从这些数据中生成GRN，使用机器学习来推断元素基因和 元素-表型关系，并使用训练好的模型来提炼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靶基因表达、基因网络的影响 活性和细胞表型。在E节中，我们将使用中尺度CRISPR来验证和改进模型 基因组元件单独和组合的干扰以及等位基因特异性的基因编辑 HPSC-胰岛类器官中葡萄糖相关突变体的筛选和顺式基因表达变化的检测 和变性人。将来自该项目的结果、数据和方法结合在一起，使用单元格类型的模型 对环境信号的快速响应和可量化的表型输出将广泛适用于 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.