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

基因组变异对环境诱导的胰腺β细胞状态变化的影响

• 批准号: 10297450
• 负责人: Hannah Kathryn Carter
• 金额: $ 128万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-07 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297450
• 关键词: 3-Dimensional; Affect; Alleles; Amino Acids; Architecture; Beta Cell; Biological; Biological Assay; Biology; Blood Glucose; CRISPR interference; Catalogs; Cell Nucleus; Cell model; Cells; Cellular Assay; Characteristics; Chromatin; Chromatin Remodeling Factor; Collaborations; Communities; Computer Models; DNA Methylation; Data; Data Collection; Development; Disease; Elements; Environment; Epigenetic Process; Exposure to; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Genetic; Genome engineering; Genomics; Glucocorticoids; Glucose; Gonadal Steroid Hormones; Health; Hormones; Hour; Human; Individual; Inflammatory; Insulin; Islets of Langerhans; Link; Lipids; Machine Learning; Maps; Measures; Mediating; Methods; Modeling; Multiomic Data; Network-based; Non-Insulin-Dependent Diabetes Mellitus; Nutrient; Organoids; Output; Pancreas; Phenotype; Process; Production; RNA; Regulation; Regulator Genes; Regulatory Element; Research Personnel; Signal Transduction; Structure of beta Cell of islet; Study models; System; Testing; Time; Training; Untranslated RNA; Variant; blood glucose regulation; cell type; combinatorial; cytokine; diabetes risk; epigenomics; experience; genetic variant; genome-wide; genomic data; genomic variation; human pluripotent stem cell; innovation; innovative technologies; insulin regulation; insulin secretion; islet; islet stem cells; model building; multidisciplinary; multiple omics; network models; novel; pancreatic juice; programs; response; stem cells; transcription factor

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.

项目总结/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 由葡萄糖调节并由许多环境信号调节，包括其他营养素，激素， 和炎性细胞因子。β细胞暴露于环境信号影响基因调控程序 这些信号依赖性的变化使胰岛素分泌适应机体的变化。 states.与胰岛素分泌测量相关的遗传变异在基因组元件中强烈富集 在β细胞中活跃，其中许多变体也与糖尿病风险有关。因此， 具有使它们成为研究信号依赖性基因调控的理想细胞模型的特性。 与人类健康和疾病有关的过程。然而，驱动信号的特定基因组程序- 在β细胞中诱导的状态变化仍然没有得到很好的表征。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一种遗传学上的 用于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。我们的团队进一步开创了 开发了许多单细胞测定法，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，以及成对的3D染色质相互作用和DNA甲基化; 我们已经成功应用于原代人胰岛和hPSC-胰岛类器官的方法。我们有 进一步开发了用于变体解释的机器学习和基于网络的方法， 单细胞RNA和表观遗传学数据。在这个计划中，我们将开发新的基因调控网络（GRN） 模型，以预测来自基因组元素，基因和表型之间的网络级关系， 单细胞多组学图谱绘制hPSC-胰岛类器官中的信号和时间依赖性变化。分段 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。 类器官暴露于十种不同的分泌信号，每个信号在四个时间点使用配对的单核 可接近的染色质和基因表达以及成对的单细胞DNA甲基化和3D染色质结构 分析。在D部分，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， 元素-表型关系，并使用训练的模型来细化GRN。从生成的GRN中，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 活性和细胞表型。在E部分，我们将使用中等规模的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基因编辑 hPSC-胰岛类器官中选择的葡萄糖相关变体，并测量顺式 总之，结果，数据和方法，从这个项目使用的模型，细胞类型， 快速响应环境信号，并具有可量化的表型输出，将广泛适用于 研究基因组调控动力学的社区。