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.

项目摘要/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，并由许多环境信号调节，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节程序 几个小时内，这些信号依赖性变化有助于使胰岛素分泌适应有机体的变化 国家。与胰岛素分泌量度相关的遗传变异非常丰富基因组元素 活跃在β细胞中，其中许多变体也与糖尿病的风险有关。因此，β细胞 拥有特征，使其成为研究信号依赖基因调节的理想细胞模型 与人类健康和疾病有关的过程。但是，驱动信号的特定基因组程序 - β细胞中诱导的状态变化的表征仍然很差。人类发展的最新进展 多能干细胞（HPSC）衍生的多细胞胰岛类器官模型和其他人提供了遗传学的 将基因组活性与细胞表型联系起来的可疗法β细胞模型。我们的小组进一步开创了 开发众多单细胞测定法，包括染色质可及性，超高通量配对 染色质访问性和基因表达，并配对3D染色质相互作用和DNA甲基化； 我们成功地应用于原发性人类胰岛和HPSC-伊斯特类器官的方法。我们有 进一步开发了机器学习和基于网络的方法，用于变体解释，包括 单细胞RNA和表观遗传数据。在此提案中，我们将开发新的基因调节网络（GRN） 预测基因组元素，基因和表型之间网络级关系的模型 单细胞多构图图表了HPSC-异源器官中的信号和时间依赖性变化。在各节中 B和C，我们将测量HPSC- iSLET中的基因组元素活性，基因表达和胰岛素分泌 使用成对的单核在四个时间点上暴露于十个不同分泌信号的器官 可访问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 测定。在D节中，我们将从这些数据中产生一个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有素的模型来完善GRN。从由此产生的grn中，我们将 预测遗传变异在特定基因组元素中对靶基因表达，基因网络的影响 活性和细胞表型。在E节中，我们将通过中等规模的CRIS验证和完善模型 基因组元素单独，组合以及等位基因特异性基因编辑的干扰 在HPSC-异源器官中选定的葡萄糖相关变体，并测量CIS的基因表达变化 和trans。共同使用该项目的结果，数据和方法，使用单元类型的模型 迅速响应环境信号，具有可量化的表型输出将广泛适用于 研究基因组调节动态的社区。