Systematic mapping and prediction of gene-enhancer connections

基因增强子连接的系统绘图和预测

• 批准号: 10318508
• 负责人: JESSE M ENGREITZ
• 金额: $ 0.15万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318508
• 关键词: 3-Dimensional; Address; Architecture; Biology; CRISPR interference; Cell model; Chromatin; Chromosome Mapping; Disease; Enhancers; Gene Expression; Genes; Genetic; Genome; Genomics; Human; Human Genome; Laboratories; Location; Maps; Measures; Mentorship; Methods; Modeling; Modernization; Parents; Population; Regulator Genes; Regulatory Element; Research; Science; Single Nucleotide Polymorphism; Training; Training Programs; Underrepresented Minority; United States National Institutes of Health; Universities; Untranslated RNA; Writing; base; career development; cell type; disorder risk; experience; genetic variant; genome wide association study; human disease; insight; network architecture; novel; programs; tool; undergraduate student; underrepresented minority student

PROJECT SUMMARY The training program described in this supplement aims to bring diversity to NIH’s scientific workforce by supporting an underrepresented minority (URM) undergraduate student in an independent research experience to identify new mechanisms of disease risk in the human population. A fundamental challenge in modern biology is to identify the noncoding regulatory elements (REs) that control gene expression, which could inform the interpretation of the thousands of noncoding genetic variants associated with human diseases through genome-wide association studies (GWAS). Interpreting the functions of REs and noncoding genetic variants has been challenging because we have lacked the ability to systematically perturb REs in their native locations in the genome. To address this challenge, we recently developed a high-throughput method to map the functions of thousands of REs in their native genomic contexts and measure their quantitative effects on gene expression (CRISPRi tiling). We also developed a novel analytical approach to model and predict gene-RE connections based on maps of chromatin state and 3D folding. Together, these advances motivate a strategy to allow systematic mapping of all of the REs that control any given gene in any given cell type. The parent proposal aims to apply these tools to characterize the network architecture of gene-RE connections across hundreds of cell types, and edit single-nucleotide variants identified by the model in cellular models to characterize their effects on gene expression. These aims will provide insights into the mechanisms and architecture of gene-RE connectivity, generate tools for mapping gene-RE connectivity in any cell type, and reveal mechanisms underlying common diseases. The project supplement described here will use this project as a platform to provide a personalized training and mentorship program for a URM student involving an independent project at the interface of computational and experimental genomics; one-on-one mentorship on science, writing, and career development; and collaborative interactions with a broader scientific team in the Engreitz Laboratory in the Department of Genetics at Stanford University. Together, this training program will catapult a URM student to the forefront of genomics research to identify new mechanisms of disease risk in the human population.

项目摘要 本增刊中描述的培训计划旨在通过以下方式为NIH的科学工作人员带来多样性： 支持代表性不足的少数民族（URM）本科生在独立的研究经验 以确定人类疾病风险的新机制。 现代生物学中的一个基本挑战是确定控制基因表达的非编码调控元件（RE）。 基因表达，这可能会为解释数千种相关的非编码遗传变异提供信息。 通过全基因组关联研究（GWAS），解释可再生能源的功能， 非编码遗传变异一直是一个挑战，因为我们缺乏系统地干扰 RE在基因组中的天然位置。为了应对这一挑战，我们最近开发了一种高通量 一种方法来映射数千个RE在其天然基因组环境中的功能，并测量其定量 对基因表达的影响（CRISPRi平铺）。我们还开发了一种新的分析方法来建模和预测 基于染色质状态图和3D折叠的基因-RE连接。总之，这些进步激励了一个 策略，以允许系统映射的所有RE，控制任何给定的基因在任何给定的细胞类型。 父提案旨在应用这些工具来表征基因RE连接的网络架构 跨越数百种细胞类型，并编辑由细胞模型中的模型识别的单核苷酸变体， 描述它们对基因表达的影响。这些目标将使人们深入了解这些机制， 基因-RE连接性的架构，生成用于在任何细胞类型中映射基因-RE连接性的工具，以及 揭示常见疾病的潜在机制。 这里所描述的项目补充将利用这个项目作为一个平台，提供个性化的培训， URM学生的导师计划，涉及在计算和 实验基因组学;一对一的指导科学，写作和职业发展;和协作 与斯坦福大学遗传学系恩格雷茨实验室的一个更广泛的科学团队进行互动 大学总之，这个培训计划将弹射URM学生到基因组学研究的最前沿， 确定人群中疾病风险的新机制。