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Systematic mapping and prediction of gene-enhancer connections

基因增强子连接的系统绘图和预测

基本信息

批准号：
10365988
负责人：
JESSE M ENGREITZ
金额：
$ 24.14万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10365988
关键词：
3-Dimensional Address Affect Alleles Architecture B-Lymphocytes Biological Biological Models Biology CRISPR interference CRISPR/Cas technology Cell Line Cell model Cell physiology Cells ChIP-seq Chromatin Chromosome Mapping Chromosomes Collaborations Complex Computer Models DNase I hypersensitive sites sequencing Data Data Element Development Disease Distal Enhancers Environment Experimental Models Gene Expression Gene Expression Regulation Genes Genetic Diseases Genetic Variation Genome Genomics Heritability Hi-C Human Human Cell Line Human Genome Immune Immune System Diseases Individual Laboratories Lead Ligands Location Maps Measures Medicine Methods Modeling Modernization Molecular Molecular Conformation Phase Play Property Regulation Regulator Genes Regulatory Element Research Role Science Single Nucleotide Polymorphism Specific qualifier value Specificity System T-Lymphocyte Testing United States National Institutes of Health Universities Untranslated RNA Update Variant Work base career cell type collaborative environment disorder risk experimental study genetic variant genome editing genome wide association study genome-wide human disease insight monocyte network architecture novel pleiotropism predictive tools promoter system architecture therapeutic development tool working group

项目摘要

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, I 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). I 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. In the K99 phase, I propose to: (i) apply CRISPRi tiling to map ~6,000 additional gene-RE connections, and (ii) use these data to extend and optimize a model to predict gene-RE connections from chromatin state. I will use human immune cells as a model system to compare predictions across cell types. In the R00 phase, I will apply these tools to (iii) characterize the network architecture of gene-RE connections across hundreds of cell types, and (iv) edit single-nucleotide variants identified by the model in cellular models to characterize their effects on gene expression. Together, 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. Stanford University is an ideal environment for my independent laboratory, providing all of the facilities needed for the proposed research and a rich interdisciplinary environment for collaborative studies. Together, these aims will launch my independent scientific career at the interface of regulatory genomics and disease genetics.

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，这可以解释成千上万的非编码遗传变异，通过全基因组关联研究（GWAS）发现与人类疾病相关的基因。解释 RE和非编码基因变异的功能一直是一个挑战，因为我们缺乏能力，系统地干扰RE在基因组中的天然位置。为了应对这一挑战，我最近开发了一种高通量的方法来定位数千个RE在其天然基因组中的功能，背景并测量其对基因表达的定量影响（CRISPRi平铺）。我还写了一本小说基于染色质状态和3D图谱的建模和预测基因-RE连接的分析方法折页.总之，这些进步促使制定一项战略，以便系统地绘制所有可再生能源的地图，控制任何给定细胞类型中的任何给定基因。在K99阶段，我建议：（i）将CRISPRi平铺应用于地图 ~ 6，000个额外的基因-RE连接，以及（ii）使用这些数据来扩展和优化模型以预测基因-RE连接从染色质状态。我将使用人类免疫细胞作为模型系统来比较预测细胞类型。在R 00阶段，我将应用这些工具（iii）描述网络的特征构建跨越数百种细胞类型的基因-RE连接，以及（iv）编辑单核苷酸变体通过细胞模型中的模型来识别它们，以表征它们对基因表达的影响。所有这些目的将提供深入了解基因-RE连接的机制和架构，绘制任何细胞类型中的基因-RE连接，并揭示常见疾病的潜在机制。斯坦福大学是我独立实验室的理想环境，提供了所有的设施所需的拟议研究和丰富的跨学科环境的合作研究。我们一起努力，这些目标将在调控基因组学和疾病的界面上开启我的独立科学生涯遗传学