Multiscale genome engineering to map cis-regulatory variants in human and mouse

多尺度基因组工程绘制人类和小鼠顺式调控变异图谱

基本信息

批准号：
10737026
负责人：
Tuuli Lappalainen
金额：
$ 113.33万
依托单位：
NEW YORK GENOME CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10737026
关键词：
Alleles Architecture Autoimmune Autoimmune Diseases Biological CRISPR interference CRISPR screen Cell Survival Cell physiology Cell surface Cells Chromatin Chromosome Mapping Clustered Regularly Interspaced Short Palindromic Repeats Complement Complex Crohn&apos s disease Data Data Analyses Dimensions Disease Elements Enhancers Etiology Gene Expression Gene Expression Regulation Genes Genetic Genetic Complementation Test Genetic Transcription Genetic Variation Genome Genome Mappings Genome engineering Genomics Goals Heritability Homeostasis Human Human Genetics Immune Immune response Immunology Immunophenotyping In Vitro Inflammation Inflammatory Infusion procedures Insulin-Dependent Diabetes Mellitus Intervention Large Intestine Link Maps Measurable Measurement Measures Methods Molecular Mus Mutagenesis Nucleotides Open Reading Frames Organ Pathogenicity Pathway interactions Phenotype Physiological Proteome Psoriasis Regulation Regulator Genes Regulatory Element Regulatory Pathway Repression Research Resolution Resources Role Shapes Site Small Intestines Statistical Models Surface System Systemic Lupus Erythematosus T cell differentiation T-Lymphocyte Technology Testing Tissues Ulcerative Colitis Umbilical Cord Blood Untranslated RNA Variant base editing candidate selection causal variant cell motility cell type chromatin immunoprecipitation disease phenotype disorder risk functional genomics gain of function genetic association genetic variant genome editing genome wide association study genome-wide genome-wide analysis genomic data genomic locus genomic variation gut homeostasis gut inflammation high throughput analysis human disease immune function in vivo in vivo Model insight knowledgebase loss of function migration molecular phenotype mouse model multi-scale atlas multiple omics new technology next generation novel peripheral blood prevent technological innovation therapeutic development trait transcriptome transcriptomics

项目摘要

PROJECT SUMMARY Genome-wide association studies (GWAS) have linked 1000s of genomic loci with human traits and diseases. However, the mechanistic inner workings of these loci are largely unknown, leaving the principal goal of GWAS – illuminating the causal biological etiology of heritable phenotypes – unfulfilled. Most GWAS loci occur within noncoding regions of the genome whose functional impact on gene regulation is difficult to unravel. Here, we propose to develop a high-throughput, integrated genome engineering toolbox to build context-specific maps of enhancers and variants for immune traits and autoimmune disorders. Our multi-PI team consists of experts in complementary fields: molecular genomics and CRISPR screens, large-scale human genetics and functional genomics data analysis, immunology, statistical modeling, and single-cell multiomics. Specifically, we propose to: 1) Identify genes and cis-regulatory elements (CREs) relevant for T cell function. T cells are a central cell type implicated in multiple autoimmune diseases. We will first perform genome-wide loss- and gain-of-function screens for 9 phenotypes reflecting T-cell differentiation and activation using primary human T cells. For top- ranked genes, we will interrogate CREs near each gene and explore their mechanisms via single-cell profiling and saturation mutagenesis. 2) Build a context-specific enhancer map of GWAS loci in T cells. We will test 1,000 candidate CREs that overlap GWAS loci using CRISPRi/a screens in the same primary T-cell system, complemented by single-cell ECCITE-seq to measure effects on the transcriptome and surface proteome. This will produce a comprehensive map of regulatory elements for a large number of loci, and their context-specific impact on transcriptomic and cellular phenotypes. Then, we will construct a context-specific variant map of regulatory elements in T cells by inserting specific alleles via base editing at 100 validated CREs. This will produce a fine-resolution map of regulatory sites within CREs. 3) Test 100 syntenic CREs from in mouse models of gut homeostasis and inflammation in vivo. We will focus on T-cell tissue accumulation (reflecting activation and migration) and alterations in transcriptional and cell surface phenotypes of the migrating cells. By doing so, we will determine if the relevant variants have similar roles in human disease and provide pathways towards targeting the pathogenic functions of those genes. Through these Aims, we will build a highly-generalizable toolkit for multi-scale interrogation of noncoding elements and an accessible, open, and reusable resource of enhancer and variant effects on molecular, cellular, and physiological traits. Altogether, we will analyze the regulatory architecture of the genome, leveraging our diverse perturbations and phenotypic layers, and characterize functional mechanisms of loci associated with autoimmune disorders.

项目摘要全基因组关联研究（GWAS）已将1000个基因组基因座与人类特征和疾病联系起来。但是，这些地区的机械内部运作在很大程度上是未知的，留下了GWAS的主要目标 - 阐明可遗传表型的因果生物学病因 - 无法实现。大多数GWAS基因座发生在难以阐明其功能对基因调节的功能影响的基因组的非编码区域。在这里，我们提出开发高通量的集成基因组工程工具箱，以构建特定于上下文的地图免疫特征和自身免疫性疾病的增强剂和变体。我们的Multi-Pi团队由专家组成完全领域：分子基因组学和CRISPR筛选，大规模的人类遗传学和功能基因组学数据分析，免疫学，统计建模和单细胞多组学。具体来说，我们建议至：1）识别与T细胞功能相关的基因和顺式调节元件（CRE）。 T细胞是中央细胞类型中隐含多种自身免疫性疾病。我们将首先执行全基因组的损失和功能获得使用原代人T细胞反映T细胞分化和激活的9个表型的筛选。对于顶排名的基因，我们将在每个基因附近询问CRE，并通过单细胞分析探索其机制和饱和诱变。 2）在T细胞中构建GWAS基因座的上下文特异性增强子图。我们将测试1,000 使用CRISPRI/A屏幕在同一主要T细胞系统中重叠GWAS基因座的候选CRE，由单细胞Eccite-seq完成，以测量对转录组和表面蛋白质组的影响。这将为大量基因座及其特定于上下文的综合监管元素图制作全面的地图对转录组和细胞表型的影响。然后，我们将构建一个特定于上下文的变体图通过在100个经过验证的CRE下插入特定等位基因，在T细胞中的调节元件。这会生成CRE内调节地点的精细分辨率图。 3）在鼠标模型中测试100个同步板体内肠稳态和炎症。我们将专注于T细胞组织的积累（反映激活）和迁移）以及迁移细胞转录和细胞表面表型的改变。这样，我们将确定相关变体在人类疾病中是否具有相似的作用，并为通往靶向这些基因的致病功能。通过这些目标，我们将建立一个高度的可替代用于多尺度询问非编码元素的工具包，以及可访问，开放和可重复使用的资源增强子和变体对分子，细胞和物理性状的影响。总共，我们将分析基因组的监管结构，利用我们的潜水员扰动和表型层，以及表征与自身免疫性疾病相关的局部功能机制。