Precision mapping of regulatory causal variants by expression CROPseq

通过表达 CROPseq 精确绘制调控因果变异

基本信息

批准号：
10095869
负责人：
Gang Bao
金额：
$ 71.05万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-04 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10095869
关键词：
Adopted Affect Algorithms Alleles Autoimmune Autoimmune Diseases Bioinformatics Biological Assay CRISPR interference CRISPR/Cas technology Cell Line Cells Chromatin Clustered Regularly Interspaced Short Palindromic Repeats Collaborations Communicable Diseases Complement Complex Congenital Abnormality Disease Disease susceptibility Engineering Evaluation Evolution Gene Expression Genes Genetic Genetic Polymorphism Genome engineering Genomics Guide RNA HL60 Hand Human Genetics Immune Immune System Diseases Immunologic Deficiency Syndromes Individual Inflammatory Bowel Diseases Jurkat Cells Knock-out Knowledge Laboratory Study Lymphocyte Lymphoid Lymphoid Cell Maps Measures Mediating Methods Minor Molecular Diagnosis Monitor Mutagenesis Mutation Myelogenous Myeloid Cells Nucleotide Mapping Pathogenicity Penetrance Promoter Regions Property Quantitative Genetics Quantitative Trait Loci Reporter Rice Scanning Series Signal Transduction Single Nucleotide Polymorphism Site Surveys T-Lymphocyte Technology Testing Transcript Transfection Universities Variant base causal variant cell preparation cost epigenomics experimental study gang genetic association genome editing genome wide association study insertion/deletion mutation microdeletion monocyte precise genome editing promoter rare variant risk variant single-cell RNA sequencing therapeutic target tool trait transcriptome sequencing translational medicine

项目摘要

ABSTRACT Genetic differences among individuals ultimately trace to single nucleotide polymorphisms, the majority of which affect traits, including disease susceptibility, by influencing the expression of nearby genes. Over the past decade, tens of thousands of loci of this type have been mapped to what are called credible intervals, namely sets of anywhere from a handful to over one hundred polymorphisms that have similar statistical signals. The next step is to resolve the identities of the causal variant(s) within these credible intervals. Similarly, there is a parallel pressing need to validate that de novo and ultra-rare variants in the promoter of a gene thought to contribute to a congenital abnormality actually disrupt expression of the gene. This project proposes a systematic effort to fine map causal variants for hundreds of genes that influence autoimmune and other immune conditions. It utilizes a combination of genome engineering and single cell genomics, in a recently developed assay called expression CROP-seq, or eCROPseq. The idea is to knock out each of the variants in a credible interval with a pool of CRISPR-Cas9 guide RNAs, each targeting one SNP for microdeletion of mutation, and taken up by about 100 cells. The expression of the gene in those cells is then compared with the expression in all other cells that receive different guide RNAs in the same experiment. Aim 1 is to use eCROPseq to identify causal variants in up to 600 credible intervals associated with 350 immune loci, measured in both a myeloid (HL60) and lymphoid (Jurkat) cell line. Aim 2 is to perform a similar analysis of up to 500 rare variants in the promoter regions of these genes to evaluate whether new mutations can cause extreme levels of aberrant gene expression. With these results in hand, Aim 3 utilizes a more precise genome editing tool, search-and-replace CRISPR (also known as prime editing) to substitute one allele for another instead of just evaluating mutations. Then Aim 4 asks whether the effects observed in the cell lines are also seen in primary T cells from eight different people, and whether the magnitude of effect differs among people. Collectively the proposed experiments will systematically map the causal regulatory variants for hundreds of autoimmune genes, and establish a tool that should be readily adapted by even small labs to test the function of new genome-wide associations.

抽象的个体之间的遗传差异最终可以追溯到单核苷酸多态性，大多数通过影响附近基因的表达来影响性状，包括疾病易感性。超过过去十年，数以万计的此类基因座已被映射到所谓的可信区间，即具有相似统计数据的从少数到一百多个多态性的集合信号。下一步是在这些可信区间内确定因果变异的身份。同样，也迫切需要验证启动子中的从头变异和超罕见变异被认为导致先天性异常的基因实际上会破坏该基因的表达。这个项目提出了一项系统性的努力来精细绘制影响自身免疫的数百个基因的因果变异图谱和其他免疫状况。它结合了基因组工程和单细胞基因组学，最近开发的一种名为表达 CROP-seq 或 eCROPseq 的检测方法。这个想法是淘汰每一个使用 CRISPR-Cas9 引导 RNA 库在可信区间内生成变体，每个引导 RNA 都针对一个 SNP 突变的微缺失，并被大约100个细胞占据。该基因在这些细胞中的表达是与在同一实验中接受不同向导RNA的所有其他细胞中的表达进行比较。目标 1 是使用 eCROPseq 识别与 350 个相关的多达 600 个可信区间内的因果变异在骨髓 (HL60) 和淋巴 (Jurkat) 细胞系中测量的免疫位点。目标 2 是执行类似的操作分析这些基因启动子区域中多达 500 个罕见变异，以评估是否存在新突变可能导致极端水平的异常基因表达。有了这些结果，Aim 3 利用了更多精确的基因组编辑工具，搜索并替换 CRISPR（也称为 Prime Editing）来替代另一个等位基因，而不仅仅是评估突变。然后目标 4 询问是否观察到效果在来自八个不同人的原代 T 细胞中也发现了细胞系，以及影响的程度是否因人而异。总的来说，拟议的实验将系统地绘制因果调节图数百个自身免疫基因的变体，并建立一个工具，甚至可以很容易地适应小型实验室来测试新的全基因组关联的功能。