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：
10341085
负责人：
Gang Bao
金额：
$ 69.72万
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/10341085
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 Persons 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 prime 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跟踪到单核苷酸多态性，大多数通过影响附近基因的表达来影响特征，包括疾病的敏感性。在过去十年，这种类型的数万个基因座已映射到所谓的可靠间隔，即，从少数到一百多个具有相似统计的多态性的地方信号。下一步是在这些可靠的间隔内解决因果变体的身份。同样，在启动子中的启动子中的从头和超稀有变体都有平行的迫切需要一个被认为有助于先天性异常的基因实际上破坏了基因的表达。这个项目提出了一个系统的努力，以精细地图为影响自动免疫的数百个基因的因果变体和其他免疫条件。它利用了基因组工程和单细胞基因组学的结合，最近开发的称为表达作物seq或ecropseq的测定法。这个想法是淘汰每个具有可靠间隔的变体，带有CRISPR-CAS9指南RNA，每个变体都针对一个SNP 突变的微骨骼，并被约100个细胞占用。那么基因在这些细胞中的表达是与在同一实验中接收不同引导RNA的所有其他细胞中的表达相比。 AIM 1是使用Ecropseq识别与350相关的600个可靠间隔中的因果变体免疫基因座，在髓样（HL60）和淋巴样（Jurkat）细胞系中测量。目标2是执行类似的分析这些基因的启动子区域中多达500种稀有变体，以评估是否新突变可能导致极度水平的异常基因表达。通过这些结果，AIM 3使用了更多精确的基因组编辑工具，搜索和重新定位CRISPR（也称为Prime编辑）代替一个等位基因，而不仅仅是评估突变。然后AIM 4询问是否在在来自八个不同人的原代T细胞中也可以看到细胞系，以及效果的幅度是否人之间有所不同。拟议的实验将系统地绘制因果调节数百个自身免疫基因的变体，并建立一个应容易被修改的工具小实验室测试全基因组关联的功能。