Precision mapping of regulatory causal variants by expression CROPseq

通过表达 CROPseq 精确绘制调控因果变异

• 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
• 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

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.

摘要 个体之间的遗传差异最终可以追溯到单核苷酸多态，大多数 这些基因通过影响附近基因的表达来影响包括疾病易感性在内的特征。超过了 在过去的十年里，数以万计的这种类型的基因被映射到了所谓的可信区间， 即从几个到一百多个具有相似统计数据的多态的集合 信号。下一步是在这些可信的区间内解决因果变量(S)的身份。 同样，还有一个同样迫切的需要来验证从头开始的和超罕见的变异体在启动子中的作用 一种被认为是导致先天性异常的基因实际上会扰乱该基因的表达。这个项目 提出了一项系统化的努力，以精细定位影响自身免疫的数百个基因的因果变异 以及其他免疫状况。它利用基因组工程和单细胞基因组学的组合，在 最近开发的一种名为表达作物序列或eCROPseq的分析方法。我们的想法是击倒每一个 变体在可信的区间内与CRISPR-Cas9引导RNA池，每个针对一个SNP 突变的微缺失，并被约100个细胞占据。然后基因在这些细胞中的表达 与同一实验中接受不同引导RNA的所有其他细胞中的表达进行比较。 目标1是使用eCROPseq在多达600个可信区间内识别与350个变量相关的因果变量 免疫位点，在髓系(HL60)和淋巴系(Jurkat)细胞系中测量。目标2是执行类似的 对这些基因启动子区域的500个罕见变异进行分析，以评估新的突变 会导致极端水平的基因表达异常。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(也称为主要编辑)以取代 等位基因，而不是仅仅评估突变。然后，目标4问到，在 在来自8个不同人的原代T细胞中也可以看到细胞系，以及影响的程度 因人而异。总的来说，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种，并建立一个甚至应该很容易适应的工具 小型实验室来测试新的全基因组关联的功能。