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: 10557093
• 负责人: Gang Bao
• 金额: $ 69.69万
• 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/10557093
• 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; Deletion Mutation; 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; 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 Assessment; Testing; Transcript; Transfection; Universities; Variant; 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。来 在过去的十年里，成千上万的这种类型的基因座已经被定位到所谓的可信区间， 即从少数到超过100个具有相似统计学特征的多态性的集合， 信号.下一步是在这些可信区间内解决因果变量的身份。 同样，有一个平行的迫切需要，以验证从头和超罕见的变异，在启动子中的 一个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 提出了一个系统的努力，精细地图因果变异的数百个基因，影响自身免疫性 和其他免疫疾病。它利用基因组工程和单细胞基因组学的组合， 最近开发的一种称为表达CROP-seq或eCROPseq的检测方法。我们的想法是， 这些变体与CRISPR-Cas9向导RNA库处于可信区间，每个向导RNA靶向一个SNP， 突变的微缺失，并被约100个细胞摄取。这些细胞中基因的表达 与在同一实验中接受不同指导RNA的所有其他细胞中的表达相比。 目的1是使用eCROPseq在多达600个可信区间内识别与350个相关的因果变异。 免疫基因座，在骨髓（HL 60）和淋巴（Jurkat）细胞系中测量。目标2是执行类似的 分析这些基因启动子区多达500种罕见变异，以评估新突变是否 会导致极端的基因表达异常有了这些结果，Aim 3利用了更多的 精确的基因组编辑工具，搜索和替换CRISPR（也称为总理编辑），以取代一个 而不仅仅是评估突变。然后目标4问， 在来自八个不同的人的原代T细胞中也可以看到细胞系， 人与人之间的差异。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体，并建立一个工具，即使是 小型实验室来测试新的全基因组关联的功能。