Interrogating regulatory variants by multiplexed genome editing

通过多重基因组编辑询问调控变异

基本信息

批准号：
9761568
负责人：
Alon Goren
金额：
$ 23.63万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-09 至 2020-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9761568
关键词：
Address Affect Allelic Imbalance Automobile Driving Binding Biological Assay CRISPR/Cas technology Cell Fraction Cells ChIP-seq Code Computing Methodologies DNA Data Set Diabetes Mellitus Enhancers Etiology Evaluation Exhibits Frequencies Gene Expression Genes Genome Genomic DNA Genomics Genotype Goals Gold HNF4A gene Heart Diseases HepG2 Hepatocyte Human Individual Machine Learning Malignant Epithelial Cell Maps Measures Messenger RNA Methods Molecular Mutate Mutation Nucleic Acid Regulatory Sequences Oligonucleotides Open Reading Frames Phenotype Physiological Plasmids Point Mutation Population Primary carcinoma of the liver cells Proteins Quantitative Trait Loci Regulatory Element Reporter Reporter Genes Resources Schizophrenia Sorting - Cell Movement Techniques Testing Untranslated RNA Variant cell type epigenomics genetic variant genome editing genome wide association study histone modification human disease improved innovation insight interest learning strategy mRNA Expression molecular phenotype novel transcription factor

项目摘要

A major result from recent genome wide association studies (GWAS) is that the majority of genetic variants driving common human diseases lie in regulatory, rather than protein-coding, regions. Massive efforts to map epigenomic features such as localization of histone modifications (HMs) and transcription factors (TFs) have paved the way toward understanding the regulatory genome. However, dissecting the impact of an individual non-coding variant remains an unsolved challenge. A variety of computational methods have been proposed, such as quantitative trait loci (QTL) studies and machine learning techniques. However, these methods still do not provide conclusive information about causality of any specific non-coding mutation and lack gold-standard experimental results for evaluation. Several techniques are used to experimentally test the impact of individual regulatory variants. For example, massively parallel reporter assays (MPRA) synthesize thousands of oligonucleotides encoding mutated versions of putative regulatory elements placed in plasmids upstream of reporter genes. However, a major limitation is that tested sequences are outside of their endogenous chromosomal locus, and hence do not necessarily provide physiological relevance. CRISPR enables targeted editing of genomic DNA. Indeed, CRISPR is widely used, but studies of individual point mutations have been primarily on a small scale and are usually limited to a handful of variants or to a single gene. The major throughput challenge in studying a specific variant using genome editing is in tying genotype to phenotype. Introducing individual mutations exhibits low efficiency, and thus there is a need for enrichment of the genotype or phenotype of interest prior to assessing the impact of a mutation on a phenotype, such as gene expression. Current enrichment methods either disrupt the physiological context or are low throughput. Recent efforts overcame these challenges using pooled editing to analyze thousands of mutations simultaneously, but were limited to variants in protein coding regions. This proposal aims to develop a novel technique merging multiplexed genome editing of putative regulatory variants followed by chromatin immunoprecipitation sequencing (ChIP-seq) to simultaneously measure the impact of hundreds of non-coding variants on regulatory potential in their native genomic context. The key insight of the proposed approach is that mutations impacting epigenomic features can be measured both in genomic DNA and in phenotypic readouts such as ChIP-seq of TFs or HMs, avoiding the need for a selection step to connect genotypes with phenotypes. Aim 1 develops the pooled editing technique on a pilot set of previously validated regulatory variants. Aim 2 scales this approach to interrogate thousands of mutations at once. Aim 3 integrates experimental predictions with state of the art machine learning methods to evaluate and optimize computational methods for regulatory variant effect prediction.

最近基因组广泛关联研究（GWAS）的主要结果是，大多数通用驱动常见人类疾病的变体在于调节区而不是蛋白质编码区域。巨大的努力绘制诸如组蛋白修饰（HMS）和转录因子（TFS）之类的表观基因组学特征已经铺平了理解调节基因组的道路。但是，剖析了单个非编码变体仍然是未解决的挑战。已经提出了多种计算方法，例如定量性状区域（QTL）研究和机器学习技术。但是，这些方法仍然没有提供有关的结论性信息任何特定的非编码突变的因果关系，缺乏用于评估的金标准实验结果。几种技术用于实验测试单个调节变体的影响。例如，大规模平行的记者测定（MPRA）合成了数千个编码突变的寡核苷酸放置在报告基因上游质粒中的推定调节元件的版本。但是，一个专业限制是测试序列不在其内源性染色体位点之外，因此不在必然提供身体上的相关性。 CRISPR启用了基因组DNA的有针对性编辑。确实，CRISPR被广泛使用，但是单个点突变主要是小规模的，通常仅限于少数变体或单个基因。使用基因组编辑研究特定变体的主要吞吐量挑战是将基因型与表型联系起来。引入个别突变表现出低效率，因此有必要在评估突变对A的影响之前，要富集基因型或感兴趣的表型表型，例如基因表达。当前的富集方法要么破坏物理环境或低吞吐量。最近的努力使用汇总编辑克服了这些挑战，以分析数千个突变很简单，但仅限于蛋白质编码区域的变体。该建议旨在开发一种新技术合并推定的多路复用基因组编辑调节性变体，然后是染色质免疫沉淀测序（CHIP-SEQ）衡量数百种非编码变体对其天然基因组环境中调节潜力的影响。提出方法的关键见解是可以测量影响表观基因组特征的突变在基因组DNA和表型读数中，例如TFS或HMS的chip-seq 将基因型与表型联系起来的选择步骤。 AIM 1在飞行员上开发了汇总编辑技术一组先前验证的法规变体。目标2量表这种审问数千种的方法一次突变。 AIM 3与最先进的机器学习方法的集成实验预测到评估和优化调节变体效应预测的计算方法。