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.

最近全基因组关联研究（GWAS）的一个主要结果是，大多数遗传导致人类常见疾病的变异在于调控区域，而不是蛋白质编码区域。绘制表观基因组特征，例如组蛋白修饰 (HM) 和转录因子 (TF) 的定位然而，剖析基因组的影响为理解调控基因组铺平了道路。个体非编码变体仍然是一个未解决的挑战。已经提出了多种计算方法，例如数量性状基因座（QTL）研究然而，这些方法仍然没有提供有关的结论性信息。任何特定非编码突变的因果关系，并且缺乏评估的金标准实验结果。使用多种技术来通过实验测试各个监管变体的影响。大规模并行报告分析 (MPRA) 合成数千个编码突变的寡核苷酸然而，放置在报告基因上游的质粒中的假定调控元件的版本。限制是测试的序列位于其内源染色体位点之外，因此不必然提供生理相关性。 CRISPR 能够实现基因组 DNA 的靶向编辑。事实上，CRISPR 已被广泛使用，但其研究仍处于起步阶段。单个点突变主要是小规模的，通常仅限于少数变体使用基因组编辑研究特定变体的主要通量挑战在于。将基因型与表型联系起来的方法效率较低，因此有必要。在评估突变对目标的影响之前富集感兴趣的基因型或表型表型，例如基因表达，当前的富集方法要么破坏生理环境，要么破坏。最近的努力克服了这些挑战，使用合并编辑来分析数千个。同时发生突变，但仅限于蛋白质编码区的变异。该提案旨在开发一种融合多重基因组编辑的新技术调节变异，然后进行染色质免疫沉淀测序 (ChIP-seq)，以同时测量数百种非编码变异对其天然基因组环境中的调控潜力的影响。该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（例如 TF 或 HM 的 ChIP-seq）中，避免了对将基因型与表型联系起来的选择步骤目标 1 在试点上开发汇集编辑技术。目标 2 扩展了这种方法以询问数千个。目标 3 将实验预测与最先进的机器学习方法相结合评估和优化调节变异效应预测的计算方法。