Capturing the phenotypic landscape of single-nucleotide variation via systematic genome editing

通过系统基因组编辑捕获单核苷酸变异的表型景观

• 批准号: 10390038
• 负责人: Lars M Steinmetz
• 金额: $ 25万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-18 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10390038
• 关键词: Bar Codes; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Collection; Communities; DNA; Data Set; Dependence; Disease; Disease susceptibility; Engineering; Environment; Environmental Exposure; Evolution; Exposure to; Genes; Genetic Variation; Genetic study; Genome; Growth; Haplotypes; Human; Individual; Investigation; Measures; Medical; Modeling; Nucleotides; Outcome; Pathway interactions; Pharmaceutical Preparations; Phenotype; Quantitative Trait Loci; Research Personnel; Resources; Role; Saccharomyces cerevisiae; Single Nucleotide Polymorphism; Stress; Technology; Testing; Therapeutic; Tissues; Variant; Visualization; Work; base; causal variant; cell type; conditioning; design; disease phenotype; disorder risk; experimental study; fitness; genetic architecture; genetic variant; genome editing; genome wide association study; genome-wide; human disease; indexing; loss of function; precision medicine; recombinase; study population; trait

PROJECT SUMMARY A major challenge common to understanding phenotypic diversity, modeling selection in evolution, and developing precision medicine is enhancing our currently limited ability to predict disease and phenotypic outcomes based on genome sequence and environmental exposures. A comprehensive understanding of genetic variation and its role in conditioning phenotypes requires systematic, perturbation-based testing of genetic variants across the genome in multiple environments and in an isogenic background. Previous systematic genome perturbation efforts have focused primarily on engineering loss-of-function, but naturally occurring variants have the most relevance to understanding medically relevant phenotypes like human traits and disease. Such variants have been studied via genome-wide association studies (GWAS) and quantitative trait locus (QTL) analysis, but these approaches are limited to the haplotypes that appear in the study population, and only in few cases have the actual causative variants been identified. Advances in genome editing technologies have made engineering specific genetic variants feasible at a large scale. This proposal aims to systematically engineer and functionally profile a genome-wide `variation collection' in three genetically distinct strains that cover all natural single-nucleotide variants (SNVs) in the Saccharomyces cerevisiae species as well as SNVs associated with human diseases. The collection will be constructed by a high-throughput CRISPR approach, leveraging an in-house sequence parsing technology (Recombinase Directed Indexing, or REDI) that will allow rapid, inexpensive isolation of sequence-verified variant strains among the millions that will be generated. Because some variants only exert their effects in certain environments, this strain collection will be profiled in hundreds of conditions, including exposure to various stresses and drugs. DNA barcodes integrated into the genome of each strain will enable pooled, competitive growth, and allow the comprehensive identification of variants in a genome that modulate fitness in a given condition in a single experiment. Finally, to dissect the genetic architecture of pathways underlying diseases and identify key interactions, strains carrying combinations of SNVs will be analyzed. The strain collection will be made available to the community for further phenotypic investigations. In addition to the gene x environment (GxE) dataset that will likely be the largest produced to date, the technological, analytical, and visualization pipelines will be publicly shared and integrated into community resources. This work will constitute an unprecedented investigation of the consequences of genetic variation and their dependence upon environment, while providing valuable resources for the scientific community. It will lay technological and conceptual groundwork for systematic perturbation-based studies of genetic variation in human cells that will inform the prediction of disease risk and the design of therapeutic strategies based on genome sequence.

项目摘要 理解表型多样性，模拟进化中的选择， 发展精准医学正在增强我们目前有限的预测疾病和表型的能力， 基于基因组序列和环境暴露的结果。全面了解 遗传变异及其在调节表型中的作用需要系统的，基于扰动的测试， 在多个环境和同基因背景中的基因组中的遗传变异。先前 系统性基因组扰动工作主要集中在工程功能丧失上，但自然地 发生的变异与理解医学相关的表型（如人类特征）最相关， 和疾病已经通过全基因组关联研究（GWAS）和定量分析研究了这些变体。 性状基因座（QTL）分析，但这些方法仅限于研究中出现的单倍型 人口，只有在少数情况下，已确定的实际致病变种。基因组研究进展 编辑技术已经使得大规模工程化特定遗传变异成为可能。这项建议 旨在系统地设计和功能性地描绘三个基因组范围内的“变异集合”， 涵盖酵母属中所有天然单核苷酸变体（SNV）的遗传上不同的菌株 酿酒酵母物种以及与人类疾病相关的SNV。该集合将由 一种高通量CRISPR方法，利用内部序列解析技术（CRISase） 定向索引，或REDI），这将允许快速，廉价的分离序列验证的变异株 将产生数百万个。因为有些变体只在某些特定的 环境中，这种菌株收集将在数百种条件下进行分析，包括暴露于各种环境中， 压力和毒品整合到每种菌株基因组中的DNA条形码将使汇集的、竞争性的 生长，并允许全面鉴定基因组中调节给定基因组中的适应性的变体。 在一个单一的实验条件。最后，为了剖析潜在疾病的遗传结构， 并确定关键的相互作用，将分析携带SNV组合的菌株。菌株收集将 提供给社区用于进一步的表型研究。除了基因x环境 (GxE)这可能是迄今为止最大的数据集，技术，分析和可视化 管道将公开共享并融入社区资源。这项工作将构成一个 对遗传变异的后果及其对环境的依赖性进行了前所未有的研究。 环境，同时为科学界提供宝贵的资源。它将奠定技术和 为人类细胞遗传变异的系统扰动研究奠定了概念基础， 为疾病风险预测和基于基因组序列的治疗策略设计提供信息。

项目成果

Thioesterase-Catalyzed Aminoacylation and Thiolation of Polyketides in Fungi.

• DOI: 10.1021/jacs.9b01083
• 发表时间: 2019-05
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Mancheng Tang;Curt R. Fischer;Jason V. Chari;D. Tan;Sundari Suresh;A. Chu;Molly Miranda;Justin Smith;Zhuan Zhang;N. Garg;Robert P. St. Onge;Yi Tang

Dissecting quantitative trait nucleotides by saturation genome editing.

通过饱和基因组编辑来剖析数量性状核苷酸。

• DOI: 10.1101/2024.02.02.577784
• 发表时间: 2024
• 期刊: bioRxiv : the preprint server for biology
• 作者: Roy,KevinR;Smith,JustinD;Li,Shengdi;Vonesch,SibylleC;Nguyen,Michelle;Burnett,WallaceT;Orsley,KevinM;Lee,Cheng-Sheng;Haber,JamesE;StOnge,RobertP;Steinmetz,LarsM
• 通讯作者: Steinmetz,LarsM