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.

项目概要 理解表型多样性、进化中的建模选择以及 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 在多种环境和同基因背景下跨基因组的遗传变异。以前的 系统的基因组扰动工作主要集中在工程功能丧失上，但自然 发生的变异与理解医学相关表型（如人类特征）最相关 和疾病。此类变异已通过全基因组关联研究（GWAS）和定量研究进行了研究 性状位点（QTL）分析，但这些方法仅限于研究中出现的单倍型 人群中，只有在少数情况下才确定了实际的致病变异。基因组的进展 编辑技术使得大规模改造特定的遗传变异变得可行。这个提议 旨在系统地设计和功能分析三个基因组范围的“变异集合” 遗传上不同的菌株，涵盖了酵母菌中所有天然的单核苷酸变体 (SNV) 酿酒酵母物种以及与人类疾病相关的 SNV。该集合将由 高通量 CRISPR 方法，利用内部序列解析技术（重组酶 定向索引（REDI）将允许快速、廉价地分离经过序列验证的变异菌株 其中将产生数以百万计的资金。因为某些变体仅在某些特定情况下发挥作用 环境中，该菌株集合将在数百种条件下进行分析，包括暴露于各种环境 压力和药物。整合到每个菌株基因组中的 DNA 条形码将实现汇集、竞争 生长，并允许全面识别基因组中调节给定适应度的变异 单个实验中的条件。最后，剖析潜在疾病途径的遗传结构 并确定关键的相互作用，对携带 SNV 组合的菌株进行分析。菌株收集将 提供给社区进行进一步的表型研究。除了基因x环境 (GxE) 数据集可能是迄今为止最大的技术、分析和可视化数据集 管道将公开共享并整合到社区资源中。这项工作将构成一个 对遗传变异的后果及其依赖性的前所未有的调查 环境，同时为科学界提供宝贵的资源。它将奠定技术和 人类细胞遗传变异的基于扰动的系统研究的概念基础 为疾病风险的预测和基于基因组序列的治疗策略的设计提供信息。

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