Mapping genetic interactions between growth-promoting mutations in yeast

绘制酵母促生长突变之间的遗传相互作用

• 批准号: 10397048
• 负责人: Gregory I Lang
• 金额: $ 33.04万
• 依托单位: LEHIGH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397048
• 关键词: Affect; Antifungal Agents; Bar Codes; Biological Assay; Carbon; Cell Cycle; Cell Polarity; Cell physiology; Chromosome Mapping; Complex; Data; Data Set; Dimensions; Disease; Environment; Environmental Risk Factor; Essential Genes; Evolution; Gene Targeting; Genes; Genetic; Genetic Variation; Genetic study; Genome; Genome Scan; Genotype; Goals; Growth; Human; Human Genetics; Knowledge; Laboratories; Link; Malignant Neoplasms; Maps; Measures; Methods; Mutation; Nitrogen; Organism; Pharmaceutical Preparations; Phenotype; Population; Quantitative Genetics; Quantitative Trait Loci; Research; Research Personnel; Resources; Saccharomyces cerevisiae; Source; Survey Methodology; Surveys; Temperature; Testing; Variant; Work; Yeasts; base; causal variant; cdc Genes; cell cycle genetics; experimental study; fitness; gene conservation; gene function; genetic linkage; genetic variant; genome-wide; genome-wide analysis; human disease; insight; knock-down; loss of function mutation; mutant; novel; prevent; rapid growth; therapeutic target; trait; two-dimensional; yeast genetics

A global understanding of genetic interaction networks, and how network perturbations affect cellular function, is crucial to preventing and treating human disease. Currently there is a fundamental gap in our understanding of these networks. Most of our knowledge of genetic interactions comes from the systematic analysis of double deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. However, the reality is that loss-of-function mutations are rarely beneficial and account for less than 5% of the known natural genetic variation. Continued existence of this gap is a significant problem because many biomedically-important interactions are likely missed by current methods. The proposed research will identify genetic interactions involving alteration-of-function variants, variants of essential genes, and higher-order interactions using a novel “Evolve-and-Map” approach, which combines experimental evolution and quantitative-trait locus mapping. The rationale for this approach is that experimental evolution efficiently selects for perturbations to the genetic interaction network that promote rapid growth, and that the genetic variants isolated in this way will be comparable to the natural genetic variants underlying complex traits in other organisms, including humans. AIM 1 will leverage the power of evolutionary “replay” experiments to identify a local network of genetic interactions between cell polarity genes and cell cycle genes. These interactions are strongly supported by preliminary laboratory evolution experiments, but are largely absent from the double-deletion genetic interaction network. AIM 2 will extend this analysis genome-wide, producing the largest data set to date on the genetic interactions between variants that arose in the context of experimental evolution. Thousands of double-barcoded segregants will be generated from crosses between evolved lines and their ancestor or between pairs of evolved lines. Each segregant will be genotyped by low-coverage sequencing and its fitness will be measured using a highly-multiplexed barcode-sequencing-based assay that is capable of measuring the fitness of thousands of segregants en masse. These data will be used to detect additive effects as well as pairwise and three-way genetic interactions. Since these mapping populations contain far fewer variants than is typical in a genome-wide scan, the power of this method to detect genetic interactions is very high. AIM 3 will determine the extent to which these genetic interactions persist across environments, including different carbon and nitrogen sources, inhibitory concentrations of antifungals, and non-optimal temperatures. This will add an important new dimension to genetic interaction networks. Overall the results obtained from this work will test the ability of the double-deletion genetic interaction network to predict interactions between growth-promoting variants, and will advance our understanding of genetic interaction networks and the evolution of complex traits.

对遗传相互作用网络以及网络扰动如何影响的全球理解 细胞功能，对于预防和治疗人类疾病至关重要。目前有一个基本的 我们对这些网络的理解存在差距。我们关于遗传相互作用的大部分知识来自 主要在酵母中对双缺失（或敲低）突变体的系统分析 酿酒酵母。然而，现实情况是功能丧失突变很少有好处 占已知自然遗传变异的不到 5%。这种差距的持续存在是 这是一个重大问题，因为许多生物医学上重要的相互作用可能被当前的技术所忽略 方法。拟议的研究将确定涉及功能改变变异的遗传相互作用， 重要基因的变体，以及使用新颖的“进化和映射”方法的高阶相互作用， 它结合了实验进化和数量性状基因座作图。这样做的理由 方法是实验进化有效地选择对遗传相互作用的扰动 网络促进快速增长，并且以这种方式分离的遗传变异将具有可比性 包括人类在内的其他生物体复杂特征的自然遗传变异。目标 1 将 利用进化“重放”实验的力量来识别遗传相互作用的局部网络 细胞极性基因和细胞周期基因之间的关系。这些互动得到了以下机构的大力支持 初步的实验室进化实验，但在双缺失遗传实验中基本上没有 互动网络。 AIM 2 将在全基因组范围内扩展该分析，产生迄今为止最大的数据集 研究实验进化背景下出现的变异之间的遗传相互作用。 数以千计的双条形码分离子将通过进化品系和 他们的祖先或进化线对之间。每个分离体将通过低覆盖率进行基因分型 测序及其适用性将使用基于高度多重条形码测序的测定进行测量 它能够测量数千名隔离者的健康状况。这些数据将被使用 检测加性效应以及成对和三向遗传相互作用。由于这些映射 群体中包含的变异比全基因组扫描中典型的变异要少得多，这种方法的力量 检测到遗传相互作用非常高。 AIM 3 将确定这些遗传因素的程度 相互作用在不同环境中持续存在，包括不同的碳源和氮源、抑制性 抗真菌药物的浓度和非最佳温度。这将增加一个重要的新维度 遗传相互作用网络。总的来说，从这项工作中获得的结果将考验我们的能力。 双缺失遗传相互作用网络来预测促生长变异之间的相互作用， 并将增进我们对遗传相互作用网络和复杂性状进化的理解。