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的全球理解，以及网络扰动如何影响 细胞功能，对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。我们关于基因相互作用的大部分知识来自于 主要在酵母中的双缺失(或敲除)突变体的系统分析 酿酒酵母。然而，现实情况是，功能丧失突变很少是有益的 只占已知自然遗传变异的不到5%。这种差距的持续存在是一个 重大问题，因为当前可能会错过许多生物医学上重要的相互作用 方法：研究方法。这项拟议的研究将确定涉及功能变化变体的遗传交互作用， 基本基因的变种，以及使用新的“进化和图谱”方法的更高阶相互作用， 它结合了实验进化和数量性状基因座定位。这样做的理由是 方法是实验进化有效地选择了对遗传相互作用的扰动 促进快速增长的网络，以这种方式分离的基因变异将具有可比性 包括人类在内的其他生物体复杂特征背后的自然遗传变异。目标1将 利用进化“回放”实验的力量来确定基因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方面的大力支持 初步的实验室进化实验，但在很大程度上缺失了双缺失基因 互动网络。AIM 2将把这种分析扩展到整个基因组，产生迄今为止最大的数据集 在实验进化的背景下出现的变异之间的遗传相互作用。 进化的品系之间的杂交将产生数千个双条码分离物 它们的祖先在成对进化的品系之间。每个分离者将通过低覆盖率进行基因分型 测序及其适合性将使用基于条形码测序的高度多元化的化验来测量 这能够衡量数千名种族隔离者的整体适合性。将使用这些数据 以检测加性效应以及成对和三向遗传交互作用。由于这些映射 群体包含的变异比全基因组扫描中的典型变异要少得多，这种方法的力量在于 检测到遗传交互作用是非常高的。目标3将确定这些基因的程度 相互作用在不同的环境中持续存在，包括不同的碳和氮源，抑制 抗真菌药物的浓度，以及非最佳温度。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 预测促生长变异体之间相互作用的双缺失遗传相互作用网络， 并将促进我们对遗传相互作用网络和复杂性状进化的理解。