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: 10386335
• 负责人: Gregory I Lang
• 金额: $ 5万
• 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 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10386335
• 关键词: Affect; Antifungal Agents; Bar Codes; Biological Assay; Carbon; Cell Polarity; Cell physiology; Chromosome Mapping; Complex; Data; Data Set; Dimensions; Disease; Environment; Environmental Risk Factor; Essential Genes; Evolution; Genes; Genetic; Genetic Variation; Genome Scan; Genotype; Growth; Human; Knowledge; Laboratories; Maps; Measures; Methods; Mutation; Nitrogen; Organism; Population; Quantitative Trait Loci; Research; Saccharomyces cerevisiae; Source; Survey Methodology; Temperature; Testing; Variant; Work; Yeasts; base; cdc Genes; experimental study; fitness; genetic variant; genome-wide analysis; human disease; insight; knock-down; loss of function mutation; mutant; novel; prevent; rapid growth; therapeutic target; trait

Below is the original Summary to fill this Mandatory Field PROJECT SUMMARY/ABSTRACT 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.

以下是填写此强制性字段的原始摘要 项目摘要/摘要 全球对遗传相互作用网络的理解，以及网络扰动如何影响 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 我们对这些网络的理解差距。我们对遗传相互作用的大多数知识都来自 双重缺失（或敲低）突变体的系统分析，主要是在酵母中 酿酒酵母。但是，现实是功能丧失突变很少有益 占已知自然遗传变异的不到5％。持续存在这个差距是 重大问题，因为当前可能会错过许多生物医学重要的互动 方法。拟议的研究将确定涉及功能变化的遗传相互作用， 基本基因的变体以及使用新颖的“进化和图”方法的高阶相互作用， 结合了实验进化和定量特征基因座映射。理由 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，并且以这种方式孤立的遗传变异将是可比的 到包括人类在内的其他生物体中的复杂性状的自然遗传变异。目标1意志 利用进化“重播”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。这些互动得到了强有力的支持 初步实验室进化实验，但在很大程度上是遗传遗传的 交互网络。 AIM 2将扩展此分析全基因组，并生成迄今为止最大的数据集 关于在实验进化的背景下出现的变体之间的遗传相互作用。 将从演变的线之间的十字和 他们的祖先或在成对的成对线之间。每个隔离剂将通过低覆盖物进行基因分型 测序及其适应性将使用高度多重的条形码测定法测量 这能够测量数以千计的隔离剂的适应性。这些数据将被使用 检测加法作用以及成对和三向遗传相互作用。由于这些映射 种群包含的变体要比全基因组扫描中的典型变体要少得多，该方法的力量是 检测遗传相互作用非常高。 AIM 3将确定这些遗传学的程度 跨环境的相互作用持续存在，包括不同的碳和氮源，抑制性 抗真菌性的浓度和非最佳温度。这将增加一个重要的新维度 到遗传相互作用网络。总体而言，从这项工作中获得的结果将测试 双层遗传相互作用网络，以预测促进生长变体之间的相互作用， 并将促进我们对遗传相互作用网络和复杂性状的演变的理解。