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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

基本信息

批准号：
10590346
负责人：
Gregory I Lang
金额：
$ 1.04万
依托单位：
LEHIGH UNIVERSITY
依托单位国家：
美国
项目类别：
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

项目摘要

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.

以下是填写此必填字段的原始摘要项目总结/摘要对遗传相互作用网络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有差距。我们对基因相互作用的大部分知识都来自于双缺失（或敲低）突变体的系统分析，主要是在酵母中酿酒酵母然而，现实情况是，功能丧失突变很少是有益的占已知自然遗传变异的不到5%。这一差距的持续存在是一个这是一个重要的问题，因为目前的研究可能会错过许多生物医学上重要的相互作用。方法.拟议中的研究将确定涉及功能改变变体的遗传相互作用，必需基因的变体，以及使用新的“进化和地图”方法的高阶相互作用，它结合了实验进化和数量性状基因定位。这样做的理由方法是实验进化有效地选择了遗传相互作用的扰动网络，促进快速增长，并以这种方式分离的遗传变异将是可比的与其他生物体（包括人类）复杂特征背后的自然遗传变异有关。AIM 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联系。这些相互作用得到了初步的实验室进化实验，但在很大程度上缺席的双缺失遗传互动网络AIM 2将在全基因组范围内扩展这种分析，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。数千个双条形码分离体将从进化品系之间的杂交中产生，它们的祖先或成对的进化线之间。每个分离子将通过低覆盖率进行基因分型将使用基于高度多重条形码测序的测定来测量测序及其适应性它能够同时测量数千个分离体的适合度。这些数据将用于以检测加性效应以及成对和三向遗传相互作用。由于这些映射群体包含的变异比全基因组扫描中的典型变异少得多，这种方法的能力，检测基因相互作用的概率非常高。AIM 3将确定这些基因在多大程度上相互作用持续跨环境，包括不同的碳和氮源，抑制抗真菌剂的浓度和非最佳温度。这将增加一个重要的新层面遗传互动网络。总的来说，从这项工作中获得的结果将测试的能力，双缺失遗传相互作用网络来预测促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进化的理解。