Toward a mechanistic understanding of genetic interactions

对遗传相互作用的机械理解

基本信息

批准号：
10414870
负责人：
Christine Queitsch
金额：
$ 53.29万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10414870
关键词：
Address Affect Animal Model Caenorhabditis elegans Complex Copy Number Polymorphism DNA DNA Repair Gene DNA biosynthesis Data Disease Dominant-Negative Mutation Elements Fertility Gene Deletion Gene Expression Genes Genetic Genetic Epistasis Genetic Variation Genome Stability Genomics Genotype Human Human Genetics Human Genome Longevity Maps Measurement Mitochondria Molecular Mutation Partner in relationship Pathway interactions Pharmaceutical Preparations Phenotype Population Proteins Resistance Ribosomal DNA Robotics Saccharomyces cerevisiae Single Nucleotide Polymorphism Stress Surface Technology Testing Variant Yeasts fitness genetic technology genetic testing genome-wide genomic locus healthspan high throughput analysis new technology polypeptide protein folding tool trait

Address Affect Animal Model Caenorhabditis elegans Complex Copy Number Polymorphism DNA DNA Repair Gene DNA biosynthesis Data Disease Dominant-Negative Mutation Elements Fertility Gene Deletion Gene Expression Genes Genetic Genetic Epistasis Genetic Variation Genome Stability Genomics Genotype Human Human Genetics Human Genome Longevity Maps Measurement Mitochondria Molecular Mutation Partner in relationship Pathway interactions Pharmaceutical Preparations Phenotype Population Proteins Resistance Ribosomal DNA Robotics Saccharomyces cerevisiae Single Nucleotide Polymorphism Stress Surface Technology Testing Variant Yeasts fitness genetic technology genetic testing genome-wide genomic locus healthspan high throughput analysis new technology polypeptide protein folding tool trait

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项目摘要

A key challenge of the post-genomic era is the functional interpretation of the vast numbers of single nucleotide variants found in human genomes. This challenge is compounded by the fact that these variants contribute to complex traits and diseases by interacting with one another and with genetic variation in repetitive DNA elements. Assessing the phenotypic consequences of all genetic interactions amounts to an impossible numbers game. In order to prioritize certain variant combinations, I will use model organisms with powerful genetics, namely the yeast S. cerevisiae and the worm C. elegans, to identify and characterize genetic interactions with large impact on complex phenotypes. I propose three projects that capitalize on our previous studies. These projects are united by their focus on genetic interactions (i.e. epistasis), albeit they address different types of variant combinations and different mechanisms. The first project focuses on rDNA, a highly variable repetitive DNA element. Variation in rDNA copy number impacts gene expression, replication, genome stability, and mitochondrial abundance. Like other repetitive loci, rDNA is predisposed to interact epistatically with other variants because of its high mutation rate. Using newly developed C. elegans mapping populations and robotics-enabled phenotyping, our preliminary data show that rDNA copy number variation affects lifespan and fitness through epistasis. High-throughput analyses of healthspan traits such as stress resistance and fertility are ongoing. We will pursue fine-mapping of the most significant genomic loci implicated in epistasis with rDNA because their identity, possibly DNA replication or repair genes, may point to the molecular mechanism by which rDNA variation affects phenotype. In both yeast and worms, we will use the entire tool box of genetics and genomics to directly interrogate the pathways by which rDNA copy number variation affects replication, genome stability, and mitochondrial abundance. To enable accurate high-throughput measurements of rDNA copy number in model organisms and humans, we will optimize a promising FISH technology. The second project relies on the detailed genotype–phenotype maps we established for genes in the yeast mating pathway. Selecting single nucleotide variants of small and intermediate effects, we will combine variants in two genes and test the combinations for mating efficiency while also perturbing strong genetic modifiers and applying common stresses. To do so, we developed a sequencing strategy that allows us to simultaneously phenotype tens of thousands of single nucleotide variant combinations between pairs of genes. The third project will apply a technology of dominant negative polypeptides that we recently developed to identify at genome scale protein interaction surfaces and their dynamics. In yeast, we will explore to what extent genetic interactions reflect direct protein interactions. We will ask how easily (or not) protein interaction surfaces are perturbed by mutation, by evolutionary divergence, or by drugs or stress that perturb protein folding. Together, the results of these three projects will yield a broad and deep assessment of epistasis, testable hypotheses for human genetics and novel technologies for testing them.

后基因组时代的主要挑战是大量单核苷酸变体的功能解释在人类基因组中发现。这些变体有助于复杂的特征和通过彼此相互作用并与重复性DNA元素的遗传变异来疾病。评估表型所有遗传相互作用的后果等于不可能的数字游戏。为了确定某些变体组合，我将使用具有强大遗传学的模型生物，即酵母菌S. cerevisiae和蠕虫。秀丽隐杆线，以识别并表征对复杂表型的巨大影响的遗传相互作用。我提出了三个利用我们以前的研究的项目。这些项目的重点是遗传相互作用（即 epitsasis），尽管它们解决了不同类型的变体组合和不同机制。第一个项目重点在rDNA上，高度可变的重复DNA元素。 rDNA拷贝数的变化会影响基因表达，复制，基因组稳定性和线粒体抽象。像其他重复的语言环境一样，rDNA易于相互作用由于其高突变率，具有其他变体。使用新开发的秀丽隐杆线虫映射种群和启用机器人的表型，我们的初步数据表明，rDNA拷贝数变化会影响寿命和寿命通过上位性的健身。对健康状态（例如压力抗性和生育率）的高通量分析正在进行中。我们将追求用rDNA实施在上学中实施的最重要的基因组基因局的精细映射，因为它们身份，可能的DNA复制或修复基因，可能指向rDNA变异的分子机制影响表型。在酵母和蠕虫中，我们将使用整个遗传学和基因组学的工具盒直接询问rDNA拷贝数变化影响复制，基因组稳定性和线粒体的途径抽象。为了实现模型生物和人类中rDNA拷贝数的准确高通量测量，我们将优化承诺的鱼类技术。第二个项目依赖于详细的基因型 - 表型地图建立在酵母交配途径中的基因。选择小型和中间作用的单个核苷酸变体，我们将在两个基因中结合变体，并测试配合效率的组合，同时也会扰动强大遗传修饰符并应用共同应力。为此，我们制定了一种测序策略，使我们能够类似地，基因对之间的数以万计的表型在基因对之间成千上万。第三项目将应用我们最近开发的主要负多肽技术，以在基因组规模上识别蛋白质相互作用表面及其动力学。在酵母中，我们将探索遗传相互作用在多大程度上反映的直接蛋白质相互作用。我们将询问蛋白质相互作用表面有多容易被突变，通过进化差异，或通过扰动蛋白质折叠的药物或应力。这三个项目的结果在一起将对上毒，可检验的人类遗传学的可检验假设以及测试新技术进行广泛而深的评估他们。