Mapping dynamic functional networks across environments and genetic backgrounds

绘制跨环境和遗传背景的动态功能网络

• 批准号: 10063947
• 负责人: Brenda Jean ANDREWS
• 金额: $ 53.37万
• 依托单位: UNIVERSITY OF TORONTO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-26 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063947
• 关键词: Address; Affect; Alleles; Architecture; Awareness; Binding; Biological; CRISPR/Cas technology; Cell model; Cell physiology; Cells; Chromosome Mapping; Complex; Data; Data Set; Diploidy; Disease; Environment; Essential Genes; Eukaryota; Funding; Genes; Genetic; Genetic Polymorphism; Genetic Suppression; Genetic Variation; Genome; Genotype; Grant; Hereditary Disease; Heritability; Human; Human Genetics; Human Genome; Individual; Knowledge; Lead; Link; Maps; Methods; Modeling; Organism; Penetrance; Phenotype; Play; Ploidies; Population; Prevalence; Problem Solving; Property; Research; Resources; Role; Saccharomyces cerevisiae; Saccharomycetales; Severities; Shapes; Source; Stress; Structure; Surveys; System; Technology; Temperature; Testing; Translating; Variant; Work; Yeasts; base; design; disorder risk; dosage; drug action; experimental study; gene function; genetic analysis; genetic makeup; genetic predictors; genetic variant; genome sequencing; genome-wide; human disease; improved; innovation; insight; model design; mutant; novel; personalized medicine; predictive test; response; scaffold; tool; trait; whole genome; yeast genetics

Whole genome sequencing has provided unprecedented information about human genetic variation. There is growing awareness that interactions between variants play a major role in determining phenotype. Yet, we lack an understanding of how genetic variation translates into genetic interactions that affect an individual. A key to solving this problem requires an understanding of the rules governing genetic networks. During our current funding period, we used the Synthetic Genetic Array method, which we developed to automate yeast genetics, to complete a reference genetic interaction map for yeast. This network provides a global view of the functional organization of a cell and reveals a hierarchical model of cell function. The reference network enabled our efforts to explore biological network dynamics in response to environmental and genetic perturbations, including genetic suppression and triple mutant interactions. Our work underscores the potential of genetic interactions to impact the inference of phenotype from genome sequence information. Although gene editing approaches offer the promise to accelerate similar studies in other systems, many types of important genetic interactions remain relatively unexplored, and can only be mapped on a genome-scale in yeast. Thus, we propose continued analysis of complex genetic interaction networks in yeast, which we will use as a model for designing informative experiments to explore genetic interactions in human cells. Aim 1: An iterative computational-experimental approach to map a condition-specific genetic network. We will test specific gene-condition combinations expected to yield many genetic interactions. Based on preliminary analyses, we estimate that a systematic analysis of condition-specific interactions will expand the global genetic interaction network by ~3-fold, providing a resource to explore the influence of environment on genetic network wiring. Aim 2: Large-scale mapping of suppressor genetic networks in yeast. We will map extragenic and dosage suppression genetic interaction networks for yeast essential genes. This analysis will uncover novel relationships between genes and provide a template for similar studies in more complex systems. Aim 3: Elucidating the landscape and principles of complex genetic interactions. We will map Complex HaploInsufficiency (CHI) interactions between heterozygous alleles of essential genes in a diploid strain and also test the potential of genetic interactions involving naturally occurring genetic variants to modify phenotype. These studies will offer insights into how ploidy and natural variation shape the penetrance of mutant phenotypes and complex traits. Aim 4: Translating insights from the global yeast genetic interaction network to human cells. Applying our knowledge of the yeast reference network, we will select and screen an informative set of query gene mutants to efficiently map a scaffold genetic network for a human cell. This network will identify genetic network properties that are generally conserved and provide a resource for annotating human gene function.

全基因组测序为人类遗传变异提供了前所未有的信息。有 越来越多的人认识到变异体之间的相互作用在决定表型方面起着重要作用。然而，我们缺乏 了解遗传变异如何转化为影响个体的遗传相互作用。的关键 要解决这个问题，需要了解基因网络的规则。 在我们目前的资助期间，我们使用了合成基因阵列方法，我们开发了该方法， 酵母遗传学，完成酵母的参考遗传相互作用图谱。这个网络提供了一个全球视野 的功能组织的细胞，并揭示了细胞功能的层次模型。参考网络 使我们能够努力探索生物网络动态，以应对环境和遗传 干扰，包括遗传抑制和三重突变体的相互作用。我们的工作强调了 遗传相互作用影响从基因组序列信息推断表型。尽管基因 编辑方法提供了在其他系统中加速类似研究的承诺，许多类型的重要 遗传相互作用仍然相对未被探索，并且只能在酵母的基因组尺度上被绘制。因此，在本发明中， 我们建议继续分析酵母中复杂的遗传相互作用网络，并将其用作模型 设计信息实验，探索人类细胞中的遗传相互作用。 目的1：迭代计算-实验方法来映射特定条件的遗传网络。 我们将测试特定的基因条件组合，预计将产生许多遗传相互作用。基于 初步分析，我们估计，特定条件下的相互作用的系统分析将扩大 全球遗传相互作用网络的~3倍，提供了一个资源，探索环境的影响， 基因网络连接 目的2：酵母中抑制基因网络的大规模定位。我们将绘制基因外和剂量 酵母必需基因的抑制遗传相互作用网络。这种分析将揭示新的关系 并为更复杂系统中的类似研究提供了模板。 目的3：阐明复杂遗传相互作用的景观和原则。我们将绘制复杂 二倍体品系中必需基因的杂合等位基因之间的单倍性不足（CHI）相互作用，以及 测试涉及自然发生的遗传变异的遗传相互作用以改变表型的潜力。这些 研究将提供深入了解倍性和自然变异如何塑造突变表型的突变率， 复杂的特征 目标4：将全球酵母遗传相互作用网络的见解转化为人类细胞。应用 我们的酵母参考网络的知识，我们将选择和筛选查询基因突变体的信息集 以有效地绘制人类细胞的支架遗传网络。该网络将识别遗传网络属性 它们通常是保守的，并为注释人类基因功能提供了资源。