Mapping dynamic functional networks across environments and backgrounds

基因组测序为人类基因变异的程度提供了前所未有的视角。然而，我们链接的能力 表型的特定遗传变异仍然有限。此外，复杂的基因组合之间的遗传相互作用 变种可能是这一挑战的原因之一。为了发现控制遗传相互作用网络的规则，我们之前 构建了所有可能的~1800万酵母双突变株，以生成全球酵母遗传相互作用图，这揭示了 真核细胞的功能“接线图”。在上一个供资周期中，我们系统地分析了 全球酵母遗传相互作用网络对不同的条件做出反应，我们发现它非常健壮 对环境的干扰。另一方面，我们对三元化合物三元相互作用的系统分析 涉及自然变异的突变体和遗传交互作用揭示了复杂的遗传交互作用和它们的 改变表型的巨大潜力。为了探索人类细胞中的基因功能和遗传网络，我们还建立了 一个高效的全基因组CRSPR-Cas9平台用于绘制遗传互作图，我们构建了一个遗传 建立人类细胞参照系的网络。与酵母遗传网络一样，人类网络的拓扑结构也是信息量很大的 并表明遗传网络的一般性质是高度保守的。 在这里，我们建议继续对酵母中复杂的遗传相互作用网络和表型进行系统分析，并 将结果应用于令人信服的实验设计，以继续绘制人类细胞的遗传网络图。 目的1：不同遗传背景下的条件表型和遗传网络动力学。我们 将对野生、遗传多样性的酵母菌株进行系统的表型和遗传分析，以确定基因 背景特异性基因重要性的潜在修饰物。我们还将绘制野生酵母分离株的遗传相互作用图，以 量化遗传背景对遗传网络的影响，以及更广泛地说，基因-表型关系。 目的2：定量单细胞读数用于分析遗传变异的表型后果。我们会 产生与基因扰动相关的定量细胞生物学表型图谱，并探索细胞的影响 状态对遗传扰动的影响，使用蛋白质组动力学作为表型读出。这些项目将绘制 亚细胞形态的遗传决定因素，揭示保守的隔室之间的联系，并建立 方法以蛋白质组为读数，进行基因分型分析。 目的3：绘制一个人类细胞系的全球遗传相互作用网络。基于我们目前的人类基因 相互作用数据集，我们将选择和筛选一组信息丰富的查询基因突变体，重点是必需基因， 为了扩展我们的支架遗传网络并有效地绘制一组具有代表性的功能蛋白质的网络 复合体。该网络将为人类基因功能的诠释和保守基因的鉴定提供强大的资源 可用于发现疾病基因修饰物的网络特性，包括那些潜在的癌细胞基因 依赖关系。

Genome sequencing has provided an unprecedented view into the extent of human genetic variation. Yet, our ability to link specific genetic variants to phenotypes remains limited. Moreover, genetic interactions between complex combinations of variants likely contribute to the challenge. To discover rules governing genetic interaction networks, we previously constructed all possible ~18 million yeast double mutants to generate a global yeast genetic interaction map, which reveals a functional ‘wiring diagram’ of a eukaryotic cell. In the context of the last funding cycle, we systematically analyzed how the global yeast genetic interaction network responds to different conditions, and we discovered that it is remarkably robust to environmental perturbation. On the other hand, our systematic analysis of trigenic interactions associated with triple mutants and genetic interactions involving natural variants revealed the prevalence of complex genetic interactions and their immense potential to modify phenotype. To explore gene function and genetic networks in human cells, we also established an efficient genome-wide CRSPR-Cas9 platform for mapping genetic interactions, and we constructed a ‘scaffold’ genetic network for a reference human cell line. Like the yeast genetic network, the topology of the human network is informative of gene function and suggests that general properties of genetic networks are highly conserved. Here, we propose continued systematic analysis of complex genetic interaction networks and phenotypes in yeast, and the application of the results for the cogent design of experiments to continue mapping genetic networks in human cells. Aim 1: Conditional phenotypes and genetic networks dynamics in the context of diverse genetic backgrounds. We will perform systematic phenotypic and genetic analyses in wild, genetically diverse yeast strains to identify genetic modifiers underlying background-specific gene essentiality. We will also map genetic interactions in wild yeast isolates to quantify the effect of genetic background on genetic networks and more generally, the genotype-phenotype relationship. Aim 2: Quantitative single cell read-outs for assaying the phenotypic consequences of genetic variation. We will produce quantitative cell biological phenotypic profiles associated with gene perturbation and explore the influence of cell state on the effects of genetic perturbation, using proteome dynamics as a phenotypic read-out. These projects will map genetic determinants of subcellular morphology, reveal connections between conserved compartments, and establish methods to use the proteome as a read-out for genotype-phenotype analysis. Aim 3: Mapping a global genetic interaction network for a human cell line. Based on our current human genetic interaction dataset, we will select and screen an informative set of query gene mutants, with an emphasis on essential genes, to expand our scaffold genetic network and efficiently map networks underlying a set of functionally representative protein complexes. This network will provide a powerful resource for annotating human gene function and identify conserved network properties that can be used to discover disease gene modifiers, including those underlying cancer cell genetic dependencies.