Mapping dynamic functional networks across environments and backgrounds

基因组测序为人类遗传变异的程度提供了前所未有的视角。然而，我们将人类 表型的特定遗传变异仍然有限。此外，基因的复杂组合之间的相互作用， 变异可能会导致这一挑战。为了发现控制遗传相互作用网络的规则，我们以前 构建了所有可能的~ 1800万酵母双突变体，以生成全球酵母遗传相互作用图谱， 真核细胞的功能“接线图”。在上一个融资周期的背景下，我们系统地分析了如何 全球酵母遗传相互作用网络对不同的条件做出反应，我们发现它非常强大， 环境扰动。另一方面，我们对与三重相关的三基因相互作用的系统分析， 突变体和涉及自然变异的遗传相互作用揭示了复杂的遗传相互作用及其 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 一个有效的全基因组CRSPR-Cas9平台，用于绘制遗传相互作用，我们构建了一个“支架”遗传学模型， 网络的参考人类细胞系。就像酵母遗传网络一样，人类网络的拓扑结构也是信息丰富的 的基因功能，并表明遗传网络的一般属性是高度保守的。 在这里，我们建议继续系统分析酵母中复杂的遗传相互作用网络和表型， 将结果应用于令人信服的实验设计，以继续绘制人类细胞中的遗传网络。 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 将在野生的、遗传多样的酵母菌株中进行系统的表型和遗传分析， 背景特异性基因重要性的修饰语。我们还将绘制野生酵母分离株中的遗传相互作用， 量化遗传背景对遗传网络的影响，更一般地说，基因型-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。我们将 产生与基因扰动相关的定量细胞生物学表型谱，并探索细胞 国家对遗传扰动的影响，使用蛋白质组动力学作为表型读出。这些项目将映射 亚细胞形态的遗传决定因素，揭示了保守区室之间的联系，并建立 使用蛋白质组作为基因型-表型分析的读数的方法。 目标3：绘制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 交互数据集，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，并有效地映射一组功能代表性蛋白质的网络， 配合物该网络将为人类基因功能的注释和保守基因的鉴定提供强有力的资源 可用于发现疾病基因修饰剂的网络特性，包括那些潜在的癌细胞遗传修饰剂。 个依赖项

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.