A global genetic interaction network maps a wiring diagram of cellular function

INTRODUCTION: Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE: Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS: We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION: A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships.

引言：遗传相互作用（genetic interaction）指的是两个或多个基因的突变组合后，产生意料之外的表型（phenotype）的现象。当两个各自不会致死的突变组合后引发细胞死亡时，就会出现极端负面遗传相互作用，即合成致死（synthetic lethal）遗传相互作用。反之，当两个突变产生的表型严重程度低于预期时，则会发生阳性遗传相互作用。遗传相互作用能够揭示基因间的功能关联，可用于生物学发现与治疗靶点的挖掘；同时，它也可解释人类疾病相关未探明遗传机制的重要组成部分。本文将介绍真核细胞（eukaryotic cell）综合遗传相互作用网络的构建与分析过程。
研究背景：基因组测序项目让我们得以以前所未有的视角观察遗传变异。然而，我们解读遗传信息以预测遗传性表型的能力依然有限，这在很大程度上源于基因组存在广泛的缓冲机制，使得大多数单个真核基因对于生物体存活并非必需。为了探究遗传相互作用在揭示细胞功能、参与复杂表型形成方面的作用程度，并挖掘遗传网络的通用原则，我们借助自动化酿酒酵母（Saccharomyces cerevisiae）遗传学技术构建了全局遗传相互作用网络。
研究结果：我们针对酿酒酵母（Saccharomyces cerevisiae）中约6000个基因的绝大多数，开展了所有可能的成对遗传相互作用检测，共鉴定得到近100万对遗传相互作用，其中包含约55万对负面相互作用与约35万对阳性相互作用，覆盖了酿酒酵母近90%的基因。必需基因（essential gene）属于网络枢纽，其携带的相互作用数量是非必需基因的5倍。单基因的遗传相互作用集合或遗传相互作用谱（genetic interaction profile）可作为功能的定量衡量指标；基于遗传相互作用谱相似性构建的全局网络，揭示了反映细胞功能架构的模块层级结构。负面遗传相互作用连接功能相关基因，映射核心生物过程，并鉴定出多效性基因；而阳性遗传相互作用则通常关联与细胞周期进程缺陷或细胞蛋白质稳态（proteostasis）异常相关的通用调控连接。尤为重要的是，该全局网络阐明了连贯的负面或阳性遗传相互作用集合如何连接蛋白质复合物（protein complex）与通路，从而绘制出细胞的功能连接图谱。
研究结论：全局遗传相互作用网络凸显了细胞的功能组织方式，并为预测基因与通路功能提供了宝贵资源。该网络强调了遗传相互作用的普遍性，以及其加剧单个突变相关表型的潜力。负面遗传相互作用往往连接功能相关基因，因此可通过其他功能信息进行预测。尽管阳性遗传相互作用的功能指向性相对较弱，但它们可为遗传抑制或细胞韧性的通用机制提供研究思路。我们预期，全局遗传网络的有序拓扑结构（遗传相互作用在蛋白质复合物与通路内部及之间实现连贯连接）可被用于解析基因型（genotype）到表型（phenotype）的关联机制。