Data from: 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）指两个或多个基因的突变组合后产生意外表型的现象。当两个单独均不致死的突变组合引发细胞死亡时，便会产生极端负向遗传相互作用或合成致死遗传相互作用（Synthetic lethal genetic interaction）。反之，当两个突变产生的表型比预期更轻微时，则会发生正向遗传相互作用。遗传相互作用可用于揭示基因间的功能关联，助力生物学发现与治疗靶点鉴定，同时也可能解释人类疾病相关的大量未被发现的遗传组分。本文报道了真核细胞全范围遗传相互作用网络的构建与分析流程。
研究依据：基因组测序项目让我们对遗传变异有了前所未有的认知，但我们解读遗传信息以预测遗传性表型的能力仍然有限，这在很大程度上是因为基因组存在广泛的缓冲效应，使得大多数真核单个基因对于生物体生存而言并非必需。为探究遗传相互作用在揭示细胞功能、促成复杂表型形成中的作用范围，并发掘遗传网络的通用原理，本研究利用自动化酵母遗传学技术构建了全球范围的遗传相互作用网络。
研究结果：本研究针对酿酒酵母（Saccharomyces cerevisiae）约6000个基因的所有成对遗传相互作用进行了检测，共鉴定出近100万种相互作用，其中约55万种负向相互作用、约35万种正向相互作用，覆盖了约90%的酵母基因。必需基因（Essential genes）作为网络枢纽，其相互作用数量是非必需基因（Nonessential genes）的5倍。单基因的遗传相互作用组或遗传相互作用谱（Genetic interaction profile）可提供定量的功能度量，基于遗传相互作用谱相似性构建的全局网络可揭示反映细胞功能架构的模块层级结构。负向相互作用连接功能相关的基因，定位核心生物过程，并鉴定出多效性基因（Pleiotropic genes）；而正向相互作用则常映射与细胞周期进程缺陷或细胞蛋白稳态（Proteostasis）相关的通用调控关联。重要的是，该全局网络展示了成组的负向或正向遗传相互作用如何连接蛋白质复合物（Protein complex）与通路（Pathway），从而绘制出细胞的功能接线图。
结论：全局遗传相互作用网络凸显了细胞的功能组织方式，可为预测基因与通路功能提供研究资源。该网络强调了遗传相互作用的普遍性，以及它们可能加剧单个突变相关表型的潜力。负向遗传相互作用通常连接功能相关的基因，因此可通过其他功能信息进行预测。尽管正向相互作用的功能信息量相对较少，但它们可助力揭示遗传抑制或恢复力的通用机制。我们预计，全局遗传网络的有序拓扑结构——即遗传相互作用在蛋白质复合物与通路内部及之间形成连贯连接——可被用于解析基因型到表型的关联关系。