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

Michael Costanzo(University of Toronto), Benjamin VanderSluis(University of Minnesota), Elizabeth N. Koch(University of Minnesota), Anastasia Baryshnikova(Princeton University), Carles Pons(University of Minnesota), Guihong Tan(University of Toronto), Wen Wang(University of Minnesota), Matej Ušaj(University of Toronto), Julia Hanchard(University of Toronto), Susan D. Lee(Tufts University), Vicent Pelechano(European Molecular Biology Laboratory), Erin B. Styles(University of Toronto), Maximilian Billmann(German Cancer Research Center), Jolanda van Leeuwen(University of Toronto), Nydia van Dyk(University of Toronto), Zhen-Yuan Lin(Lunenfeld-Tanenbaum Research Institute), Elena Kuzmin(University of Toronto), Justin Nelson(University of Minnesota), Jeff S. Piotrowski(University of Toronto), Tharan Srikumar(Princess Margaret Cancer Centre), Sondra Bahr(University of Toronto), Yiqun Chen(University of Toronto), Raamesh Deshpande(University of Minnesota), Christoph F. Kurat(University of Toronto), Sheena C. Li(University of Toronto), Zhijian Li(University of Toronto), Mojca Mattiazzi Ušaj(University of Toronto), Hiroki Okada(The University of Tokyo), Natasha Pascoe(University of Toronto), Bryan-Joseph San Luis(University of Toronto), Sara Sharifpoor(University of Toronto), Emira Shuteriqi(University of Toronto), Scott W. Simpkins(University of Minnesota), Jamie Snider(University of Toronto), Harsha Garadi Suresh(University of Toronto), Yizhao Tan(University of Toronto), Hongwei Zhu(University of Toronto), Noël Malod‐Dognin(Department of Science and Technology), Vuk Janjić(Imperial College London), Nataša Pržulj(Department of Science and Technology), Olga G. Troyanskaya(Princeton University), Igor Štagljar(University of Toronto), Tian Xia(University of Minnesota), Yoshikazu Ohya(The University of Tokyo), Anne‐Claude Gingras(University of Toronto), Brian Raught(Princess Margaret Cancer Centre), Michael Boutros(German Cancer Research Center), Lars M. Steinmetz(Stanford Medicine), Claire Moore(Tufts University), Adam P. Rosebrock(University of Toronto), Amy A. Caudy(University of Toronto), Chad L. Myers(University of Minnesota), Brenda Andrews(University of Toronto), Charles Boone(University of Toronto)
Science
September 22, 2016
Cited by 1,394Open Access
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Abstract

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. A global network of genetic interaction profile similarities. ( Left ) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. ( Right ) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms.


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