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

Michael Costanzo, Benjamin VanderSluis, Elizabeth N. Koch, Anastasia Baryshnikova, Carles Pons, Guihong Tan, Wen Wang, Matej Usaj, Julia Hanchard, Susan D. Lee, Vicent Pelechano, Erin B. Styles, Maximilian Billmann, Jolanda Van Leeuwen, Nydia Van Dyk, Zhen Yuan Lin, Elena Kuzmin, Justin Nelson, Jeff S. Piotrowski, Tharan SrikumarSondra Bahr, Yiqun Chen, Raamesh Deshpande, Christoph F. Kurat, Sheena C. Li, Zhijian Li, Mojca Mattiazzi Usaj, Hiroki Okada, Natasha Pascoe, Bryan Joseph San Luis, Sara Sharifpoor, Emira Shuteriqi, Scott W. Simpkins, Jamie Snider, Harsha Garadi Suresh, Yizhao Tan, Hongwei Zhu, Noel Malod-Dognin, Vuk Janjic, Natasa Przulj, Olga G. Troyanskaya, Igor Stagljar, Tian Xia, Yoshikazu Ohya, Anne Claude Gingras, Brian Raught, Michael Boutros, Lars M. Steinmetz, Claire L. Moore, Adam P. Rosebrock, Amy A. Caudy, Chad L. Myers, Brenda Andrews, Charles Boone

Research output: Contribution to journalArticlepeer-review

458 Scopus citations

Abstract

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Original languageEnglish (US)
Article number1420
JournalScience
Volume353
Issue number6306
DOIs
StatePublished - Sep 23 2016

Bibliographical note

Funding Information:
We thank D. Botstein, H. Bussey, A. Fraser, H. Friesen, M. Meneghini, and M. Tyers for critical comments. This work was primarily supported by the National Institutes of Health (R01HG005853) (C.B., B.A., and C.L.M.), Canadian Institutes of Health Research (FDN-143264 and FDN-143265) (C.B. and B.A.), RIKEN Strategic Programs for R&D (C.B.), JSPS Kakenhi (15H04483) (C.B.), National Institutes of Health (R01HG005084 and R01GM104975) (C.L.M), and the National Science Foundation (DBI\0953881) (C.L.M.). Computing resources and data storage services were partially provided by the Minnesota Supercomputing Institute and the UMN Office of Information Technology, respectively. Additional support was provided by the Canadian Institutes of Health Research (A.A.C.), National Science Foundation (MCB\1244043) (C.M.), European Research Council (ERC) Advanced Investigator Grant (AdG-294542) (L.M.S), ERC Advanced Grant (European Commission) (M.B.), Ministry of Education, Culture, Sports, Sciences and Technology, MEXT (15H04402) (Y.O.), Canadian Institutes of Health Research (FDN143301), Genome Canada Genome Innovation network (through the Ontario Genomics Institute) (A.-C.G), the Ontario Genomics Institute, Canadian Cystic Fibrosis Foundation, Canadian Cancer Society, Pancreatic Cancer Canada, University Health Network (I.S.), National Science Foundation, Cyber-Enabled Discover and Innovation (CDI) (OIA-1028394), ERC Starting Independent Researcher Grant (278212), ARRS project (J1-5424), Serbian Ministry of Education and Science Project 11144006 (N.P.), National Natural Science Foundation of China (T.X), RIKEN Foreign Postdoctoral Researcher Program (J.S.P., S.C.L.), National Science Foundation Graduate Research Fellowship (NSF 00039202) (E.N.K. and S.W.S), U. of Minnesota Doctoral Dissertation Fellowship (B.V. and E.N.K.). O.G.T, C.L.M, B.A., and C.B are fellows of the Canadian Institute for Advanced Research (CIFAR). All data files (Data Files S1 to 17) associated with this study are described in detail in the supplementary materials and can be downloaded from http://boonelab.ccbr.utoronto.ca/supplement/costanzo2016. Data Files S1 to S17 were also deposited in the DRYAD Digital Repository (doi:10.5061/dryad.4291s). Raw mass spectrometry data and downloadable identification and SAINTexpress results tables were deposited in the MassIVE repository housed at the Center for Computational Mass Spectrometry at UCSD (http://proteomics.ucsd.edu/ProteoSAFe/datasets.jsp). The endogenously tagged GFP and Gal-inducible hemagglutinin data sets have been assigned the MassIVE IDs MSV000079157 and MSV000079368 and are available for FTP download at ftp://MSV000079157@massive.ucsd.edu and ftp://MSV000079368@massive.ucsd.edu, respectively. The data sets were assigned the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) identifiers PXD002368 and PXD003147 (data set password: SGA).

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