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      The Genetic Landscape of a Cell

      research-article
      Author(s): 1 , 2 , 1 , 2 , 3 , 3 , 4 , 4 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 5 , 1 , 2 , 6 , 3 , 7 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 8 , 3 , 1 , 2 , 9 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 10 , 1 , 5 , 11 , 1 , 2 , 1 , 2 , 7 , 1 , 5 , 12 , 13 , 1 , 13 , 8 , 2 , 10 , 1 , 2 , 14 , 15 , 1 , 2 , 9 , 1 , 2 , 5 , 1 , 2 , 4 , 3 , † , 1 , 2 , † , 1 , 2 , †
      Publication date PMC-release:
      Journal: Science (New York, N.Y.)

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          Abstract

          A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

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          Perspective: Evolution and detection of genetic robustness.

          J Arjan G M de Visser, Joachim Hermisson, Günter P. Wagner … (2003)
          Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness--genetic robustness--and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness: and (3) exploring the implications of genetic robustness for future evolution.
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            Defining genetic interaction.

            Ramamurthy Mani, Robert St. Onge, John Hartman … (2008)
            Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction.
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              Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways.

              Ainslie B Parsons, Renee L. Brost, Huiming Ding … (2004)
              Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H(+)-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 0404511
                Journal ID (pubmed-jr-id): 7473
                Journal ID (nlm-ta): Science
                Journal ID (iso-abbrev): Science
                Title: Science (New York, N.Y.)
                ISSN (Print): 0036-8075
                ISSN (Electronic): 1095-9203
                Publication date Nihms-submitted: 24 August 2017
                Publication date (Print): 22 January 2010
                Publication date PMC-release: 15 September 2017
                Volume: 327
                Issue: 5964
                Pages: 425-431
                Affiliations
                [1 ]Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
                [2 ]Department of Molecular Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
                [3 ]Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
                [4 ]Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
                [5 ]Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada
                [6 ]Department of Biochemistry, Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA
                [7 ]Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
                [8 ]Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
                [9 ]Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario M5G 1X5, Canada
                [10 ]Department of Pharmacy, University of Toronto, Toronto, Ontario M5S 3E1, Canada
                [11 ]Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA
                [12 ]S&P Robotics, Inc., 1181 Finch Avenue West, North York, Ontario M3J 2V8, Canada
                [13 ]Institute of Biochemistry, Biological Research Center, H-6701 Szeged, Hungary
                [14 ]Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08544, USA
                [15 ]Biology Department, McGill University, Montreal, Quebec H3A 1B1, Canada
                Author notes
                [† ]To whom correspondence should be addressed. cmyers@ 123456cs.umn.edu (C.L.M.); brenda.andrews@ 123456utoronto.ca (B.J.A.); charlie.boone@ 123456utoronto.ca (C.B.)
                Article
                Accession ID: PMC5600254 Pmcid ID: PMC5600254 Pmc-uid ID: 5600254 Manuscript ID: ems73362
                DOI: 10.1126/science.1180823
                PMC ID: 5600254
                PubMed ID: 20093466
                SO-VID: 05331766-3c04-4c2b-8ec0-bfc4fe0875d7
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