. 2016 Sep 23;353(6306):aaf1420.

doi: 10.1126/science.aaf1420.

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

Michael Costanzo¹, Benjamin VanderSluis², Elizabeth N Koch³, Anastasia Baryshnikova⁴, Carles Pons³, Guihong Tan¹, Wen Wang³, Matej Usaj¹, Julia Hanchard⁵, Susan D Lee⁶, Vicent Pelechano⁷, Erin B Styles⁵, Maximilian Billmann⁸, Jolanda van Leeuwen¹, Nydia van Dyk¹, Zhen-Yuan Lin⁹, Elena Kuzmin⁵, Justin Nelson¹⁰, Jeff S Piotrowski¹¹, Tharan Srikumar¹², Sondra Bahr¹, Yiqun Chen¹, Raamesh Deshpande³, Christoph F Kurat¹, Sheena C Li¹¹, Zhijian Li¹, Mojca Mattiazzi Usaj¹, Hiroki Okada¹³, Natasha Pascoe⁵, Bryan-Joseph San Luis¹, Sara Sharifpoor¹, Emira Shuteriqi¹, Scott W Simpkins¹⁰, Jamie Snider¹, Harsha Garadi Suresh¹, Yizhao Tan¹, Hongwei Zhu¹, Noel Malod-Dognin¹⁴, Vuk Janjic¹⁵, Natasa Przulj¹⁶, Olga G Troyanskaya¹⁷, Igor Stagljar¹⁸, Tian Xia¹⁹, Yoshikazu Ohya¹³, Anne-Claude Gingras²⁰, Brian Raught¹², Michael Boutros⁸, Lars M Steinmetz²¹, Claire L Moore⁶, Adam P Rosebrock⁵, Amy A Caudy⁵, Chad L Myers²², Brenda Andrews²³, Charles Boone²⁴

Affiliations

¹ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1.
² Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Simons Center for Data Analysis, Simons Foundation, 160 Fifth Avenue, New York, NY 10010, USA.
³ Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
⁴ Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
⁵ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1.
⁶ Department of Developmental, Molecular and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA.
⁷ European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany.
⁸ Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, Heidelberg, Germany.
⁹ Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON, Canada.
¹⁰ Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
¹¹ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan.
¹² Princess Margaret Cancer Centre, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto ON, Canada.
¹³ Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan 277-8561.
¹⁴ Computer Science Deptartment, University College London, London WC1E 6BT, UK.
¹⁵ Department of Computing, Imperial College London, UK.
¹⁶ Computer Science Deptartment, University College London, London WC1E 6BT, UK. School of Computing (RAF), Union University, Belgrade, Serbia.
¹⁷ Simons Center for Data Analysis, Simons Foundation, 160 Fifth Avenue, New York, NY 10010, USA. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
¹⁸ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Biochemistry, University of Toronto, Toronto, ON, Canada.
¹⁹ Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China, 430074.
²⁰ Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON, Canada.
²¹ European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany. Department of Genetics, School of Medicine and Stanford Genome Technology Center Stanford University, Palo Alto, CA 94304, USA.
²² Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. cmyers@cs.umn.edu brenda.andrews@utoronto.ca charlie.boone@utoronto.ca.
²³ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. cmyers@cs.umn.edu brenda.andrews@utoronto.ca charlie.boone@utoronto.ca.
²⁴ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan. cmyers@cs.umn.edu brenda.andrews@utoronto.ca charlie.boone@utoronto.ca.

PMID: 27708008
PMCID: PMC5661885
DOI: 10.1126/science.aaf1420

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

Michael Costanzo et al. Science. 2016.

. 2016 Sep 23;353(6306):aaf1420.

doi: 10.1126/science.aaf1420.

Authors

Affiliations

¹ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1.
² Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Simons Center for Data Analysis, Simons Foundation, 160 Fifth Avenue, New York, NY 10010, USA.
³ Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
⁴ Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
⁵ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1.
⁶ Department of Developmental, Molecular and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA.
⁷ European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany.
⁸ Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, Heidelberg, Germany.
⁹ Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON, Canada.
¹⁰ Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
¹¹ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan.
¹² Princess Margaret Cancer Centre, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto ON, Canada.
¹³ Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan 277-8561.
¹⁴ Computer Science Deptartment, University College London, London WC1E 6BT, UK.
¹⁵ Department of Computing, Imperial College London, UK.
¹⁶ Computer Science Deptartment, University College London, London WC1E 6BT, UK. School of Computing (RAF), Union University, Belgrade, Serbia.
¹⁷ Simons Center for Data Analysis, Simons Foundation, 160 Fifth Avenue, New York, NY 10010, USA. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
¹⁸ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Biochemistry, University of Toronto, Toronto, ON, Canada.
¹⁹ Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China, 430074.
²⁰ Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON, Canada.
²¹ European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany. Department of Genetics, School of Medicine and Stanford Genome Technology Center Stanford University, Palo Alto, CA 94304, USA.
²² Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. cmyers@cs.umn.edu brenda.andrews@utoronto.ca charlie.boone@utoronto.ca.
²³ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. cmyers@cs.umn.edu brenda.andrews@utoronto.ca charlie.boone@utoronto.ca.
²⁴ The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan. cmyers@cs.umn.edu brenda.andrews@utoronto.ca charlie.boone@utoronto.ca.

PMID: 27708008
PMCID: PMC5661885
DOI: 10.1126/science.aaf1420

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.

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Figures

**Figure 1. A global network of genetic interaction profile similarities**
**(A)** The essential similarity network was constructed by computing Pearson correlation coefficients (PCCs) for genetic interaction profiles (edges) of all pairs of genes (nodes) in the essential genetic interaction matrix (ExE). Gene pairs whose profile similarity exceeded a PCC > 0.2 were connected and graphed using a spring-embedded layout algorithm. Genes sharing similar genetic interactions profiles map proximal to each other, whereas genes with less similar genetic interaction profiles are positioned further apart. (B) A genetic profile similarity network for the nonessential genetic interaction matrix (NxN). (C) A global genetic profile similarity network encompassing all nonessential and essential genes was constructed from the combined NxN, ExE and NxE genetic interaction matrices. **(D)** The essential similarity network was annotated using the Spatial Analysis of Functional Enrichment (SAFE), identifying network regions enriched for similar GO biological process terms, which are color-coded. (E) The nonessential similarity network annotated using SAFE. (F) The global similarity network annotated using SAFE.

**Figure 2. The global genetic interaction profile similarity network reveals a hierarchy of cellular function**
**(A)** A schematic representation of a genetic interaction profile-derived hierarchy. Genes with highly correlated genetic interaction profiles (PCC > 0.4) form small, densely connected clusters representing specific pathways or protein complexes. At an intermediate range of profile similarity (0.2 < PCC < 0.4), sibling clusters representing distinct pathway or complexes combine together into larger biological process enriched clusters. At a lower range of profile similarity (0.05 < PCC < 0.2), bioprocess-enriched clusters, in turn, combine together to form larger clusters corresponding to different cell compartments. The grey-white scale bar illustrates enrichment of sibling clusters for the same set of terms from the indicated functional standard. See also fig. S7. (B) The genetic network hierarchy visualized using SAFE analysis, which identified regions in the global similarity network enriched for specific cellular compartments, biological processes or protein complexes.

**Figure 3. Genetic interaction profile similarity sub-networks**
Genes belonging to the indicated biological process-enriched clusters were extracted from the global network and laid out using a spring-embedded layout algorithm. Sub-networks were annotated using SAFE to identify network regions enriched for specific protein complexes. **(A)** Protein complexes localized within the Protein Degradation, Mitosis & Chromosome Segregation, and DNA Replication & Repair, enriched bioprocess clusters shown in Fig. 1F. **(B)** Protein complexes localized within the Transcription & Chromatin Organization and mRNA Processing-enriched bioprocess clusters shown in Fig. 1F. **(C)** Protein complexes localized within the Glycosylation, Protein folding/Targeting, Cell Wall Biosynthesis and Vesicle Traffic-enriched bioprocess clusters shown in Fig. 1F. **(D)** Protein complexes localized within the MVB Sorting & pH-dependent Signaling, Cell Polarity & Morphogenesis, and Cytokinesis enriched bioprocess clusters shown in Fig. 1F.

**Figure 4. Using network connectivity to explore gene function**
**(A)** Highly connected hub genes identified either as pleiotropic (blue nodes), or functionally specific (yellow nodes), are highlighted on a schematic representation of the global similarity network. Examples of high (blue text) and low (yellow text) pleiotropy genes, grouped based on their general function, are shown. **(B)** Poorly characterized genes that localize within, or in the vicinity of, a specific biological process-enriched cluster on the global similarity network. For genes whose genetic interaction profile similarity to other genes does not exceed a PCC > 0.2 and would otherwise not appear on the global similarity network, an estimated position based on the most similar genes appearing in the network is indicated (*). **(C)** A genetic interaction profile similarity subnetwork for the uncharacterized essential gene, *IPA1* (yellow node), extracted from the Transcription & Chromatin Organization enriched biological process cluster. **(D)** Polyadenylation profiles for a representative gene, *RTG2*, generated from genome-wide sequencing of mRNA purified from a wild-type strain (WT) and strains carrying TS mutations of *PCF11*, *CFT2* or *IPA1.* The horizontal arrow indicates the orientation of the *RTG2* open reading frame, the vertical arrows indicate the mutant, increased aberrant, 3′ mRNA cleavage and polyadenylation. **(E)** A genetic interaction profile similarity sub-network for *MTC2, MTC4, MTC6, CSF1, DLT1* and *MAY24* genes (yellow nodes) extracted from the network region in the vicinity of the Cell Polarity & Morphogenesis biological process cluster. **(F)** The MTC pathway genetic interaction network. Nodes are grouped according to genetic interaction profile similarity and edges represent negative (blue) and positive (yellow) interactions (genetic interaction score, |ε| > 0.08, P < 0.05). **(G)** Distribution of *ARO1* negative (blue) and positive (yellow) genetic interactions (|ε| > 0.08, P < 0.05; gene pairs that failed to meet threshold for interactions are colored grey). Functions enriched among genes that displayed an extreme negative interaction with *ARO1* are indicated and a subset of these genes is shown. Closed circles indicate members of the MTC pathway and arrows indicate amino acid permease encoding genes. **(H)** Representative cell images illustrating Bap2-GFP localization in wild type, *mtc2*Δ and *may24*Δ deletion mutant strains (Top panel). Vacuolar intensity (total GFP signal in the vacuole/vacuolar area) and percent of total cellular GFP present at the cell periphery (cell periphery GFP/total cellular GFP signal) were quantified for wild type cells and MTC pathway mutants (Bottom panel). Error bars indicate standard deviation from three replicate experiments. **(I)** Cellular uptake of ¹⁴C-labeled phenylalanine in wild type and deletion mutant strains. Error bars indicate standard deviation from three replicate experiments. **(J)** Metabolite levels for the indicated mutants were analyzed by full scan LC-MS (Top panel). The levels of selected metabolites are presented as log₂ ratios relative to wild type cells. Schematic diagram illustrating aromatic amino acid and *de novo* NAD+ biosynthesis pathways (bottom panel).

**Figure 5. Negative and positive genetic interactions connecting nonessential and essential genes**
**(A)** The network density of negative (blue) and positive (yellow) genetic interactions, expressed as a fraction of all tested gene pairs, associated with nonessential and essential genes, at a defined threshold (genetic interaction score, |ε| >0.08, P < 0.05). Error bars indicate the standard deviation across multiple samplings of the alleles for essential genes, where each gene is represented by a single, randomly selected allele in each sampling. **(B)** Plots of precision versus recall (number of true positives (TP)) for negative (blue) and positive (yellow) interactions for nonessential and essential genes, as determined by our genetic interaction score (|ε| >0.08, P < 0.05). True positive interactions were defined as those involving gene pairs co-annotated to a gold standard set of GO terms. The precision and recall values were calculated as described (8). **(C)** Fold enrichment for negative (blue) and positive (yellow) genetic interactions among co-localized, co-expressed, or physically interacting, gene pairs were calculated for either nonessential or essential gene pairs. **(D)** Network density of genetic interactions within and across biological processes. The fraction of screened nonessential and essential gene pairs exhibiting negative or positive interactions, as determined by our genetic interaction score (|ε| >0.08, P < 0.05), was measured for the 17 gene sets enriched for specific biological processes, as defined in Fig. 1F. Node size reflects the fraction of interacting gene pairs observed for a given pair of biological processes. Dark blue and dark yellow nodes indicate that the frequency of interaction is significantly above random expectation. Light blue and light yellow nodes represent a frequency of interaction that is not significantly higher than random expectation. Nodes on the diagonal represent the frequency of interactions among genes belonging to the same biological process. Nodes off the diagonal represent the frequency of interactions between different biological processes.

**Figure 6. Mapping negative and positive interactions across the genetic network based functional hierarchy**
**(A)** Schematic representation of the genetic network-based functional hierarchy illustrating interactions between genes within the same complex, biological process, or cellular compartment, as well as distant interactions that span different cellular compartments. **(B)** The network density of genetic interactions between genes in the same cluster, at a given level of profile similarity (PCC) in the genetic network hierarchy for negative (blue) or positive (yellow) genetic interactions (genetic interaction score, |ε| > 0.08, P < 0.05). Dashed lines indicate the PCC range within which clusters in the genetic network hierarchy were enriched for cell compartments, bioprocesses, and protein complexes. **(C)** The functional distribution of all negative (blue) and all positive (yellow) interactions (|ε| > 0.08, P < 0.05) among genes in the genetic network hierarchy. The percentage of all interactions connecting nonessential gene pairs and essential gene pairs in the same clusters corresponding to a cell compartment, bioprocess or complex/pathway is shown. The combined fraction of functionally related interactions (i.e. interactions connecting genes in the same compartment, bioprocess, complex or pathway) is also indicated (*). **(D)** The percentage of negative (blue) and positive (yellow) interactions within a specified genetic interaction score (ε) range that connects genes belonging to the same cluster at the indicated level of the genetic network-based hierarchy. Different shades of blue and yellow correspond to levels of functional relatedness shown in (C). The white area corresponds to the fraction of interactions that connect genes in different cellular compartments (i.e. Distant).

**Figure 7. Genetic interactions within and between protein complexes**
**(A)** The percentage of nonessential and essential complexes whose members were enriched for genetic interactions with each other and biased (i.e. coherent) for either mostly negative (blue) or mostly positive (yellow) interactions. **(B)** The percentage of nonessential-nonessential, essential-essential or essential-nonessential complex-complex pairs found to be enriched for genetic interactions and biased (i.e. coherent) for either mostly negative (blue) or mostly positive (yellow) interactions. Black dashed lines indicate the background rate of coherent genetic interaction enrichment within individual complexes or between pairs of protein complexes. Error bars indicate the standard deviation across multiple samplings of the alleles for essential genes, where each gene is represented by a single, randomly selected allele in each sampling.

**Figure 8. Functional wiring diagrams for specific protein complexes**
**(A)** Genetic interaction map for the ORC (Origin Recognition Complex) (i) Regions of the global similarity network significantly enriched for genes exhibiting negative (blue) or positive (yellow) genetic interactions with ORC members were mapped using SAFE. (ii) Protein complexes that showed coherent negative or positive genetic interactions with ORC were placed on a schematic representation of the global similarity network based on the average genetic interaction profile similarity of the complex and connected with blue or yellow edges, respectively. (iii) A subset of protein complexes from (ii) that showed coherent negative (blue) or positive (yellow) genetic interactions with genes encoding the ORC are shown. **(B)** Genetic interaction map for the 19S proteasome. The 19S proteasome networks shown in (i–iii) were constructed are as described in (A). **(C)** Distribution of positive genetic interaction enrichment for protein complexes screened against the essential gene array (TSA). Protein complexes enriched for positive interactions with essential genes (yellow bars) tend to be associated with proteostasis-related functions (2.3X, P < 10⁻⁷, Fisher’s Exact Test), including the 19S and 20S proteasome subunits as well as the chaperonin-containing T-complex (CCT) and prefoldin chaperone complexes (indicated on the graph). **(D)** Distribution of positive vs. negative genetic interactions for protein complexes enriched for positive interactions shown in (C). Essential protein complexes that show a bias towards positive interactions, such as the ORC, RFC, and GINS, are often required for normal cell cycle progression (2X, P < 7×10⁻⁴, Fisher’s Exact Test).

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References

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

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

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