Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Scott W. Simpkins, Justin Nelson, Raamesh Deshpande, Sheena C. Li, Jeff S. Piotrowski, Erin H. Wilson, Abraham A. Gebre, Hamid Safizadeh, Reika Okamoto, Mami Yoshimura, Michael Costanzo, Yoko Yashiroda, Yoshikazu Ohya, Hiroyuki Osada, Minoru Yoshida, Charles Boone, Chad L. Myers

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Original languageEnglish (US)
Article numbere1006532
JournalPLoS computational biology
Volume14
Issue number10
DOIs
StatePublished - Oct 2018

Bibliographical note

Funding Information:
This work was partially supported by the National Institutes of Health (https://www.nih.gov/) (R01HG005084, R01GM104975) and the National Science Foundation (https://www.nsf.gov/) (DBI 0953881). SWS is supported by an NSF Graduate Research Fellowship (00039202), an NIH Biotechnology training grant (T32GM008347), and a one-year fellowship from the University of Minnesota Bioinformatics and Computational Biology Graduate Program (https://r.umn.edu/academics-research/graduate-programs/bicb). SCL and JSP are supported by a RIKEN (http://www.riken.jp/en/) Foreign Postdoctoral Research Fellowship. SCL is supported by a RIKEN CSRS (http://www.csrs.riken.jp/en/) Research Topics for Cooperative Projects Award (201601100228), and a RIKEN FY2017 Incentive Research Projects Grant. YO is supported through Grants-in-Aid for Scientific Research (15H04402) from the Ministry of Education, Culture, Sports, Science and Technology, Japan (www.mext.go.jp/en/). CB and YO are supported by JSPS KAKENHI grant number 15H04483 (http://www.jsps.go.jp/english/). CB and YY are supported by a JSPS Grant-in-Aid for Scientific Research on Innovative Areas (17H06411). CLM and CB are fellows in the Canadian Institute for Advanced Research (CIFAR, https://www.cifar.ca/) Genetic Networks Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SWS would like to thank Henry Neil Ward for his proofreading of the manuscript. Computing resources and data storage services were partially provided by the Minnesota Supercomputing Institute and the UMN Office of Information Technology, respectively. Software licensing services were provided by the UMN Office for Technology Commercialization.

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