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

• Main Authors: 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
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2018-10-01
• Series: PLoS Computational Biology
• Online Access: http://europepmc.org/articles/PMC6226211?pdf=render

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.