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Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks

View ORCID ProfileScott W. Simpkins, Justin Nelson, Raamesh Deshpande, Sheena C. Li, Jeff S. Piotrowski, Erin H. Wilson, Abraham A. Gebre, Reika Okamoto, Mami Yoshimura, Michael Costanzo, Yoko Yashiroda, Yoshikazu Ohya, Hiroyuki Osada, Minoru Yoshida, Charles Boone, View ORCID ProfileChad L. Myers
doi: https://doi.org/10.1101/111252
Scott W. Simpkins
1University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Graduate Program, Minneapolis, Minnesota, USA
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  • ORCID record for Scott W. Simpkins
Justin Nelson
1University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Graduate Program, Minneapolis, Minnesota, USA
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Raamesh Deshpande
2University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, USA
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Sheena C. Li
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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Jeff S. Piotrowski
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
6Present address: Yumanity Therapeutics, Cambridge, MA, USA
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Erin H. Wilson
2University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, USA
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Abraham A. Gebre
4University of Tokyo, Department of Integrated Biosciences, Graduate School of Frontier Sciences, Kashiwa, Chiba, Japan
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Reika Okamoto
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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Mami Yoshimura
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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Michael Costanzo
5University of Toronto, Donnelly Centre, Toronto, Ontario, Canada
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Yoko Yashiroda
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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Yoshikazu Ohya
4University of Tokyo, Department of Integrated Biosciences, Graduate School of Frontier Sciences, Kashiwa, Chiba, Japan
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Hiroyuki Osada
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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Minoru Yoshida
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
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Charles Boone
3RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
5University of Toronto, Donnelly Centre, Toronto, Ontario, Canada
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  • For correspondence: chadm@umn.edu charlie.boone@utoronto.ca
Chad L. Myers
1University of Minnesota-Twin Cities, Bioinformatics and Computational Biology Graduate Program, Minneapolis, Minnesota, USA
2University of Minnesota-Twin Cities, Department of Computer Science and Engineering, Minneapolis, Minnesota, USA
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  • ORCID record for Chad L. Myers
  • For correspondence: chadm@umn.edu charlie.boone@utoronto.ca
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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. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent 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. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed 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 present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.

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Posted May 18, 2018.
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Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks
Scott W. Simpkins, Justin Nelson, Raamesh Deshpande, Sheena C. Li, Jeff S. Piotrowski, Erin H. Wilson, Abraham A. Gebre, Reika Okamoto, Mami Yoshimura, Michael Costanzo, Yoko Yashiroda, Yoshikazu Ohya, Hiroyuki Osada, Minoru Yoshida, Charles Boone, Chad L. Myers
bioRxiv 111252; doi: https://doi.org/10.1101/111252
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Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks
Scott W. Simpkins, Justin Nelson, Raamesh Deshpande, Sheena C. Li, Jeff S. Piotrowski, Erin H. Wilson, Abraham A. Gebre, Reika Okamoto, Mami Yoshimura, Michael Costanzo, Yoko Yashiroda, Yoshikazu Ohya, Hiroyuki Osada, Minoru Yoshida, Charles Boone, Chad L. Myers
bioRxiv 111252; doi: https://doi.org/10.1101/111252

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