201. Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions
- Author
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Justin Nelson, Jeff S. Piotrowski, Sheena C. Li, Yoshikazu Ohya, Erin H. Wilson, Hamid Safizadeh, Abraham A Gebre, Charles Boone, Chad L. Myers, Mami Yoshimura, Minoru Yoshida, Raamesh Deshpande, Reika Okamoto, Yoko Yashiroda, Michael Costanzo, Hiroyuki Osada, and Scott W. Simpkins
- Subjects
0301 basic medicine ,False discovery rate ,Genetic Screens ,Computer science ,Gene Identification and Analysis ,Yeast and Fungal Models ,Genetic Networks ,Biochemistry ,Polymerization ,Tubulin ,Yeasts ,Drug Discovery ,Gene Regulatory Networks ,Cell Cycle and Cell Division ,lcsh:QH301-705.5 ,Ecology ,biology ,Systems Biology ,Cell Cycle ,Chemical Reactions ,Eukaryota ,Tubulin Modulators ,Chemistry ,Computational Theory and Mathematics ,Experimental Organism Systems ,Cell Processes ,Modeling and Simulation ,Physical Sciences ,Saccharomyces Cerevisiae ,Network Analysis ,Research Article ,Computer and Information Sciences ,Saccharomyces cerevisiae ,Computational biology ,Research and Analysis Methods ,Small Molecule Libraries ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Saccharomyces ,Model Organisms ,Tubulins ,Genetics ,Bioprocess ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Organisms ,Fungi ,Biology and Life Sciences ,Proteins ,Protein Complexes ,Reproducibility of Results ,Cell Biology ,Biological process ,biology.organism_classification ,Polymer Chemistry ,Genetic translation ,Yeast ,Cytoskeletal Proteins ,030104 developmental biology ,lcsh:Biology (General) ,Genetic Interactions ,Animal Studies ,Protein Multimerization ,Colchicine ,Function (biology) ,Genetic screen - 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., Author summary Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.
- Published
- 2018