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.
Highlights
The discovery of chemical compounds with desirable and interesting biological activity advances our understanding of how compounds and biological systems interact
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
Summary
The discovery of chemical compounds with desirable and interesting biological activity advances our understanding of how compounds and biological systems interact. Chemicalgenetic interaction profiling enables this discovery by measuring the response of defined gene mutants to chemical compounds [1,2,3,4,5,6,7,8]. Recent advances in DNA sequencing technology have enabled dramatic increases in the throughput of chemical-genetic interaction screens (into the range of thousands of compounds) via multiplexed analysis of pooled mutant libraries [6,7,9]. Genetic interactions identify pairs of gene mutations whose combined phenotypes are more or less severe than expected given the phenotypes of the individual mutants. In S. cerevisiae, the vast majority of all possible gene double-mutant pairs have been constructed and scored for fitness-based genetic interactions, yielding a global compendium of genome-wide genetic interaction profiles that quantitatively describe each gene’s function. Similarity between two genes’ genetic interaction profiles implies that these genes perform similar functions, enabling the functional annotation of uncharacterized genes and the construction of a global hierarchy of cellular function [5,10]
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