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Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways

Abstract

Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H+-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.

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Figure 1: Chemical-genetic interactions can be modeled by synthetic genetic interactions.
Figure 2: The set of viable gene deletion mutants were screened for hypersensitivity to each of 12 inhibitory compounds (cyclosporin A, FK506, tunicamycin, sulfometuron methyl, wortmannin, caffeine, rapamycin, fluconazole, camptothecin, hydroxyurea, cycloheximide and benomyl).
Figure 3: To classify multidrug resistance genes, we identified 65 deletion strains that were sensitive to at least four of ten diverse compounds: wortmannin, benomyl, tunicamycin, rapamycin, sulfometuron methyl, fluconazole, cycloheximide, FK506, caffeine and hydroxyurea.
Figure 4: Overlap between the chemical-genetic profile of fluconazole and the genetic interaction profile of ERG11.
Figure 5: Overlap between the chemical-genetic profiles of FK506 and CsA and the genetic interaction profile of CNB1.
Figure 6: Two-dimensional hierarchical clustering analysis of chemical-genetic and genetic interaction profiles.

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Acknowledgements

We thank D. Drubin for tub2-403 and M. Cyert, J. Anderson and S. Brill for gifts of FK506, fluconazole and camptothecin, respectively. We thank H. Lu, X. Xin and V. Ghandi for assistance with drug screening and confirmations and Y. Chen and X. Cheng for assistance with SGA screening. This work was supported by grants from the Canadian Institute of Health Research (CIHR) to C.B. and T.R.H. and from the National Cancer Institute of Canada (NCIC) to G.W.B. A.B.P holds a Natural Sciences and Engineering Research Council of Canada (NSERC) graduate student fellowship.

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Parsons, A., Brost, R., Ding, H. et al. Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat Biotechnol 22, 62–69 (2004). https://doi.org/10.1038/nbt919

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