Identification of drug targets by chemogenomic and metabolomic profiling in yeast

Manhong Wu, Ming Zheng, Weiruo Zhang, Sundari Suresh, Ulrich Schlecht, William L. Fitch, Sofia Aronova, Stephan Baumann, Ronald Davis, Robert St.Onge, David L. Dill, Gary Peltz

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

OBJECTIVE: To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all of its genes, chemogenomic profiling in yeast has been used to identify drug targets. As measurement of drug-induced changes in cellular metabolites can yield considerable information about the effects of a drug, we investigated whether combining chemogenomic and metabolomic profiling in yeast could improve the characterization of drug targets. BASIC METHODS: We used chemogenomic and metabolomic profiling in yeast to characterize the target for five drugs acting on two biologically important pathways. A novel computational method that uses a curated metabolic network was also developed, and it was used to identify the genes that are likely to be responsible for the metabolomic differences found. RESULTS AND CONCLUSION: The combination of metabolomic and chemogenomic profiling, along with data analyses carried out using a novel computational method, could robustly identify the enzymes targeted by five drugs. Moreover, this novel computational method has the potential to identify genes that are causative of metabolomic differences or drug targets. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.
Original languageEnglish (US)
Pages (from-to)877-886
Number of pages10
JournalPharmacogenetics and Genomics
Volume22
Issue number12
DOIs
StatePublished - Dec 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: G.P. and M. W. were partially supported by funding from a transformative RO1 award (1R01DK090992-01). D. L. D. and W.Z. were supported by a King Abdullah University of Science and Technology (KAUST) research grant under the KAUST Stanford Academic Excellence Alliance program. S. S., U. S., R. D. and B. S. were partially supported by funding from the National Human Genome Research Institute (2R01HG003317-05) provided to R.D.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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