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.
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.
Authors: In Young Choi; Sun-Young Kim; Jhin Goo Chang; Hoo Rim Song; Woo Jung Kim; Su Young Lee; Hyun-Soo Kim; Minha Hong Journal: Soa Chongsonyon Chongsin Uihak Date: 2021-01-01
Authors: Ron Caspi; Tomer Altman; Richard Billington; Kate Dreher; Hartmut Foerster; Carol A Fulcher; Timothy A Holland; Ingrid M Keseler; Anamika Kothari; Aya Kubo; Markus Krummenacker; Mario Latendresse; Lukas A Mueller; Quang Ong; Suzanne Paley; Pallavi Subhraveti; Daniel S Weaver; Deepika Weerasinghe; Peifen Zhang; Peter D Karp Journal: Nucleic Acids Res Date: 2013-11-12 Impact factor: 16.971
Authors: Justin D Smith; Sundari Suresh; Ulrich Schlecht; Manhong Wu; Omar Wagih; Gary Peltz; Ronald W Davis; Lars M Steinmetz; Leopold Parts; Robert P St Onge Journal: Genome Biol Date: 2016-03-08 Impact factor: 13.583