Discrimination of modes of action of antifungal substances by use of metabolic footprinting.
Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.
Department of Biological Sciences, University of Wales, Aberystwyth, UK.
Jess
J
Allen
Hazel M
HM
Davey
David
D
Broadhurst
Jem J
JJ
Rowland
Stephen G
SG
Oliver
Douglas B
DB
Kell
D000935:Antifungal Agents / Q000145:classification / Q000494:pharmacology*; D000963:Antimetabolites / Q000494:pharmacology; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D013058:Mass Spectrometry; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000187:drug effects* / Q000254:growth & development / Q000378:metabolism*
D000935:Antifungal Agents; D000963:Antimetabolites