Discrimination of the mode of action of antifungal substances using metabolic footprinting

Jess Allen, Hazel Marie Davey, David Iain Broadhurst, Jeremy John Rowland, Stephen G. Oliver, Douglas B. Kell

Research output: Contribution to journalArticlepeer-review

72 Citations (SciVal)


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.
Original languageEnglish
Pages (from-to)6157-6165
Number of pages9
JournalApplied and Environmental Microbiology
Issue number10
Publication statusPublished - Oct 2004


Dive into the research topics of 'Discrimination of the mode of action of antifungal substances using metabolic footprinting'. Together they form a unique fingerprint.

Cite this