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

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

71 Dyfyniadau(SciVal)

Crynodeb

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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)6157-6165
Nifer y tudalennau9
CyfnodolynApplied and Environmental Microbiology
Cyfrol70
Rhif cyhoeddi10
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - Hyd 2004

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