TY - JOUR
T1 - Discrimination of the mode of action of antifungal substances using metabolic footprinting
AU - Allen, Jess
AU - Davey, Hazel Marie
AU - Broadhurst, David Iain
AU - Rowland, Jeremy John
AU - Oliver, Stephen G.
AU - Kell, Douglas B.
N1 - Allen, J., Davey, H. M., Broadhurst, D., Rowland, J. J., Oliver, S. G., Kell, D. B. (2004). Discrimination of the mode of action of antifungal substances using metabolic footprinting. Applied and Environmental Microbiology, 70, (10), 6157-6165.
Sponsorship: BBSRC
PY - 2004/10
Y1 - 2004/10
N2 - 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.
AB - 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.
U2 - 10.1128/AEM.70.10.6157-6165.2004
DO - 10.1128/AEM.70.10.6157-6165.2004
M3 - Article
C2 - 15466562
SN - 0099-2240
VL - 70
SP - 6157
EP - 6165
JO - Applied and Environmental Microbiology
JF - Applied and Environmental Microbiology
IS - 10
ER -