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Discrimination of modes of action of antifungal substances by use of metabolic footprinting.
- Source :
-
Applied and environmental microbiology [Appl Environ Microbiol] 2004 Oct; Vol. 70 (10), pp. 6157-65. - Publication Year :
- 2004
-
Abstract
- 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.
- Subjects :
- Antifungal Agents classification
Antimetabolites pharmacology
Artificial Intelligence
Discriminant Analysis
Mass Spectrometry
Models, Biological
Saccharomyces cerevisiae growth & development
Antifungal Agents pharmacology
Saccharomyces cerevisiae drug effects
Saccharomyces cerevisiae metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 0099-2240
- Volume :
- 70
- Issue :
- 10
- Database :
- MEDLINE
- Journal :
- Applied and environmental microbiology
- Publication Type :
- Academic Journal
- Accession number :
- 15466562
- Full Text :
- https://doi.org/10.1128/AEM.70.10.6157-6165.2004