Use of GC-MS based metabolic fingerprinting for fast exploration of fungicide modes of action

Abstract

Background: The widespread occurrence of fungicide resistance in fungal plant pathogens requires the development of new compounds with different mode(s) of action (MOA) to avoid cross resistance. This will require a rapid method to identify MOAs. Results: Here, gas chromatography-mass spectrometry (GC-MS) based metabolic fingerprinting was used to elucidate the MOAs of fungicides. Botrytis cinerea, an important pathogen of vegetables and flowers, can be inhibited by a wide range of chemical fungicides with different MOAs. A sensitive strain of B. cinerea was exposed to EC50 concentrations of 13 fungicides with different known MOAs and one with unknown MOA. The mycelial extracts were analyzed for their "metabolic fingerprint" using GC-MS. A comparison among the GC-MS vector' profiles of cultures treated with fungicides were performeded. A model based on hierarchical clustering was established which allowed these antifungal compounds to be distinguished and classified coinciding with their MOAs. Thus, metabolic fingerprinting represents a rapid, convenient, and information-rich method for classifying the MOAs of antifungal substances. The biomarkers of fungicide MOAs were also established by an analysis of variance and included succinate for succinate dehydrogenase inhibitors and cystathionine for methionine synthesis inhibitors. Using the metabolic model and the common perturbation of metabolites, the new fungicide SYP-14288 was identified as having the same MOA as fluazinam. Conclusion: This study provides a comprehensive database of the metabolic perturbations of B. cinerea induced by diverse MOA inhibitors and highlights the utility of metabolic fingerprinting for defining MOAs, which will assist in the development and optimization of new fungicides.