Metabolomics based on LC-HRMS for the identification of maize metabolites involved in resistance against Fusarium verticillioides infection

Fusarium verticillioides is the causal agent of Fusarium ear rot (FER) in maize and contaminates the grain with fumonisins, a family of mycotoxins that threats human and animal health. A metabolomic approach was applied in order to reveal the mechanism of host resistance towards this pathogen. The metabolic profiles of uninoculated and 72 hours after inoculation maize kernels, belonging to resistant (CO441) and susceptible (CO354) genotypes, were investigated with liquid chromatography in combination with high resolution mass spectrometry (LC-HRMS). The major challenges of all untargeted metabolomics experiments are represented by the efficient processing of raw chromatograms and annotation/identification of metabolites, excluding non-metabolite related signals. The use in this experiment of stable isotopic labelling (SIL) combined to MetExtract algorithm allowed the automatic detection and prediction of carbon atoms of compounds of biological origin. 830 and 1135 peaks resulted per maize samples in negative and positive ionization mode, respectively. Metabolite annotation was carried out by matching accurate m/z value, number of carbon atoms and type of ion species against PlantCyc, CornCyc and KEGG databases. Uni- and multivariate statistics were applied on consistently found metabolites (701 and 992 for negative and positive mode, respectively) and revealed a clear separation between cultivars and treatments. Metabolites were set in maize metabolic pathways and a large amount of them resulted involved in defence mechanism, e.g. biosynthesis of aromatic amino acids, phenylpropanoids, flavonoids, linoleic and α-linolenic acid metabolism. Metabolite quantification and identification will be performed by comparing the fragmentation spectra of standards in MS/MS. Differentially expressed metabolites among genotypes can be used as biomarkers associated with resistant trait