Quantitative resistance is generally controlled by several genes. More than 100 resistance quantitative trait loci (QTLs) have been identified in wheat and barley against Fusarium head blight (FHB), caused by Gibberella zeae (anamorph: Fusarium graminearum), implying the possible occurrence of several resistance mechanisms. The objective of this study was to apply metabolomics to identify the metabolites in barley that are related to resistance against FHB. Barley genotypes, Chevron and Stander, were inoculated with mock or pathogen during the anthesis stage. The disease severity was assessed as the proportion of spikelets diseased. The genotype Chevron (0.33) was found to have a higher level of quantitative resistance than Stander (0.88). Spikelet samples were harvested at 48 h post-inoculation; metabolites were extracted and analysed using an LC-ESI-LTQ-Orbitrap (Thermo Fisher, Waltham, MA, USA). The output was imported to an XCMS 1.12.1 platform, the peaks were deconvoluted and the adducts were sieved. Of the 1826 peaks retained, a t-test identified 496 metabolites with significant treatment effects. Among these, 194 were resistance-related (RR) constitutive metabolites, whose abundance was higher in resistant mock-inoculated than in susceptible mock-inoculated genotypes. Fifty metabolites were assigned putative names on the basis of accurate mass, fragmentation pattern and number of carbons in the formula. The RR metabolites mainly belonged to phenylpropanoid, flavonoid, fatty acid and terpenoid metabolic pathways. Selected RR metabolites were assayed in vitro for antifungal activity on the basis of fungal biomass production. The application of these RR metabolites as potential biomarkers for screening and the potential of mass spectrometry-based metabolomics for the identification of gene functions are discussed.

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Inoculation with the virulence factor deoxynivalenol (DON) can induce disease symptoms in wheat ( Triticum aestivum L.) spikelets, even though it is not needed for the initial invasion by Fusarium graminearum Schwabe, thus the mechanism of plant defense against both the pathogen and DON, was investigated. Wheat cultivars that are resistant (‘Sumai3’) or susceptible (‘Roblin’) to fusarium head blight (FHB) were inoculated with F. graminearum, DON, or water. Inoculated spikelets were harvested 48 h after inoculation, the metabolites were extracted in methanol–water and chloroform, then derivatized and analyzed by gas chromatography – mass spectrometry. The metabolite peaks were deconvoluted and identified by manually matching the mass spectra with those in the NIST and GMD libraries. The peaks were aligned, and abundances were measured. A total of 117 metabolites were tentatively identified, including several antimicrobial metabolites and signal molecules or their precursors. Out of these 117 metabolites, 15 and 18 were identified as possible resistance-related (RR) metabolites, following F. graminearum (RRIF) and DON (RRID) inoculations, respectively, with 4 metabolites common to both. Canonical discriminant analysis of marginally significant metabolites (105) identified those with constitutive and induced resistance functions. The metabolites with high canonical loading to the canonical vectors were used to explain these functions. The putative roles of these RR metabolites in plant defense, their metabolic pathways, and their potential application for screening of wheat breeding lines for resistance to FHB are discussed.

Gas chromatography-mass spectrometry (GC/MS) technology was used to profile volatile metabolites from the headspace gas of McIntosh apple (Malus domestica Borkh.), which were noninoculated or inoculated with four different fungi, Botrytis cinerea Pers, Penicillium expansum Link, Mucor piriformis Fischer and Monilinia sp. The study yielded a total of 498 different volatile metabolites. Among them only 35 occurred relatively consistently in six replicates over three incubation periods. Of the consistent metabolites, 20 were specific to one or more inoculation agents/diseases, including seven that were unique to apples inoculated with different pathogens. Fluoroethene and 3,4-dimethyl-1-hexene were specific for Penicillium, while butanoic acid butyl ester, 4-methyl-1-hexene and 2-methyltetrazole were specific for Mucor. Similarly, acetic acid methyl ester and fluoroethane were specific to Botrytis and Monilinia, respectively. The method developed in this study can be used by storage managers to detect apple diseases at an early stage of disease progress and use this to manage apple diseases in storage, after further validation under commercial conditions.