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.

Fusarium head blight (FHB) is an economically important disease of the family Triticeae, as, apart from yield reduction it also causes quality deterioration by producing mycotoxins. Host resistance is the most promising way to control the disease. Metabolic profiling was applied to identify resistance related (RR) metabolites against Fusarium graminearum in five FHB-resistant genotypes ('Chevron', 'H5277-44', 'H5277-164', 'M92-513' and 'M122') relative to one FHB-susceptible genotype ('Stander'). The disease severity was assessed in greenhouse to group the genotypes based on FHB-resistance. The disease was quantified as the proportion of diseased spikelets (PSD) and the area under the disease progress curve (AUDPC). Spikelets were collected at 72 h post inoculation. Metabolites were extracted into an aqueous solution of methanol and analyzed using a LC-hybrid-MS system. Metabolite abundances were subjected to a resistant versus susceptible pair-wise analysis, using a t test. Resistance related (RR) metabolites, both constitutive (RRC) and induced (RRI), were identified amongst metabolites whose levels were significantly higher in resistant genotype than in susceptible. Among 1,430 RR metabolites, 115 were putatively identified. These RR metabolites belonged to different chemical groups: fatty acids: linolenic acid; phenylpropanoids: p-coumaric, sinapic acid; flavonoids: naringenin, kaempferol glucoside, catechol glucoside. In addition, resistance indicator metabolites, such as deoxynivalenol (DON) and DON-3-O-glucoside (D3G) were also detected. The amount of total DON synthesized converted to D3G (PDC) was the greatest in resistant genotype 'Chevron' (PDC = 0.76). The role of the resistance-related and resistance-indicator metabolites on plant defense, and their use as potential biomarkers to screen barley genotypes for FHB resistance is discussed.

The mechanisms of resistance in barley to fusarium head blight (FHB), caused by Gibberella zeae are complex. Metabolomics technology was explored to phenotype resistance. Spikelets of barley genotypes with contrasting levels of resistance to FHB, mock inoculated or with the pathogen, were extracted with aqueous methanol and the metabolites were analyzed using liquid chromatography and hybrid mass spectrometry. Peaks were de-convoluted using XCMS and annotated using CAMERA and IntelliXtract bioinformatics tools. A t-test, of a total of 1608 purified peaks, selected 626 metabolites with significant treatment effects, of which 161 were identified as resistance related (RR) metabolites. A total of 53 metabolites, that are RR or pathogenicity related (PR), were assigned with putative compound names. These mainly belonged to three metabolic pathways: fatty acid (jasmonic acid, methyl jasmonate, 9,10-dihydroisojasmonate, linolenic acid, linoleic acid, traumatic acid), phenylpropanoid (p-coumaric acid, caffeyl alcohol, dimethoxy-4-phenylcoumarin, rosmarinic acid, diphyllin, 5-methoxypodophyllotoxin) and flavonoid (naringenin, catechin, quercetin, and alpinumisoflavone). A few PR/RR metabolites significantly reduced mycelial growth of G. zeae in vitro.

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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.