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.

Quantitative trait locus (QTL) main effects and QTL by environment (QTL × E) interactions for seven agronomic traits (grain yield, days to heading, days to maturity, plant height, lodging severity, kernel weight, and test weight) were investigated in a two-row barley (Hordeura vulgare L.) cross, Harrington/TR306. A 127-point base map was constructed from markers (mostly RFLP) scored in 146 random double-haploid (DH) lines from the Harrington/TR306 cross. Field experiments involving the two parents and 145 random DH lines were grown in 1992 and/or 1993 at 17 locations in North America. Analysis of QTL was based on simple and composite interval mapping. Primary QTL were declared at positions where both methods gave evidence for QTL. The number of primary QTL ranged from three to six per trait, collectively explaining 34 to 52% of the genetic variance. None of these primary QTL showed major effects, but many showed effects that were consistent across environments. The addition of secondary QTL gave models that explained 39 to 80% of the genetic variance. The QTL were dispersed throughout the barley genome and some were detected in regions where QTL have been found in previous studies. Eight chromosome regions contained pleiotropic loci and/or linked clusters of loci that affected multiple traits. One region on chromosome 7 affected all traits except days to heading. This study was an intensive effort to evaluate QTL in a narrow-base population grown in a large set of environments. The results reveal the types and distributions of QTL effects manipulated by plant breeders and provide opportunities for future testing of marker-assisted selection. M OLECULAR MAPS of plant genomes, used in conjunction with phenotypic measurements, can provide information about chromosome regions that affect quantitative traits. Although knowing whether such regions represent individual quantitative trait loci (QTL)

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.

The effects of the hulless (nud) and rough‐awned (Raw1) genes are not fully understood in hulless barley (Hordeum vulgare L.). A study was initiated to (i) determine the potential of hulless lines in a hulless × covered cross, (ii) detect additive × additive epistasis and estimate genetic correlations, and (iii) determine the effects of hulless and rough‐awned genes on 11 agronomic traits of barley. Fifty covered lines and 48 hulless lines derived from a ‘Kunlun no. 1’ × ‘CIMMYT no. 6’ cross were evaluated for grain yield, test weight, seed weight, height, heading date, and maturity at two locations in Eastern Canada (Charlottetown and Ottawa) in 1998. Plant density, smut resistance, and scald resistance were also recorded at Charlottetown, while spike density was estimated at Ottawa. The 48 hulless lines contained 82 to 100% hulless kernels. At least one hulless line yielded similar to the highest yielding line if it was adjusted by the weight loss of the hull. This suggests that it is possible to breed for high‐yielding hulless barley cultivars. Additive × additive epistasis was detected for some of the traits. Yield was significantly correlated with test weight, seed weight, and height. In Eastern Canada, hullessness was associated with 17% lower plant density, 11 to 18% shorter plant height, 15 to 19% lower seed weight, 20 to 21% higher test weight, and 21 to 36% yield reduction. Hullessness, however, was not associated with heading date, maturity, smut resistance, scald resistance, and spike density. Since hulless progeny could have lower emergence rates and shorter plant heights, hulless barley breeding programs should avoid propagating segregating materials from hulless × covered crosses in bulk populations, as many hulless plants may be eliminated by competition. Rough‐awned hulless barley had more hulless kernels than smooth‐awned. Therefore, selection for rough‐awned plants could improve the threshability of hulless barley.