Metabolic profiling and multivariate analysis to phenotype cultivars of wheat varying in resistance to fusarium head blight

Bread wheat (Triticum aestivum L. em. Thell.) is the most important cultivated crop in Canada. Fusarium head blight (FHB), caused by Fusarium graminearum Schwabe [teleomorph Gibberella zeae (Schwein) Petch] is the principal disease of wheat in North America, causing severe losses in grain yield and quality. Breeding for cultivar resistance is considered the most practical way to manage this disease. High spatiotemporal variance makes screening for disease resistance based on visual assessment of symptoms slow, difficult, and expensive. Development of a cheaper, rapid, accurate, and high throughput tool for screening resistance is the highest priority for wheat breeders.;Several hundred peaks were detected, but only 55, 79, and 120 metabolites were identified in studies 1, 2, and 3, respectively. The metabolites significantly varied in abundance among cultivars/NILs varying in resistance. A resistance biomarker metabolite was defined based on univariate analysis or with high factor/canonical loading to vectors that discriminated resistant genotypes or groups of genotypes. The resistance biomarker metabolites included metabolites related to the phenylpropanoid pathway such as p-coumaric acid, cinnamic acid, several coumarins, and benzoic acid; important signaling molecules such as myo-inositol; signal-related metabolites including hexadecanoic/octadecanoic acid; and other resistance-related metabolites such as aminobutyric acid. Metabolite profiling technology has enormous potential as a high throughput tool for screening resistance to FHB in wheat genotypes.;A novel method based on metabolite profiling technology was applied to discriminate resistance in wheat genotypes against FHB. The present investigation reports on three linked studies. The first study involved the detection and application of metabolites to distinguish susceptible (Roblin) and resistant (Sumai3) wheat cultivars. In the second study, cultivars varying in level of resistance were discriminated (descending order: Wangshubai, AW488, Nobeoka Bozu, BRS177, Frontana, CEP24). Finally, biomarker metabolites related to susceptible and resistant near isogenic lines (NILs) with alternate alleles for FHB resistance on the 2DL chromosome were identified. The metabolites were extracted from spikelets in a mixture of methanol-water/chloroform, and analyzed using GC-ion trap-MS (studies 1-2) or GC-TOF-MS (study 3). Compound identification and quantification was achieved manually and/or using automated software for peak deconvolution (AMDIS), library search (MSRI and NIST libraries), and peak alignment and quantification (MET-IDEA).