RESUMO
Plant physiology and metabolism are important components of a plant response to microbial pathogens. Physiological resistance of common bean (Phaseolus vulgaris L.) to the fungal pathogen Sclerotinia sclerotiorum has been established, but the mechanisms of resistance are largely unknown. Here, the physiological and metabolic responses of bean varieties that differ in physiological resistance to S. sclerotiorum are investigated. Upon infection, the resistant bean variety A195 had a unique physiological response that included reduced photosynthesis and maintaining a higher leaf surface pH during infection. Leaf metabolomics was performed on healthy tissue adjacent to the necrotic lesion at 16, 24, and 48 hr post inoculation, and 144 metabolites were detected that varied between A195 and Sacramento following infection. The metabolites that varied in leaves included amines/amino acids, organic acids, phytoalexins, and ureides. The metabolic pathways associated with resistance included amine metabolism, uriede-based nitrogen remobilization, antioxidant production, and bean-specific phytoalexin production. A second experiment was conducted in stems of 13 bean genotypes with varying resistance. Stem resistance was associated with phytoalexin production, but unlike leaf metabolism, lipid changes were associated with susceptibility. Taken together, the data supports a multifaceted, physiometabolic response of common bean to S. sclerotiorum that mediates resistance.
Assuntos
Ascomicetos/patogenicidade , Interações Hospedeiro-Patógeno/fisiologia , Phaseolus/fisiologia , Folhas de Planta/metabolismo , Resistência à Doença , Concentração de Íons de Hidrogênio , Ácido Cinurênico/metabolismo , Nitrogênio/metabolismo , Phaseolus/microbiologia , Fotossíntese , Doenças das Plantas/microbiologia , Folhas de Planta/fisiologia , Caules de Planta/metabolismo , Estômatos de Plantas/fisiologiaRESUMO
Metabolomics is an emerging method to improve our understanding of how genetic diversity affects phenotypic variation in plants. Recent studies have demonstrated that genotype has a major influence on biochemical variation in several types of plant tissues, however, the association between metabolic variation and variation in morphological and physiological traits is largely unknown. Sorghum bicolor (L.) is an important food and fuel crop with extensive genetic and phenotypic variation. Sorghum lines have been bred for differing phenotypes beneficial for production of grain (food), stem sugar (food, fuel), and cellulosic biomass (forage, fuel), and these varying phenotypes are the end products of innate metabolic programming which determines how carbon is allocated during plant growth and development. Further, sorghum has been adapted among highly diverse environments. Because of this geographic and phenotypic variation, the sorghum metabolome is expected to be highly divergent; however, metabolite variation in sorghum has not been characterized. Here, we utilize a phenotypically diverse panel of sorghum breeding lines to identify associations between leaf metabolites and morpho-physiological traits. The panel (11 lines) exhibited significant variation for 21 morpho-physiological traits, as well as broader trends in variation by sorghum type (grain vs. biomass types). Variation was also observed for cell wall constituents (glucan, xylan, lignin, ash). Non-targeted metabolomics analysis of leaf tissue showed that 956 of 1181 metabolites varied among the lines (81%, ANOVA, FDR adjusted p < 0.05). Both univariate and multivariate analyses determined relationships between metabolites and morpho-physiological traits, and 384 metabolites correlated with at least one trait (32%, p < 0.05), including many secondary metabolites such as glycosylated flavonoids and chlorogenic acids. The use of metabolomics to explain relationships between two or more morpho-physiological traits was explored and showed chlorogenic and shikimic acid to be associated with photosynthesis, early plant growth and final biomass measures in sorghum. Taken together, this study demonstrates the integration of metabolomics with morpho-physiological datasets to elucidate links between plant metabolism, growth, and architecture.