Metabolic profiles of sunflower genotypes with contrasting response to Sclerotinia sclerotiorum infection

• Published in: Phytochemistry (Oxford) Vol. 71; no. 1; pp. 70 - 80
• Main Authors: Peluffo, Lucila, Lia, Verónica, Troglia, Carolina, Maringolo, Carla, Norma, Paniego, Escande, Alberto, Esteban Hopp, H., Lytovchenko, Anna, Fernie, Alisdair R., Heinz, Ruth, Carrari, Fernando
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 2010
• Subjects: Ascomycota; Carbon - metabolism; chemical constituents of plants; cluster analysis; Compositae; disease resistance; enzyme activity; fungal diseases of plants; gas chromatography; genetic variation; Genotype; Head rot disease; Helianthus; Helianthus - genetics; Helianthus - metabolism; Helianthus - microbiology; Helianthus annuus; Helianthus annuus L; Immunity, Innate - genetics; mass spectrometry; Metabolic Networks and Pathways; Metabolite profiling; Metabolome; oil crops; oilseed crops; Plant Diseases - genetics; Plant Diseases - microbiology; plant pathogenic fungi; principal component analysis; resistance mechanisms; Sclerotinia sclerotiorum; Sink organ metabolism; Sunflower; Head rot disease; Helianthus annuus L; Sunflower; Compositae; Sink organ metabolism; Metabolite profiling
• ISSN: 0031-9422; 1873-3700
• DOI: 10.1016/j.phytochem.2009.09.018

Understanding primary metabolism of sink organs comprising main targets for pathogen infections is of crucial importance in design of efficient strategies for control of plant diseases. Here utilizing a GC–MS-based metabolic profiling platform, it was found that, under field conditions, primary metabolic events in sunflower florets are differentially synchronized in genotypes displaying contrasting behavior to Sclerotinia sclerotiorum infection. We report a comprehensive primary metabolite profiling of sunflower ( Helianthus annuus) genotypes displaying contrasting behavior to Sclerotinia sclerotiorum infection. Applying a GC–MS-based metabolite profiling approach, we were able to identify differential patterns involving a total of 63 metabolites including major and minor sugars and sugar alcohols, organic acids, amino acids, fatty acids and few soluble secondary metabolites in the sunflower capitulum, the main target organ of pathogen attack. Metabolic changes and disease incidence of the two contrasting genotypes were determined throughout the main infection period (R5.2–R6). Both point-by-point and non-parametric statistical analyses showed metabolic differences between genotypes as well as interaction effects between genotype and time after inoculation. Network correlation analyses suggested that these metabolic changes were synchronized in a time-dependent manner in response to the pathogen. Concerted differential metabolic changes were detected to a higher extent in the susceptible, rather than the resistant genotype, thereby allowing differentiation of modules composed by intermediates of the same pathway which are highly interconnected in the susceptible line but not in the resistant one. Evaluation of these data also demonstrated a genotype specific regulation of distinct metabolic pathways, suggesting the importance of detection of metabolic patterns rather than specific metabolite changes when looking for metabolic markers differentially responding to pathogen infection. In summary, the GC–MS strategy developed in this study was suitable for detection of differences in carbon primary metabolism in sunflower capitulum, a tissue which is the main entry point for this and other pathogens which cause great detrimental impact on crop yield.