Metabolomic characterization of sunflower leaf allows discriminating genotype groups or stress levels with a minimal set of metabolic markers

• DOI: 10.1007/s11306-019-1515-4
• Publication Date: 2019-03-30T09:02:46Z
• Authors: Olivier Fernandez; Maria Urrutia; Thierry Berton; Stéphane Bernillon; Catherine Deborde; Daniel Jacob; Mickaël Maucourt; Pierre Maury; Harold Duruflé; Yves Gibon; Nicolas B. Langlade; Annick Moing
• Keywords: 2. Zero hunger; 0303 health sciences; Vegetal Biology; Genotype; Water stress; [SDV]Life Sciences [q-bio]; Maintainer–restorer lines; 630; Droughts; Sunflower; Plant Leaves; Metabolic markers; Maintainer–restorer lines;Metabolic markers;Metabolomics;Sunflower;Water stress; 03 medical and health sciences; Gene Expression Regulation, Plant; Stress, Physiological; Helianthus; Metabolomics; Original Article; [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition; Biologie végétale; Biomarkers

Plant and crop metabolomic analyses may be used to study metabolism across genetic and environmental diversity. Complementary analytical strategies are useful for investigating metabolic changes and searching for biomarkers of response or performance.The experimental material consisted in eight sunflower lines with two line status, four restorers (R, used as males) and four maintainers (B, corresponding to females) routinely used for sunflower hybrid varietal production, respectively to complement or maintain the cytoplasmic male sterility PET1. These lines were either irrigated at full soil capacity (WW) or submitted to drought stress (DS). Our aim was to combine targeted and non-targeted metabolomics to characterize sunflower leaf composition in order to investigate the effect of line status genotypes and environmental conditions and to find the best and smallest set of biomarkers for line status and stress response using a custom-made process of variables selection.Five hundred and eighty-eight metabolic variables were measured by using complementary analytical methods such as 1H-NMR, MS-based profiles and targeted analyses of major metabolites. Based on statistical analyses, a limited number of markers were able to separate WW and DS samples in a more discriminant manner than previously published physiological data. Another metabolic marker set was able to discriminate line status.This study underlines the potential of metabolic markers for discriminating genotype groups and environmental conditions. Their potential use for prediction is discussed.