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Metabolites Differentiating Asymptomatic and Symptomatic Grapevine Plants (Vitis vinifera ‘Malvasia-Fina’) Infected with Esca Complex Disease-Associated Fungi
* 1 , 2 , 1
1  Centre for the Research and Technology of Agro-Environment and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro
2  Universidade de Trás-os-Montes e Alto Douro
Academic Editor: Iker Aranjuelo

Abstract:

Esca complex disease is a grapevine disease mainly caused by the fungi Fomitiporia mediterranea, Phaeomoniella chlamydospora, and Phaeoacremonium minimum. Colonization by esca-associated fungi is restricted to the canes, spurs, cordons, and trunks, and the disease usually exhibits a latency time of 4 to 10 years between wood invasion by fungi and visible foliar symptoms. The goals of this study were to identify metabolites differentiating asymptomatic and symptomatic grapevine leaves and to gain better insights into the mechanisms involved in the delayed appearance of foliar symptoms. Experiments were performed using healthy, asymptomatic and symptomatic leaves of Vitis vinifera L. ‘Malvasia-fina’ naturally infected in the vineyard. A global metabolic profile of the samples was obtained with ultrahigh performance liquid chromatography system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap high resolution/accurate mass spectrometer. In total, 513 metabolites were identified in the leaves, including 436 compounds of known identity and 77 compounds of unknown structural identity, belonging to 9 biochemical families (amino acids, carbohydrates, lipids, cofactors + prosthetic groups + electron carriers, nucleotides, peptides, hormones, secondary metabolites, and xenobiotics). Hormone and lipid data showed that systemic signals are transferred from the infected wood to the leaves. Secondary metabolites data indicated that defence compounds are mostly locally induced following the onset of foliar symptoms. Several primary metabolites showed interesting changed patterns related to the modulation of grapevine metabolism. Random Forest analysis could classify the samples with 96% accuracy, which allowed selecting 30 metabolites with the largest contribution to the differentiation of leaf groups.

Keywords: grapevine trunk diseases; metabolomic; lipidomic; biochemical pathways; disease onset; symptom appearance; brown wood streaking; white rot; leaf stripe
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