Metabolic biomarker-based phenotyping unveils quantitative effects of plant resistance and pathogen aggressiveness in the grapevine (Vitis spp.) - downy mildew (Plasmopara viticola) pathosystem

• Major and minor loci have both a significant role in resistance pyramiding • A defeated locus can conserve a residual positive effect on the plant resistance • Plant resistance-pathogen aggressiveness are differentiated early in the infection • Pathogen strains develop different level of adaptation to the host resistance • Metabolic biomarkers allow a quantitative evaluation of plant-pathogen interactions Grape varieties carrying “Resistance to Plasmopara viticola ” ( Rpv ) loci represent an effective solution to mitigate the environmental impact of fungicide application in viticulture. However, P. viticola strains able to overcome major Rpv have become a main threat to their cultivation. Pyramiding resistance loci in the same variety enhances plant resistance, but interactions involving stacked and defeated Rpv and different P. viticola strains are poorly documented. Investigation of these interactions may uncover information for the efficient deployment of Rpv and durable resistances. In the present study, a grapevine offspring carrying single and pyramided Rpv1, Rpv3.1 and Rpv10 was phenotyped for the resistance to P. viticola using a naive strain and a strain virulent towards Rpv10 . By using high-resolution phenotyping applied to grapevine phytoalexins and P. viticola metabolic biomarkers, we demonstrated that the efficacy of Rpv combinations and aggressiveness of P. viticola strains are differentiated in the early phase of infection. Quantitative early and late infection outcomes based on P. viticola lipid biomarkers were closely related and provided complementary information. In particular, they evidenced the residual effect of a defeated Rpv and its importance in pyramiding, thereby providing keys to streamline resistance research and utilization in grapevine. In contrast, the accumulation of grapevine stilbenoids depended on infection severity, Rpv combination and genotype, highlighting the need for caution in their use as resistance biomarkers. The implementation of pathogen metabolic biomarkers-based phenotyping in resistance breeding represents an innovation that can be adapted to other plant pathosystems.