The surprising role of VvLYK6 in grapevine immune responses triggered by chitin oligomers

The plant rhizosphere microbial communities are an essential component of plant microbiota, which is crucial for sustaining the production of healthy crops. The main drivers of the composition of such communities are the growing environment and the planted genotype. Recent viticulture studies focus on understanding the effects of these factors on soil microbial composition since microbial biodiversity is an important determinant of plant phenotype, and of wine’s organoleptic properties. Microbial biodiversity of different wine regions, for instance, is an important determinant of wine terroir.

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.

Trichoderma is one of the most widely used fungal biocontrol agents on vineyards due to its multiple benefits on this crop, such as its fungicidal and growth promoting capacity. In this work, we have analyzed the effect on the concentration of nutrients in grapevine leaves and on the quality of the grape must after spraying an autochthonous strain of Trichoderma harzianum and one of the main secondary metabolites produced by this genus, 6-pentyl-α-pyrone (6PP).

The root and shoot abscisic acid (ABA) accumulation in response to water deficit and its relation with stomatal conductance is longtime known in grapevine. ABA-dependent and ABA-independent signalling response to osmotic stress coexist in sessile plants. In grapevine, the signaling role of ABA in response to water stress conditions and its influence on berry quality is critical to manage grapevine acclimation to climate change.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).