Untargeted metabolomics to identify potential chemical markers responsible for the permissiveness of red wines against Brettanomyces bruxellensis

Viticulture is facing many challenges due to climate change effects with increasingly attention to save resources, such as water, considering that drought events have been predicted to dramatically increase over the next future. Thanks to the -omics techniques, research pushed forward knowledge to deepen facets of drought response in diverse grapevine-rootstock combinations. However, the regulatory mechanisms orchestrating adaptation strategies during drought and recovery in grafted grapevines need further exploration. Herein, we combined ecophysiological, biochemical and molecular approaches to unravel drought and recovery-induced changes in potted Nebbiolo (NE) plants grafted onto three different rootstocks (3309, Kober5BB, Gravesac), by analysing root and leaf tissues.

Abstract
Wildfires are an increasing concern for wine-producing regions worldwide, as they generate smoke containing volatile organic compounds that can be transported over long distances and can be absorbed by wine grapes [1].

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Wine contains so many odorants that all its olfaction-related perceptions are, inevitably, the result of the interaction between many odorants.

Obtaining high-quality RNA from grape tissues, including berry pulp, berry skins, stems, rachis, or roots, is challenging due to their composition, which includes polysaccharides, phenolic compounds, sugars, and organic acids that can negatively affect RNA extraction. For instance, polyphenols and other secondary metabolites can bind to RNA, making it difficult to extract a pure sample. Additionally, RNA can co-precipitate with polysaccharides, leading to lower extraction yield. Also, sugars and organic acids can interfere with the pH and ionic properties of the extraction buffer. To address these challenges, we optimized a protocol for RNA isolation from grape tissues.