What metabolomics teaches us about wine shelf life

The main objective is to unravel the yeast biosynthetic pathways for MEL, SER and HT by using the respective labelled amino acids precursors: 15N2-L tryptophan and 13C-tyrosine.
The alcoholic fermentation experiments are performed with two different commercial
S cereviseae yeasts using synthetic must with the addition of the labelled compounds and the bioactive compounds were followed during the fermentation process. Six biological replicates of the fermentations were considered. MEL, SER and HT were analysed by UHPLC coupled to High Resolution Mass Spectrometry (HRMS). Accurate mass determination allowed to unequivocally distinguishing labelled and unlabelled compounds.

As the frequency and intensity of drought events increase, understanding the mechanisms of plant resilience to water deficit is crucial. To maintain an appropriate plant yield, a common practice is the application of high amounts of fertilizers with negative environmental impacts. The single and combined effect of water deficit and nutrient availability, namely nitrogen (N) and potassium (K), in Vitis Vinifera L. cv. Cabernet Sauvignon and Grenache was evaluated. Two-year-old grapevine plants grafted on SO4 rootstock were transferred in pots under semi-environmental conditions. During the growing season, plants were either maintained well-watered (100% ETc) or subjected to a controlled water deficit irrigation (33% ETc).

The above-ground parts of plants, which constitute the phyllosphere, have long been considered devoid of bacteria and fungi, at least in their internal tissues and microbial presence there was long considered a sign of disease. However, recent studies have shown that plants harbour complex bacterial communities, the so-called “microbiome”[1]. We are only beginning to unravel the origin of these bacterial plant inhabitants, their community structure and their roles, which in analogy to the gut microbiome, are likely to be of essential nature. Among their multifaceted metabolic possibilities, bacteria have been recently demonstrated to emit a wide range of volatile organic compounds (VOCs), which can greatly impact the growth and development of both the plant and its disease-causing agents.

The reaction between Strecker amino acids and α-dicarbonyls is a key pathway in the formation of Strecker aldehydes (SA), which are crucial oxidation-related odorants in wine [1].

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.