Evolution of several biochemical compounds during the development of Merlot wine in the vinegrowing “Terroir” of Valea Călugăreasa

Copper (Cu) is widely and historically used in viticulture as a fungicide against mildew. Cu has a strong affinity for soil organic matter and accumulates in topsoil horizons. Thus, Cu may negatively affect soil organisms and plants, consequently reducing soil fertility and productivity. The Bordeaux vineyards have the largest vineyard surfaces (26%) within French controlled appellation and a great proportion of French wine production (around 5 million hl per year). Considering the local context of vineyard surfaces decreasing (vine uprooting) and possible new crop plantation, the issue of Cu potential toxicity rises. Therefore, the aims of this work are firstly to evaluate the Cu contamination in vineyard soils of Bordeaux, secondly to produce a risk assessment map for new vine or crop plantation. We used soil analyses from several local studies to build a database with 4496 soil horizon samples. The database was enhanced by means of pedotransfer functions in order to estimate the bioaccessible (EDTA-extractable) Cu in soils of samples without measurements. From this database, 1797 georeferenced samples with CuEDTA concentrations in the topsoil (0-50 cm depth) were used for kriging interpolation in order to produce the spatial distribution map of CuEDTA in vineyard soils. Then, the spatial distribution of Cu was crossed with vine uprooting surfaces and municipality boundaries. CuEDTAconcentrations ranged from 0.52 to 459 mg/kg and showed clear anomalies. Our results from spatial analysis showed that almost 50% of vineyard soil surfaces have CuEDTA concentrations higher than 30 mg/kg (moderate risk for new plantation) and 20% with concentrations higher than 50 mg/kg (high risk for new plantation). A decision-support map based on municipalities was realised to provide a simple tool to stakeholders concerned by land use management.

Oxidative stability is a critical factor in maintaining wine quality during its shelf-life. SO₂ is commonly added to wine due to its strong antioxidant activity, although there is a general push to reduce SO₂ use in vinification.

Yeast cell walls (CWs) may adsorb wine components with a significant impact on wine quality. When dealing with red wines, this adsorption is mainly related to physicochemical interactions between wine polyphenols and cell wall mannoproteins. However, mannoproteins are a heterogeneous family of complex peptidoglycans including long and highly branched N-linked oligosaccharides and short linear O-linked oligosaccharides, resulting in a huge structural diversity.

Understanding the physiological and molecular bases of grapevine responses to mild to moderate water deficits is fundamental to optimize vineyard irrigation management and identify the most suitable varieties. In Mediterranean regions, the higher frequency of heat waves and droughts highlights the importance of precision irrigation to meet vine water demands and demonstrates the necessity for a deeper understanding of the different physiological responses among varieties under water stress. In this context, previous reports show an interplay between stomatal regulation of transpiration and changes in leaf hydraulic conductivity, also with the involvement of aquaporins (AQPs), particularly under water stress. However, how those signaling mechanisms are regulated in different grapevine varieties along phenological phases is unclear.

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses involved in phenotypic distinction.