Can yeast cells sense other yeasts beyond competition interactions?

Brettanomyces is a world-renowned yeast that negatively impacts the chemical composition of wines through the production of metabolites that negatively impact the sensory properties of the final product. Its resilience in wine conditions and ability to produce off-flavors make it a challenge for winemakers. Currently, the primary control technique involves adding sulfur dioxide (SO2); however, some Brettanomyces strains are developing resistance to this preservative agent. [1] Therefore, new management strategies are necessary to control this spoilage yeast.

Grapevine cultivation is threatened by the global warming, which combines high temperatures and reduced rainfall, impacting in wine quality and even plant survival. Breeding for varieties resilient to these challenges must address plant traits such as tolerance to supraoptimal temperatures and optimized water use efficiency while minimizing productivity and quality losses. Stomatal abundance (SA) determines the maximum leaf potential for transpiration and thus water loss and cooling. Since SA results from a developmental process during leaf emergence and growth, knowledge on the genetic control of this process would provide specific targets for modification.

Remote sensing technology captures spectral data beyond the visible range, making it useful for monitoring plant stress. Vis-NIR (Visible-Near Infrared) spectroscopy (400-1000 nm) is commonly used to indirectly assess plant status during drought. One example is the widespread use of normalized difference vegetation index (NDVI) that is strongly linked to green biomass. However, a knowledge gap exists regarding the applicability of this method to all the drought conditions and if it is a direct correlation to the water status of the plant.

Drought is one of the main challenges for viticulture in the context of global change. The choice of rootstock could be leveraged for vineyard adaptation to drought as we can improve plant performance without modifying the scion variety. However, most of the existing rootstocks, selected over a century ago, have a narrow genetic background which could compromise their adaptive potential.

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).