Three Metschnikowia strains marketed as bioprotection yeasts were studied to compare their antimicrobial effect on a mixture of two Hanseniaspora yeast strains in synthetic must at 12 °C, mimicking pre-fermentative maceration by combining different approaches. The growth of the different strains was monitored, their nitrogen and oxygen requirements were characterised, and their metabolomic footprint in single and co-cultures studied. Only the M. fructicola strain and one M. pulcherrima strains colonised the must and induced the rapid decline of Hanseniaspora. The efficiency of these two strains followed different inhibition kinetics. Furthermore, the initial ratio between Metschnikowia and Hanseniaspora was an important factor to ensure optimal bioprotection. Nutrient consumption kinetics showed that apiculate yeasts competed with Metschnikowia strains for nutrient accessibility. However, this competition did not explain the observed bioprotective effect, because of the considerable nitrogen content remaining on the single and co-cultures. The antagonistic effect of Metschnikowia on Hanseniaspora probably implied another form of amensalism. For the first time, metabolomic analyses of the interaction in a bioprotection context were performed after the pre-fermentative maceration step. A specific footprint of the interaction was observed, showing the strong impact of the interaction on the metabolic modulation of the yeasts, especially on the nitrogen and vitamin pathways.

Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly strain-dependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems.