Biodiversity among Brettanomyces bruxellensis Strains Isolated from Different Wine Regions of Chile: Key Factors Revealed about its Tolerance to Sulphite

Molecular approaches improving our understanding of Brettanomyces physiology

Brettanomyces species and particularly B. bruxellensis as the most studied representative, are strongly linked to industrial fermentation processes. This association is considered either positive or undesirable depending on the industry. While in some brewing applications and in kombucha production Brettanomyces yeasts contribute to the flavour and aroma profile of these beverages, in winemaking and bioethanol production Brettanomyces is considered a spoilage or contaminant microorganism. Nevertheless, understanding Brettanomyces biology and metabolism in detail will benefit all industries. This review discusses recent molecular biology tools including genomics, transcriptomics and genetic engineering techniques that can improve our understanding of Brettanomyces physiology and how these approaches can be used to make the industrial potential of this species a reality.

Prediction of Genetic Groups within Brettanomyces bruxellensis through Cell Morphology Using a Deep Learning Tool

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.