Intron splice site PCR analysis as a tool to discriminate Dekkera bruxellensis strains

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.

Intraspecific variations of Dekkera/Brettanomyces bruxellensis genome studied by capillary electrophoresis separation of the intron splice site profiles

In enology, "Brett" character refers to the wine spoilage caused by the yeast Dekkera/Brettanomyces bruxellensis and its production of volatile phenolic off-flavours. However, the spoilage potential of this yeast is strain-dependent. Therefore, a rapid and reliable recognition at the strain level is a key point to avoid serious economic losses. The present work provides an operative tool to assess the genetic intraspecific variation in this species through the use of introns as molecular targets. Firstly, the available partial D./B. bruxellensis genome sequence was investigated in order to build primers annealing to introns 5' splice site sequence (ISS). This analysis allowed the detection of a non-random vocabulary flanking the site and, exploiting this feature, the creation of specific probes for strain discrimination. Secondly, the separation of the intron splice site PCR fragments was obtained throughout the set up of a capillary electrophoresis protocol, giving a 94% repeatability threshold in our experimental conditions. The comparison of results obtained with ISS-PCR/CE versus the ones performed by mtDNA RFLP revealed that the former protocol is more discriminating and allowed a reliable identification at strain level. Actually sixty D./B. bruxellensis isolates were recognised as unique strains, showing a level of similarity below 79% and confirming the high genetic polymorphism existing within the species. Two main clusters were grouped at similarity levels of about 46% and 47%, respectively, showing a poor correlation with the geographic area of isolation. Moreover, from the evolutionary point of view, the proposed technique could determine the frequency of the genome rearrangements that can occur in D./B. bruxellesis populations.