Abstract
Saccharomyces cerevisiae is a common yeast with several applications, among which the most ancient is winemaking. Because individuals belonging to this species show a wide genetic and phenotypic variability, the possibility to identify the strains driving fermentation is pivotal when aiming at stable and palatable products. Metagenomic sequencing is increasingly used to decipher the fungal populations present in complex samples such as musts. However, it does not provide information at the strain level. Microsatellites are commonly used to describe the genotype of single strains. Here we developed a population-level microsatellite profiling approach, SID (Saccharomyces cerevisiae IDentifier), to identify the strains present in complex environmental samples. We optimized and assessed the performances of the analytical procedure on patterns generated in silico by computationally pooling Saccharomyces cerevisiae microsatellite profiles, and on samples obtained by pooling DNA of different strains, proving its ability to characterize real samples of grape wine fermentations. SID showed clear differences among S. cerevisiae populations in grape fermentation samples, identifying strains that are likely composing the populations and highlighting the impact of the inoculation of selected exogenous strains on natural strains. This tool can be successfully exploited to identify S. cerevisiae strains in any kind of complex samples.
Highlights
Facilitate the growth of indigenous S. cerevisiae strains by setting up a hostile environment for other fungal species[16]
We used these pools to test the ability of Generalized linear model (GLM) (Generalized Linear Model) analysis to identify the parental strains
We further evaluated lasso[34] analysis performances on a large number of samples by analysing a dataset generated in silico and composed by randomly combined single strains profiles
Summary
Facilitate the growth of indigenous S. cerevisiae strains (fitter than the alien ones when in competition) by setting up a hostile environment for other fungal species[16]. Reports indicate that stressed bees show higher amounts of yeasts than usual, but it is not clear whether this is a consequence or a cause of the stress[26] In all these situations, the ability to rapidly identify the S. cerevisiae strains present in the complex matrix could help understanding the role of different strains in the host health. In this work we propose a technology based on SSRs analysis to characterize complex blends of S. cerevisiae strains This new approach allows the rapid and exhaustive investigation of different S. cerevisiae populations at the strain level by evaluating which combination of strains, chosen from a representative reference dataset, is present in the given complex sample. We have developed a new open-access tool, SID (Saccharomyces cerevisiae IDentifier, https://sidentifier.shinyapps.io/SIDentifier/), through which specialized and not-specialized workers (i.e. wine-makers) could characterize the S. cerevisiae strains driving fermentations
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