SaccharomycesIDentifier, SID: strain-level analysis of Saccharomyces cerevisiae populations by using microsatellite meta-patterns
2017
Stefanini, Irene | Albanese, Davide | Sordo, Maddalena | Legras, Jean-Luc | de Filippo, Carlotta | Cavalieri, Duccio | Donati, Claudio | Computational Biology Unit [Bergen] (CBU) ; University of Bergen (UiB) | Sciences Pour l'Oenologie (SPO) ; Université Montpellier 1 (UM1)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | Institute of Agricultural Biology and Biotechnology ; National Research Council (CNR) | Integrated Programme Agreement 'METAFOODLABS' - Autonomous Province of Trento [S116/2012/537723]; research office of the Autonomous Province of Trento
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.
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