Multiplex variable number of tandem repeats for Oenococcus oeni and applications

Area of Science:

• Microbiology
• Food Science
• Genomics

Background:

• Oenococcus oeni drives malolactic fermentation in wine, exhibiting significant genomic diversity.
• Variable starter-culture efficiency necessitates monitoring indigenous and selected O. oeni strains for survival and development during winemaking.
• Previous VNTR typing methods were effective but time-consuming compared to newer techniques.

Purpose of the Study:

• To develop a faster, multiplexed protocol for Oenococcus oeni typing using multicolor capillary electrophoresis.
• To adapt PCR conditions for simultaneous amplification of five polymorphic VNTR loci.
• To enable robust, accurate, and rapid analysis of O. oeni strains from various sources.

Main Methods:

• Multiplex PCR amplification of five O. oeni VNTR loci using fluorescently labeled primers.
• Optimization of PCR conditions for simultaneous amplification at a single annealing temperature.
• Analysis of amplified fragments via multicolor capillary electrophoresis.
• Application of Fast Technology for Analysis of Nucleic Acids (FTA) for sample preparation.
• Construction of predictive models for phylogenetic relationships and freeze-drying resistance.

Main Results:

• A robust, accurate, and fast multiplexed assay for O. oeni VNTR typing was successfully established.
• All tested O. oeni strains were reliably typed using the five VNTR loci.
• The primers demonstrated high specificity for O. oeni.
• The method effectively analyzed isolated colonies, mixed colonies, and bacteria from wine samples.
• Predictive models successfully linked fragment length analysis (FLA) profiles to genotypic and phenotypic traits, including freeze-drying resistance.

Conclusions:

• The developed multiplex PCR and capillary electrophoresis protocol significantly enhances the speed and efficiency of O. oeni strain typing.
• This method provides a valuable tool for monitoring O. oeni populations in winemaking environments.
• The established FLA profiles and predictive models offer insights into bacterial adaptation and survival strategies, aiding in strain selection and process optimization.