Predictability of the community-function landscape in wine yeast ecosystems

Area of Science:

• Microbial Ecology
• Synthetic Biology
• Fermentation Science

Background:

• Predicting microbial community function from taxonomic composition is crucial for engineering microbial consortia.
• Alcoholic fermentation in wine yeast communities is a key ecological and biotechnological process.

Purpose of the Study:

• To link taxonomic composition with quantitative ecosystem functions in wine yeast communities.
• To establish predictive models for alcoholic fermentation based on yeast taxonomy.
• To provide a framework for managing microbial processes in biotechnology.

Main Methods:

• Exhaustive phenotyping of a large collection of naturally occurring wine yeast strains.
• Phylogenetic analysis to identify trait correlations with taxonomy.
• Development of quantitative rules to predict community function from individual strain contributions.

Main Results:

• Ecologically and industrially relevant traits in wine yeasts show significant phylogenetic signal.
• Taxonomic composition can predict the functional traits of wine yeast communities.
• Individual wine yeast strains contribute quantitatively to community function according to simple, predictable rules.

Conclusions:

• Taxonomy serves as a reliable predictor of function in wine yeast communities.
• Quantitative rules governing strain contributions allow for the accurate prediction of consortium function.
• The findings offer a blueprint for rationally managing microbial fermentation processes in biotechnology.