Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

Area of Science:

• Microbiology
• Biotechnology
• Industrial Biotechnology

Background:

• Adaptive Laboratory Evolution (ALE) is crucial for optimizing microorganisms in industrial processes.
• Stochasticity and genetic background effects can limit ALE's success, leading to suboptimal outcomes.
• Developing scalable ALE platforms is essential for efficient microbial strain improvement.

Purpose of the Study:

• To introduce a high-throughput ALE platform for parallelized clonal evolution at an unprecedented scale.
• To evolve yeast populations for multiple wine fermentation-related traits simultaneously.
• To overcome limitations of traditional ALE, such as stochasticity and genetic background effects.

Main Methods:

• Parallelized clonal evolution of 10^4 yeast populations from diverse strains.
• Selection for eight desired wine fermentation-related traits.
• Genomic analysis to identify evolutionary changes and side effects.

Main Results:

• ALE replicates and lineage expansions broadened the evolutionary search spectrum.
• Improved wine yeasts were obtained without significant unwanted side effects.
• Metabolic gains often correlated with chromosome amplifications and niche-specific side-effect syndromes.
• Several high-performing ALE strains demonstrated desirable fermentation kinetics in larger cultures.

Conclusions:

• The high-throughput ALE platform enables rapid optimization of microbial traits for industrial applications.
• This approach accelerates the development of improved yeast strains for winemaking.
• The platform offers a scalable solution for microbial engineering challenges, potentially saving years of development time.