Model-guided development of an evolutionarily stable yeast chassis.

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Biotechnology

Background:

• First-principle metabolic modeling offers a theoretical framework for designing robust microbial chassis resistant to adaptive mutations.
• Experimental validation of model-based chassis design remains a critical, yet underexplored, area in synthetic biology.

Purpose of the Study:

• To develop Saccharomyces cerevisiae chassis strains for enhanced dicarboxylic acid production using genome-scale metabolic modeling.
• To experimentally validate model-driven engineering strategies for microbial cell factories.

Main Methods:

• Genome-scale metabolic modeling and flux balance analysis were employed to design yeast chassis.
• Introduction of targeted TCA cycle disruptions and adaptive laboratory evolution were used to enhance metabolite secretion.
• Multi-omics analyses (transcriptomics, proteomics, metabolomics) were integrated with modeling to confirm flux re-routing.

Main Results:

• Engineered yeast strains showed increased flux towards succinate, fumarate, and malate, though initial secretion was limited.
• Model-predicted TCA cycle disruptions successfully enabled the secretion of corresponding dicarboxylic acids.
• Adaptive evolution improved succinate and fumarate production, demonstrating strain robustness. Multi-omics revealed a previously unmodeled peroxisomal flux bypass for malate production.

Conclusions:

• Computer-aided design using first-principle metabolic modeling is effective for engineering microbial chassis.
• Experimental validation and multi-omics integration are crucial for refining metabolic models and optimizing production.
• This study advances the design principles for robust microbial cell factories for chemical production.