Reusable and modular combinatorial libraries for iterative metabolic engineering of Saccharomyces cerevisiae

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Industrial Biotechnology

Background:

• Industrial metabolic engineering aims to optimize microbial metabolism for efficient molecule production.
• Current methods for constructing combinatorial libraries are labor-intensive and limited by genomic integration.
• Iterative strain engineering is crucial for developing high-performing industrial cell factories.

Purpose of the Study:

• To develop an automation-friendly framework for generating reusable, modular, integration-based combinatorial libraries.
• To engineer Saccharomyces cerevisiae for enhanced production of betacyanins, a red food colorant.
• To demonstrate the framework's utility in accelerating the development of high-performing cell factories.

Main Methods:

• Developed a framework for creating reusable and modular integration-based combinatorial libraries.
• Applied the framework to engineer betacyanin biosynthesis, precursor, and cofactor pathways in Saccharomyces cerevisiae.
• Conducted seven rounds of iterative engineering using combinatorial libraries with large design spaces.

Main Results:

• Achieved consistent 1.2-5.7-fold improvements in betacyanin production per engineering cycle.
• Generated insights into betacyanin and yeast metabolism, identifying the role of Saccharomyces cerevisiae cytochrome b5.
• Demonstrated the framework's effectiveness in accelerating cell factory development.

Conclusions:

• The developed framework enables efficient, iterative engineering of high-performing cell factories.
• This approach is well-suited for industrial fermentation processes requiring optimized microbial strains.
• The study provides a scalable method for advancing metabolic engineering and industrial biotechnology.