Evolution-guided optimization of biosynthetic pathways.

Area of Science:

• Synthetic biology
• Metabolic engineering
• Biotechnology

Background:

• Optimizing biosynthetic pathways for chemical production is complex, often requiring extensive host cell metabolic engineering.
• Designing optimal pathways a priori is challenging, necessitating the construction and evaluation of numerous pathway variants.

Purpose of the Study:

• To develop a general strategy for enhancing microbial chemical production through a combination of mutagenesis and evolutionary methods.
• To create a system that links intracellular chemical presence to cellular fitness for effective selection.

Main Methods:

• Implemented targeted genome-wide mutagenesis to generate diverse pathway variants.
• Utilized a sensor-reporter system to link chemical production to cell survival under selective pressure.
• Employed a negative selection scheme to eliminate non-producing 'cheater' cells while maintaining library diversity.
• Performed multiple rounds of evolution (approx. 10^9 cells/round) to enrich for high-producing strains.

Main Results:

• Achieved 36-fold and 22-fold increases in naringenin and glucaric acid production, respectively, after up to four rounds of evolution.
• Naringenin production reached 61 mg/L from glucose, surpassing previous reported titers.
• Whole-genome sequencing identified beneficial, untargeted mutations, suggesting novel optimization pathways.

Conclusions:

• The developed strategy effectively enhances microbial production of target chemicals.
• This approach accelerates metabolic engineering by combining targeted mutagenesis with high-throughput evolution.
• Identified new genetic targets for future pathway optimization efforts.