A Refined Adaptive Laboratory Evolution Strategy With Biosensor-Assisted Selection Resolves the Tolerance-Efficiency Trade-Off in Toxic Chemical Biosynthesis

结合生物传感器辅助选择的改进型自适应实验室进化策略解决了有毒化学物质生物合成中的耐受性和效率之间的权衡问题。

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Abstract

Enhancing microbial tolerance to target chemicals through conventional adaptive laboratory evolution (ALE) is time-consuming, labor-intensive, and further constrained by the challenge of balancing improved tolerance with maintaining optimal biosynthetic efficiency. Here, this work proposes a refined ALE strategy that combines initial mutagenesis with an automated microdroplet cultivation (MMC) system, thereby expediting the acquisition of tolerance phenotypes. Integrating a biosensor-assisted high-throughput screening platform enables identification of strains exhibiting advantageous "win-win" phenotypes, characterized by simultaneous improvements in both tolerance and biosynthetic capacity. Using E. coli for the biosynthesis of 3-hydroxypropionic acid (3-HP) as a model system, this work rapidly evolves strains capable of tolerating 720 mM 3-HP within 12 days. Leveraging a newly developed and validated 3-HP-responsive biosensor, this work efficiently screens and isolates superior strains. The top-performing strain produced 86.3 g L(-1) 3-HP with a yield of 0.82 mol mol(-1) glycerol. Transcriptomic analysis provide insights into mechanisms underlying this "win-win" phenotype. Collectively, this study establishes an effective ALE framework for accelerating the development of microbial chassis tailored for high-efficiency biochemical production.

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