The sequencing results of biosensor-guided genome-wide mutagenesis using a transposon

The economic viability of bio-based chemical production relies on microbial strain improvement, which optimizes titer, yield, and productivity of the process. This improvement necessitates redirecting cellular metabolism towards target molecule synthesis, requiring not only heterologous gene introduction but also endogenous genomic modifications to reconfigure metabolic networks originally optimized for cellular growth. However, identifying engineering targets is complex due to the intricate nature of metabolic networks and the cumulative, interactive effects of multiple genes on compound production. Consequently, there is a critical need for technologies capable of elucidating unpredictable engineering targets and synergistic gene combinations that enhance product formation in microbial strains. This study presents an innovative, integrated methodology named Tn-MAGE for rapidly identifying unpredictable engineering targets and synergistic gene combinations to enhance microbial strain performance in compound production. Our approach synergistically combines two key strategies: (1) integration of Tn-seq with biosensor-assisted ALE in a single batch culture, and (2) utilization of MAGE for combinatorial knockout library creation coupled with biosensor-assisted high-throughput screening. This integrated Tn-MAGE methodology offers a powerful tool for discovering unpredictable targets across the genome and their combinations, facilitating the development of high-performing strains for bio-based production of valuable compounds. The versatility of this approach suggests its potential applicability in various metabolic engineering contexts, promising significant advancements in strain improvement strategies. Overall design: We cultivated the E. coli BL21*(DE3)?sucC?fumC strain with a production plasmid harboring naringenin biosynthetic pathways, a naringenin sensor plasmid containing the tetA gene as a reporter, and a single-copy plasmid carrying Himar1C9. The naringenin sensor couples naringenin production with cell growth, enabling naringenin-overproducing strains to become the dominant population under tetracycline conditions. When transposition occurs, and thereby nonessential genes for naringenin production or genes in competing metabolic pathways are properly knocked out, strains can enhance naringenin production, becoming enriched in the population. Using next-generation sequencing, we identified knockout gene candidates beneficial for naringenin overproduction and validated their effects.