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Updated: May 14, 2026

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
Published on: June 24, 2019
Rare-codon-tuned fluorescent biosensor for high-throughput selection of Escherichia coli strain capable of L-arginine
Daixue Kou1,2, Guanhua Jiao1,2, Yinuo Li1,2
1State Key Laboratory of Green Papermaking and Resource Recycling, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
Background:
Rapid advancements in complex engineered metabolic system design have not been matched by corresponding screening and identification capabilities for high-performing microbial variants, creating considerable pacing constraints in strain development. Therefore, advancing high-throughput phenotyping methods is essential for propelling synthetic biology and metabolic engineering towards scalable biomanufacturing.
Results:
Accordingly, we developed a rare codon-dependent fluorescent biosensor that enables real-time, high-content monitoring of intracellular L-arginine accumulation. This system employed L-arginine-rich peptide modules that are engineered with AGG rare codons fused to the StayGold fluorescent protein, developing a stringent link between fluorescence intensity and cytoplasmic L-arginine levels. By integrating ultraviolet mutagenesis with fluorescence-activated cell sorting, we efficiently isolated superior producers from an engineered Escherichia coli ARG library, achieving a screening efficiency of 55.12%. The top-performing isolate, E. coli ARG-B10, exhibited a 94.8% enhancement in L-arginine production. Its plasmid-cured derivative, E. coli B10, was used for scale-up fermentation, attaining a 120.5 g/L titer and 0.45 g/g glucose yield under industrially relevant conditions. Genomic analysis revealed missense mutations in key metabolic genes (coaBC, gst, yihU, and fruB), indicating improvements in the precursor supply and redox management.
Conclusion:
This biosensor platform operates independently of orthogonal translation systems and is readily applicable to wild-type strains, offering a powerful and generalizable tool for accelerating microbial strain optimization.
