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1.
Metab Eng ; 76: 146-157, 2023 03.
Article in English | MEDLINE | ID: mdl-36758663

ABSTRACT

L-arginine is a value-added amino acid with promising applications in the pharmaceutical and nutraceutical industries. Further unleashing the potential of microbial cell factories to make L-arginine production more competitive remains challenging due to the sophisticated intracellular interaction networks and the insufficient knowledge of global metabolic regulation. Here, we combined multilevel rational metabolic engineering with biosensor-assisted mutagenesis screening to exploit the L-arginine production potential of Escherichia coli. First, multiple metabolic pathways were systematically reprogrammed to redirect the metabolic flux into L-arginine synthesis, including the L-arginine biosynthesis, TCA cycle, and L-arginine export. Specifically, a toggle switch responding to special cellular physiological conditions was designed to dynamically control the expression of sucA and pull more carbon flux from the TCA cycle toward L-arginine biosynthesis. Subsequently, a biosensor-assisted high-throughput screening platform was designed and applied to further exploit the L-arginine production potential. The best-engineered ARG28 strain produced 132 g/L L-arginine in a 5-L bioreactor with a yield of 0.51 g/g glucose and productivity of 2.75 g/(L ⋅ h), which were the highest values reported so far. Through whole genome sequencing and reverse engineering, Frc frameshift mutant, PqiB A78P mutant, and RpoB P564T mutant were revealed for enhancing the L-arginine biosynthesis. Our study exhibited the power of coupling rational metabolic reprogramming and biosensor-assisted mutagenesis screening to unleash the cellular potential for value-added metabolite production.


Subject(s)
Biosensing Techniques , Escherichia coli , Escherichia coli/genetics , Escherichia coli/metabolism , Metabolic Networks and Pathways , Metabolic Engineering , Arginine/genetics , Arginine/metabolism , Mutagenesis
2.
Metab Eng ; 68: 220-231, 2021 11.
Article in English | MEDLINE | ID: mdl-34688880

ABSTRACT

L-citrulline is a high-value amino acid with promising application in medicinal and food industries. Construction of highly efficient microbial cell factories for L-citrulline production is still an open issue due to complex metabolic flux distribution and L-arginine auxotrophy. In this study, we constructed a nonauxotrophic cell factory in Escherichia coli for high-titer L-citrulline production by coupling modular engineering strategies with dynamic pathway regulation. First, the biosynthetic pathway of L-citrulline was enhanced after blockage of the degradation pathway and introduction of heterologous biosynthetic genes from Corynebacterium glutamicum. Specifically, a superior recycling biosynthetic pathway was designed to replace the native linear pathway by deleting native acetylornithine deacetylase. Next, the carbamoyl phosphate and L-glutamate biosynthetic modules, the NADPH generation module, and the efflux module were modified to increase L-citrulline titer further. Finally, a toggle switch that responded to cell density was designed to dynamically control the expression of the argG gene and reconstruct a nonauxotrophic pathway. Without extra supplement of L-arginine during fermentation, the final CIT24 strain produced 82.1 g/L L-citrulline in a 5-L bioreactor with a yield of 0.34 g/g glucose and a productivity of 1.71 g/(L ⋅ h), which were the highest values reported by microbial fermentation. Our study not only demonstrated the successful design of cell factory for high-level L-citrulline production but also provided references of coupling the rational module engineering strategies and dynamic regulation strategies to produce high-value intermediate metabolites.


Subject(s)
Biosynthetic Pathways , Corynebacterium glutamicum , Biosynthetic Pathways/genetics , Citrulline/metabolism , Corynebacterium glutamicum/genetics , Corynebacterium glutamicum/metabolism , Escherichia coli/genetics , Escherichia coli/metabolism , Fermentation , Metabolic Engineering
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