Genome‐scale Metabolic Modeling Guided <i>Escherichia coli</i> Engineering for <i>De Novo</i> Biosynthesis of Chrysanthemic Acid

Chrysanthemic acid is an unconventional monoterpene moiety of the natural pesticide pyrethrins with notable anti-insect activity, making its industrial biosynthesis a promising avenue for sustainable agriculture. Here, an E. coli cell factory is designed and build for highly efficient chrysanthemic acid production guided by Genome-scale metabolic models (GEM). The biosynthetic pathway is reconstructed and simulated the metabolic changes caused by exogenous modules are simulated. A key metabolic branch point catalyzed by ispA is identified by this model, and inhibiting its expression using synthetic small RNA redirected the metabolic flux, resulting in the titers of precursor chrysanthemol and chrysanthemic acid increasing by 162% and 59%, respectively. The effect of the expression level of downstream dehydrogenases on chrysanthemic acid titer is also predicated using GEM, and the further optimization of copy number for dehydrogenase genes led to a notably 570% increase in chrysanthemic acid titer experimentally. By integrating the debranching strategy with copy number optimization, a record chrysanthemic acid titer 141.78 mg L<sup>-1</sup> is achieved in a bioreactor. The work seamlessly integrated in silico modeling optimization with wet-lab practices that significantly enhance target metabolite titer through metabolic network engineering, offering a new route for constructing efficient cell factories for natural bioproducts.

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Journal
Advanced Science
Published
2025-11-21
DOI
https://doi.org/10.1002/advs.202512736
Citations
1
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
0.81
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article

Genome‐scale Metabolic Modeling Guided Escherichia coli Engineering for De Novo Biosynthesis of Chrysanthemic Acid

Jie Wang, Jingquan Yu, Xun Wang, Kai Cheng et al.
1 citations
Advanced Science
Microbial Metabolic Engineering and Bioproduction
0.81
article

Genome‐scale Metabolic Modeling Guided Escherichia coli Engineering for De Novo Biosynthesis of Chrysanthemic Acid

Jie Wang, Jingquan Yu, Xun Wang, Kai Cheng, Wei Li, Cheng Zhao, Shenyang Qu
article en
1 citations

Abstract

Chrysanthemic acid is an unconventional monoterpene moiety of the natural pesticide pyrethrins with notable anti-insect activity, making its industrial biosynthesis a promising avenue for sustainable agriculture. Here, an E. coli cell factory is designed and build for highly efficient chrysanthemic acid production guided by Genome-scale metabolic models (GEM). The biosynthetic pathway is reconstructed and simulated the metabolic changes caused by exogenous modules are simulated. A key metabolic branch point catalyzed by ispA is identified by this model, and inhibiting its expression using synthetic small RNA redirected the metabolic flux, resulting in the titers of precursor chrysanthemol and chrysanthemic acid increasing by 162% and 59%, respectively. The effect of the expression level of downstream dehydrogenases on chrysanthemic acid titer is also predicated using GEM, and the further optimization of copy number for dehydrogenase genes led to a notably 570% increase in chrysanthemic acid titer experimentally. By integrating the debranching strategy with copy number optimization, a record chrysanthemic acid titer 141.78 mg L<sup>-1</sup> is achieved in a bioreactor. The work seamlessly integrated in silico modeling optimization with wet-lab practices that significantly enhance target metabolite titer through metabolic network engineering, offering a new route for constructing efficient cell factories for natural bioproducts.

Advanced ScienceVol. 13(2)
Huazhong Agricultural University (CN), Agricultural Genomics Institute at Shenzhen (CN)
Openalex Percentile: Top 21%
Microbial Metabolic Engineering and Bioproduction
0.81
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Genome‐scale Metabolic Modeling Guided <i>Escherichia coli</i> Engineering for <i>De Novo</i> Biosynthesis of Chrysanthemic Acid — Jie Wang, Jingquan Yu, et al. · Advanced Science (2025) | TGRS Research Map | TGRS