Developing a dynamic equilibrium system in Escherichia coli to improve the production of recombinant proteins

Zhang, Zixu; Wang, Yuzhou; Nong, Fang-Tong; Xu, Yan; Ye, Chao; Gu, Yang; Sun, Xiao‐Man; Huang, He

doi:10.1007/s00253-022-12145-0

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…By contrast, enzyme-constrained models (ecModels) introduce enzyme kinetic information into a GEM, thus reflecting the protein resource limitation faced during cell growth, enabling them to identify the rate-limiting enzymes in the pathway and further guide rational metabolic engineering. As a consequence, ecModels have been successfully applied to guide the production of L-lysine [ 9 ], poly-glutamic acid [ 10 ], heme [ 11 ] and recombinant proteins [ 12 ]. Currently, three methods exist to automate the construction of ecModels, including GECKO [ 13 ], AutoPACMEN [ 14 ] and ECMpy [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we developed the simplified Python-based workflow ECMpy, which allows the construction of an ecModel by directly adding a total enzyme amount constraint into a GEM [ 15 ]. Recently, ecModels have been constructed for several species, including Escherichia coli [ 9 , 12 , 15 ], Saccharomyces cerevisiae [ 13 ], Aspergillus niger [ 16 ], Corynebacterium glutamicum [ 17 ] and B. subtilis [ 10 ]. The first ecModel for B. subtilis (ec_iYO844) only integrated enzyme kinetic parameters for 17 reactions located in the central carbon metabolism using the GECKO method, but this model allowed more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to the GEM [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

Mao

et al. 2023

Microorganisms

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Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

Mao

et al. 2023

Microorganisms

View full text Add to dashboard Cite

show abstract

“…For example, T7RNAP expression activity ( Li Z. J. et al, 2022 ; Chee et al, 2022 ) or growth decoupling system ( Li et al, 2016 ; Darlington et al, 2018 ) can be specifically regulated when expressing toxic proteins, but this method is not universally applicable. Zhang et al (2022) developed a dynamic equilibrium system that can achieve the overexpression of basic growth-related genes (rRNA, RNAP core enzyme, sigma factor), accurately predict and express key proteins using an ec_iECBD_1354 enzyme constraint model, and dynamically regulate the expression intensity of key growth-related proteins based on a load-driven promoter. This system alleviates the host burden effect, improves the production of recombinant proteins, and is helpful to efficiently develop expression hosts based on the properties of target proteins.…”

Section: Cell Culturementioning

confidence: 99%

1Progress, applications, challenges and prospects of protein purification technology

Hou

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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Protein is one of the most important biological macromolecules in life, which plays a vital role in cell growth, development, movement, heredity, reproduction and other life activities. High quality isolation and purification is an essential step in the study of the structure and function of target proteins. Therefore, the development of protein purification technologies has great theoretical and practical significance in exploring the laws of life activities and guiding production practice. Up to now, there is no forthcoming method to extract any proteins from a complex system, and the field of protein purification still faces significant opportunities and challenges. Conventional protein purification generally includes three steps: pretreatment, rough fractionation, and fine fractionation. Each of the steps will significantly affect the purity, yield and the activity of target proteins. The present review focuses on the principle and process of protein purification, recent advances, and the applications of these technologies in the life and health industry as well as their far-reaching impact, so as to promote the research of protein structure and function, drug development and precision medicine, and bring new insights to researchers in related fields.

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“…guide rational metabolic engineering. As a consequence, ecModels have been successfully applied to guide the production of L-lysine [9], poly-glutamic acid [10], heme [11] and recombinant proteins [12]. Currently, three methods exist to automate the construction of ecModels, including GECKO [13], AutoPACMEN [14] and ECMpy [15].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we developed the simplified Python-based workflow ECMpy, which allows the construction of an ecModel by directly adding a total enzyme amount constraint into a GEM [15]. Recently, ecModels have been constructed for several species, including Escherichia coli [9,12,15], Saccharomyces cerevisiae [13], Aspergillus niger [16], Corynebacterium glutamicum [17] and B. subtilis [10]. The first ecModel for B. subtilis (ec_iYO844) only integrated enzyme kinetic parameters for 17 reactions located in the central carbon metabolism using the GECKO method, but this model allowed more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to the GEM [10].…”

Section: Introductionmentioning

confidence: 99%

ecBSU1: A Genome-scale Enzyme-constrained Model of <em>Bacillus subtilis</em> based on the ECMpy Workflow

Wu¹,

Mao²,

Mao³

et al. 2022

Preprint

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Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Then, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

show abstract

Developing a dynamic equilibrium system in Escherichia coli to improve the production of recombinant proteins

Cited by 10 publications

References 60 publications

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

1Progress, applications, challenges and prospects of protein purification technology

ecBSU1: A Genome-scale Enzyme-constrained Model of <em>Bacillus subtilis</em> based on the ECMpy Workflow

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