A new genome-scale metabolic model of Corynebacterium glutamicum and its application

Zhang, Yu; Cai, Jingyi; Shang, Xiuling; Wang, Bo; Liu, Shuwen; Chai, Xin; Tan, Tianwei; Zhang, Yun; Wen, Tingyi

doi:10.1186/s13068-017-0856-3

Cited by 76 publications

(69 citation statements)

References 76 publications

(108 reference statements)

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“…ProB is the limiting enzyme owing to the feedback inhibition by L-proline and the overexpression of the feedback-resistant proB* leads to a significant increase in L-proline production (Sleator, Gahan, & Hill, 2001;Y. Zhang et al, 2017).…”

Section: Fine-tuning Of Prob* and P4h Abundances To Increase The Yimentioning

confidence: 99%

“…The fermentative production of Hyp from glucose was achieved in an L-proline-accumulating E. coli by heterologously expressing the p4h gene derived from Dactylosporangium sp. Zhang et al, 2017). The high-concentration oxygen was necessary for the hydroxylation of proline; therefore, the expression of the vgb gene encoding hemoglobin derived from Vitreoscilla increased Hyp production due to the alleviation of oxygen limitation in E. coli (Zhao et al, 2017).…”

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confidence: 99%

“…An isoleucine bradytrophy C. glutamicum strain expressing the p4h gene produced 7.1 g/L of Hyp from glucose by limiting an external isoleucine supply in a fed-batch cultivation (Falcioni et al, 2015). Zhang et al, 2017), Hyp converted from L-proline under one-step catalysis of P4H, has not been efficiently synthesized. Zhang et al, 2017), Hyp converted from L-proline under one-step catalysis of P4H, has not been efficiently synthesized.…”

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confidence: 99%

“…C. glutamicum, an important industrial producer, shows a high metabolic flux toward L-glutamate and has been engineered to produce L-proline with a high yield on glucose (Y. Zhang et al, 2017). This makes C. glutamicum a better candidate for the fermentative production of Hyp (Lee, Na, Kim, Lee, & Kim, 2016).…”

mentioning

confidence: 99%

“…In spite of the high production of L-proline in C. glutamicum (Y. Zhang et al, 2017), Hyp converted from L-proline under one-step catalysis of P4H, has not been efficiently synthesized. Hence, it is still challenging for developing Hyp-producing strains with excellent fermentation performances.…”

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confidence: 99%

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Reconstruction of tricarboxylic acid cycle in Corynebacterium glutamicum with a genome‐scale metabolic network model for trans‐4‐hydroxyproline production

Zhang

Shang

Wang

et al. 2018

Biotech & Bioengineering

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trans-4-Hydroxy- l-proline (Hyp) is an abundant component of mammalian collagen and functions as a chiral synthon for the syntheses of anti-inflammatory drugs in the pharmaceutical industry. Proline 4-hydroxylase (P4H) can catalyze the conversion of l-proline to Hyp; however, it is still challenging for the fermentative production of Hyp from glucose using P4H due to the low yield and productivity. Here, we report the metabolic engineering of Corynebacterium glutamicum for the fermentative production of Hyp by reconstructing tricarboxylic acid (TCA) cycle together with heterologously expressing the p4h gene from Dactylosporangium sp. strain RH1. In silico model-based simulation showed that α-ketoglutarate was redirected from the TCA cycle toward Hyp synthetic pathway driven by P4H when the carbon flux from succinyl-CoA to succinate descended to zero. The interruption of the TCA cycle by the deletion of sucCD-encoding the succinyl-CoA synthetase (SUCOAS) led to a 60% increase in Hyp production and had no obvious impact on the growth rate. Fine-tuning of plasmid-borne ProB* and P4H abundances led to a significant increase in the yield of Hyp on glucose. The final engineered Hyp-7 strain produced up to 21.72 g/L Hyp with a yield of 0.27 mol/mol (Hyp/glucose) and a volumetric productivity of 0.36 g·L ·hr in the shake flask fermentation. To our knowledge, this is the highest yield and productivity achieved by microbial fermentation in a glucose-minimal medium for Hyp production. This strategy provides new insights into engineering C. glutamicum by flux coupling for the fermentative production of Hyp and related products.

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Section: Fine-tuning Of Prob* and P4h Abundances To Increase The Yimentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Reconstruction of tricarboxylic acid cycle in Corynebacterium glutamicum with a genome‐scale metabolic network model for trans‐4‐hydroxyproline production

Zhang

Shang

Wang

et al. 2018

Biotech & Bioengineering

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Co‐Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction

Xue,

Liu,

Yang

et al. 2023

Advanced Science

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Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome‐scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co‐cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of “independent + global”. This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co‐culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.

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Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

Kuriya

Inoue

Yamamoto

et al. 2021

Biotechnology Journal

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Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.

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A new genome-scale metabolic model of Corynebacterium glutamicum and its application

Cited by 76 publications

References 76 publications

Reconstruction of tricarboxylic acid cycle in Corynebacterium glutamicum with a genome‐scale metabolic network model for trans‐4‐hydroxyproline production

Reconstruction of tricarboxylic acid cycle in Corynebacterium glutamicum with a genome‐scale metabolic network model for trans‐4‐hydroxyproline production

Co‐Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction

Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering

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