Improving lysine production through construction of an <i>Escherichia coli</i> enzyme‐constrained model

Ye, Chao; Luo, Qiuling; Guo, Liang; Gao, Cong; Xu, Nan; Zhang, Li; Li, Liming; Chen, Xiulai

doi:10.1002/bit.27485

Cited by 56 publications

(53 citation statements)

References 46 publications

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“…GECKO used enzyme utilization and value to expand the stoichiometric matrix S so that the model can integrate quantitative proteomics data more directly and easily. GECKO has shown good flexibility and more accurate prediction ability, which has been confirmed in yeast, Bacillus subtilis, and Escherichia coli [14,20,21].…”

Section: Introductionmentioning

confidence: 83%

Integration of Enzyme Constraints in a Genome-scale Metabolic Model of Aspergillus Niger Improves Phenotype Predictions

Zhou

Zhuang

Xia

2021

Preprint

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BackgroundGenome-scale Metabolic Model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.ResultsBased on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale B values, predicting the differential expression of enzymes under different growth conditions. ConclusionsThis study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of Aspergillus niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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Section: Introductionmentioning

confidence: 83%

Integration of Enzyme Constraints in a Genome-scale Metabolic Model of Aspergillus Niger Improves Phenotype Predictions

Zhou

Zhuang

Xia

2021

Preprint

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“…Metabolic engineering to improve provision of the precursor metabolites L-lysine and S-adenosyL-methionine (SAM) may increase de novo production of L-carnitine by the E. coli strain constructed here. Strategies to overproduce L-lysine by E. coli are well established (Wendisch, 2020) and an E. coli strain overproducing L-lysine to a titer of 194 g L −1 from glucose and ammonium has recently been described (Ye et al, 2020). Regeneration of SAM, the major co-substrate for methyltransferases, using the renewable feedstock methanol as source of the methyl group proved very efficient in E. coli (Okano et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

L-Carnitine Production Through Biosensor-Guided Construction of the Neurospora crassa Biosynthesis Pathway in Escherichia coli

Kugler

Trumm

Frese

et al. 2021

Front. Bioeng. Biotechnol.

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L-Carnitine is a bioactive compound derived from L-lysine and S-adenosyl-L-methionine, which is closely associated with the transport of long-chain fatty acids in the intermediary metabolism of eukaryotes and sought after in the pharmaceutical, food, and feed industries. The L-carnitine biosynthesis pathway has not been observed in prokaryotes, and the use of eukaryotic microorganisms as natural L-carnitine producers lacks economic viability due to complex cultivation and low titers. While biotransformation processes based on petrochemical achiral precursors have been described for bacterial hosts, fermentative de novo synthesis has not been established although it holds the potential for a sustainable and economical one-pot process using renewable feedstocks. This study describes the metabolic engineering of Escherichia coli for L-carnitine production. L-carnitine biosynthesis enzymes from the fungus Neurospora crassa that were functionally active in E. coli were identified and applied individually or in cascades to assemble and optimize a four-step L-carnitine biosynthesis pathway in this host. Pathway performance was monitored by a transcription factor-based L-carnitine biosensor. The engineered E. coli strain produced L-carnitine from supplemented L-Nε-trimethyllysine in a whole cell biotransformation, resulting in 15.9 μM carnitine found in the supernatant. Notably, this strain also produced 1.7 μM L-carnitine de novo from glycerol and ammonium as carbon and nitrogen sources through endogenous Nε-trimethyllysine. This work provides a proof of concept for the de novoL-carnitine production in E. coli, which does not depend on petrochemical synthesis of achiral precursors, but makes use of renewable feedstocks instead. To the best of our knowledge, this is the first description of L-carnitine de novo synthesis using an engineered bacterium.

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“…Since the first implementation of the GECKO method 38 , its principles of enzyme constraints have been incorporated into GEMs for B. subtilis 40 , E. coli 41 , B. coagulans 42 , Streptomyces coelicolor 43 and even for diverse human cancer cell-lines 2 , showing the applicability of the method even for non-model organisms.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Domenzain

Sánchez

Anton

et al. 2021

Preprint

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Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.

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Improving lysine production through construction of an Escherichia coli enzyme‐constrained model

Cited by 56 publications

References 46 publications

Integration of Enzyme Constraints in a Genome-scale Metabolic Model of Aspergillus Niger Improves Phenotype Predictions

Integration of Enzyme Constraints in a Genome-scale Metabolic Model of Aspergillus Niger Improves Phenotype Predictions

L-Carnitine Production Through Biosensor-Guided Construction of the Neurospora crassa Biosynthesis Pathway in Escherichia coli

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

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