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Background: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study., Results: Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based k cat data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct k cat collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted k cat values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila., Conclusions: In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based k cat data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism., (© 2024. The Author(s).)

Constraint-based genome-scale metabolic network models (genome-scale metabolic models, GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric constraints, other constraints such as enzyme availability and thermodynamic feasibility may also limit the cellular phenotype solution space. Recently, extended GEM models considering either enzymatic or thermodynamic constraints have been developed to improve model prediction accuracy. This review summarizes the recent progresses on metabolic models with multiple constraints (MCGEMs). We presented the construction methods and various applications of MCGEMs including the simulation of gene knockout, prediction of biologically feasible pathways and identification of bottleneck steps. By integrating multiple constraints in a consistent modeling framework, MCGEMs can predict the metabolic bottlenecks and key controlling and modification targets for pathway optimization more precisely, and thus may provide more reliable design results to guide metabolic engineering of industrially important microorganisms.

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (ec i ML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli ( i ML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using ec i ML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction., (Copyright © 2021 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Background: Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in multiplex genome editing technologies. However, simultaneously modulating multiple genes on the chromosome remains challenging in Bacillus subtilis . Thus, developing an efficient and convenient method for B. subtilis multiplex genome editing is imperative., Results: Here, we developed a CRISPR/Cas9n-based multiplex genome editing system for iterative genome editing in B. subtilis . This system enabled us to introduce various types of genomic modifications with more satisfying efficiency than using CRISPR/Cas9, especially in multiplex gene editing. Our system achieved at least 80% efficiency for 1-8 kb gene deletions, at least 90% efficiency for 1-2 kb gene insertions, near 100% efficiency for site-directed mutagenesis, 23.6% efficiency for large DNA fragment deletion and near 50% efficiency for three simultaneous point mutations. The efficiency for multiplex gene editing was further improved by regulating the nick repair mechanism mediated by ligD gene, which finally led to roughly 65% efficiency for introducing three point mutations on the chromosome. To demonstrate its potential, we applied our system to simultaneously fine-tune three genes in the riboflavin operon and significantly improved the production of riboflavin in a single cycle., Conclusions: We present not only the iterative CRISPR/Cas9n system for B. subtilis but also the highest efficiency for simultaneous modulation of multiple genes on the chromosome in B. subtilis reported to date. We anticipate this CRISPR/Cas9n mediated system to greatly enhance the optimization of diverse biological systems via metabolic engineering and synthetic biology., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2019.)

To enrich the resource pool of endophytic fungi from plants which produce taxol, a taxol-producing endophytic fungus TMS-26 was isolated from the stem of Taxus Media. The result of high performance liquid chromatography (HPLC) showed that TMS-26 extract exhibited similar chromatographic peaks and retention time (4.545 min) with authentic taxol. Then mass spectrometry (MS) analysis further confirmed that TMS-26 extracts contained the same mass peaks with authentic taxol ((M+Na)+=876). These indicated that the isolated endophytic fungus TMS-26 can produce taxol. According to the morphological characteristics, the molecular analysis of 18S rDNA and internal transcribed spacer nuclear rDNA gene sequence, the fungus was identified as Aspergillus fumigatus TMS-26.