Metabolic engineering最新文献

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Protocatechuic acid (3, 4-dihydroxybenzoic acid, PCA) is widely used in the pharmaceuticals, health food, and cosmetics industries owing to its diverse biological activities. However, the inhibition of 3-dehydroshikimate dehydratase (AroZ) by PCA and its toxicity to cells limit the efficient production of PCA in Escherichia coli. In this study, a high-level strain of 3-dehydroshikimate, E. coli DHS01, was developed by blocking the carbon flow from the shikimate-overproducing strain E. coli SA09. Additionally, the PCA biosynthetic pathway was established in DHS01 by introducing the high-activity ApAroZ. Subsequently, the protein structure and catalytic mechanism of 3-dehydroshikimate dehydratase from Acinetobacter pittii PHEA-2 (ApAroZ) were clarified. The variant ApAroZ^R363A, achieved by modulating the conformational dynamics of ApAroZ, effectively relieved product inhibition. Additionally, the tolerance of the strain E. coli PCA04 to PCA was enhanced by adaptive laboratory evolution, and a biosensor-assisted high-throughput screening method was designed and implemented to expedite the identification of high-performance PCA-producing strains. Finally, in a 5 L bioreactor, the final strain PCA05 achieved the highest PCA titer of 46.65 g/L, a yield of 0.23 g/g, and a productivity of 1.46 g/L/h for PCA synthesis from glucose using normal fed-batch fermentation. The strategies described herein serve as valuable guidelines for the production of other high-value and toxic products.

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the “IPP-bypass” pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

Chemically defined media for cultivation of Saccharomyces cerevisiae strains are commonly supplemented with a mixture of multiple Class-B vitamins, whose omission leads to strongly reduced growth rates. Fast growth without vitamin supplementation is interesting for industrial applications, as it reduces costs and complexity of medium preparation and may decrease susceptibility to contamination by auxotrophic microbes. In this study, suboptimal growth rates of S. cerevisiae CEN.PK113-7D in the absence of pantothenic acid, para-aminobenzoic acid (pABA), pyridoxine, inositol and/or biotin were corrected by single or combined overexpression of ScFMS1, ScABZ1/ScABZ2, ScSNZ1/ScSNO1, ScINO1 and Cyberlindnera fabianii BIO1, respectively. Several strategies were explored to improve growth of S. cerevisiae CEN.PK113-7D in thiamine-free medium. Overexpression of ScTHI4 and/or ScTHI5 enabled thiamine-independent growth at 83% of the maximum specific growth rate of the reference strain in vitamin-supplemented medium. Combined overexpression of seven native S. cerevisiae genes and CfBIO1 enabled a maximum specific growth rate of 0.33 ± 0.01 h⁻¹ in vitamin-free synthetic medium. This growth rate was only 17 % lower than that of a congenic reference strain in vitamin-supplemented medium. Physiological parameters of the engineered vitamin-independent strain in aerobic glucose-limited chemostat cultures (dilution rate 0.10 h⁻¹) grown on vitamin-free synthetic medium were similar to those of similar cultures of the parental strain grown on vitamin-supplemented medium. Transcriptome analysis revealed only few differences in gene expression between these cultures, which primarily involved genes with roles in Class-B vitamin metabolism. These results pave the way for development of fast-growing vitamin-independent industrial strains of S. cerevisiae.

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

Precise control over mammalian cell growth dynamics poses a major challenge in biopharmaceutical manufacturing. Here, we present a multi-level cell engineering strategy for the tunable regulation of growth phases in mammalian cells. Initially, we engineered mammalian death phase by employing CRISPR/Cas9 to knockout pro-apoptotic proteins Bax and Bak, resulting in a substantial attenuation of apoptosis by improving cell viability and extending culture lifespan. The second phase introduced a growth acceleration system, akin to a “gas pedal”, based on an abscidic acid inducible system regulating cMYC gene expression, enabling rapid cell density increase and cell cycle control. The third phase focused on a stationary phase inducing system, comparable to a “brake pedal”. A tetracycline inducible genetic circuit based on BLIMP1 gene led to cell growth cessation and arrested cell cycle upon activation. Finally, we developed a dual controllable system, combining the “gas and brake pedals”, enabling for dynamic and precise orchestration of mammalian cell growth dynamics. This work exemplifies the application of synthetic biology tools and combinatorial cell engineering, offering a sophisticated framework for manipulating mammalian cell growth and providing a unique paradigm for reprogramming cell behaviour for enhancing biopharmaceutical manufacturing and further biomedical applications.

Odd-numbered fatty acids (FAs) have been widely used in nutrition, agriculture, and chemical industries. Recently, some studies showed that they could be produced from bacteria or yeast, but the products are almost exclusively odd-numbered long-chain FAs. Here we report the design and construction of two biosynthetic pathways in Saccharomyces cerevisiae for de novo production of odd-numbered medium-chain fatty acids (OMFAs) via ricinoleic acid and 10-hydroxystearic acid, respectively. The production of OMFAs was enabled by introducing a hydroxy fatty acid cleavage pathway, including an alcohol dehydrogenase from Micrococcus luteus, a Baeyer-Villiger monooxygenase from Pseudomonas putida, and a lipase from Pseudomonas fluorescens. These OMFA biosynthetic pathways were optimized by eliminating the rate-limiting step, generating heptanoic acid, 11-hydroxyundec-9-enoic acid, nonanoic acid, and 9-hydroxynonanoic acid at 7.83 mg/L, 9.68 mg/L, 9.43 mg/L and 13.48 mg/L, respectively. This work demonstrates the biological production of OMFAs in a sustainable manner in S. cerevisiae.

Microbial synthesis has emerged as a promising and sustainable alternative to traditional chemical synthesis and plant extraction. However, the competition between synthetic pathways and central metabolic pathways for cellular resources may impair final production efficiency. Moreover, when the synthesis of target product requires multiple precursors from the same node, the conflicts of carbon flux have further negative impacts on yields. In this study, a self-regulated network was developed to relieve the competition of precursors in complex synthetic pathways. Using 4-hydroxycoumarin (4-HC) synthetic pathway as a proof of concept, we employed an intermediate as a trigger to dynamically rewire the metabolic flux of pyruvate and control the expression levels of genes in 4-HC synthetic pathway, achieving self-regulation of multiple precursors and enhanced titer. Transcriptomic analysis results additionally demonstrated that the gene transcriptional levels of both pyruvate kinase PykF and synthetic pathway enzyme SdgA dynamically changed according to the intermediate concentrations. Overall, our work established a self-regulated network to dynamically balance the metabolic flux of two precursors in 4-HC biosynthesis, providing insight into balancing biosynthetic pathways where multiple precursors compete and interfere with each other.

Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. It’s still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.