Effects of n-butanol production on metabolism and the photosystem in Synecococcus elongatus PCC 7942 based on metabolic flux and target proteome analyses

Although n-butanol (BuOH) is an ideal fuel because of its superior physical properties, it has toxicity to microbes. Previously, a Synechococcus elongatus PCC 7942 derivative strain that produces BuOH from CO2 was developed by introducing six heterologous genes (BUOH-SE strain). To identify the bottleneck in BuOH production, the effects of BuOH production and its toxicity on central metabolism and the photosystem were investigated. Parental (WT) and BUOH-SE strains were cultured under autotrophic conditions. Consistent with the results of a previous study, BuOH production was observed only in the BUOH-SE strain. Isotopically non-stationary 13C-metabolic flux analysis revealed that the CO2 fixation rate was much larger than the BuOH production rate in the BUOH-SE strain (1.70 vs 0.03 mmol gDCW-1 h-1), implying that the carbon flow for BuOH biosynthesis was less affected by the entire flux distribution. No large difference was observed in the flux of metabolism between the WT and BUOH-SE strains. Contrastingly, in the photosystem, the chlorophyll content and maximum O2 evolution rate per dry cell weight of the BUOH-SE strain were decreased to 81% and 43% of the WT strain, respectively. Target proteome analysis revealed that the amounts of some proteins related to antennae (ApcA, ApcD, ApcE, and CpcC), photosystem II (PsbB, PsbU, and Psb28-2), and cytochrome b6f complex (PetB and PetC) in photosystems decreased in the BUOH-SE strain. The activation of photosynthesis would be a novel approach for further enhancing BuOH production in S. elongatus PCC 7942.

A genome-scale dynamic constraint-based modelling (gDCBM) framework predicts growth dynamics, medium composition and intracellular flux distributions in CHO clonal variations

Optimizing mammalian cell growth and bioproduction is a tedious task. However, due to the inherent complexity of eukaryotic cells, heuristic experimental approaches such as, metabolic engineering and bioprocess design, are frequently integrated with mathematical models of cell culture to improve biological process efficiency and find paths for improvement. Constraint-based metabolic models have evolved over the last two decades to be used for dynamic modelling in addition to providing a linear description of steady-state metabolic systems. Formulation and implementation of the underlying optimization problems require special attention to the model's performance and feasibility, lack of defects in the definition of system components, and consideration of optimal alternate solutions, in addition to processing power limitations. Here, the time-resolved dynamics of a genome-scale metabolic network of Chinese hamster ovary (CHO) cell metabolism are shown using a genome-scale dynamic constraint-based modelling framework (gDCBM). The metabolic network was adapted from a reference model of CHO genome-scale metabolic model (GSMM), iCHO_DG44_v1, and dynamic restrictions were imposed to its exchange fluxes based on experimental results. We used this framework for predicting physiological changes in CHO clonal variants. Because of the methodical creation of the components for the flux balance analysis optimization problem and the integration of a switch time, this model can generate sequential predictions of intracellular fluxes during growth and non-growth phases (per hour of culture time) and transparently reveal the shortcomings in such practice. As a result of the differences exploited by various clones, we can understand the relevance of changes in intracellular flux distribution and exometabolomics. The integration of various omics data into the given gDCBM framework, as well as the reductionist analysis of the model, can further help bioprocess optimization.

Application of a genome-scale model in tandem with enzyme assays for identification of metabolic signatures of high and low CHO cell producers

Biopharmaceutical industrial processes are based on high yielding stable recombinant Chinese Hamster Ovary (CHO) cells that express monoclonal antibodies. However, the process and feeding regimes need to be adapted for each new cell line, as they all have a slightly different metabolism and product performance. A main limitation for accelerating process development is that the metabolic pathways underlying this physiological variability are not yet fully understood. This study describes the evolution of intracellular fluxes during the process for 4 industrial cell lines, 2 high producers and 2 low producers (n = 3), all of them producing a different antibody. In order to understand from a metabolic point of view the phenotypic differences observed, and to find potential targets for improving specific productivity of low producers, the analysis was supported by a tailored genome-scale model and was validated with enzymatic assays performed at different days of the process. A total of 59 reactions were examined from different key pathways, namely glycolysis, pentose phosphate pathway, TCA cycle, lipid metabolism, and oxidative phosphorylation. The intracellular fluxes did not show a metabolic correlation between high producers, but the degree of similitude observed between cell lines could be confirmed with additional experimental observations. The whole analysis led to a better understanding of the metabolic requirements for all the cell lines, allowed to the identification of metabolic bottlenecks and suggested targets for further cell line engineering. This study is a successful application of a curated genome-scale model to multiple industrial cell lines, which makes the metabolic model suitable for process platform.

13C-metabolic flux analysis for mevalonate-producing strain of Escherichia coli

Mevalonate (MVA) is used to produce various useful products such as drugs, cosmetics and food additives. An MVA-producing strain of Escherichia coli (engineered) was constructed by introducing mvaES genes from Enterococcus faecalis. The engineered strain produced 1.84 mmol/gDCW/h yielding 22% (C-mol/C-mol) of MVA from glucose in the aerobic exponential growth phase. The mass balance analysis revealed that the MVA yield of the engineered strain was close to the upper limit at the biomass yield. Since MVA is synthesized from acetyl-CoA using NADPH as a cofactor, the production of MVA affects central metabolism in terms of carbon utilization and NADPH requirements. The reason for this highly efficient MVA production was investigated based on ¹³C-metabolic flux analysis. The estimated flux distributions revealed that the fluxes of acetate formation and the TCA cycle in the engineered strain were lower than those in the control strain. Although the oxidative pentose phosphate pathway is considered as the NADPH generating pathway in E. coli, no difference of the flux was observed between the control and engineered strains. The production/consumption balance of NADPH suggested that additional requirement of NADPH for MVA synthesis was obtained from the transhydrogenase reaction in the engineered strain. Comparison between the measured flux distribution and the ideal values for MVA production proposes a strategy for further engineering to improve the MVA production in E. coli.

Improving the flux distributions simulated with genome-scale metabolic models of Saccharomyces cerevisiae

Genome-scale metabolic models (GEMs) can be used to evaluate genotype-phenotype relationships and their application to microbial strain engineering is increasing in popularity. Some of the algorithms used to simulate the phenotypes of mutant strains require the determination of a wild-type flux distribution. However, the accuracy of this reference, when calculated with flux balance analysis, has not been studied in detail before.

Here, the wild-type simulations of selected GEMs for Saccharomyces cerevisiae have been analysed and most of the models tested predicted erroneous fluxes in central pathways, especially in the pentose phosphate pathway. Since the problematic fluxes were mostly related to areas of the metabolism consuming or producing NADPH/NADH, we have manually curated all reactions including these cofactors by forcing the use of NADPH/NADP⁺ in anabolic reactions and NADH/NAD⁺ for catabolic reactions. The curated models predicted more accurate flux distributions and performed better in the simulation of mutant phenotypes.

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The flux distributions of the genome-scale models of Saccharomyces cerevisiae were evaluated

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Most of the tested models showed fluxes inconsistent with experimental data

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A manual curation process was performed on all reactions including NADH or NADPH

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The curated models showed flux distributions more consistent with experimental data

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Phenotype simulations improved when the curated flux distributions were used

Validation of the Serpent 2 code on TRIGA Mark II benchmark experiments

The main aim of this paper is the development and validation of a 3D computational model of TRIGA research reactor using Serpent 2 code. The calculated parameters were compared to the experimental results and to calculations performed with the MCNP code. The results show that the calculated normalized reaction rates and flux distribution within the core are in good agreement with MCNP and experiment, while in the reflector the flux distribution differ up to 3% from the measurements.