Intracellular Flux Distribution Research Articles

Characterization of cellular responses and cell lysis to elevated hydrodynamic stress from benchtop perfusion bioreactors.

The effective design of perfusion cell culture is currently challenging regarding balancing the operating parameters associated with the hydrodynamic conditions due to increased system complexity. To address this issue, cellular responses of an industrial CHO cell line to different types of hydrodynamic stress in benchtop perfusion bioreactors originating from agitation, sparging, and hollow fibers (HF) in the cell retention devices were systematically investigated here with the analysis of cell lysis. It was found that cell lysis was very common and most associated with the sparging stress, followed by the HF and lastly the agitation, consequently heavily impacting the estimation of process descriptors related to biomass. The results indicated that the agitation stress led to a reduced cell growth with a shift toward a more productive phenotype, suggesting an energy redirection from biomass formation to product synthesis, whereas the sparging stress had a small impact on the intracellular metabolic flux distribution but increased the cell death rate drastically. For HF stress, a similar cell maintenance profile was found as the sparging while the activity of glycolysis and the TCA cycle was significantly impeded, potentially leading to the lack of energy and thus a substantial decrease in cell-specific productivity. Moreover, a novel concept of volume average shear stress was developed to further understand the relations of different types of stress and the observed responses for an improved insight for the perfusion cell culture.

Genetic inactivation of the Carnitine/Acetyl-Carnitine mitochondrial carrier of Yarrowia lipolytica leads to enhanced odd-chain fatty acid production

BackgroundMitochondrial carriers (MCs) can deeply affect the intracellular flux distribution of metabolic pathways. The manipulation of their expression level, to redirect the flux toward the production of a molecule of interest, is an attractive target for the metabolic engineering of eukaryotic microorganisms. The non-conventional yeast Yarrowia lipolytica is able to use a wide range of substrates. As oleaginous yeast, it directs most of the acetyl-CoA therefrom generated towards the synthesis of lipids, which occurs in the cytoplasm. Among them, the odd-chain fatty acids (OCFAs) are promising microbial-based compounds with several applications in the medical, cosmetic, chemical and agricultural industries.ResultsIn this study, we have identified the MC involved in the Carnitine/Acetyl-Carnitine shuttle in Y. lipolytica, YlCrc1. The Y. lipolytica Ylcrc1 knock-out strain failed to grow on ethanol, acetate and oleic acid, demonstrating the fundamental role of this MC in the transport of acetyl-CoA from peroxisomes and cytoplasm into mitochondria. A metabolic engineering strategy involving the deletion of YlCRC1, and the recombinant expression of propionyl-CoA transferase from Ralstonia eutropha (RePCT), improved propionate utilization and its conversion into OCFAs. These genetic modifications and a lipogenic medium supplemented with glucose and propionate as the sole carbon sources, led to enhanced accumulation of OCFAs in Y. lipolytica.ConclusionsThe Carnitine/Acetyl-Carnitine shuttle of Y. lipolytica involving YlCrc1, is the sole pathway for transporting peroxisomal or cytosolic acetyl-CoA to mitochondria. Manipulation of this carrier can be a promising target for metabolic engineering approaches involving cytosolic acetyl-CoA, as demonstrated by the effect of YlCRC1 deletion on OCFAs synthesis.

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.

A Dynamic Constraint-based Modelling (DCBM) Approach With Alternative Metabolic Objective Functions Predicts The impact of Oxidative Stress on Stored Red Blood Cells (RBCs)

Mathematical metabolic modelling is a systematic endeavour to allow identifying the main causes of an observed metabolic change and to estimate the consequences of an imposed metabolic perturbation regarding a biosystem. Dynamic Constraint-based modelling (DCBM) has delivered promising results in metabolic engineering and in bioprocess design by providing mechanistically relevant systems-level knowledge of a network of bioreactions. Here, we seek to establish a DCBM approach that leverages convex optimization and nonlinear regression mathematical toolkit to estimate dynamic intracellular metabolic flux distributions in stored Red Blood Cells (RBCs) for transfusion purposes. First, we developed an ad-hoc metabolic network including 77 reactions and 74 metabolites, second, we adapted Flux Variability Analysis (FVA) technique to quantify the connection between exometabolomic dynamics and the dynamics of feasible intracellular reaction flux ranges. We have obtained fine-grained flux range dynamics of the intracellular reactions for the benchmark data published in (Bordbar et al., 2016). Then, we defined four objective functions regarding the accumulation of oxidative stress in stored RBCs for performing a dynamic Flux Balance Analysis (DFBA). In all four cases, time-resolved flux predictions were obtained respecting the imposed equality and inequality constraints. Last, we adapted a quadratic programming (QP) approach to calculate the Euclidean distance between the dynamic optimum flux vectors. The DCBM approach we have developed herein along with the developed metabolic network showed being suitable for the computational analysis of RBCs metabolic behaviour, and it is thought to be useful for other biosystems.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

BackgroundSeveral computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values.ResultsHere, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes.ConclusionWe showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Metabolic engineering of Vibrio natriegens for anaerobic succinate production.

SummaryThe biotechnological production of succinate bears serious potential to fully replace existing petrochemical approaches in the future. In order to establish an economically viable bioprocess, obtaining high titre, yield and productivity is of central importance. In this study, we present a straightforward engineering approach for anaerobic succinate production with Vibrio natriegens, consisting of essential metabolic engineering and optimization of process conditions. The final producer strain V. natriegens Δlldh Δdldh Δpfl Δald Δdns::pyc Cg (Succ1) yielded 1.46 mol of succinate per mol of glucose under anaerobic conditions (85% of the theoretical maximum) and revealed a particularly high biomass‐specific succinate production rate of 1.33 gSucc gCDW −1 h−1 compared with well‐established production systems. By applying carbon and redox balancing, we determined the intracellular flux distribution and show that under the tested conditions the reductive TCA as well as the oxidative TCA/glyoxylate pathway contributed to succinate formation. In a zero‐growth bioprocess using minimal medium devoid of complex additives and expensive supplements, we obtained a final titre of 60.4 gSucc l−1 with a maximum productivity of 20.8 gSucc l−1 h−1 and an overall volumetric productivity of 8.6 gSucc l−1 h−1 during the 7 h fermentation. The key performance indicators (titre, yield and productivity) of this first engineering approach in V. natriegens are encouraging and compete with costly tailored microbial production systems.

Metabolic control analysis of L-tryptophan producing Escherichia coli applying targeted perturbation with shikimate

L-tryptophan production from glycerol with Escherichia coli was analysed by perturbation studies and metabolic control analysis. The insertion of a non-natural shikimate transporter into the genome of an Escherichia coli L-tryptophan production strain enabled targeted perturbation within the product pathway with shikimate during parallelised short-term perturbation experiments with cells withdrawn from a 15 L fed-batch production process. Expression of the shikimate/H+-symporter gene (shiA) from Corynebacterium glutamicum did not alter process performance within the estimation error. Metabolic analyses and subsequent extensive data evaluation were performed based on the data of the parallel analysis reactors and the production process. Extracellular rates and intracellular metabolite concentrations displayed evident deflections in cell metabolism and particularly in chorismate biosynthesis due to the perturbations with shikimate. Intracellular flux distributions were estimated using a thermodynamics-based flux analysis method, which integrates thermodynamic constraints and intracellular metabolite concentrations to restrain the solution space. Feasible flux distributions, Gibbs reaction energies and concentration ranges were computed simultaneously for the genome-wide metabolic model, with minimum bias in relation to the direction of metabolic reactions. Metabolic control analysis was applied to estimate elasticities and flux control coefficients, predicting controlling sites for L-tryptophan biosynthesis. The addition of shikimate led to enhanced deviations in chorismate biosynthesis, revealing a so far not observed control of 3-dehydroquinate synthase on L-tryptophan formation. The relative expression of the identified target genes was analysed with RT-qPCR. Transcriptome analysis revealed disparities in gene expression and the localisation of target genes to further improve the microbial L-tryptophan producer by metabolic engineering.

Effects of Glucose and Maltose Substrates on the Intracellular Metabolic Flux Distribution of Curdlan Polysaccharides Biosynthesis by Alcaligenes faecalis

Effects of Glucose and Maltose Substrates on the Intracellular Metabolic Flux Distribution of Curdlan Polysaccharides Biosynthesis by Alcaligenes faecalis

Identifying metabolic features and engineering targets for productivity improvement in CHO cells by integrated transcriptomics and genome-scale metabolic model

In this study, we presented an integrated systems biology approach to elucidate the key characteristics of cellular metabolism in Chinese hamster ovary (CHO) cells producing monoclonal antibodies (mAb). The cellular metabolism in high and low producers under batch conditions was interrogated dynamically both within and among cells. First, transcriptomics analysis was used to study the time-course change in the metabolic pathway within cells that was correlated with mAb productivity increase. Second, differentially regulated pathways between high and low producers were sought at each growth phase. Several up-regulated pathways were identified in the high producer at the late growth phase, including citrate cycle, oxidative phosphorylation, and pentose phosphate pathway. These activities were further analyzed by intracellular flux distributions estimated through a genome-scale CHO model. Our results revealed that these key pathways are identified to be characteristics of high mAb production, not only for the high-producing cell line but also a dynamic phenomenon in mAb-producing cell cultures. This study showed that the approach of integrating transcriptomics and flux analysis leads to a better understanding of cellular metabolism related to mAb productivity. In turn, this allows for the identification of metabolic bottlenecks and potential engineering targets for cell line development and process optimization.

Effect of dissolved oxygen on L-methionine production from glycerol by Escherichia coli W3110BL using metabolic flux analysis method.

L-Methionine is an essential amino acid in humans, which plays an important role in the synthesis of some important amino acids and proteins. In this work, metabolic flux of batch fermentation of L-methionine with recombinant Escherichia coli W3110BL was analyzed using the flux balance analysis method, which estimated the intracellular flux distributions under different dissolved oxygen conditions. The results revealed the producing L-methionine flux of 4.8mmol/(g cell·h) [based on the glycerol uptake flux of 100mmol/(g cell·h)] was obtained at 30% dissolved oxygen level which was higher than that of other dissolved oxygen levels. The carbon fluxes for synthesizing L-methionine were mainly obtained from the pathway of phosphoenolpyruvate to oxaloacetic acid [15.6mmol/(g cell·h)] but not from the TCA cycle. Hence, increasing the flow from phosphoenolpyruvate to oxaloacetic acid by enhancing the enzyme activity of phosphoenolpyruvate carboxylase might be conducive to the production of L-methionine. Additionally, pentose phosphate pathway could provide a large amount of reducing power NADPH for the synthesis of amino acids and the flux could increase from 41mmol/(g cell·h) to 51mmol/(g cell·h) when changing the dissolved oxygen levels, thus meeting the requirement of NADPH for L-methionine production and biomass synthesis. Therefore, the following modification of the strains should based on the improvement of the key pathway and the NAD(P)/NAD(P)H metabolism.

Comparison and Analysis of Published Genome-scale Metabolic Models of Yarrowia lipolytica

Genome-scale metabolic models (GEMs) are powerful tools for predicting metabolic flux distributions, understanding complex cell physiology, and guiding the improvement of cell metabolism and production. Yarrowia lipolytica is known for its ability to accumulate lipids and has been widely employed to produce many important metabolites as an ideal host microorganism. There are six GEMs reconstructed for this strain by different research groups, which, however, may cause confusion for model users. It is therefore necessary to understand and analyze the existing models comprehensively. Different simulation results of the published GEMs of Y. lipolytica were analyzed based on experimental data, in order to understand the differences among models and identify whether there were common problems in model construction. First, specific growth rates (μ) under various culture conditions were simulated by different models, showing that the biomass generation equation in models had significant influence on the accuracy of simulation results. In addition, simulation and analysis of intracellular flux distributions revealed several inaccurate descriptions on the reversibility of reactions involving currency metabolites in the models. Finally, specific metabolite formation rates were predicted for different target products, and large discrepancies among the different models were observed. The corresponding solutions were then proposed according to the findings of the above model problems. We have corrected the existing GEMs of Y. lipolytica and the prediction performances of the models have been significantly improved. Several suggestions for better construction and refinement of genome-scale metabolic network models were also provided.

Reconstruction and analysis of a carbon-core metabolic network for Dunaliella salina

BackgroundThe green microalga Dunaliella salina accumulates a high proportion of β-carotene during abiotic stress conditions. To better understand the intracellular flux distribution leading to carotenoid accumulation, this work aimed at reconstructing a carbon core metabolic network for D. salina CCAP 19/18 based on the recently published nuclear genome and its validation with experimental observations and literature data.ResultsThe reconstruction resulted in a network model with 221 reactions and 212 metabolites within three compartments: cytosol, chloroplast and mitochondrion. The network was implemented in the MATLAB toolbox CellNetAnalyzer and checked for feasibility. Furthermore, a flux balance analysis was carried out for different light and nutrient uptake rates. The comparison of the experimental knowledge with the model prediction revealed that the results of the stoichiometric network analysis are plausible and in good agreement with the observed behavior. Accordingly, our model provides an excellent tool for investigating the carbon core metabolism of D. salina.ConclusionsThe reconstructed metabolic network of D. salina presented in this work is able to predict the biological behavior under light and nutrient stress and will lead to an improved process understanding for the optimized production of high-value products in microalgae.

P13CMFA: Parsimonious 13C metabolic flux analysis.

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

A preliminary study on the impact of exogenous A-Factor analogue 1,4-butyrolactone on stimulating bitespiramycin biosynthesis.

Bitespiramycin is composed of nine main acylated spiramycin components with isovaleryspiramycin as the major component. However, even with excellent therapeutic effects, its application and industrialization are restricted due to its low titer. In this study, the exogenous addition of A-Factor analogue 1,4-butyrolactone (1,4-BL) stimulated an improvement in bitespiramycin biological titer by 29% with a tiny influence on concentration of major component. Moreover, the mechanism of 1,4-BL stimulating effect was preliminarily explored by the analyses of three key enzyme activities, intracellular metabolite profiling and metabolic flux distribution. All results coordinately revealed that the extensive accumulation of methylmalonyl-CoA and acetyl-CoA was the direct reason for the enhanced bitespiramycin biosynthesis. This study would provide theoretical and technical basis for the application of 1,4-BL addition strategy to industrial bitespiramycin production.

Metabolic switches from quiescence to growth in synchronized Saccharomyces cerevisiae

IntroductionThe switch from quiescence (G0) into G1 and cell cycle progression critically depends on specific nutrients and metabolic capabilities. Conversely, metabolic networks are regulated by enzyme–metabolite interaction and transcriptional regulation that lead to flux modifications to support cell growth. How cells process and integrate environmental information into coordinated responses is challenging to analyse and not yet described quantitatively.ObjectivesTo quantitatively monitor the central carbon metabolism during G0 exit and the first 2 h after reentering the cell cycle from synchronized Saccharomyces cerevisiae.MethodsDynamic tailored 13C metabolic flux analysis was used to observe the intracellular metabolite flux changes, and the metabolome and proteome were observed to identify regulatory mechanisms.ResultsG0 cells responded immediately to an extracellular increase of glucose. The intracellular metabolic flux changed in time and specific events were observed. High fluxes into trehalose and glycogen synthesis were observed during the G0 exit. Both fluxes then decreased, reaching a minimum at t = 65 min. Here, storage degradation contributed significantly (i.e. 21%) to the glycolytic flux. In contrast to these changes, the glucose uptake rate remained constant after the G0 exit. The flux into the oxidative pentose phosphate pathway was highest (29-fold increase, 36.4% of the glucose uptake) at t = 65 min, while it was very low at other time points. The maximum flux seems to correlate with a late G1 state preparing for the S phase transition. In the G1/S phase (t = 87 min), anaplerotic reactions such as glyoxylate shunt increased. Protein results show that during this transition, proteins belonging to clusters related with ribosome biogenesis and assembly, and initiation transcription factors clusters were continuously synthetised.ConclusionThe intracellular flux distribution changes dynamically and these major rearrangements highlight the coordinate reorganization of metabolic flux to meet requirements for growth during different cell state.

An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells—the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an “essential nutrient minimization” approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.

Improvement of ε-poly-L-lysine production of Streptomyces albulus by continuous introduction of streptomycin resistance

• A modified ribosome engineering was developed and used for Streptomyces improvement. • The modified ribosome engineering improved the ε-PL production by 1.79 folds. • Streptomycin resistance resulted in mutants occurring mutations in rps L and rsm G. • Some regulators and molecular chaperones expression levels in mutant were increased. • The high-yield mechanism of mutant was discussed. ε-Poly- l -lysine (ε-PL) is an excellent antimicrobial agent and produced by Streptomyces albulus . To improve the ε-PL production of S. albulus M-Z18, a modified method of ribosome engineering with continuous introduction of streptomycin resistance was adopted in this study. The highest-yield mutant S. albulus SS-62 was generated from 1631 mutants screening, and achieved ε-PL production of 3.04 g/L in shake-flask fermentation, which was 1.79-fold higher than the parent strain. In order to reveal the high-yield mechanism of S. albulus SS-62, mutation sites, global protein expression, key enzymes activities and the corresponding gene transcriptions, intracellular flux distribution and cofactors in the ε-PL biosynthesis pathway were investigated. We found continuous introduction of streptomycin resistance caused mutations in rps L (encoding ribosomal protein S12) and rsm G (encoding 16S rRNA methyltransferase). Moreover, the expression changes of regulators and molecular chaperones related to transcription and translation process were occurred, and some key enzymes activities and metabolic flux in the pathway of ε-PL biosynthesis were increased. It could be reasonably speculated that these changes eventually led to a significant increase of ε-PL production in S. albulus SS-62. Taken together, the present study not only enhanced the ε-PL production of S. albulus M-Z18 but also provided an insight into the high-yield mechanism of S. albulus SS-62.

Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13C-Labeling Data.

Synechococcus elongatus UTEX 2973 (Synechococcus 2973) has the shortest reported doubling time (2.1 h) among cyanobacteria, making it a promising platform for the solar-based production of biochemicals. In this meta-analysis, its intracellular flux distribution was recomputed using genome-scale isotopic nonstationary 13C-metabolic flux analysis given the labeling dynamics of 13 metabolites reported in an earlier study. To achieve this, a genome-scale mapping model, namely imSyu593, was constructed using the imSyn617 mapping model for Synechocystis sp. PCC 6803 (Synechocystis 6803) as the starting point encompassing 593 reactions. The flux elucidation revealed nearly complete conversion (greater than 96%) of the assimilated carbon into biomass in Synechococcus 2973. In contrast, Synechocystis 6803 achieves complete conversion of only 86% of the assimilated carbon. This high biomass yield was enabled by the reincorporation of the fixed carbons lost in anabolic and photorespiratory pathways in conjunction with flux rerouting through a nondecarboxylating reaction such as phosphoketolase. This reincorporation of lost CO2 sustains a higher flux through the photorespiratory C2 cycle that fully meets the glycine and serine demands for growth. In accordance with the high carbon efficiency drive, acetyl-coenzyme A was entirely produced using the carbon-efficient phosphoketolase pathway. Comparison of the Synechococcus 2973 flux map with that of Synechocystis 6803 revealed differences in the use of Calvin cycle and photorespiratory pathway reactions. The two species used different reactions for the synthesis of metabolites such as fructose-6-phosphate, glycine, sedoheptulose-7-phosphate, and Ser. These findings allude to a highly carbon-efficient metabolism alongside the fast carbon uptake rate in Synechococcus 2973, which explains its faster growth rate.

Engineering and systems-level analysis of Pseudomonas chlororaphis for production of phenazine-1-carboxamide using glycerol as the cost-effective carbon source

BackgroundGlycerol, an inevitable byproduct of biodiesel, has become an attractive feedstock for the production of value-added chemicals due to its availability and low price. Pseudomonas chlororaphis HT66 can use glycerol to synthesize phenazine-1-carboxamide (PCN), a phenazine derivative, which is strongly antagonistic to fungal phytopathogens. A systematic understanding of underlying mechanisms for the PCN overproduction will be important for the further improvement and industrialization.ResultsWe constructed a PCN-overproducing strain (HT66LSP) through knocking out three negative regulatory genes, lon, parS, and prsA in HT66. The strain HT66LSP produced 4.10 g/L of PCN with a yield of 0.23 (g/g) from glycerol, which was of the highest titer and the yield obtained among PCN-producing strains. We studied gene expression, metabolomics, and dynamic 13C tracer in HT66 and HT66LSP. In response to the phenotype changes, the transcript levels of phz biosynthetic genes, which are responsible for PCN biosynthesis, were all upregulated in HT66LSP. Central carbon was rerouted to the shikimate pathway, which was shown by the modulation of specific genes involved in the lower glycolysis, the TCA cycle, and the shikimate pathway, as well as changes in abundances of intracellular metabolites and flux distribution to increase the precursor availability for PCN biosynthesis. Moreover, dynamic 13C-labeling experiments revealed that the presence of metabolite channeling of 3-phosphoglyceric acid to phosphoenolpyruvate and shikimate to trans-2,3-dihydro-3-hydroxyanthranilic acid in HT66LSP could enable high-yielding synthesis of PCN.ConclusionsThe integrated analysis of gene expression, metabolomics, and dynamic 13C tracer enabled us to gain a more in-depth insight into complex mechanisms for the PCN overproduction. This study provides important basis for further engineering P. chlororaphis for high PCN production and efficient glycerol conversion.

Metabolic Flux Analysis Using 13C Isotopes (13C-MFA). 1. Experimental Basis of the Method and the Present State of Investigations

Quantitatively characterizing the intracellular carbon flux distribution provides useful information for both fundamental and applied investigations into the cellular metabolism at the system level, such as the roles of different metabolic pathways and individual reactions, metabolic state characterization, metabolic differences between the strains, and clues regarding strategies for producer-strain improvement. A variety of methods have been developed to characterize the metabolic state of the cell by determining its intracellular flux distribution, and together, they are called metabolic flux analysis (MFA) or fluxomics. These methods, in addition to other X-omics technologies (i.e., genomics, transcriptomics, proteomics, and metabolomics) constitute a recent arsenal of the system biology estimation approaches. One of the most well-developed approaches for intracellular carbon flux estimation in vivo in (quasi) steady-state conditions is 13C-MFA, which uses substrates that are labeled with a heavy carbon (13C). Applying 13C-MFA requires the coordination of experts in biochemistry, applied mathematics and nuclear magnetic resonance (NMR) or mass spectrometry. Therefore, the authors have prepared a three-part review highlighting the different but equally important aspects of 13C-MFA. In the first part, which is presented below, the focus is on the basic principles of 13C-MFA, such as stoichiometric model development, labeling experiments and experimental data extraction. The principles of the labeling experiments modeling and quantitative carbon flux estimation and statistics are discussed in the second part. The final part reviews recent achievements in fundamental and applied investigations of bacterial metabolism achieved using 13C-MFA.