Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly when using glutamate and aspartate. Depletion of glutamate and aspartate causes a distinct shift in fluxes of the central carbon and nitrogen metabolism. In the current work, hurdles encountered in flux analysis at a genome-scale level are addressed using hierarchical flux balance analysis and uniform sampling of the constrained solution space. This general framework can now be adopted in further studies of S. lividans, e.g., as a host for heterologous protein production.     

Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation

We have developed a reactor-scale model of Escherichia coli metabolism and growth in a 1000L process for the production of a recombinant therapeutic protein. The model consists of two distinct parts: (1) a dynamic, process specific portion that describes the time evolution of 37 process variables of relevance and (2) a flux balance based, 123-reaction metabolic model of E. coli metabolism. This model combines several previously reported modeling approaches including a growth rate-dependent biomass composition, maximum growth rate objective function, and dynamic flux balancing. In addition, we introduce concentration-dependent boundary conditions of transport fluxes, dynamic maintenance demands, and a state-dependent cellular objective. This formulation was able to describe specific runs with high-fidelity over process conditions including rich media, simultaneous acetate and glucose consumption, glucose minimal media, and phosphate depleted media. Furthermore, the model accurately describes the effect of process perturbations—such as glucose overbatching and insufficient aeration—on growth, metabolism, and titer.     

Metabolic flux and robustness analysis of glycerol metabolism in <Emphasis Type="Italic">Klebsiella pneumoniae</Emphasis>

The knowledge of the mechanism of flux distribution will benefit understanding cell physiology and regulation of metabolism. In this study, the measured fluxes obtained under steady-state conditions were used to estimate intracellular fluxes and identify the robustness of branch points of the anaerobic glycerol metabolism in Klebsiella pneumoniae for the production of 1,3-propanediol by metabolic flux analysis. The biomass concentration increased as NADH₂/NAD⁺ decreased at low initial concentration and inversed at high initial glycerol concentration. The flux distribution revealed that the branch points of glycerol and dihydroxyacetonephosphate were rigid to the environmental conditions. However, the pyruvate and acetyl coenzyme A metabolisms gave cells the flexibility to regulate the energy and intermediate fluxes under various environmental conditions. Additionly, it was found that the formation rate of ethanol and the ratio of pyruvate dehydrogenase to pyruvate formate lyase appeared visible fluctuations at high glycerol uptake rate.     

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade‐off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper‐osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper‐osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.     

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of ¹³C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.     

Shewanella oneidensis MR-1 fluxome under various oxygen conditions

The central metabolic fluxes of Shewanella oneidensis MR-1 were examined under carbon-limited (aerobic) and oxygen-limited (microaerobic) chemostat conditions, using 13C-labeled lactate as the sole carbon source. The carbon labeling patterns of key amino acids in biomass were probed using both gas chromatography-mass spectrometry (GC-MS) and 13C nuclear magnetic resonance (NMR). Based on the genome annotation, a metabolic pathway model was constructed to quantify the central metabolic flux distributions. The model showed that the tricarboxylic acid (TCA) cycle is the major carbon metabolism route under both conditions. The Entner-Doudoroff and pentose phosphate pathways were utilized primarily for biomass synthesis (with a flux below 5% of the lactate uptake rate). The anaplerotic reactions (pyruvate to malate and oxaloacetate to phosphoenolpyruvate) and the glyoxylate shunt were active. Under carbon-limited conditions, a substantial amount (9% of the lactate uptake rate) of carbon entered the highly reversible serine metabolic pathway. Under microaerobic conditions, fluxes through the TCA cycle decreased and acetate production increased compared to what was found for carbon-limited conditions, and the flux from glyoxylate to glycine (serine-glyoxylate aminotransferase) became measurable. Although the flux distributions under aerobic, microaerobic, and shake flask culture conditions were different, the relative flux ratios for some central metabolic reactions did not differ significantly (in particular, between the shake flask and aerobic-chemostat groups). Hence, the central metabolism of S. oneidensis appears to be robust to environmental changes. Our study also demonstrates the merit of coupling GC-MS with 13C NMR for metabolic flux analysis to reduce the use of 13C-labeled substrates and to obtain more-accurate flux values.     

Metabolic regulation analysis of wild-type and arcA mutant Escherichia coli under nitrate conditions using different levels of omics data

It is of practical interest to investigate the effect of nitrates on bacterial metabolic regulation of both fermentation and energy generation, as compared to aerobic and anaerobic growth without nitrates. Although gene level regulation has previously been studied for nitrate assimilation, it is important to understand this metabolic regulation in terms of global regulators. In the present study, therefore, we measured gene expression using DNA microarrays, intracellular metabolite concentrations using CE-TOFMS, and metabolic fluxes using the (13)C-labeling technique for wild-type E. coli and the ΔarcA (a global regulatory gene for anoxic response control, ArcA) mutant to compare the metabolic state under nitrate conditions to that under aerobic and anaerobic conditions without nitrates in continuous culture conditions at a dilution rate of 0.2 h(-1). In wild-type, although the measured metabolite concentrations changed very little among the three culture conditions, the TCA cycle and the pentose phosphate pathway fluxes were significantly different under each condition. These results suggested that the ATP production rate was 29% higher under nitrate conditions than that under anaerobic conditions, whereas the ATP production rate was 10% lower than that under aerobic conditions. The flux changes in the TCA cycle were caused by changes in control at the gene expression level. In ΔarcA mutant, the TCA cycle flux was significantly increased (4.4 times higher than that of the wild type) under nitrate conditions. Similarly, the intracellular ATP/ADP ratio increased approximately two-fold compared to that of the wild-type strain.     

13C‐metabolic flux analysis for batch culture of Escherichia coli and its pyk and pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites

Since most bio‐production processes are conducted in a batch or fed‐batch manner, the evaluation of metabolism with respect to time is highly desirable. Toward this aim, we applied ¹³C‐metabolic flux analysis to nonstationary conditions by measuring the mass isotopomer distribution of intracellular metabolites. We performed our analysis on batch cultures of wild‐type Escherichia coli, as well as on Pyk and Pgi mutants, obtained the fluxes and metabolite concentrations as a function of time. Our results for the wild‐type indicated that the TCA cycle flux tended to increase during growth on glucose. Following glucose exhaustion, cells controlled the branch ratio between the glyoxylate pathway and the TCA cycle, depending on the availability of acetate. In the Pyk mutant, the concentrations of glycolytic intermediates changed drastically over time due to the dumping and feedback inhibition caused by PEP accumulation. Nevertheless, the flux distribution and free amino acid concentrations changed little. The growth rate and the fluxes remained constant in the Pgi mutant and the glucose‐6‐phosphate dehydrogenase reaction was the rate‐limiting step. The measured fluxes were compared with those predicted by flux balance analysis using maximization of biomass yield or ATP production. Our findings indicate that the objective function of biosynthesis became less important as time proceeds on glucose in the wild‐type, while it remained highly important in the Pyk mutant. Furthermore, ATP production was the primary objective function in the Pgi mutant. This study demonstrates how cells adjust their metabolism in response to environmental changes and/or genetic perturbations in the batch cultivation. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Metabolic flux analysis of embryonic stem cells using three distinct differentiation protocols and comparison to gene expression patterns

Metabolic flux analysis (MFA) was performed on mouse embryonic stem cells cultured under three distinct differentiation conditions: classical embryoid body formation (EB), and on surfaces coated with either gelatin (GEL) or matrigel (MAT). MFA was based on 15 metabolic reactions and eight transport steps, and was carried out based on measurements of four substrates and/or metabolites: glucose, lactate, glutamine, and glutamate. Fluxes representing biomass production remained fairly constant for all three culture conditions with at most a 40% variation. In contrast, major temporal variations, up to 500%, were observed for all other major central metabolic fluxes across all culture conditions. Fluxes were compared to gene-expression patterns measured by microarray analysis. Particularly interesting is the correlation between the metabolic fluxes with expression patterns of the corresponding genes of the pyruvate to lactate reaction, whereby the genes for several isoforms of the lactate dehydrogenase enzyme were examined. The patterns of this flux were notably different in the EB cultures compared to the GEL and MAT cultures and reflected differences in oxygen and nutrient transport in EB vs. the GEL and MAT cultures. Another novel finding of this study is an event occurring between Days 4 and 5 of differentiation, which was identified by a notable change in both the metabolic fluxes and gene-expression patterns. This suggests that metabolic patterns can be used as effective beacons of changes in differentiating stem cells. Overall, and qualitatively, core metabolic fluxes, under the three culture conditions examined, correlated well with gene-expression patterns.     

Comparison and analysis of objective functions in flux balance analysis

Flux balance analysis (FBA) is currently one of the most important and used techniques for estimation of metabolic reaction rates (fluxes). This mathematical approach utilizes an optimization criterion in order to select a distribution of fluxes from the feasible space delimited by the metabolic reactions and some restrictions imposed over them, assuming that cellular metabolism is in steady state. Therefore, the obtained flux distribution depends on the specific objective function used. Multiple studies have been aimed to compare distinct objective functions at given conditions, in order to determine which of those functions produces values of fluxes closer to real data when used as objective in the FBA; in other words, what is the best objective function for modeling cell metabolism at a determined environmental condition. However, these comparative studies have been designed in very dissimilar ways, and in general, several factors that can change the ideal objective function in a cellular condition have not been adequately considered. Additionally, most of them have used only one dataset for representing one condition of cell growth, and different measuring techniques have been used. For these reasons, a rigorous study on the effect of factors such as the quantity of used data, the number and type of fluxes utilized as input data, and the selected classification of growth conditions, are required in order to obtain useful conclusions for these comparative studies, allowing limiting clearly the application range on any of those results. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:985–991, 2014     

Shewanella oneidensis MR-1 Fluxome under Various Oxygen Conditions

The central metabolic fluxes of Shewanella oneidensis MR-1 were examined under carbon-limited (aerobic) and oxygen-limited (microaerobic) chemostat conditions, using ¹³C-labeled lactate as the sole carbon source. The carbon labeling patterns of key amino acids in biomass were probed using both gas chromatography-mass spectrometry (GC-MS) and ¹³C nuclear magnetic resonance (NMR). Based on the genome annotation, a metabolic pathway model was constructed to quantify the central metabolic flux distributions. The model showed that the tricarboxylic acid (TCA) cycle is the major carbon metabolism route under both conditions. The Entner-Doudoroff and pentose phosphate pathways were utilized primarily for biomass synthesis (with a flux below 5% of the lactate uptake rate). The anaplerotic reactions (pyruvate to malate and oxaloacetate to phosphoenolpyruvate) and the glyoxylate shunt were active. Under carbon-limited conditions, a substantial amount (9% of the lactate uptake rate) of carbon entered the highly reversible serine metabolic pathway. Under microaerobic conditions, fluxes through the TCA cycle decreased and acetate production increased compared to what was found for carbon-limited conditions, and the flux from glyoxylate to glycine (serine-glyoxylate aminotransferase) became measurable. Although the flux distributions under aerobic, microaerobic, and shake flask culture conditions were different, the relative flux ratios for some central metabolic reactions did not differ significantly (in particular, between the shake flask and aerobic-chemostat groups). Hence, the central metabolism of S. oneidensis appears to be robust to environmental changes. Our study also demonstrates the merit of coupling GC-MS with ¹³C NMR for metabolic flux analysis to reduce the use of ¹³C-labeled substrates and to obtain more-accurate flux values.     

Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements

A stoichiometric model of metabolism was developed to describe the balance of metabolic reactions during steady-state growth of Escherichia coli on glucose (or metabolic intermediates) and mineral salts. The model incorporates 153 reversible and 147 irreversible reactions and 289 metabolites from several metabolic data bases for the biosynthesis of the macromolecular precursors, coenzymes, and prosthetic groups necessary for synthesis of all cellular macromolecules. Correlations describing how the cellular composition changes with growth rate were developed from experimental data and were used to calculate the drain of precursors to macromolecules, coenzymes, and prosthetic groups from the metabolic network for the synthesis of those macromolecules at a specific growth rate. Energy requirements for macromolecular polymerization and proofreading, transport of metabolites, and maintenance of transmembrane gradients were included in the model rather than a lumped maintenance energy term. The underdetermined set of equations was solved using the Simplex algorithm, employing realistic objective functions and constraints; the drain of precursors, coenzymes, and prosthetic groups and the energy requirements for the synthesis of macromolecules served as the primary set of constraints. The model accurately predicted experimentally determined metabolic fluxes for aerobic growth on acetate or acetate plus glucose. In addition, the model predicted the genetic and metabolic regulation that must occur for growth under different conditions, such as the opening of the glyoxylate shunt during growth on acetate and the branching of the tricarboxylic acid cycle under anaerobic growth. Sensitivity analyses were performed to determine the flexibility of pathways and the effects of different rates and growth conditions on the distribution of fluxes. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 398-421, 1997.     

Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks

Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.     

ΔFBA—Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.     

Quantification of metabolism in Saccharomyces cerevisiae under hyperosmotic conditions using elementary mode analysis

Yeast metabolism under hyperosmotic stress conditions was quantified using elementary mode analysis to obtain insights into the metabolic status of the cell. The fluxes of elementary modes were determined as solutions to a linear program that used the stoichiometry of the elementary modes as constraints. The analysis demonstrated that distinctly different sets of elementary modes operate under normal and hyperosmotic conditions. During the adaptation phase, elementary modes that only produce glycerol are active, while elementary modes that yield biomass, ethanol, and glycerol become active after the adaptive phase. The flux distribution in the metabolic network, calculated using the fluxes in the elementary modes, was employed to obtain the flux ratio at key nodes. At the glucose 6-phosphate (G6P) node, 25% of the carbon influx was diverted towards the pentose phosphate pathway under normal growth conditions, while only 0.3% of the carbon flux was diverted towards the pentose phosphate pathway during growth at 1?M NaCl, indicating that cell growth is arrested under hyperosmotic conditions. Further, objective functions were used in the linear program to obtain optimal solution spaces corresponding to the different accumulation rates. The analysis demonstrated that while biomass formation was optimal under normal growth conditions, glycerol synthesis was closer to optimal during adaptation to osmotic shock.     

Flux balance analysis in the production of clavulanic acid by Streptomyces clavuligerus

In this work, in silico flux balance analysis is used for predicting the metabolic behavior of Streptomyces clavuligerus during clavulanic acid production. To choose the best objective function for use in the analysis, three different optimization problems are evaluated inside the flux balance analysis formulation: (i) maximization of the specific growth rate, (ii) maximization of the ATP yield, and (iii) maximization of clavulanic acid production. Maximization of ATP yield showed the best predictions for the cellular behavior. Therefore, flux balance analysis using ATP as objective function was used for analyzing different scenarios of nutrient limitations toward establishing the effect of limiting the carbon, nitrogen, phosphorous, and oxygen sources on the growth and clavulanic acid production rates. Obtained results showed that ammonia and phosphate limitations are the ones most strongly affecting clavulanic acid biosynthesis. Furthermore, it was possible to identify the ornithine flux from the urea cycle and the α‐ketoglutarate flux from the TCA cycle as the most determinant internal fluxes for promoting clavulanic acid production. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1226–1236, 2015     

Response of fluxome and metabolome to temperature-induced recombinant protein synthesis in Escherichia coli

The response of the central carbon metabolism of Escherichia coli to temperature-induced recombinant production of human fibroblast growth factor was studied on the level of metabolic fluxes and intracellular metabolite levels. During production, E. coli TG1:plambdaFGFB, carrying a plasmid encoded gene for the recombinant product, revealed stress related characteristics such as decreased growth rate and biomass yield and enhanced by-product excretion (acetate, pyruvate, lactate). With the onset of production, the adenylate energy charge dropped from 0.85 to 0.60, indicating the occurrence of a severe energy limitation. This triggered an increase of the glycolytic flux which, however, was not sufficient to compensate for the increased ATP demand. The activation of the glycolytic flux was also indicated by the readjustment of glycolytic pool sizes leading to an increased driving force for the reaction catalyzed by phosphofructokinase. Moreover, fluxes through the TCA cycle, into the pentose phosphate pathway and into anabolic pathways decreased significantly. The strong increase of flux into overflow pathways, especially towards acetate was most likely caused by a flux redirection from pyruvate dehydrogenase to pyruvate oxidase. The glyoxylate shunt, not active during growth, was the dominating anaplerotic pathway during production. Together with pyruvate oxidase and acetyl CoA synthase this pathway could function as a metabolic by-pass to overcome the limitation in the junction between glycolysis and TCA cycle and partly recycle the acetate formed back into the metabolism.