Quantification of intracellular metabolic fluxes from fractional enrichment and 13C-13C coupling constraints on the isotopomer distribution in labeled biomass components

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Systems metabolic engineering for citric acid production by Aspergillus niger in the post-genomic era

Citric acid is the world’s largest consumed organic acid and is widely used in beverage, food and pharmaceutical industries. Aspergillus niger is the main industrial workhorse for citric acid production. Since the release of the genome sequence, extensive multi-omic data are being rapidly obtained, which greatly boost our understanding of the citric acid accumulation mechanism in A. niger to a molecular and system level. Most recently, the rapid development of CRISPR/Cas9 system facilitates highly efficient genome-scale genetic perturbation in A. niger. In this review, we summarize the impact of systems biology on the citric acid molecular regulatory mechanisms, the advances in metabolic engineering strategies for enhancing citric acid production and discuss the development and application of CRISPR/Cas9 systems for genome editing in A. niger. We believe that future systems metabolic engineering efforts will redesign and engineer A. niger as a highly optimized cell factory for industrial citric acid production.

Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Typically, ¹³C flux analysis relies on assumptions of both metabolic and isotopic steady state. If metabolism is steady but isotope labeling is not allowed to fully equilibrate, isotopically nonstationary metabolic flux analysis (INST-MFA) can be used to estimate fluxes. This requires solution of differential equations that describe the time-dependent labeling of network metabolites, while iteratively adjusting the flux and pool size parameters to match the transient labeling measurements. INST-MFA holds a number of unique advantages over approaches that rely solely upon steady-state isotope enrichments. First, INST-MFA can be applied to estimate fluxes in autotrophic systems, which consume only single-carbon substrates. Second, INST-MFA is ideally suited to systems that label slowly due to the presence of large intermediate pools or pathway bottlenecks. Finally, INST-MFA provides increased measurement sensitivity to estimate reversible exchange fluxes and metabolite pool sizes, which represents a potential framework for integrating metabolomic analysis with ¹³C flux analysis. This review highlights the unique capabilities of INST-MFA, describes newly available software tools that automate INST-MFA calculations, presents several practical examples of recent INST-MFA applications, and discusses the technical challenges that lie ahead.

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Pseudomonas aeruginosa is a metabolically versatile wide-ranging opportunistic pathogen. In humans P. aeruginosa causes infections of the skin, urinary tract, blood, and the lungs of Cystic Fibrosis patients. In addition, P. aeruginosa's broad environmental distribution, relatedness to biotechnologically useful species, and ability to form biofilms have made it the focus of considerable interest. We used ¹³C metabolic flux analysis (MFA) and flux balance analysis to understand energy and redox production and consumption and to explore the metabolic phenotypes of one reference strain and five strains isolated from the lungs of cystic fibrosis patients. Our results highlight the importance of the oxidative pentose phosphate and Entner-Doudoroff pathways in P. aeruginosa growth. Among clinical strains we report two divergent metabolic strategies and identify changes between genetically related strains that have emerged during a chronic infection of the same patient. MFA revealed that the magnitude of fluxes through the glyoxylate cycle correlates with growth rates.

Mathematical modeling of isotope labeling experiments for metabolic flux analysis

Isotope labeling experiments (ILEs) offer a powerful methodology to perform metabolic flux analysis. However, the task of interpreting data from these experiments to evaluate flux values requires significant mathematical modeling skills. Toward this, this chapter provides background information and examples to enable the reader to (1) model metabolic networks, (2) simulate ILEs, and (3) understand the optimization and statistical methods commonly used for flux evaluation. A compartmentalized model of plant glycolysis and pentose phosphate pathway illustrates the reconstruction of a typical metabolic network, whereas a simpler example network illustrates the underlying metabolite and isotopomer balancing techniques. We also discuss the salient features of commonly used flux estimation software 13CFLUX2, Metran, NMR2Flux+, FiatFlux, and OpenFLUX. Furthermore, we briefly discuss methods to improve flux estimates. A graphical checklist at the end of the chapter provides a reader a quick reference to the mathematical modeling concepts and resources.

Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination

Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from (13) C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of (13) C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the (13) C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the (13) C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in (13) C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC-MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in (13) C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.

Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary ¹³C flux analysis

Background

Several studies have shown that the utilization of mixed carbon feeds instead of methanol as sole carbon source is beneficial for protein production with the methylotrophic yeast Pichia pastoris. In particular, growth under mixed feed conditions appears to alleviate the metabolic burden related to stress responses triggered by protein overproduction and secretion. Yet, detailed analysis of the metabolome and fluxome under mixed carbon source metabolizing conditions are missing. To obtain a detailed flux distribution of central carbon metabolism, including the pentose phosphate pathway under methanol-glucose conditions, we have applied metabolomics and instationary ¹³C flux analysis in chemostat cultivations.

Results

Instationary ¹³C-based metabolic flux analysis using GC-MS and LC-MS measurements in time allowed for an accurate mapping of metabolic fluxes of glycolysis, pentose phosphate and methanol assimilation pathways. Compared to previous results from NMR-derived stationary state labelling data (proteinogenic amino acids, METAFoR) more fluxes could be determined with higher accuracy. Furthermore, using a thermodynamic metabolic network analysis the metabolite measurements and metabolic flux directions were validated. Notably, the concentration of several metabolites of the upper glycolysis and pentose phosphate pathway increased under glucose-methanol feeding compared to the reference glucose conditions, indicating a shift in the thermodynamic driving forces. Conversely, the extracellular concentrations of all measured metabolites were lower compared with the corresponding exometabolome of glucose-grown P. pastoris cells.

The instationary ¹³C flux analysis resulted in fluxes comparable to previously obtained from NMR datasets of proteinogenic amino acids, but allowed several additional insights. Specifically, i) in vivo metabolic flux estimations were expanded to a larger metabolic network e.g. by including trehalose recycling, which accounted for about 1.5% of the glucose uptake rate; ii) the reversibility of glycolytic/gluconeogenesis, TCA cycle and pentose phosphate pathways reactions was estimated, revealing a significant gluconeogenic flux from the dihydroxyacetone phosphate/glyceraldehydes phosphate pool to glucose-6P. The origin of this finding could be carbon recycling from the methanol assimilatory pathway to the pentose phosphate pool. Additionally, high exchange fluxes of oxaloacetate with aspartate as well as malate indicated amino acid pool buffering and the activity of the malate/Asp shuttle; iii) the ratio of methanol oxidation vs utilization appeared to be lower (54 vs 79% assimilated methanol directly oxidized to CO₂).

Conclusions

In summary, the application of instationary ¹³C-based metabolic flux analysis to P. pastoris provides an experimental framework with improved capabilities to explore the regulation of the carbon and energy metabolism of this yeast, particularly for the case of methanol and multicarbon source metabolism.

Integration of in vivo and in silico metabolic fluxes for improvement of recombinant protein production

The filamentous fungus Aspergillus niger is an efficient host for the recombinant production of the glycosylated enzyme fructofuranosidase, a biocatalyst of commercial interest for the synthesis of pre-biotic sugars. In batch culture on a minimal glucose medium, the recombinant strain A. niger SKAn1015, expressing the fructofuranosidase encoding suc1 gene secreted 45U/mL of the target enzyme, whereas the parent wild type SKANip8 did not exhibit production. The production of the recombinant enzyme induced a significant change of in vivo fluxes in central carbon metabolism, as assessed by (13)C metabolic flux ratio analysis. Most notably, the flux redistribution enabled an elevated supply of NADPH via activation of the cytosolic pentose phosphate pathway (PPP) and mitochondrial malic enzyme, whereas the flux through energy generating TCA cycle was reduced. In addition, the overall possible flux space of fructofuranosidase producing A. niger was investigated in silico by elementary flux mode analysis. This provided theoretical flux distributions for multiple scenarios with differing production capacities. Subsequently, the measured flux changes linked to improved production performance were projected into the in silico flux space. This provided a quantitative evaluation of the achieved optimization and a priority ranked target list for further strain engineering. Interestingly, the metabolism was shifted largely towards the optimum flux pattern by sole expression of the recombinant enzyme, which seems an inherent attractive property of A. niger. Selected fluxes, however, changed contrary to the predicted optimum and thus revealed novel targets-including reactions linked to NADPH metabolism and gluconate formation.

Fluxomers: a new approach for 13C metabolic flux analysis

Background

The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.

Results

This article addresses the mathematical and computational formulation of ¹³C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.

Conclusions

Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.

Flux Design: In silico design of cell factories based on correlation of pathway fluxes to desired properties

Background

The identification of genetic target genes is a key step for rational engineering of production strains towards bio-based chemicals, fuels or therapeutics. This is often a difficult task, because superior production performance typically requires a combination of multiple targets, whereby the complex metabolic networks complicate straightforward identification. Recent attempts towards target prediction mainly focus on the prediction of gene deletion targets and therefore can cover only a part of genetic modifications proven valuable in metabolic engineering. Efficient in silico methods for simultaneous genome-scale identification of targets to be amplified or deleted are still lacking.

Results

Here we propose the identification of targets via flux correlation to a chosen objective flux as approach towards improved biotechnological production strains with optimally designed fluxes. The approach, we name Flux Design, computes elementary modes and, by search through the modes, identifies targets to be amplified (positive correlation) or down-regulated (negative correlation). Supported by statistical evaluation, a target potential is attributed to the identified reactions in a quantitative manner. Based on systems-wide models of the industrial microorganisms Corynebacterium glutamicum and Aspergillus niger, up to more than 20,000 modes were obtained for each case, differing strongly in production performance and intracellular fluxes. For lysine production in C. glutamicum the identified targets nicely matched with reported successful metabolic engineering strategies. In addition, simulations revealed insights, e.g. into the flexibility of energy metabolism. For enzyme production in A.niger flux correlation analysis suggested a number of targets, including non-obvious ones. Hereby, the relevance of most targets depended on the metabolic state of the cell and also on the carbon source.

Conclusions

Objective flux correlation analysis provided a detailed insight into the metabolic networks of industrially relevant prokaryotic and eukaryotic microorganisms. It was shown that capacity, pathway usage, and relevant genetic targets for optimal production partly depend on the network structure and the metabolic state of the cell which should be considered in future metabolic engineering strategies. The presented strategy can be generally used to identify priority sorted amplification and deletion targets for metabolic engineering purposes under various conditions and thus displays a useful strategy to be incorporated into efficient strain and bioprocess optimization.

Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels

BACKGROUND

Analysis of the cell operation at the metabolic level requires collecting data of different types and to determine their confidence level. In addition, the acquired information has to be combined in order to obtain a consistent operational view. In the case of Pichia pastoris, information of its biomass composition at macromolecular and elemental level is scarce particularly when different environmental conditions, such as oxygen availability or, genetic backgrounds (e.g. recombinant protein production vs. non production conditions) are compared.

RESULTS

P. pastoris cells growing in carbon-limited chemostat cultures under different oxygenation conditions (% O2 in the bioreactor inlet gas: 21%, 11% and 8%, corresponding to normoxic, oxygen-limiting and hypoxic conditions, respectively), as well as under recombinant protein (antibody fragment, Fab) producing and non-producing conditions, were analyzed from different points of view. On the one hand, the macromolecular and elemental composition of the biomass was measured using different techniques at the different experimental conditions and proper reconciliation techniques were applied for gross error detection of the measured substrates and products conversion rates. On the other hand, fermentation data was analyzed applying elemental mass balances. This allowed detecting a previously missed by-product secreted under hypoxic conditions, identified as arabinitol (aka. arabitol). After identification of this C5 sugar alcohol as a fermentation by-product, the mass balances of the fermentation experiments were validated.

CONCLUSIONS

After application of a range of analytical and statistical techniques, a consistent view of growth parameters and compositional data of P. pastoris cells growing under different oxygenation conditions was obtained. The obtained data provides a first view of the effects of oxygen limitation on the physiology of this microorganism, while recombinant Fab production seems to have little or no impact at this level of analysis. Furthermore, the results will be highly useful in other complementary quantitative studies of P. pastoris physiology, such as metabolic flux analysis.

A possibilistic framework for constraint-based metabolic flux analysis

BACKGROUND

Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements.

RESULTS

Herein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data--even if those are scarce--to distinguish possible from impossible flux states in a gradual way.

CONCLUSION

We introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.

Metabolic flux analysis of Escherichia coli creB and arcA mutants reveals shared control of carbon catabolism under microaerobic growth conditions

Escherichia coli has several elaborate sensing mechanisms for response to availability of oxygen and other electron acceptors, as well as the carbon source in the surrounding environment. Among them, the CreBC and ArcAB two-component signal transduction systems are responsible for regulation of carbon source utilization and redox control in response to oxygen availability, respectively. We assessed the role of CreBC and ArcAB in regulating the central carbon metabolism of E. coli under microaerobic conditions by means of (13)C-labeling experiments in chemostat cultures of a wild-type strain, DeltacreB and DeltaarcA single mutants, and a DeltacreB DeltaarcA double mutant. Continuous cultures were conducted at D = 0.1 h(-1) under carbon-limited conditions with restricted oxygen supply. Although all experimental strains metabolized glucose mainly through the Embden-Meyerhof-Parnas pathway, mutant strains had significantly lower fluxes in both the oxidative and the nonoxidative pentose phosphate pathways. Significant differences were also found at the pyruvate branching point. Both pyruvate-formate lyase and the pyruvate dehydrogenase complex contributed to acetyl-coenzyme A synthesis from pyruvate, and their activity seemed to be modulated by both ArcAB and CreBC. Strains carrying the creB deletion showed a higher biomass yield on glucose compared to the wild-type strain and its DeltaarcA derivative, which also correlated with higher fluxes from building blocks to biomass. Glyoxylate shunt and lactate dehydrogenase were active mainly in the DeltaarcA strain. Finally, it was observed that the tricarboxylic acid cycle reactions operated in a rather cyclic fashion under our experimental conditions, with reduced activity in the mutant strains.

Advances in analysis of microbial metabolic fluxes via (13)C isotopic labeling

Metabolic flux analysis via (13)C labeling ((13)C MFA) quantitatively tracks metabolic pathway activity and determines overall enzymatic function in cells. Three core techniques are necessary for (13)C MFA: (1) a steady state cell culture in a defined medium with labeled-carbon substrates; (2) precise measurements of the labeling pattern of targeted metabolites; and (3) evaluation of the data sets obtained from mass spectrometry measurements with a computer model to calculate the metabolic fluxes. In this review, we summarize recent advances in the (13)C-flux analysis technologies, including mini-bioreactor usage for tracer experiments, isotopomer analysis of metabolites via high resolution mass spectrometry (such as GC-MS, LC-MS, or FT-ICR), high performance and large-scale isotopomer modeling programs for flux analysis, and the integration of fluxomics with other functional genomics studies. It will be shown that there is a significant value for (13)C-based metabolic flux analysis in many biological research fields.

Kinetic flux profiling for quantitation of cellular metabolic fluxes

This protocol enables quantitation of metabolic fluxes in cultured cells. Measurements are based on the kinetics of cellular incorporation of stable isotope from nutrient into downstream metabolites. At multiple time points, after cells are rapidly switched from unlabeled to isotope-labeled nutrient, metabolism is quenched, metabolites are extracted and the extract is analyzed by chromatography-mass spectrometry. Resulting plots of unlabeled compound versus time follow variants of exponential decay, with the flux equal to the decay rate multiplied by the intracellular metabolite concentration. Because labeling is typically fast (t(1/2)<or=5 min for central metabolites in Escherichia coli), variations on this approach can effectively probe dynamically changing metabolic fluxes. This protocol is exemplified using E. coli and nitrogen labeling, for which quantitative flux data for approximately 15 metabolites can be obtained over 3 d of work. Applications to adherent mammalian cells are also discussed.

Replacing Escherichia coli NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPDH) with a NADP-dependent enzyme from Clostridium acetobutylicum facilitates NADPH dependent pathways

Reactions requiring reducing equivalents, NAD(P)H, are of enormous importance for the synthesis of industrially valuable compounds such as carotenoids, polymers, antibiotics and chiral alcohols among others. The use of whole-cell biocatalysis can reduce process cost by acting as catalyst and cofactor regenerator at the same time; however, product yields might be limited by cofactor availability within the cell. Thus, our study focussed on the genetic manipulation of a whole-cell system by modifying metabolic pathways and enzymes to improve the overall production process. In the present work, we genetically engineered an Escherichia coli strain to increase NADPH availability to improve the productivity of products that require NADPH in its biosynthesis. The approach involved an alteration of the glycolysis step where glyceraldehyde-3-phosphate (GAP) is oxidized to 1,3 bisphophoglycerate (1,3-BPG). This reaction is catalyzed by NAD-dependent endogenous glyceraldehyde-3-phosphate dehydrogenase (GAPDH) encoded by the gapA gene. We constructed a recombinant E. coli strain by replacing the native NAD-dependent gapA gene with a NADP-dependent GAPDH from Clostridium acetobutylicum, encoded by the gene gapC. The beauty of this approach is that the recombinant E. coli strain produces 2 mol of NADPH, instead of NADH, per mole of glucose consumed. Metabolic flux analysis showed that the flux through the pentose phosphate (PP) pathway, one of the main pathways that produce NADPH, was reduced significantly in the recombinant strain when compared to that of the parent strain. The effectiveness of the NADPH enhancing system was tested using the production of lycopene and epsilon-caprolactone as model systems using two different background strains. The recombinant strains, with increased NADPH availability, consistently showed significant higher productivity than the parent strains.

Metabolic model integration of the bibliome, genome, metabolome and reactome of Aspergillus niger

The release of the genome sequences of two strains of Aspergillus niger has allowed systems-level investigations of this important microbial cell factory. To this end, tools for doing data integration of multi-ome data are necessary, and especially interesting in the context of metabolism. On the basis of an A. niger bibliome survey, we present the largest model reconstruction of a metabolic network reported for a fungal species. The reconstructed gapless metabolic network is based on the reportings of 371 articles and comprises 1190 biochemically unique reactions and 871 ORFs. Inclusion of isoenzymes increases the total number of reactions to 2240. A graphical map of the metabolic network is presented. All levels of the reconstruction process were based on manual curation. From the reconstructed metabolic network, a mathematical model was constructed and validated with data on yields, fluxes and transcription. The presented metabolic network and map are useful tools for examining systemwide data in a metabolic context. Results from the validated model show a great potential for expanding the use of A. niger as a high-yield production platform.

Identification of hexose hydrolysis products in metabolic flux analytes: a case study of levulinic acid in plant protein hydrolysate

Biosynthetically directed fractional (13)C labeling, a popular methodology of metabolic flux analysis, involves culture on a mixture of (13)C and (12)C substrates and preparation a 'metabolic flux analyte' (typically protein hydrolysate) from the biomass. Metabolic flux analytes prepared from complex eukaryotes may contain additional compounds than those prepared from microorganisms. We report the presence of such compounds (hexose hydrolysis products) in a plant metabolic flux analyte (acid hydrolyzed protein from soybean embryos). We designed NMR experiments to systematically identify these compounds, and found that they were levulinic acid (LVA; major) and hydroxyacetone (HyA; minor). These acid hydrolysis products of hexoses (glucose and mannose) were generated during acid hydrolysis of glycosylating sugars (glucosamine and mannose) associated with soybean embryo protein. Analysis of LVA by two-dimensional [(13)C, (1)H] NMR and measurement of its J-coupling constants revealed long-range coupling between atoms C3 and C5, which enables LVA to provide more isotopomer information than its precursor hexose. Furthermore, we found that LVA and HyA preserve the isotopomeric composition of the metabolic hexose from which they are derived. An important consequence of these results is that comparison of LVA and HyA isotopomers from two separate metabolic flux analytes (protein hydrolysate and starch hydrolysate) from the same plant tissue can distinguish between parallel glycolysis and pentose phosphate pathways in different subcellular compartments.

Flux quantification in central carbon metabolism of Catharanthus roseus hairy roots by 13C labeling and comprehensive bondomer balancing

Methods for accurate and efficient quantification of metabolic fluxes are desirable in plant metabolic engineering and systems biology. Toward this objective, we introduce the application of "bondomers", a computationally efficient and intuitively appealing alternative to the commonly used isotopomer concept, to flux evaluation in plants, by using Catharanthus roseus hairy roots as a model system. We cultured the hairy roots on (5% w/w U-(13)C, 95% w/w naturally abundant) sucrose, and acquired two-dimensional [(13)C, (1)H] and [(1)H, (1)H] NMR spectra of hydrolyzed aqueous extract from the hairy roots. Analysis of these spectra yielded a data set of 116 bondomers of beta-glucans and proteinogenic amino acids from the hairy roots. Fluxes were evaluated from the bondomer data by using comprehensive bondomer balancing. We identified most fluxes in a three-compartmental model of central carbon metabolism with good precision. We observed parallel pentose phosphate pathways in the cytosol and the plastid with significantly different fluxes. The anaplerotic fluxes between phosphoenolpyruvate and oxaloacetate in the cytosol and between malate and pyruvate in the mitochondrion were relatively high (60.1+/-2.5 mol per 100 mol sucrose uptake, or 22.5+/-0.5 mol per 100 mol mitochondrial pyruvate dehydrogenase flux). The development of a comprehensive flux analysis tool for this plant hairy root system is expected to be valuable in assessing the metabolic impact of genetic or environmental changes, and this methodology can be extended to other plant systems.

NMR-based fluxomics: quantitative 2D NMR methods for isotopomers analysis

We have investigated the reliability of 2D-COSY and 2D-TOCSY experiments to provide accurate measurements of (13)C-enrichments in complex mixtures of (13)C-labelled metabolites. This was done from both theoretical considerations and experimental investigations. The results showed that 2D-TOCSY but not 2D-COSY could provide accurate measurements of (13)C-enrichments, provided efficient zero-quantum filters were applied during the mixing period. This approach extends the range of NMR methods applicable in (13)C-labelling experiments and is suitable to investigating the dynamic behaviour of metabolic systems.

Experimental and mathematical approaches to modeling plant metabolic networks

To support their sessile and autotrophic lifestyle higher plants have evolved elaborate networks of metabolic pathways. Dynamic changes in these metabolic networks are among the developmental forces underlying the functional differentiation of organs, tissues and specialized cell types. They are also important in the various interactions of a plant with its environment. Further complexity is added by the extensive compartmentation of the various interconnected metabolic pathways in plants. Thus, although being used widely for assessing the control of metabolic flux in microbes, mathematical modeling approaches that require steady-state approximations are of limited utility for understanding complex plant metabolic networks. However, considerable progress has been made when manageable metabolic subsystems were studied. In this article, we will explain in general terms and using simple examples the concepts underlying stoichiometric modeling (metabolic flux analysis and metabolic pathway analysis) and kinetic approaches to modeling (including metabolic control analysis as a special case). Selected studies demonstrating the prospects of these approaches, or combinations of them, for understanding the control of flux through particular plant pathways are discussed. We argue that iterative cycles of (dry) mathematical modeling and (wet) laboratory testing will become increasingly important for simulating the distribution of flux in plant metabolic networks and deriving rational experimental designs for metabolic engineering efforts.

The YfiD protein contributes to the pyruvate formate-lyase flux in an Escherichia coli arcA mutant strain

The product of yfiD gene is similar to pyruvate formate-lyase (PFL) activase and it has been reported to activate PFL by replacing the glycyl radical domain. To quantitate the effect of YfiD on the cell metabolism in microaerobic cultures, glucose-limited chemostat cultures were conducted with Escherichia coli yfiD mutant and yfiDarcA mutant strains. The microaerobic condition was controlled by purging the culture media with 2.5% O(2) in N(2). The intracellular metabolic flux distributions in these cultures were estimated based on C-13 labeling experiments. By comparing with the flux distributions in wild-type E. coli and the arcA mutant, it was shown that YfiD contributes to about 18% of the PFL flux in the arcA mutant, but it did not contribute to the PFL flux in wild-type E. coli. It was also shown that the cell used both PFL and pyruvate dehydrogenase (PDH) to supplement the acetyl-coenzyme A (AcCoA) pool under microaerobic conditions. The flux through PDH was about 22-30% of the total flux toward AcCoA in the wild-type, the yfiD mutant and yfiDarcA mutant strains. Relatively higher lactate production was seen in the yfiDarcA mutant than the other strains, which was due to the lower total flux through PFL and PDH toward AcCoA in this strain.

Flux analysis shows that hypoxia-inducible-factor-1-alpha minimally affects intracellular metabolism in tumor spheroids

Heterogeneous metabolic microenvironments in tumors affect local cell growth, survival, and overall therapeutic efficacy. Hypoxia-inducible-factor-1alpha (HIF-1alpha) is a transcription factor that responds to low-oxygen environments by upregulating genes for cell survival and metabolism. To date, the metabolic effects of HIF-1alpha in three-dimensional tissue have not been investigated. Preliminary experiments have shown that the effects of HIF-1alpha are dependent on glucose availability. Based on this observation, we hypothesized that HIF-1alpha would not affect cell survival and metabolism in the center of spheroids, where the concentrations of oxygen and glucose are low, similar to hypoxic regions found in tumors. To test this hypothesis we used fluorescence microscopy and the tumor cylindroid model to quantify cellular viability in three-dimensional tissue. Isotope labeling and metabolic flux analysis were also used to quantity the intracellular metabolism of wild-type and HIF-1alpha-null spheroids. As hypothesized, cell survival and intracellular metabolism were not different between wild-type and HIF-1alpha-null tissues. In addition, small spheroids, which contain less quiescent cells and are less nutritionally limited, were found to have increased carbon flux through the biosynthetic pentose phosphate and pyruvate carboxylase pathways. These results show how nutrient gradients affect cell growth and metabolism in spheroids and suggest that metabolic microenvironment should be taken into account when developing HIF-1alpha-based therapies.

Fluxome analysis using GC-MS

Fluxome analysis aims at the quantitative analysis of in vivo carbon fluxes in metabolic networks, i. e. intracellular activities of enzymes and pathways. It allows investigating the effects of genetic or environmental modifications and thus precisely provides a global perspective on the integrated genetic and metabolic regulation within the intact metabolic network. The experimental and computational approaches developed in this area have revealed fascinating insights into metabolic properties of various biological systems. Most of the comprehensive approaches for metabolic flux studies today involve isotopic tracer studies and GC-MS for measurement of the labeling pattern of metabolites. Initially developed and applied mainly in the field of biomedicine these GC-MS based metabolic flux approaches have been substantially extended and optimized during recent years and today display a key technology in metabolic physiology and biotechnology.

Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition

Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using 13C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from 13C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the 13C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied.

Global gene expression analysis of glucose overflow metabolism in Escherichia coli and reduction of aerobic acetate formation

During aerobic growth on glucose, Escherichia coli produces acetate in the so-called overflow metabolism. DNA microarray analysis was used to determine the global gene expression patterns of chemostat cultivations of E. coli MG1655 that were characterized by different acetate formation rates during aerobic growth on glucose. A correlation analysis identified that expression of ten genes (sdhCDAB, sucB, sucC, acnB, lpdA, fumC and mdh) encoding the TCA cycle enzymes succinate dehydrogenase, alpha-ketoglutarate dehydrogenase, succinyl-CoA synthetase, aconitase, fumarase and malate dehydrogenase, respectively, and of the acs-yjcH-actP operon for acetate utilization correlated negatively with acetate formation. Relieving transcriptional control of the sdhCDAB-b0725-sucABCD operon by chromosomal promoter exchange mutagenesis yielded a strain with increased specific activities of the TCA cycle enzymes succinate dehydrogenase, alpha-ketoglutarate dehydrogenase and succinyl-CoA synthetase, which are encoded by this operon. The resulting strain produced less acetate and directed more carbon towards carbon dioxide formation than the parent strain MG1655 while maintaining high growth and glucose consumption rates.

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.

Determination of metabolic flux changes during fed-batch cultivation from measurements of intracellular amino acids by LC-MS/MS

Metabolic flux analysis using (13)C-labeled substrates is a well-developed method for investigating cellular behavior in steady-state culture condition. To extend its application, in particular to typical industrial conditions, such as batch and fed-batch cultivations, a novel method of (13)C metabolic flux analysis is proposed. An isotopomer balancing model was developed to elucidate flux distributions in the central metabolism and all amino acids synthetic pathways. A lysine-producing strain of Escherichia coli was cultivated by fed-batch mode in a growth medium containing yeast extract. Mass distribution data was derived from both intracellular free amino acids and proteinogenic amino acids measured by LC-MS/MS, and a correction parameter for the protein turnover effect on the mass distributions of intracellular amino acids was introduced. Metabolic flux distributions were determined in both exponential and stationary phases. Using this new approach, a culture phase-dependent metabolic shift was detected in the fed-batch culture. The approach presented here has great potential for investigating cellular behavior in industrial processes, independent of cultivation modes, metabolic phase and growth medium.

Effect of the global redox sensing/regulation networks on Escherichia coli and metabolic flux distribution based on C-13 labeling experiments

Escherichia coli has several elaborate sensing mechanisms for response to the availability of oxygen and the presence of other electron acceptors. Among them, the one component Fnr protein and the two-component Arc system coordinate the adaptive responses to oxygen availability. To systematically investigate the contribution of Arc- and Fnr-dependent regulation in catabolism, glucose-limited chemostat cultures were conducted on wild-type E. coli, an arcA mutant, an fnr mutant, and an arcAfnr double mutant strains under a well-defined semi-aerobic condition. The metabolic flux distributions of the cultures of these strains were estimated based on C-13 labeling experiments. It was shown that the oxidative pentose phosphate (PP) pathway was functioning at low level under semi-aerobic condition. The fluxes through pyruvate dehydrogenase (PDH) and tricarboxylic acid (TCA) cycle were found to be lower in the arcA mutant and the arcAfnr double mutant strains than that in the wild-type strain, although the expression of the genes involved in these pathways have been proved to be derepressed in the mutant strains ([Shalel-Levanon, S., San, K.Y., Bennett, G.N., 2005a. Effect of ArcA and FNR on the expression of genes related to the oxygen regulation and the glycolysis pathway in Escherichia coli under microaerobic growth conditions. Biotechnol. Bioeng. 92, 147-159; Shalel-Levanon, S., San, K.Y., Bennett, G.N., 2005c. Effect of oxygen, and ArcA and FNR regulators on the expression of genes related to the electron transfer chain and the TCA cycle in Escherichia coli. Metab. Eng. 7, 364-374]). The significantly higher lactate production in the arcAfnr double mutant strain was shown to be an indirect effect caused by the reduced pyruvate formate-lyase (PFL) and PDH fluxes as well as the intracellular redox state.

Estradiol stimulates the biosynthetic pathways of breast cancer cells: detection by metabolic flux analysis

Selective estrogen receptor (ER) modulators are highly successful breast cancer therapies, but they are not effective in patients with ER negative and selective estrogen receptor modulator (SERM)-resistant tumors. Understanding the mechanisms of estrogen-stimulated proliferation may provide a route to design estrogen-independent therapies that would be effective in these patients. In this study, metabolic flux analysis was used to determine the intracellular fluxes that are significantly affected by estradiol stimulation in MCF-7 breast cancer cells. Intracellular fluxes were calculated from nuclear magnetic resonance (NMR)-generated isotope enrichment data and extracellular metabolite fluxes, using a specific flux analysis algorithm. The metabolic pathway model used by the algorithm includes glycolysis, the tricarboxylic acid cycle (TCA cycle), the pentose phosphate pathway, glutamine catabolism, pyruvate carboxylase, and malic enzyme. The pathway model also incorporates mitochondrial compartmentalization and reversible trans-mitochondrial membrane reactions to more accurately describe the role of mitochondria in cancer cell proliferation. Flux results indicate that estradiol significantly increases carbon flow through the pentose phosphate pathway and increases glutamine consumption. In addition, intra-mitochondrial malic enzyme was found to be inactive and the malate-aspartate shuttle (MAS) was only minimally active. The inactivity of these enzymes indicates that glutamine is not oxidized within mitochondria, but is consumed primarily to provide biosynthetic precursors. The excretion of glutamine carbons from the mitochondria has the secondary effect of limiting nicotinamide adenine dinucleotide (NADH) recycle, resulting in NADH buildup in the cytosol and the excretion of lactate. The observed dependence of breast cancer cells on pentose phosphate pathway activity and glutamine consumption for estradiol-stimulated biosynthesis suggests that these pathways may be targets for estrogen-independent breast cancer therapies.

Planning optimal measurements of isotopomer distributions for estimation of metabolic fluxes

MOTIVATION

Flux estimation using isotopomer information of metabolites is currently the most reliable method to obtain quantitative estimates of the activity of metabolic pathways. However, the development of isotopomer measurement techniques for intermediate metabolites is a demanding task. Careful planning of isotopomer measurements is thus needed to maximize the available flux information while minimizing the experimental effort.

RESULTS

In this paper we study the question of finding the smallest subset of metabolites to measure that ensure the same level of isotopomer information as the measurement of every metabolite in the metabolic network. We study the computational complexity of this optimization problem in the case of the so-called positional enrichment data, give methods for obtaining exact and fast approximate solutions, and evaluate empirically the efficacy of the proposed methods by analyzing a metabolic network that models the central carbon metabolism of Saccharomyces cerevisiae.

A novelgndmutation leading to increased l-lysine production inCorynebacterium glutamicum

Toward more efficient L-lysine production, we have been challenging genome-based strain breeding by the approach of assembling only relevant mutations in a single wild-type background. Following the creation of a new L-lysine producer Corynebacterium glutamicum AHP-3 that carried three useful mutations (lysC311, hom59, and pyc458) on the relevant downstream pathways, we shifted our target to the pentose phosphate pathway. Comparative genomic analysis for the pathway between a classically derived L-lysine producer and its parental wild-type identified several mutations. Among these mutations, a Ser-361-->Phe mutation in the 6-phosphogluconate dehydrogenase gene (gnd) was defined as a useful mutation for L-lysine production. Introduction of the gnd mutation into strain AHP-3 by allelic replacement led to approximately 15% increased L-lysine production. Enzymatic analysis revealed that the mutant enzyme was less sensitive than the wild-type enzyme to allosteric inhibition by intracellular metabolites, such as fructose 1,6-bisphosphate, D-glyceraldehyde 3-phosphate, phosphoribosyl pyrophosphate, ATP, and NADPH, which were known to inhibit this enzyme. Isotope-based metabolic flux analysis demonstrated that the gnd mutation resulted in 8% increased carbon flux through the pentose phosphate pathway during L-lysine production. These results indicate that the gnd mutation is responsible for diminished allosteric regulation and contributes to redirection of more carbon to the pentose phosphate pathway that was identified as the primary source for NADPH essential for L-lysine biosynthesis, thereby leading to improved product formation.

Quantification of compartmented metabolic fluxes in developing soybean embryos by employing biosynthetically directed fractional (13)C labeling, two-dimensional [(13)C, (1)H] nuclear magnetic resonance, and comprehensive isotopomer balancing

Metabolic flux quantification in plants is instrumental in the detailed understanding of metabolism but is difficult to perform on a systemic level. Toward this aim, we report the development and application of a computer-aided metabolic flux analysis tool that enables the concurrent evaluation of fluxes in several primary metabolic pathways. Labeling experiments were performed by feeding a mixture of U-(13)C Suc, naturally abundant Suc, and Gln to developing soybean (Glycine max) embryos. Two-dimensional [(13)C, (1)H] NMR spectra of seed storage protein and starch hydrolysates were acquired and yielded a labeling data set consisting of 155 (13)C isotopomer abundances. We developed a computer program to automatically calculate fluxes from this data. This program accepts a user-defined metabolic network model and incorporates recent mathematical advances toward accurate and efficient flux evaluation. Fluxes were calculated and statistical analysis was performed to obtain sds. A high flux was found through the oxidative pentose phosphate pathway (19.99 +/- 4.39 micromol d(-1) cotyledon(-1), or 104.2 carbon mol +/- 23.0 carbon mol per 100 carbon mol of Suc uptake). Separate transketolase and transaldolase fluxes could be distinguished in the plastid and the cytosol, and those in the plastid were found to be at least 6-fold higher. The backflux from triose to hexose phosphate was also found to be substantial in the plastid (21.72 +/- 5.00 micromol d(-1) cotyledon(-1), or 113.2 carbon mol +/-26.0 carbon mol per 100 carbon mol of Suc uptake). Forward and backward directions of anaplerotic fluxes could be distinguished. The glyoxylate shunt flux was found to be negligible. Such a generic flux analysis tool can serve as a quantitative tool for metabolic studies and phenotype comparisons and can be extended to other plant systems.

Identification of the Entner-Doudoroff pathway in an antibiotic-producing actinomycete species

The metabolic network of the central carbon metabolism represents the backbone of cellular metabolism and provides the precursors and cofactors required for synthesis of secondary metabolites. It is therefore pivotal to map the operating metabolic network in the central carbon metabolism in order to design metabolic engineering strategies towards construction of more efficient producers of specific metabolites. In this context, methods that allow rapid and reliable mapping of the central carbon metabolism are valuable. In the present study, a (13)C labelling-based method was used to identify the primary metabolic pathways of the poorly characterized antibiotic-producing actinomycete Nonomuraea sp. ATCC 39727. Surprisingly, it was found that Nonomuraea sp. ATCC 39272 predominantly metabolizes glucose via the Entner-Doudoroff (ED) pathway. This represents the first time that the ED pathway has been recognized as the main catabolic pathway in an actinomycete. The Nonomuraea genes encoding the key enzymes of the ED pathway were subsequently identified, sequenced and functionally described.

Responses of the central metabolism in Escherichia coli to phosphoglucose isomerase and glucose-6-phosphate dehydrogenase knockouts

The responses of Escherichia coli central carbon metabolism to knockout mutations in phosphoglucose isomerase and glucose-6-phosphate (G6P) dehydrogenase genes were investigated by using glucose- and ammonia-limited chemostats. The metabolic network structures and intracellular carbon fluxes in the wild type and in the knockout mutants were characterized by using the complementary methods of flux ratio analysis and metabolic flux analysis based on [U-(13)C]glucose labeling and two-dimensional nuclear magnetic resonance (NMR) spectroscopy of cellular amino acids, glycerol, and glucose. Disruption of phosphoglucose isomerase resulted in use of the pentose phosphate pathway as the primary route of glucose catabolism, while flux rerouting via the Embden-Meyerhof-Parnas pathway and the nonoxidative branch of the pentose phosphate pathway compensated for the G6P dehydrogenase deficiency. Furthermore, additional, unexpected flux responses to the knockout mutations were observed. Most prominently, the glyoxylate shunt was found to be active in phosphoglucose isomerase-deficient E. coli. The Entner-Doudoroff pathway also contributed to a minor fraction of the glucose catabolism in this mutant strain. Moreover, although knockout of G6P dehydrogenase had no significant influence on the central metabolism under glucose-limited conditions, this mutation resulted in extensive overflow metabolism and extremely low tricarboxylic acid cycle fluxes under ammonia limitation conditions.

Analysis of Escherichia coli anaplerotic metabolism and its regulation mechanisms from the metabolic responses to altered dilution rates and phosphoenolpyruvate carboxykinase knockout

The gluconeogenic phosphoenolpyruvate (PEP) carboxykinase is active in Escherichia coli during its growth on glucose. The present study investigated the influence of growth rates and PEP carboxykinase knockout on the anaplerotic fluxes in E. coli. The intracellular fluxes were determined using the complementary methods of flux ratio analysis and metabolic flux analysis based on [U-(13)C(6)]glucose labeling experiments and 2D nuclear magnetic resonance (NMR) spectroscopy of cellular amino acids and glycerol. Significant activity of PEP carboxykinase was identified in wild-type E. coli, and the ATP dissipation for the futile cycling via this reaction accounted for up to 8.2% of the total energy flux. Flux analysis of pck deletion mutant revealed that abolishment of PEP carboxykinase activity resulted in a remarkably reduced flux through the anaplerotic PEP carboxylase and the activation of the glyoxylate shunt, with 23% of isocitrate found being channeled in the glyoxylate shunt. The changes in intracellular metabolite concentrations and specific enzyme activities associated with different growth rates and pck deletion, were also determined. Combining the measurement data of in vivo fluxes, metabolite concentrations and enzyme activities, the in vivo regulations of PEP carboxykinase flux, PEP carboxylation, and glyoxylate shunt in E. coli are discussed.

Reconstruction of the central carbon metabolism ofAspergillus niger

The topology of central carbon metabolism of Aspergillus niger was identified and the metabolic network reconstructed, by integrating genomic, biochemical and physiological information available for this microorganism and other related fungi. The reconstructed network may serve as a valuable database for annotation of genes identified in future genome sequencing projects on aspergilli. Based on the metabolic reconstruction, a stoichiometric model was set up that includes 284 metabolites and 335 reactions, of which 268 represent biochemical conversions and 67 represent transport processes between the different intracellular compartments and between the cell and the extracellular medium. The stoichiometry of the metabolic reactions was used in combination with biosynthetic requirements for growth and pseudo-steady state mass balances over intracellular metabolites for the quantification of metabolic fluxes using metabolite balancing. This framework was employed to perform an in silico characterisation of the phenotypic behaviour of A. niger grown on different carbon sources. The effects on growth of single reaction deletions were assessed and essential biochemical reactions were identified for different carbon sources. Furthermore, application of the stoichiometric model for assessing the metabolic capabilities of A. niger to produce metabolites was evaluated by using succinate production as a case study.

Metabolic flux analysis of Escherichia coli K12 grown on 13C-labeled acetate and glucose using GC-MS and powerful flux calculation method

A new algorithm was developed for the estimation of the metabolic flux distribution based on GC-MS data of proteinogenic amino acids. By using a sensitive GC-MS protocol as well as by combining the global search algorithm such as the genetic algorithm with the local search algorithm such as the Levenberg-Marquardt algorithm, not only the distribution of the net fluxes in the entire network, but also certain exchange fluxes which contribute significantly to the isotopomer distribution could be quantified. This mass isotopomer analysis could identify the biochemical changes involved in the regulation where acetate or glucose was used as a main carbon source. The metabolic flux analysis clearly revealed that when the specific growth rate increased, only a slight change in flux distribution was observed for acetate metabolism, indicating that subtle regulation mechanism exists in certain key junctions of this network system. Different from acetate metabolism, when glucose was used as a carbon source, as the growth rate increased, a significant increase in relative pentose phosphate pathway (PPP) flux was observed for Escherichia coli K12 at the expense of the citric acid cycle, suggesting that when growing on glucose, the flux catalyzed by isocitrate dehydrogenase could not fully fulfill the NADPH demand for cell growth, causing the oxidative PPP to be utilized to a larger extent so as to complement the NADPH demand. The GC-MS protocol as well as the new algorithm demonstrated here proved to be a powerful tool for characterizing metabolic regulation and can be utilized for strain improvement and bioprocess optimization.

Cumulative bondomers: a new concept in flux analysis from 2D [13C,1H] COSY NMR data

A well-established way of determining metabolic fluxes is to measure 2D [(13)C,(1)H] COSY NMR spectra of components of biomass grown on uniformly (13)C-labeled carbon sources. When using the entire set of measured data to simultaneously determine all fluxes in a proposed metabolic network model, the (13)C-labeling distribution in all measured compounds has to be simulated. This requires very large sets of isotopomer or cumomer balances. This article introduces the new concept of bondomers; entities that only vary in the numbers and positions of C-C bonds that have remained intact since the medium substrate molecule entered the metabolism. Bondomers are shown to have many analogies to isotopomers. One of these is that bondomers can be transformed to cumulative bondomers, just like isotopomers can be transformed to cumomers. Similarly to cumomers, cumulative bondomers allow an analytical solution of the entire set of balances describing a metabolic network. The main difference is that cumulative bondomer models are considerably smaller than corresponding cumomer models. This saves computational time, allows easier identifiability analysis, and yields new insights in the information content of 2D [(13)C,(1)H] COSY NMR data. We illustrate the theoretical concepts by means of a realistic example of the glycolytic and pentose phosphate pathways. The combinations of 2D [(13)C,(1)H] COSY NMR data that allow identification of all metabolic fluxes in these pathways are analyzed, and it is found that the NMR data contain less information than was previously expected.

Genealogy profiling through strain improvement by using metabolic network analysis: metabolic flux genealogy of several generations of lysine-producing corynebacteria

A comprehensive approach of metabolite balancing, (13)C tracer studies, gas chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time of flight mass spectrometry, and isotopomer modeling was applied for comparative metabolic network analysis of a genealogy of five successive generations of lysine-producing Corynebacterium glutamicum. The five strains examined (C. glutamicum ATCC 13032, 13287, 21253, 21526, and 21543) were previously obtained by random mutagenesis and selection. Throughout the genealogy, the lysine yield in batch cultures increased markedly from 1.2 to 24.9% relative to the glucose uptake flux. Strain optimization was accompanied by significant changes in intracellular flux distributions. The relative pentose phosphate pathway (PPP) flux successively increased, clearly corresponding to the product yield. Moreover, the anaplerotic net flux increased almost twofold as a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes to cover the increased demand for lysine formation; thus, the overall increase was a consequence of concerted regulation of C(3) carboxylation and C(4) decarboxylation fluxes. The relative flux through isocitrate dehydrogenase dropped from 82.7% in the wild type to 59.9% in the lysine-producing mutants. In contrast to the NADPH demand, which increased from 109 to 172% due to the increasing lysine yield, the overall NADPH supply remained constant between 185 and 196%, resulting in a decrease in the apparent NADPH excess through strain optimization. Extrapolated to industrial lysine producers, the NADPH supply might become a limiting factor. The relative contributions of PPP and the tricarboxylic acid cycle to NADPH generation changed markedly, indicating that C. glutamicum is able to maintain a constant supply of NADPH under completely different flux conditions. Statistical analysis by a Monte Carlo approach revealed high precision for the estimated fluxes, underlining the fact that the observed differences were clearly strain specific.

Computing positional isotopomer distributions from tandem mass spectrometric data

The isotopomer distributions of metabolites are invaluable pieces of information in the computation of the flux distribution in a metabolic network. We describe the use of tandem mass spectrometry with the daughter ion scanning technique in the discovery of positional isotopomer distributions (PID). This technique increases the possibilities of mass spectrometry since given the same fragment ions, it uncovers more information than the full scanning mode. The mathematics of the new technique is slightly more complicated than the techniques needed by full scanning mode methods. Our experiments, however, show that in practice the inadequacy of the fragmentation of amino acids in the tandem mass spectrometer does not allow uncovering the PID exactly even if the daughter ion scanning is used. The computational techniques have been implemented in a MATLAB application called PIDC (Positional Isotopomer Distribution Calculator).

Metabolic flux analysis in Synechocystis using isotope distribution from 13C-labeled glucose

Using the carbon isotope labeling technique, the response of cyanobacterial central carbon metabolism to the change in environmental conditions was investigated. Synechocystis was grown in the heterotrophic and mixotrophic cultures fed with 13C-labeled glucose. The labeling patterns of the amino acids in biomass hydrolysates for both cultures were detected by the two-dimensional 1H-13C correlation nuclear magnetic resonance (2D 1H-13C COSY NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) technique. The in vivo intracellular flux distributions were then quantitated from the labeling measurements and metabolite balances using a parameters fitting approach. From the estimated flux distributions, it was found that the pentose phosphate pathway was the major pathway of glucose catabolism in the heterotrophic culture, while in the mixotrophic culture, the flux of CO2 fixation through the Calvin cycle was about two-fold of the glucose input flux. The relative flux through the phosphoenolpyruvate carboxylase was very high in both cultures, and this reaction represented about 25% of the assimilated CO2 in the mixotrophic culture. More importantly, we found a substantial outflow from the tricarboxylic acid cycle to glycolysis pathway carried by the malic enzyme, demonstrating the operation of a C4 pathway in cyanobacterial cells through the PEP carboxylase and malic enzyme. The estimated flux distributions also revealed that the NADPH synthesis was in excess relative to its requirement, and the excess NADPH might be reoxidized in cyanobacterial respiration to provide the energy for cellular requirement. Moreover, the analyzed result also suggested that the activity of the respiratory electron transport chain in cyanobacterial cells was not inhibited by light.

Analysis of flux estimates based on (13)C-labelling experiments

Modelling of the fluxes in central metabolism can be performed by combining labelling experiments with metabolite balancing. Using this approach, multiple samples from a cultivation of Saccharomyces cerevisiae in metabolic and isotopic steady state were analysed, and the metabolic fluxes in central metabolism were estimated. In the various samples, the estimates of the central metabolic pathways, the tricarboxylic acid cycle, the oxidative pentose phosphate pathway and the anaplerotic pathway, showed an unprecedented reproducibility. The high reproducibility was obtained with fractional labellings of individual carbon atoms as the calculational base, illustrating that the more complex modelling using isotopomers is not necessarily superior with respect to reproducibility of the flux estimates. Based on these results some general difficulties in flux estimation are discussed.

Metabolic network analysis of an adipoyl-7-ADCA-producing strain of Penicillium chrysogenum: elucidation of adipate degradation

An adipoyl-7-ADCA-producing, recombinant strain of Penicillium chrysogenum was characterized by metabolic network analysis, with special focus on the degradation of adipate and determination of the metabolic fluxes. Degradation of the side-chain precursor, adipate, causes an undesired consumption of adipate in the production of 7-ADCA. Using (13)C-labeled glucose and measurement of metabolite labeling patterns, it was shown that adipate was degraded by beta-oxidation to succinyl-CoA and acetyl-CoA. The labeling analysis indicated that degradation of adipate was taking place in the microbodies and the formed acetyl-CoA was metabolized in the glyoxylate shunt. This hypothesis was further substantiated by an enzyme assay, which showed activity of the key enzyme in the glyoxylate shunt. Flux estimations in two chemostat cultures, one with and one without adipate in the feed, revealed that degradation of adipate replaces the net anaplerotic reaction from pyruvate to oxaloacetate. Thus, with a combination of labeling experiments and enzyme assays, the pathway of adipate degradation was elucidated, and the effect of adipate degradation on the primary metabolism was quantified.

Control motifs for intracellular regulatory networks

A number of technological innovations are yielding unprecedented data on the networks of biochemical, genetic, and biophysical reactions that underlie cellular behavior and failure. These networks are composed of hundreds to thousands of chemical species and structures, interacting via nonlinear and possibly stochastic physical processes. A central goal of modern biology is to optimally use the data on these networks to understand how their design leads to the observed cellular behaviors and failures. Ultimately, this knowledge should enable cellular engineers to redesign cellular processes to meet industrial needs (such as optimal natural product synthesis), aid in choosing the most effective targets for pharmaceuticals, and tailor treatment for individual genotypes. The size and complexity of these networks and the inevitable lack of complete data, however, makes reaching these goals extremely difficult. If it proves possible to modularize these networks into functional subnetworks, then these smaller networks may be amenable to direct analysis and might serve as regulatory motifs. These motifs, recurring elements of control, may help to deduce the structure and function of partially known networks and form the basis for fulfilling the goals described above. A number of approaches to identifying and analyzing control motifs in intracellular networks are reviewed.

Metabolic flux responses to pyruvate kinase knockout in Escherichia coli

The intracellular carbon flux distribution in wild-type and pyruvate kinase-deficient Escherichia coli was estimated using biosynthetically directed fractional 13C labeling experiments with [U-13C6]glucose in glucose- or ammonia-limited chemostats, two-dimensional nuclear magnetic resonance (NMR) spectroscopy of cellular amino acids, and a comprehensive isotopomer model. The general response to disruption of both pyruvate kinase isoenzymes in E. coli was a local flux rerouting via the combined reactions of phosphoenolpyruvate (PEP) carboxylase and malic enzyme. Responses in the pentose phosphate pathway and the tricarboxylic acid cycle were strongly dependent on the environmental conditions. In addition, high futile cycling activity via the gluconeogenic PEP carboxykinase was identified at a low dilution rate in glucose-limited chemostat culture of pyruvate kinase-deficient E. coli, with a turnover that is comparable to the specific glucose uptake rate. Furthermore, flux analysis in mutant cultures indicates that glucose uptake in E. coli is not catalyzed exclusively by the phosphotransferase system in glucose-limited cultures at a low dilution rate. Reliability of the flux estimates thus obtained was verified by statistical error analysis and by comparison to intracellular carbon flux ratios that were independently calculated from the same NMR data by metabolic flux ratio analysis.