Parallel labeling experiments with [U-(13)C]glucose validate E. coli metabolic network model for (13)C metabolic flux analysis

(13)C-metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. (13)C MFA relies on several key assumptions: (1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; (2) (13)C-labeling measurements are accurate and precise; and (3) enzymes and transporters do not discriminate between (12)C- and (13)C-labeled metabolites. In this study, we tested these inherent assumptions of (13)C MFA for wild-type E. coli by parallel labeling experiments with [U-(13)C]glucose as tracer. Cells were grown in six parallel cultures in custom-constructed mini-bioreactors, starting from the same inoculum, on medium containing different mixtures of natural glucose and fully labeled [U-(13)C]glucose, ranging from 0% to 100% [U-(13)C]glucose. Macroscopic growth characteristics of E. coli showed no observable kinetic isotope effect. The cells grew equally well on natural glucose, 100% [U-(13)C]glucose, and mixtures thereof. (13)C MFA was then used to determine intracellular metabolic fluxes for several metabolic network models: an initial network model from literature; and extended network models that accounted for potential dilution effects of isotopic labeling. The initial network model did not give statistically acceptable fits and produced inconsistent flux results for the parallel labeling experiments. In contrast, an extended network model that accounted for dilution of intracellular CO(2) by exchange with extracellular CO(2) produced statistically acceptable fits, and the estimated metabolic fluxes were consistent for the parallel cultures. This study illustrates the importance of model validation for (13)C MFA. We show that an incomplete network model can produce statistically unacceptable fits, as determined by a chi-square test for goodness-of-fit, and return biased metabolic fluxes. The validated metabolic network model for E. coli from this study can be used in future investigations for unbiased metabolic flux measurements.     

Rational design of 13C-labeling experiments for metabolic flux analysis in mammalian cells

ABSTRACT: BACKGROUND: 13C-Metabolic flux analysis (13C-MFA) is a standard technique to probe cellular metabolism and elucidate in vivo metabolic fluxes. 13C-Tracer selection is an important step in conducting 13C-MFA, however, current methods are restricted to trial-and-error approaches, which commonly focus on an arbitrary subset of the tracer design space. To systematically probe the complete tracer design space, especially for complex systems such as mammalian cells, there is a pressing need for new rational approaches to identify optimal tracers. RESULTS: Recently, we introduced a new framework for optimal 13C-tracer design based on elementary metabolite units (EMU) decomposition, in which a measured metabolite is decomposed into a linear combination of so-called EMU basis vectors. In this contribution, we applied the EMU method to a realistic network model of mammalian metabolism with lactate as the measured metabolite. The method was used to select optimal tracers for the two free fluxes in the system, the oxidative pentose phosphate pathway (oxPPP) flux and anaplerosis by pyruvate carboxylase (PC). Our approach was based on sensitivity analysis of EMU basis vector coefficients with respect to free fluxes. Through efficient grouping of coefficient sensitivities, simple tracer selection rules were derived for high-resolution quantification of the fluxes in the mammalian network model. The approach resulted in a significant reduction of the number of possible tracers and the feasible tracers were evaluated using numerical simulations. Two optimal, novel tracers were identified that have not been previously considered for 13C-MFA of mammalian cells, specifically [2,3,4,5,6-13C]glucose for elucidating oxPPP flux and [3,4-13C]glucose for elucidating PC flux. We demonstrate that 13C-glutamine tracers perform poorly in this system in comparison to the optimal glucose tracers. CONCLUSIONS: In this work, we have demonstrated that optimal tracer design does not need to be a pure simulation-based trial-and-error process; rather, rational insights into tracer design can be gained through the application of the EMU basis vector methodology. Using this approach, rational labeling rules can be established a priori to guide the selection of optimal 13C-tracers for high-resolution flux elucidation in complex metabolic network models.     

Oxidation of glutamine by the splanchnic bed in humans

[1,2-(13)C(2)]glutamine and [ring-(2)H(5)]phenylalanine were infused for 7 h into five postabsorptive healthy subjects on two occasions. On one occasion, the tracers were infused intravenously for 3.5 h and then by a nasogastric tube for 3.5 h. The order of infusion was reversed on the other occasion. From the plasma tracer enrichment measurements at plateau during the intravenous and nasogastric infusion periods, we determined that 27 +/- 2% of the enterally delivered phenylalanine and 64 +/- 2% of the glutamine were removed on the first pass by the splanchnic bed. Glutamine flux was 303 +/- 8 micromol. kg(-1). h(-1). Of the enterally delivered [(13)C]glutamine tracer, 73 +/- 2% was recovered as exhaled CO(2) compared with 58 +/- 1% of the intravenously infused tracer. The fraction of the enterally delivered tracer that was oxidized specifically on the first pass by the splanchnic bed was 53 +/- 2%, comprising 83% of the total tracer extracted. From the appearance of (13)C in plasma glucose, we estimated that 7 and 10% of the intravenously and nasogastrically infused glutamine tracers, respectively, were converted to glucose. The results for glutamine flux and first-pass extraction were similar to our previously reported values when a [2-(15)N]glutamine tracer [Matthews DE, Morano MA, and Campbell RG, Am J Physiol Endocrinol Metab 264: E848-E854, 1993] was used. The results of [(13)C]glutamine tracer disposal demonstrate that the major fate of enteral glutamine extraction is for oxidation and that only a minor portion is used for gluconeogenesis.     

Optimal tracers for parallel labeling experiments and 13C metabolic flux analysis: A new precision and synergy scoring system

¹³C-Metabolic flux analysis (¹³C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by ¹³C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for ¹³C-MFA. The best single tracers were doubly ¹³C-labeled glucose tracers, including [1,6-¹³C]glucose, [5,6-¹³C]glucose and [1,2-¹³C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6-¹³C]glucose and [1,2-¹³C]glucose. Combined analysis of [1,6-¹³C]glucose and [1,2-¹³C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1-¹³C]glucose +20% [U-¹³C]glucose.     

Selection of tracers for 13C-metabolic flux analysis using elementary metabolite units (EMU) basis vector methodology

Metabolic flux analysis (MFA) is a powerful technique for elucidating in vivo fluxes in microbial and mammalian systems. A key step in (13)C-MFA is the selection of an appropriate isotopic tracer to observe fluxes in a proposed network model. Despite the importance of MFA in metabolic engineering and beyond, current approaches for tracer experiment design are still largely based on trial-and-error. The lack of a rational methodology for selecting isotopic tracers prevents MFA from achieving its full potential. Here, we introduce a new technique for tracer experiment design based on the concept of elementary metabolite unit (EMU) basis vectors. We demonstrate that any metabolite in a network model can be expressed as a linear combination of so-called EMU basis vectors, where the corresponding coefficients indicate the fractional contribution of the EMU basis vector to the product metabolite. The strength of this approach is the decoupling of substrate labeling, i.e. the EMU basis vectors, from the dependence on free fluxes, i.e. the coefficients. In this work, we demonstrate that flux observability inherently depends on the number of independent EMU basis vectors and the sensitivities of coefficients with respect to free fluxes. Specifically, the number of independent EMU basis vectors places hard limits on how many free fluxes can be determined in a model. This constraint is used as a guide for selecting feasible substrate labeling. In three example models, we demonstrate that by maximizing the number of independent EMU basis vectors the observability of a system is improved. Inspection of sensitivities of coefficients with respect to free fluxes provides additional constraints for proper selection of tracers. The present contribution provides a fresh perspective on an important topic in metabolic engineering, and gives practical guidelines and design principles for a priori selection of isotopic tracers for (13)C-MFA studies.     

Combining metabolic flux analysis tools and 13C NMR to estimate intracellular fluxes of cultured astrocytes

In this work, brain cell metabolism was investigated by (13)C NMR spectroscopy and metabolic flux analysis (MFA). Monotypic cultures of astrocytes were incubated with labeled glucose for 38 h, and the distribution of the label was analyzed by (13)C NMR spectroscopy. The analysis of the spectra reveals two distinct physiological states characterized by different ratios of pyruvate carboxylase to pyruvate dehydrogenase activities (PC/PDH). Intracellular flux distributions for both metabolic states were estimated by MFA using the isotopic information and extracellular rate measurements as constraints. The model was subsequently checked with the consistency index method. From a biological point of view, the occurrence of the two physiological states appears to be correlated with the presence or absence of extracellular glutamate. Concerning the model, it can be stated that the metabolic network and the set of constraints adopted provide a consistent and robust characterization of the astrocytic metabolism, allowing for the calculation of central intracellular fluxes such as pyruvate recycling, the anaplerotic flux mediated by pyruvate carboxylase, and the glutamine formation through glutamine synthetase.     

Metabolic network reconstruction,growth characterization and 13C-metabolic flux analysis of the extremophile Thermus thermophilus HB8

Thermus thermophilus is an extremely thermophilic bacterium with significant biotechnological potential. In this work, we have characterized aerobic growth characteristics of T. thermophilus HB8 at temperatures between 50 and 85 °C, constructed a metabolic network model of its central carbon metabolism and validated the model using ¹³C-metabolic flux analysis (¹³C–MFA). First, cells were grown in batch cultures in custom constructed mini-bioreactors at different temperatures to determine optimal growth conditions. The optimal temperature for T. thermophilus grown on defined medium with glucose was 81 °C. The maximum growth rate was 0.25 h⁻¹. Between 50 and 81 °C the growth rate increased by 7-fold and the temperature dependence was described well by an Arrhenius model with an activation energy of 47 kJ/mol. Next, we performed a ¹³C-labeling experiment with [1,2-¹³C] glucose as the tracer and calculated intracellular metabolic fluxes using ¹³C–MFA. The results provided support for the constructed network model and highlighted several interesting characteristics of T. thermophilus metabolism. We found that T. thermophilus largely uses glycolysis and TCA cycle to produce biosynthetic precursors, ATP and reducing equivalents needed for cells growth. Consistent with its proposed metabolic network model, we did not detect any oxidative pentose phosphate pathway flux or Entner-Doudoroff pathway activity. The biomass precursors erythrose-4-phosphate and ribose-5-phosphate were produced via the non-oxidative pentose phosphate pathway, and largely via transketolase, with little contribution from transaldolase. The high biomass yield on glucose that was measured experimentally was also confirmed independently by ¹³C–MFA. The results presented here provide a solid foundation for future studies of T. thermophilus and its metabolic engineering applications.     

Isotopic non-stationary 13C gluconate tracer method for accurate determination of the pentose phosphate pathway split-ratio in Penicillium chrysogenum

Current (13)C labeling experiments for metabolic flux analysis (MFA) are mostly limited by either the requirement of isotopic steady state or the extremely high computational effort due to the size and complexity of large metabolic networks. The presented novel approach circumvents these limitations by applying the isotopic non-stationary approach to a local metabolic network. The procedure is demonstrated in a study of the pentose phosphate pathway (PPP) split-ratio of Penicillium chrysogenum in a penicillin-G producing chemostat-culture grown aerobically at a dilution rate of 0.06h(-1) on glucose, using a tracer amount of uniformly labeled [U-(13)C(6)] gluconate. The rate of labeling inflow can be controlled by using different cell densities and/or different fractions of the labeled tracer in the feed. Due to the simplicity of the local metabolic network structure around the 6-phosphogluconate (6pg) node, only three metabolites need to be measured for the pool size and isotopomer distribution. Furthermore, the mathematical modeling of isotopomer distributions for the flux estimation has been reduced from large scale differential equations to algebraic equations. Under the studied cultivation condition, the estimated split-ratio (41.2+/-0.6%) using the novel approach, shows statistically no difference with the split-ratio obtained from the originally proposed isotopic stationary gluconate tracing method.     

Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli

The use of parallel labeling experiments for ¹³C metabolic flux analysis (¹³C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-¹³C]glucose and mixtures of [1-¹³C]glucose and [U-¹³C]glucose, four novel tracers were applied in this study: [2,3-¹³C]glucose, [4,5,6-¹³C]glucose, [2,3,4,5,6-¹³C]glucose and a mixture of [1-¹³C]glucose and [4,5,6-¹³C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-¹³C]glucose+20% [U-¹³C]glucose, while [4,5,6-¹³C]glucose and [5-¹³C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.     

Metabolic flux analysis in mammalian cell culture

Mammalian cell culture metabolism is characterized by glucoglutaminolysis, that is, high glucose and glutamine uptake combined with a high rate of lactate and non-essential amino acid secretion. Stress associated with acid neutralization and ammonia accumulation necessitates complex feeding schemes and limits cell densities achieved in fed-batch culture. Conventional and constraint-based metabolic flux analysis has been successfully used to study the metabolic phenotype of mammalian cells in culture, while ¹³C tracer analysis has been used to study small network models and validate assumptions of metabolism. Large-scale ¹³C metabolic flux analysis, which is required to improve confidence in the network models and their predictions, remains a major challenge. Advances in both modeling and analytical techniques are bringing this challenge within sight.     

Towards dynamic metabolic network measurements by multi-dimensional NMR-based fluxomics

Novel technologies for measuring biological systems and methods for visualizing data have led to a revolution in the life sciences. Nuclear magnetic resonance (NMR) techniques can provide information on metabolite structure and metabolic dynamics at the atomic level. We have been developing a new method for measuring the dynamic metabolic network of crude extracts that combines [(13)C(6)]glucose stable isotope labeling of Arabidopsis thaliana and multi-dimensional heteronuclear NMR analysis, whereas most conventional metabolic flux analyses examine proteinogenic amino acids that are specifically labeled with partially labeled substrates such as [2-(13)C(1)]glucose or 10% [(13)C(6)]glucose. To show the validity of our method, we investigated how to obtain information about biochemical reactions, C-C bond formation, and the cleavage of the main metabolites, such as free amino acids, in crude extracts based on the analysis of the (13)C-(13)C coupling pattern in 2D-NMR spectra. For example, the combination of different extraction solvents allows one to distinguish complicated (13)C-(13)C fine couplings at the C2 position of amino acids. As another approach, f1-f3 projection of the HCACO spectrum also helps in the analysis of (13)C-(13)C connectivities. Using these new methods, we present an example that involves monitoring the incorporation profile of [(13)C(6)]glucose into A. thaliana and its metabolic dynamics, which change in a time-dependent manner with atmospheric (12)CO(2) assimilation.     

Respirometric 13C flux analysis--Part II: in vivo flux estimation of lysine-producing Corynebacterium glutamicum

A novel method for metabolic flux studies of central metabolism which is based on respirometric (13)C flux analysis, i.e., parallel (13)C tracer studies with online CO(2) labeling measurements is applied to flux quantification of a lysine-producing mutant of Corynebacterium glutamicum. For this purpose, 3 respirometric (13)C labeling experiments with [1-(13)C(1)], [6-(13)C(1)] and [1,6-(13)C(2)] glucose were carried out in parallel. All fluxes comprising the reactions of glycolysis, of TCA cycle, of C3- and C4-metabolite interconversion and of lysine biosynthesis as well as the net reactions in the pentose phosphate pathway could be quantified solely using experimental data obtained from CO(2) labeling and extracellular rate measurements. At key branch points, 68+/-5% of glucose 6-phosphate were observed to be metabolized into pentose phosphate pathway and 48+/-1% of pyruvate into TCA cycle via pyruvate dehydrogenase. The results showed a good agreement with the previous studies using (13)C tracer cultivation and GC/MS analysis of proteinogenic amino acids. Also, respiratory quotient calculated from flux estimates using redox balance showed a high accordance with the value determined directly from the measured specific rates of O(2) consumption and CO(2) production. The results strongly support that the respirometric (13)C metabolic flux analysis is suited as an alternative to the conventional methods to study functional and regulatory activities of cells. The developed method is applicable to study growing or non-growing cells, primary and secondary metabolism and immobilized cells. Due to the non-accumulating nature of CO(2) labeling and instantaneous nature of the resulting fluxes, the method can also be used for dynamic profiling of metabolic activities. Therefore, it is complementary to conventional methods for metabolic flux analysis.     

13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split ratio in Penicillium chrysogenum

In this study we developed a new method for accurately determining the pentose phosphate pathway (PPP) split ratio, an important metabolic parameter in the primary metabolism of a cell. This method is based on simultaneous feeding of unlabeled glucose and trace amounts of [U-13C]gluconate, followed by measurement of the mass isotopomers of the intracellular metabolites surrounding the 6-phosphogluconate node. The gluconate tracer method was used with a penicillin G-producing chemostat culture of the filamentous fungus Penicillium chrysogenum. For comparison, a 13C-labeling-based metabolic flux analysis (MFA) was performed for glycolysis and the PPP of P. chrysogenum. For the first time mass isotopomer measurements of 13C-labeled primary metabolites are reported for P. chrysogenum and used for a 13C-based MFA. Estimation of the PPP split ratio of P. chrysogenum at a growth rate of 0.02 h(-1) yielded comparable values for the gluconate tracer method and the 13C-based MFA method, 51.8% and 51.1%, respectively. A sensitivity analysis of the estimated PPP split ratios showed that the 95% confidence interval was almost threefold smaller for the gluconate tracer method than for the 13C-based MFA method (40.0 to 63.5% and 46.0 to 56.5%, respectively). From these results we concluded that the gluconate tracer method permits accurate determination of the PPP split ratio but provides no information about the remaining cellular metabolism, while the 13C-based MFA method permits estimation of multiple fluxes but provides a less accurate estimate of the PPP split ratio.     

Metabolic profiling by 13C-NMR spectroscopy: [1,2-13C2]glucose reveals a heterogeneous metabolism in human leukemia T cells

Metabolic profiling is defined as the simultaneous assessment of substrate fluxes within and among the different pathways of metabolite synthesis and energy production under various physiological conditions. The use of stable-isotope tracers and the analysis of the distribution of labeled carbons in various intermediates, by both mass spectrometry and NMR spectroscopy, allow the role of several metabolic processes in cell growth and death to be defined. In the present paper we describe the metabolic profiling of Jurkat cells by isotopomer analysis using (13)C-NMR spectroscopy and [1,2-(13)C(2)]glucose as the stable-isotope tracer. The isotopomer analysis of the lactate, alanine, glutamate, proline, serine, glycine, malate and ribose-5-phosphate moiety of nucleotides has allowed original integrated information regarding the pentose phosphate pathway, TCA cycle, and amino acid metabolism in proliferating human leukemia T cells to be obtained. In particular, the contribution of the glucose-6-phosphate dehydrogenase and transketolase activities to phosphoribosyl-pyrophosphate synthesis was evaluated directly by the determination of isotopomers of the [1'-(13)C], [4',5'-(13)C(2)]ribosyl moiety of nucleotides. Furthermore, the relative contribution of the glycolysis and pentose cycle to lactate production was estimated via analysis of lactate isotopomers. Interestingly, pyruvate carboxylase and pyruvate dehydrogenase flux ratios measured by glutamate isotopomers and the production of isotopomers of several metabolites showed that the metabolic processes described could not take place simultaneously in the same macrocompartments (cells). Results revealed a heterogeneous metabolism in an asynchronous cell population that may be interpreted on the basis of different metabolic phenotypes of subpopulations in relation to different cell cycle phases.     

Modeling and experimental design for metabolic flux analysis of lysine-producing Corynebacteria by mass spectrometry

Experimental design of (13)C-tracer studies for metabolic flux analysis with mass spectrometric determination of labeling patterns was performed for the central metabolism of Corynebacterium glutamicum comprising various flux scenarios. Ratio measurement of mass isotopomer pools of Corynebacterium products lysine, alanine, and trehalose is sufficient to quantify the flux partitioning ratios (i) between glycolysis and pentose phosphate pathways (Phi(PPP)), (ii) between the split pathways in the lysine biosynthesis (Phi(DH)), (iii) at the pyruvate node (Phi(PC)), and reversibilities of (iv) glucose 6-phosphate isomerase (zeta(PGI)), (v) at the pyruvate node (zeta(PC/PEPCK)), and (vi) of transaldolase and transketolases in the PPP. Weighted sensitivities for flux parameters were derived from partial derivatives to quantitatively evaluate experimental approaches and predict precision for estimated flux parameters. Deviation of intensity ratios from ideal values of 1 was used as weighting function. Weighted flux sensitivities can be used to identify optimal type and degree of tracer labeling or potential intensity ratios to be measured. Experimental design for lysine-producing strain C. glutamicum MH 20-22B (Marx et al., Biotechnol. Bioeng. 49, 111-129, 1996) and various potential mutants with different alterations in the flux pattern showed that specific tracer labelings are optimal to quantify a certain flux parameter uninfluenced by the overall flux situation. Identified substrates of choice are [1-(13)C]glucose for the estimation of Phi(PPP) and zeta(PGI) and a 1 : 1 mixture of [U-(12)C/U-(13)C]glucose for the determination of zeta(PC/PEPCK). Phi(PC) can be quantified by feeding [4-(13)C]glucose or [U-(12)C/U-(13)C]glucose (1 : 1), whereas Phi(DH) is accessible via [4-(13)C]glucose. The sensitivity for the quantification of a certain flux parameter can be influenced by superposition through other flux parameters in the network, but substrate and measured mass isotopomers of choice remain the same. In special cases, reduced labeling degree of the tracer substrate can increase the precision of flux analysis. Enhanced precision and flux information can be achieved via multiply labeled substrates. The presented approach can be applied for effective experimental design of (13)C tracer studies for metabolic flux analysis. Intensity ratios of other products such as glutamate, valine, phenylalanine, and riboflavin also sensitively reflect flux parameters, which underlines the great potential of mass spectrometry for flux analysis.     

The thermodynamic meaning of metabolic exchange fluxes

Metabolic flux analysis (MFA) deals with the experimental determination of steady-state fluxes in metabolic networks. An important feature of the ¹³C MFA method is its capability to generate information on both directions of bidirectional reaction steps given by exchange fluxes. The biological interpretation of these exchange fluxes and their relation to thermodynamic properties of the respective reaction steps has never been systematically investigated. As a central result, it is shown here that for a general class of enzyme reaction mechanisms the quotients of net and exchange fluxes measured by ¹³C MFA are coupled to Gibbs energies of the reaction steps. To establish this relation the concept of apparent flux ratios of enzymatic isotope-labeling networks is introduced and some computing rules for these flux ratios are given. Application of these rules reveals a conceptional pitfall of ¹³C MFA, which is the inherent dependency of measured exchange fluxes on the chosen tracer atom. However, it is shown that this effect can be neglected for typical biochemical reaction steps under physiological conditions. In this situation, the central result can be formulated as a two-sided inequality relating fluxes, pool sizes, and standard Gibbs energies. This relation has far-reaching consequences for metabolic flux analysis, quantitative metabolomics, and network thermodynamics.     

Improving glucose and glutamine metabolism of human HEK 293 and Trichoplusia ni insect cells engineered to express a cytosolic pyruvate carboxylase enzyme

Metabolic engineering has been defined as a directed improvement of product formation or cellular properties by modification of specific biochemical pathways or introduction of new enzymatic reactions by recombinant DNA technology. The use of metabolic flux analysis (MFA) has helped in the understanding of the key limitation in the metabolic pathways of cultured animal cells. The MFA of the major nutrients glucose and glutamine showed that the flux of glucose to the TCA cycle and its subsequent utilization is limited as a result of the lack of certain key enzymes in the pathway. One of the key enzymes controlling this flux is pyruvate carboxylase. Introduction of this enzyme into mammalian cells has been shown to improve the utilization of glucose and limit the production of lactate and ammonia, which are deleterious to cell growth. In the present work a yeast pyruvate carboxylase gene has been introduced into mammalian (HEK 293) and insect (Trichoplusia ni High-Five) cells, resulting in the cytosolic expression of the enzyme. In both cases the resulting transfected cells were able to utilize glucose and glutamine more efficiently and produce lower amounts of lactate and ammonia. Differences in the amino acid utilization pattern were also observed, indicating changes in the basic metabolism of the cells. The performance of the transfected cells as expression systems for adenovirus and baculovirus vectors, respectively, has also been examined. The results obtained and their impact on the process development for protein and viral vector production are discussed.     

Evaluation of isotope discrimination in C-based metabolic flux analysis

In a ¹³C experiment for metabolic flux analysis (¹³C MFA), we examined isotope discrimination by measuring the labeling of glucose, amino acids, and hexose monophosphates via mass spectrometry. When Escherichia coli grew in a mix of 20% fully labeled and 80% naturally labeled glucose medium, the cell metabolism favored light isotopes and the measured isotopic ratios (δ¹³C) were in the range of −35 to −92. Glucose transporters might play an important role in such isotopic fractionation. Flux analysis showed that both isotopic discrimination and isotopic impurities in labeled substrates could affect the solution of ¹³C MFA.     

Simultaneous tracers and a unified model of positional and mass isotopomers for quantification of metabolic flux in liver

Computational models based on the metabolism of stable isotope tracers can yield valuable insight into the metabolic basis of disease. The complexity of these models is limited by the number of tracers and the ability to characterize tracer labeling in downstream metabolites. NMR spectroscopy is ideal for multiple tracer experiments since it precisely detects the position of tracer nuclei in molecules, but it lacks sensitivity for detecting low-concentration metabolites. GC-MS detects stable isotope mass enrichment in low-concentration metabolites, but lacks nuclei and positional specificity. We performed liver perfusions and in vivo infusions of ²H and ¹³C tracers, yielding complex glucose isotopomers that were assigned by NMR and fit to a newly developed metabolic model. Fluxes regressed from ²H and ¹³C NMR positional isotopomer enrichments served to validate GC-MS-based flux estimates obtained from the same experimental samples. NMR-derived fluxes were largely recapitulated by modeling the mass isotopomer distributions of six glucose fragment ions measured by GC-MS. Modest differences related to limited fragmentation coverage of glucose C1–C3 were identified, but fluxes such as gluconeogenesis, glycogenolysis, cataplerosis and TCA cycle flux were tightly correlated between the methods. Most importantly, modeling of GC-MS data could assign fluxes in primary mouse hepatocytes, an experiment that is impractical by ²H or ¹³C NMR.