INCA 2.0: A tool for integrated,dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state ¹³C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different ¹³C acetate tracers, we show that MFA based on dynamic ¹³C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined ¹³C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.     

Simultaneous Steady-state and Dynamic 13C NMR Can Differentiate Alternative Routes of Pyruvate Metabolism in Living Cancer Cells

Metabolic reprogramming facilitates cancer cell growth, so quantitative metabolic flux measurements could produce useful biomarkers. However, current methods to analyze flux in vivo provide either a steady-state overview of relative activities (infusion of ¹³C and analysis of extracted metabolites) or a dynamic view of a few reactions (hyperpolarized ¹³C spectroscopy). Moreover, although hyperpolarization has successfully quantified pyruvate-lactate exchanges, its ability to assess mitochondrial pyruvate metabolism is unproven in cancer. Here, we combined ¹³C hyperpolarization and isotopomer analysis to quantify multiple fates of pyruvate simultaneously. Two cancer cell lines with divergent pyruvate metabolism were incubated with thermally polarized [3-¹³C]pyruvate for several hours, then briefly exposed to hyperpolarized [1-¹³C]pyruvate during acquisition of NMR spectra using selective excitation to maximize detection of H[¹³C]O₃⁻ and [1-¹³C]lactate. Metabolites were then extracted and subjected to isotopomer analysis to determine relative rates of pathways involving [3-¹³C]pyruvate. Quantitation of hyperpolarized H[¹³C]O₃⁻ provided a single definitive metabolic rate, which was then used to convert relative rates derived from isotopomer analysis into quantitative fluxes. This revealed that H[¹³C]O₃⁻ appearance reflects activity of pyruvate dehydrogenase rather than pyruvate carboxylation followed by subsequent decarboxylation reactions. Glucose substantially altered [1-¹³C]pyruvate metabolism, enhancing exchanges with [1-¹³C]lactate and suppressing H[¹³C]O₃⁻ formation. Furthermore, inhibiting Akt, an oncogenic kinase that stimulates glycolysis, reversed these effects, indicating that metabolism of pyruvate by both LDH and pyruvate dehydrogenase is subject to the acute effects of oncogenic signaling on glycolysis. The data suggest that combining ¹³C isotopomer analyses and dynamic hyperpolarized ¹³C spectroscopy may enable quantitative flux measurements in living tumors.     

Mass spectrometry for metabolic flux analysis

Mass spectrometry in combination with tracer experiments based on 13C substrates can serve as a powerful tool for the modeling and analysis of intracellular fluxes and the investigation of biochemical networks. The theoretical background for the application of mass spectrometry to metabolic flux analysis is discussed. Mass spectrometry methods are especially useful to determine mass distribution of metabolites. Additional information gained from fragmentation of metabolites, e.g., by electron impact ionization, allows further localization of labeling positions, up to complete resolution of isotopomer pools. To effectively handle mass distributions in simulation experiments, a matrix based general methodology is formulated. The natural isotope distribution of carbon, oxygen, hydrogen and nitrogen in the target metabolites is considered by introduction of correction matrices. It is shown by simulation results for the central carbon metabolism that neglecting natural isotope distributions causes significant errors in intracellular flux distributions. By varying relative fluxes into pentosephosphate pathway and pyruvate carboxylation reaction, marked changes in the mass distributions of metabolites result, which are illustrated for pyruvate, oxaloacetate, and alpha-ketoglutarate. In addition mass distributions of metabolites are significantly influenced over a broad range by the degree of reversibility of transaldolase and transketolase reactions in the pentosephosphate pathway. The mass distribution of metabolites is very sensitive towards intracellular flux patterns and can be measured with high accuracy by routine mass spectrometry methods. Copyright 1999 John Wiley & Sons, Inc.     

COMPLETE-MFA: Complementary parallel labeling experiments technique for metabolic flux analysis

We have developed a novel approach for measuring highly accurate and precise metabolic fluxes in living cells, termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. The COMPLETE-MFA method is based on combined analysis of multiple isotopic labeling experiments, where the synergy of using complementary tracers greatly improves the precision of estimated fluxes. In this work, we demonstrate the COMPLETE-MFA approach using all singly labeled glucose tracers, [1-¹³C], [2-¹³C], [3-¹³C], [4-¹³C], [5-¹³C], and [6-¹³C]glucose to determine precise metabolic fluxes for wild-type Escherichia coli. Cells were grown in six parallel cultures on defined medium with glucose as the only carbon source. Mass isotopomers of biomass amino acids were measured by gas chromatography–mass spectrometry (GC–MS). The data from all six experiments were then fitted simultaneously to a single flux model to determine accurate intracellular fluxes. We obtained a statistically acceptable fit with more than 300 redundant measurements. The estimated flux map is the most precise flux result obtained thus far for E. coli cells. To our knowledge, this is the first time that six isotopic labeling experiments have been successfully integrated for high-resolution ¹³C-flux analysis.     

An improved method for statistical analysis of metabolic flux analysis using isotopomer mapping matrices with analytical expressions

Analytical expressions were derived for calculating the sensitivities of isotopomer distribution vectors, the weighted output matrix with respect to the fluxes, and the covariance matrix for the metabolic flux analysis based on isotopomers mapping matrices (IMM). These expressions allow us to implement efficient statistical analysis, avoiding the time-consuming Monte Carlo techniques for estimating the confidence interval of the fluxes. The analytical expressions are also useful in implementing a faster design of experiment, which requires repetitive computation of the covariance matrix that is not straightforward to make in practice with the numerical techniques based on the conventional IMM. The proposed method was applied for analyzing the central carbon metabolism of the mixotrophically cultivated Synechocystis sp. PCC6803, and the confidence intervals of all its fluxes were computed based on the isotopomer distribution measured using NMR and GC-MS. It was found that the best feasible mixture for labeling experiment is 70% unlabeled, 10% [U-13C] and 20% [1,2-13C2] labeled glucose to obtain the most reliable metabolic fluxes.     

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

A method for the quantification of intracellular metabolic flux distributions from steady-state mass balance constraints and from the constraints posed by the measured 13C labeling state of biomass components is presented. Two-dimensional NMR spectroscopy is used to analyze the labeling state of cell protein hydrolysate and cell wall components. No separation of the biomass hydrolysate is required to measure the degree of 13C-13C coupling and the fractional 13C enrichment in various carbon atom positions. A mixture of [1-13C]glucose and uniformly labeled [13C6]glucose is applied to make fractional 13C enrichment data and measurements of the degree of 13C-13C coupling informative with respect to the intracellular flux distribution. Simulation models that calculate the complete isotopomer distribution in biomass components on the basis of isotopomer mapping matrices are used for the estimation of intracellular fluxes by least-squares minimization. The statistical quality of the estimated intracellular flux distributions is assessed by Monte Carlo methods. Principal component analysis is performed on the outcome of the Monte Carlo procedure to identify groups of fluxes that contribute major parts to the total variance in the multiple flux estimations. The methods described are applied to a steady-state culture of a glucoamylase-producing recombinant Aspergillus niger strain.     

Current status of 13C-metabolic flux analysis and future perspectives

Current ¹³C-metabolic flux analysis methods were reviewed as well as the weakness of the conventional metabolic flux analysis without ¹³C-labeled experiments. Although it has been recognized that ¹³C-labeling technique is powerful in estimating the metabolic fluxes, and the program-based flux analysis is necessary, one may not be confident with the result obtained without experiences and exhaustive trial and errors in practice due to its black box nature. In the present article, we call attention to the importance of investigating the relationships between fluxes and isotopomer or mass isotopomer distributions to understand the mechanism of generating specific isotopomers. Then, the experimental design for the preferred mixture of the specific ¹³C-labeled substrate was discussed. The effect of the reversibility in the bidirectional flux on the isotopomer distribution was also mentioned, and it was shown why the reliability of the bidirectional fluxes becomes lower. Moreover, by noting that recent development of measurement techniques enables us to measure the isotopomer patterns of intracellular metabolites instead of proteinogenic amino acids, it is mentioned that this enables us to estimate the flux changes during time-variant batch culture. Some future perspectives are discussed in relation to the integration of different levels of information in the cell.     

Isotopomer enrichment assay for very short chain fatty acids and its metabolic applications

The present work illustrated an accurate GC/MS measurement for the low isotopomer enrichment assay of formic acid, acetic acid, propionic aicd, butyric acid, and pentanoic acid. The pentafluorobenzyl bromide derivatives of these very short chain fatty acids have high sensitivity of isotopoic enrichment due to their low natural isotopomer distribution in negative chemical ionization mass spectrometric mode. Pentafluorobenzyl bromide derivatization reaction was optimized in terms of pH, temperature, reaction time, and the amount of pentafluorobenzyl bromide versus sample. The precision, stability, and accuracy of this method for the isotopomer analysis were validated. This method was applied to measure the enrichments of formic acid, acetic acid, and propionic acid in the perfusate from rat liver exposed to Krebs–Ringer bicarbonate buffer only, 0–1 mM [3,4-¹³C₂]-4-hydroxynonanoate, and 0–2 mM [5,6,7-¹³C₃]heptanoate. The enrichments of acetic acid and propionic acid in the perfusate are comparable to the labeling pattern of acetyl-CoA and propionyl-CoA in the rat liver tissues. The enrichment of the acetic acid assay is much more sensitive and precise than the enrichment of acetyl-CoA by LC-MS/MS. The reversibility of propionyl-CoA from succinyl-CoA was confirmed by the low labeling of M1 and M2 of propionic acid from [5,6,7-¹³C₃]heptanoate perfusates.     

Determination of full 13C isotopomer distributions for metabolic flux analysis using heteronuclear spin echo difference NMR spectroscopy

13C-isotopomer labeling experiments play an increasingly important role in the analysis of intracellular metabolic fluxes for genetic engineering purposes. 13C NMR spectroscopy is a key technique in the experimental determination of isotopomer distributions. However, only subsets of isotopomers can be quantitated using this technique due to redundancies in the scalar coupling patterns and due to invisibility of the 12C isotope in NMR. Therefore, we developed and describe in this paper a 1H NMR spectroscopy method that allows to determine the complete isotopomer distribution in metabolites having a backbone consisting of up to at least four carbons. The proposed pulse sequences employ up to three alternately applied frequency-selective inversion pulses in the 13C channel. In a first application study, the complete isotopomer distribution of aspartate isolated from [1-13C]ethanol-grown Ashbya gossypii was determined. A tentative model of the central metabolism of this organism was constructed and used for metabolic flux analysis. The aspartate isotopomer NMR data played a key role in the successful determination of the flux through the peroxisomal glyoxylate pathway. The new NMR method can be highly instrumental in generating the data upon which isotopomer labeling experiments for flux analysis, that are becoming increasingly important, are based.     

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.     

Gas chromatography-mass spectrometry analysis of 13C labeling in sugars for metabolic flux analysis

Metabolic flux analysis, using 13C labeled substrates, has become a powerful methodology for quantifying intracellular fluxes. Most often, analysis is restricted to nuclear magnetic resonance or mass spectrometry measurement of 13C label incorporation into protein amino acids. However, amino acid isotopomer distribution insufficiently covers the entire network of central metabolism, especially in plant cells with highly compartmented metabolism, and analysis of other metabolites is required. Analysis of label in saccharides provides complementary data to better define fluxes around hexose, pentose, and triose phosphate pools. Here, we propose a gas chromatography-mass spectrometry (GC-MS) method to analyze 13C labeling in glucose and fructose moieties of sucrose, free glucose, fructose, maltose, inositol, and starch. Our results show that saccharide labeling for isotopomer quantification is better analyzed by chemical ionization than by electron ionization. The structure of the generated fragments was simulated and validated using labeled standards. The method is illustrated by analysis of saccharides extracted from developing rapeseed (Brassica napus L.) embryos. It is shown that glucose 6-phosphate isomerase and plastidial glucose 6-phosphate transport reactions are not at equilibrium, and light is shed on the pathways leading to fructose, maltose, and inositol synthesis.     

Effects of Potassium and Glutamine on Metabolism of Glucose in Astrocytes

The metabolic effects of extracellular glutamine (2.5 mM) or high potassium (25 mM) on glucose metabolism were studied in cultured cerebellar astrocytes. High potassium caused an increased glycolytic flux and an increase in glutamine release. Exposure to glutamine increased glycolytic flux and alanine formation, indicating that glutamine uptake is an energy requiring process. The effects of glutamine and high potassium on glycolytic flux were additive. Formation of metabolites from [1-¹³C]glucose and [2-¹³C]acetate confirmed the effects of glutamine and high potassium on glycolytic metabolism. In the presence of extracellular glutamine, analysis of the ¹³C labeling patterns of citrate and glutamine indicated a decrease in the cycling ratio and/or pyruvate carboxylation and glutamine synthesis from [1-¹³C]glucose did occur, but was decreased. Exposure to high potassium led to extracellular accumulation of acetate, presumably through non-enzymatic decarboxylation of pyruvate.     

13C metabolic flux analysis for larger scale cultivation using gas chromatography-combustion-isotope ratio mass spectrometry

¹³C-based metabolic flux analysis (¹³CMFA) is limited to smaller scale experiments due to very high costs of labeled substrates. We measured ¹³C enrichment in proteinogenic amino acid hydrolyzates using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) from a series of parallel batch cultivations of Corynebacterium glutamicum utilizing mixtures of natural glucose and [1-¹³C] glucose, containing 0%, 0.5%, 1%, 2%, and 10% [1-¹³C] glucose. Decreasing the [1-¹³C] glucose content, kinetic isotope effects played an increasing role but could be corrected. From the corrected ¹³C enrichments in vivo fluxes in the central metabolism were determined by numerical optimization. The obtained flux distribution was very similar to those obtained from parallel labeling experiments using conventional high labeling GC-MS method and to published results. The GC-C-IRMS-based method involving low labeling degree of expensive tracer substrate, e.g. 1%, is well suited for larger laboratory and industrial pilot scale fermentations.     

Multiple Mass Isotopomer Tracing of Acetyl-CoA Metabolism in Langendorff-perfused Rat Hearts: CHANNELING OF ACETYL-CoA FROM PYRUVATE DEHYDROGENASE TO CARNITINE ACETYLTRANSFERASE*

We developed an isotopic technique to assess mitochondrial acetyl-CoA turnover (≈citric acid flux) in perfused rat hearts. Hearts are perfused with buffer containing tracer [¹³C₂,²H₃]acetate, which forms M5 + M4 + M3 acetyl-CoA. The buffer may also contain one or two labeled substrates, which generate M2 acetyl-CoA (e.g. [¹³C₆]glucose or [1,2-¹³C₂]palmitate) or/and M1 acetyl-CoA (e.g. [1-¹³C]octanoate). The total acetyl-CoA turnover and the contributions of fuels to acetyl-CoA are calculated from the uptake of the acetate tracer and the mass isotopomer distribution of acetyl-CoA. The method was applied to measurements of acetyl-CoA turnover under different conditions (glucose ± palmitate ± insulin ± dichloroacetate). The data revealed (i) substrate cycling between glycogen and glucose-6-P and between glucose-6-P and triose phosphates, (ii) the release of small excess acetyl groups as acetylcarnitine and ketone bodies, and (iii) the channeling of mitochondrial acetyl-CoA from pyruvate dehydrogenase to carnitine acetyltransferase. Because of this channeling, the labeling of acetylcarnitine and ketone bodies released by the heart are not proxies of the labeling of mitochondrial acetyl-CoA.     

Evidence for transketolase-like TKTL1 flux in CHO cells based on parallel labeling experiments and 13C-metabolic flux analysis

The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. It provides precursors for the biosynthesis of nucleotides and contributes to the production of reducing power in the form of NADPH. It has been hypothesized that mammalian cells may contain a hidden reaction in PPP catalyzed by transketolase-like protein 1 (TKTL1) that is closely related to the classical transketolase enzyme; however, until now there has been no direct experimental evidence for this reaction. In this work, we have applied state-of-the-art techniques in ¹³C metabolic flux analysis (¹³C-MFA) based on parallel labeling experiments and integrated flux fitting to estimate the TKTL1 flux in CHO cells. We identified a set of three parallel labeling experiments with [1-¹³C]glucose+[4,5,6-¹³C]glucose, [2-¹³C]glucose+[4,5,6-¹³C]glucose, and [3-¹³C]glucose+[4,5,6-¹³C]glucose and developed a new method to measure ¹³C-labeling of fructose 6-phosphate by GC-MS that allows intuitive interpretation of mass isotopomer distributions to determine key fluxes in the model, including glycolysis, oxidative PPP, non-oxidative PPP, and the TKTL1 flux. Using these tracers we detected a significant TKTL1 flux in CHO cells at the stationary phase. The flux results suggest that the main function of oxidative PPP in CHO cells at the stationary phase is to fuel the TKTL1 reaction. Overall, this study demonstrates for the first time that carbon atoms can be lost in the PPP, by means other than the oxidative PPP, and that this loss of carbon atoms is consistent with the hypothesized TKTL1 reaction in mammalian cells.     

Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices

Within the last decades NMR spectroscopy has undergone tremendous development and has become a powerful analytical tool for the investigation of intracellular flux distributions in biochemical networks using (13)C-labeled substrates. Not only are the experiments much easier to conduct than experiments employing radioactive tracer elements, but NMR spectroscopy also provides additional information on the labeling pattern of the metabolites. Whereas the maximum amount of information obtainable with (14)C-labeled substrates is the fractional enrichment in the individual carbon atom positions, NMR spectroscopy can also provide information on the degree of labeling at neighboring carbon atom positions by analyzing multiplet patterns in NMR spectra or using 2-dimensional NMR spectra. It is possible to quantify the mole fractions of molecules that show a specific labeling pattern, i.e., information of the isotopomer distribution in metabolite pools can be obtained. The isotopomer distribution is the maximum amount of information that in theory can be obtained from (13)C-tracer studies. The wealth of information contained in NMR spectra frequently leads to overdetermined algebraic systems. Consequently, fluxes must be estimated by nonlinear least squares analysis, in which experimental labeling data is compared with simulated steady state isotopomer distributions. Hence, mathematical models are required to compute the steady state isotopomer distribution as a function of a given set of steady state fluxes. Because 2(n) possible labeling patterns exist in a molecule of n carbon atoms, and each pattern corresponds to a separate state in the isotopomer model, these models are inherently complex. Model complexity, so far, has restricted usage of isotopomer information to relatively small metabolic networks. A general methodology for the formulation of isotopomer models is described. The model complexity of isotopomer models is reduced to that of classical metabolic models by expressing the 2(n) isotopomer mass balances of a metabolite pool in a single matrix equation. Using this approach an isotopomer model has been implemented that describes label distribution in primary carbon metabolism, i.e., in a metabolic network including the Embden-Meyerhof-Parnas and pentose phosphate pathway, the tricarboxylic acid cycle, and selected anaplerotic reaction sequences. The model calculates the steady state label distribution in all metabolite pools as a function of the steady state fluxes and is applied to demonstrate the effect of selected anaplerotic fluxes on the labeling pattern of the pathway intermediates. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55:831-840, 1997.     

Parallel labeling experiments validate Clostridium acetobutylicum metabolic network model for 13C metabolic flux analysis

In this work, we provide new insights into the metabolism of Clostridium acetobutylicum ATCC 824 obtained using a systematic approach for quantifying fluxes based on parallel labeling experiments and ¹³C-metabolic flux analysis (¹³C-MFA). Here, cells were grown in parallel cultures with [1-¹³C]glucose and [U-¹³C]glucose as tracers and ¹³C-MFA was used to quantify intracellular metabolic fluxes. Several metabolic network models were compared: an initial model based on current knowledge, and extended network models that included additional reactions that improved the fits of experimental data. While the initial network model did not produce a statistically acceptable fit of ¹³C-labeling data, an extended network model with five additional reactions was able to fit all data with 292 redundant measurements. The model was subsequently trimmed to produce a minimal network model of C. acetobutylicum for ¹³C-MFA, which could still reproduce all of the experimental data. The flux results provided valuable new insights into the metabolism of C. acetobutylicum. First, we found that TCA cycle was effectively incomplete, as there was no measurable flux between α-ketoglutarate and succinyl-CoA, succinate and fumarate, and malate and oxaloacetate. Second, an active pathway was identified from pyruvate to fumarate via aspartate. Third, we found that isoleucine was produced exclusively through the citramalate synthase pathway in C. acetobutylicum and that CAC3174 was likely responsible for citramalate synthase activity. These model predictions were confirmed in several follow-up tracer experiments. The validated metabolic network model established in this study can be used in future investigations for unbiased ¹³C-flux measurements in C. acetobutylicum.     

Characterization of Acetate and Pyruvate Metabolism in Suspension Cultures of Zea mays by C NMR Spectroscopy

Carbon-13 nuclear magnetic resonance (NMR) spectroscopy has been applied to the direct observation of acetate and pyruvate metabolism in suspension cultures of Zea mays (var Black Mexican Sweet). Growth of the corn cells in the presence of 2 millimolar [2-¹³C]acetate resulted in a rapid uptake of the substrate from the medium and initial labeling (0-4 hours) of primarily the intracellular glutamate and malate pools. Further metabolism of these intermediates resulted in labeling of glutamine, aspartate, and alanine. With [1-¹³C]acetate as the substrate very little incorporation into intermediary metabolites was observed in the ¹³C NMR spectra due to loss of the label as ¹³CO₂. Uptake of [3-¹³C]pyruvate by the cells was considerably slower than with [2-¹³C]acetate; however, the labelling patterns were similar with the exception of increased [3-¹³C] alanine generation with pyruvate as the substrate. Growth of the cells for up to 96 hours with 2 millimolar [3-¹³C]pyruvate ultimately resulted in labeling of valine, leucine, isoleucine, threonine, and the polyamine putrescine.     

Cortical metabolism in pyruvate dehydrogenase deficiency revealed by ex vivo multiplet C NMR of the adult mouse brain

The pyruvate dehydrogenase complex (PDC), required for complete glucose oxidation, is essential for brain development. Although PDC deficiency is associated with a severe clinical syndrome, little is known about its effects on either substrate oxidation or synthesis of key metabolites such as glutamate and glutamine. Computational simulations of brain metabolism indicated that a 25% reduction in flux through PDC and a corresponding increase in flux from an alternative source of acetyl-CoA would substantially alter the ¹³C NMR spectrum obtained from brain tissue. Therefore, we evaluated metabolism of [1,6-¹³C₂]glucose (oxidized by both neurons and glia) and [1,2-¹³C₂]acetate (an energy source that bypasses PDC) in the cerebral cortex of adult mice mildly and selectively deficient in brain PDC activity, a viable model that recapitulates the human disorder. Intravenous infusions were performed in conscious mice and extracts of brain tissue were studied by ¹³C NMR. We hypothesized that mice deficient in PDC must increase the proportion of energy derived from acetate metabolism in the brain. Unexpectedly, the distribution of ¹³C in glutamate and glutamine, a measure of the relative flux of acetate and glucose into the citric acid cycle, was not altered. The ¹³C labeling pattern in glutamate differed significantly from glutamine, indicating preferential oxidation of [1,2-¹³C]acetate relative to [1,6-¹³C]glucose by a readily discernible metabolic domain of the brain of both normal and mutant mice, presumably glia. These findings illustrate that metabolic compartmentation is preserved in the PDC-deficient cerebral cortex, probably reflecting intact neuron–glia metabolic interactions, and that a reduction in brain PDC activity sufficient to induce cerebral dysgenesis during development does not appreciably disrupt energy metabolism in the mature brain.     

<Superscript>13</Superscript>C metabolite profiling to compare the central metabolic flux in two yeast strains

¹³C metabolite profiling to quantify the dynamic changes of central carbon metabolites was attempted using mass isotopomer distribution analysis in two yeast strains, Saccharomyces cerevisiae and Kluyveromyces marxianus. Mass and isotopomer balances of the intermediates were examined and calculated in both yeast species and central carbon metabolic fluxes were successfully determined. Metabolic fluxes of pentose phosphate pathway in K. marxianus were 1.66 times higher than S. cerevisiae. The flux difference was also supported by relatively high abundance of partially labeled fructose 6-phosphate and 3-phosphoglycerate as well as an increased concentration of labeled L-valine in K. marxianus. Metabolic flux analysis combined with dynamic metabolite profiling has provided better understanding in the central carbon metabolic pathways of two model organisms and can be applied as a method to analyze more complicated metabolic networks in other organisms.