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.     

Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis

Glucose and xylose are the two most abundant sugars derived from the breakdown of lignocellulosic biomass. While aerobic glucose metabolism is relatively well understood in E. coli, until now there have been only a handful of studies focused on anaerobic glucose metabolism and no ¹³C-flux studies on xylose metabolism. In the absence of experimentally validated flux maps, constraint-based approaches such as MOMA and RELATCH cannot be used to guide new metabolic engineering designs. In this work, we have addressed this critical gap in current understanding by performing comprehensive characterizations of glucose and xylose metabolism under aerobic and anaerobic conditions, using recent state-of-the-art techniques in ¹³C metabolic flux analysis (¹³C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated ¹³C-MFA using the optimal tracers [1,2-¹³C]glucose, [1,6-¹³C]glucose, [1,2-¹³C]xylose and [5-¹³C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U-¹³C]glucose and [U-¹³C]xylose tracers. We demonstrated that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO₂ originates from biomass turnover. Using knockout strains, we also demonstrated that β-oxidation is critical for anaerobic growth on xylose. Quantitative analysis of co-factor balances (NADH/FADH₂, NADPH, and ATP) for different growth conditions provided new insights regarding the interplay of energy and redox metabolism and the impact on E. coli cell physiology.     

Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by 13C metabolic flux analysis and whole genome sequencing

We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and ¹³C-metabolic flux analysis (¹³C-MFA) with [1,6-¹³C]glucose, [5-¹³C]xylose, and [1,6-¹³C]glucose+[5-¹³C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~2-fold), increased biomass yield, increased tolerance to high temperatures up to 90 °C, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81 °C, the maximum growth rate on glucose and xylose was 0.44 and 0.46 h⁻¹, respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. ¹³C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, ¹³C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, ¹³C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.     

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.     

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.     

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.     

Metabolic flux analysis of CHO cells at growth and non-growth phases using isotopic tracers and mass spectrometry

Chinese hamster ovary (CHO) cells are the main platform for production of biotherapeutics in the biopharmaceutical industry. However, relatively little is known about the metabolism of CHO cells in cell culture. In this work, metabolism of CHO cells was studied at the growth phase and early stationary phase using isotopic tracers and mass spectrometry. CHO cells were grown in fed-batch culture over a period of six days. On days 2 and 4, [1,2-¹³C] glucose was introduced and the labeling of intracellular metabolites was measured by gas chromatography-mass spectrometry (GC–MS) at 6, 12 and 24 h following the introduction of tracer. Intracellular metabolic fluxes were quantified from measured extracellular rates and ¹³C-labeling dynamics of intracellular metabolites using non-stationary ¹³C-metabolic flux analysis (¹³C-MFA). The flux results revealed significant rewiring of intracellular metabolic fluxes in the transition from growth to non-growth, including changes in energy metabolism, redox metabolism, oxidative pentose phosphate pathway and anaplerosis. At the exponential phase, CHO cell metabolism was characterized by a high flux of glycolysis from glucose to lactate, anaplerosis from pyruvate to oxaloacetate and from glutamate to α-ketoglutarate, and cataplerosis though malic enzyme. At the stationary phase, the flux map was characterized by a reduced flux of glycolysis, net lactate uptake, oxidative pentose phosphate pathway flux, and reduced rate of anaplerosis. The fluxes of pyruvate dehydrogenase and TCA cycle were similar at the exponential and stationary phase. The results presented here provide a solid foundation for future studies of CHO cell metabolism for applications such as cell line development and medium optimization for high-titer production of recombinant proteins.     

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.     

Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis

Vibrio natriegens is a fast-growing, non-pathogenic bacterium that is being considered as the next-generation workhorse for the biotechnology industry. However, little is known about the metabolism of this organism which is limiting our ability to apply rational metabolic engineering strategies. To address this critical gap in current knowledge, here we have performed a comprehensive analysis of V. natriegens metabolism. We constructed a detailed model of V. natriegens core metabolism, measured the biomass composition, and performed high-resolution ¹³C metabolic flux analysis (¹³C-MFA) to estimate intracellular fluxes using parallel labeling experiments with the optimal tracers [1,2−¹³C]glucose and [1,6−¹³C]glucose. During exponential growth in glucose minimal medium, V. natriegens had a growth rate of 1.70 1/h (doubling time of 24 min) and a glucose uptake rate of 3.90 g/g/h, which is more than two 2-fold faster than E. coli, although slower than the fast-growing thermophile Geobacillus LC300. ¹³C-MFA revealed that the core metabolism of V. natriegens is similar to that of E. coli, with the main difference being a 33% lower normalized flux through the oxidative pentose phosphate pathway. Quantitative analysis of co-factor balances provided additional insights into the energy and redox metabolism of V. natriegens. Taken together, the results presented in this study provide valuable new information about the physiology of V. natriegens and establish a solid foundation for future metabolic engineering efforts with this promising microorganism.     

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.     

Metabolic Modeling of Dynamic Brain 13C NMR Multiplet Data: Concepts and Simulations with a Two-Compartment Neuronal-Glial Model

Metabolic modeling of dynamic ¹³C labeling curves during infusion of ¹³C-labeled substrates allows quantitative measurements of metabolic rates in vivo. However metabolic modeling studies performed in the brain to date have only modeled time courses of total isotopic enrichment at individual carbon positions (positional enrichments), not taking advantage of the additional dynamic ¹³C isotopomer information available from fine-structure multiplets in ¹³C spectra. Here we introduce a new ¹³C metabolic modeling approach using the concept of bonded cumulative isotopomers, or bonded cumomers. The direct relationship between bonded cumomers and ¹³C multiplets enables fitting of the dynamic multiplet data. The potential of this new approach is demonstrated using Monte-Carlo simulations with a brain two-compartment neuronal-glial model. The precision of positional and cumomer approaches are compared for two different metabolic models (with and without glutamine dilution) and for different infusion protocols ([1,6-¹³C₂]glucose, [1,2-¹³C₂]acetate, and double infusion [1,6-¹³C₂]glucose?+?[1,2-¹³C₂]acetate). In all cases, the bonded cumomer approach gives better precision than the positional approach. In addition, of the three different infusion protocols considered here, the double infusion protocol combined with dynamic bonded cumomer modeling appears the most robust for precise determination of all fluxes in the model. The concepts and simulations introduced in the present study set the foundation for taking full advantage of the available dynamic ¹³C multiplet data in metabolic modeling.     

Parallel labeling experiments and metabolic flux analysis: Past,present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for ¹³C-metabolic flux analysis (¹³C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of ¹³C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for ¹³C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.     

Analysis of 13C labeling enrichment in microbial culture applying metabolic tracer experiments using gas chromatography-combustion-isotope ratio mass spectrometry

The applicability of gas chromatography–combustion–isotope ratio mass spectrometry (GC–C–IRMS) for the quantification of ¹³C enrichment of proteinogenic amino acids in metabolic tracer experiments was evaluated. Measurement of the ¹³C enrichment of proteinogenic amino acids from cell hydrolyzates of Corynebacterium glutamicum growing on different mixtures containing between 0.5 and 10% [1-¹³C]glucose shows the significance of kinetic isotope effects in metabolic flux studies at low degree of labeling. We developed a method to calculate the ¹³C enrichment. The approach to correct for these effects in metabolic flux studies using δ¹³C measurement by GC–C–IRMS uses two parallel experiments applying substrate with natural abundance and ¹³C-enriched tracer substrate, respectively. The fractional enrichment obtained in natural substrate is subtracted from that of the enriched one. Tracer studies with C. glutamicum resulted in a statistically identical relative fractional enrichment of ¹³C in proteinogenic amino acids over the whole range of applied concentrations of [1-¹³C]glucose. The current findings indicate a great potential of GC–C–IRMS for labeling quantification in ¹³C metabolic flux analysis with low labeling degree of tracer substrate directly in larger scale bioreactors.     

13C metabolic flux analysis of microbial and mammalian systems is enhanced with GC-MS measurements of glycogen and RNA labeling

¹³C metabolic flux analysis (¹³C-MFA) is a widely used tool for quantitative analysis of microbial and mammalian metabolism. Until now, ¹³C-MFA was based mainly on measurements of isotopic labeling of amino acids derived from hydrolyzed biomass proteins and isotopic labeling of extracted intracellular metabolites. Here, we demonstrate that isotopic labeling of glycogen and RNA, measured with gas chromatography-mass spectrometry (GC-MS), provides valuable additional information for ¹³C-MFA. Specifically, we demonstrate that isotopic labeling of glucose moiety of glycogen and ribose moiety of RNA greatly enhances resolution of metabolic fluxes in the upper part of metabolism; importantly, these measurements allow precise quantification of net and exchange fluxes in the pentose phosphate pathway. To demonstrate the practical importance of these measurements for ¹³C-MFA, we have used Escherichia coli as a model microbial system and CHO cells as a model mammalian system. Additionally, we have applied this approach to determine metabolic fluxes of glucose and xylose co-utilization in the E. coli ΔptsG mutant. The convenience of measuring glycogen and RNA, which are stable and abundant in microbial and mammalian cells, offers the following key advantages: reduced sample size, no quenching required, no extractions required, and GC-MS can be used instead of more costly LC-MS/MS techniques. Overall, the presented approach for ¹³C-MFA will have widespread applicability in metabolic engineering and biomedical research.     

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.     

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.     

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.     

Induced pluripotent stem cells can utilize lactate as a metabolic substrate to support proliferation

Human-induced pluripotent stem cells (iPSCs) hold the promise to improve cell-based therapies. Yet, to meet rising demands and become clinically impactful, sufficient high-quality iPSCs in quantity must be generated, a task that exceeds current capabilities. In this study, K3 iPSCs cultures were examined using parallel-labeling metabolic flux analysis (¹³C-MFA) to quantify intracellular fluxes at relevant bioprocessing stages: glucose concentrations representative of initial media concentrations and high lactate concentrations representative of fed-batch culture conditions, prior to and after bolus glucose feeds. The glucose and lactate concentrations are also representative of concentrations that might be encountered at different locations within 3D cell aggregates. Furthermore, a novel method was developed to allow the isotopic tracer [U-¹³C₃] lactate to be used in the ¹³C-MFA model. The results indicated that high extracellular lactate concentrations decreased glucose consumption and lactate production, while glucose concentrations alone did not affect rates of aerobic glycolysis. Moreover, for the high lactate cultures, lactate was used as a metabolic substrate to support oxidative mitochondrial metabolism. These results demonstrate that iPSCs have metabolic flexibility and possess the capacity to metabolize lactate to support exponential growth, and that high lactate concentrations alone do not adversely impact iPSC proliferation.     

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.     

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.