Shewanella oneidensis MR-1 Fluxome under Various Oxygen Conditions

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

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.     

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.     

Gas chromatography-mass spectrometry (GC-MS) techniques for metabolic flux analysis of the Bifido shunt pathway

The presence of the Bifido shunt in Bifidobacterium is predicted to lead to the uptake and metabolic conversion of fructose to acetate and lactate. We propose an approach to quantifying the carbon flux through the Bifido shunt by measuring specific ¹³C-labeled carbohydrate-derived isotopomers by gas chromatography-mass spectrometry (GC-MS). The techniques described may provide an alternative approach for determining the in vitro prebiotic potential of dietary oligosaccharides.     

The relationships between the metabolic fluxes and 13C-labeled isotopomer distribution for the flux analysis of the main metabolic pathways

It has been known that ¹³C-labeling technique is quite useful in estimating the metabolic fluxes. Although the program-based flux analysis is powerful, it is not easy to be confident with the result obtained without experiences and exhaustive trial and errors based on statistical analysis for the confidence intervals in practice. It is, therefore, quite important to grasp the relationship between the fluxes and the ¹³C-labeled isotopomer distribution to get deeper insight into the metabolic flux analysis. In the present research, it was shown explicitly how the isotopomer distribution changes with respect to the fluxes in relation to the labeling patterns of the substrate, where either labeled glucose, acetate, or pyruvate was used as a carbon source. Some of the analytical expressions were derived based on the matrix representation, and they were utilized for analysis. It was shown that the isotopomer pattern does not necessarily change uniformly with respect to fluxes, but changes in a complicated way in particular for the case of using pyruvate as a carbon source where some isotopomers do not necessarily change monotonically. It was shown to be quite important to grasp how the isotopomer pattern changes with respect to fluxes and the labeling pattern of the substrate for flux determination and the experimental design. It was also shown that the mixture of [1-¹³C] acetate and [2-¹³C] acetate should not be used from the information index point of view. Some of the experimental data were evaluated from the present approach. It was also shown that the isotopomer distribution is less sensitive to the bidirectional fluxes in the reversible pathway.     

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.     

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.     

C isotopomer analysis of glutamate by heteronuclear multiple quantum coherence-total correlation spectroscopy (HMQC-TOCSY)

¹³C has become an important tracer isotope for studies of intermediary metabolism. Information about relative flux through pathways is encoded by the distribution of ¹³C isotopomers in an intermediate pool such as glutamate. This information is commonly decoded either by mass spectrometry or by measuring relative multiplet areas in a ¹³C NMR spectrum. We demonstrate here that groups of glutamate ¹³C isotopomers may be quantified by indirect detection of protons in a 2D HMQC-TOCSY NMR spectrum and that fitting of these data to a metabolic model provides an identical measure of the ¹³C fractional enrichment of acetyl-CoA and relative anaplerotic flux to that given by direct ¹³C NMR analysis. The sensitivity gain provided by HMQC-TOCSY spectroscopy will allow an extension of ¹³C isotopomer analysis to tissue samples not amenable to direct ¹³C detection (∼10 mg soleus muscle) and to tissue metabolites other than glutamate that are typically present at lower concentrations.     

Method for the simultaneous quantitation of apolipoprotein E isoforms using tandem mass spectrometry

Using apolipoprotein E (ApoE) as a model protein, we developed a protein isoform analysis method utilizing stable isotope labeling tandem mass spectrometry (SILT MS). ApoE isoforms are quantitated using the intensities of the b and y ions of the ¹³C-labeled tryptic isoform-specific peptides versus unlabeled tryptic isoform-specific peptides. The ApoE protein isoform analysis using SILT allows for the simultaneous detection and relative quantitation of different ApoE isoforms from the same sample. This method provides a less biased assessment of ApoE isoforms compared to antibody-dependent methods, and may lead to a better understanding of the biological differences between isoforms.     

A carbon-13 nuclear magnetic resonance investigation of the metabolic fluxes associated withy glucose metabolism in human erythrocytes

We have used [2-¹³C]d-glucose and carbon-13 nuclear magnetic resonance (NMR) spectroscopy to investigate metabolic fluxes through the major pathways of glucose metabolism in intact human erythrocytes and to determine the interactions among these pathways under conditions that perturb metabolism. Using the method described, we have been able to measure fluxes through the pentose phosphate pathway, phosphofructokinase, the 2,3-diphosphoglycerate bypass, and phosphoglycerate kinase, as well as glucose uptake, concurrently and in a single experiment. We have measured these fluxes in normal human erythrocytes under the following conditions: (1) fully oxygenated; (2) treated with methylene blue; and (3) deoxygenated. This method makes it possible to monitor various metabolic effects of stresses in normal and pathological states. Not only has ¹³C-NMR spectroscopy proved to be a useful method for measuring in vivo flux through the pentose phosphate pathway, but it has also provided additional information about the cycling of metabolites through the non-oxidative portion of the pentose phosphate pathway. Our evidence from experiments with [1-¹³C]-, [2-¹³C]-, and [3-¹³C]d-glucoses indicates that there is an observable reverse flux of fructose 6-phosphate through the reactions catalyzed by transketolase and transaldolase, even in the presence of a net flux through the pentose phosphate pathway.     

Sensitivity summation theorems for stochastic biochemical reaction systems

We investigate how stochastic reaction processes are affected by external perturbations. We describe an extension of the deterministic metabolic control analysis (MCA) to the stochastic regime. We introduce stochastic sensitivities for mean and covariance values of reactant concentrations and reaction fluxes and show that there exist MCA-like summation theorems among these sensitivities. The summation theorems for flux variances is shown to depend on the size of the measurement time window (?) within which reaction events are counted for measuring a single flux. It is found that the degree of the ?-dependency can become significant for processes involving multi-time-scale dynamics and is estimated by introducing a new measure of time-scale separation. This ?-dependency is shown to be closely related to the power-law scaling observed in flux fluctuations in various complex networks.     

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.     

Numerical bias estimation for mass spectrometric mass isotopomer analysis

Mass spectrometric (MS) isotopomer analysis has become a standard tool for investigating biological systems using stable isotopes. In particular, metabolic flux analysis uses mass isotopomers of metabolic products typically formed from ¹³C-labeled substrates to quantitate intracellular pathway fluxes. In the current work, we describe a model-driven method of numerical bias estimation regarding MS isotopomer analysis. Correct bias estimation is crucial for measuring statistical qualities of measurements and obtaining reliable fluxes. The model we developed for bias estimation corrects a priori unknown systematic errors unique for each individual mass isotopomer peak. For validation, we carried out both computational simulations and experimental measurements. From stochastic simulations, it was observed that carbon mass isotopomer distributions and measurement noise can be determined much more precisely only if signals are corrected for possible systematic errors. By removing the estimated background signals, the residuals resulting from experimental measurement and model expectation became consistent with normality, experimental variability was reduced, and data consistency was improved. The method is useful for obtaining systematic error-free data from ¹³C tracer experiments and can also be extended to other stable isotopes. As a result, the reliability of metabolic fluxes that are typically computed from mass isotopomer measurements is increased.     

Kinetic analyses of liver phosphatidylcholine and phosphatidylethanolamine biosynthesis using 13C NMR spectroscopy

Choline and ethanolamine are substrates for de novo synthesis of phosphatidylcholine (PtdC) and phosphatidylethanolamine (PtdE) through the CDP-choline and CDP-ethanolamine pathways. In liver, PtdE can also be converted to PtdC by PtdE N-methyltransferase (PEMT). We investigated these kinetics in rat liver during a 60 min infusion with ¹³C-labeled choline and ethanolamine. NMR analyses of liver extracts provided concentrations and ¹³C enrichments of phosphocholine (Pcho), phosphoethanolamine (Peth), PtdC, and PtdE. Kinetic models showed that the de novo and PEMT pathways are ‘channeled’ processes. The intermediary metabolites directly derived from exogenous choline and ethanolamine do not completely mix with the intracellular pools, but are preferentially used for phospholipid synthesis. Of the newly synthesized PtdC, about 70% was derived de novo and 30% was by PEMT. PtdC and PtdE de novo syntheses displayed different kinetics. A simple model assuming constant fluxes yielded a modest fit to the data; allowing upregulated fluxes significantly improved the fit. The ethanolamine-to-Peth flux exceeded choline-to-Pcho, and the rate of PtdE synthesis (1.04 μmol/h/g liver) was 2–3 times greater than that of PtdC de novo synthesis. The metabolic pathway information provided by these studies makes the NMR method superior to earlier radioisotope studies.     

Hepatic anaplerotic outflow fluxes are redirected from gluconeogenesis to lactate synthesis in patients with Type 1a glycogen storage disease

Hepatic glucose production and relative Krebs cycle fluxes (indexed to a citrate synthase flux of 1.0) were evaluated with [U-¹³C]glycerol tracer in 5 fed healthy controls and 5 Type 1a glycogen storage disease (GSD1a) patients. Plasma glucose, hepatic glucose-6-phosphate (G6P) and glutamine ¹³C-isotopomers were analyzed by ¹³C NMR via blood sampling and chemical biopsy. In healthy subjects, 35±14% of plasma glucose originated from hepatic G6P while GSD1a patients had no detectable G6P contribution. Compared to controls, GSD1a patients had an increased fraction of acetyl-CoA from pyruvate (0.5±0.2 vs. 0.3±0.1, p<0.01), and increased pyruvate recycling fluxes (14.4±3.8 vs. 8.7±2.8, p<0.05). Despite negligible gluconeogenic flux, net anaplerotic outflow was not significantly different from controls (2.2±0.8 vs. 1.3±0.5). The enrichment of lactate with ¹³C-isotopomers derived from the Krebs cycle suggests that lactate was the main anaplerotic product in GSD1a patients.     

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.     

Carbon Flux Analysis by 13C Nuclear Magnetic Resonance To Determine the Effect of CO2 on Anaerobic Succinate Production by Corynebacterium glutamicum

Wild-type Corynebacterium glutamicum produces a mixture of lactic, succinic, and acetic acids from glucose under oxygen deprivation. We investigated the effect of CO₂ on the production of organic acids in a two-stage process: cells were grown aerobically in glucose, and subsequently, organic acid production by nongrowing cells was studied under anaerobic conditions. The presence of CO₂ caused up to a 3-fold increase in the succinate yield (1 mol per mol of glucose) and about 2-fold increase in acetate, both at the expense of l-lactate production; moreover, dihydroxyacetone formation was abolished. The redistribution of carbon fluxes in response to CO₂ was estimated by using ¹³C-labeled glucose and ¹³C nuclear magnetic resonance (NMR) analysis of the labeling patterns in end products. The flux analysis showed that 97% of succinate was produced via the reductive part of the tricarboxylic acid cycle, with the low activity of the oxidative branch being sufficient to provide the reducing equivalents needed for the redox balance. The flux via the pentose phosphate pathway was low (∼5%) regardless of the presence or absence of CO₂. Moreover, there was significant channeling of carbon to storage compounds (glycogen and trehalose) and concomitant catabolism of these reserves. The intracellular and extracellular pools of lactate and succinate were measured by in vivo NMR, and the stoichiometry (H⁺:organic acid) of the respective exporters was calculated. This study shows that it is feasible to take advantage of natural cellular regulation mechanisms to obtain high yields of succinate with C. glutamicum without genetic manipulation.     

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.     

The Ability of Flux Balance Analysis to Predict Evolution of Central Metabolism Scales with the Initial Distance to the Optimum

The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a ¹³C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600–800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate.     

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.