Metabolic flux profiling based on the analysis of distribution of stable isotope tracer in metabolites is an important method widely used in cancer research to understand the regulation of cell metabolism and elaborate new therapeutic strategies. Recently, we developed software Isodyn, which extends the methodology of kinetic modeling to the analysis of isotopic isomer distribution for the evaluation of cellular metabolic flux profile under relevant conditions. This tool can be applied to reveal the metabolic effect of proapoptotic drug edelfosine in leukemia Jurkat cell line, uncovering the mechanisms of induction of apoptosis in cancer cells.
The study of 13C distribution of Jukat cells exposed to low edelfosine concentration, which induces apoptosis in ≤5% of cells, revealed metabolic changes previous to the development of apoptotic program. Specifically, it was found that low dose of edelfosine stimulates the TCA cycle. These metabolic perturbations were coupled with an increase of nucleic acid synthesis de novo, which indicates acceleration of biosynthetic and reparative processes. The further increase of the TCA cycle fluxes, when higher doses of drug applied, eventually enhance reactive oxygen species (ROS) production and trigger apoptotic program.
The application of Isodyn to the analysis of mechanism of edelfosine-induced apoptosis revealed primary drug-induced metabolic changes, which are important for the subsequent initiation of apoptotic program. Initiation of such metabolic changes could be exploited in anticancer therapy.
Stable isotope tracing is a powerful technique for following the fate of individual atoms through metabolic pathways. Measuring isotopic enrichment in metabolites provides quantitative insights into the biosynthetic network and enables flux analysis as a function of external perturbations. NMR and mass spectrometry are the techniques of choice for global profiling of stable isotope labeling patterns in cellular metabolites. However, meaningful biochemical interpretation of the labeling data requires both quantitative analysis and complex modeling. Here, we demonstrate a novel approach that involved acquiring and modeling the timecourses of 13C isotopologue data for UDP-N-acetyl-D-glucosamine (UDP-GlcNAc) synthesized from [U-13C]-glucose in human prostate cancer LnCaP-LN3 cells. UDP-GlcNAc is an activated building block for protein glycosylation, which is an important regulatory mechanism in the development of many prominent human diseases including cancer and diabetes.
We utilized a stable isotope resolved metabolomics (SIRM) approach to determine the timecourse of 13C incorporation from [U-13C]-glucose into UDP-GlcNAc in LnCaP-LN3 cells. 13C Positional isotopomers and isotopologues of UDP-GlcNAc were determined by high resolution NMR and Fourier transform-ion cyclotron resonance-mass spectrometry. A novel simulated annealing/genetic algorithm, called 'Genetic Algorithm for Isotopologues in Metabolic Systems' (GAIMS) was developed to find the optimal solutions to a set of simultaneous equations that represent the isotopologue compositions, which is a mixture of isotopomer species. The best model was selected based on information theory. The output comprises the timecourse of the individual labeled species, which was deconvoluted into labeled metabolic units, namely glucose, ribose, acetyl and uracil. The performance of the algorithm was demonstrated by validating the computed fractional 13C enrichment in these subunits against experimental data. The reproducibility and robustness of the deconvolution were verified by replicate experiments, extensive statistical analyses, and cross-validation against NMR data.
This computational approach revealed the relative fluxes through the different biosynthetic pathways of UDP-GlcNAc, which comprises simultaneous sequential and parallel reactions, providing new insight into the regulation of UDP-GlcNAc levels and O-linked protein glycosylation. This is the first such analysis of UDP-GlcNAc dynamics, and the approach is generally applicable to other complex metabolites comprising distinct metabolic subunits, where sufficient numbers of isotopologues can be unambiguously resolved and accurately measured.
Metabolic Flux Analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and NMR measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called elementary metabolite units, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical 13C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with 2H, 13C, and 18O tracers requires only 354 EMUs, compared to more than 2 million isotopomers.
Metabolic flux analysis; network analysis; isotopomers; elementary metabolite units; network decomposition
We tested the hypothesis that glycogen is preferentially oxidized in isolated working rat heart. This was accomplished by measuring the proportion of glycolytic flux (oxidation plus lactate production) specifically from glycogen which is metabolized to lactate, and comparing it to the same proportion determined concurrently from exogenous glucose during stimulation with epinephrine. After prelabeling of glycogen with either 14C or 3H, a dual isotope technique was used to simultaneously trace the disposition of glycogen and exogenous glucose between oxidative and non-oxidative pathways. Immediately after the addition of epinephrine (1 microM), 40-50% of flux from glucose was directed towards lactate. Glycogen, however, did not contribute to lactate, being almost entirely oxidized. Further, glycogen utilization responded promptly to the abrupt increase in contractile performance with epinephrine, during the lag in stimulation of utilization of exogenous glucose, suggesting that glycogen serves as substrate reservoir to buffer rapid increases in demand. Preferential oxidation of glycogen may serve to ensure efficient generation of ATP from a limited supply of endogenous substrate, or as a mechanism to limit lactate accumulation during rapid glycogenolysis.
The rationale of this dose matching/dose escalating study was to compare a panel of flavonoids—luteolin, resveratrol, and quercetin—against the metabolite flux-controlling properties of a synthetic targeted fatty acid synthase inhibitor drug C75 on multiple macromolecule synthesis pathways in pancreatic tumor cells using [1,2-13C2]-d-glucose as the single precursor metabolic tracer. MIA PaCa-2 pancreatic adenocarcinoma cells were cultured for 48 h in the presence of 0.1% DMSO (control), or 50 or 100 μM of each test compound, while intracellular glycogen, RNA ribose, palmitate and cholesterol as well as extra cellular 13CO2, lactate and glutamate production patterns were measured using gas chromatography/mass spectrometry (GC/MS) and stable isotope-based dynamic metabolic profiling (SiDMAP). The use of 50% [1,2-13C2]-d-glucose as tracer resulted in an average of 24 excess 13CO2 molecules for each 1,000 CO2 molecule in the culture media, which was decreased by 29 and 33% (P < 0.01) with 100 μM C75 and luteolin treatments, respectively. Extracellular tracer glucose-derived 13C-labeled lactate fractions (Σm) were between 45.52 and 47.49% in all cultures with a molar ratio of 2.47% M + 1/Σm lactate produced indirectly by direct oxidation of glucose in the pentose cycle in control cultures; treatment with 100 μM C75 and luteolin decreased this figure to 1.80 and 1.67%. The tracer glucose-derived 13C labeled fraction (Σm) of ribonucleotide ribose was 34.73% in controls, which was decreased to 20.58 and 8.45% with C75, 16.15 and 6.86% with luteolin, 27.66 and 19.25% with resveratrol, and 30.09 and 25.67% with quercetin, respectively. Luteolin effectively decreased nucleotide precursor synthesis pentose cycle flux primarily via the oxidative branch, where we observed a 41.74% flux (M + 1/Σm) in control cells, in comparison with only a 37.19%, 32.74%, or a 26.57%, 25.47% M + 1/Σm flux (P < 0.001) after 50 or 100 μM C75 or luteolin treatment. Intracellular de novo fatty acid palmitate (C16:0) synthesis was severely and equally blocked by C75 and luteolin treatments indicated by the 5.49% (control), 2.29 or 2.47% (C75) and 2.21 or 2.73% (luteolin) tracer glucose-derived 13C-labeled fractions, respectively. On the other hand there was a significant 192 and 159% (P < 0.001), and a 103 and 117% (P < 0.01) increase in tracer glucose-derived cholesterol after C75 or luteolin treatment. Only resveratrol and quercetin at 100 μM inhibited tracer glucose-derived glycogen labeling (Σm) and turnover by 34.8 and 23.8%, respectively. The flavonoid luteolin possesses equal efficacy to inhibit fatty acid palmitate de novo synthesis as well as nucleotide RNA ribose turnover via the oxidative branch of the pentose cycle in comparison with the targeted fatty acid synthase inhibitor synthetic compound C75. Luteolin is also effective in stringently controlling glucose entry and anaplerosis in the TCA cycle, while it promotes less glucose flux towards cholesterol synthesis than that of C75. In contrast, quercetin and resveratrol inhibit glycogen synthesis and turnover as their underlying mechanism of controlling tumor cell proliferation. Therefore the flavonoid luteolin controls fatty and nucleic acid syntheses as well as energy production with pharmacological strength, which can be explored as a non-toxic natural treatment modality for pancreatic cancer.
Metabolic profile; Phytochemicals; Pancreatic cancer; Fatty acid synthase; Lipogenesis; Luteolin; Resveratrol; Quercetin; C75
The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution.
The open-source software “Least Square Mass Isotopomer Analyzer” (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman’s least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed.
The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.
The functional status of a cell is expressed in its metabolic activity. We have applied stable isotope tracing methods to determine the differences in metabolic pathways in proliferating Rhabdomysarcoma cells (Rh30) and human primary myocytes in culture. Uniformly 13C-labeled glucose was used as a source molecule to follow the incorporation of 13C into more than 40 marker metabolites using NMR and GC-MS. These include metabolites that report on the activity of glycolysis, Krebs' cycle, pentose phosphate pathway and pyrimidine biosynthesis.
The Rh30 cells proliferated faster than the myocytes. Major differences in flux through glycolysis were evident from incorporation of label into secreted lactate, which accounts for a substantial fraction of the glucose carbon utilized by the cells. Krebs' cycle activity as determined by 13C isotopomer distributions in glutamate, aspartate, malate and pyrimidine rings was considerably higher in the cancer cells than in the primary myocytes. Large differences were also evident in de novo biosynthesis of riboses in the free nucleotide pools, as well as entry of glucose carbon into the pyrimidine rings in the free nucleotide pool. Specific labeling patterns in these metabolites show the increased importance of anaplerotic reactions in the cancer cells to maintain the high demand for anabolic and energy metabolism compared with the slower growing primary myocytes. Serum-stimulated Rh30 cells showed higher degrees of labeling than serum starved cells, but they retained their characteristic anabolic metabolism profile. The myocytes showed evidence of de novo synthesis of glycogen, which was absent in the Rh30 cells.
The specific 13C isotopomer patterns showed that the major difference between the transformed and the primary cells is the shift from energy and maintenance metabolism in the myocytes toward increased energy and anabolic metabolism for proliferation in the Rh30 cells. The data further show that the mitochondria remain functional in Krebs' cycle activity and respiratory electron transfer that enables continued accelerated glycolysis. This may be a common adaptive strategy in cancer cells.
A current trend in neuroscience research is the use of stable isotope tracers in order to address metabolic processes in vivo. The tracers produce a huge number of metabolite forms that differ according to the number and position of labeled isotopes in the carbon skeleton (isotopomers) and such a large variety makes the analysis of isotopomer data highly complex. On the other hand, this multiplicity of forms does provide sufficient information to address cell operation in vivo. By the end of last millennium, a number of tools have been developed for estimation of metabolic flux profile from any possible isotopomer distribution data. However, although well elaborated, these tools were limited to steady state analysis, and the obtained set of fluxes remained disconnected from their biochemical context. In this review we focus on a new numerical analytical approach that integrates kinetic and metabolic flux analysis. The related computational algorithm estimates the dynamic flux based on the time-dependent distribution of all possible isotopomers of metabolic pathway intermediates that are generated from a labeled substrate. The new algorithm connects specific tracer data with enzyme kinetic characteristics, thereby extending the amount of data available for analysis: it uses enzyme kinetic data to estimate the flux profile, and vice versa, for the kinetic analysis it uses in vivo tracer data to reveal the biochemical basis of the estimated metabolic fluxes.
Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.
Metabolic network; isotopomer dynamics; MFA; mathematical modeling; compartmentalization; 13C NMR
13C metabolic flux analysis (MFA) is the most comprehensive means of characterizing cellular metabolic states. Uniquely labeled isotopic tracers enable more focused analyses to probe specific reactions within the network. As a result, the choice of tracer largely determines the precision with which one can estimate metabolic fluxes, especially in complex mammalian systems that require multiple substrates. Here we have experimentally determined metabolic fluxes in a tumor cell line, successfully recapitulating the hallmarks of cancer cell metabolism. Using these data, we computationally evaluated specifically labeled 13C glucose and glutamine tracers for their ability to precisely and accurately estimate fluxes in central carbon metabolism. These methods enabled us to to identify the optimal tracer for analyzing individual fluxes, specific pathways, and central carbon metabolism as a whole. [1,2-13C2]glucose provided the most precise estimates for glycolysis, the pentose phosphate pathway, and the overall network. Tracers such as [2-13C]glucose and [3-13C]glucose also outperformed the more commonly used [1-13C]glucose. [U-13C5]glutamine emerged as the preferred isotopic tracer for analysis of the tricarboxylic acid (TCA) cycle. These results provide valuable, quantitative information on the performance of 13C-labeled substrates and can aid in the design of more informative MFA experiments in mammalian cell culture.
Metabolic flux analysis; confidence intervals; isotopic tracers; tumor cells
Summary: Most current stable isotope-based methodologies are targeted and focus only on the well-described aspects of metabolic networks. Here, we present NTFD (non-targeted tracer fate detection), a software for the non-targeted analysis of all detectable compounds derived from a stable isotope-labeled tracer present in a GC/MS dataset. In contrast to traditional metabolic flux analysis approaches, NTFD does not depend on any a priori knowledge or library information. To obtain dynamic information on metabolic pathway activity, NTFD determines mass isotopomer distributions for all detected and labeled compounds. These data provide information on relative fluxes in a metabolic network. The graphical user interface allows users to import GC/MS data in netCDF format and export all information into a tab-separated format.
Availability: NTFD is C++- and Qt4-based, and it is freely available under an open-source license. Pre-compiled packages for the installation on Debian- and Redhat-based Linux distributions, as well as Windows operating systems, along with example data, are provided for download at http://ntfd.mit.edu/.
This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−13C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.
systems biology; multivariate regression; stable isotopes; metabolism; gluconeogenesis
This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U–13C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.
systems biology; multivariate regression; stable isotopes; metabolism; gluconeogenesis
A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraint-based modeling framework with isotopic label tracing on a large-scale. When cells are fed a growth substrate with certain carbon positions labeled with 13C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the anti-malarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-13C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.
Metabolic flux analysis; Isotope labeling; Constraint-based modeling; Nonlinear optimization; Statistical analysis
Metabolomics provides a readout of the state of metabolism in cells or tissue and their responses to external perturbations. For this reason, the approach has great potential in clinical diagnostics. Clinical metabolomics using stable isotope resolved metabolomics (SIRM) for pathway tracing represents an important new approach to obtaining metabolic parameters in human cancer subjects in situ. Here we provide an overview of the technology development of labeling from cells in culture and mouse models. The high throughput analytical methods NMR and mass spectrometry, especially Fourier transform ion cyclotron resonance, for analyzing the resulting metabolite isotopomers and isotopologues are described with examples of applications in cancer biology. Special technical considerations for clinical applications of metabolomics using stable isotope tracers are described. The whole process from concept to analysis will be exemplified by our on-going study of nonsmall cell lung cancer (NSCLC) metabolomics. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells, including the upregulation of anaplerosis via pyruvate carboxylation in NSCLC.
Protonated molecular peptide ions and their product ions generated by tandem mass spectrometry appear as isotopologue clusters due to the natural isotopic variations of carbon, hydrogen, nitrogen, oxygen and sulfur. Quantitation of the isotopic composition of peptides can be employed in experiments involving isotope effects, isotope exchange, isotopic labeling by chemical reactions, and studies of metabolism by stable isotope incorporation. Both ion trap and quadrupole-time of flight mass spectrometry are shown to be capable of determining the isotopic composition of peptide product ions obtained by tandem mass spectrometry with both precision and accuracy. Tandem mass spectra obtained in profile-mode of clusters of isotopologue ions are fit by non-linear least squares to a series of Gaussian peaks (described in the accompanying manuscript) which quantify the Mn/M0 values which define the isotopologue distribution (ID). To determine the isotopic composition of product ions from their ID, a new algorithm that predicts the Mn/M0 ratios is developed which obviates the need to determine the intensity of all of the ions of an ID. Consequently a precise and accurate determination of the isotopic composition a product ion may be obtained from only the initial values of the ID, however the entire isotopologue cluster must be isolated prior to fragmentation. Following optimization of the molecular ion isolation width, fragmentation energy and detector sensitivity, the presence of isotopic excess (2H, 13C, 15N, 18O) is readily determined within 1%. The ability to determine the isotopic composition of sequential product ions permits the isotopic composition of individual amino acid residues in the precursor ion to be determined.
isotopologue distribution; mass isotopomer distribution; tandem mass spectrometry; deuterium incorporation; isotopic excess; isotope quantitation; H/D exchange; protein turnover
GKAs (glucokinase activators) are promising agents for the therapy of Type 2 diabetes, but little is known about their effects on hepatic intermediary metabolism. We monitored the fate of 13C-labelled glucose in both a liver perfusion system and isolated hepatocytes. MS and NMR spectroscopy were deployed to measure isotopic enrichment. The results demonstrate that the stimulation of glycolysis by GKA led to numerous changes in hepatic metabolism: (i) augmented flux through the TCA (tricarboxylic acid) cycle, as evidenced by greater incorporation of 13C into the cycle (anaplerosis) and increased generation of 13C isotopomers of citrate, glutamate and aspartate (cataplerosis); (ii) lowering of hepatic [Pi] and elevated [ATP], denoting greater phosphorylation potential and energy state; (iii) stimulation of glycogen synthesis from glucose, but inhibition of glycogen synthesis from 3-carbon precursors; (iv) increased synthesis of N-acetylglutamate and consequently augmented ureagenesis; (v) increased synthesis of glutamine, alanine, serine and glycine; and (vi) increased production and outflow of lactate. The present study provides a deeper insight into the hepatic actions of GKAs and uncovers the potential benefits and risks of GKA for treatment of diabetes. GKA improved hepatic bioenergetics, ureagenesis and glycogenesis, but decreased gluconeogenesis with a potential risk of lactic acidosis and fatty liver.
gluconeogenesis; glycogenesis; glycolysis; N-acetylglutamate (NAG); tricarboxylic acid (TCA) cycle; ureagenesis
A comprehensive approach to 13C tracer studies, labeling measurements by gas chromatography-mass spectrometry, metabolite balancing, and isotopomer modeling, was applied for comparative metabolic network analysis of lysine-producing Corynebacterium glutamicum on glucose or fructose. Significantly reduced yields of lysine and biomass and enhanced formation of dihydroxyacetone, glycerol, and lactate in comparison to those for glucose resulted on fructose. Metabolic flux analysis revealed drastic differences in intracellular flux depending on the carbon source applied. On fructose, flux through the pentose phosphate pathway (PPP) was only 14.4% of the total substrate uptake flux and therefore markedly decreased compared to that for glucose (62.0%). This result is due mainly to (i) the predominance of phosphoenolpyruvate-dependent phosphotransferase systems for fructose uptake (PTSFructose) (92.3%), resulting in a major entry of fructose via fructose 1,6-bisphosphate, and (ii) the inactivity of fructose 1,6-bisphosphatase (0.0%). The uptake of fructose during flux via PTSMannose was only 7.7%. In glucose-grown cells, the flux through pyruvate dehydrogenase (70.9%) was much less than that in fructose-grown cells (95.2%). Accordingly, flux through the tricarboxylic acid cycle was decreased on glucose. Normalized to that for glucose uptake, the supply of NADPH during flux was only 112.4% on fructose compared to 176.9% on glucose, which might explain the substantially lower lysine yield of C. glutamicum on fructose. Balancing NADPH levels even revealed an apparent deficiency of NADPH on fructose, which is probably overcome by in vivo activity of malic enzyme. Based on these results, potential targets could be identified for optimization of lysine production by C. glutamicum on fructose, involving (i) modification of flux through the two PTS for fructose uptake, (ii) amplification of fructose 1,6-bisphosphatase to increase flux through the PPP, and (iii) knockout of a not-yet-annotated gene encoding dihydroxyacetone phosphatase or kinase activity to suppress overflow metabolism. Statistical evaluation revealed high precision of the estimates of flux, so the observed differences for metabolic flux are clearly substrate specific.
Metabolic models have been used to elucidate important aspects of brain metabolism in recent years. This work applies for the first time the concept of isotopic transient 13C metabolic flux analysis (MFA) to estimate intracellular fluxes in primary cultures of astrocytes. This methodology comprehensively explores the information provided by 13C labeling time-courses of intracellular metabolites after administration of a 13C-labeled substrate. Cells were incubated with medium containing [1-13C]glucose for 24 h and samples of cell supernatant and extracts collected at different time points were then analyzed by mass spectrometry and/or high performance liquid chromatography. Metabolic fluxes were estimated by fitting a carbon labeling network model to isotopomer profiles experimentally determined. Both the fast isotopic equilibrium of glycolytic metabolite pools and the slow labeling dynamics of TCA cycle intermediates are described well by the model. The large pools of glutamate and aspartate which are linked to the TCA cycle via reversible aminotransferase reactions are likely to be responsible for the observed delay in equilibration of TCA cycle intermediates. Furthermore, it was estimated that 11% of the glucose taken up by astrocytes was diverted to the pentose phosphate pathway. In addition, considerable fluxes through pyruvate carboxylase [PC; PC/pyruvate dehydrogenase (PDH) ratio = 0.5], malic enzyme (5% of the total pyruvate production), and catabolism of branched-chained amino acids (contributing with ∼40% to total acetyl-CoA produced) confirmed the significance of these pathways to astrocytic metabolism. Consistent with the need of maintaining cytosolic redox potential, the fluxes through the malate–aspartate shuttle and the PDH pathway were comparable. Finally, the estimated glutamate/α-ketoglutarate exchange rate (∼0.7 μmol mg prot−1 h−1) was similar to the TCA cycle flux. In conclusion, this work demonstrates the potential of isotopic transient MFA for a comprehensive analysis of energy metabolism.
metabolic fluxes; intracellular pools; 13C-labeling time-courses; [1-13C]glucose; mass spectrometry
This protocol enables quantitation of metabolic fluxes in cultured cells. Measurements are based on the kinetics of cellular incorporation of stable isotope from nutrient into downstream metabolites. At multiple time points, after cells are rapidly switched from unlabeled to isotope-labeled nutrient, metabolism is quenched, metabolites are extracted and the extract is analyzed by chromatography–mass spectrometry. Resulting plots of unlabeled compound versus time follow variants of exponential decay, with the flux equal to the decay rate multiplied by the intracellular metabolite concentration. Because labeling is typically fast (t1/2≤5 min for central metabolites in Escherichia coli), variations on this approach can effectively probe dynamically changing metabolic fluxes. This protocol is exemplified using E. coli and nitrogen labeling, for which quantitative flux data for ~15 metabolites can be obtained over 3 d of work. Applications to adherent mammalian cells are also discussed.
Fluxome analysis aims at the quantitative analysis of in vivo carbon fluxes in metabolic networks, i. e. intracellular activities of enzymes and pathways. It allows investigating the effects of genetic or environmental modifications and thus precisely provides a global perspective on the integrated genetic and metabolic regulation within the intact metabolic network. The experimental and computational approaches developed in this area have revealed fascinating insights into metabolic properties of various biological systems. Most of the comprehensive approaches for metabolic flux studies today involve isotopic tracer studies and GC-MS for measurement of the labeling pattern of metabolites. Initially developed and applied mainly in the field of biomedicine these GC-MS based metabolic flux approaches have been substantially extended and optimized during recent years and today display a key technology in metabolic physiology and biotechnology.
Intramolecular correlations among the 18O-labels of metabolic oligophosphates, mapped by J-decoupled 31P NMR 2D chemical shift correlation spectroscopy, impart stringent constraints to the 18O-isotope distributions over the whole oligophosphate moiety. The multiple deduced correlations of isotopic labels enable determination of site-specific fractional isotope enrichments and unravel the isotopologue statistics. This approach ensures accurate determination of 18O-labeling rates of phosphometabolites, critical in biochemical energy conversion and metabolic flux transmission. The biological usefulness of the J-decoupled 31P NMR 2D chemical shift correlation maps was validated on adenosine tri-phosphate fractionally 18O labeled in perfused mammalian hearts.
31P NMR chemical shift correlation; 18O/16O isotopic effects; ATP turnover; Isotopologue distribution; Oxygen-18; High-energy phosphoryl dynamics; Phosphotransfer; Phosphometabolomics
Precise and accurate measurements of isotopologue distributions (IDs) in biological molecules are needed for the determination of isotope effects, quantitation by isotope dilution and quantifying isotope tracers employed in both metabolic and biophysical studies. While single ion monitoring (SIM) yields significantly greater sensitivity and signal/noise than profile mode acquisitions, we show that small changes in the SIM window width and/or center can alter experimentally determined isotope ratios by up to 5%, resulting in significant inaccuracies. This inaccuracy is attributed to mass granularity, the differential distribution of digital data points across the m/z ranges sampled by SIM. Acquiring data in the profile mode and fitting the data to an equation describing a series of equally spaced and identically shaped peaks eliminates the inaccuracies associated with mass granularity with minimal loss of precision. Additionally a method of using the complete ID profile data that inherently corrects for “spillover” and for the natural abundance ID has been used to determine 18O/16O ratios for 5′,3′-guanosine bis-[18O1]phosphate and TM[18O1]P with precisions of ~0.005. The analysis protocol is also applied to quadrupole-time of flight tandem mass spectrometry using [2-18O]uridine and 3′-UM[18O1]P which enhances signal/noise and minimizes concerns for background contamination.
isotope ratio; isotopologue distribution; mass isotopomer distribution; tandem mass spectrometry; heavy atom isotope effect; oxygen-18; nucleotide; mass granularity; whole molecule mass spectrometry
We investigated the carbon metabolism of three strains of Fibrobacter succinogenes and one strain of Fibrobacter intestinalis. The four strains produced the same amounts of the metabolites succinate, acetate, and formate in approximately the same ratio (3.7/1/0.3). The four strains similarly stored glycogen during all growth phases, and the glycogen-to-protein ratio was close to 0.6 during the exponential growth phase. 13C nuclear magnetic resonance (NMR) analysis of [1-13C]glucose utilization by resting cells of the four strains revealed a reversal of glycolysis at the triose phosphate level and the same metabolic pathways. Glycogen futile cycling was demonstrated by 13C NMR by following the simultaneous metabolism of labeled [13C]glycogen and exogenous unlabeled glucose. The isotopic dilutions of the CH2 of succinate and the CH3 of acetate when the resting cells were metabolizing [1-13C]glucose and unlabeled glycogen were precisely quantified by using 13C-filtered spin-echo difference 1H NMR spectroscopy. The measured isotopic dilutions were not the same for succinate and acetate; in the case of succinate, the dilutions reflected only the contribution of glycogen futile cycling, while in the case of acetate, another mechanism was also involved. Results obtained in complementary experiments are consistent with reversal of the succinate synthesis pathway. Our results indicated that for all of the strains, from 12 to 16% of the glucose entering the metabolic pathway originated from prestored glycogen. Although genetically diverse, the four Fibrobacter strains studied had very similar carbon metabolism characteristics.
Despite the long-established therapeutic efficacy of lithium in the treatment of bipolar disorder (BPD), its molecular mechanism of action remains elusive. Newly developed stable isotope-resolved metabolomics (SIRM) is a powerful approach that can be used to elucidate systematically how lithium impacts glial and neuronal metabolic pathways and activities, leading ultimately to deciphering its molecular mechanism of action. The effect of lithium on the metabolism of three different 13C-labeled precursors ([U-13C]-glucose, 13C-3-lactate or 13C-2,3-alanine) was analyzed in cultured rat astrocytes and neurons by nuclear magnetic resonance (NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Using [U-13C]-glucose, lithium was shown to enhance glycolytic activity and part of the Krebs cycle activity in both astrocytes and neurons, particularly the anaplerotic pyruvate carboxylation (PC). The PC pathway was previously thought to be active in astrocytes but absent in neurons. Lithium also stimulated the extracellular release of 13C labeled-lactate, -alanine (Ala), -citrate, and -glutamine (Gln) by astrocytes. Interrogation of neuronal pathways using 13C-3-lactate or 13C-2,3-Ala as tracers indicated a high capacity of neurons to utilize lactate and Ala in the Krebs cycle, particularly in the production of labeled Asp and Glu via PC and normal cycle activity. Prolonged lithium treatment enhanced lactate metabolism via PC but inhibited lactate oxidation via the normal Krebs cycle in neurons. Such lithium modulation of glycolytic, PC and Krebs cycle activity in astrocytes and neurons as well as release of fuel substrates by astrocytes should help replenish Krebs cycle substrates for Glu synthesis while meeting neuronal demands for energy. Further investigations into the molecular regulation of these metabolic traits should provide new insights into the pathophysiology of mood disorders and early diagnostic markers, as well as new target(s) for effective therapies.
Bipolar disorder; Lithium; 13C-labeled tracers; Astrocytes; Neurons; Pyruvate carboxylation; Glu/Gln cycling