Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https: //github. com/cfoguet/iso2flux/releases/tag/0. 7. 2).
Fluxomics is the omics field that analyses metabolic fluxes (i. e. , reaction and transport rates in living cells) which are a close reflection of the metabolic phenotype. As such, quantitative tracking of metabolic fluxes is vital for deciphering the regulation mechanisms of metabolic networks subjected to perturbations, including disease states and drug-induced stress. However, unlike other omics data that can be quantified directly, the fluxome can only be estimated through an indirect interpretation of experimental data[1–3]. There are two main model-based approaches to quantifying metabolic fluxes, Flux Balance Analysis (FBA) and 13C Metabolic Flux Analysis (13C MFA). Both methods use stoichiometric, thermodynamic and experimental constraints to find the range of feasible fluxes across a metabolic network and then find the flux distributions within that space that optimize a given objective function. However, both techniques differ in the type of objective function optimized. In FBA, the objective function is a set of fluxes to be minimized or maximized. These fluxes must represent a biological objective deemed desirable in the conditions of study (e. g. , synthesis of biomass components for proliferating systems) [4]. A significant limitation of FBA is that the choice of objective (s) can significantly influence the predicted flux distributions. In 13C MFA, the objective function is to minimize the difference between simulated and measured 13C enrichment in metabolites [5,6]. 13C enrichment is quantified in metabolic products and intermediates after incubating samples with metabolic substrates labeled with 13C (tracers) and provides information about how carbons are redistributed along metabolic pathways[7]. Compared to FBA, 13C MFA has a greater capacity to elucidate the fluxes of central carbon metabolism. However, 13C MFA is more complex to solve than FBA due to the non-linear nature of the 13C MFA objective. A significant limitation of FBA is that there is generally a wide range of optimal flux distributions[8]. This is not usually the case with 13C MFA which can generally determine flux distributions with a high degree of accuracy. 13C MFA achieves this by integrating large sets of measured isotopologue fractions from parallel experiments with tracers optimized for different parts of the network[9–16]. However, when 13C MFA is used in large metabolic networks and with a limited set of measurements, it can also suffer from the same limitation as FBA and result on a wide interval of flux values for part of the metabolic network[5,17–19]. On FBA, an approach to reduce the range of optimal solutions consists in running a second optimization step on the optimal solution range. One of such methods is parsimonious FBA (pFBA) [20]. This approach, which follows the principle of parsimony or simplicity, consists on finding the optimal value of the primary objective function through FBA and then running a second optimization step where the sum of reaction fluxes is minimized while maintaining the optimal primary objective. The GIMME (and its derivative GIM3E) algorithms[21,22] are based on a similar principle as pFBA. Unlike standard pFBA, where all reactions fluxes are minimized with equal weight, GIMME integrates gene expression data to give greater weight to the minimization of fluxes through reactions catalyzed by lowly expressed enzymes. Different to FBA, for 13C MFA, there is currently no approach that relies on a second optimization to reduce the solution space when experimental data is insufficient to constrain the system towards a unique solution. In addition to model-based approaches (e. g. , FBA or 13C MFA), metabolic fluxes can also be analyzed through the direct or semidirect interpretation of 13C data. This approach primarily consists of predicting the contribution of a labeled substrate to the synthesis of a given metabolite (nutrient contribution) and predicting the relative activity of pathways (pathway activity analysis). Pathway activity analysis assumes that the isotopologue fractions used as a surrogate for the pathways of interest are primarily generated through them. This assumption is generally based on the assertion that the pathways of interest are the most direct way to generate such fractions from the labeled substrate used in the experiment[2,7, 23–25]. Unlike 13C MFA, direct interpretation of 13C data is generally not able to quantify network-wide flux maps. Instead, it provides a series of qualitative or semiquantitative flux predictions around each analyzed metabolite. Strategies that couple direct interpretation of 13C data to regression and correlation analyses are widely applied to unveil the effect of an external perturbation, such as a therapeutic intervention, on central carbon metabolism[26–30]. Here we present parsimonious 13C MFA (p13CMFA), a new model-based approach to flux estimation. p13CMFA first minimizes the difference between experimental and simulated 13C enrichment in metabolites (13C MFA) and then applies the flux minimization principle to select the best solution among the solutions that fit experimental 13C data. Hence, p13CMFA can be used to select the best flux map in instances where experimental 13C measurements are not enough to fully constrain the 13C MFA solution space. Furthermore, the minimization can be weighted by gene expression allowing seamless integration of 13C with gene expression data (Fig 1). We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software (https: //github. com/cfoguet/iso2flux/releases/tag/0. 7. 2). As a proof of concept, we have applied it to the analysis of the metabolic flux distribution in HUVECs (Human umbilical vein endothelial cells) through the integration of a small set of 13C enrichment measurements and transcriptomics data. Furthermore, we validated the predictive capacity of p13CMFA using data from a published study of HTC116 cells where fluxes had been estimated with a high degree of confidence[14]. Using only a small subset of the measurements from such study, p13CMFA was able to achieve significantly better flux predictions than both 13C MFA and GIMME.
p13CMFA consists of two consecutive optimizations: first, the optimal solution to the 13C MFA problem is identified (Eq 1); secondly, the weighted sum of reaction fluxes is minimized within the optimal solution space of 13C MFA (Eq 2). The 13C MFA optimization (Eq 1) identifies the flux distribution that minimizes the difference between measured and simulated isotopologue fractions [5,7]: Xopt=min∑j (Ej−Yj (v) σj) 2 (Eq 1) SubjecttoS. v=0, lb≤v≤ub where, v is a vector of flux values describing a valid steady-state flux distribution; Xopt is the optimal value of the 13C MFA objective; Ej is the experimentally quantified fraction for isotopologue j; Yj (v) is the simulated isotopologue fraction for isotopologue j with flux distribution v. Such simulation is performed by solving a complex non-linear system of equations built around isotopologues balances [1]. σj is the experimental standard deviation of the measurements of isotopologue j; S is the stoichiometric matrix; lb and ub are vectors defining the upper and lower bounds for flux values. Flux bounds can be used to integrate experimental flux measurements; Either in large metabolic networks or when small sets of 13C measurements are integrated, the 13C MFA problem can be undetermined and there can be a wide range of possible solutions. Such indetermination emerges from cycles and alternative pathways in the metabolic network, which lead to many possible flux combinations that can result in the measured 13C label patterns. Furthermore, many of the 13C MFA solutions can involve large fluxes through futile cycles, which are usually artifacts of the optimization process as in vivo enzyme activities cannot support such large flux values. Therefore, to select the best solution among the many solutions that fit experimental 13C data, p13CMFA runs a second optimization where the weighted sum of fluxes is minimized (Eq 2): min∑i|vi|⋅wi (Eq 2) subjecttoS. v=0, lb≤v≤ub, ∑j (Ej−Yj (v) σj) 2≤Xopt+T where: wi is the weight given to the minimization of flux through reaction i; T is the maximum value that the 13C MFA objective can deviate from its optimal value (primary objective tolerance) when fluxes are minimized; The difference between the optimal 13C MFA objective function value and the objective function value when the total reaction flux is minimized can be assumed to follow a Chi2-distribution with one degree of freedom. Therefore, setting T to 3. 84 gives a p13CMFA solution within the 95% confidence intervals of 13C MFA[5]. With p13CMFA, the activity through cycles is minimized to the minimum amount needed to account for experimental measurements. Furthermore, gene expression measurements can be integrated to give greater weight to the minimization of fluxes through reactions catalyzed by lowly expressed enzymes. Then, in instances where multiple pathways can result in similar label patterns, those pathways with stronger gene expression evidence are selected. Hence, p13CMFA reduces the solution space towards a unique solution without requiring a simplification of the metabolic network or additional 13C measurements (Fig 1). As an example of a potential application of p13CMFA, we applied it to analyze the metabolic flux distribution in HUVECs using a publicly available dataset not large enough to make meaningful flux predictions with conventional 13C MFA. In this study, available in the MetaboLights repository[31] (accession number MTBLS412), HUVECs were incubated in the presence of the tracer [1,2-13C2]-glucose, and the relative abundance of 13C isotopologues was quantified in glycogen, ribose, lactate, and glutamate. The rates of production/consumption of glucose, glycogen, lactate, glutamate, and glutamine were also quantified. The data were integrated into a stoichiometric model of central metabolism which includes glycolysis, glycogen metabolism, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, fatty acid synthesis, and energy and redox metabolism (S1 ZIP). To predict the flux distribution using conventional 13C MFA, 95% confidence intervals were computed for each predicted flux value. From this analysis, the space of flux solutions consistent with the measured 13C enrichment was estimated. The resulting space of solution was still mostly undetermined and, in general, 13C MFA was unable to significantly constraint the flux ranges emerging from the stoichiometric and thermodynamic constraints and the measured extracellular fluxes (Fig 2, S1 Table). For instance, despite integrating measurements of 13C enrichment in ribose, it was not possible to conclude whether the oxidative branch of the pentose phosphate pathway contributed more to de novo ribose synthesis than the non-oxidative branch or vice versa. Nevertheless, p13CMFA can be applied to select the best solution in the 13C MFA solution space. With this aim, transcriptomic data taken from the literature[32] were used to add additional penalties to the flux through lowly expressed enzymes. Indeed, by applying p13CMFA, we can now conclude that, under the condition of the study, glucose is mostly directed towards lactate production except for a small part going to the TCA cycle through pyruvate dehydrogenase (Fig 2, Fig 3). Glutamine is mainly metabolized to glutamate or directed to glycolysis through the TCA cycle and phosphoenolpyruvate carboxykinase. In the PPP, the non-oxidative branch contributes to roughly 60% of the net ribose synthesis. Only the glycogen phosphorylase/glycogen synthase futile cycle is predicted to be active, while the remaining futile cycles (i. e. , the hexokinase/glucose 6-phosphatase, phosphofructokinase/fructose bis-phosphatase, pyruvate carboxylase/phosphoenolpyruvate carboxykinase, and glutaminase/glutamine synthase cycles) are predicted to be inactive. Concerning redox metabolism, most of the reduced NAD+ (NADH) produced in the mitochondria is exported to the cytosol through the malate-aspartate shuttle, where it is used to reduce pyruvate to lactate. To evaluate the contribution of 13C MFA to the p13CMFA solution, GIMME (i. e. , flux minimization weighted by gene expression without integrating 13C data) was also performed (Fig 2, S1 Table). Lacking 13C data, GIMME does not predict any activity in the oxidative branch of the pentose phosphate pathway, nor on the glycogen phosphorylase/glycogen synthase futile cycle. Furthermore, GIMME predicts a significantly larger flux through pyruvate dehydrogenase than p13CMFA. Interestingly, p13CMFA predicts an increased activity of the TCA cycle compared to the GIMME solution. This increased activity is fueled by alternative sources of acetyl-CoA such as fatty acid oxidation or catabolism of ketogenic amino acids. Hence, p13CMFA is able to take advantage of measured 13C enrichments and predict significantly different flux maps than those derived from flux minimization alone. To validate the p13CMFA method, we used data from a metabolic characterization of the colon cancer cell line HCT 116 published by Tarrado-Castellarnau et al. [14]. In this study, 25 direct flux measurements and 24 sets of isotopologue fractions, measured after incubation with either [1,2-13C2]-glucose or [U-13C5]-glutamine, had been integrated in the framework of 13C MFA. With such a large set of experimental measurements, 13C MFA had been able to estimate the flux through 62 reactions with a high degree of accuracy. In the same study, transcriptomics data were also collected. From this large data set, we selected a partial data set consisting of 7 experimental flux measurements (the rates of uptake/secretion of glucose, lactate, glutamine, glutamate and, oxygen and the rate of protein and glycogen synthesis) and 4 sets of isotopologue fractions (isotopologue fractions in ribose, lactate, glutamate and glycogen measured after incubation with 1,2-13C2]-glucose). Those are the sets of isotopologues and fluxes that were analyzed in the HUVECs case study with the addition of the rate of protein synthesis and oxygen consumption which Tarrado-Castellarnau et al. described as key determinants of the metabolic phenotype of HCT 116 cells. The partial data set was used to apply pFBA, GIMME, 13C MFA and p13CMFA in the framework of the metabolic network defined by Tarrado-Castellarnau et al. [14] (S2 Zip). p13CMFA was applied both with and without integrating gene expression data (p13CMFA+ge and p13CMFA-ge, respectively). Two complementary metrics, Pearson’s correlation and Euclidian distance, were used to evaluate the similarity between the predicted flux distributions and the flux maps estimated by Tarrado-Castellarnau et al. using the full dataset[14] (Fig 4, S2 Table). The results show that p13CMFA-ge yields a significantly more accurate flux prediction than both pFBA (i. e. , flux minimization without integrating 13C data), and 13C MFA. Interestingly, while integrating gene expression significantly enhances the accuracy of p13CMFA (p13CMFA+ge compared to p13CMFA-ge), such effect is less marked than the effect of adding gene expression to standard flux minimization (GIMME compared to pFBA). This is due to the fact that p13CMFA-ge flux predictions have already a remarkable level of accuracy; hence, less information can be gained by adding transcriptomics data. Nevertheless, even if GIMME achieves flux predictions of similar accuracy to p13CMFA-ge, p13CMFA+ge results on flux predictions that are significantly more accurate than those obtained with GIMME. Hence, in instances were only a limited number of 13C measurements are available, p13CMFA is a valid method for obtaining accurate flux estimations, regardless of the availability of gene expression data.
13C MFA is a well-established technique and has proven to be an extremely valuable tool in quantifying metabolic fluxes[9–18]. However, to fully determine fluxes through a large metabolic network, parallel labeling experiments must be performed and 13C propagation must be quantified in many metabolites in the network[19]. Indeed, when applying 13C MFA either with a small set of experimental data or with a large metabolic network, part of the 13C MFA solution space can be too wide to draw meaningful conclusions about the underlying flux distribution. This solution space can be reduced by removing degrees of freedom from the system, for instance, by removing reactions from the network or making reactions irreversible. However, this can introduce an arbitrary bias in the resulting flux distribution. Here we describe p13CMFA, a new approach for 13C data integration which can overcome these limitations of 13C MFA and estimate a realistic solution within an undetermined 13C MFA solution space. This solution will be the flux distribution within the 13C MFA solution space that minimizes the weighted sum of reaction fluxes. Thus, it will be the most enzymatically efficient solution. In that regard, p13CMFA is partially based on a similar principle as pathway activity analysis (i. e. , the assumption that specific fractions of isotopologues are primarily generated through the simplest combinations of pathways). However, unlike pathway activity analysis, p13CMFA is able to integrate all quantified isotopologue fractions and flux measurements (e. g. rates of metabolite uptake and secretion) to generate network-wide flux maps consistent with such data. Furthermore, p13CMFA is highly flexible; for instance, here we show that it can be used to seamlessly integrate gene expression data by giving higher weight to the minimization of the fluxes through lowly expressed enzymes. As a proof of concept, we exemplified how p13CMFA can be used to estimate flux distributions integrating only limited sets of 13C measurements in a test case where traditional 13C MFA was unable to provide a narrow solution space. Furthermore, we demonstrated that, when a limited set of measurements are integrated, p13CMFA can yield more accurate flux predictions than both 13C MFA and GIMME. p13C MFA does not aim to be a replacement of 13C MFA; instead, it seeks to supplement it by identifying the more straightforward solution in parts of the network that cannot be uniquely determined. In that regard, it can be used to quantitatively study flux distributions in instances where not enough information can be obtained with conventional 13C MFA. Nor does it aim to replace the direct interpretation of 13C data. The latter is still a suitable technique when the goal of the analysis is to compare the relative activity of well-established pathways across conditions or quantify substrate contributions rather than to generate complete flux maps. 13C data has been widely used to assist in drug discovery. In this regard, tracer analysis coupled with regression and correlation analyses is frequently used to characterize drug response [26–29]. Such approach uses regression and correlation statistics with binary, numeric and visual analysis to integrate drug dosage, time points, as well as all necessary biological variables in order to diagnose disturbed stable isotope labeled matrices[29]. p13CMFA could further expand the role of 13C in drug discovery by allowing the integration of 13C and transcriptomic data in the framework of genome-scale metabolic models. In the framework of such models, drug targets are identified by systematically simulating the effect of reactions or genes knock out to cell function[34]. This is usually attained by applying the ROOM[35] or MOMA[36] algorithms, which take a unique flux solution as input (wild-type flux distribution) to predict the most likely effect of a gene KO. Hence, p13CMFA results could be potentially used as ROOM/MOMA inputs allowing to take full advantage of the flux information derived from both 13C and transcriptomics data to predict new drug targets. With atom mappings now available on a genome-scale[37], the main obstacle to applying p13CMFA at a genome-scale is the high computational complexity of solving the resulting non-linear problem which increases with the size of the network. Hence, the next challenge for p13CMFA will be optimizing its implementation for genome-scale networks.
The flux spectrum[38] (i. e. , the feasible range of fluxes for a given set of stoichiometric, thermodynamic and flux boundary constraints) was determined using flux variability analysis [8]. Under this approach, each flux is minimized (Eq 3) and maximized (Eq 4) subject to constraints to find the minimum (vminiFS) and maximum (vmaxiFS) feasible values for each flux: vminiFS=minvi (Eq 3) subjecttoS. v=0, lb≤v≤ub vmaxiFS=maxvi (Eq 4) subjecttoS. v=0, lb≤v≤ub The 13C MFA solution space is estimated by computing the confidence intervals for each flux. Such intervals are obtained by minimizing (Eq 5) and maximizing (Eq 6) each flux subject to constraints[5]. vmini=minvi (Eq 5) subjecttoS. v=0, lb≤v≤ub, ∑j (Ej−Yj (v) σj) 2≤Xopt+T vmaxi=maxvi (Eq 6) subjecttoS. v=0, lb≤v≤ub, ∑j (Ej−Yj (v) σj) 2≤Xopt+T where, vmini: is the lower bound of the confidence interval for flux i with tolerance T; vmaxi: is the upper bound of the confidence interval for flux i with tolerance T; Provided that the same primary objective tolerance (T) is used in computing both the p13CMFA solution and the 13C MFA confidence intervals, the p13CMFA solution will always fall within the boundaries of 13C MFA confidence intervals (vmini≤vi≤vmaxi). To apply GIMME and pFBA, the sum of fluxes is minimized subject only to network stoichiometry and flux boundaries (Eq 7). In GIMME, flux minimization weights are derived from gene expression measurements, whereas in pFBA all reactions are given the same minimization weight[20,22]. Transcriptomic data of HUVECs and HCT 116 cells published by Weigand et al. [32] and Tarrado-Castellarnau[14] et al. , respectively, were obtained from the Gene Expression Omnibus repository[39]. A Robust Multichip Analysis gene-level normalization[40] was performed with the Oligo package for R[41]. Using gene-protein-reaction rules, normalized transcript intensities were mapped to each enzyme-catalyzed reaction or protein-facilitated transport process. The weight given to the minimization of fluxes was assigned according to the following equation: wi=1+max (Th−gei, 0) (Eq 8) where, gei is the gene expression value assigned to reaction i; Th is the gene expression threshold. Fluxes through reactions with gene expression levels below this threshold are given additional minimization weight; Using the same criteria as GIM3E[22], Th was set at the maximum gene expression value found in the set of genes mapped to the metabolic network (Eq 9): Th=max (ge) (Eq 9) Using this threshold, the information gained from integrating available gene expression measurements is maximized. Other Th values were tested in the validation case study[14] and using the maximum gene expression as the threshold was found to yield the most accurate flux predictions (S3 Table). p13CMFA was implemented in Iso2Flux, our in-house developed 13C MFA software (https: //github. com/cfoguet/iso2flux/releases/tag/0. 7. 2). Iso2Flux computes steady-state flux distributions as the product of the null space of the stoichiometric matrix and the vector of free fluxes. Reversible reactions are split into forward and reverse reactions. For each reversible reaction, a turnover variable (ti) is introduced defining the flux that is common to the forward (vif) and reverse (vir) reactions. These variables are used to assign values to the fluxes of the forward and reverse reactions as a function of the steady-state net flux (vi). Iso2flux uses the Elementary Metabolite Unit (EMU) framework[1] to build the 13C propagation model. This framework is based on a highly efficient decomposition method that identifies the minimum amount of isotopologue transitions required to simulate the experimentally quantified isotopologues according to the defined carbon propagation rules. The isotopologue transitions are grouped into decoupled systems based on isotopologue size. Balance equations are built around each isotopologue fraction under the assumption of isotopic steady state (S1 Fig). Using the steady-state flux distribution as an input, systems of equations around isotopologues balances are solved sequentially starting with the smallest isotopologue size [1] using the fsolve function of the SciPy library (https: //scipy. org/scipylib/index. html). Solving such system predicts the isotopologue distribution associated with a given steady-state flux distribution (Yj (v) ). The self-adaptive differential evolution (SADE) algorithm from PyGMO (Python Parallel Global Multiobjective Optimizer, https: //github. com/esa/pagmo2) was used to find the optimal solution of the 13C MFA (Eq 1) and p13CMFA (Eq 2) problems. SADE was parallelized using the generalized island-model paradigm. Under such implementation, SADE is run in parallel in different CPU processes (islands). After a given number of SADE iterations (generations), the best solutions (individuals) in each SADE process (island) are shared to parallel SADE processes (migrate to adjacent islands). To prevent bias from the starting solutions (starting populations), the islands are seeded through random sampling of all variables. Free fluxes variables are sampled using the optGpSampler implemented into COBRApy[42,43]. Turnover variables are sampled using the random. uniform function built into python. The algorithm was run with 7 islands, each with a population of 60, and with migrations between islands set to occur every 400 generations. For the analyzed 13C MFA and p13CMFA problems, repeated iterations of the algorithm were shown to reliably converge towards the same minimal objective function value. At the beginning of a 13C experiment, all internal metabolites are unlabeled (m0). Progressively, these products are enriched in 13C, with the subsequent decrease in m0. Isotopic steady state is quickly reached for small pools of metabolites but not necessarily for larger pools such as those of fatty acids, glycogen or metabolites present in large concentrations in the external medium[44]. For these larger pools, unlabeled isotopologues m0 are oversized and might not quickly decrease to the theoretical value that should be reached at steady-state. However, it is possible to represent the effect of large pools in the framework of steady-state 13C MFA through the addition of a virtual reaction. This reaction replaces labeled isotopologues by unlabeled isotopologues in metabolites with large pools. With p13CMFA, the flux through this virtual reaction can be minimized. Effectively, this allows correcting steady-state 13C simulations for large pools while identifying the solutions that require the minimum amount of correction. The statistical significance of the difference between correlation coefficients was evaluated using the Fisher r-to-z transformation[33]. Following this approach, Pearson’s correlation coefficients (r) can be converted to a z-score (r’): r′=12·Ln (1+r1−r) (Eq 12) The variance of z (Sz) will depend only on the sample size (n): Sz=1n−3 (Eq 13) From Eq 12 and Eq 13, the significance of the difference between two correlation coefficients (r1 and r2) can be evaluated by computing the z score corresponding to such difference (Eq 14) and its associated p-value. Human Umbilical Vein Endothelial Cells (HUVECs-pooled, Lonza) were maintained on 1% gelatin-coated flasks at 37°C in a humidified atmosphere of 5% CO2 and 95% air in MCDB131 (Gibco) medium, supplemented with the recommended quantity of endothelial growth medium (EGM) SingleQuots (Lonza), 10% fetal bovine serum (FBS) (Gibco), 2 mM glutamine (Gibco) and 0. 1% Streptomycin (100 μg/mL) /Penicillin (100 units/mL) (S/P) (Gibco). 1 × 106 HUVECs were seeded in 1% gelatin-coated cell culture plates for 6h, and then the maintenance medium was replaced with the MCDB131 basal medium, supplemented with 2% FBS, 2 mM glutamine and 0. 1% S/P and cells were incubated overnight for nutrient deprivation. After nutrient deprivation, the medium was replaced with a restricted medium containing MCDB131 medium supplemented with 2% FBS, 2 mM glutamine and 0. 1% S/P with 10 mM of 50% [1,2-13C2]-glucose (Sigma-Aldrich) and cells were incubated for 40h in a humidified atmosphere with 5% CO2 and 1% O2 at 37°C. Both at the beginning (t = 0h) and the end (t = 40h) of incubation, media and pellets were collected. On the one hand, media and cell pellets were used for analyzing isotopologue abundances for glucose, lactate, glutamate, RNA ribose and glycogen. Raw data are publicly available in the MetaboLights repository at http: //www. ebi. ac. uk/metabolights [31], with accession number MTBLS412. Isolation, derivatization and analysis details are described in MetaboLights. Glucose, lactate, glutamate, and glutamine concentrations were determined in media samples for estimation of secretion or uptake rates of these metabolites using spectrophotometric methods[45]. Also, the net rate of glycogen re-utilization into glucose was estimated by quantifying glycogen content at initial and final time points using [U-13C-D7]-glucose as recovery standard[46]. All biochemical data were normalized by cell number, and by incubation time (h). The resulting rates–expressed in micromoles of metabolite consumed/produced/transformed per hour per million cells (μmol·h-1·million-cells-1) –were 0. 463,0. 099,0. 050 and 1. 169 for glucose uptake, glutamine uptake, glutamate secretion, and lactate secretion, respectively, and a net transformation of glycogen of 0. 000175.
13C Metabolic Flux Analysis (13C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a 13C-labeled substrate, 13C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. 13C MFA integrates measurements of 13C enrichment in metabolites to identify the flux distributions consistent with the measured 13C propagation. However, there is often a range of flux values that can lead to the observed 13C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the13C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of 13C MFA. We have termed this new approach parsimonious 13C MFA (p13CMFA).
carbohydrate metabolism
medicine and health sciences
metabolic networks
glucose metabolism
metabolites
network analysis
pharmacology
molecular biology techniques
drug metabolism
research and analysis methods
cell labeling
computer and information sciences
gene expression
metabolic pathways
molecular biology
pharmacokinetics
metabolic labeling
biochemistry
glycogens
genetics
biology and life sciences
metabolism
glycobiology
p13CMFA: Parsimonious 13C metabolic flux analysis