Metabolic Flux Analysis Methods Research Articles

The intricacies of mitochondrial calcium and enzyme regulation in liver metabolism

Mitochondrial Ca2+ plays a positive role in regulating pyruvate dehydrogenase, as well as the TCA cycle enzymes isocitrate dehydrogenase and α-ketoglutarate dehydrogenase. This regulation boosts the production of reducing equivalents that fuel the electron transport chain, ultimately driving ATP production. The Mitochondrial Calcium Uniporter (MCU) is the highly selective channel responsible for mitochondrial Ca2+ uptake when local Ca2+ levels reach the threshold for channel activation. In a recent study, LaMoia et al. used an innovative [13C5]glutamine-based metabolic flux analysis method (Q-flux) to measure in vivo hepatic metabolic fluxes in liver-specific MCU-/- mice. Surprisingly, they observed increased flux through isocitrate dehydrogenase and α-ketoglutarate dehydrogenase. Metabolic pathways are continuously reorganized in response to intrinsic cellular signals, as well as hormonal and nutritional inputs. Integrating metabolic flux analysis into complex systems can provide deeper insights into how metabolic adaptations occur under different conditions.

195-OR: A Novel 13C5 Glutamine Tracer Method (Q Flux) Reveals a Key Role of Succinyl CoA Anaplerosis in Promoting Increased Rates of Hepatic Gluconeogenesis during Hyperglucagonemia

Hyperglucagonemia is a hallmark of type 2 diabetes mellitus (T2DM) and contributes to increased rates of endogenous glucose production (EGP) , mostly through increased rates of hepatic gluconeogenesis (GNG) , but the anaplerotic pathways and respective substrates that support this process are unclear. To address this question, we developed a novel and extensively validated 13C5 glutamine-based in vivo metabolic flux analysis method (Q Flux) , which allows for quantitation of both the relative and absolute rates of hepatic gluconeogenesis from pyruvate (via pyruvate carboxylase [PC]) , succinyl CoA (via methylmalonyl CoA mutase [MUT]) , glutamine (via glutaminase [GLS2]) , and glycerol. Male Sprague-Dawley rats (∼300 g) were infused for 90 minutes with either low-dose (LD) glucagon (1.25 ng/[kg-min]) or high-dose (HD) glucagon (ng/[kg-min]) , plus insulin (0.5 mU/[kg-min]) , somatostatin (4 μg/[kg-min]) , and 13C5 glutamine (3 μmol/[kg-min]) after a 16-hour fast to deplete hepatic glycogen. HD animals displayed significantly increased plasma glucose concentrations and EGP relative to LD controls. Analyses of liver tissues collected at sacrifice revealed that in LD animals, 70% of mitochondrial GNG was from pyruvate, 15% from succinyl CoA, and 15% from glutamine. In HD animals, 59% of mitochondrial GNG was derived from pyruvate (p=0.vs. LD) , 26% from succinyl CoA (p=0.0vs. LD) , and 15% from glutamine (p=0.99 vs. LD) . Conclusion: We find an unexpected role for succinyl CoA anaplerosis and methylmalonyl CoA mutase flux in supporting increased rates of mitochondrial GNG during hyperglucagonemia. Furthermore, relative contributions of glutamine to hepatic gluconeogenesis were unchanged, contrary to previous reports. Taken together, these results identify novel targets to reduce hyperglucagonemia-induced increases in rates of gluconeogenesis and hyperglycemia in T2DM. Disclosure B.T.Hubbard: None. G.I.Shulman: Advisory Panel; 89bio, Inc., AstraZeneca, Equator Therapeutics, Inc., Janssen Research & Development, LLC, Merck & Co., Inc., Consultant; DiCerna Pharmaceuticals, Inc. , Novo Nordisk, Other Relationship; Generian Pharmaceuticals, iMetabolic Biopharma Corporation, Maze Therapeutics, The Liver Company, Stock/Shareholder; Levels Health, Inc. .

Effect of dissolved oxygen on L-methionine production from glycerol by Escherichia coli W3110BL using metabolic flux analysis method.

L-Methionine is an essential amino acid in humans, which plays an important role in the synthesis of some important amino acids and proteins. In this work, metabolic flux of batch fermentation of L-methionine with recombinant Escherichia coli W3110BL was analyzed using the flux balance analysis method, which estimated the intracellular flux distributions under different dissolved oxygen conditions. The results revealed the producing L-methionine flux of 4.8mmol/(g cell·h) [based on the glycerol uptake flux of 100mmol/(g cell·h)] was obtained at 30% dissolved oxygen level which was higher than that of other dissolved oxygen levels. The carbon fluxes for synthesizing L-methionine were mainly obtained from the pathway of phosphoenolpyruvate to oxaloacetic acid [15.6mmol/(g cell·h)] but not from the TCA cycle. Hence, increasing the flow from phosphoenolpyruvate to oxaloacetic acid by enhancing the enzyme activity of phosphoenolpyruvate carboxylase might be conducive to the production of L-methionine. Additionally, pentose phosphate pathway could provide a large amount of reducing power NADPH for the synthesis of amino acids and the flux could increase from 41mmol/(g cell·h) to 51mmol/(g cell·h) when changing the dissolved oxygen levels, thus meeting the requirement of NADPH for L-methionine production and biomass synthesis. Therefore, the following modification of the strains should based on the improvement of the key pathway and the NAD(P)/NAD(P)H metabolism.

IMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis.

Background: Constraint-based metabolic network models have been widely used in pheno-typic prediction and metabolic engineering design. In recent years, researchers have attempted to im-prove prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results: We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction bounda-ries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion: Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise pre-dictions of metabolic fluxes by using expression values normalized based on GO.

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis.

BackgroundMetabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. However, classical metabolomics techniques only provide a very static view on metabolism. Metabolic flux analysis methods, on the other hand, are highly targeted and require detailed knowledge on metabolism beforehand.ResultsWe present a novel workflow to analyze non-targeted metabolome-wide stable isotope labeling data to detect metabolic flux changes in a non-targeted manner. Furthermore, we show how similarity-analysis of isotopic enrichment patterns can be used for pathway contextualization of unidentified compounds. We illustrate our approach with the analysis of changes in cellular metabolism of human adenocarcinoma cells in response to decreased oxygen availability. Starting without a priori knowledge, we detect metabolic flux changes, leading to an increased glutamine contribution to acetyl-CoA production, reveal biosynthesis of N-acetylaspartate by N-acetyltransferase 8-like (NAT8L) in lung cancer cells and show that NAT8L silencing inhibits proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells.ConclusionsDifferential stable isotope labeling analysis provides qualitative metabolic flux information in a non-targeted manner. Furthermore, similarity analysis of enrichment patterns provides information on metabolically closely related compounds. N-acetylaspartate and NAT8L are important players in cancer cell metabolism, a context in which they have not received much attention yet.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0150-z) contains supplementary material, which is available to authorized users.

MID Max: LC-MS/MS Method for Measuring the Precursor and Product Mass Isotopomer Distributions of Metabolic Intermediates and Cofactors for Metabolic Flux Analysis Applications.

The analytical challenges to acquire accurate isotopic data of intracellular metabolic intermediates for stationary, nonstationary, and dynamic metabolic flux analysis (MFA) are numerous. This work presents MID Max, a novel LC-MS/MS workflow, acquisition, and isotopomer deconvolution method for MFA that takes advantage of additional scan types that maximizes the number of mass isotopomer distributions (MIDs) that can be acquired in a given experiment. The analytical method was found to measure the MIDs of 97 metabolites, corresponding to 74 unique metabolite-fragment pairs (32 precursor spectra and 42 product spectra) with accuracy and precision. The compounds measured included metabolic intermediates in central carbohydrate metabolism and cofactors of peripheral metabolism (e.g., ATP). Using only a subset of the acquired MIDs, the method was found to improve the precision of flux estimations and number of resolved exchange fluxes for wild-type E. coli compared to traditional methods and previously published data sets.

Comprehensive evaluation of two genome-scale metabolic network models for Scheffersomyces stipitis.

Genome-scale metabolic network models represent the link between the genotype and phenotype of the organism, which are usually reconstructed based on the genome sequence annotation and relevant biochemical and physiological information. These models provide a holistic view of the organism's metabolism, and constraint-based metabolic flux analysis methods have been used extensively to study genome-scale cellular metabolic networks. It is clear that the quality of the metabolic network model determines the outcome of the application. Therefore, it is critically important to determine the accuracy of a genome-scale model in describing the cellular metabolism of the modeled strain. However, because of the model complexity, which results in a system with very high degree of freedom, a good agreement between measured and computed substrate uptake rates and product secretion rates is not sufficient to guarantee the predictive capability of the model. To address this challenge, in this work we present a novel system identification based framework to extract the qualitative biological knowledge embedded in the quantitative simulation results from the metabolic network models. The extracted knowledge can serve two purposes: model validation during model development phase, which is the focus of this work, and knowledge discovery once the model is validated. This framework bridges the gap between the large amount of numerical results generated from genome-scale models and the knowledge that can be easily understood by biologists. The effectiveness of the proposed framework is demonstrated by its application to the analysis of two recently published genome-scale models of Scheffersomyces stipitis.

Engineering the supply chain for protein production/secretion in yeasts and mammalian cells.

Metabolic bottlenecks play an increasing role in yeasts and mammalian cells applied for high-performance production of proteins, particularly of pharmaceutical ones that require complex posttranslational modifications. We review the present status and developments focusing on the rational metabolic engineering of such cells to optimize the supply chain for building blocks and energy. Methods comprise selection of beneficial genetic modifications, rational design of media and feeding strategies. Design of better producer cells based on whole genome-wide metabolic network analysis becomes increasingly possible. High-resolution methods of metabolic flux analysis for the complex networks in these compartmented cells are increasingly available. We discuss phenomena that are common to both types of organisms but also those that are different with respect to the supply chain for the production and secretion of pharmaceutical proteins.

Investigation of glutamine metabolism in CHO cells by dynamic metabolic flux analysis

Background Glutamine metabolism represents one of the major targets in metabolic engineering and process optimization due to its importance as cellular energy, carbon and nitrogen source. Metabolic flux analysis represents a powerful method to investigate the physiology and metabolism of cells [1]. Classical metabolic flux analysis methods require steady state conditions. However, industrially relevant cultivation conditions, i.e. batch and fed-batch cultivations, are characterized by changing environmental conditions and metabolic shifts [2]. We used dynamic metabolic flux analysis to study the impact of glutamine availability or limitation on the physiology of CHO K1 cells capturing metabolic dynamics during batchand fed-batch cultivations.

Metabolomic analysis of key central carbon metabolism carboxylic acids as their 3‐nitrophenylhydrazones by UPLC/ESI‐MS

Multiple hydroxy-, keto-, di-, and tri-carboxylic acids are among the cellular metabolites of central carbon metabolism (CCM). Sensitive and reliable analysis of these carboxylates is important for many biological and cell engineering studies. In this work, we examined 3-nitrophenylhydrazine as a derivatizing reagent and optimized the reaction conditions for the measurement of ten CCM-related carboxylic compounds, including glycolate, lactate, malate, fumarate, succinate, citrate, isocitrate, pyruvate, oxaloacetate, and α-ketoglutarate as their 3-nitrophenylhydrazones using LC/MS with ESI. With the derivatization protocol which we have developed, and using negative-ion multiple-reaction monitoring on a triple-quadrupole instrument, all of the carboxylates showed good linearity within a dynamic range of ca. 200 to more than 2000. The on-column LODs and LOQs were from high femtomoles to low picomoles. The analytical accuracies for eight of the ten analytes were determined to be between 89.5 to 114.8% (CV≤7.4%, n = 6). Using a QTOF instrument, the isotopic distribution patterns of these carboxylates, extracted from a (13) C-labeled mouse heart, were successfully determined by UPLC/MS with full-mass detection, indicating the possible utility of this analytical method for metabolic flux analysis. In summary, this work demonstrates an efficient chemical derivatization LC/MS method for metabolomic analysis of these key CCM intermediates in a biological matrix.

PREDICTING GROWTH AND FINDING BIOMASS PRODUCTION USING THE GENERAL GROWTH MECHANISM

First, we briefly describe the general growth mechanism, which governs the growth of living organisms, and its mathematical representation, the growth equation. Using the growth equation, we compute the growth curve for S. cerevisiae and show that it corresponds to available experimental data. Then, we propose a new method for finding the amount of synthesized biomass without complicated stoichiometric computations and apply this method to evaluation of biomass production by S. cerevisiae. We find that obtained results are very close to values obtained by methods of metabolic flux analysis. Since methods of metabolic flux analysis require finding produced biomass, which is one of the most important parameters affecting stoichiometric models, a priori knowledge of produced biomass can significantly improve methods of metabolic flux analysis in many aspects, which we also discuss. Besides, based on the general growth mechanism, we considered evolutionary development of S. cerevisiae and find that it is a more ancient organism than S. pombe and is apparently its direct predecessor.

Eukaryotic metabolism: Measuring compartment fluxes

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Effect of culture conditions on producing and uptake hydrogen flux of biohydrogen fermentation by metabolic flux analysis method

In this work, metabolic flux analysis (MFA) method was used to estimate the effects of the culture conditions on both the producing and uptake hydrogen flux inside the cell of Klebsiella pneumoniae ECU-15. The results indicated that higher temperature could reduce the amount of the uptake hydrogen and enhance the hydrogen production from the NADH pathway. Moreover, both the producing hydrogen flux from formate and the uptake hydrogen flux were attained to the maximum at pH 7.0–7.5. The producing hydrogen flux was higher at 5g/L initial glucose than that of the other concentrations, and the uptake hydrogen flux showed the minimum value under the same condition. The apparent hydrogen generation was caused by the combined action of producing hydrogenase, uptake hydrogenase and bidirectional hydrogenase. These results were helpful to deeply understand the mechanism of the biohydrogen evolving process and establish the suitable molecular strategies for improving hydrogen production.

Quantitative characterization of metabolism and metabolic shifts during growth of the new human cell line AGE1.HN using time resolved metabolic flux analysis

For the improved production of vaccines and therapeutic proteins, a detailed understanding of the metabolic dynamics during batch or fed-batch production is requested. To study the new human cell line AGE1.HN, a flexible metabolic flux analysis method was developed that is considering dynamic changes in growth and metabolism during cultivation. This method comprises analysis of formation of cellular components as well as conversion of major substrates and products, spline fitting of dynamic data and flux estimation using metabolite balancing. During batch cultivation of AGE1.HN three distinct phases were observed, an initial one with consumption of pyruvate and high glycolytic activity, a second characterized by a highly efficient metabolism with very little energy spilling waste production and a third with glutamine limitation and decreasing viability. Main events triggering changes in cellular metabolism were depletion of pyruvate and glutamine. Potential targets for the improvement identified from the analysis are (i) reduction of overflow metabolism in the beginning of cultivation, e.g. accomplished by reduction of pyruvate content in the medium and (ii) prolongation of phase 2 with its highly efficient energy metabolism applying e.g. specific feeding strategies. The method presented allows fast and reliable metabolic flux analysis during the development of producer cells and production processes from microtiter plate to large scale reactors with moderate analytical and computational effort. It seems well suited to guide media optimization and genetic engineering of producing cell lines.Electronic supplementary materialThe online version of this article (doi:10.1007/s00449-010-0502-y) contains supplementary material, which is available to authorized users.

Tandem mass spectrometry: A novel approach for metabolic flux analysis

The goal of metabolic flux analysis (MFA) is the accurate estimation of intracellular fluxes in metabolic networks. Here, we introduce a new method for MFA based on tandem mass spectrometry (MS) and stable-isotope tracer experiments. We demonstrate that tandem MS provides more labeling information than can be obtained from traditional full scan MS analysis and allows estimation of fluxes with better precision. We present a modeling framework that takes full advantage of the additional labeling information obtained from tandem MS for MFA. We show that tandem MS data can be computed for any network model, any compound and any tandem MS fragmentation using linear mapping of isotopomers. The inherent advantages of tandem MS were illustrated in two network models using simulated and literature data. Application of tandem MS increased the observability of the models and improved the precision of estimated fluxes by 2- to 5-fold compared to traditional MS analysis.

Measuring non-steady-state metabolic fluxes in starch-converting faecal microbiota in vitro

This paper explores human gut bacterial metabolism of starch using a combined analytical and computational modelling approach for metabolite and flux analysis. Non-steady-state isotopic labelling experiments were performed with human faecal microbiota in a well-established in vitro model of the human colon. After culture stabilisation, [U-13C] starch was added and samples were taken at regular intervals. Metabolite concentrations and 13C isotopomeric distributions were measured amongst other things for acetate, propionate and butyrate by mass spectrometry and NMR. The vast majority of metabolic flux analysis methods based on isotopomer analysis published to date are not applicable to metabolic non-steady-state experiments. We therefore developed a new ordinary differential equation-based representation of a metabolic model of human faecal microbiota to determine eleven metabolic parameters that characterised the metabolic flux distribution in the isotope labelling experiment. The feasibility of the model parameter quantification was demonstrated on noisy in silico data using a downhill simplex optimisation, matching simulated labelling patterns of isotopically labelled metabolites with measured metabolite and isotope labelling data. Using the experimental data, we determined an increasing net label influx from starch during the experiment from 94±1 µmol/l/min to 133±3 µmol/l/min. Only about 12% of the total carbon flux from starch reached propionate. Propionate production mainly proceeded via succinate with a small contribution via acrylate. The remaining flux from starch yielded acetate (35%) and butyrate (53%). Interpretation of 13C NMR multiplet signals further revealed that butyrate, valerate and caproate were mainly synthesised via cross-feeding, using acetate as a co-substrate. This study demonstrates for the first time that the experimental design and the analysis of the results by computational modelling allows the determination of time-resolved effects of nutrition on the flux distribution within human faecal microbiota in metabolic non-steady-state.

Metabolic flux analysis and pharmaceutical production

Rational engineering of biological systems is an inherently complex process due to their evolved nature. Metabolic engineering emerged and developed over the past 20 years as a field in which methodologies for the rational engineering of biological systems is now being applied to specific industrial, medical, or scientific problems. Of considerable interest is the determination of metabolic fluxes within the cell itself, called metabolic flux analysis. This special issue and this review have a particular interest in the application of metabolic flux analysis for improving the pharmaceutical production process (for both small and large molecules). Though metabolic flux analysis has been somewhat limited in application towards pharmaceutical production, the overall goal is to: (1) have a better understanding of the organism and/or process in question, and (2) provide a rational basis to further engineer (on both metabolic and process scales) improved pharmaceutical production in these organisms. The focus of this review article is to present how experimental and computational methods of metabolic flux analysis have matured, mirroring the maturation of the metabolic engineering field itself, while highlighting some of the successful applications towards both small- and large-molecule pharmaceuticals.

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity. This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.

Metabolic flux analysis as a tool in metabolic engineering of plants

Methods of metabolic flux analysis (MFA) provide insights into the theoretical capabilities of metabolic networks and allow probing the in vivo performance of cellular metabolism. In recent years, an increasing awareness has developed that network analysis methods within the systems biology toolbox are serving to improve our understanding and ability to manipulate metabolism. In this minireview the potential of MFA to increase the chances of success in metabolic engineering of plants is presented, recent progress related to engineering and flux analysis in central metabolism of plants is discussed, and some recent advances in flux analysis methodology are highlighted.

The Thermodynamic Meaning of Metabolic Exchange Fluxes

Metabolic flux analysis (MFA) deals with the experimental determination of steady-state fluxes in metabolic networks. An important feature of the 13C MFA method is its capability to generate information on both directions of bidirectional reaction steps given by exchange fluxes. The biological interpretation of these exchange fluxes and their relation to thermodynamic properties of the respective reaction steps has never been systematically investigated. As a central result, it is shown here that for a general class of enzyme reaction mechanisms the quotients of net and exchange fluxes measured by 13C MFA are coupled to Gibbs energies of the reaction steps. To establish this relation the concept of apparent flux ratios of enzymatic isotope-labeling networks is introduced and some computing rules for these flux ratios are given. Application of these rules reveals a conceptional pitfall of 13C MFA, which is the inherent dependency of measured exchange fluxes on the chosen tracer atom. However, it is shown that this effect can be neglected for typical biochemical reaction steps under physiological conditions. In this situation, the central result can be formulated as a two-sided inequality relating fluxes, pool sizes, and standard Gibbs energies. This relation has far-reaching consequences for metabolic flux analysis, quantitative metabolomics, and network thermodynamics.