INCA: a computational platform for isotopically non-stationary metabolic flux analysis

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses

¹³C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary ¹³C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open ¹³C-MFA software that works in various operating systems will enable more researchers to perform ¹³C-MFA and to further modify and develop the package.

A guide to 13C metabolic flux analysis for the cancer biologist

Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, ¹³C metabolic flux analysis (¹³C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with ¹³C-MFA. We describe best practices in ¹³C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism.

Tracing tagged molecules can help researchers understand the altered metabolism of cancer cells. The abilities of cancer cells to multiply rapidly and invade new tissues are supported by metabolic alterations, which can be investigated by feeding tagged molecules to cells and tracing how they are metabolized. These techniques, such as ¹³C metabolic flux analysis (¹³C-MFA), have been perceived as difficult to use, but recent advances are making them more accessible. Maciek Antoniewicz, University of Delaware, Newark, USA, has published a practical guide for researchers wanting to use 13C-MFA. The review includes best practices, pitfalls, alternative approaches, and new developments, especially new user-friendly software that allows researchers without extensive training in mathematics, statistics, or coding to perform 13C-MFA. Broadening access to tools for investigating altered metabolic pathways may spur development of new cancer therapies targeting these pathways.

On structural identifiability analysis of the cascaded linear dynamic systems in isotopically non-stationary 13C labelling experiments

The isotopically non-stationary 13C labelling experiments, as an emerging experimental technique, can estimate the intracellular fluxes of the cell culture under an isotopic transient period. However, to the best of our knowledge, the issue of the structural identifiability analysis of non-stationary isotope experiments is not well addressed in the literature. In this work, the local structural identifiability analysis for non-stationary cumomer balance equations is conducted based on the Taylor series approach. The numerical rank of the Jacobian matrices of the finite extended time derivatives of the measured fractions with respect to the free parameters is taken as the criterion. It turns out that only one single time point is necessary to achieve the structural identifiability analysis of the cascaded linear dynamic system of non-stationary isotope experiments. The equivalence between the local structural identifiability of the cascaded linear dynamic systems and the local optimum condition of the nonlinear least squares problem is elucidated in the work. Optimal measurements sets can then be determined for the metabolic network. Two simulated metabolic networks are adopted to demonstrate the utility of the proposed method.

Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Typically, ¹³C flux analysis relies on assumptions of both metabolic and isotopic steady state. If metabolism is steady but isotope labeling is not allowed to fully equilibrate, isotopically nonstationary metabolic flux analysis (INST-MFA) can be used to estimate fluxes. This requires solution of differential equations that describe the time-dependent labeling of network metabolites, while iteratively adjusting the flux and pool size parameters to match the transient labeling measurements. INST-MFA holds a number of unique advantages over approaches that rely solely upon steady-state isotope enrichments. First, INST-MFA can be applied to estimate fluxes in autotrophic systems, which consume only single-carbon substrates. Second, INST-MFA is ideally suited to systems that label slowly due to the presence of large intermediate pools or pathway bottlenecks. Finally, INST-MFA provides increased measurement sensitivity to estimate reversible exchange fluxes and metabolite pool sizes, which represents a potential framework for integrating metabolomic analysis with ¹³C flux analysis. This review highlights the unique capabilities of INST-MFA, describes newly available software tools that automate INST-MFA calculations, presents several practical examples of recent INST-MFA applications, and discusses the technical challenges that lie ahead.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Combinatorial CRISPR-Cas9 Metabolic Screens Reveal Critical Redox Control Points Dependent on the KEAP1-NRF2 Regulatory Axis

The metabolic pathways fueling tumor growth have been well characterized, but the specific impact of transforming events on network topology and enzyme essentiality remains poorly understood. To this end, we performed combinatorial CRISPR-Cas9 screens on a set of 51 carbohydrate metabolism genes that represent glycolysis and the pentose phosphate pathway (PPP). This high-throughput methodology enabled systems-level interrogation of metabolic gene dispensability, interactions, and compensation across multiple cell types. The metabolic impact of specific combinatorial knockouts was validated using 13C and 2H isotope tracing, and these assays together revealed key nodes controlling redox homeostasis along the KEAP-NRF2 signaling axis. Specifically, targeting KEAP1 in combination with oxidative PPP genes mitigated the deleterious effects of these knockouts on growth rates. These results demonstrate how our integrated framework, combining genetic, transcriptomic, and flux measurements, can improve elucidation of metabolic network alterations and guide precision targeting of metabolic vulnerabilities based on tumor genetics.

Hepatic Gi signaling regulates whole-body glucose homeostasis

An increase in hepatic glucose production (HGP) is a key feature of type 2 diabetes. Excessive signaling through hepatic Gs-linked glucagon receptors critically contributes to pathologically elevated HGP. Here, we tested the hypothesis that this metabolic impairment can be counteracted by enhancing hepatic Gi signaling. Specifically, we used a chemogenetic approach to selectively activate Gi-type G proteins in mouse hepatocytes in vivo. Unexpectedly, activation of hepatic Gi signaling triggered a pronounced increase in HGP and severely impaired glucose homeostasis. Moreover, increased Gi signaling stimulated glucose release in human hepatocytes. A lack of functional Gi-type G proteins in hepatocytes reduced blood glucose levels and protected mice against the metabolic deficits caused by the consumption of a high-fat diet. Additionally, we delineated a signaling cascade that links hepatic Gi signaling to ROS production, JNK activation, and a subsequent increase in HGP. Taken together, our data support the concept that drugs able to block hepatic Gi-coupled GPCRs may prove beneficial as antidiabetic drugs.

Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease

Metabolic dysfunction has reemerged as an essential hallmark of tumorigenesis, and metabolic phenotypes are increasingly being integrated into pre-clinical models of disease. The complexity of these metabolic networks requires systems-level interrogation, and metabolic flux analysis (MFA) with stable isotope tracing present a suitable conceptual framework for such systems. Here we review efforts to elucidate mechanisms through which metabolism influences tumor growth and survival, with an emphasis on applications using stable isotope tracing and MFA. Through these approaches researchers can now quantify pathway fluxes in various in vitro and in vivo contexts to provide mechanistic insights at molecular and physiological scales respectively. Knowledge and discoveries in cancer models are paving the way toward applications in other biological contexts and disease models. In turn, MFA approaches will increasingly help to uncover new therapeutic opportunities that enhance human health.

Cell-Type Specific Metabolic Flux Analysis: A Challenge for Metabolic Phenotyping and a Potential Solution in Plants

Stable isotope labelling experiments are used routinely in metabolic flux analysis (MFA) to determine the metabolic phenotype of cells and tissues. A complication arises in multicellular systems because single cell measurements of transcriptomes, proteomes and metabolomes in multicellular organisms suggest that the metabolic phenotype will differ between cell types. In silico analysis of simulated metabolite isotopomer datasets shows that cellular heterogeneity confounds conventional MFA because labelling data averaged over multiple cell types does not necessarily yield averaged flux values. A potential solution to this problem—the use of cell-type specific reporter proteins as a source of cell-type specific labelling data—is proposed and the practicality of implementing this strategy in the roots of Arabidopsis thaliana seedlings is explored. A protocol for the immunopurification of ectopically expressed green fluorescent protein (GFP) from Arabidopsis thaliana seedlings using a GFP-binding nanobody is developed, and through GC-MS analysis of protein hydrolysates it is established that constitutively expressed GFP reports accurately on the labelling of total protein in root tissues. It is also demonstrated that the constitutive expression of GFP does not perturb metabolism. The principal obstacle to the implementation of the method in tissues with cell-type specific GFP expression is the sensitivity of the GC-MS system.

Loss of hepatic AMP-activated protein kinase impedes the rate of glycogenolysis but not gluconeogenic fluxes in exercising mice

Pathologies including diabetes and conditions such as exercise place an unusual demand on liver energy metabolism, and this demand induces a state of energy discharge. Hepatic AMP-activated protein kinase (AMPK) has been proposed to inhibit anabolic processes such as gluconeogenesis in response to cellular energy stress. However, both AMPK activation and glucose release from the liver are increased during exercise. Here, we sought to test the role of hepatic AMPK in the regulation of in vivo glucose-producing and citric acid cycle-related fluxes during an acute bout of muscular work. We used ²H/¹³C metabolic flux analysis to quantify intermediary metabolism fluxes in both sedentary and treadmill-running mice. Additionally, liver-specific AMPK α1 and α2 subunit KO and WT mice were utilized. Exercise caused an increase in endogenous glucose production, glycogenolysis, and gluconeogenesis from phosphoenolpyruvate. Citric acid cycle fluxes, pyruvate cycling, anaplerosis, and cataplerosis were also elevated during this exercise. Sedentary nutrient fluxes in the postabsorptive state were comparable for the WT and KO mice. However, the increment in the endogenous rate of glucose appearance during exercise was blunted in the KO mice because of a diminished glycogenolytic flux. This lower rate of glycogenolysis was associated with lower hepatic glycogen content before the onset of exercise and prompted a reduction in arterial glucose during exercise. These results indicate that liver AMPKα1α2 is required for maintaining glucose homeostasis during an acute bout of exercise.

De novo MYC addiction as an adaptive response of cancer cells to CDK4/6 inhibition

Cyclin‐dependent kinases (CDK) are rational cancer therapeutic targets fraught with the development of acquired resistance by tumor cells. Through metabolic and transcriptomic analyses, we show that the inhibition of CDK4/6 leads to a metabolic reprogramming associated with gene networks orchestrated by the MYC transcription factor. Upon inhibition of CDK4/6, an accumulation of MYC protein ensues which explains an increased glutamine metabolism, activation of the mTOR pathway and blunting of HIF‐1α‐mediated responses to hypoxia. These MYC‐driven adaptations to CDK4/6 inhibition render cancer cells highly sensitive to inhibitors of MYC, glutaminase or mTOR and to hypoxia, demonstrating that metabolic adaptations to antiproliferative drugs unveil new vulnerabilities that can be exploited to overcome acquired drug tolerance and resistance by cancer cells.

Understanding metabolism with flux analysis: From theory to application

Quantitative and qualitative knowledge of metabolic rates (i.e. fluxes) over a metabolic network and in specific cellular compartments gives insights into the regulation of metabolism and helps to understand the contribution of metabolic alterations to pathology. In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis. Finally we discuss applications using metabolic flux analysis to elucidate mechanisms pertinent to tumor cell metabolism. We hope that this review gives the readers a brief introduction of how flux analysis is conducted, how technical issues related to it are addressed, and how its application has contributed to our knowledge of tumor cell metabolism.

To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis

¹³ C Metabolic Fluxes Analysis (¹³ C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of ¹³ C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to ¹³ C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in ¹³ C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer.

Distinct carbon sources affect structural and functional maturation of cardiomyocytes derived from human pluripotent stem cells

The immature phenotype of human pluripotent stem cell derived cardiomyocytes (hPSC-CMs) constrains their potential in cell therapy and drug testing. In this study, we report that shifting hPSC-CMs from glucose-containing to galactose- and fatty acid-containing medium promotes their fast maturation into adult-like CMs with higher oxidative metabolism, transcriptional signatures closer to those of adult ventricular tissue, higher myofibril density and alignment, improved calcium handling, enhanced contractility, and more physiological action potential kinetics. Integrated "-Omics" analyses showed that addition of galactose to culture medium improves total oxidative capacity of the cells and ameliorates fatty acid oxidation avoiding the lipotoxicity that results from cell exposure to high fatty acid levels. This study provides an important link between substrate utilization and functional maturation of hPSC-CMs facilitating the application of this promising cell type in clinical and preclinical applications.

Rerouting of carbon flux in a glycogen mutant of cyanobacteria assessed via isotopically non-stationary 13 C metabolic flux analysis

Cyanobacteria, which constitute a quantitatively dominant phylum, have attracted attention in biofuel applications due to favorable physiological characteristics, high photosynthetic efficiency and amenability to genetic manipulations. However, quantitative aspects of cyanobacterial metabolism have received limited attention. In the present study, we have performed isotopically non-stationary ¹³ C metabolic flux analysis (INST-¹³ C-MFA) to analyze rerouting of carbon in a glycogen synthase deficient mutant strain (glgA-I glgA-II) of the model cyanobacterium Synechococcus sp. PCC 7002. During balanced photoautotrophic growth, 10-20% of the fixed carbon is stored in the form of glycogen via a pathway that is conserved across the cyanobacterial phylum. Our results show that deletion of glycogen synthase gene orchestrates cascading effects on carbon distribution in various parts of the metabolic network. Carbon that was originally destined to be incorporated into glycogen gets partially diverted toward alternate storage molecules such as glucosylglycerol and sucrose. The rest is partitioned within the metabolic network, primarily via glycolysis and tricarboxylic acid cycle. A lowered flux toward carbohydrate synthesis and an altered distribution at the glucose-1-phosphate node indicate flexibility in the network. Further, reversibility of glycogen biosynthesis reactions points toward the presence of futile cycles. Similar redistribution of carbon was also predicted by Flux Balance Analysis. The results are significant to metabolic engineering efforts with cyanobacteria where fixed carbon needs to be re-routed to products of interest. Biotechnol. Bioeng. 2017;114: 2298-2308. © 2017 Wiley Periodicals, Inc.

Substrates and oxygen dependent citric acid production by Yarrowia lipolytica: insights through transcriptome and fluxome analyses

BACKGROUND

Unlike the well-studied backer yeast where catabolite repression represents a burden for mixed substrate fermentation, Yarrowia lipolytica, an oleaginous yeast, is recognized for its potential to produce single cell oils and citric acid from different feedstocks. These versatilities of Y. lipolytica with regards to substrate utilization make it an attractive host for biorefinery application. However, to develop a commercial process for the production of citric acid by Y. lipolytica, it is necessary to better understand the primary metabolism and its regulation, especially for growth on mixed substrate.

RESULTS

Controlling the dissolved oxygen concentration (pO2) in Y. lipolytica cultures enhanced citric acid production significantly in cultures grown on glucose in mono- or dual substrate fermentations, whereas with glycerol as mono-substrate no significant effect of pO2 was found on citrate production. Growth on mixed substrate with glucose and glycerol revealed a relative preference of glycerol utilization by Y. lipolytica. Under optimized conditions with pO2 control, the citric acid titer on glucose in mono- or in dual substrate cultures was 55 and 50 g/L (with productivity of 0.6 g/L*h in both cultures), respectively, compared to a maximum of 18 g/L (0.2 g/L*h) with glycerol in monosubstrate culture. Additionally, in dual substrate fermentation, glycerol limitation was found to trigger citrate consumption despite the presence of enough glucose in pO2-limited culture. The metabolic behavior of this yeast on different substrates was investigated at transcriptomic and 13C-based fluxomics levels.

CONCLUSION

Upregulation of most of the genes of the pentose phosphate pathway was found in cultures with highest citrate production with glucose in mono- or in dual substrate fermentation with pO2 control. The activation of the glyoxylate cycle in the oxygen limited cultures and the imbalance caused by glycerol limitation might be the reason for the re-consumption of citrate in dual substrate fermentations. This study provides interesting targets for metabolic engineering of this industrial yeast.

Isotopically nonstationary 13C flux analysis of cyanobacterial isobutyraldehyde production

We applied isotopically nonstationary ¹³C metabolic flux analysis (INST-MFA) to compare the pathway fluxes of wild-type (WT) Synechococcus elongatus PCC 7942 to an engineered strain (SA590) that produces isobutyraldehyde (IBA). The flux maps revealed a potential bottleneck at the pyruvate kinase (PK) reaction step that was associated with diversion of flux into a three-step PK bypass pathway involving the enzymes PEP carboxylase (PEPC), malate dehydrogenase (MDH), and malic enzyme (ME). Overexpression of pk in SA590 led to a significant improvement in IBA specific productivity. Single-gene overexpression of the three enzymes in the proposed PK bypass pathway also led to improvements in IBA production, although to a lesser extent than pk overexpression. Combinatorial overexpression of two of the three genes in the proposed PK bypass pathway (mdh and me) led to improvements in specific productivity that were similar to those achieved by single-gene pk overexpression. Our work demonstrates how ¹³C flux analysis can be used to identify potential metabolic bottlenecks and novel metabolic routes, and how these findings can guide rational metabolic engineering of cyanobacteria for increased production of desired molecules.

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed "unsteady-state flux balance analysis" (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through ¹³C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Background

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.

Results

The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and ¹³C Metabolic Flux Analysis. Moreover, it introduces the capability to use ¹³C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale ¹³C Metabolic Flux Analysis (2S-¹³C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.

Conclusions

jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1615-y) contains supplementary material, which is available to authorized users.

Fluxomics links cellular functional analyses to whole-plant phenotyping

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.

Modeling framework for isotopic labeling of heteronuclear moieties

BACKGROUND

Isotopic labeling is an analytic technique that is used to track the movement of isotopes through reaction networks. In general, the applicability of isotopic labeling techniques is limited to the investigation of reaction networks that consider homonuclear moieties, whose atoms are of one tracer element with two isotopes, distinguished by the presence of one additional neutron.

RESULTS

This article presents a reformulation of the modeling framework for isotopic labeling, generalized to arbitrarily large, heteronuclear moieties, arbitrary numbers of isotopic tracer elements, and arbitrary numbers of isotopes per element, distinguished by arbitrary numbers of additional neutrons.

CONCLUSIONS

With this work, it is now possible to simulate the isotopic labeling states of metabolites in completely arbitrary biochemical reaction networks.

The importance of accurately correcting for the natural abundance of stable isotopes

The use of isotopically labeled tracer substrates is an experimental approach for measuring in vivo and in vitro intracellular metabolic dynamics. Stable isotopes that alter the mass but not the chemical behavior of a molecule are commonly used in isotope tracer studies. Because stable isotopes of some atoms naturally occur at non-negligible abundances, it is important to account for the natural abundance of these isotopes when analyzing data from isotope labeling experiments. Specifically, a distinction must be made between isotopes introduced experimentally via an isotopically labeled tracer and the isotopes naturally present at the start of an experiment. In this tutorial review, we explain the underlying theory of natural abundance correction of stable isotopes, a concept not always understood by metabolic researchers. We also provide a comparison of distinct methods for performing this correction and discuss natural abundance correction in the context of steady state 13C metabolic flux, a method increasingly used to infer intracellular metabolic flux from isotope experiments.

Metabolic Flux Analysis in Isotope Labeling Experiments Using the Adjoint Approach

Comprehension of metabolic pathways is considerably enhanced by metabolic flux analysis (MFA-ILE) in isotope labeling experiments. The balance equations are given by hundreds of algebraic (stationary MFA) or ordinary differential equations (nonstationary MFA), and reducing the number of operations is therefore a crucial part of reducing the computation cost. The main bottleneck for deterministic algorithms is the computation of derivatives, particularly for nonstationary MFA. In this article, we explain how the overall identification process may be speeded up by using the adjoint approach to compute the gradient of the residual sum of squares. The proposed approach shows significant improvements in terms of complexity and computation time when it is compared with the usual (direct) approach. Numerical results are obtained for the central metabolic pathways of Escherichia coli and are validated against reference software in the stationary case. The methods and algorithms described in this paper are included in the sysmetab software package distributed under an Open Source license at http://forge.scilab.org/index.php/p/sysmetab/.

Liver AMP-Activated Protein Kinase Is Unnecessary for Gluconeogenesis but Protects Energy State during Nutrient Deprivation

AMPK is an energy sensor that protects cellular energy state by attenuating anabolic and promoting catabolic processes. AMPK signaling is purported to regulate hepatic gluconeogenesis and substrate oxidation; coordination of these processes is vital during nutrient deprivation or pathogenic during overnutrition. Here we directly test hepatic AMPK function in regulating metabolic fluxes that converge to produce glucose and energy in vivo. Flux analysis was applied in mice with a liver-specific deletion of AMPK (L-KO) or floxed control littermates to assess rates of hepatic glucose producing and citric acid cycle (CAC) fluxes. Fluxes were assessed in short and long term fasted mice; the latter condition is a nutrient stressor that increases liver AMP/ATP. The flux circuit connecting anaplerosis with gluconeogenesis from the CAC was unaffected by hepatic AMPK deletion in short and long term fasting. Nevertheless, depletion of hepatic ATP was exacerbated in L-KO mice, corresponding to a relative elevation in citrate synthase flux and accumulation of branched-chain amino acid-related metabolites. L-KO mice also had a physiological reduction in flux from glycogen to G6P. These results demonstrate AMPK is unnecessary for maintaining gluconeogenic flux from the CAC yet is critical for stabilizing liver energy state during nutrient deprivation.

Exo-MFA - A 13C metabolic flux analysis framework to dissect tumor microenvironment-secreted exosome contributions towards cancer cell metabolism

Dissecting the pleiotropic roles of tumor micro-environment (TME) on cancer progression has been brought to the foreground of research on cancer pathology. Extracellular vesicles such as exosomes, transport proteins, lipids, and nucleic acids, to mediate intercellular communication between TME components and have emerged as candidates for anti-cancer therapy. We previously reported that cancer-associated fibroblast (CAF) derived exosomes (CDEs) contain metabolites in their cargo that are utilized by cancer cells for central carbon metabolism and promote cancer growth. However, the metabolic fluxes involved in donor cells towards packaging of metabolites in extracellular vesicles and exosome-mediated metabolite flux upregulation in recipient cells are still not known. Here, we have developed a novel empirical and computational technique, exosome-mediated metabolic flux analysis (Exo-MFA) to quantify flow of cargo from source cells to recipient cells via vesicular transport. Our algorithm, which is based on ¹³C metabolic flux analysis, successfully predicts packaging fluxes to metabolite cargo in CAFs, dynamic changes in rate of exosome internalization by cancer cells, and flux of cargo release over time. We find that cancer cells internalize exosomes rapidly leading to depletion of extracellular exosomes within 24h. However, metabolite cargo significantly alters intracellular metabolism over the course of 24h by regulating glycolysis pathway fluxes via lactate supply. Furthermore, it can supply up to 35% of the TCA cycle fluxes by providing TCA intermediates and glutamine. Our algorithm will help gain insight into (i) metabolic interactions in multicellular systems (ii) biogenesis of extracellular vesicles and their differential packaging of cargo under changing environments, and (iii) regulation of cancer cell metabolism by its microenvironment.

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary ¹³C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO₂ over a diurnal period implying that the metabolic steady state is limited to less than 12 h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient ¹³CO₂ labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis

BACKGROUND

Synechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973's fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, ¹³C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions.

RESULTS

The Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO₂ conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions.

CONCLUSIONS

This study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for CO₂ assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

LKB1 promotes metabolic flexibility in response to energy stress

The Liver Kinase B1 (LKB1) tumor suppressor acts as a metabolic energy sensor to regulate AMP-activated protein kinase (AMPK) signaling and is commonly mutated in various cancers, including non-small cell lung cancer (NSCLC). Tumor cells deficient in LKB1 may be uniquely sensitized to metabolic stresses, which may offer a therapeutic window in oncology. To address this question we have explored how functional LKB1 impacts the metabolism of NSCLC cells using ¹³C metabolic flux analysis. Isogenic NSCLC cells expressing functional LKB1 exhibited higher flux through oxidative mitochondrial pathways compared to those deficient in LKB1. Re-expression of LKB1 also increased the capacity of cells to oxidize major mitochondrial substrates, including pyruvate, fatty acids, and glutamine. Furthermore, LKB1 expression promoted an adaptive response to energy stress induced by anchorage-independent growth. Finally, this diminished adaptability sensitized LKB1-deficient cells to combinatorial inhibition of mitochondrial complex I and glutaminase. Together, our data implicate LKB1 as a major regulator of adaptive metabolic reprogramming and suggest synergistic pharmacological strategies for mitigating LKB1-deficient NSCLC tumor growth.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

The airway epithelium undergoes metabolic reprogramming in individuals at high risk for lung cancer

The molecular determinants of lung cancer risk remain largely unknown. Airway epithelial cells are prone to assault by risk factors and are considered to be the primary cell type involved in the field of cancerization. To investigate risk-associated changes in the bronchial epithelium proteome that may offer new insights into the molecular pathogenesis of lung cancer, proteins were identified in the airway epithelial cells of bronchial brushing specimens from risk-stratified individuals by shotgun proteomics. Differential expression of selected proteins was validated by parallel reaction monitoring mass spectrometry in an independent set of individual bronchial brushings. We identified 2,869 proteins, of which 312 proteins demonstrated a trend in expression. Pathway analysis revealed enrichment of carbohydrate metabolic enzymes in high-risk individuals. Glucose consumption and lactate production were increased in human bronchial epithelial BEAS2B cells treated with cigarette smoke condensate for 7 months. Increased lipid biosynthetic capacity and net reductive carboxylation were revealed by metabolic flux analyses of [U-¹³C₅] glutamine in this in vitro model, suggesting profound metabolic reprogramming in the airway epithelium of high-risk individuals. These results provide a rationale for the development of potentially new chemopreventive strategies and selection of patients for surveillance programs.

Quantitative metabolic flux analysis reveals an unconventional pathway of fatty acid synthesis in cancer cells deficient for the mitochondrial citrate transport protein

The mitochondrial citrate transport protein (CTP), encoded by SLC25A1, accommodates bidirectional trafficking of citrate between the mitochondria and cytosol, supporting lipid biosynthesis and redox homeostasis. Genetic CTP deficiency causes a fatal neurodevelopmental syndrome associated with the accumulation of L- and D-2-hydroxyglutaric acid, and elevated CTP expression is associated with poor prognosis in several types of cancer, emphasizing the importance of this transporter in multiple human pathologies. Here we describe the metabolic consequences of CTP deficiency in cancer cells. As expected from the phenotype of CTP-deficient humans, somatic CTP loss in cancer cells induces broad dysregulation of mitochondrial metabolism, resulting in accumulation of lactate and of the L- and D- enantiomers of 2-hydroxyglutarate (2HG) and depletion of TCA cycle intermediates. It also eliminates mitochondrial import of citrate from the cytosol. To quantify the impact of CTP deficiency on metabolic flux, cells were cultured with a set of ¹³C-glucose and ¹³C-glutamine tracers with resulting data integrated by metabolic flux analysis (MFA). CTP-deficient cells displayed a major restructuring of central carbon metabolism, including suppression of pyruvate dehydrogenase (PDH) and induction of glucose-dependent anaplerosis through pyruvate carboxylase (PC). We also observed an unusual lipogenic pathway in which carbon from glucose supplies mitochondrial production of alpha-ketoglutarate (AKG), which is then trafficked to the cytosol and used to supply reductive carboxylation by isocitrate dehydrogenase 1 (IDH1). The resulting citrate is cleaved to produce lipogenic acetyl-CoA, thereby completing a novel pathway of glucose-dependent reductive carboxylation. In CTP deficient cells, IDH1 inhibition suppresses lipogenesis from either glucose or glutamine, implicating IDH1 as a required component of fatty acid synthesis in states of CTP deficiency.

WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism

Background

Flux analyses, including flux balance analysis (FBA) and ¹³C-metabolic flux analysis (¹³C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the ¹³C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the ¹³C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB^®.

Results

We constructed an open-source platform for steady-state ¹³C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify ¹³C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results.

Conclusion

Open-source software for ¹³C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1314-0) contains supplementary material, which is available to authorized users.

Combined 13C-assisted metabolomics and metabolic flux analysis reveals the impacts of glutamate on the central metabolism of high β-galactosidase-producing Pichia pastoris

Background

Pichia pastoris is a popular recombinant protein expression system for its accessibility of efficient gene manipulation and high protein production. Sufficient supply of precursors, energy, and redox cofactors is crucial for high recombinant protein production. In our present work, we found that the addition of glutamate improved the recombinant β-galactosidase (β-gal) production by P. pastoris G1HL.

Methods

To elucidate the impacts of glutamate on the central metabolism in detail, a combined ¹³C-assisted metabolomics and ¹³C metabolic flux analysis was conducted based on LC–MS/MS and GC–MS data.

Results

The pool sizes of intracellular amino acids were obviously higher on glucose/glutamate (Glc/Glu). The fluxes in EMP entry reaction and in downstream TCA cycle were 50 and 67% higher on Glc/Glu than on Glc, respectively. While the fluxes in upstream TCA cycle kept almost unaltered, the fluxes in PPP oxidative branch decreased.

Conclusion

The addition of glutamate leads to a remarkable change on the central metabolism of high β-galactosidase-producing P. pastoris G1HL. To meet the increased demands of redox cofactors and energy for higher β-galactosidase production on Glc/Glu, P. pastoris G1HL redistributes the fluxes in central metabolism through the inhibitions and/or activation of the enzymes in key nodes together with the energy and redox status.

Electronic supplementary material

The online version of this article (doi:10.1186/s40643-016-0124-6) contains supplementary material, which is available to authorized users.

SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis

Metabolic fluxes are a cornerstone of cellular physiology that emerge from a complex interplay of enzymes, carriers, and nutrients. The experimental assessment of in vivo intracellular fluxes using stable isotopic tracers is essential if we are to understand metabolic function and regulation. Flux estimation based on 13C or 2H labeling relies on complex simulation and iterative fitting; processes that necessitate a level of expertise that ordinarily preclude the non-expert user. To overcome this, we have developed SUMOFLUX, a methodology that is broadly applicable to the targeted analysis of 13C-metabolic fluxes. By combining surrogate modeling and machine learning, we trained a predictor to specialize in estimating flux ratios from measurable 13C-data. SUMOFLUX targets specific flux features individually, which makes it fast, user-friendly, applicable to experimental design and robust in terms of experimental noise and exchange flux magnitude. Collectively, we predict that SUMOFLUX's properties realistically pave the way to high-throughput flux analyses.

Tracing insights into human metabolism using chemical engineering approaches

Metabolism coordinates the conversion of available nutrients toward energy, biosynthetic intermediates, and signaling molecules to mediate virtually all biological functions. Dysregulation of metabolic pathways contributes to many diseases, so a detailed understanding of human metabolism has significant therapeutic implications. Over the last decade major technological advances in the areas of analytical chemistry, computational estimation of intracellular fluxes, and biological engineering have improved our ability to observe and engineer metabolic pathways. These approaches are reminiscent of the design, operation, and control of industrial chemical plants. Immune cells have emerged as an intriguing system in which metabolism influences diverse biological functions. Application of metabolic flux analysis and related approaches to macrophages and T cells offers great therapeutic opportunities to biochemical engineers.

13 C-metabolic flux analysis of human adenovirus infection: Implications for viral vector production

Adenoviruses are human pathogens increasingly used as gene therapy and vaccination vectors. However, their impact on cell metabolism is poorly characterized. We performed carbon labeling experiments with [1,2-¹³ C]glucose or [U-¹³ C]glutamine to evaluate metabolic alterations in the amniocyte-derived, E1-transformed 1G3 cell line during production of a human adenovirus type 5 vector (AdV5). Nonstationary ¹³ C-metabolic flux analysis revealed increased fluxes of glycolysis (17%) and markedly PPP (over fourfold) and cytosolic AcCoA formation (nearly twofold) following infection of growing cells. Interestingly, infection of growth-arrested cells increased overall carbon flow even more, including glutamine anaplerosis and TCA cycle activity (both over 1.5-fold), but was unable to stimulate the PPP and was associated with a steep drop in AdV5 replication (almost 80%). Our results underscore the importance of nucleic and fatty acid biosynthesis for adenovirus replication. Overall, we portray a metabolic blueprint of human adenovirus infection, highlighting similarities with other viruses and cancer, and suggest strategies to improve AdV5 production. Biotechnol. Bioeng. 2017;114: 195-207. © 2016 Wiley Periodicals, Inc.

Distinct Metabolic States Can Support Self-Renewal and Lipogenesis in Human Pluripotent Stem Cells under Different Culture Conditions

Recent studies have suggested that human pluripotent stem cells (hPSCs) depend primarily on glycolysis and only increase oxidative metabolism during differentiation. Here, we demonstrate that both glycolytic and oxidative metabolism can support hPSC growth and that the metabolic phenotype of hPSCs is largely driven by nutrient availability. We comprehensively characterized hPSC metabolism by using (13)C/(2)H stable isotope tracing and flux analysis to define the metabolic pathways supporting hPSC bioenergetics and biosynthesis. Although glycolytic flux consistently supported hPSC growth, chemically defined media strongly influenced the state of mitochondrial respiration and fatty acid metabolism. Lipid deficiency dramatically reprogramed pathways associated with fatty acid biosynthesis and NADPH regeneration, altering the mitochondrial function of cells and driving flux through the oxidative pentose phosphate pathway. Lipid supplementation mitigates this metabolic reprogramming and increases oxidative metabolism. These results demonstrate that self-renewing hPSCs can present distinct metabolic states and highlight the importance of medium nutrients on mitochondrial function and development.

RhoC GTPase Is a Potent Regulator of Glutamine Metabolism and N-Acetylaspartate Production in Inflammatory Breast Cancer Cells

Inflammatory breast cancer (IBC) is an extremely lethal cancer that rapidly metastasizes. Although the molecular attributes of IBC have been described, little is known about the underlying metabolic features of the disease. Using a variety of metabolic assays, including (13)C tracer experiments, we found that SUM149 cells, the primary in vitro model of IBC, exhibit metabolic abnormalities that distinguish them from other breast cancer cells, including elevated levels of N-acetylaspartate, a metabolite primarily associated with neuronal disorders and gliomas. Here we provide the first evidence of N-acetylaspartate in breast cancer. We also report that the oncogene RhoC, a driver of metastatic potential, modulates glutamine and N-acetylaspartate metabolism in IBC cells in vitro, revealing a novel role for RhoC as a regulator of tumor cell metabolism that extends beyond its well known role in cytoskeletal rearrangement.

Reductive carboxylation supports redox homeostasis during anchorage-independent growth

Cells receive growth and survival stimuli through their attachment to an extracellular matrix (ECM). Overcoming the addiction to ECM-induced signals is required for anchorage-independent growth, a property of most malignant cells. Detachment from ECM is associated with enhanced production of reactive oxygen species (ROS) owing to altered glucose metabolism. Here we identify an unconventional pathway that supports redox homeostasis and growth during adaptation to anchorage independence. We observed that detachment from monolayer culture and growth as anchorage-independent tumour spheroids was accompanied by changes in both glucose and glutamine metabolism. Specifically, oxidation of both nutrients was suppressed in spheroids, whereas reductive formation of citrate from glutamine was enhanced. Reductive glutamine metabolism was highly dependent on cytosolic isocitrate dehydrogenase-1 (IDH1), because the activity was suppressed in cells homozygous null for IDH1 or treated with an IDH1 inhibitor. This activity occurred in absence of hypoxia, a well-known inducer of reductive metabolism. Rather, IDH1 mitigated mitochondrial ROS in spheroids, and suppressing IDH1 reduced spheroid growth through a mechanism requiring mitochondrial ROS. Isotope tracing revealed that in spheroids, isocitrate/citrate produced reductively in the cytosol could enter the mitochondria and participate in oxidative metabolism, including oxidation by IDH2. This generates NADPH in the mitochondria, enabling cells to mitigate mitochondrial ROS and maximize growth. Neither IDH1 nor IDH2 was necessary for monolayer growth, but deleting either one enhanced mitochondrial ROS and reduced spheroid size, as did deletion of the mitochondrial citrate transporter protein. Together, the data indicate that adaptation to anchorage independence requires a fundamental change in citrate metabolism, initiated by IDH1-dependent reductive carboxylation and culminating in suppression of mitochondrial ROS.

Metabolic flux profiling of MDCK cells during growth and canine adenovirus vector production

Canine adenovirus vector type 2 (CAV2) represents an alternative to human adenovirus vectors for certain gene therapy applications, particularly neurodegenerative diseases. However, more efficient production processes, assisted by a greater understanding of the effect of infection on producer cells, are required. Combining [1,2-(13)C]glucose and [U-(13)C]glutamine, we apply for the first time (13)C-Metabolic flux analysis ((13)C-MFA) to study E1-transformed Madin-Darby Canine Kidney (MDCK) cells metabolism during growth and CAV2 production. MDCK cells displayed a marked glycolytic and ammoniagenic metabolism, and (13)C data revealed a large fraction of glutamine-derived labelling in TCA cycle intermediates, emphasizing the role of glutamine anaplerosis. (13)C-MFA demonstrated the importance of pyruvate cycling in balancing glycolytic and TCA cycle activities, as well as occurrence of reductive alphaketoglutarate (AKG) carboxylation. By turn, CAV2 infection significantly upregulated fluxes through most central metabolism, including glycolysis, pentose-phosphate pathway, glutamine anaplerosis and, more prominently, reductive AKG carboxylation and cytosolic acetyl-coenzyme A formation, suggestive of increased lipogenesis. Based on these results, we suggest culture supplementation strategies to stimulate nucleic acid and lipid biosynthesis for improved canine adenoviral vector production.