Isotopically nonstationary 13C metabolic flux analysis

The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis

In the future, plants may encounter increased light and elevated CO2 levels. How consequent alterations in photosynthetic rates will impact fluxes in photosynthetic carbon metabolism remains uncertain. Respiration in light (RL) is pivotal in plant carbon balance and a key parameter in photosynthesis models. Understanding the dynamics of photosynthetic metabolism and RL under varying environmental conditions is essential for optimizing plant growth and agricultural productivity. However, measuring RL under high light and high CO2 (HLHC) conditions poses challenges using traditional gas exchange methods. In this study, we employed isotopically nonstationary metabolic flux analysis (INST-MFA) to estimate RL and investigate photosynthetic carbon flux, unveiling nuanced adjustments in Camelina sativa under HLHC. Despite numerous flux alterations in HLHC, RL remained stable. HLHC affects several factors influencing RL, such as starch and sucrose partitioning, vo/vc ratio, triose phosphate partitioning, and hexose kinase activity. Analysis of A/Ci curve operational points reveals that HLHC's major changes primarily stem from CO2 suppressing photorespiration. Integration of these fluxes into a simplified model predicts changes in CBC labeling under HLHC. This study extends our prior discovery that incomplete CBC labeling is due to unlabeled carbon reimported during RL, offering insights into manipulating labeling through adjustments in photosynthetic rates.

Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

Effects of n-butanol production on metabolism and the photosystem in Synecococcus elongatus PCC 7942 based on metabolic flux and target proteome analyses

Although n-butanol (BuOH) is an ideal fuel because of its superior physical properties, it has toxicity to microbes. Previously, a Synechococcus elongatus PCC 7942 derivative strain that produces BuOH from CO2 was developed by introducing six heterologous genes (BUOH-SE strain). To identify the bottleneck in BuOH production, the effects of BuOH production and its toxicity on central metabolism and the photosystem were investigated. Parental (WT) and BUOH-SE strains were cultured under autotrophic conditions. Consistent with the results of a previous study, BuOH production was observed only in the BUOH-SE strain. Isotopically non-stationary 13C-metabolic flux analysis revealed that the CO2 fixation rate was much larger than the BuOH production rate in the BUOH-SE strain (1.70 vs 0.03 mmol gDCW-1 h-1), implying that the carbon flow for BuOH biosynthesis was less affected by the entire flux distribution. No large difference was observed in the flux of metabolism between the WT and BUOH-SE strains. Contrastingly, in the photosystem, the chlorophyll content and maximum O2 evolution rate per dry cell weight of the BUOH-SE strain were decreased to 81% and 43% of the WT strain, respectively. Target proteome analysis revealed that the amounts of some proteins related to antennae (ApcA, ApcD, ApcE, and CpcC), photosystem II (PsbB, PsbU, and Psb28-2), and cytochrome b6f complex (PetB and PetC) in photosystems decreased in the BUOH-SE strain. The activation of photosynthesis would be a novel approach for further enhancing BuOH production in S. elongatus PCC 7942.

Metabolic flux analysis in adipose tissue reprogramming

Obesity is a growing epidemic in the United States and worldwide and is associated with insulin resistance and cardiovascular disease, among other comorbidities. Understanding of the pathology that links overnutrition to these disease processes is ongoing. Adipose tissue is a heterogeneous organ comprised of multiple different cell types and it is likely that dysregulated metabolism within these cell populations disrupts both inter- and intracellular interactions and is a key driver of human disease. In recent years, metabolic flux analysis, which offers a precise quantification of metabolic pathway fluxes in biological systems, has emerged as a candidate strategy for uncovering the metabolic changes that stoke these disease processes. In this mini review, we discuss metabolic flux analysis as an experimental tool, with a specific emphasis on mass spectrometry with isotope tracing as this is the technique most frequently used for metabolic flux analysis in adipocytes. Furthermore, we examine existing literature that uses metabolic flux analysis to further our understanding of adipose tissue biology. Our group has a specific interest in understanding the role of white adipose tissue inflammation in the progression of cardiometabolic disease, as we know that in obesity the accumulation of pro-inflammatory adipose tissue macrophages is associated with significant morbidity, so we use this as a paradigm throughout our review for framing the application of these experimental techniques. However, there are many other biological applications to which they can be applied to further understanding of not only adipose tissue biology but also systemic homeostasis.

13CO2-labelling and Sampling in Algae for Flux Analysis of Photosynthetic and Central Carbon Metabolism

The flux in photosynthesis can be studied by performing 13CO2 pulse labelling and analysing the temporal labelling kinetics of metabolic intermediates using gas or liquid chromatography linked to mass spectrometry. Metabolic flux analysis (MFA) is the primary approach for analysing metabolic network function and quantifying intracellular metabolic fluxes. Different MFA approaches differ based on the metabolic state (steady vs. non-steady state) and the use of stable isotope tracers. The main methodology used to investigate metabolic systems is metabolite steady state associated with stable isotope labelling experiments. Specifically, in biological systems like photoautotrophic organisms, isotopic non-stationary 113C metabolic flux analysis at metabolic steady state with transient isotopic labelling (13C-INST-MFA) is required. The common requirement for metabolic steady state, alongside its very short half-timed reactions, complicates robust MFA of photosynthetic metabolism. While custom gas chambers design has addressed these challenges in various model plants, no similar tools were developed for liquid photosynthetic cultures (e.g., algae, cyanobacteria), where diffusion and equilibration of inorganic carbon species in the medium entails a new dimension of complexity. Recently, a novel tailor-made microfluidics labelling system has been introduced, supplying short 13CO2 pulses at steady state, and resolving fluxes across most photosynthetic metabolic pathways in algae. The system involves injecting algal cultures and medium containing pre-equilibrated inorganic 13C into a microfluidic mixer, followed by rapid metabolic quenching, enabling precise seconds-level label pulses. This was complemented by a 13CO2-bubbling-based open labelling system (photobioreactor), allowing long pulses (minutes-hours) required for investigating fluxes into central C metabolism and major products. This combined labelling procedure provides a comprehensive fluxome cover for most algal photosynthetic and central C metabolism pathways, thus allowing comparative flux analyses across algae and plants.

MYC overrides HIF-1α to regulate proliferating primary cell metabolism in hypoxia

Hypoxia requires metabolic adaptations to sustain energetically demanding cellular activities. While the metabolic consequences of hypoxia have been studied extensively in cancer cell models, comparatively little is known about how primary cell metabolism responds to hypoxia. Thus, we developed metabolic flux models for human lung fibroblast and pulmonary artery smooth muscle cells proliferating in hypoxia. Unexpectedly, we found that hypoxia decreased glycolysis despite activation of hypoxia-inducible factor 1α (HIF-1α) and increased glycolytic enzyme expression. While HIF-1α activation in normoxia by prolyl hydroxylase (PHD) inhibition did increase glycolysis, hypoxia blocked this effect. Multi-omic profiling revealed distinct molecular responses to hypoxia and PHD inhibition, and suggested a critical role for MYC in modulating HIF-1α responses to hypoxia. Consistent with this hypothesis, MYC knockdown in hypoxia increased glycolysis and MYC over-expression in normoxia decreased glycolysis stimulated by PHD inhibition. These data suggest that MYC signaling in hypoxia uncouples an increase in HIF-dependent glycolytic gene transcription from glycolytic flux.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kβ inhibition on metabolic pathway activities in a PTEN null breast cancer cell line

The combination of high-resolution LC-MS untargeted metabolomics with stable isotope-resolved tracing is a promising approach for the global exploration of metabolic pathway activities. In our established workflow we combine targeted isotopologue feature extraction with the non-targeted X¹³CMS routine. Metabolites, detected by X¹³CMS as differentially labeled between two biological conditions are subsequently integrated into the original targeted library. This strategy enables monitoring of changes in known pathways as well as the discovery of hitherto unknown metabolic alterations. Here, we demonstrate this workflow in a PTEN (phosphatase and tensin homolog) null breast cancer cell line (MDA-MB-468) exploring metabolic pathway activities in the absence and presence of the selective PI3Kβ inhibitor AZD8186. Cells were fed with [U-¹³C] glucose and treated for 1, 3, 6, and 24 h with 0.5 µM AZD8186 or vehicle, extracted by an optimized sample preparation protocol and analyzed by LC-QTOF-MS. Untargeted differential tracing of labels revealed 286 isotope-enriched features that were significantly altered between control and treatment conditions, of which 19 features could be attributed to known compounds from targeted pathways. Other 11 features were unambiguously identified based on data-dependent MS/MS spectra and reference substances. Notably, only a minority of the significantly altered features (11 and 16, respectively) were identified when preprocessing of the same data set (treatment vs. control in 24 h unlabeled samples) was performed with tools commonly used for label-free (i.e. w/o isotopic tracer) non-targeted metabolomics experiments (Profinder´s batch recursive feature extraction and XCMS). The structurally identified metabolites were integrated into the existing targeted isotopologue feature extraction workflow to enable natural abundance correction, evaluation of assay performance and assessment of drug-induced changes in pathway activities. Label incorporation was highly reproducible for the majority of isotopologues in technical replicates with a RSD below 10%. Furthermore, inter-day repeatability of a second label experiment showed strong correlation (Pearson R² > 0.99) between tracer incorporation on different days. Finally, we could identify prominent pathway activity alterations upon PI3Kβ inhibition. Besides pathways in central metabolism, known to be changed our workflow revealed additional pathways, like pyrimidine metabolism or hexosamine pathway. All pathways identified represent key metabolic processes associated with cancer metabolism and therapy.

Sink/Source Balance of Leaves Influences Amino Acid Pools and Their Associated Metabolic Fluxes in Winter Oilseed Rape (Brassica napus L.)

Nitrogen remobilization processes from source to sink tissues in plants are determinant for seed yield and their implementation results in a complete reorganization of the primary metabolism during sink/source transition. Here, we decided to characterize the impact of the sink/source balance on amino acid metabolism in the leaves of winter oilseed rape grown at the vegetative stage. We combined a quantitative metabolomics approach with an instationary ¹⁵N-labeling experiment by using [¹⁵N]_L-glycine as a metabolic probe on leaf ranks with a gradual increase in their source status. We showed that the acquisition of the source status by leaves was specifically accompanied by a decrease in asparagine, glutamine, proline and S-methyl-l-cysteine sulphoxide contents and an increase in valine and threonine contents. Dynamic analysis of ¹⁵N enrichment and concentration of amino acids revealed gradual changes in the dynamics of amino acid metabolism with respect to the sink/source status of leaf ranks. Notably, nitrogen assimilation into valine, threonine and proline were all decreased in source leaves compared to sink leaves. Overall, our results suggested a reduction in de novo amino acid biosynthesis during sink/source transition at the vegetative stage.

Mass Isotopologue Distribution of dimer ion adducts of intracellular metabolites for potential applications in 13C Metabolic Flux Analysis

¹³C Metabolic Flux Analysis (¹³C-MFA) is a powerful tool for quantification of carbon flux distribution in metabolic pathways. However, the requirement to obtain accurate labeling patterns, especially for compounds with low abundance, poses a challenge. Chromatographic separation and high sensitivity of the modern mass spectrometers (MS) alleviate this problem to a certain extent. However, the presence of derivatives such as in-source fragments, multimer ion adducts, and multiply charged ions result in reduced intensity of the molecular ion. While multimer ion adducts have been reported in the field of metabolomics, their presence is considered undesirable in quantitative studies. Here, we demonstrate a novel application of dimer ion adducts in calculating the mass isotopologue distribution (MIDs) of the corresponding monomer ions for public domain and in-house generated datasets comprising of ¹³C-labeling time-course experiments. Out of the 100 standard compounds analyzed, we could detect multimer ion adducts in 24 of the intermediate metabolites. Further, a subset of these multimer ions were detected in all the biological samples analyzed. Majority of these ion adducts were either not detected in the original study or labeled as a putative features. Regression analysis was performed to estimate the monomer MIDs from those of the dimer. This resulted in accurate estimation regardless of the biological system, chromatographic method, the MS hardware, or the relative abundance of the dimer ion. We argue that this analysis may be useful in cases where satisfactory data cannot be extracted from the chromatographic peaks of the monomer ions.

Dynamic 13C Labeling of Fast Turnover Metabolites for Analysis of Metabolic Fluxes and Metabolite Channeling

Dynamic or isotopically nonstationary ¹³C labeling experiments are a powerful tool not only for precise carbon flux quantification (e.g., metabolic flux analysis of photoautotrophic organisms) but also for the investigation of pathway bottlenecks, a cell's phenotype, and metabolite channeling. In general, isotopically nonstationary metabolic flux analysis requires three main components: (1) transient isotopic labeling experiments; (2) metabolite quenching and isotopomer analysis using LC-MS; (3) metabolic network construction and flux quantification. Labeling dynamics of key metabolites from ¹³C-pulse experiments allow flux estimation of key central pathways by solving ordinary differential equations to fit time-dependent isotopomer distribution data. Additionally, it is important to provide biomass requirements, carbon uptake rates, specific growth rates, and carbon excretion rates to properly and precisely balance the metabolic network. Labeling dynamics through cascade metabolites may also identify channeling phenomena in which metabolites are passed between enzymes without mixing with the bulk phase. In this chapter, we outline experimental protocols to probe metabolic pathways through dynamic labeling. We describe protocols for labeling experiments, metabolite quenching and extraction, LC-MS analysis, computational flux quantification, and metabolite channeling observations.

Isotope Tracing Untargeted Metabolomics Reveals Macrophage Polarization-State-Specific Metabolic Coordination across Intracellular Compartments

We apply stable isotope tracing, mass-spectrometry-based untargeted metabolomics, to reveal the biochemical space labeled by 13C-substrates in bone-marrow-derived macrophages. At the pathway level, classically (lipopolysaccharide [LPS]-polarized, M1) and alternatively (interleukin [IL]-4-polarized, M2) polarized macrophages were 13C-labeled with surprising concordance. Total pools of uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), an intermediate in the hexosamine biosynthetic pathway, were equally abundant in LPS- and IL-4-polarized macrophages. Informatic scrutiny of 13C-isotopologues revealed that LPS-polarized macrophages leverage the pentose phosphate pathway to generate UDP-GlcNAc, whereas IL-4-polarized macrophages rely on intact glucose and mitochondrial metabolism of glucose carbon. Labeling from [13C]glucose is competed by unlabeled fatty acids and acetoacetate, underscoring the broad roles for substrate metabolism beyond energy conversion. Finally, the LPS-polarized macrophage metabolite itaconate is imported into IL-4-polarized macrophages, in which it reprograms [13C]glucose metabolism. Thus, use of fully unsupervised isotope tracing metabolomics in macrophages reveals polarization-state-specific metabolic pathway connectivity, substrate competition, and metabolite allocation among cellular compartments.

Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

An improved method for extraction of polar and charged metabolites from cyanobacteria

A key requirement for 13C Metabolic flux analysis (13C-MFA), a widely used technique to estimate intracellular metabolic fluxes, is an efficient method for the extraction of intermediate metabolites for analysis via liquid chromatography mass spectrometry (LC/MS). The 13C isotopic labeling results in further distribution of an already sparse pool of intermediate metabolites into isotopologues, each appearing as a separate chromatographic feature. We examined some of the reported solvent systems for the extraction of polar intracellular metabolites from three strains of cyanobacteria of the genus Synechococcus, viz., Synechococcus sp. PCC 7002, Synechococcus elongatus PCC 7942, and a newly isolated Synechococcus elongatus PCC 11801 (manuscript under review). High resolution-LC/MS was used to assess the relative abundance of the extracted metabolites. The different solvent systems used for extraction led to statistically significant changes in the extraction efficiency for a large number of metabolites. While a few hundred m/z features or potential metabolites were detected with different solvent systems, the abundance of over a quarter of all metabolites varied significantly from one solvent system to another. Further, the extraction methods were evaluated for a targeted set of metabolites that are important in 13C-MFA studies of photosynthetic organisms. While for the strain PCC 7002, the reported method using methanol-chloroform-water system gave satisfactory results, a mild base in the form of NH4OH had to be used in place of water to achieve adequate levels of extraction for PCC 7942 and PCC 11801. While minor changes in extraction solvent resulted in dramatic changes in the extraction efficiency of a number of compounds, certain metabolites such as amino acids and organic acids were adequately extracted in all the solvent systems tested. Overall, we present a new improved method for extraction using a methanol-chloroform-NH4OH system. Our method improves the extraction of polar compounds such as sugar phosphates, bisphosphates, that are central to 13C-MFA studies.

Mass Spectrometry-Based Method to Study Inhibitor-Induced Metabolic Redirection in the Central Metabolism of Cancer Cells

Cancer cells often respond to chemotherapeutic inhibitors by redirecting carbon flow in the central metabolism. To understand the metabolic redirections of inhibitor treatment on cancer cells, this study established a 13C-metabolic flux analysis (13C-MFA)-based method to evaluate metabolic redirection in MCF-7 breast cancer cells using mass spectrometry. A metabolic stationary state necessary for accurate 13C-MFA was confirmed during an 8-24 h window using low-dose treatments of various metabolic inhibitors. Further 13C-labeling experiments using [1-13C]glucose and [U-13C]glutamine, combined with gas chromatography-mass spectrometry (GC-MS) analysis of mass isotopomer distributions (MIDs), confirmed that an isotopic stationary state of intracellular metabolites was reached 24 h after treatment with paclitaxel (Taxol), an inhibitor of mitosis used for cancer treatment. Based on these metabolic and isotopic stationary states, metabolic flux distribution in the central metabolism of paclitaxel-treated MCF-7 cells was determined by 13C-MFA. Finally, estimations of the 95% confidence intervals showed that tricarboxylic acid cycle metabolic flux increased after paclitaxel treatment. Conversely, anaerobic glycolysis metabolic flux decreased, revealing metabolic redirections by paclitaxel inhibition. The gap between total regeneration and consumption of ATP in paclitaxel-treated cells was also found to be 1.2 times greater than controls, suggesting ATP demand was increased by paclitaxel treatment, likely due to increased microtubule polymerization. These data confirm that 13C-MFA can be used to investigate inhibitor-induced metabolic redirection in cancer cells. This will contribute to future pharmaceutical developments and understanding variable patient response to treatment.

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.

Comprehensive and accurate tracking of carbon origin of LC-tandem mass spectrometry collisional fragments for 13C-MFA

In recent years the benefit of measuring positionally resolved 13C-labeling enrichment from tandem mass spectrometry (MS/MS) collisional fragments for improved precision of 13C-Metabolic Flux Analysis (13C-MFA) has become evident. However, the usage of positional labeling information for 13C-MFA faces two challenges: (1) The mass spectrometric acquisition of a large number of potentially interfering mass transitions may hamper accuracy and sensitivity. (2) The positional identity of carbon atoms of product ions needs to be known. The present contribution addresses the latter challenge by deducing the maximal positional labeling information contained in LC-ESI-MS/MS spectra of product anions of central metabolism as well as product cations of amino acids. For this purpose, we draw on accurate mass spectrometry, selectively labeled standards, and published fragmentation pathways to structurally annotate all dominant mass peaks of a large collection of metabolites, some of which with a complete fragmentation pathway. Compiling all available information, we arrive at the most detailed map of carbon atom fate of LC-ESI-MS/MS collisional fragments yet, comprising 170 intense and structurally annotated product ions with unique carbon origin from 76 precursor ions of 72 metabolites. Our 13C-data proof that heuristic fragmentation rules often fail to yield correct fragment structures and we expose common pitfalls in the structural annotation of product ions. We show that the positionally resolved 13C-label information contained in the product ions that we structurally annotated allows to infer the entire isotopomer distribution of several central metabolism intermediates, which is experimentally demonstrated for malate using quadrupole-time-of-flight MS technology. Finally, the inclusion of the label information from a subset of these fragments improves flux precision in a Corynebacterium glutamicum model of the central carbon 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.

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.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms

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.

13 C flux analysis of cyanobacterial metabolism

(13)C metabolic flux analysis (MFA) has made important contributions to our understanding of the physiology of model strains of E. coli and yeast, and it has been widely used to guide metabolic engineering efforts in these microorganisms. Recent advancements in (13)C MFA methodology combined with publicly available software tools are creating new opportunities to extend this approach to examine less characterized microbes. In particular, growing interest in the use of cyanobacteria as industrial hosts for photosynthetic production of biofuels and biochemicals has led to a critical need to better understand how cyanobacterial metabolic fluxes are regulated in response to changes in growth conditions or introduction of heterologous pathways. In this contribution, we review several prior studies that have applied isotopic steady-state (13)C MFA to examine heterotrophic or mixotrophic growth of cyanobacteria, as well as recent studies that have pioneered the use of isotopically nonstationary MFA (INST-MFA) to study autotrophic cultures. We also provide recommendations for the design and analysis of INST-MFA experiments in cyanobacteria, based on our previous experience and a series of simulation studies used to assess the selection of measurements and sample time points. We anticipate that this emerging knowledgebase of prior (13)C MFA studies, optimized experimental protocols, and public software tools will catalyze increasing use of (13)C MFA techniques by the cyanobacteria research community.

How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.

(13)C metabolic flux analysis of recombinant expression hosts

Identifying host cell metabolic phenotypes that promote high recombinant protein titer is a major goal of the biotech industry. (13)C metabolic flux analysis (MFA) provides a rigorous approach to quantify these metabolic phenotypes by applying isotope tracers to map the flow of carbon through intracellular metabolic pathways. Recent advances in tracer theory and measurements are enabling more information to be extracted from (13)C labeling experiments. Sustained development of publicly available software tools and standardization of experimental workflows is simultaneously encouraging increased adoption of (13)C MFA within the biotech research community. A number of recent (13)C MFA studies have identified increased citric acid cycle and pentose phosphate pathway fluxes as consistent markers of high recombinant protein expression, both in mammalian and microbial hosts. Further work is needed to determine whether redirecting flux into these pathways can effectively enhance protein titers while maintaining acceptable glycan profiles.

Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks

The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.

Metabolic flux analysis using ¹³C peptide label measurements

¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady-state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady-state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable 'single-sample' spatially and temporally resolved steady-state flux analysis, we investigated the suitability of peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC-MS measurement-based approach. Deconvolution of PMDs of the storage protein β-conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC-MS-derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.

Quantitation of cellular metabolic fluxes of methionine

Methionine is an essential proteogenic amino acid. In addition, it is a methyl donor for DNA and protein methylation and a propylamine donor for polyamine biosynthesis. Both the methyl and propylamine donation pathways involve metabolic cycles, and methods are needed to quantitate these cycles. Here, we describe an analytical approach for quantifying methionine metabolic fluxes that accounts for the mixing of intracellular and extracellular methionine pools. We observe that such mixing prevents isotope tracing experiments from reaching the steady state due to the large size of the media pools and hence precludes the use of standard stationary metabolic flux analysis. Our approach is based on feeding cells with (13)C methionine and measuring the isotope-labeling kinetics of both intracellular and extracellular methionine by liquid chromatography-mass spectrometry (LC-MS). We apply this method to quantify methionine metabolism in a human fibrosarcoma cell line and study how methionine salvage pathway enzyme methylthioadenosine phosphorylase (MTAP), frequently deleted in cancer, affects methionine metabolism. We find that both transmethylation and propylamine transfer fluxes amount to roughly 15% of the net methionine uptake, with no major changes due to MTAP deletion. Our method further enables the quantification of flux through the pro-tumorigenic enzyme ornithine decarboxylase, and this flux increases 2-fold following MTAP deletion. The analytical approach used to quantify methionine metabolic fluxes is applicable for other metabolic systems affected by mixing of intracellular and extracellular metabolite pools.

Moving on from GWAS: functional studies on the G6PC2 gene implicated in the regulation of fasting blood glucose

Genome-wide association studies (GWAS) have shown that single-nucleotide polymorphisms (SNPs) in G6PC2 are the most important common determinants of variations in fasting blood glucose (FBG) levels. Molecular studies examining the functional impact of these SNPs on G6PC2 gene transcription and splicing suggest that they affect FBG by directly modulating G6PC2 expression. This conclusion is supported by studies on G6pc2 knockout (KO) mice showing that G6pc2 represents a negative regulator of basal glucose-stimulated insulin secretion that acts by hydrolyzing glucose-6-phosphate, thereby reducing glycolytic flux and opposing the action of glucokinase. Suppression of G6PC2 activity might, therefore, represent a novel therapy for lowering FBG and the risk of cardiovascular-associated mortality. GWAS and G6pc2 KO mouse studies also suggest that G6PC2 affects other aspects of beta cell function. The evolutionary benefit conferred by G6PC2 remains unclear, but it is unlikely to be related to its ability to modulate FBG.

Towards high resolution analysis of metabolic flux in cells and tissues

Metabolism extracts chemical energy from nutrients, uses this energy to form building blocks for biosynthesis, and interconverts between various small molecules that coordinate the activities of cellular pathways. The metabolic state of a cell is increasingly recognized to determine the phenotype of not only metabolically active cell types such as liver, muscle, and adipose, but also other specialized cell types such as neurons and immune cells. This review focuses on methods to quantify intracellular reaction flux as a measure of cellular metabolic activity, with emphasis on studies involving cells of mammalian tissue. Two key areas are highlighted for future development, single cell metabolomics and noninvasive imaging, which could enable spatiotemporally resolved analysis and thereby overcome issues of heterogeneity, a distinctive feature of tissue metabolism.