Metabolite channeling and compartmentation in the human cell line AGE1.HN determined by 13C labeling experiments and 13C metabolic flux analysis

Anti-Inflammatory Effects of Quercetin on High-Glucose and Pro-Inflammatory Cytokine Challenged Vascular Endothelial Cell Metabolism

SCOPE

Pro-inflammatory stimuli such as hyperglycemia and cytokines have been shown to negatively affect endothelial cell functions. The aim of this study is to assess the potential of quercetin and its human metabolites to overcome the deleterious effects of hyperglycemic or inflammatory conditions on the vascular endothelium by modulating endothelial cell metabolism.

METHODS AND RESULTS

A metabolomics approach enabled identification and quantification of 27 human umbilical vein endothelial cell (HUVEC) metabolites. Treatment of HUVECs with high-glucose concentrations causes significant increases in lactate and glutamate concentrations. Quercetin inhibits glucose-induced increases in lactate and adenosine 5'-triphosphate (ATP) and also increased inosine concentrations. Tumor necrosis factor α-treatment (TNFα) of HUVECs causes increases in asparagine and decreases in aspartate concentrations. Co-treatment with quercetin reduces pyruvate concentrations compared to TNFα-only treated controls. Subsequently, it was shown that quercetin and its HUVEC phase-2 conjugates inhibit adenosine deaminase, xanthine oxidase and 5'nucleotidase (CD73) but not ectonucleoside triphosphate diphosphohydrolase-1 (CD39) or purine nucleoside phosphorylase activities.

CONCLUSION

Quercetin was shown to alter the balance of HUVEC metabolites towards a less inflamed phenotype, both alone and in the presence of pro-inflammatory stimuli. These changes are consistent with the inhibition of particular enzymes involved in purine metabolism by quercetin and its HUVEC metabolites.

A dynamic model linking cell growth to intracellular metabolism and extracellular by-product accumulation

Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1-antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by-products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α-ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.

p13CMFA: Parsimonious 13C metabolic flux analysis

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are ¹³C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using ¹³C Metabolic Flux Analysis (¹³C MFA) to provide quantitative metabolic maps of flux distributions. However, ¹³C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious ¹³C MFA (p¹³CMFA), an approach that runs a secondary optimization in the ¹³C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with ¹³C data. As proof of concept, we demonstrate how p¹³CMFA can be used to estimate intracellular flux distributions from ¹³C measurements and transcriptomics data. We have implemented p¹³CMFA in Iso2Flux, our in-house developed isotopic steady-state ¹³C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

¹³C Metabolic Flux Analysis (¹³C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a ¹³C-labeled substrate, ¹³C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. ¹³C MFA integrates measurements of ¹³C enrichment in metabolites to identify the flux distributions consistent with the measured ¹³C propagation. However, there is often a range of flux values that can lead to the observed ¹³C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the¹³C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of ¹³C MFA. We have termed this new approach parsimonious ¹³C MFA (p¹³CMFA).

Intersubject and Intrasubject Variability of Potential Plasma and Urine Metabolite and Protein Biomarkers in Healthy Human Volunteers

A limited understanding of intersubject and intrasubject variability hampers effective biomarker translation from in vitro/in vivo studies to clinical trials and clinical decision support. Specifically, variability of biomolecule concentration can play an important role in interpretation, power analysis, and sampling time designation. In the present study, a wide range of 749 plasma metabolites, 62 urine biogenic amines, and 1,263 plasma proteins were analyzed in 10 healthy male volunteers measured repeatedly during 12 hours under tightly controlled conditions. Three variability components in relative concentration data are determined using linear mixed models: between (intersubject), time (intrasubject), and noise (intrasubject). Biomolecules such as 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, platelet-derived growth factor C, and cathepsin D with low noise potentially detect changing conditions within a person. If also the between component is low, biomolecules can easier differentiate conditions between persons, for example cathepsin D, CD27 antigen, and prolylglycine. Variability over time does not necessarily inhibit translatability, but requires choosing sampling times carefully.

From nutritional wealth to autophagy: In vivo metabolic dynamics in the cytosol, mitochondrion and shuttles of IgG producing CHO cells

To fulfil the optimization needs of current biopharmaceutical processes the knowledge how to improve cell specific productivities is of outmost importance. This requires a detailed understanding of cellular metabolism on a subcellular level inside compartments such as cytosol and mitochondrion. Using IgG1 producing Chinese hamster ovary (CHO) cells, a pioneering protocol for compartment-specific metabolome analysis was applied. Various production-like growth conditions ranging from ample glucose and amino acid supply via moderate to severe nitrogen limitation were investigated in batch cultures. The combined application of quantitative metabolite pool analysis, ¹³C tracer studies and non-stationary flux calculations revealed that Pyr/H⁺ symport (MPC1/2) bore the bulk of the mitochondrial transport under ample nutrient supply. Glutamine limitation induced the concerted adaptation of the bidirectional Mal/aKG (OGC) and the Mal/HPO₄^2- antiporter (DIC), even installing completely reversed shuttle fluxes. As a result, NADPH and ATP formation were adjusted to cellular needs unraveling the key role of cytosolic malic enzyme for NADPH production. Highest cell specific IgG1 productivities were closely correlated to a strong mitochondrial malate export according to the anabolic demands. The requirement to install proper NADPH supply for optimizing the production of monoclonal antibodies is clearly outlined. Interestingly, it was observed that mitochondrial citric acid cycle activity was always maintained enabling constant cytosolic adenylate energy charges at physiological levels, even under autophagy conditions.

Passing the Baton: Substrate Channelling in Respiratory Metabolism

Despite species-specific differences in the pathways of respiratory metabolism are remarkably conserved across the kingdoms of life with glycolysis, the tricarboxylic acid cycle, and mitochondrial electron transport chain representing the major components of the process in the vast majority of organisms. In addition to being of critical importance in fueling life itself these pathways serve as interesting case studies for substrate channelling with research on this theme having been carried out for over 40 years. Here we provide a cross-kingdom review of the ample evidence for protein-protein interaction and enzyme assemblies within the three component pathways as well as describing the scarcer available evidence for substrate channelling itself.

The role of dynamic enzyme assemblies and substrate channelling in metabolic regulation

Transient physical association between enzymes appears to be a cardinal feature of metabolic systems, yet the purpose of this metabolic organisation remains enigmatic. It is generally assumed that substrate channelling occurs in these complexes. However, there is a lack of information concerning the mechanisms and extent of substrate channelling and confusion regarding the consequences of substrate channelling. In this review, we outline recent advances in the structural characterisation of enzyme assemblies and integrate this with new insights from reaction-diffusion modelling and synthetic biology to clarify the mechanistic and functional significance of the phenomenon.

Improvements in protein production in mammalian cells from targeted metabolic engineering

Bioprocess optimization has yielded powerful clones for biotherapeutic production. However, new genomic technologies allow more targeted approaches to cell line development. Here we review efforts to enhance protein production in mammalian cells through metabolic engineering. Most efforts aimed to reduce toxic byproducts accumulation to enhance protein productivity. However, recent work highlights the possibility of regulating other desirable traits (e.g., apoptosis and glycosylation) by targeting central metabolism since these processes are interconnected. Therefore, as we further detail the pathways underlying cell growth and protein production and deploy diverse algorithms for their analysis, opportunities will arise to move beyond simple cell line designs and facilitate cell engineering strategies with complex combinations of genes that together underlie a phenotype of interest.

Forward Individualized Medicine from Personal Genomes to Interactomes

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

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

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

Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation

BACKGROUND

Mapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation.

RESULTS

Intracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism.

CONCLUSIONS

The CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.

Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation

BACKGROUND

Mapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation.

RESULTS

Intracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism.

CONCLUSIONS

The CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.

The influence of cell growth and enzyme activity changes on intracellular metabolite dynamics in AGE1.HN.AAT cells

Optimization of bioprocesses with mammalian cells mainly concentrates on cell engineering, cell screening and medium optimization to achieve enhanced cell growth and productivity. For improving cell lines by cell engineering techniques, in-depth understandings of the regulation of metabolism and product formation as well as the resulting demand for the different medium components are needed. In this work, the relationship of cell specific growth and uptake rates and of changes in maximum in vitro enzyme activities with intracellular metabolite pools of glycolysis, pentose phosphate pathway, citric acid cycle and energy metabolism were determined for batch cultivations with AGE1.HN.AAT cells. Results obtained by modeling cell growth and consumption of main substrates showed that the dynamics of intracellular metabolite pools is primarily linked to the dynamics of specific glucose and glutamine uptake rates. By analyzing maximum in vitro enzyme activities we found low activities of pyruvate dehydrogenase and pyruvate carboxylase which suggest a reduced metabolite transfer into the citric acid cycle resulting in lactate release (Warburg effect). Moreover, an increase in the volumetric lactate production rate during the transition from exponential to stationary growth together with a transient accumulation of fructose 1,6-bisphosphate, fructose 1-phosphate and ribose 5-phosphate point toward an upregulation of PK via FBP. Glutaminase activity was about 44-fold lower than activity of glutamine synthetase. This seemed to be sufficient for the supply of intermediates for biosynthesis but might lead to unnecessary dissipation of ATP. Taken together, our results elucidate regulation of metabolic networks of immortalized mammalian cells by changes of metabolite pools over the time course of batch cultivations. Eventually, it enables the use of cell engineering strategies to improve the availability of building blocks for biomass synthesis by increasing glucose as well as glutamine fluxes. An additional knockdown of the glutamine synthetase might help to prevent unnecessary dissipation of ATP, to yield a cell line with optimized growth characteristics and increased overall productivity.

Metabolic control at the cytosol-mitochondria interface in different growth phases of CHO cells

Metabolism at the cytosol-mitochondria interface and its regulation is of major importance particularly for efficient production of biopharmaceuticals in Chinese hamster ovary (CHO) cells but also in many diseases. We used a novel systems-oriented approach combining dynamic metabolic flux analysis and determination of compartmental enzyme activities to obtain systems level information with functional, spatial and temporal resolution. Integrating these multiple levels of information, we were able to investigate the interaction of glycolysis and TCA cycle and its metabolic control. We characterized metabolic phases in CHO batch cultivation and assessed metabolic efficiency extending the concept of metabolic ratios. Comparing in situ enzyme activities including their compartmental localization with in vivo metabolic fluxes, we were able to identify limiting steps in glycolysis and TCA cycle. Our data point to a significant contribution of substrate channeling to glycolytic regulation. We show how glycolytic channeling heavily affects the availability of pyruvate for the mitochondria. Finally, we show that the activities of transaminases and anaplerotic enzymes are tailored to permit a balanced supply of pyruvate and oxaloacetate to the TCA cycle in the respective metabolic states. We demonstrate that knowledge about metabolic control can be gained by correlating in vivo metabolic flux dynamics with time and space resolved in situ enzyme activities.

Labelling analysis for ¹³C MFA using NMR spectroscopy

NMR spectroscopy is an efficient method for analyzing (13)C labelling of cellular metabolites. The strength of it is especially the ability to provide direct quantitative positional information on the (13)C labelling status of carbon atoms in metabolites. NMR spectroscopic methods allow also for detection of contiguously (13)C-labelled fragments in the carbon backbones of the metabolites. Furthermore, the recent developments of NMR spectroscopy hardware have substantially improved the sensitivity of the methods. In this chapter we describe a method for analyzing the (13)C labelling of the biomass amino acids for metabolic flux analysis, sample preparation for NMR spectroscopy, acquiring and processing the NMR spectra, and extracting the (13)C labelling information from the NMR data. Different NMR methods are applied depending on the (13)C labelling strategy chosen. These strategies include uniform (13)C labelling, positional (13)C labelling, or a combination of both. Not only the preparation of sample for analysis of (13)C labelling in proteinogenic amino acids in biomass is described, but also the necessary modifications to the method when analysis of (13)C labelling in free metabolic intermediates is of interest. Finally the strategies for using the different NMR-detected (13)C labelling data in (13)C MFA are discussed.

Customization of ¹³C-MFA strategy according to cell culture system

(13)C-MFA is far from being a simple assay for quantifying metabolic activity. It requires considerable up-front experimental planning and familiarity with the cell culture system in question, as well as optimized analytics and adequate computation frameworks. The success of a (13)C-MFA experiment is ultimately rated by the ability to accurately quantify the flux of one or more reactions of interest. In this chapter, we describe the different (13)C-MFA strategies that have been developed for the various fermentation or cell culture systems, as well as the limitations of the respective strategies. The strategies are affected by many factors and the (13)C-MFA modeling and experimental strategy must be tailored to conditions. The prevailing philosophy in the computation process is that any metabolic processes that produce significant systematic bias in the labeling pattern of the metabolites being measured must be described in the model. It is equally important to plan a labeling strategy by analytical screening or by heuristics.

Metabolic flux rewiring in mammalian cell cultures

Continuous cell lines (CCLs) engage in 'wasteful' glucose and glutamine metabolism that leads to accumulation of inhibitory byproducts, primarily lactate and ammonium. Advances in techniques for mapping intracellular carbon fluxes and profiling global changes in enzyme expression have led to a deeper understanding of the molecular drivers underlying these metabolic alterations. However, recent studies have revealed that CCLs are not necessarily entrenched in a glycolytic or glutaminolytic phenotype, but instead can shift their metabolism toward increased oxidative metabolism as nutrients become depleted and/or growth rate slows. Progress to understand dynamic flux regulation in CCLs has enabled the development of novel strategies to force cultures into desirable metabolic phenotypes, by combining fed-batch feeding strategies with direct metabolic engineering of host cells.

Probing the metabolism of an inducible mammalian expression system using extracellular isotopomer analysis

In an effort to quantitatively assess the impact of recombinant protein expression on the primary metabolism of mammalian cells in culture, we have employed an efficient inducible expression system and conducted a comparative study of the intracellular flux map distribution with and without the induction of recombinant protein synthesis. Cells were grown in parallel semi-continuous cultures with various singly and uniformly labeled substrates and the resulting mass isotopomer distributions of lactate and extracellular amino acids were measured by mass spectrometry. These distributions were used to quantify the main intracellular fluxes. The analysis revealed that, under mild hypothermic conditions, the onset of protein expression is correlated with small but significant changes in several key pathways related to ATP and NADPH formation. More specifically, we observed that induced cells exhibit a more efficient utilization of glucose, characterized by an increased flux of pyruvate into the TCA cycle. In contrast, the catabolic rates of most amino acids remained relatively unaffected. Such analysis is instrumental to guide the identification of robust biomarkers of productivity, as well as the development of medium formulations optimized for recombinant protein production.

Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for (13)C-metabolic flux analysis ((13)C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of (13)C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for (13)C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.

From measurement to implementation of metabolic fluxes

The intracellular reaction rates (fluxes) are the ultimate outcome of the activities of the complete inventory (from DNA to metabolite) and in their sum determine the cellular phenotype. The genotype-phenotype relationship is fundamental in such different fields as cancer research and biotechnology. Here, we summarize the developments in determining metabolic fluxes, inferring major pathways from the DNA-sequence, estimating optimal flux distributions, and how these flux distributions can be achieved in vivo. The technical advances to intervene with the many levels of the cellular architecture allow the implementation of new strategies in for example Metabolic Engineering.

Metabolic modelling in the development of cell factories by synthetic biology

Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.

Stable isotope-assisted metabolomics to detect metabolic flux changes in mammalian cell cultures

The determination of metabolic fluxes provides detailed information of cellular physiology, and the assessment of metabolic flux changes upon a certain perturbation can help to improve biotechnological and pharmaceutical processes. Stable isotope-assisted metabolomics using tracer-labeled substrates is the method of choice to determine the fluxes. Though well-established for microbial cultures, the application to mammalian cells is generally complex and still limited. However, there have been great achievements in recent years and it is now emerging that stable isotope-assisted metabolic flux analysis in mammalian cell cultures will help improving biotechnological production and will also support drug development and discovery.

Recent advances in mapping environmental microbial metabolisms through 13C isotopic fingerprints

After feeding microbes with a defined (13)C substrate, unique isotopic patterns (isotopic fingerprints) can be formed in their metabolic products. Such labelling information not only can provide novel insights into functional pathways but also can determine absolute carbon fluxes through the metabolic network via metabolic modelling approaches. This technique has been used for finding pathways that may have been mis-annotated in the past, elucidating new enzyme functions, and investigating cell metabolisms in microbial communities. In this review paper, we summarize the applications of (13)C approaches to analyse novel cell metabolisms for the past 3 years. The isotopic fingerprints (defined as unique isotopomers useful for pathway identifications) have revealed the operations of the Entner-Doudoroff pathway, the reverse tricarboxylic acid cycle, new enzymes for biosynthesis of central metabolites, diverse respiration routes in phototrophic metabolism, co-metabolism of carbon nutrients and novel CO(2) fixation pathways. This review also discusses new isotopic methods to map carbon fluxes in global metabolisms, as well as potential factors influencing the metabolic flux quantification (e.g. metabolite channelling, the isotopic purity of (13)C substrates and the isotopic effect). Although (13)C labelling is not applicable to all biological systems (e.g. microbial communities), recent studies have shown that this method has a significant value in functional characterization of poorly understood micro-organisms, including species relevant for biotechnology and human health.

Quercetin treatment changes fluxes in the primary metabolism and increases culture longevity and recombinant α₁-antitrypsin production in human AGE1.HN cells

Addition of the flavonoid quercetin to cultivations of the α(1)-antitrypsin (A1AT) producing human AGE1.HN.AAT cell line resulted in alterations of the cellular physiology and a remarkable improvement of the overall performance of these cells. In a first screening in 96-well plate format, toxicity and the effect of quercetin on the lactate/glucose ratio was analyzed. It was found that quercetin treatment reduced the lactate/glucose ratio dose dependently. An increase in culture longevity, viable cell density (160% of control), and A1AT concentration (from 0.39 g/L in the control to 0.76 g/L with quercetin, i.e., 195% of the control) was observed in batch cultivation with 10 μM quercetin compared to the control. A detailed analysis of quercetin effects on primary metabolism revealed dose-dependent alterations in metabolic fluxes. Quercetin addition resulted in an improved channeling of pyruvate into the mitochondria accompanied by reduced waste product formation and stimulation of TCA cycle activity. The observed changes in cellular physiology can be explained by different properties of quercetin and its metabolites, e.g., inhibition of specific enzymes, stimulation of oxidation of cytoplasmic, and mitochondrial NADH resulting in reduced NADH/NAD(+) ratio, and cytoprotective activity. The present study shows that the addition of specific effectors to the culture medium represents a promising strategy to improve the cellular metabolic phenotype and the production of biopharmaceuticals. The provided results contribute, additionally, to an improved understanding of quercetin action on the metabolism of human cells in a general physiological context.

Eukaryotic metabolism: measuring compartment fluxes

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