Bioreaction network topology and metabolic flux ratio analysis by biosynthetic fractional 13C labeling and two-dimensional NMR spectroscopy

Messenger RNA in lipid nanoparticles rescues HEK 293 cells from lipid-induced mitochondrial dysfunction as studied by real time pulse chase NMR, RTPC-NMR, spectroscopy

Analytical tools to study cell physiology are critical for optimizing drug-host interactions. Real time pulse chase NMR spectroscopy, RTPC-NMR, was introduced to monitor the kinetics of metabolite production in HEK 293T cells treated with COVID-19 vaccine-like lipid nanoparticles, LNPs, with and without mRNA. Kinetic flux parameters were resolved for the incorporation of isotopic label into metabolites and clearance of labeled metabolites from the cells. Changes in the characteristic times for alanine production implicated mitochondrial dysfunction as a consequence of treating the cells with lipid nanoparticles, LNPs. Mitochondrial dysfunction was largely abated by inclusion of mRNA in the LNPs, the presence of which increased the size and uniformity of the LNPs. The methodology is applicable to all cultured cells.

Quantitative metabolic fluxes regulated by trans-omic networks

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Quantitative Isotopomer Rates in Real-Time Metabolism of Cells Determined by NMR Methods

Tracer-based metabolism is becoming increasingly important for studying metabolic mechanisms in cells. NMR spectroscopy offers several approaches to measure label incorporation in metabolites, including ¹³ C- and ¹ H-detected spectra. The latter are generally more sensitive, but quantification depends on the proton-carbon ¹ J_CH coupling constant, which varies significantly between different metabolites. It is therefore not possible to have one experiment optimised for all metabolites, and quantification of ¹ H-edited spectra such as HSQCs requires precise knowledge of coupling constants. Increasing interest in tracer-based and metabolic flux analysis requires robust analyses with reasonably small acquisition times. Herein, we compare ¹³ C-filtered and ¹³ C-edited methods for quantification and show the applicability of the methods for real-time NMR spectroscopy of cancer-cell metabolism, in which label incorporations are subject to constant flux. We find an approach using a double filter to be most suitable and sufficiently robust to reliably obtain ¹³ C incorporations from difference spectra. This is demonstrated for JJN3 multiple myeloma cells processing glucose over 24 h. The proposed method is equally well suited for calculating the level of label incorporation in labelled cell extracts in the context of metabolic flux analysis.

Metabolic and physiological perturbations of Escherichia coli W3100 by bacterial small RNA RyhB

RyhB is a key regulator of iron level in Escherichia coli (E. coli), which assists in conserving iron for life-sustaining cellular functions when cytoplasmic levels of the ferrous form of iron is limited. RyhB affects glucose metabolism. Seventy percent of the genes that are regulated by RyhB are related to metabolism. We demonstrated for the first time that the activity of the pentose phosphate pathway increased upon ryhB activation using a¹³C stable isotope-based technique called METAFoR (Metabolic flux ratio analysis). U-¹³C glucose-based studies showed that the reversible exchange activity of serine and glycine was enhanced by flux redistribution, which further favors NADPH formation. In addition, Entner-Doudoroff (ED) pathway activity was inhibited in the ryhB-defective cells. Quantitative physiology-based experiments highlighted a significant increase in the levels of reactive oxygen species (ROS) in ryhB-induced W3100 E. coli cells in batch culture. A simultaneous decrease in NADH/NAD⁺ and NADPH/NADP⁺ ratios outlined the potentially direct roles of NADH and NADPH in antagonizing the excess ROS formed after ryhB activation. Our observations offer a new perspective regarding the roles of RyhB and highlight that this small RNA can significantly affect cell metabolism in addition to its role as a regulator of gene expression.

A framework for tracer-based metabolism in mammalian cells by NMR

Metabolism changes extensively during the normal proliferation and differentiation of mammalian cells, and in cancer and inflammatory diseases. Since changes in the metabolic network reflect interactions between genetic, epigenetic and environmental changes, it is helpful to study the flow of label from isotopically labelled precursors into other metabolites rather than static metabolite levels. For this Nuclear Magnetic Resonance (NMR) spectroscopy is an attractive technique as it can quantify site-specific label incorporation. However, for applications using human cells and cell lines, the challenge is to optimize the process to maximize sensitivity and reproducibility. Here we present a new framework to analyze metabolism in mammalian cell lines and primary cells, covering the workflow from the preparation of cells to the acquisition and analysis of NMR spectra. We have applied this new approach in hematological and liver cancer cell lines and confirm the feasibility of tracer-based metabolism in primary liver cells.

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.

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.

13 C metabolic flux profiling of Pichia pastoris grown in aerobic batch cultures on glucose revealed high relative anabolic use of TCA cycle and limited incorporation of provided precursors of branched-chain amino acids

Carbon metabolism of Crabtree-negative yeast Pichia pastoris was profiled using ¹³ C nuclear magnetic resonance (NMR) to delineate regulation during exponential growth and to study the import of two precursors for branched-chain amino acid biosynthesis, α-ketoisovalerate and α-ketobutyrate. Cells were grown in aerobic batch cultures containing (a) only glucose, (b) glucose along with the precursors, or (c) glucose and Val. The study provided the following new insights. First, ¹³ C flux ratio analyses of central metabolism reveal an unexpectedly high anaplerotic supply of the tricarboxylic acid cycle for a Crabtree-negative yeast, and show that a substantial fraction of glucose catabolism proceeds through the pentose phosphate pathway. A comparison with previous flux ratio analyses for batch cultures of Crabtree-negative Pichia stipitis and Crabtree-positive Saccharomyces cerevisiae indicate that the overall regulation of central carbon metabolism in P. pastoris is intermediate in between P. stipitis and S. cerevisiae. Second, excess α-ketoisovalerate in the medium is not transported into the cytoplasm indicating that P. pastoris lacks a suitable transporter. In contrast, excess Val is efficiently taken up and largely fulfills demands for both Val and Leu for protein synthesis. Third, excess α-ketobutyrate is transported into the mitochondria for Ile biosynthesis. However, the import does not efficiently inhibit the synthesis of α-ketobutyrate from pyruvate indicating that P. pastoris has not been optimized evolutionarily to take full advantage of this carbon source. These findings have direct implications for preparing uniformly ² H,¹³ C,¹⁵ N-labeled proteins containing protonated Ile, Val, and Leu methyl groups in P. pastoris for NMR-based structural biology.

ENZYMES

Acetohydroxy acid isomeroreductase (EC 1.1.1.86), branched-chain amino acid aminotransferase (BCAT, EC 2.6.1.42), fumarase (EC 4.2.1.2), malic enzyme (EC 1.1.1.39/1.1.1.40), phosphoenolpyruvate carboxykinase (EC 4.1.1.49), pyruvate carboxylase (EC 6.4.1.1), pyruvate kinase (EC 2.7.1.40), l-serine hydroxymethyltransferase (EC 2.1.2.1), threonine aldolase (EC 4.1.2.5), threonine dehydratase (EC 4.3.1.19); transketolase (EC 2.2.1.1), transaldolase (EC 2.2.1.2).

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Carbon 13-Metabolic Flux Analysis derived constraint-based metabolic modelling of Clostridium acetobutylicum in stressed chemostat conditions

The metabolism of butanol producing bacteria Clostridium acetobutylicum was studied in chemostat with glucose limited conditions, butanol stimulus, and as a reference cultivation. COnstraint-Based Reconstruction and Analysis (COBRA) was applied using additional constraints from (13)C Metabolic Flux Analysis ((13)C-MFA) and experimental measurement results. A model consisting of 451 metabolites and 604 reactions was utilized in flux balance analysis (FBA). The stringency of the flux spaces considering different optimization objectives, i.e. growth rate maximization, ATP maintenance, and NADH/NADPH formation, for flux variance analysis (FVA) was studied in the different modelled conditions. Also a previously uncharacterized exopolysaccharide (EPS) produced by C. acetobutylicum was characterized on monosaccharide level. The major monosaccharide components of the EPS were 40n-% rhamnose, 34n-% glucose, 13n-% mannose, 10n-% galactose, and 2n-% arabinose. The EPS was studied to have butanol adsorbing property, 70(butanol)mg(EPS)g(-1) at 37°C.

CO2-fixing one-carbon metabolism in a cellulose-degrading bacterium Clostridium thermocellum

Clostridium thermocellum can ferment cellulosic biomass to formate and other end products, including CO₂ This organism lacks formate dehydrogenase (Fdh), which catalyzes the reduction of CO₂ to formate. However, feeding the bacterium ¹³C-bicarbonate and cellobiose followed by NMR analysis showed the production of ¹³C-formate in C. thermocellum culture, indicating the presence of an uncharacterized pathway capable of converting CO₂ to formate. Combining genomic and experimental data, we demonstrated that the conversion of CO₂ to formate serves as a CO₂ entry point into the reductive one-carbon (C1) metabolism, and internalizes CO₂ via two biochemical reactions: the reversed pyruvate:ferredoxin oxidoreductase (rPFOR), which incorporates CO₂ using acetyl-CoA as a substrate and generates pyruvate, and pyruvate-formate lyase (PFL) converting pyruvate to formate and acetyl-CoA. We analyzed the labeling patterns of proteinogenic amino acids in individual deletions of all five putative PFOR mutants and in a PFL deletion mutant. We identified two enzymes acting as rPFOR, confirmed the dual activities of rPFOR and PFL crucial for CO₂ uptake, and provided physical evidence of a distinct in vivo "rPFOR-PFL shunt" to reduce CO₂ to formate while circumventing the lack of Fdh. Such a pathway precedes CO₂ fixation via the reductive C1 metabolic pathway in C. thermocellum These findings demonstrated the metabolic versatility of C. thermocellum, which is thought of as primarily a cellulosic heterotroph but is shown here to be endowed with the ability to fix CO₂ as well.

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.

Elucidating central metabolic redox obstacles hindering ethanol production in Clostridium thermocellum

Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model's capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. The model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol and other reduced metabolites.

A roadmap for interpreting (13)C metabolite labeling patterns from cells

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

¹³C NMR metabolomics: applications at natural abundance

(13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C-(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum.

Decoding the dynamics of cellular metabolism and the action of 3-bromopyruvate and 2-deoxyglucose using pulsed stable isotope-resolved metabolomics

BACKGROUND

Cellular metabolism is highly dynamic and continuously adjusts to the physiological program of the cell. The regulation of metabolism appears at all biological levels: (post-) transcriptional, (post-) translational, and allosteric. This regulatory information is expressed in the metabolome, but in a complex manner. To decode such complex information, new methods are needed in order to facilitate dynamic metabolic characterization at high resolution.

RESULTS

Here, we describe pulsed stable isotope-resolved metabolomics (pSIRM) as a tool for the dynamic metabolic characterization of cellular metabolism. We have adapted gas chromatography-coupled mass spectrometric methods for metabolomic profiling and stable isotope-resolved metabolomics. In addition, we have improved robustness and reproducibility and implemented a strategy for the absolute quantification of metabolites.

CONCLUSIONS

By way of examples, we have applied this methodology to characterize central carbon metabolism of a panel of cancer cell lines and to determine the mode of metabolic inhibition of glycolytic inhibitors in times ranging from minutes to hours. Using pSIRM, we observed that 2-deoxyglucose is a metabolic inhibitor, but does not directly act on the glycolytic cascade.

¹³C-based metabolic flux analysis of recombinant Pichia pastoris

Overexpression of a foreign protein may negatively affect several cell growth parameters, as well as cause cellular stress. Central (or core) metabolism plays a crucial role since it supplies energy, reduction equivalents, and precursor molecules for the recombinant product, cell's maintenance, and growth needs. However, the number of quantitative physiology studies of the impact of recombinant protein production on the central metabolic pathways of yeast cell factories has been traditionally rather limited, thereby hampering the application of rational strain engineering strategies targeting central metabolism.The development and application of quantitative physiology and modelling tools and methodologies is allowing for a systems-level understanding of the effect of bioprocess parameters such as growth rate, temperature, oxygen availability, and substrate(s) choice on metabolism, and its subsequent interactions with recombinant protein synthesis, folding, and secretion.Here, we review the recent developments and applications of (13)C-based metabolic flux analysis ((13)C-MFA) of Pichia pastoris and the gained understanding of the metabolic behavior of this yeast in recombinant protein production bioprocesses. We also discuss the potential of multilevel studies integrating (13)C-MFA with other omics analyses, as well as future perspectives on the metabolic modelling approaches to study and design metabolic engineering strategies for improved protein production.

¹³C-based metabolic flux analysis in yeast: the Pichia pastoris case

Metabolic flux analysis based on tracing patterns of stable isotopes, particularly (13)C, comprises a set of methodologies to experimentally quantify intracellular biochemical reaction rates, i.e., to measure carbon flux distributions through a metabolic network. This allows quantifying the response of a metabolic network to an environmental or genetic perturbation (i.e., the metabolic phenotype). Here, we describe a protocol based on growing yeast on a (13)C-labelled substrate and subsequent NMR detection of (13)C-patterns in proteinogenic amino acids. To calculate metabolic fluxes, we describe two complementary mathematical approaches using available software; namely, an approach based on the estimation of local ratios in network nodes, and a method based on a global iterative fitting approach. Furthermore, we consider specificities of these protocols for their application to the yeast Pichia pastoris growing on multicarbon substrates other than glucose (glycerol), as well as the case when methanol is used as co-substrate in combination with glucose or glycerol.

Low mass MS/MS fragments of protonated amino acids used for distinction of their 13C-isotopomers in metabolic studies

Glu, Gln, Pro, and Ala are the main amino acids involved in ammonia detoxification in mosquitoes. In order to develop a tandem mass spectrometry method (MS(2)) to monitor each carbon of the above isotopically-labeled (13)C-amino acids for metabolic studies, the compositions and origins of atoms in fragments of the protonated amino acid should be first elucidated. Thus, various electrospray (ESI)-based MS(2) tools were employed to study the fragmentation of these unlabeled and isotopically-labeled amino acids and better understand their dissociation pathways. A broad range of fragments, including previously-undescribed low m/z fragments was revealed. The formulae of the fragments (from m/z 130 down to m/z 27) were confirmed by their accurate masses. The structures and conformations of the larger fragments of Glu were also explored by ion mobility mass spectrometry (IM-MS) and gas-phase hydrogen/deuterium exchange (HDX) experiments. It was found that some low m/z fragments (m/z 27-30) are common to Glu, Gln, Pro, and Ala. The origins of carbons in these small fragments are discussed and additional collision induced dissociation (CID) MS(2) fragmentation pathways are proposed for them. It was also found that small fragments (≤m/z 84) of protonated, methylated Glu, and methylated Gln are the same as those of the underivatized Glu and Gln. Taken together, the new approach of utilizing low m/z fragments can be applied to distinguish, identify, and quantify (13)C-amino acids labeled at various positions, either in the backbone or side chain.

Using multiple tracers for 13C metabolic flux analysis

(13)C-Metabolic flux analysis ((13)C-MFA) is a powerful technique for quantifying intracellular metabolic fluxes in living cells. These in vivo fluxes provide important information on the physiology of cells in culture, which can be used for metabolic engineering purposes and serve as inputs for systems biology modeling. The (13)C-MFA technique consists of several steps: (1) selecting appropriate tracers for a given system of interest, (2) performing isotopic labeling experiments, (3) measuring isotopic labeling distributions in metabolic products, (4) estimating metabolic fluxes using least-squares regression, and (5) evaluating the goodness of fit and computing confidence intervals for estimated fluxes. In this chapter, we provide guidelines for performing (13)C-MFA studies using multiple isotopic tracers, a technique that is especially useful for elucidating fluxes in complex biological systems where multiple carbon sources are present. Here, as an example, we describe key steps and decision points for designing (13)C-MFA studies for microbes grown on mixtures of glucose and xylose. The general concepts described in this chapter are applicable to many other biological systems. For example, the same procedures can be applied to design (13)C-MFA studies in mammalian cells, which are generally grown in complex media containing multiple substrates such as glucose and amino acids.

Flux-p: automating metabolic flux analysis

Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via ¹³C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize ¹³C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses.

Metabolic flux profiling of recombinant protein secreting Pichia pastoris growing on glucose:methanol mixtures

Background

The methylotrophic yeast Pichia pastoris has emerged as one of the most promising yeast hosts for the production of heterologous proteins. Mixed feeds of methanol and a multicarbon source instead of methanol as sole carbon source have been shown to improve product productivities and alleviate metabolic burden derived from protein production. Nevertheless, systematic quantitative studies on the relationships between the central metabolism and recombinant protein production in P. pastoris are still rather limited, particularly when growing this yeast on mixed carbon sources, thus hampering future metabolic network engineering strategies for improved protein production.

Results

The metabolic flux distribution in the central metabolism of P. pastoris growing on a mixed feed of glucose and methanol was analyzed by Metabolic Flux Analysis (MFA) using ¹³C-NMR-derived constraints. For this purpose, we defined new flux ratios for methanol assimilation pathways in P. pastoris cells growing on glucose:methanol mixtures. By using this experimental approach, the metabolic burden caused by the overexpression and secretion of a Rhizopus oryzae lipase (Rol) in P. pastoris was further analyzed. This protein has been previously shown to trigger the unfolded protein response in P. pastoris. A series of ¹³C-tracer experiments were performed on aerobic chemostat cultivations with a control and two different Rol producing strains growing at a dilution rate of 0.09 h⁻¹ using a glucose:methanol 80:20 (w/w) mix as carbon source.

The MFA performed in this study reveals a significant redistristribution of carbon fluxes in the central carbon metabolism when comparing the two recombinant strains vs the control strain, reflected in increased glycolytic, TCA cycle and NADH regeneration fluxes, as well as higher methanol dissimilation rates.

Conclusions

Overall, a further ¹³C-based MFA development to characterise the central metabolism of methylotrophic yeasts when growing on mixed methanol:multicarbon sources has been implemented, thus providing a new tool for the investigation of the relationships between central metabolism and protein production. Specifically, the study points at a limited but significant impact of the conformational stress associated to secretion of recombinant proteins on the central metabolism, occurring even at modest production levels.

Measuring complete isotopomer distribution of aspartate using gas chromatography/tandem mass spectrometry

We have developed a simple and accurate method for determining the complete positional isotopomer distribution of aspartate carbon atoms by gas chromatography/tandem mass spectrometry for (13)C-metabolic flux analysis. First, we screened tandem mass spectrometry (MS) spectra of the tert-butyldimethylsilyl (TBDMS) derivative of aspartate for daughter fragments with the necessary carbon atom fragmentations to fully resolve all 16 isotopomers of aspartate. Tandem MS scanning parameters were optimized for each daughter fragment, and the accuracy of tandem MS measurements were evaluated. We selected five accurate fragments that provided a redundant set of 47 labeling measurements to quantify the complete isotopomer distribution of aspartate by least-squares regression. The validity of the approach was demonstrated using six (13)C-labeled aspartate standards and natural aspartate.

Metabolomics of Mycobacterium tuberculosis reveals compartmentalized co-catabolism of carbon substrates

Metabolic adaptation to the host environment is a defining feature of the pathogenicity of Mycobacterium tuberculosis (Mtb), but we lack biochemical knowledge of its metabolic networks. Many bacteria use catabolite repression as a regulatory mechanism to maximize growth by consuming individual carbon substrates in a preferred sequence and growing with diauxic kinetics. Surprisingly, untargeted metabolite profiling of Mtb growing on ¹³C-labeled carbon substrates revealed that Mtb could catabolize multiple carbon sources simultaneously to achieve enhanced monophasic growth. Moreover, when co-catabolizing multiple carbon sources, Mtb differentially catabolized each carbon source through the glycolytic, pentose phosphate, and/or tricarboxylic acid pathways to distinct metabolic fates. This unusual topologic organization of bacterial intermediary metabolism has not been previously observed and may subserve the pathogenicity of Mtb.

Subpopulation-specific metabolic pathway usage in mixed cultures as revealed by reporter protein-based 13C analysis

Most large-scale biological processes, like global element cycling or decomposition of organic matter, are mediated by microbial consortia. Commonly, the different species in such consortia exhibit mutual metabolic dependencies that include the exchange of nutrients. Despite the global importance, surprisingly little is known about the metabolic interplay between species in particular subpopulations. To gain insight into the intracellular fluxes of subpopulations and their interplay within such mixed cultures, we developed here a (13)C flux analysis approach based on affinity purification of the recombinant fusion glutathione S-transferase (GST) and green fluorescent protein (GFP) as a reporter protein. Instead of detecting the (13)C labeling patterns in the typically used amino acids from the total cellular protein, our method detects these (13)C patterns in amino acids from the reporter protein that has been expressed in only one species of the consortium. As a proof of principle, we validated our approach by mixed-culture experiments of an Escherichia coli wild type with two metabolic mutants. The reporter method quantitatively resolved the expected mutant-specific metabolic phenotypes down to subpopulation fractions of about 1%.

A multi-level study of recombinant Pichia pastoris in different oxygen conditions

Background

Yeasts are attractive expression platforms for many recombinant proteins, and there is evidence for an important interrelation between the protein secretion machinery and environmental stresses. While adaptive responses to such stresses are extensively studied in Saccharomyces cerevisiae, little is known about their impact on the physiology of Pichia pastoris. We have recently reported a beneficial effect of hypoxia on recombinant Fab secretion in P. pastoris chemostat cultivations. As a consequence, a systems biology approach was used to comprehensively identify cellular adaptations to low oxygen availability and the additional burden of protein production. Gene expression profiling was combined with proteomic analyses and the ¹³C isotope labelling based experimental determination of metabolic fluxes in the central carbon metabolism.

Results

The physiological adaptation of P. pastoris to hypoxia showed distinct traits in relation to the model yeast S. cerevisiae. There was a positive correlation between the transcriptomic, proteomic and metabolic fluxes adaptation of P. pastoris core metabolism to hypoxia, yielding clear evidence of a strong transcriptional regulation component of key pathways such as glycolysis, pentose phosphate pathway and TCA cycle. In addition, the adaptation to reduced oxygen revealed important changes in lipid metabolism, stress responses, as well as protein folding and trafficking.

Conclusions

This systems level study helped to understand the physiological adaptations of cellular mechanisms to low oxygen availability in a recombinant P. pastoris strain. Remarkably, the integration of data from three different levels allowed for the identification of differences in the regulation of the core metabolism between P. pastoris and S. cerevisiae. Detailed comparative analysis of the transcriptomic data also led to new insights into the gene expression profiles of several cellular processes that are not only susceptible to low oxygen concentrations, but might also contribute to enhanced protein secretion.

A systematic investigation of Escherichia coli central carbon metabolism in response to superoxide stress

Background

The cellular responses of bacteria to superoxide stress can be used to model adaptation to severe environmental changes. Superoxide stress promotes the excessive production of reactive oxygen species (ROS) that have detrimental effects on cell metabolic and other physiological activities. To antagonize such effects, the cell needs to regulate a range of metabolic reactions in a coordinated way, so that coherent metabolic responses are generated by the cellular metabolic reaction network as a whole. In the present study, we have used a quantitative metabolic flux analysis approach, together with measurement of gene expression and activity of key enzymes, to investigate changes in central carbon metabolism that occur in Escherichia coli in response to paraquat-induced superoxide stress. The cellular regulatory mechanisms involved in the observed global flux changes are discussed.

Results

Flux analysis based on nuclear magnetic resonance (NMR) and mass spectroscopy (MS) measurements and computation provided quantitative results on the metabolic fluxes redistribution of the E. coli central carbon network under paraquat-induced oxidative stress. The metabolic fluxes of the glycolytic pathway were redirected to the pentose phosphate pathway (PP pathway). The production of acetate increased significantly, the fluxes associated with the TCA cycle decreased, and the fluxes in the glyoxylate shunt increased in response to oxidative stress. These global flux changes resulted in an increased ratio of NADPH:NADH and in the accumulation of α-ketoglutarate.

Conclusions

Metabolic flux analysis provided a quantitative and global picture of responses of the E. coli central carbon metabolic network to oxidative stress. Systematic adjustments of cellular physiological state clearly occurred in response to changes in metabolic fluxes induced by oxidative stress. Quantitative flux analysis therefore could reveal the physiological state of the cell at the systems level and is a useful complement to molecular systems approaches, such as proteomics and transcription analyses.

13C-metabolic flux ratio and novel carbon path analyses confirmed that Trichoderma reesei uses primarily the respirative pathway also on the preferred carbon source glucose

Background

The filamentous fungus Trichoderma reesei is an important host organism for industrial enzyme production. It is adapted to nutrient poor environments where it is capable of producing large amounts of hydrolytic enzymes. In its natural environment T. reesei is expected to benefit from high energy yield from utilization of respirative metabolic pathway. However, T. reesei lacks metabolic pathway reconstructions and the utilization of the respirative pathway has not been investigated on the level of in vivo fluxes.

Results

The biosynthetic pathways of amino acids in T. reesei supported by genome-level evidence were reconstructed with computational carbon path analysis. The pathway reconstructions were a prerequisite for analysis of in vivo fluxes. The distribution of in vivo fluxes in both wild type strain and cre1, a key regulator of carbon catabolite repression, deletion strain were quantitatively studied by performing ¹³C-labeling on both repressive carbon source glucose and non-repressive carbon source sorbitol. In addition, the ¹³C-labeling on sorbitol was performed both in the presence and absence of sophorose that induces the expression of cellulase genes. Carbon path analyses and the ¹³C-labeling patterns of proteinogenic amino acids indicated high similarity between biosynthetic pathways of amino acids in T. reesei and yeast Saccharomyces cerevisiae. In contrast to S. cerevisiae, however, mitochondrial rather than cytosolic biosynthesis of Asp was observed under all studied conditions. The relative anaplerotic flux to the TCA cycle was low and thus characteristic to respiratory metabolism in both strains and independent of the carbon source. Only minor differences were observed in the flux distributions of the wild type and cre1 deletion strain. Furthermore, the induction of the hydrolytic gene expression did not show altered flux distributions and did not affect the relative amino acid requirements or relative anabolic and respirative activities of the TCA cycle.

Conclusion

High similarity between the biosynthetic pathways of amino acids in T. reesei and yeast S. cerevisiae was concluded. In vivo flux distributions confirmed that T. reesei uses primarily the respirative pathway also when growing on the repressive carbon source glucose in contrast to Saccharomyces cerevisiae, which substantially diminishes the respirative pathway flux under glucose repression.

OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis

Background

The quantitative analysis of metabolic fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on ¹³C substrates, (ii) ¹³C labelling analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis.

Results

We have developed OpenFLUX as a user friendly, yet flexible software application for small and large scale ¹³C metabolic flux analysis. The application is based on the new Elementary Metabolite Unit (EMU) framework, significantly enhancing computation speed for flux calculation. From simple notation of metabolic reaction networks defined in a spreadsheet, the OpenFLUX parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial flux study with 71 reactions, 8 free flux parameters and mass isotopomer distribution of 10 metabolites, OpenFLUX allowed to automatically compile the EMU-based model from an Excel file containing metabolic reactions and carbon transfer mechanisms, showing it's user-friendliness. It reliably reproduced the published data and optimum flux distributions for the network under study were found quickly (<20 sec).

Conclusion

We have developed a fast, accurate application to perform steady-state ¹³C metabolic flux analysis. OpenFLUX will strongly facilitate and enhance the design, calculation and interpretation of metabolic flux studies. By providing the software open source, we hope it will evolve with the rapidly growing field of fluxomics.

Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A

BACKGROUND

The yeast Saccharomyces cerevisiae is able to adjust to external oxygen availability by utilizing both respirative and fermentative metabolic modes. Adjusting the metabolic mode involves alteration of the intracellular metabolic fluxes that are determined by the cell's multilevel regulatory network. Oxygen is a major determinant of the physiology of S. cerevisiae but understanding of the oxygen dependence of intracellular flux distributions is still scarce.

RESULTS

Metabolic flux distributions of S. cerevisiae CEN.PK113-1A growing in glucose-limited chemostat cultures at a dilution rate of 0.1 h-1 with 20.9%, 2.8%, 1.0%, 0.5% or 0.0% O2 in the inlet gas were quantified by 13C-MFA. Metabolic flux ratios from fractional [U-13C]glucose labelling experiments were used to solve the underdetermined MFA system of central carbon metabolism of S. cerevisiae.While ethanol production was observed already in 2.8% oxygen, only minor differences in the flux distribution were observed, compared to fully aerobic conditions. However, in 1.0% and 0.5% oxygen the respiratory rate was severely restricted, resulting in progressively reduced fluxes through the TCA cycle and the direction of major fluxes to the fermentative pathway. A redistribution of fluxes was observed in all branching points of central carbon metabolism. Yet only when oxygen provision was reduced to 0.5%, was the biomass yield exceeded by the yields of ethanol and CO2. Respirative ATP generation provided 59% of the ATP demand in fully aerobic conditions and still a substantial 25% in 0.5% oxygenation. An extensive redistribution of fluxes was observed in anaerobic conditions compared to all the aerobic conditions. Positive correlation between the transcriptional levels of metabolic enzymes and the corresponding fluxes in the different oxygenation conditions was found only in the respirative pathway.

CONCLUSION

13C-constrained MFA enabled quantitative determination of intracellular fluxes in conditions of different redox challenges without including redox cofactors in metabolite mass balances. A redistribution of fluxes was observed not only for respirative, respiro-fermentative and fermentative metabolisms, but also for cells grown with 2.8%, 1.0% and 0.5% oxygen. Although the cellular metabolism was respiro-fermentative in each of these low oxygen conditions, the actual amount of oxygen available resulted in different contributions through respirative and fermentative pathways.

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

Background

Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³C isotopomer data are typically needed.

Results

We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³C isotopomer measurement techniques, substrates and substrate labelling patterns.

By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.

Conclusion

The core of ¹³C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.

Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates

The metabolic pathways associated with the tricarboxylic acid cycle intermediates of Pichia pastoris were studied using biosynthetically directed fractional 13C labelling. Cells were grown aerobically in a chemostat culture fed at two dilution rates (1.39x10(-5) s-1 and 4.44x10(-5) s-1) with varying mixtures of glycerol and methanol as sole carbon sources. The results show that, with co-assimilation of methanol, the common amino acids are synthesized as in P. pastoris cells grown on glycerol only. During growth at the lower dilution rate, when both substrates are entirely consumed, the incorporation of methanol into the biomass increases as the methanol fraction in the feed is increased. Moreover, the co-assimilation of methanol impacts on how key intermediates of the pentose phosphate pathway (PPP) are synthesized. In contrast, such an impact on the PPP is not observed at the higher dilution rate, where methanol is only partially consumed. This finding possibly indicates that the distribution of methanol carbon into assimilatory and dissimilatory (direct oxidation to CO2) pathways are different at the two dilution rates. Remarkably, distinct flux ratios were registered at each of the two growth rates, while the dependency of the flux ratios on the varying fraction of methanol in the medium was much less pronounced. This study brings new insights into the complex regulation of P. pastoris methanol metabolism in the presence of a second carbon source, revealing important implications for biotechnological applications.

Fluxome analysis using GC-MS

Fluxome analysis aims at the quantitative analysis of in vivo carbon fluxes in metabolic networks, i. e. intracellular activities of enzymes and pathways. It allows investigating the effects of genetic or environmental modifications and thus precisely provides a global perspective on the integrated genetic and metabolic regulation within the intact metabolic network. The experimental and computational approaches developed in this area have revealed fascinating insights into metabolic properties of various biological systems. Most of the comprehensive approaches for metabolic flux studies today involve isotopic tracer studies and GC-MS for measurement of the labeling pattern of metabolites. Initially developed and applied mainly in the field of biomedicine these GC-MS based metabolic flux approaches have been substantially extended and optimized during recent years and today display a key technology in metabolic physiology and biotechnology.

Determination of Metabolic Flux Ratios From 13C-Experiments and Gas Chromatography-Mass Spectrometry Data

Network topology is a necessary fundament to understand function and properties of microbial reaction networks. A valuable method for experimental elucidation of metabolic network topology is metabolic flux ratio analysis, which quantifies the relative contribution of two or more converging pathways to a given metabolite. It is based on 13C-labeling experiments, gas chromatography-mass spectrometry analysis, and probabilistic equations that relate mass distributions in proteinogenic amino acids to pathway activity. Here, we describe the protocol for sample generation and illustrate the principles underlying the calculation of metabolic flux ratios with three examples. These principles are also implemented in the publicly available software FiatFlux, which directly calculates flux ratios from the mass spectra of amino acids.

Global metabolic changes following loss of a feedback loop reveal dynamic steady states of the yeast metabolome

Metabolic enzymes control cellular metabolite concentrations dynamically in response to changing environmental and intracellular conditions. Such real-time feedback regulation suggests the global metabolome may sample distinct dynamic steady states, forming "basins of stability" in the energy landscape of possible metabolite concentrations and enzymatic activities. Using metabolite, protein and transcriptional profiling, we characterize three dynamic steady states of the yeast metabolome that form by perturbing synthesis of the universal methyl donor S-adenosylmethionine (AdoMet). Conversion between these states is driven by replacement of serine with glycine+formate in the media, loss of feedback inhibition control by the metabolic enzyme Met13, or both. The latter causes hyperaccumulation of methionine and AdoMet, and dramatic global compensatory changes in the metabolome, including differences in amino acid and sugar metabolism, and possibly in the global nitrogen balance, ultimately leading to a G1/S phase cell cycle delay. Global metabolic changes are not necessarily accompanied by global transcriptional changes, and metabolite-controlled post-transcriptional regulation of metabolic enzymes is clearly evident.

Planning optimal measurements of isotopomer distributions for estimation of metabolic fluxes

MOTIVATION

Flux estimation using isotopomer information of metabolites is currently the most reliable method to obtain quantitative estimates of the activity of metabolic pathways. However, the development of isotopomer measurement techniques for intermediate metabolites is a demanding task. Careful planning of isotopomer measurements is thus needed to maximize the available flux information while minimizing the experimental effort.

RESULTS

In this paper we study the question of finding the smallest subset of metabolites to measure that ensure the same level of isotopomer information as the measurement of every metabolite in the metabolic network. We study the computational complexity of this optimization problem in the case of the so-called positional enrichment data, give methods for obtaining exact and fast approximate solutions, and evaluate empirically the efficacy of the proposed methods by analyzing a metabolic network that models the central carbon metabolism of Saccharomyces cerevisiae.

FiatFlux--a software for metabolic flux analysis from 13C-glucose experiments

BACKGROUND

Quantitative knowledge of intracellular fluxes is important for a comprehensive characterization of metabolic networks and their functional operation. In contrast to direct assessment of metabolite concentrations, in vivo metabolite fluxes must be inferred indirectly from measurable quantities in 13C experiments. The required experience, the complicated network models, large and heterogeneous data sets, and the time-consuming set-up of highly controlled experimental conditions largely restricted metabolic flux analysis to few expert groups. A conceptual simplification of flux analysis is the analytical determination of metabolic flux ratios exclusively from MS data, which can then be used in a second step to estimate absolute in vivo fluxes.

RESULTS

Here we describe the user-friendly software package FiatFlux that supports flux analysis for non-expert users. In the first module, ratios of converging fluxes are automatically calculated from GC-MS-detected 13C-pattern in protein-bound amino acids. Predefined fragmentation patterns are automatically identified and appropriate statistical data treatment is based on the comparison of redundant information in the MS spectra. In the second module, absolute intracellular fluxes may be calculated by a 13C-constrained flux balancing procedure that combines experimentally determined fluxes in and out of the cell and the above flux ratios. The software is preconfigured to derive flux ratios and absolute in vivo fluxes from [1-13C] and [U-13C]glucose experiments and GC-MS analysis of amino acids for a variety of microorganisms.

CONCLUSION

FiatFlux is an intuitive tool for quantitative investigations of intracellular metabolism by users that are not familiar with numerical methods or isotopic tracer experiments. The aim of this open source software is to enable non-specialists to adapt the software to their specific scientific interests, including other 13C-substrates, labeling mixtures, and organisms.