Possible pitfalls of flux calculations based on (13)C-labeling

Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control

For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.

Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA)

Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large E. coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA. This article is protected by copyright. All rights reserved.

Revealing the Metabolic Flexibility of "Candidatus Accumulibacter phosphatis" through Redox Cofactor Analysis and Metabolic Network Modeling

Environmental fluctuations in the availability of nutrients lead to intricate metabolic strategies. "Candidatus Accumulibacter phosphatis," a polyphosphate-accumulating organism (PAO) responsible for enhanced biological phosphorus removal (EBPR) from wastewater treatment systems, is prevalent in aerobic/anaerobic environments. While the overall metabolic traits of these bacteria are well described, the nonavailability of isolates has led to controversial conclusions on the metabolic pathways used. In this study, we experimentally determined the redox cofactor preferences of different oxidoreductases in the central carbon metabolism of a highly enriched "Ca Accumulibacter phosphatis" culture. Remarkably, we observed that the acetoacetyl coenzyme A reductase engaged in polyhydroxyalkanoate (PHA) synthesis is NADH preferring instead of showing the generally assumed NADPH dependency. This allows rethinking of the ecological role of PHA accumulation as a fermentation product under anaerobic conditions and not just a stress response. Based on previously published metaomics data and the results of enzymatic assays, a reduced central carbon metabolic network was constructed and used for simulating different metabolic operating modes. In particular, scenarios with different acetate-to-glycogen consumption ratios were simulated, which demonstrated optima using different combinations of glycolysis, glyoxylate shunt, or branches of the tricarboxylic acid (TCA) cycle. Thus, optimal metabolic flux strategies will depend on the environment (acetate uptake) and on intracellular storage compound availability (polyphosphate/glycogen). This NADH-related metabolic flexibility is enabled by the NADH-driven PHA synthesis. It allows for maintaining metabolic activity under various environmental substrate conditions, with high carbon conservation and lower energetic costs than for NADPH-dependent PHA synthesis. Such (flexible) metabolic redox coupling can explain the competitiveness of PAOs under oxygen-fluctuating environments.IMPORTANCE Here, we demonstrate how microbial storage metabolism can adjust to a wide range of environmental conditions. Such flexibility generates a selective advantage under fluctuating environmental conditions. It can also explain the different observations reported in PAO literature, including the capacity of "Ca Accumulibacter phosphatis" to act like glycogen-accumulating organisms (GAOs). These observations stem from slightly different experimental conditions, and controversy arises only when one assumes that metabolism can operate only in a single mode. Furthermore, we also show how the study of metabolic strategies is possible when combining omics data with functional cofactor assays and modeling. Genomic information can only provide the potential of a microorganism. The environmental context and other complementary approaches are still needed to study and predict the functional expression of such metabolic potential.

The plastidial pentose phosphate pathway is essential for postglobular embryo development in Arabidopsis

Many mutations that affect plastidial metabolism are embryo-lethal, as expected if the disrupted genes encode enzymes with essential housekeeping functions. However, some mutations that disrupt the plastidial oxidative pentose phosphate pathway (OPPP) cause developmental defects, as well as embryo arrest at the globular stage of development. We show that the OPPP provides the substrate for the pathway of purine synthesis, ribose-5-phosphate, and is thus essential for the generation of nucleic acids during the very early stages of embryo development. Inadequate purine synthesis leads to abnormal patterns of cell division in the embryo and blocks development beyond the globular stage. Therefore, defects in primary metabolic pathways can have profound consequences for development as well as simply reducing growth.

Large numbers of genes essential for embryogenesis in Arabidopsis encode enzymes of plastidial metabolism. Disruption of many of these genes results in embryo arrest at the globular stage of development. However, the cause of lethality is obscure. We examined the role of the plastidial oxidative pentose phosphate pathway (OPPP) in embryo development. In nonphotosynthetic plastids the OPPP produces reductant and metabolic intermediates for central biosynthetic processes. Embryos with defects in various steps in the oxidative part of the OPPP had cell division defects and arrested at the globular stage, revealing an absolute requirement for the production via these steps of ribulose-5-phosphate. In the nonoxidative part of the OPPP, ribulose-5-phosphate is converted to ribose-5-phosphate (R5P)—required for purine nucleotide and histidine synthesis—and subsequently to erythrose-4-phosphate, which is required for synthesis of aromatic amino acids. We show that embryo development through the globular stage specifically requires synthesis of R5P rather than erythrose-4-phosphate. Either a failure to convert ribulose-5-phosphate to R5P or a block in purine nucleotide biosynthesis beyond R5P perturbs normal patterning of the embryo, disrupts endosperm development, and causes early developmental arrest. We suggest that seed abortion in mutants unable to synthesize R5P via the oxidative part of the OPPP stems from a lack of substrate for synthesis of purine nucleotides, and hence nucleic acids. Our results show that the plastidial OPPP is essential for normal developmental progression as well as for growth in the embryo.

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

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

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

¹³C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. ¹³C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying ¹³C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of ¹³C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for ¹³C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of ¹³C MFA.

Comprehensive assessment of measurement uncertainty in 13C-based metabolic flux experiments

In the field of metabolic engineering ¹³C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a ¹³C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation.

Graphical abstract

Fluorometabolite biosynthesis: isotopically labelled glycerol incorporations into the antibiotic nucleocidin in Streptomyces calvus

Deuterium and carbon-13 labelled glycerols have been fed to Streptomyces calvus fermentations and isotope incorporation into the fluorine containing antibiotic nucleocidin have been evaluated by ¹⁹F-NMR. A single deuterium atom was incorporated from [²H₅]- and (R)-[²H₂]-glycerol into C-5' of the antibiotic, suggesting that an oxidation occurs at this carbon after ribose ring assembly from glycerol (pentose phosphate pathway), during nucleocidin biosynthesis.

Protein-protein interactions and metabolite channelling in the plant tricarboxylic acid cycle

Protein complexes of sequential metabolic enzymes, often termed metabolons, may permit direct channelling of metabolites between the enzymes, providing increased control over metabolic pathway fluxes. Experimental evidence supporting their existence in vivo remains fragmentary. In the present study, we test binary interactions of the proteins constituting the plant tricarboxylic acid (TCA) cycle. We integrate (semi-)quantitative results from affinity purification-mass spectrometry, split-luciferase and yeast-two-hybrid assays to generate a single reliability score for assessing protein–protein interactions. By this approach, we identify 158 interactions including those between catalytic subunits of sequential enzymes and between subunits of enzymes mediating non-adjacent reactions. We reveal channelling of citrate and fumarate in isolated potato mitochondria by isotope dilution experiments. These results provide evidence for a functional TCA cycle metabolon in plants, which we discuss in the context of contemporary understanding of this pathway in other kingdoms.

A metabolon is a complex of sequential metabolic enzymes that channels substrates directly between enzymes, thus optimizing metabolic flux. Here Zhang et al. provide protein interaction and isotope dilution data that support the existence of a metabolon that channels both citrate and fumarate in the plant TCA cycle.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

(13)C metabolic flux analysis shows that resistin impairs the metabolic response to insulin in L6E9 myotubes

Background

It has been suggested that the adipokine resistin links obesity and insulin resistance, although how resistin acts on muscle metabolism is controversial. We aimed to quantitatively analyse the effects of resistin on the glucose metabolic flux profile and on insulin response in L6E9 myotubes at the metabolic level using a tracer-based metabolomic approach and our in-house developed software, Isodyn.

Results

Resistin significantly increased glucose uptake and glycolysis, altering pyruvate utilisation by the cell. In the presence of resistin, insulin only slightly increased glucose uptake and glycolysis, and did not alter the flux profile around pyruvate induced by resistin. Resistin prevented the increase in gene expression in pyruvate dehydrogenase-E1 and the sharp decrease in gene expression in cytosolic phosphoenolpyruvate carboxykinase-1 induced by insulin.

Conclusions

These data suggest that resistin impairs the metabolic activation of insulin. This impairment cannot be explained by the activity of a single enzyme, but instead due to reorganisation of the whole metabolic flux distribution.

Pulsed stable isotope-resolved metabolomic studies of cancer cells

Metabolic reprogramming is a key step in oncogenic transformation, and it involves alterations in both bioenergetic and anabolic metabolism. Sustained by these metabolic alterations, malignant cells acquire the ability to re-enter the cell cycle and proliferate. The so-called central carbon metabolism (CCM) is the ultimate source for energy and building blocks enabling cellular growth and proliferation. The time-resolved monitoring of the conversion of stable isotope-labeled metabolites provides profound insights into the metabolic dynamics of malignant cells and enables the tracking of individual carbon routes within the CCM. Specifically, the analysis of isotope incorporation rates within short time frames by means of pulsed stable isotope-resolved metabolomics (pSIRM) can be used to determine the dynamics of glycolysis and glutaminolysis-two metabolic circuitries that are often deregulated in malignant cells. Here, we detail a pSIRM-based method that can be applied to the study of metabolic alteration in cultured cancer cells.

Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination

Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from (13) C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of (13) C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the (13) C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the (13) C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in (13) C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC-MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in (13) C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.

The shikimate pathway and aromatic amino Acid biosynthesis in plants

L-tryptophan, L-phenylalanine, and L-tyrosine are aromatic amino acids (AAAs) that are used for the synthesis of proteins and that in plants also serve as precursors of numerous natural products, such as pigments, alkaloids, hormones, and cell wall components. All three AAAs are derived from the shikimate pathway, to which ≥30% of photosynthetically fixed carbon is directed in vascular plants. Because their biosynthetic pathways have been lost in animal lineages, the AAAs are essential components of the diets of humans, and the enzymes required for their synthesis have been targeted for the development of herbicides. This review highlights recent molecular identification of enzymes of the pathway and summarizes the pathway organization and the transcriptional/posttranscriptional regulation of the AAA biosynthetic network. It also identifies the current limited knowledge of the subcellular compartmentalization and the metabolite transport involved in the plant AAA pathways and discusses metabolic engineering efforts aimed at improving production of the AAA-derived plant natural products.

Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions

Background

Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution.

Results

The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated ¹³C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells.

Conclusions

The analysis of compartmentation of metabolic networks based on the measured ¹³C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured ¹³C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.

Experimental flux measurements on a network scale

Metabolic flux is a fundamental property of living organisms. In recent years, methods for measuring metabolic flux in plants on a network scale have evolved further. One major challenge in studying flux in plants is the complexity of the plant's metabolism. In particular, in the presence of parallel pathways in multiple cellular compartments, the core of plant central metabolism constitutes a complex network. Hence, a common problem with the reliability of the contemporary results of (13)C-Metabolic Flux Analysis in plants is the substantial reduction in complexity that must be included in the simulated networks; this omission partly is due to limitations in computational simulations. Here, I discuss recent emerging strategies that will better address these shortcomings.

Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to ¹³C metabolic flux analysis

Plant oils are an important renewable resource, and seed oil content is a key agronomical trait that is in part controlled by the metabolic processes within developing seeds. A large-scale model of cellular metabolism in developing embryos of Brassica napus (bna572) was used to predict biomass formation and to analyze metabolic steady states by flux variability analysis under different physiological conditions. Predicted flux patterns are highly correlated with results from prior ¹³C metabolic flux analysis of B. napus developing embryos. Minor differences from the experimental results arose because bna572 always selected only one sugar and one nitrogen source from the available alternatives, and failed to predict the use of the oxidative pentose phosphate pathway. Flux variability, indicative of alternative optimal solutions, revealed alternative pathways that can provide pyruvate and NADPH to plastidic fatty acid synthesis. The nutritional values of different medium substrates were compared based on the overall carbon conversion efficiency (CCE) for the biosynthesis of biomass. Although bna572 has a functional nitrogen assimilation pathway via glutamate synthase, the simulations predict an unexpected role of glycine decarboxylase operating in the direction of NH₄⁺ assimilation. Analysis of the light-dependent improvement of carbon economy predicted two metabolic phases. At very low light levels small reductions in CO₂ efflux can be attributed to enzymes of the tricarboxylic acid cycle (oxoglutarate dehydrogenase, isocitrate dehydrogenase) and glycine decarboxylase. At higher light levels relevant to the ¹³C flux studies, ribulose-1,5-bisphosphate carboxylase activity is predicted to account fully for the light-dependent changes in carbon balance.

An automated growth enclosure for metabolic labeling of Arabidopsis thaliana with 13C-carbon dioxide - an in vivo labeling system for proteomics and metabolomics research

Background

Labeling whole Arabidopsis (Arabidopsis thaliana) plants to high enrichment with ¹³C for proteomics and metabolomics applications would facilitate experimental approaches not possible by conventional methods. Such a system would use the plant's native capacity for carbon fixation to ubiquitously incorporate ¹³C from ¹³CO₂ gas. Because of the high cost of ¹³CO₂ it is critical that the design conserve the labeled gas.

Results

A fully enclosed automated plant growth enclosure has been designed and assembled where the system simultaneously monitors humidity, temperature, pressure and ¹³CO₂ concentration with continuous adjustment of humidity, pressure and ¹³CO₂ levels controlled by a computer running LabView software. The enclosure is mounted on a movable cart for mobility among growth environments. Arabidopsis was grown in the enclosure for up to 8 weeks and obtained on average >95 atom% enrichment for small metabolites, such as amino acids and >91 atom% for large metabolites, including proteins and peptides.

Conclusion

The capability of this labeling system for isotope dilution experiments was demonstrated by evaluation of amino acid turnover using GC-MS as well as protein turnover using LC-MS/MS. Because this 'open source' Arabidopsis ¹³C-labeling growth environment was built using readily available materials and software, it can be adapted easily to accommodate many different experimental designs.

A flexible state-space approach for the modeling of metabolic networks II: advanced interrogation of hybridoma metabolism

Having previously introduced the mathematical framework of topological metabolic analysis (TMA) - a novel optimization-based technique for modeling metabolic networks of arbitrary size and complexity - we demonstrate how TMA facilitates unique methods of metabolic interrogation. With the aid of several hybridoma metabolic investigations as case-studies (Bonarius et al., 1995, 1996, 2001), we first establish that the TMA framework identifies biologically important aspects of the metabolic network under investigation. We also show that the use of a structured weighting approach within our objective provides a substantial modeling benefit over an unstructured, uniform, weighting approach. We then illustrate the strength of TAM as an advanced interrogation technique, first by using TMA to prove the existence of (and to quantitatively describe) multiple topologically distinct configurations of a metabolic network that each optimally model a given set of experimental observations. We further show that such alternate topologies are indistinguishable using existing stoichiometric modeling techniques, and we explain the biological significance of the topological variables appearing within our model. By leveraging the manner in which TMA implements metabolite inputs and outputs, we also show that metabolites whose possible metabolic fates are inadequately described by a given network reconstruction can be quickly identified. Lastly, we show how the use of the TMA aggregate objective function (AOF) permits the identification of modeling solutions that can simultaneously consider experimental observations, underlying biological motivations, or even purely engineering- or design-based goals.

Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae

One of the challenges in strain improvement by evolutionary engineering is to subsequently determine the molecular basis of the improved properties that were enriched from the natural genetic variation during the selective conditions. This study focuses on Saccharomyces cerevisiae IMS0002 which, after metabolic and evolutionary engineering, ferments the pentose sugar arabinose. Glucose- and arabinose-limited anaerobic chemostat cultures of IMS0002 and its non-evolved ancestor were subjected to transcriptome analysis, intracellular metabolite measurements and metabolic flux analysis. Increased expression of the GAL-regulon and deletion of GAL2 in IMS0002 confirmed that the galactose transporter is essential for growth on arabinose. Elevated intracellular concentrations of pentose-phosphate-pathway intermediates and upregulation of TKL2 and YGR043c (encoding transketolase and transaldolase isoenzymes) suggested an involvement of these genes in flux-controlling reactions in arabinose fermentation. Indeed, deletion of these genes in IMS0002 caused a 21% reduction of the maximum specific growth rate on arabinose.

Modeling with a view to target identification in metabolic engineering: a critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy.

Quantification of statin effects on hepatic cholesterol synthesis by transient (13)C-flux analysis

The present work is the first to deal with the determination of cholesterol synthesis rates in primary rat hepatocytes using transient (13)C-flux analysis. The effects of statins on cholesterol biosynthesis and central carbon fluxes were quantified at a therapeutic concentration of 50 nM atorvastatin using carbon-labeled glutamine. The flux through the cholesterol pathway decreased from 0.27 to 0.08 mmol/l(cv)h in response to the administration of the hypolipidemic drug. Isotopic steady state was reached within 4h in the central carbon metabolism but not in the cholesterol pathway, regardless of whether atorvastatin was administered or not. Marked channeling was observed for the symmetrical tricarboxylic acid cycle intermediates, succinate and fumarate. Non-stationary (13)C-based flux identification delivers both intracellular fluxes and intermediate levels, which was for the first time utilized for investigating systems-level effects of the administered drug by quantifying the flux control of the 3-hydroxy-3-methylglutaryl-coenzyme A reductase.

The topology of metabolic isotope labeling networks

Background

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.

Results

With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools.

Conclusion

Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

13CO2 as a universal metabolic tracer in isotopologue perturbation experiments

A tobacco plant was illuminated for 5h in an atmosphere containing (13)CO(2) and then maintained for 10 days under standard greenhouse conditions. Nicotine, glucose, and amino acids from proteins were isolated chromatographically. Isotopologue abundances of isolated metabolites were determined quantitatively by NMR spectroscopy and mass spectrometry. The observed non-stochastic isotopologue patterns indicate (i) formation of multiply labeled photosynthetic carbohydrates during the (13)CO(2) pulse phase followed by (ii) partial catabolism of the primary photosynthetic products, and (iii) recombination of the (13)C-labeled fragments with unlabeled intermediary metabolites during the chase period. The detected and simulated isotopologue profiles of glucose and amino acids reflect carbon partitioning that is dominated by the Calvin cycle and glycolysis/glucogenesis. Retrobiosynthetic analysis of the nicotine pattern is in line with its known formation from nicotinic acid and putrescine via aspartate, glyceraldehyde phosphate and alpha-ketoglutarate as basic building blocks. The study demonstrates that pulse/chase labeling with (13)CO(2) as precursor is a powerful tool for the analysis of quantitative aspects of plant metabolism in completely unperturbed whole plants.

The thermodynamic meaning of metabolic exchange fluxes

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

Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments

A novel approach to (13)C metabolic flux analysis (MFA) is presented using cytosolic metabolite pool sizes and their (13)C labeling data from an isotopically non-stationary (13)C labeling experiment (INST-CLE). The procedure is demonstrated with an E. coli wild type strain grown at fed batch conditions. The intra cellular labeling dynamics are excited by a sudden step increase of the (13)C portion in the substrate feed. Due to unchanged saturation of the substrate uptake system, the metabolic fluxes remain constant during the following sampling time period of only 16s, in which 20 samples are taken by an automated rapid sampling device immediately stopping metabolism by methanol quenching. Subsequent cell disruptive sample preparation and LC-MS/MS enabled simultaneous determination of pool sizes and mass isotopomers of intra cellular metabolites requiring detection limits in the nM range. Based on this data the new computational flux analysis tool 13CFLUX/INST is used to determine the intra cellular fluxes based on a complex carbon labeling network model. The measured data is in good agreement with the model predictions, thus proving the applicability of the new isotopically non-stationary (13)C metabolic flux analysis (INST-(13)C-MFA) concept. Moreover, it is shown that significant new information with respect to flux identifiability, non-measurable pool sizes, data consistency, or large storage pools can be taken from the novel kind of experimental data. This offers new insight into the biological operation of the metabolic network in vivo.

13C-Labeled metabolic flux analysis of a fed-batch culture of elutriated Saccharomyces cerevisiae

This study addresses the question of whether observable changes in fluxes in the primary carbon metabolism of Saccharomyces cerevisiae occur between the different phases of the cell division cycle. To detect such changes by metabolic flux analysis, a 13C-labeling experiment was performed with a fed-batch culture inoculated with a partially synchronized cell population obtained through centrifugal elutriation. Such a culture exhibits dynamic changes in the fractions of cells in different cell cycle phases over time. The mass isotopomer distributions of free intracellular metabolites in central carbon metabolism were measured by liquid chromatography-mass spectrometry. For four time points during the culture, these distributions were used to obtain the best estimates for the metabolic fluxes. The obtained flux fits suggested that the optimally fitted split ratio for the pentose phosphate pathway changed by almost a factor of 2 up and down around a value of 0.27 during the experiment. Statistical analysis revealed that some of the fitted flux distributions for different time points were significantly different from each other, indicating that cell cycle-dependent variations in cytosolic metabolic fluxes indeed occurred.

Determining Actinobacillus succinogenes metabolic pathways and fluxes by NMR and GC-MS analyses of 13C-labeled metabolic product isotopomers

Actinobacillus succinogenes is a promising candidate for industrial succinate production. However, in addition to producing succinate, it also produces formate and acetate. To understand carbon flux distribution to succinate and alternative products we fed A. succinogenes [1-(13)C]glucose and analyzed the resulting isotopomers of excreted organic acids, proteinaceous amino acids, and glycogen monomers by gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. The isotopomer data, together with the glucose consumption and product formation rates and the A. succinogenes biomass composition, were supplied to a metabolic flux model. Oxidative pentose phosphate pathway flux supplied, at most, 20% of the estimated NADPH requirement for cell growth. The model indicated that NADPH was instead produced primarily by the conversion of NADH to NADPH by transhydrogenase and/or by NADP-dependent malic enzyme. Transhydrogenase activity was detected in A. succinogenes cell extracts, as were formate and pyruvate dehydrogenases, which the model suggested were contributing to NADH production. Malic enzyme activity was also detected in cell extracts, consistent with the flux analysis results. Labeling patterns in amino acids and organic acids showed that oxaloacetate and malate were being decarboxylated to pyruvate. These are the first in vivo experiments to show that the partitioning of flux between succinate and alternative fermentation products can occur at multiple nodes in A. succinogenes. The implications for designing effective metabolic engineering strategies to increase A. succinogenes succinate production are discussed.

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.

Metabolic flux analysis: recent advances in carbon metabolism in plants

Isotopic tracers are used to both trace metabolic pathways and quantify fluxes through these pathways. The use of different labeling methods recently led to profound changes in our views of plant metabolism. Examples are taken from primary metabolism, with sugar interconversions, carbon partitioning between glycolysis and the pentose phosphate pathway, or metabolite inputs into the tricarboxylic acid (TCA) cycle, as well as from secondary metabolism with the relative contribution of the plastidial and cytosolic pathways to the biosynthesis of terpenoids. While labeling methods are often distinguished according to the instruments used for label detection, emphasis is put here on labeling duration. Short time labeling is adequate to study limited areas of the metabolic network. Long-term labeling, when designed to obtain metabolic and isotopic steady-state, allows to calculate various fluxes in large areas ofcentral metabolism. After longer labeling periods, large amounts of label accumulate in structural or storage compounds: their detailed study through the retrobiosynthetic method gives access to the biosynthetic pathways of otherwise undetectable precursors. This chapter presents the power and limits of the different methods, and illustrates how they can be associated with each other and with other methods of cell biology, to provide the information needed for a rational approach of metabolic engineering.

Metabolic networks in motion: 13C-based flux analysis

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

Integration of enzyme kinetic models and isotopomer distribution analysis for studies of in situ cell operation

A current trend in neuroscience research is the use of stable isotope tracers in order to address metabolic processes in vivo. The tracers produce a huge number of metabolite forms that differ according to the number and position of labeled isotopes in the carbon skeleton (isotopomers) and such a large variety makes the analysis of isotopomer data highly complex. On the other hand, this multiplicity of forms does provide sufficient information to address cell operation in vivo. By the end of last millennium, a number of tools have been developed for estimation of metabolic flux profile from any possible isotopomer distribution data. However, although well elaborated, these tools were limited to steady state analysis, and the obtained set of fluxes remained disconnected from their biochemical context. In this review we focus on a new numerical analytical approach that integrates kinetic and metabolic flux analysis. The related computational algorithm estimates the dynamic flux based on the time-dependent distribution of all possible isotopomers of metabolic pathway intermediates that are generated from a labeled substrate. The new algorithm connects specific tracer data with enzyme kinetic characteristics, thereby extending the amount of data available for analysis: it uses enzyme kinetic data to estimate the flux profile, and vice versa, for the kinetic analysis it uses in vivo tracer data to reveal the biochemical basis of the estimated metabolic fluxes.

Robustness of central carbohydrate metabolism in developing maize kernels

The central carbohydrate metabolism provides the precursors for the syntheses of various storage products in seeds. While the underlying biochemical map is well established, little is known about the organization and flexibility of carbohydrate metabolic fluxes in the face of changing biosynthetic demands or other perturbations. This question was addressed in developing kernels of maize (Zea mays L.), a model system for the study of starch and sugar metabolism. (13)C-labeling experiments were carried out with inbred lines, heterotic hybrids, and starch-deficient mutants that were selected to cover a wide range of performances and kernel phenotypes. In total, 46 labeling experiments were carried out using either [U-(13)C(6)]glucose or [U-(13)C(12)]sucrose and up to three stages of kernel development. Carbohydrate flux distributions were estimated based on glucose isotopologue abundances, which were determined in hydrolysates of starch by using quantitative (13)C-NMR and GC-MS. Similar labeling patterns in all samples indicated robustness of carbohydrate fluxes in maize endosperm, and fluxes were rather stable in response to glucose or sucrose feeding and during development. A lack of ADP-glucose pyrophosphorylase in the bt2 and sh2 mutants triggered significantly increased hexose cycling. In contrast, other mutations with similar kernel phenotypes had no effect. Thus, the distribution of carbohydrate fluxes is stable and not determined by sink strength in maize kernels.

Computational tools for isotopically instationary 13C labeling experiments under metabolic steady state conditions

(13)C metabolic flux analysis (MFA) has become an important and powerful tool for the quantitative analysis of metabolic networks in the framework of metabolic engineering. Isotopically instationary (13)C MFA under metabolic stationary conditions is a promising refinement of classical stationary MFA. It accounts for the experimental requirements of non-steady-state cultures as well as for the shortening of the experimental duration. This contribution extends all computational methods developed for classical stationary (13)C MFA to the instationary situation by using high-performance computing methods. The developed tools allow for the simulation of instationary carbon labeling experiments (CLEs), sensitivity calculation with respect to unknown parameters, fitting of the model to the measured data, statistical identifiability analysis and an optimal experimental design facility. To explore the potential of the new approach all these tools are applied to the central metabolism of Escherichia coli. The achieved results are compared to the outcome of the stationary counterpart, especially focusing on statistical properties. This demonstrates the specific strengths of the instationary method. A new ranking method is proposed making both an a priori and an a posteriori design of the sampling times available. It will be shown that although still not all fluxes are identifiable, the quality of flux estimates can be strongly improved in the instationary case. Moreover, statements about the size of some immeasurable pool sizes can be made.

Revisiting the 13C-label distribution of the non-oxidative branch of the pentose phosphate pathway based upon kinetic and genetic evidence

The currently applied reaction structure in stoichiometric flux balance models for the nonoxidative branch of the pentose phosphate pathway is not in accordance with the established ping-pong kinetic mechanism of the enzymes transketolase (EC 2.2.1.1) and transaldolase (EC 2.2.1.2). Based upon the ping-pong mechanism, the traditional reactions of the nonoxidative branch of the pentose phosphate pathway are replaced by metabolite specific, reversible, glycolaldehyde moiety (C(2)) and dihydroxyacetone moiety (C(3)) fragments producing and consuming half-reactions. It is shown that a stoichiometric model based upon these half-reactions is fundamentally different from the currently applied stoichiometric models with respect to the number of independent C(2) and C(3) fragment pools in the pentose phosphate pathway and can lead to different label distributions for (13)C-tracer experiments. To investigate the actual impact of the new reaction structure on the estimated flux patterns within a cell, mass isotopomer measurements from a previously published (13)C-based metabolic flux analysis of Saccharomyces cerevisiae were used. Different flux patterns were found. From a genetic point of view, it is well known that several micro-organisms, including Escherichia coli and S. cerevisiae, contain multiple genes encoding isoenzymes of transketolase and transaldolase. However, the extent to which these gene products are also actively expressed remains unknown. It is shown that the newly proposed stoichiometric model allows study of the effect of isoenzymes on the (13)C-label distribution in the nonoxidative branch of the pentose phosphate pathway by extending the half-reaction based stoichiometric model with two distinct transketolase enzymes instead of one. Results show that the inclusion of isoenzymes affects the ensuing flux estimates.

Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of (13)C-labeled primary metabolites

Metabolic-flux analyses in microorganisms are increasingly based on (13)C-labeling data. In this paper a new approach for the measurement of (13)C-label distributions is presented: rapid sampling and quenching of microorganisms from a cultivation, followed by extraction and detection by liquid chromatography-mass spectrometry of free intracellular metabolites. This approach allows the direct assessment of mass isotopomer distributions of primary metabolites. The method is applied to the glycolytic and pentose phosphate pathways of Saccharomyces cerevisiae strain CEN.PK113-7D grown in an aerobic, glucose-limited chemostat culture. Detailed investigations of the measured mass isotopomer distributions demonstrate the accuracy and information-richness of the obtained data. The mass fractions are fitted with a cumomer model to yield the metabolic fluxes. It is estimated that 24% of the consumed glucose is catabolized via the pentose phosphate pathway. Furthermore, it is found that turnover of storage carbohydrates occurs. Inclusion of this turnover in the model leads to a large confidence interval of the estimated split ratio.

Revealing metabolic phenotypes in plants: inputs from NMR analysis

Assessing the performance of the plant metabolic network, with its varied biosynthetic capacity and its characteristic subcellular compartmentation, remains a considerable challenge. The complexity of the network is such that it is not yet possible to build large-scale predictive models of the fluxes it supports, whether on the basis of genomic and gene expression analysis or on the basis of more traditional measurements of metabolites and their interconversions. This limits the agronomic and biotechnological exploitation of plant metabolism, and it undermines the important objective of establishing a rational metabolic engineering strategy. Metabolic analysis is central to removing this obstacle and currently there is particular interest in harnessing high-throughput and/or large-scale analyses to the task of defining metabolic phenotypes. Nuclear magnetic resonance (NMR) spectroscopy contributes to this objective by providing a versatile suite of analytical techniques for the detection of metabolites and the fluxes between them. The principles that underpin the analysis of plant metabolism by NMR are described, including a discussion of the measurement options for the detection of metabolites in vivo and in vitro, and a description of the stable isotope labelling experiments that provide the basis for metabolic flux analysis. Despite a relatively low sensitivity, NMR is suitable for high-throughput system-wide analyses of the metabolome, providing methods for both metabolite fingerprinting and metabolite profiling, and in these areas NMR can contribute to the definition of plant metabolic phenotypes that are based on metabolic composition. NMR can also be used to investigate the operation of plant metabolic networks. Labelling experiments provide information on the operation of specific pathways within the network, and the quantitative analysis of steady-state labelling experiments leads to the definition of large-scale flux maps for heterotrophic carbon metabolism. These maps define multiple unidirectional fluxes between branch-points in the metabolic network, highlighting the existence of substrate cycles and discriminating in favourable cases between fluxes in the cytosol and plastid. Flux maps can be used to define a functionally relevant metabolic phenotype and the extensive application of such maps in microbial systems suggests that they could have important applications in characterising the genotypes produced by plant genetic engineering.

Understanding flux in plant metabolic networks

The revolutionary growth in our ability to identify the 'parts list' of cellular infrastructure in plants in detail, and to alter it with precision, challenges us to develop methods to quantify how these parts function. For components of metabolism, this means mapping fluxes at the level of metabolic networks. Advances in experimental, analytical and software tools for metabolic flux analysis now allow maps of the fluxes through central metabolism to be obtained from the results of stable-isotope-labeling experiments. Such maps have led to notable successes in understanding and engineering metabolic function in microorganisms. Recent studies in plants are giving insight into particular fluxes, such as those of the pentose phosphate pathway, and into general phenomena, such as substrate- or futile-cycles and compartmentation. The importance of experimental design and statistical analysis have been illustrated, and analyses of fluxes in heterotrophic plant tissues have been carried out recently.

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model.

The oxidative pentose phosphate pathway: structure and organisation

The oxidative pentose phosphate pathway is a major source of reducing power and metabolic intermediates for biosynthetic processes. Some, if not all, of the enzymes of the pathway are found in both the cytosol and plastids, although the precise distribution of their activities varies. The apparent absence of sections of the pathway from the cytosol potentially complicates metabolism. These complications are partly offset, however, by exchange of intermediates between the cytosol and the plastids through the activities of a family of plastid phosphate translocators. Molecular analysis is confirming the widespread presence of multiple genes encoding each of the enzymes of the oxidative pentose phosphate pathway. Differential expression of these isozymes may ensure that the kinetic properties of the activity that catalyses a specific reaction match the metabolic requirements of a particular tissue. This hypothesis can be tested thanks to recent developments in the application of 13C-steady-state labelling strategies. These strategies make it possible to quantify flux through metabolic networks and to discriminate between pathways of carbohydrate oxidation in the cytosol and plastids.

Metabolic-flux profiling of the yeasts Saccharomyces cerevisiae and Pichia stipitis

The so far largely uncharacterized central carbon metabolism of the yeast Pichia stipitis was explored in batch and glucose-limited chemostat cultures using metabolic-flux ratio analysis by nuclear magnetic resonance. The concomitantly characterized network of active metabolic pathways was compared to those identified in Saccharomyces cerevisiae, which led to the following conclusions. (i) There is a remarkably low use of the non-oxidative pentose phosphate (PP) pathway for glucose catabolism in S. cerevisiae when compared to P. stipitis batch cultures. (ii) Metabolism of P. stipitis batch cultures is fully respirative, which contrasts with the predominantly respiro-fermentative metabolic state of S. cerevisiae. (iii) Glucose catabolism in chemostat cultures of both yeasts is primarily oxidative. (iv) In both yeasts there is significant in vivo malic enzyme activity during growth on glucose. (v) The amino acid biosynthesis pathways are identical in both yeasts. The present investigation thus demonstrates the power of metabolic-flux ratio analysis for comparative profiling of central carbon metabolism in lower eukaryotes. Although not used for glucose catabolism in batch culture, we demonstrate that the PP pathway in S. cerevisiae has a generally high catabolic capacity by overexpressing the Escherichia coli transhydrogenase UdhA in phosphoglucose isomerase-deficient S. cerevisiae.

Cumulative bondomers: a new concept in flux analysis from 2D [13C,1H] COSY NMR data

A well-established way of determining metabolic fluxes is to measure 2D [(13)C,(1)H] COSY NMR spectra of components of biomass grown on uniformly (13)C-labeled carbon sources. When using the entire set of measured data to simultaneously determine all fluxes in a proposed metabolic network model, the (13)C-labeling distribution in all measured compounds has to be simulated. This requires very large sets of isotopomer or cumomer balances. This article introduces the new concept of bondomers; entities that only vary in the numbers and positions of C-C bonds that have remained intact since the medium substrate molecule entered the metabolism. Bondomers are shown to have many analogies to isotopomers. One of these is that bondomers can be transformed to cumulative bondomers, just like isotopomers can be transformed to cumomers. Similarly to cumomers, cumulative bondomers allow an analytical solution of the entire set of balances describing a metabolic network. The main difference is that cumulative bondomer models are considerably smaller than corresponding cumomer models. This saves computational time, allows easier identifiability analysis, and yields new insights in the information content of 2D [(13)C,(1)H] COSY NMR data. We illustrate the theoretical concepts by means of a realistic example of the glycolytic and pentose phosphate pathways. The combinations of 2D [(13)C,(1)H] COSY NMR data that allow identification of all metabolic fluxes in these pathways are analyzed, and it is found that the NMR data contain less information than was previously expected.

Carbohydrate cycling in micro-organisms: what can (13)C-NMR tell us?

The extension of (13)C-nuclear magnetic resonance (NMR) techniques to study cellular metabolism over recent years has provided valuable data supporting the occurrence, diversity and extent of carbon cycling in the carbohydrate metabolism of micro-organisms. The occurrence of such cycles, resulting from the simultaneous operation of different and sometimes opposite individual steps, is inherently related to the network organisation of cellular metabolism. These cycles are tentatively classified here as 'reversibility', 'metabolic' and 'substrate' cycles on the basis of their balance in carbon and cofactors. Current hypotheses concerning the physiological relevance of carbohydrate cycles are discussed in light of the (13)C-NMR data. They most likely represent system-level mechanisms for coherent and timely partitioning of carbon resources to fit with the various biosynthetic, energetic or redox needs of cells and/or additional strategies in the adaptive capacity of micro-organisms to face variation in environmental conditions.

Metabolic flux analysis in Synechocystis using isotope distribution from 13C-labeled glucose

Using the carbon isotope labeling technique, the response of cyanobacterial central carbon metabolism to the change in environmental conditions was investigated. Synechocystis was grown in the heterotrophic and mixotrophic cultures fed with 13C-labeled glucose. The labeling patterns of the amino acids in biomass hydrolysates for both cultures were detected by the two-dimensional 1H-13C correlation nuclear magnetic resonance (2D 1H-13C COSY NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) technique. The in vivo intracellular flux distributions were then quantitated from the labeling measurements and metabolite balances using a parameters fitting approach. From the estimated flux distributions, it was found that the pentose phosphate pathway was the major pathway of glucose catabolism in the heterotrophic culture, while in the mixotrophic culture, the flux of CO2 fixation through the Calvin cycle was about two-fold of the glucose input flux. The relative flux through the phosphoenolpyruvate carboxylase was very high in both cultures, and this reaction represented about 25% of the assimilated CO2 in the mixotrophic culture. More importantly, we found a substantial outflow from the tricarboxylic acid cycle to glycolysis pathway carried by the malic enzyme, demonstrating the operation of a C4 pathway in cyanobacterial cells through the PEP carboxylase and malic enzyme. The estimated flux distributions also revealed that the NADPH synthesis was in excess relative to its requirement, and the excess NADPH might be reoxidized in cyanobacterial respiration to provide the energy for cellular requirement. Moreover, the analyzed result also suggested that the activity of the respiratory electron transport chain in cyanobacterial cells was not inhibited by light.

Analysis of flux estimates based on (13)C-labelling experiments

Modelling of the fluxes in central metabolism can be performed by combining labelling experiments with metabolite balancing. Using this approach, multiple samples from a cultivation of Saccharomyces cerevisiae in metabolic and isotopic steady state were analysed, and the metabolic fluxes in central metabolism were estimated. In the various samples, the estimates of the central metabolic pathways, the tricarboxylic acid cycle, the oxidative pentose phosphate pathway and the anaplerotic pathway, showed an unprecedented reproducibility. The high reproducibility was obtained with fractional labellings of individual carbon atoms as the calculational base, illustrating that the more complex modelling using isotopomers is not necessarily superior with respect to reproducibility of the flux estimates. Based on these results some general difficulties in flux estimation are discussed.

Genomics to fluxomics and physiomics - pathway engineering

Developments in microanalytical methods are enabling quantitative measurement of multiple metabolic fluxes and, in conjunction with transcript and proteomic profiling, are revolutionizing the ability of researchers to manipulate metabolism through pathway engineering in a variety of species. We review recent literature on the advances in genomics, proteomics, fluxomics and computational modeling focused on metabolic pathway engineering applications.