Metabolic cartography: experimental quantification of metabolic fluxes from isotopic labelling studies

For the past decade, flux maps have provided researchers with an in-depth perspective on plant metabolism. As a rapidly developing field, significant headway has been made recently in computation, experimentation, and overall understanding of metabolic flux analysis. These advances are particularly applicable to the study of plant metabolism. New dynamic computational methods such as non-stationary metabolic flux analysis are finding their place in the toolbox of metabolic engineering, allowing more organisms to be studied and decreasing the time necessary for experimentation, thereby opening new avenues by which to explore the vast diversity of plant metabolism. Also, improved methods of metabolite detection and measurement have been developed, enabling increasingly greater resolution of flux measurements and the analysis of a greater number of the multitude of plant metabolic pathways. Methods to deconvolute organelle-specific metabolism are employed with increasing effectiveness, elucidating the compartmental specificity inherent in plant metabolism. Advances in metabolite measurements have also enabled new types of experiments, such as the calculation of metabolic fluxes based on (13)CO(2) dynamic labelling data, and will continue to direct plant metabolic engineering. Newly calculated metabolic flux maps reveal surprising and useful information about plant metabolism, guiding future genetic engineering of crops to higher yields. Due to the significant level of complexity in plants, these methods in combination with other systems biology measurements are necessary to guide plant metabolic engineering in the future.     

Insights into plant metabolic networks from steady-state metabolic flux analysis

Steady-state metabolic flux analysis (MFA) is an experimental approach that allows the measurement of multiple fluxes in the core network of primary carbon metabolism. It is based on isotopic labelling experiments, and although well established in the analysis of micro-organisms, and some mammalian systems, the extension of the method to plant cells has been challenging because of the extensive subcellular compartmentation of the metabolic network. Despite this difficulty there has been substantial progress in developing robust protocols for the analysis of heterotrophic plant metabolism by steady-state MFA, and flux maps have now been published that reflect the metabolic phenotypes of excised root tips, developing embryos and cotyledons, hairy root cultures, and cell suspensions under a variety of physiological conditions. There has been a steady improvement in the quality, extent and statistical reliability of these analyses, and new information is emerging on the performance of the plant metabolic network and the contributions of specific pathways.     

Vacuolar compartmentation complicates the steady-state analysis of glucose metabolism and forces reappraisal of sucrose cycling in plants

Steady-state stable isotope labelling provides a method for generating flux maps of the compartmented network of central metabolism in heterotrophic plant tissues. Theoretical analysis of the contribution of the vacuole to the regeneration of glucose by endogenous processes shows that numerical fitting of isotopomeric data will only generate an accurate map of the fluxes involving intracellular glucose if information is available on the labelling of both the cytosolic and vacuolar glucose pools. In the absence of this information many of the calculated fluxes are at best unreliable or at worst indeterminate. This result suggests that the anomalously high rates of sucrose cycling and glucose resynthesis that have been reported in earlier steady-state analyses of tissues labelled with (13)C-glucose precursors may be an artefact of assuming that the labelling pattern of extracted glucose reflected the labelling of the cytosolic pool. The analysis emphasises that although subcellular information can sometimes be deduced from a steady-state analysis without recourse to subcellular fractionation, the success of this procedure depends critically on the structure of the metabolic network. It is concluded that methods need to be implemented that will allow measurement of the subcellular labelling pattern of glucose and other metabolites, as part of the routine analysis of the redistribution of label in steady-state stable isotope labelling experiments, if the true potential of network flux analysis for generating metabolic phenotypes is to be realized.     

Quantitative approaches for analysing fluxes through plant metabolic networks using NMR and stable isotope labelling

The quantitative analysis of metabolic networks is a prerequisite for understanding the integration and regulation of plant metabolism and for devising rational approaches for manipulating resource allocation in plants. The analysis of steady state stable isotope labelling experiments using nuclear magnetic resonance (NMR) spectroscopy has developed into a powerful method for determining these fluxes in micro-organisms and its application to heterotrophic plant metabolism is increasing. After an introductory discussion of the well known role of stable isotopes in pathway delineation, the review considers their application to metabolic flux analysis in plants. These applications are divided into two groups – small scale analyses of fluxes through particular pathways and large scale analyses of multiple fluxes through metabolic networks – and the problems caused by the complexity of intermediary metabolism in plants are discussed. It is concluded that metabolic flux analysis provides a powerful method for defining the metabolic phenotype of wild type, mutant and transgenic plants and that its development should be pursued.     

Quantitative analysis of metabolic fluxes in Escherichia coli, using two-dimensional NMR spectroscopy and complete isotopomer models.

Complete isotopomer models that simulate distribution of label in 13C tracer experiments are applied to the quantification of metabolic fluxes in the primary carbon metabolism of E. coli under aerobic and anaerobic conditions. The concept of isotopomer mapping matrices (IMMs) is used to simplify the formulation of isotopomer mass balances by expressing all isotopomer mass balances of a metabolite pool in a single matrix equation. A numerically stable method to calculate the steady-state isotopomer distribution in metabolic networks in introduced. Net values of intracellular fluxes and the degree of reversibility of enzymatic steps are estimated by minimization of the deviations between experimental and simulated measurements. The metabolic model applied includes the Embden-Meyerhof-Parnas and the pentose phosphate pathway, the tricarboxylic acid cycle, anaplerotic reaction sequences and pathways involved in amino acid synthesis. The study clearly demonstrates the value of complete isotopomer models for maximizing the information obtainable from 13C tracer experiments. The approach applied here offers a completely general and comprehensive analysis of carbon tracer experiments where any set of experimental data on the labeling state and extracellular fluxes can be used for the quantification of metabolic fluxes in complex metabolic networks.     

Strategies for investigating the plant metabolic network with steady-state metabolic flux analysis: lessons from an Arabidopsis cell culture and other systems

Steady-state (13)C metabolic flux analysis (MFA) is currently the experimental method of choice for generating flux maps of the compartmented network of primary metabolism in heterotrophic and mixotrophic plant tissues. While statistically robust protocols for the application of steady-state MFA to plant tissues have been developed by several research groups, the implementation of the method is still far from routine. The effort required to produce a flux map is more than justified by the information that it contains about the metabolic phenotype of the system, but it remains the case that steady-state MFA is both analytically and computationally demanding. This article provides an overview of principles that underpin the implementation of steady-state MFA, focusing on the definition of the metabolic network responsible for redistribution of the label, experimental considerations relating to data collection, the modelling process that allows a set of metabolic fluxes to be deduced from the labelling data, and the interpretation of flux maps. The article draws on published studies of Arabidopsis cell cultures and other systems, including developing oilseeds, with the aim of providing practical guidance and strategies for handling the issues that arise when applying steady-state MFA to the complex metabolic networks encountered in plants.     

Determination of metabolic fluxes in a non-steady-state system

Estimation of fluxes through metabolic networks from redistribution patterns of (13)C has become a well developed technique in recent years. However, the approach is currently limited to systems at metabolic steady-state; dynamic changes in metabolic fluxes cannot be assessed. This is a major impediment to understanding the behaviour of metabolic networks, because steady-state is not always experimentally achievable and a great deal of information about the control hierarchy of the network can be derived from the analysis of flux dynamics. To address this issue, we have developed a method for estimating non-steady-state fluxes based on the mass-balance of mass isotopomers. This approach allows multiple mass-balance equations to be written for the change in labelling of a given metabolite pool and thereby permits over-determination of fluxes. We demonstrate how linear regression methods can be used to estimate non-steady-state fluxes from these mass balance equations. The approach can be used to calculate fluxes from both mass isotopomer and positional isotopomer labelling information and thus has general applicability to data generated from common spectrometry- or NMR-based analytical platforms. The approach is applied to a GC-MS time-series dataset of (13)C-labelling of metabolites in a heterotrophic Arabidopsis cell suspension culture. Threonine biosynthesis is used to demonstrate that non-steady-state fluxes can be successfully estimated from such data while organic acid metabolism is used to highlight some common issues that can complicate flux estimation. These include multiple pools of the same metabolite that label at different rates and carbon skeleton rearrangements.     

Synthesis of inositol phosphate ligands of plant hormone-receptor complexes: pathways of inositol hexakisphosphate turnover

Reduction of phytate is a major goal of plant breeding programs to improve the nutritional quality of crops. Remarkably, except for the storage organs of crops such as barley, maize and soybean, we know little of the stereoisomeric composition of inositol phosphates in plant tissues. To investigate the metabolic origins of higher inositol phosphates in photosynthetic tissues, we have radiolabelled leaf tissue of Solanum tuberosum with myo-[2-3H]inositol, undertaken a detailed analysis of inositol phosphate stereoisomerism and permeabilized mesophyll protoplasts in media containing inositol phosphates. We describe the inositol phosphate composition of leaf tissue and identify pathways of inositol phosphate metabolism that we reveal to be common to other kingdoms. Our results identify the metabolic origins of a number of higher inositol phosphates including ones that are precursors of cofactors, or cofactors of plant hormone-receptor complexes. The present study affords alternative explanations of the effects of disruption of inositol phosphate metabolism reported in other species, and identifies different inositol phosphates from that described in photosynthetic tissue of the monocot Spirodela polyrhiza. We define the pathways of inositol hexakisphosphate turnover and shed light on the occurrence of a number of inositol phosphates identified in animals, for which metabolic origins have not been defined.     

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.     

Kinetics of labelling of organic and amino acids in potato tubers by gas chromatography-mass spectrometry following incubation in (13)C labelled isotopes

Metabolic pathways of primary metabolism of discs isolated from potato tubers were evaluated by the use of a gas chromatography-mass spectrometry (GC-MS) method generated specifically for this purpose. After testing several possible methods including chemical ionization, it was decided for reasons of sensitivity, reproducibility and speed to use electron impact ionization-based GC-MS analysis. The specific labelling and label accumulation of over 30 metabolites including a broad number of sugars, organic and amino acids was analysed following the incubation of tuber discs in [U-(13)C]glucose. The reproducibility of this method was similar to that found for other GC-MS-based analyses and comparison of flux estimates from this method with those obtained from parallel, yet less comprehensive, radiolabel experiments revealed close agreement. Therefore, the novel method allows quantitatively evaluation of a broad range of metabolic pathways without the need for laborious (and potentially inaccurate), chemical fractionation procedures commonly used in the estimation of fluxes following incubation in radiolabelled substrates. As a first experiment the GC-MS method has been applied to compare the metabolism of wild type and well-characterized transgenic potato tubers exhibiting an enhanced sucrose mobilization. The fact that this method is able to rapidly yield further comprehensive information into primary metabolism illustrates its power as a further phenotyping tool for the analysis of plant metabolism.     

Nuclear magnetic resonance and plant metabolic engineering.

Nuclear magnetic resonance (NMR) can be used to measure metabolite levels and metabolic fluxes, to probe the intracellular environment, and to follow transport and energetics nondestructively. NMR methods are therefore powerful aids to understanding plant metabolism and physiology. Both spectroscopy and imaging can help overcome the unique challenges that plants present to the metabolic engineer by detecting, identifying, quantifying, and localizing novel metabolites in vivo and in extracts; revealing the composition and physical state of cell wall and other polymers; allowing the identification of active pathways; providing quantitative measures of metabolic flux; and testing hypotheses about the effects of engineered traits on plant physiological function. The aim of this review is to highlight recent studies in which NMR has contributed to metabolic engineering of plants and to illustrate the unique characteristics of NMR measurements that give it the potential to make greater contributions in the future.     

Bidirectional reaction steps in metabolic networks: II. Flux estimation and statistical analysis

Metabolic carbon labelling experiments enable a large amount of extracellular fluxes and intracellular carbon isotope enrichments to be measured. Since the relation between the measured quantities and the unknown intracellular metabolic fluxes is given by bilinear balance equations, flux determination from this data set requires the numerical solution of a nonlinear inverse problem. To this end, a general algorithm for flux estimation from metabolic carbon labelling experiments based on the least squares approach is developed in this contribution and complemented by appropriate tools for statistical analysis. The linearization technique usually applied for the computation of nonlinear confidence regions is shown to be inappropriate in the case of large exchange fluxes. For this reason a sophisticated compactification transformation technique for nonlinear statistical analysis is developed. Statistical analysis is then performed by computing appropriate statistical quality measures like output sensitivities, parameter sensitivities and the parameter covariance matrix. This allows one to determine the order of magnitude of exchange fluxes in most practical situations. An application study with a large data set from lysine-producing Corynebacterium glutamicum demonstrates the power and limitations of the carbon-labelling technique. It is shown that all intracellular fluxes in central metabolism can be quantitated without assumptions on intracellular energy yields. At the same time several exchange fluxes are determined which is invaluable information for metabolic engineering. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 118-135, 1997.     

The metabolic architecture of plant cells. Stability of central metabolism and flexibility of anabolic pathways during the growth cycle of tomato cells

The changes in the intermediary metabolism of plant cells were quantified according to growth conditions at three different stages of the growth cycle of tomato cell suspension. Eighteen fluxes of central metabolism were calculated from (13)C enrichments after near steady-state labeling by a metabolic model similar to that described in Dieuaide-Noubhani et al. (Dieuaide-Noubhani, M., Raffard, G., Canioni, P., Pradet, A., and Raymond, P. (1995) J. Biol. Chem. 270, 13147-13159), and 10 net fluxes were obtained directly from end-product accumulation rates. The absolute flux values of central metabolic pathways gradually slowed down with the decrease of glucose influx into the cells. However, the relative fluxes of glycolysis, the pentose-P pathway, and the tricarboxylic acid cycle remained unchanged during the culture cycle at 70, 28, and 40% of glucose influx, respectively, and the futile cycle of sucrose remained high at about 6-fold the glucose influx, independently from carbon nutritional conditions. This natural resistance to flux alterations is referred to as metabolic stability. The numerous anabolic pathways, including starch synthesis, hexose accumulation, biosynthesis of wall polysaccharides, and amino and organic acid biosynthesis were comparatively low and variable. The phosphoenolpyruvate carboxylase flux decreased 5-fold in absolute terms and 2-fold in relation to the glucose influx rate during the culture cycle. We conclude that anabolic fluxes constitute the flexible part of plant cell metabolism that can fluctuate in relation to cell demands for growth.     

Physiology of microbial cell and metabolic engineering

This review is devoted to the problems of the physiology and cell biology of microorganisms in relation to metabolic engineering. The latter is considered as a branch of fundamental and applied biotechnology aimed at controlling microbial metabolism by methods of genetic engineering and classical genetics and based on intimate knowledge of cell metabolism. Attention is also given to the problems associated with the metabolic limitation of microbial biosyntheses, analysis and control of metabolic fluxes, rigidity of metabolic pathways, the role of pleiotropic (global) regulatory systems in the control of metabolic fluxes, and prospects of physiological and evolutionary approaches in metabolic engineering.     

Physiology of microbial cells and metabolic engineering

This review is devoted to the problems of the physiology and cell biology of microorganisms in relation to metabolic engineering. The latter is considered as a branch of fundamental and applied biotechnology aimed at controlling microbial metabolism by methods of genetic engineering and classical genetics and based on intimate knowledge of cell metabolism. Attention is also given to the problems associated with the metabolic limitation of microbial biosyntheses, analysis and control of metabolic fluxes, rigidity of metabolic pathways, the role of pleiotropic (global) regulatory systems in the control of metabolic fluxes, and prospects of physiological and evolutionary approaches in metabolic engineering.     

INCA 2.0: A tool for integrated,dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state ¹³C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different ¹³C acetate tracers, we show that MFA based on dynamic ¹³C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined ¹³C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.     

A transient isotopic labeling methodology for 13C metabolic flux analysis of photoautotrophic microorganisms

Metabolic flux analysis is increasingly recognized as an integral component of systems biology. However, techniques for experimental measurement of system-wide metabolic fluxes in purely photoautotrophic systems (growing on CO(2) as the sole carbon source) have not yet been developed due to the unique problems posed by such systems. In this paper, we demonstrate that an approach that balances positional isotopic distributions transiently is the only route to obtaining system-wide metabolic flux maps for purely autotrophic metabolism. The outlined transient (13)C-MFA methodology enables measurement of fluxes at a metabolic steady-state, while following changes in (13)C-labeling patterns of metabolic intermediates as a function of time, in response to a step-change in (13)C-label input. We use mathematical modeling of the transient isotopic labeling patterns of central intermediates to assess various experimental requirements for photoautotrophic MFA. This includes the need for intracellular metabolite concentration measurements and isotopic labeling measurements as a function of time. We also discuss photobioreactor design and operation in order to measure fluxes under precise environmental conditions. The transient MFA technique can be used to measure and compare fluxes under different conditions of light intensity, nitrogen sources or compare strains with various mutations or gene deletions and additions.