Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity.
This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.     

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.     

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.     

A review on metabolic pathway analysis with emphasis on isotope labeling approach

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production usingRalstonia eutropha and recombinantEscherichia coli, lactate production by recombinantSaccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeastToluropsis glabrata, lysine production usingCorynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/or the change in the culture condition.     

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.     

Metabolic flux analysis in biotechnology processes

Metabolic flux analysis (MFA) has become a fundamental tool of metabolic engineering to elucidate the metabolic state of the cell and has been applied to various biotechnological processes. In recent years, considerable technical advances have been made. Developments of analytical instruments allow us to determine ¹³C labeling distribution of intracellular metabolites with high accuracy and sensitivity. Moreover, kinetic information of intracellular label distribution during isotopic instationary enables us to calculate metabolic fluxes with shortened experimental time and decreased amount of labeled substrate. The ¹³C MFA may be one of the most promising approaches for the target estimation to improve strain performances and production processes.     

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.     

Dissecting metabolic flux in C<Subscript>4</Subscript> plants: experimental and theoretical approaches

C₄ photosynthesis is the carbon fixation pathway in specific plant species, so called C₄ plants including maize, sorghum and sugarcane. It is characterized by the carboxylation reaction that forms four-carbon (C₄) molecules, which are then used to transport CO₂ to the proximity of RubisCO in the bundle sheath cells. Since C₄ photosynthesis confers high photosynthetic as well as water and nitrogen use efficiency on plants, worldwide efforts have been made to understand the mechanisms of C₄ photosynthesis and to properly introduce the pathway into C₃ crops. Metabolic flux analysis (MFA) is a research field trying to analyze the metabolic pathway structure and activity (i.e., flux) in vivo. Constraint-based reconstruction and analysis tools theoretically study the distribution of metabolic flux in genome-scale network-based models. Different types of MFA and model-based analyses have been contributing to the discovery of C₄ photosynthetic pathways and to analyze its operation in C₄ plant species. This article reviews the studies to dissect the operation of C₄ photosynthesis and adjacent pathways, from the pioneer studies using radioisotope-based MFA to the recent stable isotope-based MFA and the model-based approaches. These studies indicate complex interconnections among metabolic pathways and the importance of the integration of experimental and theoretical approaches. Perspectives on the integrative approach and major obstacles are also discussed.     

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

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

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.     

A small metabolic flux model to identify transient metabolic regulations in Saccharomyces cerevisiae

The understanding of dynamic metabolic regulations is important for physiological studies and strain characterization tasks. The present study combined transient experiments with online metabolic flux analysis (MFA) in order to quantify metabolic regulations, namely carbon catabolite repression of respiration and transient acetic-acid production, in Saccharomyces cerevisiae during aerobic growth on glucose. The aim was to investigate which additional information can be gained from using a small metabolic flux model to study transient growth provoked by shift-up and shift-down experiments, compared to online monitoring alone. The MFA model allowed us to propose new correlations between pathways of the central metabolism. A linear correlation between glycolytic flux and respiratory capacity holds for shift-down and shift-up experiments. This confirmed that respiratory functions were subjected to carbon catabolite repression and suggested that respiratory capacity is controlled by the glycolytic flux rather than the glucose influx. Furthermore, the model showed that control of repression of respiration by the glycolytic flux was a dynamic phenomenon. Co-factor balancing within the MFA model showed that transient acetic-acid production indicated a transient limitation in another part of the central metabolism but not in oxidative phosphorylation. However, at super-critical growth rates and when coupling of anabolism and catabolism is resumed, the limitation shifts to oxidative phosphorylation, with the consequence that ethanol is formed. The online application of small metabolic flux models to transient experiments enhanced the physiological insight into transient growth and opens up the use of transient experiments as an efficient tool to understand dynamic metabolic regulations.     

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.     

Combining metabolic flux analysis tools and 13C NMR to estimate intracellular fluxes of cultured astrocytes

In this work, brain cell metabolism was investigated by (13)C NMR spectroscopy and metabolic flux analysis (MFA). Monotypic cultures of astrocytes were incubated with labeled glucose for 38 h, and the distribution of the label was analyzed by (13)C NMR spectroscopy. The analysis of the spectra reveals two distinct physiological states characterized by different ratios of pyruvate carboxylase to pyruvate dehydrogenase activities (PC/PDH). Intracellular flux distributions for both metabolic states were estimated by MFA using the isotopic information and extracellular rate measurements as constraints. The model was subsequently checked with the consistency index method. From a biological point of view, the occurrence of the two physiological states appears to be correlated with the presence or absence of extracellular glutamate. Concerning the model, it can be stated that the metabolic network and the set of constraints adopted provide a consistent and robust characterization of the astrocytic metabolism, allowing for the calculation of central intracellular fluxes such as pyruvate recycling, the anaplerotic flux mediated by pyruvate carboxylase, and the glutamine formation through glutamine synthetase.     

Improving glucose and glutamine metabolism of human HEK 293 and Trichoplusia ni insect cells engineered to express a cytosolic pyruvate carboxylase enzyme

Metabolic engineering has been defined as a directed improvement of product formation or cellular properties by modification of specific biochemical pathways or introduction of new enzymatic reactions by recombinant DNA technology. The use of metabolic flux analysis (MFA) has helped in the understanding of the key limitation in the metabolic pathways of cultured animal cells. The MFA of the major nutrients glucose and glutamine showed that the flux of glucose to the TCA cycle and its subsequent utilization is limited as a result of the lack of certain key enzymes in the pathway. One of the key enzymes controlling this flux is pyruvate carboxylase. Introduction of this enzyme into mammalian cells has been shown to improve the utilization of glucose and limit the production of lactate and ammonia, which are deleterious to cell growth. In the present work a yeast pyruvate carboxylase gene has been introduced into mammalian (HEK 293) and insect (Trichoplusia ni High-Five) cells, resulting in the cytosolic expression of the enzyme. In both cases the resulting transfected cells were able to utilize glucose and glutamine more efficiently and produce lower amounts of lactate and ammonia. Differences in the amino acid utilization pattern were also observed, indicating changes in the basic metabolism of the cells. The performance of the transfected cells as expression systems for adenovirus and baculovirus vectors, respectively, has also been examined. The results obtained and their impact on the process development for protein and viral vector production are discussed.     

Metabolic isotopomer labeling systems. Part I: global dynamic behavior

In the last few years metabolic flux analysis (MFA) using carbon labeling experiments (CLE) has become a major diagnostic tool in metabolic engineering. The mathematical centerpiece of MFA is the solution of isotopomer labeling systems (ILS). An ILS is a high-dimensional nonlinear differential equation system that describes the distribution of isotopomers over a metabolic network during a carbon labeling experiment. This contribution presents a global analysis of the dynamic behavior of general ILSs. It is proven that an ILS is globally stable under very weak conditions that are always satisfied in practice. In particular it is shown that in some sense ILSs are a nonlinear extension to the classical theory of compartmental systems. The central stability condition for compartmental systems, i.e., the non-existence of traps in linear compartmental networks, is also the major stability condition for ILSs. As an important side result of the proof, it is shown that ILSs can be transformed to a cascade of linear systems with time-dependent inhomogeneous terms. This cascade structure has considerable consequences for the development of efficient numerical algorithms for the solution of ILSs and thus for MFA.     

Application of multivariate analysis to optimize function of cultured hepatocytes

Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. To characterize intermediary metabolism in the hepatocytes, metabolic flux analysis (MFA) was applied to elucidate the changes in intracellular pathway fluxes of primary rat hepatocytes exposed to human plasma and to provide a comprehensive snapshot of the hepatic metabolic profile. In the current study, the combination of preconditioning and plasma supplementation produced distinct metabolic states. Combining the metabolic flux distribution obtained by MFA with methodologies such as Fisher discriminant analysis (FDA) and partial least squares or projection to latent structures (PLS) provided insights into the underlying structure and causal relationship within the data. With the aid of these analyses, patterns in the cellular response of the hepatocytes that contributed to the separation of the different hepatic states were identified. Of particular interest was the recognition of distal pathways that strongly correlated with a particular hepatic function. The hepatic functions investigated were intracellular triglyceride accumulation and urea production. This study illustrates a framework for optimizing hepatic function and a possibility of identifying potential targets for improving hepatic functions.     

Green pathways: Metabolic network analysis of plant systems

Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field.     

MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis

SUMMARY: MetaFluxNet is a program package for managing information on the metabolic reaction network and for quantitatively analyzing metabolic fluxes in an interactive and customized way. It allows users to interpret and examine metabolic behavior in response to genetic and/or environmental modifications. As a result, quantitative in silico simulations of metabolic pathways can be carried out to understand the metabolic status and to design the metabolic engineering strategies. The main features of the program include a well-developed model construction environment, user-friendly interface for metabolic flux analysis (MFA), comparative MFA of strains having different genotypes under various environmental conditions, and automated pathway layout creation. AVAILABILITY: http://mbel.kaist.ac.kr/ Supplementary information: A manual for MetaFluxNet is available as PDF file.     

The impact of anti-apoptotic gene Bcl-2∆ expression on CHO central metabolism

Anti-apoptosis engineering is an established technique to prolong the viability of mammalian cell cultures used for industrial production of recombinant proteins. However, the effect of overexpressing anti-apoptotic proteins on central carbon metabolism has not been systematically studied. We transfected CHO-S cells to express Bcl-2∆, an engineered anti-apoptotic gene, and selected clones that differed in their Bcl-2∆ expression and caspase activity. ¹³C metabolic flux analysis (MFA) was then applied to elucidate the metabolic alterations induced by Bcl-2∆. Expression of Bcl-2Δ reduced lactate accumulation by redirecting the fate of intracellular pyruvate toward mitochondrial oxidation during the lactate-producing phase, and it significantly increased lactate re-uptake during the lactate-consuming phase. This flux redistribution was associated with significant increases in biomass yield, peak viable cell density (VCD), and integrated VCD. Additionally, Bcl-2∆ expression was associated with significant increases in isocitrate dehydrogenase and NADH oxidase activities, both rate-controlling mitochondrial enzymes. This is the first comprehensive ¹³C MFA study to demonstrate that expression of anti-apoptotic genes has a significant impact on intracellular metabolic fluxes, especially in controlling the fate of pyruvate carbon, which has important biotechnology applications for reducing lactate accumulation and enhancing productivity in mammalian cell cultures.