Estimation of metabolic fluxes, expression levels and metabolite dynamics of a secondary metabolic pathway in potato using label pulse-feeding experiments combined with kinetic network modelling and simulation

Modeling lignin biosynthesis: a pathway to renewable chemicals

Plant biomass contains lignin that can be converted into high-value-added chemicals, fuels, and materials. The precise genetic manipulation of lignin content and composition in plant cells offers substantial environmental and economic benefits. However, the intricate regulatory mechanisms governing lignin formation challenge the development of crops with specific lignin profiles. Mathematical models and computational simulations have recently been employed to gain fundamental understanding of the metabolism of lignin and related phenolic compounds. This review article discusses the strategies used for modeling plant metabolic networks, focusing on the application of mathematical modeling for flux network analysis in monolignol biosynthesis. Furthermore, we highlight how current challenges might be overcome to optimize the use of metabolic modeling approaches for developing lignin-engineered plants.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Shoot tip culture: a step towards 13C metabolite flux analysis of sink leaf metabolism

BACKGROUND

Better understanding of the physiological and metabolic status of plants can only be obtained when metabolic fluxes are accurately assessed in a growing plant. Steady state ¹³C-MFA has been established as a routine method for analysis of fluxes in plant primary metabolism. However, the experimental system needs to be improved for continuous carbon enrichment from labelled sugars into metabolites for longer periods until complex secondary metabolism reaches steady state.

RESULTS

We developed an in vitro plant culture strategy by using peppermint as a model plant with minimizing unlabelled carbon fixation where growing shoot tip was strongly dependent on labelled glucose for their carbon necessity. We optimized the light condition and detected the satisfactory phenotypical growth under the lower light intensity. Total volatile terpenes were also highest at the same light. Analysis of label incorporation into pulegone monoterpene after continuous U-¹³C₆ glucose feeding revealed nearly 100% ¹³C, even at 15 days after first leaf visibility (DALV). Label enrichment was gradually scrambled with increasing light intensity and leaf age. This study was validated by showing similar levels of label enrichment in proteinogenic amino acids. The efficiency of this method was also verified in oregano.

CONCLUSIONS

Our shoot tip culture depicted a method in achieving long term, stable and a high percentage of label accumulation in secondary metabolites within a fully functional growing plant system. It recommends the potential application for the investigations of various facets of plant metabolism by steady state ¹³C-MFA. The system also provides a greater potential to study sink leaf metabolism. Overall, we propose a system to accurately describe complex metabolic phenotypes in a growing plant.

A 13C isotope labeling method for the measurement of lignin metabolic flux in Arabidopsis stems

BACKGROUND

Metabolic fluxes represent the functional phenotypes of biochemical pathways and are essential to reveal the distribution of precursors among metabolic networks. Although analysis of metabolic fluxes, facilitated by stable isotope labeling and mass spectrometry detection, has been applied in the studies of plant metabolism, we lack experimental measurements for carbon flux towards lignin, one of the most abundant polymers in nature.

RESULTS

We developed a feeding strategy of excised Arabidopsis stems with 13C labeled phenylalanine (Phe) for the analysis of lignin biosynthetic flux. We optimized the feeding methods and found the stems continued to grow and lignify. Consistent with lignification profiles along the stems, higher levels of phenylpropanoids and activities of lignin biosynthetic enzymes were detected in the base of the stem. In the feeding experiments, 13C labeled Phe was quickly accumulated and used for the synthesis of phenylpropanoid intermediates and lignin. The intermediates displayed two different patterns of labeling kinetics during the feeding period. Analysis of lignin showed rapid incorporation of label into all three subunits in the polymers.

CONCLUSIONS

Our feeding results demonstrate the effectiveness of the stem feeding system and suggest a potential application for the investigations of other aspects in plant metabolism. The supply of exogenous Phe leading to a higher lignin deposition rate indicates the availability of Phe is a determining factor for lignification rates.

Deuterium- and Tritium-Labelled Compounds: Applications in the Life Sciences

Hydrogen isotopes are unique tools for identifying and understanding biological and chemical processes. Hydrogen isotope labelling allows for the traceless and direct incorporation of an additional mass or radioactive tag into an organic molecule with almost no changes in its chemical structure, physical properties, or biological activity. Using deuterium-labelled isotopologues to study the unique mass-spectrometric patterns generated from mixtures of biologically relevant molecules drastically simplifies analysis. Such methods are now providing unprecedented levels of insight in a wide and continuously growing range of applications in the life sciences and beyond. Tritium (³ H), in particular, has seen an increase in utilization, especially in pharmaceutical drug discovery. The efforts and costs associated with the synthesis of labelled compounds are more than compensated for by the enhanced molecular sensitivity during analysis and the high reliability of the data obtained. In this Review, advances in the application of hydrogen isotopes in the life sciences are described.

Can stable isotope mass spectrometry replace ‎radiolabelled approaches in metabolic studies?

Metabolic pathways and the key regulatory points thereof can be deduced using isotopically labelled substrates. One prerequisite is the accurate measurement of the labeling pattern of targeted metabolites. The subsequent estimation of metabolic fluxes following incubation in radiolabelled substrates has been extensively used. Radiolabelling is a sensitive approach and allows determination of total label uptake since the total radiolabel content is easy to detect. However, the incubation of cells, tissues or the whole plant in a stable isotope enriched environment and the use of either mass spectrometry or nuclear magnetic resonance techniques to determine label incorporation within specific metabolites offers the possibility to readily obtain metabolic information with higher resolution. It additionally also offers an important complement to other post-genomic strategies such as metabolite profiling providing insights into the regulation of the metabolic network and thus allowing a more thorough description of plant cellular function. Thus, although safety concerns mean that stable isotope feeding is generally preferred, the techniques are in truth highly complementary and application of both approaches in tandem currently probably provides the best route towards a comprehensive understanding of plant cellular metabolism.

Directed multistep biocatalysis using tailored permeabilized cells

: Recent developments in the field of biocatalysis using permeabilized cells are reviewed here, with a special emphasis on the newly emerging area of multistep biocatalysis using permeabilized cells. New methods of metabolic engineering using in silico network design and new methods of genetic engineering provide the opportunity to design more complex biocatalysts for the synthesis of complex biomolecules. Methods for the permeabilization of cells are thoroughly reviewed. We provide an extended review of useful available databases and bioinformatics tools, particularly for setting up genome-scale reconstructed networks. Examples described include phosphorylated carbohydrates, sugar nucleotides, and polyketides.

Kinetic modeling of cell metabolism for microbial production

Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations.

Tracing flavonoid degradation in grapes by MS filtering with stable isotopes

Anthocyanin degradation has been proposed as one of the primary causes for reduced colour and quality in red wine grapes grown in a warm climate. To study anthocyanin degradation we infused berries with L-phenyl-(13)C₆-alanine and then tracked the fate of the anthocyanins comparing normal (25 °C) and warm (45 °C) temperature conditions. An untargeted metabolomics approach was aided by filtering the MS data using software algorithms to extract all M and M+6 isotopic peak pairs, allowing the analysis to focus solely on the metabolites of phenylalanine. A paired-comparison t-test was performed over the 8 biological replicates revealing 13 metabolites that were statistically different between 25 °C and 45 °C treatments. Most of these features had lower abundances in 45 °C samples, confirming that 45 °C treatment caused anthocyanin degradation. In addition, resveratrol was significantly reduced following heat treatment. However, 5 metabolites increased following the 45 °C treatment. These unidentified metabolites are therefore suspects for anthocyanin degradation products.

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Application of stable isotope-assisted metabolomics for cell metabolism studies

The applications of stable isotopes in metabolomics have facilitated the study of cell metabolisms. Stable isotope-assisted metabolomics requires: (1) properly designed tracer experiments; (2) stringent sampling and quenching protocols to minimize isotopic alternations; (3) efficient metabolite separations; (4) high resolution mass spectrometry to resolve overlapping peaks and background noises; and (5) data analysis methods and databases to decipher isotopic clusters over a broad m/z range (mass-to-charge ratio). This paper overviews mass spectrometry based techniques for precise determination of metabolites and their isotopologues. It also discusses applications of isotopic approaches to track substrate utilization, identify unknown metabolites and their chemical formulas, measure metabolite concentrations, determine putative metabolic pathways, and investigate microbial community populations and their carbon assimilation patterns. In addition, 13C-metabolite fingerprinting and metabolic models can be integrated to quantify carbon fluxes (enzyme reaction rates). The fluxome, in combination with other "omics" analyses, may give systems-level insights into regulatory mechanisms underlying gene functions. More importantly, 13C-tracer experiments significantly improve the potential of low-resolution gas chromatography-mass spectrometry (GC-MS) for broad-scope metabolism studies. We foresee the isotope-assisted metabolomics to be an indispensable tool in industrial biotechnology, environmental microbiology, and medical research.

Assessment of constituents in Allium by multivariate data analysis, high-resolution α-glucosidase inhibition assay and HPLC-SPE-NMR

Bulbs and leaves of 35 Allium species and cultivars bought or collected in 2010-2012 were investigated with multivariate data analysis, high-resolution α-glucosidase inhibition assays and HPLC-HRMS-SPE-NMR with the aim of exploring the potential of Allium as a future functional food for management of type 2 diabetes. It was found that 30 out of 106 crude extracts showed more than 80% inhibition of the α-glucosidase enzyme at a concentration of 40mg/mL (dry sample) or 0.4g/mL (fresh sample). High-resolution α-glucosidase biochromatograms of these extracts allowed fast identification of three analytes with α-glucosidase inhibitory activity, and subsequent HPLC-HRMS-SPE-NMR experiments allowed identification of these as N-p-coumaroyloctopamine, N-p-coumaroyltyramine, and quercetin. The distribution of these three compounds was mapped for all samples by HPLC-ESI-HRMS. Unsupervised principal component analysis of samples from 2012 indicated that a major difference between fresh material and dried material is the increased amount of quercetin, a known α-glucosidase inhibitor.

Application of metabolic flux analysis to plants

This volume compiles a series of chapters that cover the major aspects of plant metabolic flux analysis, such as but not limited to labeling of plant material, acquisition of labeling data, mathematical modeling of metabolic network at the cell, tissue, and plant level. A short revue, including methodological points and applications of flux analysis to plants, is presented in this introductory chapter.

Analysis of metabolic flux using dynamic labelling and metabolic modelling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed.

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

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

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.

Eukaryotic metabolism: measuring compartment fluxes

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

Primary metabolism in the new human cell line AGE1.HN at various substrate levels: increased metabolic efficiency and α1-antitrypsin production at reduced pyruvate load

Metabolic responses of the new neuronal human cell line AGE1.HN to various substrate levels were analyzed in this study showing that reduced substrate and especially pyruvate load improves metabolic efficiency, leading to improved growth and α(1)-antitrypsin (A1AT) production. The adaptation of the metabolism to different pyruvate and glutamine concentrations was analyzed in detail using a full factorial design. The most important finding was an increasingly inefficient use of substrates as well as the reduction of cell proliferation with increasing pyruvate concentrations in the medium. Cultivations with different feeding profiles showed that the highest viable cell density and A1AT concentration (167% of batch) was reached in the culture with the lowest glucose level and without pyruvate feeding. Analysis of metabolic fluxes in the differently fed cultures revealed a more efficient metabolic phenotype in the cultures without pyruvate feeding. The measured in vitro enzyme activities of the selected enzymes involved in pyruvate metabolism were lower in AGE1.HN compared with CHO cells, which might explain the higher sensitivity and different adaptation of AGE1.HN to increased pyruvate concentrations. The results indicate on the one hand that increasing the connectivity between glycolysis and the TCA cycle might improve substrate use and, finally, the production of A1AT. On the other hand, a better balanced substrate uptake promises a reduction of energy spilling which is increased with increasing substrate levels in this cell line. Overall, the results of this study provide important insights into the regulation of primary metabolism and into the adaptation of AGE1.HN to different substrate levels, providing guidance for further optimization of production cell lines and applied process conditions.

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

A kinetic model describes metabolic response to perturbations and distribution of flux control in the benzenoid network of Petunia hybrida

In recent years there has been much interest in the genetic enhancement of plant metabolism; however, attempts at genetic modification are often unsuccessful due to an incomplete understanding of network dynamics and their regulatory properties. Kinetic modeling of plant metabolic networks can provide predictive information on network control and response to genetic perturbations, which allow estimation of flux at any concentration of intermediate or enzyme in the system. In this research, a kinetic model of the benzenoid network was developed to simulate whole network responses to different concentrations of supplied phenylalanine (Phe) in petunia flowers and capture flux redistributions caused by genetic manipulations. Kinetic parameters were obtained by network decomposition and non-linear least squares optimization of data from petunia flowers supplied with either 75 or 150 mm(2)H(5)-Phe. A single set of kinetic parameters simultaneously accommodated labeling and pool size data obtained for all endogenous and emitted volatiles at the two concentrations of supplied (2)H(5)-Phe. The generated kinetic model was validated using flowers from transgenic petunia plants in which benzyl CoA:benzyl alcohol/phenylethanol benzoyltransferase (BPBT) was down-regulated via RNAi. The determined in vivo kinetic parameters were used for metabolic control analysis, in which flux control coefficients were calculated for fluxes around the key branch point at Phe and revealed that phenylacetaldehyde synthase activity is the primary controlling factor for the phenylacetaldehyde branch of the benzenoid network. In contrast, control of flux through the beta-oxidative and non-beta-oxidative pathways is highly distributed.

Metabolic flux analysis in eukaryotes

Metabolic flux analysis (MFA) represents a powerful tool for systems biology research on eukaryotic cells. This review describes recent advances, the challenges as well as applications of metabolic flux analysis comprising fungi, mammalian cells and plants. While MFA is widely established and applied in microorganisms, it remains still a challenge to adapt these methods to eukaryotic cell systems having a higher complexity particularly concerning compartmentation or media composition. In fungi MFA was used in the past few years to analyze a variety of conditions and factors and their effects on cellular metabolism. In mammalian cells MFA was applied mainly in cell culture technology and in medical and toxicological research. (13)C metabolic studies on native whole plants are additionally challenging by the fact that CO(2) is usually the only carbon source.

Metabolomics for functional genomics, systems biology, and biotechnology

Metabolomics now plays a significant role in fundamental plant biology and applied biotechnology. Plants collectively produce a huge array of chemicals, far more than are produced by most other organisms; hence, metabolomics is of great importance in plant biology. Although substantial improvements have been made in the field of metabolomics, the uniform annotation of metabolite signals in databases and informatics through international standardization efforts remains a challenge, as does the development of new fields such as fluxome analysis and single cell analysis. The principle of transcript and metabolite cooccurrence, particularly transcriptome coexpression network analysis, is a powerful tool for decoding the function of genes in Arabidopsis thaliana. This strategy can now be used for the identification of genes involved in specific pathways in crops and medicinal plants. Metabolomics has gained importance in biotechnology applications, as exemplified by quantitative loci analysis, prediction of food quality, and evaluation of genetically modified crops. Systems biology driven by metabolome data will aid in deciphering the secrets of plant cell systems and their application to biotechnology.

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.

Bidirectionality and compartmentation of metabolic fluxes are revealed in the dynamics of isotopomer networks

Isotope labeling is one of the few methods of revealing the in vivo bidirectionality and compartmentalization of metabolic fluxes within metabolic networks. We argue that a shift from steady state to dynamic isotopomer analysis is required to deal with these cellular complexities and provide a review of dynamic studies of compartmentalized energy fluxes in eukaryotic cells including cardiac muscle, plants, and astrocytes. Knowledge of complex metabolic behaviour on a molecular level is prerequisite for the intelligent design of genetically modified organisms able to realize their potential of revolutionizing food, energy, and pharmaceutical production. We describe techniques to explore the bidirectionality and compartmentalization of metabolic fluxes using information contained in the isotopic transient, and discuss the integration of kinetic models with MFA. The flux parameters of an example metabolic network were optimized to examine the compartmentalization of metabolites and and the bidirectionality of fluxes in the TCA cycle of Saccharomyces uvarum for steady-state respiratory growth.

Modification of the biochemical pathways of plants induced by ozone: what are the varied routes to change?

When plants are observed under a low dose of ozone, some physiological and metabolic shifts occur. Barring extreme injury such as tissue damage or stomata closure, most of these disruptive changes are likely to have been initiated at the level of gene expression. The belief is oxidative products formed in ozone exposed leaves, e.g. hydrogen peroxide, are responsible for much of the biochemical adjustments. The first line of defense is a range of antioxidants, such as ascorbate and glutathione, but if this defense is overwhelmed, subsequent actions occur, similar to systemic acquired resistance or general wounding. Yet there are seemingly unrelated metabolic responses which are also triggered, such as early senescence. We discuss here the current understanding of gene control and signal transduction/control in order to increase our comprehension of how ozone alters the basic metabolism of plants and how plants counteract or cope with ozone.

Getting to grips with the plant metabolic network

Research into plant metabolism has a long history, and analytical approaches of ever-increasing breadth and sophistication have been brought to bear. We now have access to vast repositories of data concerning enzymology and regulatory features of enzymes, as well as large-scale datasets containing profiling information of transcripts, protein and metabolite levels. Nevertheless, despite this wealth of data, we remain some way off from being able to rationally engineer plant metabolism or even to predict metabolic responses. Within the past 18 months, rapid progress has been made, with several highly informative plant network interrogations being discussed in the literature. In the present review we will appraise the current state of the art regarding plant metabolic network analysis and attempt to outline what the necessary steps are in order to further our understanding of network regulation.

Metabolic flux analysis in plants using dynamic labeling technique: application to tryptophan biosynthesis in cultured rice cells

The concept and methodology of using dynamic labeling for the MFA of plant metabolic pathways are described, based on a case study to develop a method for the MFA of the tryptophan biosynthetic pathway in cultured rice cells. Dynamic labeling traces the change in the labeling level of a metabolite in a metabolic pathway after the application of a stable isotope-labeled compound. In this study, [1-(13)C] l-serine was fed as a labeling precursor and the labeling level of Trp was determined by using the LC-MS/MS. The value of metabolic flux is determined by fitting a model describing the labeling dynamics of the pathway to the observed labeling data. The biosynthetic flux of Trp in rice suspension cultured cell was determined to be 6.0+/-1.1 nmol (gFWh)(-1). It is also demonstrated that an approximately sixfold increase in the biosynthetic flux of Trp in transgenic rice cells expressing the feedback-insensitive version of anthranilate synthase alpha-subunit gene (OASA1D) resulted in a 45-fold increase in the level of Trp. In this article, the basic workflow for the experiment is introduced and the details of the actual experimental procedures are explained. Future perspectives are also discussed by referring recent advances in the dynamic labeling approach.

Fluxomics: mass spectrometry versus quantitative imaging

The recent development of analytic high-throughput technologies enables us to take a bird's view of how metabolism is regulated in real time. We have known for a long time that metabolism is highly regulated at all levels, including transcriptional, posttranslational and allosteric controls. Flux through a metabolic or signaling pathway is determined by the activity of its individual components. Fluxomics aims to define the genes involved in regulation by following the flux. Two technologies are used to monitor fluxes. Pulse labeling of the organism or cell with a tracer, such as 13C, followed by mass spectrometric analysis of the partitioning of label into different compounds provides an efficient tool to study flux and to compare the effect of mutations on flux. The second approach is based on the use of flux sensors, proteins that respond with a conformational change to ligand binding. Fluorescence resonance energy transfer (FRET) detects the conformational change and serves as a proxy for ligand concentration. In contrast to the mass spectrometry assays, FRET nanosensors monitor only a single compound. Both methods provide high time resolution. The major advantages of FRET nanosensors are that they yield data with cellular and subcellular resolution and the method is minimally invasive.