Quantification of compartmented metabolic fluxes in developing soybean embryos by employing biosynthetically directed fractional (13)C labeling, two-dimensional [(13)C, (1)H] nuclear magnetic resonance, and comprehensive isotopomer balancing

Metabolic fluxes in an illuminated Arabidopsis rosette

Photosynthesis is the basis for life, and its optimization is a key biotechnological aim given the problems of population explosion and environmental deterioration. We describe a method to resolve intracellular fluxes in intact Arabidopsis thaliana rosettes based on time-dependent labeling patterns in the metabolome. Plants photosynthesizing under limiting irradiance and ambient CO2 in a custom-built chamber were transferred into a (13)CO2-enriched environment. The isotope labeling patterns of 40 metabolites were obtained using liquid or gas chromatography coupled to mass spectrometry. Labeling kinetics revealed striking differences between metabolites. At a qualitative level, they matched expectations in terms of pathway topology and stoichiometry, but some unexpected features point to the complexity of subcellular and cellular compartmentation. To achieve quantitative insights, the data set was used for estimating fluxes in the framework of kinetic flux profiling. We benchmarked flux estimates to four classically determined flux signatures of photosynthesis and assessed the robustness of the estimates with respect to different features of the underlying metabolic model and the time-resolved data set.

Nuclear magnetic resonance methods for metabolic fluxomics

Fluxomics, through its core methodology of metabolic flux analysis (MFA), enables quantification of carbon traffic through cellular biochemical pathways. Isotope labeling experiments aid MFA by providing information on intracellular fluxes, especially through parallel and cyclic pathways. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are two complementary methods to measure abundances of isotopomers generated in these experiments. 2-D [(13)C, (1)H] heteronuclear correlation NMR spectra can detect (13)C isotopes coupled to protons and thus noninvasively separate molecules and atoms with a specific isotopic content from a mixture of molecular species. Furthermore, the fine structures of the peaks in these spectra can reveal scalar couplings between chemically bonded carbon atoms in the sample, from which isotopomer abundances can be quantified. This chapter introduces methods for NMR sample preparation and spectral acquisition of 2-D [(13)C, (1)H] correlation maps, followed by a detailed presentation of methods to process the spectra and quantify isotopomer abundances. We explain the use of the software NMRViewJ for spectral visualization and processing, as well as MATLAB scripts developed by us for peak extraction, deconvolution of overlapping peaklets, and isotopomer abundance quantification. Finally, we discuss the applications of NMR-derived isotopomer data toward quantitatively understanding metabolic pathways.

Metabolite analysis of biological fluids and tissues by proton nuclear magnetic resonance spectroscopy

NMR-based biochemical profiling of natural products has become popular due to the development of high-resolution instruments (>400 MHz) and cryogenically cooled probes/preamplifiers, by increasing the sensitivity of NMR instruments several fold and reducing instrument noise levels. NMR provides a rapid, nondestructive, high-throughput method that requires minimal sample preparation, therefore maintaining the biological integrity of the sample. One-dimensional (1D) solution-state (1)H NMR is used in untargeted sample screening (metabolomics/metabonomics) to gain insight into spectral pattern changes associated with samples of different origins. Metabolomics and metabonomics contextually explains the systematic and quantitative measurement of metabolites that are produced from the biochemical reactions of living systems. This chapter describes some commonly used (1)H NMR experiments for identification and quantification of small molecular weight, water soluble metabolites in biological samples, some considerations for choosing the correct NMR experiment, and sample preparation protocols for isolating metabolites from a number of biological sample types.

13CFLUX2--high-performance software suite for (13)C-metabolic flux analysis

Summary:¹³C-based metabolic flux analysis (¹³C-MFA) is the state-of-the-art method to quantitatively determine in vivo metabolic reaction rates in microorganisms. 13CFLUX2 contains all tools for composing flexible computational ¹³C-MFA workflows to design and evaluate carbon labeling experiments. A specially developed XML language, FluxML, highly efficient data structures and simulation algorithms achieve a maximum of performance and effectiveness. Support of multicore CPUs, as well as compute clusters, enables scalable investigations. 13CFLUX2 outperforms existing tools in terms of universality, flexibility and built-in features. Therewith, 13CFLUX2 paves the way for next-generation high-resolution ¹³C-MFA applications on the large scale.

Availability and implementation: 13CFLUX2 is implemented in C++ (ISO/IEC 14882 standard) with Java and Python add-ons to run under Linux/Unix. A demo version and binaries are available at www.13cflux.net.

Contact:info@13cflux.net or k.noeh@fz-juelich.de

Supplementary information:Supplementary data are available at Bioinformatics online.

An integrated computational and experimental study for overproducing fatty acids in Escherichia coli

Increasing demands for petroleum have stimulated sustainable ways to produce chemicals and biofuels. Specifically, fatty acids of varying chain lengths (C₆-C₁₆) naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids from plants and other microbial production hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways. In addition, precursors for fatty acids are used along other central metabolic pathways for the production of amino acids and biomass, which further complicates the engineering of microbial hosts for higher yields. Here, we demonstrate an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis techniques to meet this challenge. The OptForce procedure was used for suggesting and prioritizing genetic manipulations that overproduce fatty acids of different chain lengths from C₆ to C₁₆ starting with wild-type E. coli. We identified some common but mostly chain-specific genetic interventions alluding to the possibility of fine-tuning overproduction for specific fatty acid chain lengths. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose (∼39% maximum theoretical yield) of C₁₄₋₁₆ fatty acids in minimal M9 medium. These results highlight the benefit of using computational strain design and flux analysis tools in the design of recombinant strains of E. coli to produce free fatty acids.

Isotope labelling of Rubisco subunits provides in vivo information on subcellular biosynthesis and exchange of amino acids between compartments

The architecture of plant metabolism includes substantial duplication of metabolite pools and enzyme catalyzed reactions in different subcellular compartments. This poses challenges for understanding the regulation of metabolism particularly in primary metabolism and amino acid biosynthesis. To explore the extent to which amino acids are made in single compartments and to gain insight into the metabolic precursors from which they derive, we used steady state (13) C labelling and analysed labelling in protein amino acids from plastid and cytosol. Ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) is a major component of green tissues and its large and small subunits are synthesized from different pools of amino acids in the plastid and cytosol, respectively. Developing Brassica napus embryos were cultured in the presence of [U-(13) C]-sucrose, [U-(13) C]-glucose, [U-(13) C]-glutamine or [U-(13) C]-alanine to generate proteins. The large subunits (LSU) and small subunits (SSU) of Rubisco were isolated and the labelling in their constituent amino acids was analysed by gas chromatography-mass spectrometry. Amino acids including alanine, glycine and serine exhibited different (13) C enrichment in the LSU and SSU, demonstrating that these pools have different metabolic origins and are not isotopically equilibrated between the plastid and cytosol on the time scale of cellular growth. Potential extensions of this novel approach to other macromolecules, organelles and cell types of eukaryotes are discussed.

Global analysis of gene expression profiles in developing physic nut (Jatropha curcas L.) seeds

BACKGROUND

Physic nut (Jatropha curcas L.) is an oilseed plant species with high potential utility as a biofuel. Furthermore, following recent sequencing of its genome and the availability of expressed sequence tag (EST) libraries, it is a valuable model plant for studying carbon assimilation in endosperms of oilseed plants. There have been several transcriptomic analyses of developing physic nut seeds using ESTs, but they have provided limited information on the accumulation of stored resources in the seeds.

METHODOLOGY/PRINCIPAL FINDINGS

We applied next-generation Illumina sequencing technology to analyze global gene expression profiles of developing physic nut seeds 14, 19, 25, 29, 35, 41, and 45 days after pollination (DAP). The acquired profiles reveal the key genes, and their expression timeframes, involved in major metabolic processes including: carbon flow, starch metabolism, and synthesis of storage lipids and proteins in the developing seeds. The main period of storage reserves synthesis in the seeds appears to be 29-41 DAP, and the fatty acid composition of the developing seeds is consistent with relative expression levels of different isoforms of acyl-ACP thioesterase and fatty acid desaturase genes. Several transcription factor genes whose expression coincides with storage reserve deposition correspond to those known to regulate the process in Arabidopsis.

CONCLUSIONS/SIGNIFICANCE

The results will facilitate searches for genes that influence de novo lipid synthesis, accumulation and their regulatory networks in developing physic nut seeds, and other oil seeds. Thus, they will be helpful in attempts to modify these plants for efficient biofuel production.

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.

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.

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.

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.

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.

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.

Comparison between elementary flux modes analysis and 13C-metabolic fluxes measured in bacterial and plant cells

BACKGROUND

(13)C metabolic flux analysis is one of the pertinent ways to compare two or more physiological states. From a more theoretical standpoint, the structural properties of metabolic networks can be analysed to explore feasible metabolic behaviours and to define the boundaries of steady state flux distributions. Elementary flux mode analysis is one of the most efficient methods for performing this analysis. In this context, recent approaches have tended to compare experimental flux measurements with topological network analysis.

RESULTS

Metabolic networks describing the main pathways of central carbon metabolism were set up for a bacteria species (Corynebacterium glutamicum) and a plant species (Brassica napus) for which experimental flux maps were available. The structural properties of each network were then studied using the concept of elementary flux modes. To do this, coefficients of flux efficiency were calculated for each reaction within the networks by using selected sets of elementary flux modes. Then the relative differences - reflecting the change of substrate i.e. a sugar source for C. glutamicum and a nitrogen source for B. napus - of both flux efficiency and flux measured experimentally were compared. For both organisms, there is a clear relationship between these parameters, thus indicating that the network structure described by the elementary flux modes had captured a significant part of the metabolic activity in both biological systems. In B. napus, the extension of the elementary flux mode analysis to an enlarged metabolic network still resulted in a clear relationship between the change in the coefficients and that of the measured fluxes. Nevertheless, the limitations of the method to fit some particular fluxes are discussed.

CONCLUSION

This consistency between EFM analysis and experimental flux measurements, validated on two metabolic systems allows us to conclude that elementary flux mode analysis could be a useful tool to complement (13)C metabolic flux analysis, by allowing the prediction of changes in internal fluxes before carbon labelling experiments.

The remarkable diversity of plant PEPC (phosphoenolpyruvate carboxylase): recent insights into the physiological functions and post-translational controls of non-photosynthetic PEPCs

PEPC [PEP (phosphoenolpyruvate) carboxylase] is a tightly controlled enzyme located at the core of plant C-metabolism that catalyses the irreversible β-carboxylation of PEP to form oxaloacetate and Pi. The critical role of PEPC in assimilating atmospheric CO2 during C4 and Crassulacean acid metabolism photosynthesis has been studied extensively. PEPC also fulfils a broad spectrum of non-photosynthetic functions, particularly the anaplerotic replenishment of tricarboxylic acid cycle intermediates consumed during biosynthesis and nitrogen assimilation. An impressive array of strategies has evolved to co-ordinate in vivo PEPC activity with cellular demands for C4–C6 carboxylic acids. To achieve its diverse roles and complex regulation, PEPC belongs to a small multigene family encoding several closely related PTPCs (plant-type PEPCs), along with a distantly related BTPC (bacterial-type PEPC). PTPC genes encode ~110-kDa polypeptides containing conserved serine-phosphorylation and lysine-mono-ubiquitination sites, and typically exist as homotetrameric Class-1 PEPCs. In contrast, BTPC genes encode larger ~117-kDa polypeptides owing to a unique intrinsically disordered domain that mediates BTPC's tight interaction with co-expressed PTPC subunits. This association results in the formation of unusual ~900-kDa Class-2 PEPC hetero-octameric complexes that are desensitized to allosteric effectors. BTPC is a catalytic and regulatory subunit of Class-2 PEPC that is subject to multi-site regulatory phosphorylation in vivo. The interaction between divergent PEPC polypeptides within Class-2 PEPCs adds another layer of complexity to the evolution, physiological functions and metabolic control of this essential CO2-fixing plant enzyme. The present review summarizes exciting developments concerning the functions, post-translational controls and subcellular location of plant PTPC and BTPC isoenzymes.

Comparative proteomics of seed maturation in oilseeds reveals differences in intermediary metabolism

Proteomics is increasingly being used to understand enzyme expression and regulatory mechanisms involved in the accumulation of storage reserves in crops with sequenced genomes. During the past six years, considerable progress has been made to characterize proteomes of both mature and developing seeds, particularly oilseeds - plants which accumulate principally oil and protein as storage reserves. This review summarizes the emerging proteomics data, with emphasis on seed filling in soy, rapeseed, castor and Arabidopsis as each of these oilseeds were analyzed using very similar proteomic strategies. These parallel studies provide a comprehensive view of source-sink relationships, specifically sucrose assimilation into organic acid intermediates for de novo amino acid and fatty acid synthesis. We map these biochemical processes for seed maturation and illustrate the differences and similarities among the four oilseeds. For example, while the four oilseeds appear capable of producing cytosolic phosphoenolpyruvate as the principal carbon intermediate, soybean and castor also express malic enzymes and malate dehydrogenase, together capable of producing malate that has been previously proposed to be the major intermediate for fatty acid synthesis in castor. We discuss these and other differences in the context of intermediary metabolism for developing oilseeds.

Central metabolic fluxes in the endosperm of developing maize seeds and their implications for metabolic engineering

¹⁴C labeling experiments performed with kernel cultures showed that developing maize endosperm is more efficient than other non-photosynthetic tissues such as sunflower and maize embryos at converting maternally supplied substrates into biomass. To characterize the metabolic fluxes in endosperm, maize kernels were labeled to isotopic steady state using ¹³C-labeled glucose. The resultant labeling in free metabolites and biomass was analyzed by NMR and GC-MS. After taking into account the labeling of substrates supplied by the metabolically active cob, the fluxes through central metabolism were quantified by computer-aided modeling. The flux map indicates that 51-69% of the ATP produced is used for biomass synthesis and up to 47% is expended in substrate cycling. These findings point to potential engineering targets for improving yield and increasing oil contents by, respectively, reducing substrate cycling and increasing the commitment of plastidic carbon into fatty acid synthesis at the level of pyruvate kinase.

A genome-scale metabolic model accurately predicts fluxes in central carbon metabolism under stress conditions

Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by (13)C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.

Not just a circle: flux modes in the plant TCA cycle

The tricarboxylic acid (TCA) cycle is one of the iconic pathways in metabolism. The cycle is commonly thought of in terms of energy metabolism, being responsible for the oxidation of respiratory substrates to drive ATP synthesis. However, the reactions of carboxylic acid metabolism are embedded in a larger metabolic network and the conventional TCA cycle is only one way in which the component reactions can be organised. Recent evidence from labelling studies and metabolic network models suggest that the organisation of carboxylic acid metabolism in plants is highly dependent on the metabolic and physiological demands of the cell. Thus, alternative, non-cyclic flux modes occur in leaves in the light, in some developing oilseeds, and under specific physiological circumstances such as anoxia.

Metabolic analysis of wild-type Escherichia coli and a pyruvate dehydrogenase complex (PDHC)-deficient derivative reveals the role of PDHC in the fermentative metabolism of glucose

Pyruvate is located at a metabolic junction of assimilatory and dissimilatory pathways and represents a switch point between respiratory and fermentative metabolism. In Escherichia coli, the pyruvate dehydrogenase complex (PDHC) and pyruvate formate-lyase are considered the primary routes of pyruvate conversion to acetyl-CoA for aerobic respiration and anaerobic fermentation, respectively. During glucose fermentation, the in vivo activity of PDHC has been reported as either very low or undetectable, and the role of this enzyme remains unknown. In this study, a comprehensive characterization of wild-type E. coli MG1655 and a PDHC-deficient derivative (Pdh) led to the identification of the role of PDHC in the anaerobic fermentation of glucose. The metabolism of these strains was investigated by using a mixture of (13)C-labeled and -unlabeled glucose followed by the analysis of the labeling pattern in protein-bound amino acids via two-dimensional (13)C,(1)H NMR spectroscopy. Metabolite balancing, biosynthetic (13)C labeling of proteinogenic amino acids, and isotopomer balancing all indicated a large increase in the flux of the oxidative branch of the pentose phosphate pathway (ox-PPP) in response to the PDHC deficiency. Because both ox-PPP and PDHC generate CO(2) and the calculated CO(2) evolution rate was significantly reduced in Pdh, it was hypothesized that the role of PDHC is to provide CO(2) for cell growth. The similarly negative impact of either PDHC or ox-PPP deficiencies, and an even more pronounced impairment of cell growth in a strain lacking both ox-PPP and PDHC, provided further support for this hypothesis. The three strains exhibited similar phenotypes in the presence of an external source of CO(2), thus confirming the role of PDHC. Activation of formate hydrogen-lyase (which converts formate to CO(2) and H(2)) rendered the PDHC deficiency silent, but its negative impact reappeared in a strain lacking both PDHC and formate hydrogen-lyase. A stoichiometric analysis of CO(2) generation via PDHC and ox-PPP revealed that the PDHC route is more carbon- and energy-efficient, in agreement with its beneficial role in cell growth.

Mathematical modeling: bridging the gap between concept and realization in synthetic biology

Mathematical modeling plays an important and often indispensable role in synthetic biology because it serves as a crucial link between the concept and realization of a biological circuit. We review mathematical modeling concepts and methodologies as relevant to synthetic biology, including assumptions that underlie a model, types of modeling frameworks (deterministic and stochastic), and the importance of parameter estimation and optimization in modeling. Additionally we expound mathematical techniques used to analyze a model such as sensitivity analysis and bifurcation analysis, which enable the identification of the conditions that cause a synthetic circuit to behave in a desired manner. We also discuss the role of modeling in phenotype analysis such as metabolic and transcription network analysis and point out some available modeling standards and software. Following this, we present three case studies—a metabolic oscillator, a synthetic counter, and a bottom-up gene regulatory network—which have incorporated mathematical modeling as a central component of synthetic circuit design.

Moonlighting function of glycerol kinase causes systems-level changes in rat hepatoma cells

Glycerol kinase (GK) is an enzyme with diverse (moonlighting) cellular functions. GK overexpression affects central metabolic fluxes substantially; therefore, to elucidate the mechanism underlying these changes, we employed a systems-level evaluation of GK overexpression in H4IIE rat hepatoma cells. Microarray analysis revealed altered expression of genes in metabolism (central carbon and lipid), which correlated with previous flux analysis, and of genes regulated by the glucocorticoid receptor (GR). Oil Red O staining showed that GK overexpression leads to increased fat storage in H4IIE cells. Network component analysis revealed that activities of peroxisome proliferator-activated receptor alpha, GR, and seven other transcription factors were altered by GK overexpression. The increased activity of GR was experimentally verified by quantitative RT-PCR of GR-responsive genes in the presence and absence of the glucocorticoid agonist, dexamethasone. This systems biology approach further emphasizes GK's essential role in central and lipid metabolism and experimentally verifies GK's alternative (moonlighting) function of affecting GR transcription factor activity.

Understanding fatty acid synthesis in developing maize embryos using metabolic flux analysis

The efficiency with which developing maize embryos convert substrates into seed storage reserves was determined to be 57-71%, by incubating developing maize embryos with uniformly labeled 14C substrates and measuring their conversion to CO2 and biomass products. To map the pattern of metabolic fluxes underlying this efficiency, maize embryos were labeled to isotopic steady state using a combination of labeled 13C-substrates. Intermediary metabolic fluxes were estimated by computer-aided modeling of the central metabolic network using the labeling data collected by NMR and GC-MS and the biomass composition. The resultant flux map reveals that even though 36% of the entering carbon goes through the oxidative pentose-phosphate pathway, this does not fully meet the NADPH demands for fatty acid synthesis. Metabolic flux analysis and enzyme activities highlight the importance of plastidic NADP-dependent malic enzyme, which provides one-third of the carbon and NADPH required for fatty acid synthesis in developing maize embryos.

Plastidial glycolysis in developing Arabidopsis embryos

During oilseed embryo development, carbon from sucrose is utilized for fatty acid synthesis in the plastid. The role of plastidial glycolysis in Arabidopsis embryo oil accumulation was investigated. Genes encoding enolases (ENO) and phosphoglyceromutases (PGlyM) were identified, and activities and subcellular locations were established by expression of recombinant and green fluorescent protein (GFP)-fusion proteins. Mutant Arabidopsis plants lacking putative plastidial isoforms were characterized with respect to isoform composition and embryo oil content. In the developing embryo, ENO1 and ENO2 account for most or all of the plastidial and cytosolic ENO activity, respectively, and PGLYM1 accounts for most or all of the plastidial PGlyM activity. The eno1 and pglym1 mutants, in which plastidic ENO and PGlyM activities were undetectable, had wild-type amounts of seed oil at maturity. It is concluded that although plastids of developing Arabidopsis embryos have the capacity to carry out the lower part of the glycolytic pathway, the cytosolic glycolytic pathway alone is sufficient to support the flux from 3-phosphoglycerate to phosphoenolpyruvate required for oil production. The results highlight the importance for oil production of translocators that facilitate interchange of glycolytic intermediates between the cytosol and the plastid stroma.

Subcellular flux analysis of central metabolism in a heterotrophic Arabidopsis cell suspension using steady-state stable isotope labeling

The presence of cytosolic and plastidic pathways of carbohydrate oxidation is a characteristic feature of plant cell metabolism. Ideally, steady-state metabolic flux analysis, an emerging tool for creating flux maps of heterotrophic plant metabolism, would capture this feature of the metabolic phenotype, but the extent to which this can be achieved is uncertain. To address this question, fluxes through the pathways of central metabolism in a heterotrophic Arabidopsis (Arabidopsis thaliana) cell suspension culture were deduced from the redistribution of label in steady-state (13)C-labeling experiments using [1-(13)C]-, [2-(13)C]-, and [U-(13)C(6)]glucose. Focusing on the pentose phosphate pathway (PPP), multiple data sets were fitted simultaneously to models in which the subcellular compartmentation of the PPP was altered. The observed redistribution of the label could be explained by any one of three models of the subcellular compartmentation of the oxidative PPP, but other biochemical evidence favored the model in which the oxidative steps of the PPP were duplicated in the cytosol and plastids, with flux through these reactions occurring largely in the cytosol. The analysis emphasizes the inherent difficulty of analyzing the PPP without predefining the extent of its compartmentation and the importance of obtaining high-quality data that report directly on specific subcellular processes. The Arabidopsis flux map also shows that the potential ATP yield of respiration in heterotrophic plant cells can greatly exceed the direct metabolic requirements for biosynthesis, highlighting the need for caution when predicting flux through metabolic networks using assumptions based on the energetics of resource utilization.

Analysis of metabolic flux phenotypes for two Arabidopsis mutants with severe impairment in seed storage lipid synthesis

Major storage reserves of Arabidopsis (Arabidopsis thaliana) seeds are triacylglycerols (seed oils) and proteins. Seed oil content is severely reduced for the regulatory mutant wrinkled1 (wri1-1; At3g54320) and for a double mutant in two isoforms of plastidic pyruvate kinase (pkpbeta(1)pkpalpha; At5g52920 and At3g22960). Both already biochemically well-characterized mutants were now studied by (13)C metabolic flux analysis of cultured developing embryos based on comparison with their respective genetic wild-type backgrounds. For both mutations, in seeds as well as in cultured embryos, the oil fraction was strongly reduced while the fractions of proteins and free metabolites increased. Flux analysis in cultured embryos revealed changes in nutrient uptakes and fluxes into biomass as well as an increase in tricarboxylic acid cycle activity for both mutations. While in both wild types plastidic pyruvate kinase (PK(p)) provides most of the pyruvate for plastidic fatty acid synthesis, the flux through PK(p) is reduced in pkpbeta(1)pkpalpha by 43% of the wild-type value. In wri1-1, PK(p) flux is even more reduced (by 82%), although the genes PKpbeta(1) and PKpalpha are still expressed. Along a common paradigm of metabolic control theory, it is hypothesized that a large reduction in PK(p) enzyme activity in pkpbeta(1)pkpalpha has less effect on PK(p) flux than multiple smaller reductions in glycolytic enzymes in wri1-1. In addition, only in the wri1-1 mutant is the large reduction in PK(p) flux compensated in part by an increased import of cytosolic pyruvate and by plastidic malic enzyme. No such limited compensatory bypass could be observed in pkpbeta(1)pkpalpha.

Ensemble modeling for aromatic production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.

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.

The role of light in soybean seed filling metabolism

Soybean (Glycine max) yields high levels of both protein and oil, making it one of the most versatile and important crops in the world. Light has been implicated in the physiology of developing green seeds including soybeans but its roles are not quantitatively understood. We have determined the light levels reaching growing soybean embryos under field conditions and report detailed redox and energy balance analyses for them. Direct flux measurements and labeling patterns for multiple labeling experiments including [U-(13)C(6)]-glucose, [U-(13)C(5)]-glutamine, the combination of [U-(14)C(12)]-sucrose + [U-(14)C(6)]-glucose + [U-(14)C(5)]-glutamine + [U-(14)C(4)]-asparagine, or (14)CO2 labeling were performed at different light levels to give further insight into green embryo metabolism during seed filling and to develop and validate a flux map. Labeling patterns (protein amino acids, triacylglycerol fatty acids, starch, cell wall, protein glycan monomers, organic acids), uptake fluxes (glutamine, asparagine, sucrose, glucose), fluxes to biomass (protein amino acids, oil), and respiratory fluxes (CO2, O2) were established by a combination of gas chromatography-mass spectrometry, (13)C- and (1)H-NMR, scintillation counting, HPLC, gas chromatography-flame ionization detection, C:N and amino acid analyses, and infrared gas analysis, yielding over 750 measurements of metabolism. Our results show: (i) that developing soybeans receive low but significant light levels that influence growth and metabolism; (ii) a role for light in generating ATP but not net reductant during seed filling; (iii) that flux through Rubisco contributes to carbon conversion efficiency through generation of 3-phosphoglycerate; and (iv) a larger contribution of amino acid carbon to fatty acid synthesis than in other oilseeds analyzed to date.

Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate

A previously developed kinetic metabolic model for plant metabolism was used in a context of identification and control of intracellular phosphate (Pi) dynamics. Experimental data from batch flask cultures of Eschscholtiza californica cells was used to calibrate the model parameters for the slow dynamics (growth, nutrition, anabolic pathways, etc.). Perturbation experiments were performed using a perfusion small-scale bioreactor monitored by in vivo(31)P NMR. Parameter identification for Pi metabolism was done by measuring the cells dynamic response to different inputs for extracellular Pi (two pulse-response experiments and a step-response experiment). The calibrated model can describe Pi translocation between the cellular pools (vacuole and cytoplasm). The effect of intracellular Pi management on ATP/ADP and phosphomonoesters concentrations is also described by the model. The calibrated model is then used to develop a control strategy on the cytoplasmic Pi pool. From the identification of the systems dynamics, a proportional-integral controller was designed and tuned. The closed-loop control was implemented in the small-scale NMR bioreactor and experimental results were in accordance with model predictions. Thus, the calibrated model is able to predict cellular behaviour for phosphate metabolism and it was demonstrated that it is possible to control the intracellular level of cytoplasmic Pi in plant cells.

The oxygen status of the developing seed

Recent applications of oxygen-sensitive microsensors have demonstrated steep oxygen gradients in developing seeds of various crops. Here, we present an overview on oxygen distribution, major determinants of the oxygen status in the developing seed and implications for seed physiology. The steady-state oxygen concentration in different seed tissues depends on developmental parameters, and is determined to a large extent by environmental factors. Photosynthetic activity of the seed significantly diminishes hypoxic constraints, and can even cause transient, local hyperoxia. Changes in oxygen availability cause rapid adjustments in mitochondrial respiration and global metabolism. We argue that nitric oxide (NO) is a key player in the oxygen balancing process in seeds, avoiding fermentation and anoxia in vivo. Molecular approaches aiming to increase oxygen availability within the seed are discussed.

Lipid and protein accumulation in developing seeds of three lupine species: Lupinus luteus L., Lupinus albus L., and Lupinus mutabilis Sweet

A comparative study was carried out on the dynamics of lipid accumulation in developing seeds of three lupine species. Lupine seeds differ in lipid content; yellow lupine (Lupinus luteus L.) seeds contain about 6%, white lupine (Lupinus albus L.) 7-14%, and Andean lupine (Lupinus mutabilis Sweet) about 20% of lipids by dry mass. Cotyledons from developing seeds were isolated and cultured in vitro for 96 h on Heller medium with 60 mM sucrose (+S) or without sucrose (-S). Each medium was additionally enriched with 35 mM asparagine or 35 mM NaNO3. Asparagine caused an increase in protein accumulation and simultaneously decreased the lipid content, but nitrate increased accumulation of both protein and lipid. Experiments with [1-14C]acetate and [2-14C]acetate showed that the decrease in lipid accumulation in developing lupine seeds resulted from exhaustion of lipid precursors rather than from degradation or modification of the enzymatic apparatus. The carbon atom from the C-1 position of acetate was liberated mainly as CO2, whereas the carbon atom from the C-2 position was preferentially used in anabolic pathways. The dominant phospholipid in the investigated lupine seed storage organs was phosphatidylcholine. The main fatty acid in yellow lupine cotyledons was linoleic acid, in white lupine it was oleic acid, and in Andean lupine it was both linoleic and oleic acids. The relationship between stimulation of lipid and protein accumulation by nitrate in developing lupine cotyledons and enhanced carbon flux through glycolysis caused by the inorganic nitrogen form is discussed.

Metabolic network fluxes in heterotrophic Arabidopsis cells: stability of the flux distribution under different oxygenation conditions

Steady-state labeling experiments with [1-(13)C]Glc were used to measure multiple metabolic fluxes through the pathways of central metabolism in a heterotrophic cell suspension culture of Arabidopsis (Arabidopsis thaliana). The protocol was based on in silico modeling to establish the optimal labeled precursor, validation of the isotopic and metabolic steady state, extensive nuclear magnetic resonance analysis of the redistribution of label into soluble metabolites, starch, and protein, and a comprehensive set of biomass measurements. Following a simple modification of the cell culture procedure, cells were grown at two oxygen concentrations, and flux maps of central metabolism were constructed on the basis of replicated experiments and rigorous statistical analysis. Increased growth rate at the higher O(2) concentration was associated with an increase in fluxes throughout the network, and this was achieved without any significant change in relative fluxes despite differences in the metabolite profile of organic acids, amino acids, and carbohydrates. The balance between biosynthesis and respiration within the tricarboxylic acid cycle was unchanged, with 38% +/- 5% of carbon entering used for biosynthesis under standard O(2) conditions and 33% +/- 2% under elevated O(2). These results add to the emerging picture of the stability of the central metabolic network and its capacity to respond to physiological perturbations with the minimum of rearrangement. The lack of correlation between the change in metabolite profile, which implied significant disruption of the metabolic network following the alteration in the oxygen supply, and the unchanging flux distribution highlights a potential difficulty in the interpretation of metabolomic data.

Ensemble modeling of metabolic networks

Complete modeling of metabolic networks is desirable, but it is difficult to accomplish because of the lack of kinetics. As a step toward this goal, we have developed an approach to build an ensemble of dynamic models that reach the same steady state. The models in the ensemble are based on the same mechanistic framework at the elementary reaction level, including known regulations, and span the space of all kinetics allowable by thermodynamics. This ensemble allows for the examination of possible phenotypes of the network upon perturbations, such as changes in enzyme expression levels. The size of the ensemble is reduced by acquiring data for such perturbation phenotypes. If the mechanistic framework is approximately accurate, the ensemble converges to a smaller set of models and becomes more predictive. This approach bypasses the need for detailed characterization of kinetic parameters and arrives at a set of models that describes relevant phenotypes upon enzyme perturbations.

Metabolic flux maps comparing the effect of temperature on protein and oil biosynthesis in developing soybean cotyledons

Metabolic flux maps developed from 13C metabolic flux analysis (13C MFA) are effective tools for assessing the response of biological systems to genetic or environmental perturbations, and for identifying possible metabolic engineering targets. Experimental treatments were designed to distinguish between temperature effects prior to, and during incubation in vitro, on primary metabolism in developing soybeans. Biomass accumulation increased with temperature as did carbon partitioning into lipids. The flux through the plastidic oxidative pentose phosphate pathway (pgl(P)) relative to sucrose intake remained fairly constant [ approximately 56% (+/-24%)] when cotyledons were transferred from an optimum growth temperature to varying temperatures in in vitro culture, signifying a rigid node under these conditions. However, pgl(P) flux ranged from 57 to 77% of sucrose intake when growth temperature in planta varied and were cultured in vitro at the same temperature (as the plant), indicating a flexible node for this case. The carbon flux through the anaplerotic reactions catalysed by plastidic malic enzyme (me(P)), cytosolic phosphoenolpyruvate (PEP) carboxylase and the malate (Mal) transporter from the cytosol to mitochondrion varied dramatically with temperature and had a direct influence on the carbon partitioning into protein and oil from the plastidic pyruvate (Pyr) pool. These results of the in vitro culture indicate that temperature during early stages of development has a dominant effect on establishing capacity for flux through certain components of central carbon metabolism.

Global metabolic effects of glycerol kinase overexpression in rat hepatoma cells

Glycerol kinase has several diverse activities in mammalian cells. Glycerol kinase deficiency is a complex, single-gene, inborn error of metabolism wherein no genotype-phenotype correlation has been established. Since glycerol kinase has been suggested to exhibit additional activities than glycerol phosphorylation, expression level perturbation in this enzyme may affect cellular physiology globally. To investigate this possibility, we conducted metabolic investigations of wild-type and two glycerol kinase-overexpressing H4IIE rat hepatoma cell lines constructed in this study. The glycerol kinase-overexpressing cell lines exhibited a significantly higher consumption of carbon sources per cell, suggesting excess carbon expenditure. Furthermore, we quantified intracellular metabolic fluxes by employing stable isotope 13C labeling with a mathematically designed substrate mixture, gas chromatography-mass spectrometry, and comprehensive isotopomer balancing. This flux analysis revealed that the pentose phosphate pathway flux in the glycerol kinase-overexpressing cell lines was 2-fold higher than that in the wild-type, in addition to subtler flux changes in other pathways of carbohydrate metabolism. Furthermore, the activity and transcript level of the lipogenic enzyme glucose-6-phosphate dehydrogenase, the rate-limiting enzyme of the pentose phosphate pathway, were also about 2-fold higher than that of the wild-type; these data corroborate the flux analysis results. This study shows that glycerol kinase affects carbon metabolism globally, possibly through its additional functions, and highlights glycerol kinase's multifaceted role in cellular physiology.

Metabolic flux analysis in plants: from intelligent design to rational engineering

Metabolic flux analysis (MFA) is a rapidly developing field concerned with the quantification and understanding of metabolism at the systems level. The application of MFA has produced detailed maps of flow through metabolic networks of a range of plant systems. These maps represent detailed metabolic phenotypes, contribute significantly to our understanding of metabolism in plants, and have led to the discovery of new metabolic routes. The presentation of thorough statistical evaluation with current flux maps has set a new standard for the quality of quantitative flux studies. In microbial systems, powerful methods have been developed for the reconstruction of metabolic networks from genomic and transcriptomic data, pathway analysis, and predictive modeling. This review brings together the recent developments in quantitative MFA and predictive modeling. The application of predictive tools to high quality flux maps in particular promises to be important in the rational metabolic engineering of plants.

Carbon conversion efficiency and central metabolic fluxes in developing sunflower (Helianthus annuus L.) embryos

The efficiency with which developing sunflower embryos convert substrates into seed storage reserves was determined by labeling embryos with [U-(14)C6]glucose or [U-(14)C5]glutamine and measuring their conversion to CO2, oil, protein and other biomass compounds. The average carbon conversion efficiency was 50%, which contrasts with a value of over 80% previously observed in Brassica napus embryos (Goffman et al., 2005), in which light and the RuBisCO bypass pathway allow more efficient conversion of hexose to oil. Labeling levels after incubating sunflower embryos with [U-(14)C4]malate indicated that some carbon from malate enters the plastidic compartment and contributes to oil synthesis. To test this and to map the underlying pattern of metabolic fluxes, separate experiments were carried out in which embryos were labeled to isotopic steady state using [1-(13)C1]glucose, [2-(13)C1]glucose, or [U-(13)C5]glutamine. The resultant labeling in sugars, starch, fatty acids and amino acids was analyzed by NMR and GC-MS. The fluxes through intermediary metabolism were then quantified by computer-aided modeling. The resulting flux map accounted well for the labeling data, was in good agreement with the observed carbon efficiency, and was further validated by testing for agreement with gas exchange measurements. The map shows that the influx of malate into oil is low and that flux through futile cycles (wasting ATP) is low, which contrasts with the high rates previously determined for growing root tips and heterotrophic cell cultures.

Identification of hexose hydrolysis products in metabolic flux analytes: a case study of levulinic acid in plant protein hydrolysate

Biosynthetically directed fractional (13)C labeling, a popular methodology of metabolic flux analysis, involves culture on a mixture of (13)C and (12)C substrates and preparation a 'metabolic flux analyte' (typically protein hydrolysate) from the biomass. Metabolic flux analytes prepared from complex eukaryotes may contain additional compounds than those prepared from microorganisms. We report the presence of such compounds (hexose hydrolysis products) in a plant metabolic flux analyte (acid hydrolyzed protein from soybean embryos). We designed NMR experiments to systematically identify these compounds, and found that they were levulinic acid (LVA; major) and hydroxyacetone (HyA; minor). These acid hydrolysis products of hexoses (glucose and mannose) were generated during acid hydrolysis of glycosylating sugars (glucosamine and mannose) associated with soybean embryo protein. Analysis of LVA by two-dimensional [(13)C, (1)H] NMR and measurement of its J-coupling constants revealed long-range coupling between atoms C3 and C5, which enables LVA to provide more isotopomer information than its precursor hexose. Furthermore, we found that LVA and HyA preserve the isotopomeric composition of the metabolic hexose from which they are derived. An important consequence of these results is that comparison of LVA and HyA isotopomers from two separate metabolic flux analytes (protein hydrolysate and starch hydrolysate) from the same plant tissue can distinguish between parallel glycolysis and pentose phosphate pathways in different subcellular compartments.

Flux quantification in central carbon metabolism of Catharanthus roseus hairy roots by 13C labeling and comprehensive bondomer balancing

Methods for accurate and efficient quantification of metabolic fluxes are desirable in plant metabolic engineering and systems biology. Toward this objective, we introduce the application of "bondomers", a computationally efficient and intuitively appealing alternative to the commonly used isotopomer concept, to flux evaluation in plants, by using Catharanthus roseus hairy roots as a model system. We cultured the hairy roots on (5% w/w U-(13)C, 95% w/w naturally abundant) sucrose, and acquired two-dimensional [(13)C, (1)H] and [(1)H, (1)H] NMR spectra of hydrolyzed aqueous extract from the hairy roots. Analysis of these spectra yielded a data set of 116 bondomers of beta-glucans and proteinogenic amino acids from the hairy roots. Fluxes were evaluated from the bondomer data by using comprehensive bondomer balancing. We identified most fluxes in a three-compartmental model of central carbon metabolism with good precision. We observed parallel pentose phosphate pathways in the cytosol and the plastid with significantly different fluxes. The anaplerotic fluxes between phosphoenolpyruvate and oxaloacetate in the cytosol and between malate and pyruvate in the mitochondrion were relatively high (60.1+/-2.5 mol per 100 mol sucrose uptake, or 22.5+/-0.5 mol per 100 mol mitochondrial pyruvate dehydrogenase flux). The development of a comprehensive flux analysis tool for this plant hairy root system is expected to be valuable in assessing the metabolic impact of genetic or environmental changes, and this methodology can be extended to other plant systems.

In vivo 13C NMR determines metabolic fluxes and steady state in linseed embryos

The dynamics of developing linseed embryo metabolism was investigated using (13)C-labelling experiments where the real-time kinetics of label incorporation into metabolites was monitored in situ using in vivo NMR. The approach took advantage of the occurrence in this plant tissue of large metabolite pools - such as sucrose or lipids - to provide direct and quantitative measurement of the evolution of the labelling state within central metabolism. As a pre-requisite for the use of steady state flux measurements it was shown that isotopic steady state was reached within 3 h at the level of central intermediates whereas it took a further 6h for the sucrose pool. Complete isotopic and metabolic steady state took 18 h to be reached. The data collected during the transient state where label was equilibrated but the metabolic steady state was incomplete, enabled the rates of lipid and sucrose synthesis to be measured in situ on the same sample. This approach is suitable to get a direct assessment of metabolic time-scales within living plant tissues and provides a valuable complement to steady state flux determinations.