Interpreting metabolic complexity via isotope-assisted metabolic flux analysis.

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.

Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness.

Stable isotope-assisted metabolic flux analysis (MFA) is a powerful method to estimate carbon flow and partitioning in metabolic networks. At its core, MFA is a parameter estimation problem wherein the fluxes and metabolite pool sizes are model parameters that are estimated, via optimization, to account for measurements of steady-state or isotopically-nonstationary isotope labeling patterns. As MFA problems advance in scale, they require efficient computational methods for fast and robust convergence. The structure of the MFA problem enables it to be cast as an equality-constrained nonlinear program (NLP), where the equality constraints are constructed from the MFA model equations, and the objective function is defined as the sum of squared residuals (SSR) between the model predictions and a set of labeling measurements. This NLP can be solved by using an algebraic modeling language (AML) that offers state-of-the-art optimization solvers for robust parameter estimation and superior scalability to large networks. When implemented in this manner, the optimization is performed with no distinction between state variables and model parameters. During each iteration of such an optimization, the system state is updated instead of being calculated explicitly from scratch, and this occurs concurrently with improvement in the model parameter estimates. This optimization approach starkly contrasts with traditional "shooting" methods where the state variables and model parameters are kept distinct and the system state is computed afresh during each iteration of a stepwise optimization. Our NLP formulation uses the MFA modeling framework of Wiechert et al. [1], which is amenable to incorporation of the model equations into an NLP. The NLP constraints consist of balances on either elementary metabolite units (EMUs) or cumomers. In this formulation, both the steady-state and isotopically-nonstationary MFA (inst-MFA) problems may be solved as an NLP. For the inst-MFA case, the ordinary differential equation (ODE) system describing the labeling dynamics is transcribed into a system of algebraic constraints for the NLP using collocation. This large-scale NLP may be solved efficiently using an NLP solver implemented on an AML. In our implementation, we used the reduced gradient solver CONOPT, implemented in the General Algebraic Modeling System (GAMS). The NLP framework is particularly advantageous for inst-MFA, scaling well to large networks with many free parameters, and having more robust convergence properties compared to the shooting methods that compute the system state and sensitivities at each iteration. Additionally, this NLP approach supports the use of tandem-MS data for both steady-state and inst-MFA when the cumomer framework is used. We assembled a software, eiFlux, written in Python and GAMS that uses the NLP approach and supports both steady-state and inst-MFA. We demonstrate the effectiveness of the NLP formulation on several examples, including a genome-scale inst-MFA model, to highlight the scalability and robustness of this approach. In addition to typical inst-MFA applications, we expect that this framework and our associated software, eiFlux, will be particularly useful for applying inst-MFA to complex MFA models, such as those developed for eukaryotes (e.g. algae) and co-cultures with multiple cell types.

Bacterial diketopiperazines stimulate diatom growth and lipid accumulation.

Diatoms are photosynthetic microalgae that fix a significant fraction of the world's carbon. Because of their photosynthetic efficiency and high-lipid content, diatoms are priority candidates for biofuel production. Here, we report that sporulating Bacillus thuringiensis and other members of the Bacillus cereus group, when in co-culture with the marine diatom Phaeodactylum tricornutum, significantly increase diatom cell count. Bioassay-guided purification of the mother cell lysate of B. thuringiensis led to the identification of two diketopiperazines (DKPs) that stimulate both P. tricornutum growth and increase its lipid content. These findings may be exploited to enhance P. tricornutum growth and microalgae-based biofuel production. As increasing numbers of DKPs are isolated from marine microbes, the work gives potential clues to bacterial-produced growth factors for marine microalgae.

NetRed, an algorithm to reduce genome-scale metabolic networks and facilitate the analysis of flux predictions.

Flux balance analysis (FBA) of large, genome-scale stoichiometric models (GSMs) is a powerful and popular method to predict cell-wide metabolic activity. FBA typically generates a flux vector containing O(1,000) fluxes. The interpretation of such a flux vector is difficult, even for expert users, because of the large size and complex topology of the underlying metabolic network. This interpretation could be simplified by condensing the network to a reduced, yet fully representative version. Toward this goal we report NetRed, an algorithm that systematically reduces a stoichiometric matrix and a corresponding flux vector to a more easily interpretable form. The reduction offered by NetRed is transparent because it relies purely on matrix algebra and not on optimization. Uniquely, it involves zero information loss; therefore, the original unreduced network can be easily recovered from the reduced network. The inputs to NetRed are (i) a stoichiometric matrix, (ii) a flux vector with numerical flux values, and (iii) a list of "protected" metabolites recommended by the user to remain in the reduced network. NetRed outputs a reduced metabolic network containing a reduced number of metabolites, of which the protected metabolites are a subset. The algorithm also generates a corresponding reduced flux vector. Due to its simplified presentation and easier interpretability, the reduced network allows the user to quickly find fluxes through metabolites and reaction modes or pathways of interest. In this manuscript, we first demonstrate NetRed on a simple network consisting of glycolysis and the pentose phosphate pathway (PPP), wherein NetRed reduced the PPP to a single net reaction. We followed this with applications of NetRed to E. coli and yeast GSMs. NetRed reduced the size of an E. coli GSM by 20- to 30-fold and enabled a comprehensive comparison of aerobic and anaerobic metabolism. The application of NetRed to a yeast GSM allowed for easy mechanistic interpretation of a double-gene knockout that rerouted flux toward dihydroartemisinic acid. When applied to an E. coli strain engineered for enhanced valine production, NetRed allowed for a holistic interpretation of the metabolic rerouting resulting from multiple genetic interventions.

Nanoparticle technology improves in-vitro attachment of cattle (Bos taurus) trophectoderm cells.

The bovine cell line, cow trophectoderm-1 (CT-1), provides an excellent in-vitro cell culture model to study early embryonic development. Obtaining consistent attachment and outgrowth, however, is difficult because enzymatic disassociation into single cells is detrimental; therefore, CT-1 cells must be passaged in clumps, which do not attach readily to the surface of the dish. We tested whether magnetic nanoparticles, NanoShuttle™-PL, could be used to improve cell attachment and subsequent proliferation of the cattle trophectoderm cell line without altering cellular metabolism or immunofluorescent detection of the lineage marker Caudal Type Homeobox 2 (CDX2). Confluency was achieved more consistently by using the NanoShuttle™-PL system to magnetically force attachment (75-100% of wells) as compared to the control (11%). Moreover, there were no alterations in characteristic morphology, nuclear-localized expression of the trophectoderm marker CDX2, or glycolytic metabolism. By enhancing attachment, magnetic nanoparticles improved culture efficiency and reproducibility in an anchorage-dependent cell line that otherwise was recalcitrant to efficient passaging.

Concurrent isotope-assisted metabolic flux analysis and transcriptome profiling reveal responses of poplar cells to altered nitrogen and carbon supply.

Reduced nitrogen is indispensable to plants. However, its limited availability in soil combined with the energetic and environmental impacts of nitrogen fertilizers motivates research into molecular mechanisms toward improving plant nitrogen use efficiency (NUE). We performed a systems-level investigation of this problem by employing multiple 'omics methodologies on cell suspensions of hybrid poplar (Populus tremula × Populus alba). Acclimation and growth of the cell suspensions in four nutrient regimes ranging from abundant to deficient supplies of carbon and nitrogen revealed that cell growth under low-nitrogen levels was associated with substantially higher NUE. To investigate the underlying metabolic and molecular mechanisms, we concurrently performed steady-state 13 C metabolic flux analysis with multiple isotope labels and transcriptomic profiling with cDNA microarrays. The 13 C flux analysis revealed that the absolute flux through the oxidative pentose phosphate pathway (oxPPP) was substantially lower (~threefold) under low-nitrogen conditions. Additionally, the flux partitioning ratio between the tricarboxylic acid cycle and anaplerotic pathways varied from 84%:16% under abundant carbon and nitrogen to 55%:45% under deficient carbon and nitrogen. Gene expression data, together with the flux results, suggested a plastidic localization of the oxPPP as well as transcriptional regulation of certain metabolic branchpoints, including those between glycolysis and the oxPPP. The transcriptome data also indicated that NUE-improving mechanisms may involve a redirection of excess carbon to aromatic metabolic pathways and extensive downregulation of potentially redundant genes (in these heterotrophic cells) that encode photosynthetic and light-harvesting proteins, suggesting the recruitment of these proteins as nitrogen sinks in nitrogen-abundant conditions.

The ATP-stimulated translocation promoter (ASTP) activity of glycerol kinase plays central role in adipogenesis.

Glycerol kinase (GK) is a multifunctional enzyme located at the interface of carbohydrate and fat metabolism. It contributes to both central carbon metabolism and adipogenesis; specifically, through its role as the ATP-stimulated translocation promoter (ASTP). GK overexpression leads to increased ASTP activity and increased fat storage in H4IIE cells. We performed metabolic flux analysis in human GK-overexpressing H4IIE cells and found that overexpressing cells had significantly altered fluxes through central carbon and lipid metabolism including increased flux through the pentose phosphate pathway and increased production of lipids. We also observed an equal contribution of glycerol to carbohydrate metabolism in all cell lines, suggesting that GK's alternate functions rather than its enzymatic function are important for these processes. To further elucidate the contributions of the enzymatic (phosphorylation) and alternative (ASTP) functions of GK in adipogenesis, we performed experiments on mammalian GK and E. coli GK. We determined that the ASTP function of GK (which is absent in E. coli GK) plays a greater role than the enzymatic activity in these processes. These studies further emphasize GK's diverse functionality and provides fundamental insights into the multiple protein functions of glycerol kinase.

Insulin sensitivity predictions in individuals with obesity and type II diabetes mellitus using mathematical model of the insulin signal transduction pathway.

Mathematical modeling approaches have been commonly used in complex signaling pathway studies such as the insulin signal transduction pathway. Our expanded mathematical model of the insulin signal transduction pathway was previously shown to effectively predict glucose clearance rates using mRNA levels of key components of the pathway in a mouse model. In this study, we re-optimized and applied our expanded model to study insulin sensitivity in other species and tissues (human skeletal muscle) with altered protein activities of insulin signal transduction pathway components. The model has now been optimized to predict the effect of short term exercise on insulin sensitivity for human test subjects with obesity or type II diabetes mellitus. A comparison between our extended model and the original model showed that our model better simulates the GLUT4 translocation events of the insulin signal transduction pathway and glucose uptake as a clinically relevant model output. Results from our extended model correlate with O'Gorman's published in-vivo results. This study demonstrates the ability to adapt this model to study insulin sensitivity to many biological systems (human skeletal muscle and mouse liver) with minimal changes in the model parameters.

Proteomics of nitrogen remobilization in poplar bark.

Seasonal nitrogen (N) cycling in temperate deciduous trees involves the accumulation of bark storage proteins (BSPs) in phloem parenchyma and xylem ray cells. BSPs are anabolized using recycled N during autumn leaf senescence and later become a source of N during spring shoot growth as they are catabolized. Little is known about the catabolic processes involved in remobilization and reutilization of N from BSPs in trees. In this study, we used multidimensional protein identification technology (MudPIT) and spectral counting to identify protein changes that occur in the bark during BSP catabolism. A total of 4,178 proteins were identified from bark prior to and during BSP catabolism. The majority (62%) of the proteins were found during BSP catabolism, indicating extensive remodeling of the proteome during renewed shoot growth and N remobilization. Among these proteins were 30 proteases, the relative abundances of which increased during BSP catabolism. These proteases spanned a range of families including members of the papain-like cysteine proteases, serine carboxypeptidases, and aspartyl proteases. These data identify, for the first time, candidate proteases that could potentially provide hydrolase activity required for N remobilization from BSPs and provide the foundation for research to advance our knowledge of poplar N cycling.

Mathematical modeling of the insulin signal transduction pathway for prediction of insulin sensitivity from expression data.

Mathematical models of biological pathways facilitate a systems biology approach to medicine. However, these models need to be updated to reflect the latest available knowledge of the underlying pathways. We developed a mathematical model of the insulin signal transduction pathway by expanding the last major previously reported model and incorporating pathway components elucidated since the original model was reported. Furthermore, we show that inputting gene expression data of key components of the insulin signal transduction pathway leads to sensible predictions of glucose clearance rates in agreement with reported clinical measurements. In one set of simulations, our model predicted that glycerol kinase knockout mice have reduced GLUT4 translocation, and consequently, reduced glucose uptake. Additionally, a comparison of our extended model with the original model showed that the added pathway components improve simulations of glucose clearance rates. We anticipate this expanded model to be a useful tool for predicting insulin sensitivity in mammalian tissues with altered expression protein phosphorylation or mRNA levels of insulin signal transduction pathway components.

Experimental evidence and isotopomer analysis of mixotrophic glucose metabolism in the marine diatom Phaeodactylum tricornutum.

BACKGROUND: Heterotrophic fermentation using simple sugars such as glucose is an established and cost-effective method for synthesizing bioproducts from bacteria, yeast and algae. Organisms incapable of metabolizing glucose have limited applications as cell factories, often despite many other advantageous characteristics. Therefore, there is a clear need to investigate glucose metabolism in potential cell factories. One such organism, with a unique metabolic network and a propensity to synthesize highly reduced compounds as a large fraction of its biomass, is the marine diatom Phaeodactylum tricornutum (Pt). Although Pt has been engineered to metabolize glucose, conflicting lines of evidence leave it unresolved whether Pt can natively consume glucose. RESULTS: Isotope labeling experiments in which Pt was mixotrophically grown under light on 100% U-(13)C glucose and naturally abundant (~99% (12)C) dissolved inorganic carbon resulted in proteinogenic amino acids with an average 13C-enrichment of 88%, thus providing convincing evidence of glucose uptake and metabolism. The dissolved inorganic carbon was largely incorporated through anaplerotic rather than photosynthetic fixation. Furthermore, an isotope labeling experiment utilizing 1-(13)C glucose and subsequent metabolic pathway analysis indicated that (i) the alternative Entner-Doudoroff and Phosphoketolase glycolytic pathways are active during glucose metabolism, and (ii) during mixotrophic growth, serine and glycine are largely synthesized from glyoxylate through photorespiratory reactions rather than from 3-phosphoglycerate. We validated the latter result for mixotrophic growth on glycerol by performing a 2-(13)C glycerol isotope labeling experiment. Additionally, gene expression assays showed that known, native glucose transporters in Pt are largely insensitive to glucose or light, whereas the gene encoding cytosolic fructose bisphosphate aldolase 3, an important glycolytic enzyme, is overexpressed in light but insensitive to glucose. CONCLUSION: We have shown that Pt can use glucose as a primary carbon source when grown in light, but cannot use glucose to sustain growth in the dark. We further analyzed the metabolic mechanisms underlying the mixotrophic metabolism of glucose and found isotopic evidence for unusual pathways active in Pt. These insights expand the envelope of Pt cultivation methods using organic substrates. We anticipate that they will guide further engineering of Pt towards sustainable production of fuels, pharmaceuticals, and platform chemicals.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease.

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks.

Genome-scale stoichiometric models (GSMs) have been widely utilized to predict and understand cellular metabolism. GSMs and the flux predictions resulting from them have proven indispensable to fields ranging from metabolic engineering to human disease. Nonetheless, it is challenging to parse these flux predictions due to the inherent size and complexity of the GSMs. Several previous approaches have reduced this complexity by identifying key pathways contained within the genome-scale flux predictions. However, a reduction method that overlays carbon atom transitions on stoichiometry and flux predictions is lacking. To fill this gap, we developed NetFlow, an algorithm that leverages genome-scale carbon mapping to extract and quantitatively distinguish biologically relevant metabolic pathways from a given genome-scale flux prediction. NetFlow extends prior approaches by utilizing both full carbon mapping and context-specific flux predictions. Thus, NetFlow is uniquely able to quantitatively distinguish between biologically relevant pathways of carbon flow within the given flux map. NetFlow simulates 13C isotope labeling experiments to calculate the extent of carbon exchange, or carbon yield, between every metabolite in the given GSM. Based on the carbon yield, the carbon flow to or from any metabolite or between any pair of metabolites of interest can be isolated and readily visualized. The resulting pathways are much easier to interpret, which enables an in-depth mechanistic understanding of the metabolic phenotype of interest. Here, we first demonstrate NetFlow with a simple network. We then depict the utility of NetFlow on a model of central carbon metabolism in E. coli. Specifically, we isolated the production pathway for succinate synthesis in this model and the metabolic mechanism driving the predicted increase in succinate yield in a double knockout of E. coli. Finally, we describe the application of NetFlow to a GSM of lycopene-producing E. coli, which enabled the rapid identification of the mechanisms behind the measured increases in lycopene production following single, double, and triple knockouts.

13C Metabolic Flux Analysis Indicates Endothelial Cells Attenuate Metabolic Perturbations by Modulating TCA Activity.

Disrupted endothelial metabolism is linked to endothelial dysfunction and cardiovascular disease. Targeted metabolic inhibitors are potential therapeutics; however, their systemic impact on endothelial metabolism remains unknown. In this study, we combined stable isotope labeling with 13C metabolic flux analysis (13C MFA) to determine how targeted inhibition of the polyol (fidarestat), pentose phosphate (DHEA), and hexosamine biosynthetic (azaserine) pathways alters endothelial metabolism. Glucose, glutamine, and a four-carbon input to the malate shuttle were important carbon sources in the baseline human umbilical vein endothelial cell (HUVEC) 13C MFA model. We observed two to three times higher glutamine uptake in fidarestat and azaserine-treated cells. Fidarestat and DHEA-treated HUVEC showed decreased 13C enrichment of glycolytic and TCA metabolites and amino acids. Azaserine-treated HUVEC primarily showed 13C enrichment differences in UDP-GlcNAc. 13C MFA estimated decreased pentose phosphate pathway flux and increased TCA activity with reversed malate shuttle direction in fidarestat and DHEA-treated HUVEC. In contrast, 13C MFA estimated increases in both pentose phosphate pathway and TCA activity in azaserine-treated cells. These data show the potential importance of endothelial malate shuttle activity and suggest that inhibiting glycolytic side branch pathways can change the metabolic network, highlighting the need to study systemic metabolic therapeutic effects.

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.

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.

Transcriptomic and network component analysis of glycerol kinase in skeletal muscle using a mouse model of glycerol kinase deficiency.

Glycerol kinase (GK) is at the interface of fat and carbohydrate metabolism and has been linked to obesity and type 2 diabetes mellitus (T2DM). The purpose of this study was to investigate the role of GK in fat metabolism and insulin signaling in skeletal muscle (an important end organ tissue in T2DM). Microarray analysis determined that there were 525 genes that were differentially expressed (1.2-fold, p value<0.05) between knockout (KO) and wild-type (WT) mice. Quantitative PCR (qPCR) confirmed the differential expression of genes including glycerol kinase (Gyk), phosphatidylinositol 3-kinase regulatory subunit, polypeptide 1 (p85 alpha) (Pik3r1), insulin-like growth factor 1 (Igf1), and growth factor receptor bound protein 2-associated protein 1 (Gab1). Network component analysis demonstrated that transcription factor activities of myogenic differentiation 1 (MYOD), myogenic regulatory factor 5 (MYF5), myogenin (MYOG), nuclear receptor subfamily 4, group A, member 1 (NUR77) are decreased in the Gyk KO whereas the activity of paired box 3 (PAX3) is increased. The activity of MYOD was confirmed using a DNA binding assay. In addition, myoblasts from Gyk KO had less ability to differentiate into myotubes compared to WT myoblasts. These findings support our previous studies in brown adipose tissue and demonstrate that the role of Gyk in muscle is due in part to its non-metabolic (moonlighting) activities.

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 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.

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.