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

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

Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems

The elucidation of organism-scale metabolic networks necessitates the development of integrative methods to analyze and interpret the systemic properties of cellular metabolism. A shift in emphasis from single metabolic reactions to systemically defined pathways is one consequence of such an integrative analysis of metabolic systems. The constraints of systemic stoichiometry, and limited thermodynamics have led to the definition of the flux space within the context of convex analysis. The flux space of the metabolic system, containing all allowable flux distributions, is constrained to a convex polyhedral cone in a high-dimensional space. From metabolic pathway analysis, the edges of the high-dimensional flux cone are vectors that correspond to systemically defined "extreme pathways" spanning the capabilities of the system. The addition of maximum flux capacities of individual metabolic reactions serves to further constrain the flux space and has led to the development of flux balance analysis using linear optimization to calculate optimal flux distributions. Here we provide the precise theoretical connections between pathway analysis and flux balance analysis allowing for their combined application to study integrated metabolic function. Shifts in metabolic behavior are calculated using linear optimization and are then interpreted using the extreme pathways to demonstrate the concept of pathway utilization. Changes to the reaction network, such as the removal of a reaction, can lead to the generation of suboptimal phenotypes that can be directly attributed to the loss of pathway function and capabilities. Optimal growth phenotypes are calculated as a function of environmental variables, such as the availability of substrate and oxygen, leading to the definition of phenotypic phase planes. It is illustrated how optimality properties of the computed flux distributions can be interpreted in terms of the extreme pathways. Together these developments are applied to an example network and to core metabolism of Escherichia coli demonstrating the connections between the extreme pathways, optimal flux distributions, and phenotypic phase planes. The consequences of changing environmental and internal conditions of the network are examined for growth on glucose and succinate in the face of a variety of gene deletions. The convergence of the calculation of optimal phenotypes through linear programming and the definition of extreme pathways establishes a different perspective for the understanding of how a defined metabolic network is best used under different environmental and internal conditions or, in other words, a pathway basis for the interpretation of the metabolic reaction norm.     

Novel proteins, putative membrane transporters, and an integrated metabolic network are revealed by quantitative proteomic analysis of Arabidopsis cell culture peroxisomes

Peroxisomes play key roles in energy metabolism, cell signaling, and plant development. A better understanding of these important functions will be achieved with a more complete definition of the peroxisome proteome. The isolation of peroxisomes and their separation from mitochondria and other major membrane systems have been significant challenges in the Arabidopsis (Arabidopsis thaliana) model system. In this study, we present new data on the Arabidopsis peroxisome proteome obtained using two new technical advances that have not previously been applied to studies of plant peroxisomes. First, we followed density gradient centrifugation with free-flow electrophoresis to improve the separation of peroxisomes from mitochondria. Second, we used quantitative proteomics to identify proteins enriched in the peroxisome fractions relative to mitochondrial fractions. We provide evidence for peroxisomal localization of 89 proteins, 36 of which have not previously been identified in other analyses of Arabidopsis peroxisomes. Chimeric green fluorescent protein constructs of 35 proteins have been used to confirm their localization in peroxisomes or to identify endoplasmic reticulum contaminants. The distribution of many of these peroxisomal proteins between soluble, membrane-associated, and integral membrane locations has also been determined. This core peroxisomal proteome from nonphotosynthetic cultured cells contains a proportion of proteins that cannot be predicted to be peroxisomal due to the lack of recognizable peroxisomal targeting sequence 1 (PTS1) or PTS2 signals. Proteins identified are likely to be components in peroxisome biogenesis, beta-oxidation for fatty acid degradation and hormone biosynthesis, photorespiration, and metabolite transport. A considerable number of the proteins found in peroxisomes have no known function, and potential roles of these proteins in peroxisomal metabolism are discussed. This is aided by a metabolic network analysis that reveals a tight integration of functions and highlights specific metabolite nodes that most probably represent entry and exit metabolites that could require transport across the peroxisomal membrane.     

293SF metabolic flux analysis during cell growth and infection with an adenoviral vector

Metabolic flux quantification of cell culture is becoming a crucial means to improve cell growth as well as protein and vector productions. The technique allows rapid determination of cell culture status, thus providing a tool for further feeding improvements. Herein, we report on key results of a metabolic investigation using 293 cells adapted to suspension and serum-free medium (293SF) during growth and infection with an adenoviral vector encoding the green fluorescence protein (GFP). The model developed contains 35 fluxes, which include the main fluxes of glycolysis, glutaminolysis, and amino acids pathways. It requires specific consumption and production rate measurements of amino acids, glucose, lactate, NH(3), and O(2), as well as DNA and total proteins biosynthesis rate measurements. Also, it was found that extracellular protein concentration measurement is important for flux calculation accuracy. With this model, we are able to describe the 293SF cell metabolism, grown under different culture conditions in a 3-L controlled bioreactor for batch and fed-batch with low glucose. The metabolism is also investigated during infection under two different feeding strategies: a fed-batch starting at the end of the growth phase and extending during infection without medium change and a fed-batch after infection following medium renewal. Differences in metabolism are observed between growth and infection, as well as between the different feeding strategies, thus providing a better understanding of the general metabolism.     

Design of substrate label for steady state flux measurements in plant systems using the metabolic network of Brassica napus embryos

Steady state metabolic flux analysis using (13)C labeled substrates is of growing importance in plant physiology and metabolic engineering. The quality of the flux estimates in (13)C metabolic flux analysis depend on the: (i) network structure; (ii) flux values; (iii) design of the labeling substrate; and (iv) label measurements performed. Whereas the first two parameters are facts of nature, the latter two are to some extent controlled by the experimenter, yet they have received little attention in most plant studies. Using the metabolic flux map of developing Brassica napus (Rapeseed) embryos, this study explores the value of optimal substrate label designs obtained with different statistical criteria and addresses the applicability of different optimal designs to biological questions. The results demonstrate the value of optimizing the choice of labeled substrates and show that substrate combinations commonly used in bacterial studies can be far from optimal for mapping fluxes in plant systems. The value of performing additional experiments and the inclusion of measurements is also evaluated.     

Capturing the essence of a metabolic network: A flux balance analysis approach

As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented.Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole.Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.     

Shaping the niche: lessons from the Drosophila testis and other model systems

Stem cells are fascinating, as they supply the cells that construct our adult bodies and replenish, as we age, worn out, damaged, and diseased tissues. Stem cell regulation relies on intrinsic signals but also on inputs emanating from the neighbouring niche. The Drosophila testis provides an excellent system for studying such processes. Although recent advances have uncovered several signalling, cytoskeletal and other factors affecting niche homeostasis and testis differentiation, many aspects of niche regulation and maintenance remain unsolved. In this review, we discuss aspects of niche establishment and integrity not yet fully understood and we compare it to the current knowledge in other model systems such as vertebrates and plants. We also address specific questions on stem cell maintenance and niche regulation in the Drosophila testis under the control of Hox genes. Finally, we provide insights on the striking functional conservation of homologous genes in plants and animals and their respective stem cell niches. Elucidating conserved mechanisms of stem cell control in both lineages could reveal the importance underlying this conservation and justify the evolutionary pressure to adapt homologous molecules for performing the same task.     

Simplified modelling of metabolic pathways for flux prediction and optimization: lessons from an in vitro reconstruction of the upper part of glycolysis

Explicit modelling of metabolic networks relies on well-known mathematical tools and specialized computer programs. However, identifying and estimating the values of the very numerous enzyme parameters inherent to the models remain a tedious and difficult task, and the rate equations of the reactions are usually not known in sufficient detail. A way to circumvent this problem is to use 'non-mechanistic' models, which may account for the behaviour of the systems with a limited number of parameters. Working on the first part of glycolysis reconstituted in vitro, we showed how to derive, from titration experiments, values of effective enzyme activity parameters that do not include explicitly any of the classical kinetic constants. With a maximum of only two parameters per enzyme, this approach produced very good estimates for the flux values, and enabled us to determine the optimization conditions of the system, i.e. to calculate the set of enzyme concentrations that maximizes the flux. This fast and easy method should be valuable in the context of integrative biology or for metabolic engineering, where the challenge is to deal with the dramatic increase in the number of parameters when the systems become complex.     

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles

Background  

In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems.     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

ANAP: an integrated knowledge base for Arabidopsis protein interaction network analysis

Protein interactions are fundamental to the molecular processes occurring within an organism and can be utilized in network biology to help organize, simplify, and understand biological complexity. Currently, there are more than 10 publicly available Arabidopsis (Arabidopsis thaliana) protein interaction databases. However, there are limitations with these databases, including different types of interaction evidence, a lack of defined standards for protein identifiers, differing levels of information, and, critically, a lack of integration between them. In this paper, we present an interactive bioinformatics Web tool, ANAP (Arabidopsis Network Analysis Pipeline), which serves to effectively integrate the different data sets and maximize access to available data. ANAP has been developed for Arabidopsis protein interaction integration and network-based study to facilitate functional protein network analysis. ANAP integrates 11 Arabidopsis protein interaction databases, comprising 201,699 unique protein interaction pairs, 15,208 identifiers (including 11,931 The Arabidopsis Information Resource Arabidopsis Genome Initiative codes), 89 interaction detection methods, 73 species that interact with Arabidopsis, and 6,161 references. ANAP can be used as a knowledge base for constructing protein interaction networks based on user input and supports both direct and indirect interaction analysis. It has an intuitive graphical interface allowing easy network visualization and provides extensive detailed evidence for each interaction. In addition, ANAP displays the gene and protein annotation in the generated interactive network with links to The Arabidopsis Information Resource, the AtGenExpress Visualization Tool, the Arabidopsis 1,001 Genomes GBrowse, the Protein Knowledgebase, the Kyoto Encyclopedia of Genes and Genomes, and the Ensembl Genome Browser to significantly aid functional network analysis. The tool is available open access at http://gmdd.shgmo.org/Computational-Biology/ANAP.     

Strategies for the structural analysis of multi-protein complexes: lessons from the 3D-Repertoire project

Structural studies of multi-protein complexes, whether by X-ray diffraction, scattering, NMR spectroscopy or electron microscopy, require stringent quality control of the component samples. The inability to produce 'keystone' subunits in a soluble and correctly folded form is a serious impediment to the reconstitution of the complexes. Co-expression of the components offers a valuable alternative to the expression of single proteins as a route to obtain sufficient amounts of the sample of interest. Even in cases where milligram-scale quantities of purified complex of interest become available, there is still no guarantee that good quality crystals can be obtained. At this step, protein engineering of one or more components of the complex is frequently required to improve solubility, yield or the ability to crystallize the sample. Subsequent characterization of these constructs may be performed by solution techniques such as Small Angle X-ray Scattering and Nuclear Magnetic Resonance to identify 'well behaved' complexes. Herein, we recount our experiences gained at protein production and complex assembly during the European 3D Repertoire project (3DR). The goal of this consortium was to obtain structural information on multi-protein complexes from yeast by combining crystallography, electron microscopy, NMR and in silico modeling methods. We present here representative set case studies of complexes that were produced and analyzed within the 3DR project. Our experience provides useful insight into strategies that are more generally applicable for structural analysis of protein complexes.     

Techniques for the regeneration and genetic transformation of Arabidopsis pumila: an ephemeral plant suitable for investigating the mechanisms for adaptation to desert environments

Arabidopsis pumila is a type of cruciferous ephemeral plant, which in China mainly grows in the desert environments of northern Xinjiang. A. pumila not only has a short growth duration, but also has high photosynthetic efficiency, seed yield, salt tolerance, and drought resistance. It is an ideal species for the study of environmental adaptations in ephemeral plants. We induced callus tissue formation on the roots and hypocotyls of 8-day-old seedlings, and on the leaves and petioles of 4-week-old seedlings, and obtained multiple adventitious shoots on these tissues grown on Murashige and Skoog induction medium supplemented with 0.5 mg/L 6-Benzylaminopurine and 0.1 mg/L α-Naphthalene acetic acid. Young roots, hypocotyls, leaves, and petioles could all induce calluses, but the induction rate was highest on young roots. In addition, the leaves and petioles of 4-week-old seedlings were used as explants, the Δ1-pyrroline-5-carboxylic acid synthase gene 1 of A. pumila controlled by 35S promoter of cauliflower mosaic virus was used as target gene, and hygromycin B was used as screening antibiotic to explore Agrobacterium tumefaciens GV3101 mediated transformation. The results showed that the callus induction rate of petiole explants was the highest when they were treated with Agrobacterium suspension (OD₆₀₀?=?0.6) for 10 min and thenco-cultured in dark for 2 days. The qRT-PCR results showed that the ApP5CS1.1 gene was overexpressed in the transgenic plants. These protocols provide working research methods for exploring the cellular level adaptative mechanisms of this species to desert environments.

     

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

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

Viral vectors for the treatment of alcoholism: Use of metabolic flux analysis for cell cultivation and vector production

The HEK293 cell line has been used for the production of adenovirus vectors to be used in the potential treatment of alcoholism using a gene therapy strategy. Culture optimization and scale-up has been achieved by first adapting the cells to serum-free media and secondly by growing them in suspension. Adenovirus production after infection was increased, resulting in higher specific glucose consumption and lactate accumulation rates compared to the growth phase. We applied media design tools and Metabolic Flux Analysis (MFA) to compare the metabolic states of cells during growth and adenovirus production and to optimize culture media according to the metabolic demand of the cells in terms of glucose and glutamine concentrations. This allowed obtaining a higher maximum cell concentration and increased adenovirus production by minimizing the production of metabolites that can have an inhibitory effect on cell growth. We have proposed a stoichiometric equation for adenovirus synthesis. MFA results allowed determination of how these changes in composition affected the way cells distribute their nutrient resources during cell growth and virus production. Virus purification was successfully achieved using chromatography and Aqueous Two-Phase Systems (ATPS).     

MetMAP: an integrated Matlab package for analysis and optimization of metabolic systems

In previous works we have presented and applied a method to predict the parameter profile that optimizes biochemical systems regarding either a single or a set of metabolic responses within physiological constraints [Vera et al., 2003a]. This optimization technique requires a previous model definition and a translation to S-system form and the use of widely available linear programming packages. However, in dealing with these issues the interested researcher has to confront additional difficulties because of a lack of connectivity among available software packages or routines specifically designed to perform different tasks. In addition to this difficulty is the unavailability of any automated package which is capable of performing such optimizations and the previous required analysis. This situation prompted us to develop an integrated software package able to deal with these tasks in a single program environment. In this paper we present a software package for the model definition, analysis and optimization of a biochemical system. It starts with a given model definition that is directly translated to its equivalent S-system form. Once the model quality assessment is performed (stability and sensitivity analysis) the program determines the parameter profile that yields the optimized response compatible with a predefined set of constraints. Moreover the package finds the set of solutions obtained when more than one system's responses are to be optimized (multiobjective optimization).     

Fumaric acid: an overlooked form of fixed carbon in Arabidopsis and other plant species

Photoassimilates are used by plants for production of energy, as carbon skeletons and in transport of fixed carbon between different plant organs. Many studies have been devoted to characterizing the factors that regulate photoassimilate concentrations in different plant species. Most studies examining photoassimilate concentrations in C₃ plants have focused on analyzing starch and soluble sugars. However, work presented here demonstrates that a number of C₃ plants, including the popular model organism Arabidopsis thaliana (L.) Heynh., and agriculturally important plants, such as soybean, Glycine max (L.) Merr., contain significant quantities of fumaric acid. In fact, fumaric acid can accumulate to levels of several milligrams per gram fresh weight in Arabidopsis leaves, often exceeding those of starch and soluble sugars. Fumaric acid is a component of the tricarboxylic acid cycle and, like starch and soluble sugars, can be metabolized to yield energy and carbon skeletons for production of other compounds. Fumaric acid concentrations increase with plant age and light intensity in Arabidopsis leaves. Moreover, Arabidopsis phloem exudates contain significant quantities of fumaric acid, raising the possibility that fumaric acid may function in carbon transport. Received: 11 February 2000 / Accepted: 1 April 2000