Understanding flux in plant metabolic networks

The revolutionary growth in our ability to identify the 'parts list' of cellular infrastructure in plants in detail, and to alter it with precision, challenges us to develop methods to quantify how these parts function. For components of metabolism, this means mapping fluxes at the level of metabolic networks. Advances in experimental, analytical and software tools for metabolic flux analysis now allow maps of the fluxes through central metabolism to be obtained from the results of stable-isotope-labeling experiments. Such maps have led to notable successes in understanding and engineering metabolic function in microorganisms. Recent studies in plants are giving insight into particular fluxes, such as those of the pentose phosphate pathway, and into general phenomena, such as substrate- or futile-cycles and compartmentation. The importance of experimental design and statistical analysis have been illustrated, and analyses of fluxes in heterotrophic plant tissues have been carried out recently.     

The stochastic evolution of catalysts in spatially resolved molecular systems.

A fully stochastic chemical modelling technique is derived which describes the influence of spatial separation and discrete population size on the evolutionary stability of coupled amplification in biopolymers. The model is analytically tractable for an infinite-dimensional space (simplex geometry), which also provides insight into evolution in normal Euclidean space. The results are compared with stochastic simulations describing the co-evolution of combinatorial families of molecular sequences both in the simplex geometry and in lower (one, two and three) space dimensions. They demonstrate analytically the generic limits which exploitation place on co-evolving multi-component amplification systems. In particular, there is an optimal diffusion (or migration) coefficient for cooperative amplification and minimal and maximal threshold values for stable cooperation. Over a bounded range of diffusion rates, the model also exhibits stable limit cycles. Furthermore, the co-operatively coupled system has a maximum tolerable error rate at intermediate rates of diffusion. A tractable model is thereby established which demonstrates that spatial effects can stabilize catalytic biological information. The analytic behaviour in infinite-dimensional simplex space is seen to provide a reasonable guide to the spatial dependence of the error threshold in physical space. Nanoscale possibilities for the evolution of catalysis on the basis of the model are outlined. We denote the modelling technique by PRESS, Probability Reduced Evolution of Spatially-discrete Species.     

On detecting and measuring competition in spatially structured plant communities

Recent models have shown that the development of spatial structure in plant mixtures may make strong competitive interactions between species hard to detect owing to spatial segregation of the competing species. Here we address the issue of measuring interspecific competition using a simulation based on a neighbourhood population model which assumes that both dispersal and competitive interactions are localized. Using known parameter combinations we use the model to test the power and efficiency of two approaches for detecting and measuring competition. The first approach is based on measuring the response of communities to the removal of neighbours. Measures of interspecific competition based on this approach are extremely biased by spatial segregation of species, although this bias may be partially overcome by altering the spatial scale at which the effects of removals are recorded. The second approach is based on multiple regression of per capita population growth rates on local densities of the interacting species. When dispersal is restricted, the regression approach provides accurate estimates of interspecific competition coefficients when the scale of the sampling unit (i.e. the quadrats within which plants are counted) is large compared to the scale at which interactions and dispersal occur. When seeds disperse globally the removal method performs best; the regression method fails because sampling units do not form closed dynamic systems. Our results highlight the importance of tailoring methods for detecting competition to the characteristics of the species in question. They also indicate that rapid nonmanipulative estimates of competition coefficients may be the best approach in communities where dispersal is restricted and competitive interactions localized, which is likely to be the case for the majority of plants.     

Metabolic network flux analysis for engineering plant systems

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Highlights► A powerful range of tools has been developed for metabolic network flux analysis. ► These tools yield insights that are used to aid microbial metabolic engineering. ► Plants present great opportunities and special challenges to applying these tools. ► Tool selection and knowledge of plant systems is key to practical success.     

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.     

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.     

Development of real-time radioisotope imaging systems for plant nutrient uptake studies

Ionic nutrition is essential for plant development. Many techniques have been developed to image and (or) measure ionic movement in plants. Nevertheless, most of them are destructive and limit the analysis. Here, we present the development of radioisotope imaging techniques that overcome such restrictions and allow for real-time imaging of ionic movement. The first system, called macroimaging, was developed to visualize and measure ion uptake and translocation between organs at a whole-plant scale. Such a device is fully compatible with illumination of the sample. We also modified fluorescent microscopes to set up various solutions for ion uptake analysis at the microscopic level. Both systems allow numerical analysis of images and possess a wide dynamic range of detection because they are based on radioactivity.     

The potential of spatially resolved spectroscopy for monitoring angiogenesis in the chorioallantoic membrane

Over the last decade, the poultry sector has sought to develop ways to monitor chicken embryonic development as to optimize the incubation conditions. One of the parameters of development which may change under different incubation conditions is the angiogenesis in the chorioallantoic membrane (CAM). To be able to quantify these changes in the angiogenesis and detect long-term effects on health, a non-destructive technique is necessary. In this article, the first steps toward such a non-destructive technique are successfully taken. A spatially resolved spectroscopy set-up is built and tested for its potential to measure changes in angiogenesis with incubation time, and differences between a normal and hypercapnic incubation. In this first study, reflectance measurements are performed directly on the CAM as the eggshell considerably complicates the analysis. This issue should be addressed in future research to come to a really non-destructive technique. An experiment was conducted in which one group was incubated under normal conditions, and another under early prenatal hypercapnic conditions (i.e., increased CO(2) concentrations). The angiogenesis in the CAM was measured at embryonic day (ED) 10, 13, and 16. The measurements showed a clear blood spectrum with an increasing amount of blood in time, and significant differences in the reflectance as function of the source-detector distances. However, no significant differences between the hypercapnia and the control group could be detected.     

Enhanced low oxygen survival in Arabidopsis through increased metabolic flux in the fermentative pathway

We manipulated the enzyme activity levels of the alcohol fermentation pathway, pyruvate decarboxylase (PDC), and alcohol dehydrogenase (ADH) in Arabidopsis using sense and antisense overexpression of the corresponding genes (PDC1, PDC2, and ADH1). Transgenic plants were analyzed for levels of fermentation and evaluated for changes in hypoxic survival. Overexpression of either Arabidopsis PDC1 or PDC2 resulted in improved plant survival. In contrast, overexpression of Arabidopsis ADH1 had no effect on flooding survival. These results support the role of PDC as the control step in ethanol fermentation. Although ADH1 null mutants had decreased hypoxic survival, attempts to reduce the level of PDC activity enough to see an effect on plant survival met with limited success. The combination of flooding survival data and metabolite analysis allows identification of critical metabolic flux points. This information can be used to design transgenic strategies to improve hypoxic tolerance in plants.     

Quantification of metabolic flux in plant secondary metabolism by a biogenetic organizational approach

Metabolic engineering represents a promising approach to enhance the yield of valuable natural products from plants. A method to quantify flux through metabolite measurements is necessary for the analysis of native and modified pathways. Rather than focusing only on the accumulation of the final products, analyzing a wide range of secondary metabolites has significant advantages. We propose a model that organizes the flux analysis by grouping metabolites of similar biosynthetic origin. To this end, we have quantified temporal profiles of metabolites from several branches of the indole alkaloid pathway in Catharanthus roseus hairy root cultures. By analyzing these data, we are able to examine the distribution of flux around key branchpoints. Furthermore, this analysis provides crucial information such as an estimate of total flux to secondary metabolism.     

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.     

Two-stage oxygen supply strategy for enhanced lipase production by Bacillus subtilis based on metabolic flux analysis

Lipase production by Bacillus subtilis CICC20034 was assessed by metabolic flux distribution analysis. Lipase production was tested under various oxygen supply conditions in a synthetic medium to obtain the optimal oxygen supply profile. Based on the metabolic flux analysis, a two-stage oxygen supply strategy (TOS) that maintained high oxygen supply conditions during early fermentation phase, and then step-wisely reduced aeration to keep a stable, smooth, and adequate changing dissolved oxygen (DO) level profile throughout the production phases was carried out. With the proposed control strategy, the final lipase activity in batch fermentation significantly increased and reached a high level at 0.56 U/ml, corresponding to a 51% increase. The relevant metabolic flux analysis verified the effectiveness of the proposed control strategy. By applying TOS in composite medium, the final lipase activity reached 5.0 U/ml.     

Recent progress in the metabolic engineering of alkaloids in plant systems

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Highlights► Recent metabolic engineering efforts for plant alkaloids. ► Characterizing, reconfiguring and fine-tuning metabolic ‘parts’ improves titers. ► Additional strategies are necessary to produce ‘unnatural’ natural products.     

Reactive oxygen species generation and antioxidant systems in plant mitochondria

In living cells, reactive oxygen species (ROS) play a key role in signaling but these compounds can also damage macromolecules. As in other compartments, the mitochondrial ROS concentrations need to be tightly controlled. Plant mitochondria contain several antioxidant systems that are not only able to scavenge ROS and limit their production but also to repair damages to macromolecules and possibly to serve as redox sensors. They comprise ascorbate- and glutathione-dependent pathways as well as systems based on thioredoxin (TRX)- and glutaredoxin (GRX)-like molecules. This review describes the various mitochondrial redox pathways for ROS control in plants with special emphasis on the poorly studied GRX and TRX systems and provides perspectives for future research in this area.     

A simple and modified manometric method for measuring oxygen uptake rate of plant cells in flask cultures

Summary A simple and convenient technique was developed based on the principle of Warburg manometric method to measure O₂ uptake rate (OUR) and CO₂ evolution rate (CER) of suspended cells in a shake flask culture. It was successfully applied to suspension cultures of rice (Oryza sativa) and Panax notoginseng cells, and some important bioprocess parameters, such as OUR, CER, respiratory quotient (RQ), specific OUR (SOUR) and specific CER (SCER), were quantitatively obtained. The measuring system is easy to operate, able to treat many samples simultaneously and is economical.     

Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments

Metabolic flux analysis (MFA) methods use external flux and isotopic measurements to quantify the magnitude of metabolic flows in metabolic networks. A key question in this analysis is choosing a set of measurements that is capable of yielding a unique flux distribution (identifiability). In this article, we introduce an optimization-based framework that uses incidence structure analysis to determine the smallest (or most cost-effective) set of measurements leading to complete flux elucidation. This approach relies on an integer linear programming formulation OptMeas that allows for the measurement of external fluxes and the complete (or partial) enumeration of the isotope forms of metabolites without requiring any of these to be chosen in advance. We subsequently query and refine the measurement sets suggested by OptMeas for identifiability and optimality. OptMeas is first tested on small to medium-size demonstration examples. It is subsequently applied to a large-scale E. coli isotopomer mapping model with more than 17,000 isotopomers. A number of additional measurements are identified leading to maximum flux elucidation in an amorphadiene producing E. coli strain.     

FluxAnalyzer: exploring structure,pathways, and flux distributions in metabolic networks on interactive flux maps

MOTIVATION: The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS: The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY: Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer.     

Comparison of two test systems for measuring plant phosphorus uptake via arbuscular mycorrhizal fungi

 Plant phosphorus uptake via external hyphae of arbuscular mycorrhizal fungi has been measured using compartmented systems where a hyphal compartment is separated from a rooting compartment by a fine mesh. By labelling the soil within the hyphal compartment with a radioactive phosphorus (P) isotope, hyphal uptake of P into the plant can be traced. The objective of this growth chamber study was to test two hyphal compartments of different design with respect to their suitabilities for measurement of hyphal P uptake. One hyphal compartment was simply a nylon mesh bag filled with ³²P-labelled soil. The labelled soil in the other hyphal compartment was completely surrounded by an 8–10 mm layer of unlabelled soil that served as a buffer zone. Mycorrhizal and non-mycorrhizal subterranean clover plants were grown in pots with a centrally positioned hyphal compartment. Uptake of radioactive P by non-mycorrhizal control plants was 25% of that by mycorrhizal plants with the mesh bag but only 3% when including the buffer zone. Based on this good control of non-mycorrhizal P uptake from within the hyphal compartment and its greater ease of handling once produced, we judged the hyphal compartment including a buffer zone to be superior to the mesh bag. Accepted: 15 September 1998