Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.     

Insights into pH‐induced metabolic switch by flux balance analysis

Lactate accumulation in mammalian cell culture is known to impede cellular growth and productivity. The control of lactate formation and consumption in a hybridoma cell line was achieved by pH alteration during the early exponential growth phase. In particular, lactate consumption was induced even at high glucose concentrations at pH 6.8, whereas highly increased production of lactate was obtained at pH 7.8. Consequently, constraint‐based metabolic flux analysis was used to examine pH‐induced metabolic states in the same growth state. We demonstrated that lactate influx at pH 6.8 led cells to maintain high fluxes in the TCA cycle and malate‐aspartate shuttle resulting in a high ATP production rate. In contrast, under increased pH conditions, less ATP was generated and different ATP sources were utilized. Gene expression analysis led to the conclusion that lactate formation at high pH was enabled by gluconeogenic pathways in addition to facilitated glucose uptake. The obtained results provide new insights into the influence of pH on cellular metabolism, and are of importance when considering pH heterogeneities typically present in large scale industrial bioreactors. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:347–357, 2015     

Proteotypic profiling of LHCI from Chlamydomonas reinhardtii provides new insights into structure and function of the complex

We used isotope dilution MS to measure the stoichiometry of light‐harvesting complex I (LHCI) proteins with the photosystem I (PSI) core complex in the green alga Chlamydomonas reinhardtii. Proteotypic peptides served as quantitative markers for each of the nine gene products (Lhca1–9) and for PSI subunits. The quantitative data revealed that the LHCI antenna of C. reinhardtii contains about 7.5 ± 1.4 subunits. It further demonstrated that the thylakoid LHCI population is heterogeneously composed and that several lhca gene products are not present in 1:1 stoichiometries with PSI. When compared with vascular plants, LHCI of C. reinhardtii possesses a lower proportion of proteins potentially contributing to far‐red fluorescence emission. In general, the strategy presented is universally applicable for exploring subunit stoichiometries within the C. reinhardtii proteome.     

Bayesian flux balance analysis applied to a skeletal muscle metabolic model

In this article, the steady state condition for the multi-compartment models for cellular metabolism is considered. The problem is to estimate the reaction and transport fluxes, as well as the concentrations in venous blood when the stoichiometry and bound constraints for the fluxes and the concentrations are given. The problem has been addressed previously by a number of authors, and optimization-based approaches as well as extreme pathway analysis have been proposed. These approaches are briefly discussed here. The main emphasis of this work is a Bayesian statistical approach to the flux balance analysis (FBA). We show how the bound constraints and optimality conditions such as maximizing the oxidative phosphorylation flux can be incorporated into the model in the Bayesian framework by proper construction of the prior densities. We propose an effective Markov chain Monte Carlo (MCMC) scheme to explore the posterior densities, and compare the results with those obtained via the previously studied linear programming (LP) approach. The proposed methodology, which is applied here to a two-compartment model for skeletal muscle metabolism, can be extended to more complex models.     

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.     

Monte Carlo sampling and principal component analysis of flux distributions yield topological and modular information on metabolic networks

The work presented here uses Monte Carlo random sampling combined with flux balance analysis and linear programming to analyse the steady-state flux distributions on the surface of the glucose-ammonia phenotypic phase plane of an Escherichia coli system grown on glucose-minimal medium. The distribution of allowable glucose and ammonia uptake rates showed a triangular shape, the apex corresponding to maximum growth rate. The exact shape, e.g. the diagonal boundary is determined by the relative amounts of nutrients required for growth. The logarithm of flux values has a normal distribution, e.g. there is a log normal distribution, and most of the reactions have an order of magnitude between 10(-1) and 1. The increase in the number of blocked reactions as growth switched from aerobic to micro-aerobic phase and the presence of alternate networks for a single optimal solution were both reflections of the variability of pathway utilization for survival and growth. Principal component analysis (PCA) provided us with significant clues on the correlations between individual reactions and correlations between sets of reactions. Furthermore, PCA identified the most influential reactions of the system. The PCA score plots clearly distinguish two different growth phases, micro-aerobic and aerobic. The loading plots for each growth phase showed both the impact of the reactions on the model and the clustering of reactions that are highly correlated. These results have proved that PCA is a promising way to analyse correlations in high-dimensional solution spaces and to detect modular patterns among reactions in a network.     

Trophic polymorphism in bluegill sunfish (<Emphasis Type="Italic">Lepomis macrochirus</Emphasis>) introduced into Lake Biwa: evidence from stable isotope analysis

Trophic polymorphism was recently reported in introduced bluegill (Lepomis macrochirus) in Lake Biwa, Japan, where three morphs are specialized in benthic invertebrates (benthivorous type), submerged aquatic plants (herbivorous type), and zooplankton (planktivorous type). We evaluated the long-term effects of food resource utilization by these trophic morphs using stable isotope ratios, δ¹⁵N and δ¹³C. A significant difference in δ¹⁵N was found between the benthivorous and planktivorous types. The planktivorous type had the higher δ¹⁵N value, which corresponded with the value expected from its prey, zooplankton. The lower δ¹⁵N value of the benthivorous type would be derived from the lower δ¹⁵N values of benthic prey organisms compared to zooplankton. These results support previous findings that the benthivorous and planktivorous types have different food resource utilization. In contrast, the δ¹⁵N and δ¹³C values of the herbivorous type were distinctly different from the expected values, indicating that this type was unlikely to utilize aquatic plants substantially, contradicting the results of the dietary analysis.