Global Metabolic Network Research Articles

Metabolomics data reveal a crucial role of cytosolic glutamine synthetase 1;1 in coordinating metabolic balance in rice

Rice plants grown in paddy fields preferentially use ammonium as a source of inorganic nitrogen. Glutamine synthetase (GS) catalyses the conversion of ammonium to glutamine. Of the three genes encoding cytosolic GS in rice, OsGS1;1 is critical for normal growth and grain filling. However, the basis of its physiological function that may alter the rate of nitrogen assimilation and carbon metabolism within the context of metabolic networks remains unclear. To address this issue, we carried out quantitative comparative analyses between the metabolite profiles of a rice mutant lacking OsGS1;1 and its background wild type (WT). The mutant plants exhibited severe retardation of shoot growth in the presence of ammonium compared with the WT. Overaccumulation of free ammonium in the leaf sheath and roots of the mutant indicated the importance of OsGS1;1 for ammonium assimilation in both organs. The metabolite profiles of the mutant line revealed: (i) an imbalance in levels of sugars, amino acids and metabolites in the tricarboxylic acid cycle, and (ii) overaccumulation of secondary metabolites, particularly in the roots under a continuous supply of ammonium. Metabolite-to-metabolite correlation analysis revealed the presence of mutant-specific networks between tryptamine and other primary metabolites in the roots. These results demonstrated a crucial function of OsGS1;1 in coordinating the global metabolic network in rice plants grown using ammonium as the nitrogen source.

Statistical methods for comparing the abundances of metabolic pathways in metagenomics

Background A major goal of metagenomic studies is to identify specific functional adaptations of microbial communities to their habitats. The functional profile and the abundances for a sample can be estimated by mapping metagenomic sequences to the global metabolic network consisting of thousands of molecular reactions. Here we describe our development of statistical methods that can identify differentially abundant subnetworks between metagenomic samples.

Two-Dimensional Statistical Recoupling for the Identification of Perturbed Metabolic Networks from NMR Spectroscopy

The development of Statistical Total Correlation Spectroscopy (STOCSY), a representation of the autocorrelation matrix of a spectral data set as a 2D pseudospectrum, has allowed more reliable assignment of one- and two-dimensional NMR spectra acquired from the complex mixtures that are usually used in metabolomics/metabonomics studies, thus, improving precise identification of candidate biomarkers contained in metabolic signatures computed by multivariate statistical analysis. However, the correlations obtained cannot always be interpreted in terms of connectivities between metabolites. In this study, we combine statistical recoupling of variables (SRV) and STOCSY to identify perturbed metabolite systems. The resulting Recoupled-STOCSY (R-STOCSY) method provides a 2D correlation landscape based on the SRV clusters representing physical, chemical, and biological entities. This enables the identification of correlations between distant clusters and extends the recoupling scheme of SRV, which was previously limited to the association of neighboring clusters. This allows the recovery of only meaningful correlations between metabolic signals and significantly enhances the interpretation of STOCSY. The method is validated through the measurement of the distances between the metabolites involved in these correlations, within the whole metabolic network, which shows that the average shortest path length is significantly shorter for the correlations detected in this new way compared to metabolite couples randomly selected from within the entire KEGG metabolic network. This enables the identification without any a priori knowledge of the perturbed metabolic network. The R-STOCSY completes the recoupling procedure between distant clusters, further reducing the high dimensionality of metabolomics/metabonomics data set and finally allows the identification of composite biomarkers, highlighting disruption of particular metabolic pathways within a global metabolic network. This allows the perturbed metabolic network to be extracted through NMR based metabolomics/metabonomics in an automated, and statistical manner.

Reconstruction and Analysis of Human Liver-Specific Metabolic Network Based on CNHLPP Data

Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.

The conservation and evolutionary modularity of metabolism

A novel evolutionary analysis of metabolic networks across 26 taxa reveals a highly-conserved but flexible core of metabolic enzymes.

A global view of antibiotic resistance

Antibiotic resistance is one of the few examples of evolution that can be addressed experimentally. The present review analyses this resistance, focusing on the networks that regulate its acquisition and its effect on bacterial physiology. It is widely accepted that antibiotics and antibiotic resistance genes play fundamental ecological roles - as weapons and shields, respectively - in shaping the structures of microbial communities. Although this Darwinian view of the role of antibiotics is still valid, recent work indicates that antibiotics and resistance mechanisms may play other ecological roles and strongly influence bacterial physiology. The expression of antibiotic resistance determinants must therefore be tightly regulated and their activity forms part of global metabolic networks. In addition, certain bacterial modes of life can trigger transient phenotypic antibiotic resistance under some circumstances. Understanding resistance thus requires the analysis of the regulatory networks controlling bacterial evolvability, the physiological webs affected and the metabolic rewiring it incurs.

Network-based prediction of human tissue-specific metabolism

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Towards Kinetic Modeling of Global Metabolic Networks: Methylobacterium extorquens AM1 Growth as Validation

Here we report a systematic method for constructing a large scale kinetic metabolic model and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium. Its central metabolic network includes formaldehyde metabolism, serine cycle, citric acid cycle, pentose phosphate pathway, gluconeogensis, PHB synthesis and acetyl-CoA conversion pathway, respiration and energy metabolism. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. Our method consists of the following major steps: 1) using a generic enzymatic rate equation to reduce the number of enzymatic parameters to a minimum set while still preserving their characteristics; 2) using a set of steady state fluxes and metabolite concentrations in the physiological range as the expected output steady state fluxes and metabolite concentrations for the kinetic model to restrict the parametric space of enzymatic reactions; 3) choosing enzyme constants K's and K' eqs optimized for reactions under physiological concentrations, if their experimental values are unknown; 4) for models which do not cover the entire metabolic network of the organisms, designing a dynamical exchange for the coupling between the metabolism represented in the model and the rest not included.

Insights into environmental bioremediation by microorganisms through functional genomics and proteomics

Environmental pollutants in the soil are a major concern worldwide. Bioremediation mediated by microorganisms is a highly promising technology that is environmentally friendly, safe, and effective. However, incomplete biological information regarding the cellular responses in many microbial communities restricts progress in the site-specific mineralization process. The application of proteomics in environmental bioremediation research provides a global view of the protein compositions of the microbial cells and offers a promising approach to address the molecular mechanisms of bioremediation. With the combination of proteomics, functional genomics provide an insight into global metabolic and regulatory networks that can enhance the understanding of gene functions. This article deals with the applications of functional genomics and proteomics to dissect the cellular responses to environmental stimuli, such as stress response, induction and expressions of regulatory proteins/enzymes in response to aromatic hydrocarbons and heavy metals. An understanding of the growth conditions governing the expression of the proteome (for example, enzymes and regulatory proteins of aromatic hydrocarbon degradation, energy generation pathways, transport and stress-related proteins) in a specific environment is essential for developing rational strategies for successful bioremediation.

KEGG spider: interpretation of genomics data in the context of the global gene metabolic network

KEGG spider is a web-based tool for interpretation of experimentally derived gene lists in order to gain understanding of metabolism variations at a genomic level. KEGG spider implements a 'pathway-free' framework that overcomes a major bottleneck of enrichment analyses: it provides global models uniting genes from different metabolic pathways. Analyzing a number of experimentally derived gene lists, we demonstrate that KEGG spider provides deeper insights into metabolism variations in comparison to existing methods.

Global reconstruction of the human metabolic network based on genomic and bibliomic data.

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.

Hierarchical modularity of nested bow-ties in metabolic networks.

BackgroundThe exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks.ResultsIn this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively.ConclusionThe highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.

Phylophenetic properties of metabolic pathway topologies as revealed by global analysis.

BackgroundAs phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the post-genomic era, it is possible to measure the "phylophenetic" contents of different pathways topologies from a global perspective.ResultsWe reconstructed phylophenetic trees for all available metabolic pathways based on topological similarities, and compared them to the corresponding 16S rRNA-based trees. Similarity values for each pair of trees ranged from 0.044 to 0.297. Using the quartet method, single pathways trees were merged into a comprehensive tree containing information from a large part of the entire metabolic networks. This tree showed considerably higher similarity (0.386) to the corresponding 16S rRNA-based tree than any tree based on a single pathway, but was, on the other hand, sufficiently distinct to preserve unique phylogenetic information not reflected by the 16S rRNA tree.ConclusionWe observed that the topology of different metabolic pathways provided different phylogenetic and phenetic information, depicting the compromise between phylogenetic information and varying evolutionary pressures forming metabolic pathway topologies in different organisms. The phylogenetic information content of the comprehensive tree is substantially higher than that of any tree based on a single pathway, which also gave clues to constraints working on the topology of the global metabolic networks, information that is only partly reflected by the topologies of individual metabolic pathways.

Biosynthesis and Regulation of the Branched-Chain Amino Acids†.

This review focuses on more recent studies concerning the systems biology of branched-chain amino acid biosynthesis, that is, the pathway-specific and global metabolic and genetic regulatory networks that enable the cell to adjust branched-chain amino acid synthesis rates to changing nutritional and environmental conditions. It begins with an overview of the enzymatic steps and metabolic regulatory mechanisms of the pathways and descriptions of the genetic regulatory mechanisms of the individual operons of the isoleucine-leucine-valine (ilv) regulon. This is followed by more-detailed discussions of recent evidence that global control mechanisms that coordinate the expression of the operons of this regulon with one another and the growth conditions of the cell are mediated by changes in DNA supercoiling that occur in response to changes in cellular energy charge levels that, in turn, are modulated by nutrient and environmental signals. Since the parallel pathways for isoleucine and valine biosynthesis are catalyzed by a single set of enzymes, and because the AHAS-catalyzed reaction is the first step specific for valine biosynthesis but the second step of isoleucine biosynthesis, valine inhibition of a single enzyme for this enzymatic step might compromise the cell for isoleucine or result in the accumulation of toxic intermediates. The operon-specific regulatory mechanisms of the operons of the ilv regulon are discussed in the review followed by a consideration and brief review of global regulatory proteins such as integration host factor (IHF), Lrp, and CAP (CRP) that affect the expression of these operons.

Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms

Information from fully sequenced genomes makes it possible to reconstruct strain-specific global metabolic network for structural and functional studies. These networks are often very large and complex. To properly understand and analyze the global properties of metabolic networks, methods for rationally representing and quantitatively analyzing their structure are needed. In this work, the metabolic networks of 80 fully sequenced organisms are in silico reconstructed from genome data and an extensively revised bioreaction database. The networks are represented as directed graphs and analyzed by using the 'breadth first searching algorithm to identify the shortest pathway (path length) between any pair of the metabolites. The average path length of the networks are then calculated and compared for all the organisms. Different from previous studies the connections through current metabolites and cofactors are deleted to make the path length analysis physiologically more meaningful. The distribution of the connection degree of these networks is shown to follow the power law, indicating that the overall structure of all the metabolic networks has the characteristics of a small world network. However, clear differences exist in the network structure of the three domains of organisms. Eukaryotes and archaea have a longer average path length than bacteria. The reaction database in excel format and the programs in VBA (Visual Basic for Applications) are available upon request. Bioinformatics Online.