In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.     

A network perspective on the topological importance of enzymes and their phylogenetic conservation

Background  

A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes.     

Inferring meaningful pathways in weighted metabolic networks

An approach is presented for computing meaningful pathways in the network of small molecule metabolism comprising the chemical reactions characterized in all organisms. The metabolic network is described as a weighted graph in which all the compounds are included, but each compound is assigned a weight equal to the number of reactions in which it participates. Path finding is performed in this graph by searching for one or more paths with lowest weight. Performance is evaluated systematically by computing paths between the first and last reactions in annotated metabolic pathways, and comparing the intermediate reactions in the computed pathways to those in the annotated ones. For the sake of comparison, paths are computed also in the un-weighted raw (all compounds and reactions) and filtered (highly connected pool metabolites removed) metabolic graphs, respectively. The correspondence between the computed and annotated pathways is very poor (<30%) in the raw graph; increasing to approximately 65% in the filtered graph; reaching approximately 85% in the weighted graph. Considering the best-matching path among the five lightest paths increases the correspondence to 92%, on average. We then show that the average distance between pairs of metabolites is significantly larger in the weighted graph than in the raw unfiltered graph, suggesting that the small-world properties previously reported for metabolic networks probably result from irrelevant shortcuts through pool metabolites. In addition, we provide evidence that the length of the shortest path in the weighted graph represents a valid measure of the "metabolic distance" between enzymes. We suggest that the success of our simplistic approach is rooted in the high degree of specificity of the reactions in metabolic pathways, presumably reflecting thermodynamic constraints operating in these pathways. We expect our approach to find useful applications in inferring metabolic pathways in newly sequenced genomes.     

Analysis of metabolic networks using a pathway distance metric through linear programming

The solution of the shortest path problem in biochemical systems constitutes an important step for studies of their evolution. In this paper, a linear programming (LP) algorithm for calculating minimal pathway distances in metabolic networks is studied. Minimal pathway distances are identified as the smallest number of metabolic steps separating two enzymes in metabolic pathways. The algorithm deals effectively with circularity and reaction directionality. The applicability of the algorithm is illustrated by calculating the minimal pathway distances for Escherichia coli small molecule metabolism enzymes, and then considering their correlations with genome distance (distance separating two genes on a chromosome) and enzyme function (as characterised by enzyme commission number). The results illustrate the effectiveness of the LP model. In addition, the data confirm that propinquity of genes on the genome implies similarity in function (as determined by co-involvement in the same region of the metabolic network), but suggest that no correlation exists between pathway distance and enzyme function. These findings offer insight into the probable mechanism of pathway evolution.     

'Slave' metabolites and enzymes. A rapid way of delineating metabolic control.

Although control of fluxes and concentrations tends to be distributed rather than confined to a single rate-limiting enzyme, the extent of control can differ widely between enzymes in a metabolic network. In some cases, there are enzymes that lack control completely. This paper identifies one surprising origin of such lack of control: If, in a metabolic system, there is a metabolite that affects the catalytic rate of only one enzyme, the corresponding enzyme cannot control any metabolic variable other than the concentration of that metabolite. We call such enzymes 'slave enzymes', and the corresponding metabolites 'slave metabolites'. Implications of the existence of slave enzymes for the control properties of enzymes further down the metabolic pathway are discussed and examined for the glycolytic pathway of yeast. Inadvertent assumptions in metabolic models may cause the latter incorrectly to calculate absence of metabolic control. The phenomenon of slave enzymes may well be important in enhancing metabolic signal transduction.     

Helicobacter pylori 26695 基因组尺度代谢研究进展

代谢网络在代谢功能研究、生物代谢过程控制、疾病诊断分析和药物靶标设计等方面具有重要理论和实践意义。生物信息学研究利用序列同源、结构模拟、对接等手段与生化实验有效结合促进了生物体代谢网络的进一步完善。本文作者在构建幽门螺杆菌（Helicobacter pylori 26695,H.pylori 26695）代谢网络的工作基础上综合了近年来研究者对H.pylori 26695代谢通路关键酶的研究成果,并结合基因组信息,综述了H.pylori 26695特异性的重要代谢通路。本文从基因组水平阐明代谢通路与基因的关系,并详细分析了关键酶对H.pylori 26695生理的重要作用,最后探讨了重构一个连续、完整的代谢网络面临的困难及其在药物靶标设计方面的研究前景。     

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.     

Mapping determinants of variation in energy metabolism, respiration and flight in Drosophila

We employed quantitative trait locus (QTL) mapping to dissect the genetic architecture of a hierarchy of functionally related physiological traits, including metabolic enzyme activity, metabolite storage, metabolic rate, and free-flight performance in recombinant inbred lines of Drosophila melanogaster. We identified QTL underlying variation in glycogen synthase, hexokinase, phosphoglucomutase, and trehalase activity. In each case variation mapped away from the enzyme-encoding loci, indicating that trans-acting regions of the genome are important sources of variation within the metabolic network. Individual QTL associated with variation in metabolic rate and flight performance explained between 9 and 35% of the phenotypic variance. Bayesian QTL analysis identified epistatic effects underlying variation in flight velocity, metabolic rate, glycogen content, and several metabolic enzyme activities. A region on the third chromosome was associated with expression of the glucose-6-phosphate branchpoint enzymes and with metabolic rate and flight performance. These genomic regions are of special interest as they may coordinately regulate components of energy metabolism with effects on whole-organism physiological performance. The complex biochemical network is encoded by an equally complex network of interacting genetic elements with potentially pleiotropic effects. This has important consequences for the evolution of performance traits that depend upon these metabolic networks.     

Biological roles of specific peptides in enzymes

It has recently been shown (Kunik et al., PLOS Comput Biol 2007;3(8):e167) that the occurrence of specific peptides (SPs) on sequences of enzymes allows for accurate EC classification of enzymes. We inquire whether these SPs play important roles in bringing about the enzymatic function. This is assessed by cross-checking the occurrence of SPs on enzymes with Swiss-Prot annotations and PDB spatial structures of enzymes. Analyzing the coverage of functional annotations of enzymes, we demonstrate that SPs contain major fractions of all annotated features. This result is statistically highly significant and associates over 10% of all SPs with important biological markers. Concentrating on DNA binding regions, relevant to LexA repressor enzymes, we find interesting coverage patterns. Moreover, for the same data, we demonstrate that SPs allow for subclassification of the relevant bacteria into phylogenetic classes. An analysis of mutagen annotations on SPs appearing on all enzymes leads to the conclusion that mutations on SPs tend to damage the enzymatic function much more than expected from a background model, hence SPs are of high importance to enzymatic functions. SPs that lie in 3D pockets that are shared by active and binding sites, are shown to be significantly enriched by glycine, leading to the hypothesis that they are responsible for conformational plasticity. Finally we show that SPs can partially resolve outstanding difficult problems of convergent evolution by representing correctly enzyme functions in spite of remote homologies in sequence and in structure.     

Genes encoding hub and bottleneck enzymes of the Arabidopsis metabolic network preferentially retain homeologs through whole genome duplication

Background

Whole genome duplication (WGD) occurs widely in angiosperm evolution. It raises the intriguing question of how interacting networks of genes cope with this dramatic evolutionary event.

Results

In study of the Arabidopsis metabolic network, we assigned each enzyme (node) with topological centralities (in-degree, out-degree and between-ness) to measure quantitatively their centralities in the network. The Arabidopsis metabolic network is highly modular and separated into 11 interconnected modules, which correspond well to the functional metabolic pathways. The enzymes with higher in-out degree and between-ness (defined as hub and bottleneck enzymes, respectively) tend to be more conserved and preferentially retain homeologs after WGD. Moreover, the simultaneous retention of homeologs encoding enzymes which catalyze consecutive steps in a pathway is highly favored and easily achieved, and enzyme-enzyme interactions contribute to the retention of one-third of WGD enzymes.

Conclusions

Our analyses indicate that the hub and bottleneck enzymes of metabolic network obtain great benefits from WGD, and this event grants clear evolutionary advantages in adaptation to different environments.     

Genome-wide analysis of enzyme structure-function combination across three domains of life

To investigate diverse enzyme structure-function combination (SFC) types in different species, 34 different genome sequences were annotated using the protein catalytic domain database SCOPEC (http://www.enzome.com/enzome/), in which both the structure and function for each entry are known. Annotated enzymes with catalytic domains from the same SCOP superfamily are considered to have an identical structure. Annotated enzymes sharing the identical three-digit EC number are considered to have the same enzymatic function. Results reveal that the different SFC types for enzymes identified in archaea, bacteria and eukaryota are 137, 300 and 313, respectively. About 80% of the SFCs identified in archaea can be consistently found in bacteria and eukaryota species, whereas 28% and 35% combination types in bacteria and eukaryota respectively are unique to their corresponding groups. The number of functions per structure and the number of structures per function for the annotated sequences were measured in different species. Furthermore, a new concept was proposed to represent enzymatic structures as a functional similarity network. Thus, the current study will be helpful to enhance the global view on the evolution of enzymatic structure and function.     

4-Hydroxyphenylglycine biosynthesis in Herpetosiphon aurantiacus: a case of gene duplication and catalytic divergence

The nonproteinogenic amino acid 4-hydroxyphenylglycine (HPG) arises from the diversion of the tyrosine degradation pathway into secondary metabolism, and its biosynthesis requires a set of three enzymes. The gene cassette for HPG biosynthesis is widely spread in actinomycete bacteria, which incorporate the amino acid as a building block into various peptide antibiotics, but it has never been reported from another taxonomic group of eubacteria. A genome mining study has now revealed a putative HPG pathway in the predatory bacterium Herpetosiphon aurantiacus, which is phylogenetically distinct from Actinomycetes. Anomalies in the active center of one annotated key enzyme raised questions about the true product of this pathway, prompting an in vitro reconstitution attempt. This study confirmed the capability of H. aurantiacus for HPG production. Sequence analysis of the aberrant 4-hydroxymandelate synthase refines the existing model on the catalytic differentiation of iron(II)-dependent dioxygenases. Furthermore, we report a comprehensive analysis on the phylogeny of these enzymes, which sheds light on the evolution of paralogous gene sets and the ensuing metabolic diversity in a barely studied bacterium.     

Use of pathway analysis and genome context methods for functional genomics of Mycoplasma pneumoniae nucleotide metabolism

Elementary modes analysis allows one to reveal whether a set of known enzymes is sufficient to sustain functionality of the cell. Moreover, it is helpful in detecting missing reactions and predicting which enzymes could fill these gaps. Here, we perform a comprehensive elementary modes analysis and a genomic context analysis of Mycoplasma pneumoniae nucleotide metabolism, and search for new enzyme activities. The purine and pyrimidine networks are reconstructed by assembling enzymes annotated in the genome or found experimentally. We show that these reaction sets are sufficient for enabling synthesis of DNA and RNA in M. pneumoniae. Special focus is on the key modes for growth. Moreover, we make an educated guess on the nutritional requirements of this micro-organism. For the case that M. pneumoniae does not require adenine as a substrate, we suggest adenylosuccinate synthetase (EC 6.3.4.4), adenylosuccinate lyase (EC 4.3.2.2) and GMP reductase (EC 1.7.1.7) to be operative. GMP reductase activity is putatively assigned to the NRDI_MYCPN gene on the basis of the genomic context analysis. For the pyrimidine network, we suggest CTP synthase (EC 6.3.4.2) to be active. Further experiments on the nutritional requirements are needed to make a decision. Pyrimidine metabolism appears to be more appropriate as a drug target than purine metabolism since it shows lower plasticity.     

A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks

A set of linear pathways often does not capture the full range of behaviors of a metabolic network. The concept of 'elementary flux modes' provides a mathematical tool to define and comprehensively describe all metabolic routes that are both stoichiometrically and thermodynamically feasible for a group of enzymes. We have used this concept to analyze the interplay between the pentose phosphate pathway (PPP) and glycolysis. The set of elementary modes for this system involves conventional glycolysis, a futile cycle, all the modes of PPP function described in biochemistry textbooks, and additional modes that are a priori equally entitled to pathway status. Applications include maximizing product yield in amino acid and antibiotic synthesis, reconstruction and consistency checks of metabolism from genome data, analysis of enzyme deficiencies, and drug target identification in metabolic networks.     

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Background

The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.

Results

Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.

Conclusion

Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network.     

Comparative metabolic capabilities for Micrococcus luteus NCTC 2665, the "Fleming" strain, and actinobacteria

Putative gene predictions of the Gram positive actinobacteria Micrococcus luteus (NCTC 2665, "Fleming strain") was used to construct a genome scale reconstruction of the metabolic network for this organism. The metabolic network comprises 586 reactions and 551 metabolites, and accounts for 21% of the genes in the genome. The reconstruction was based on the annotated genome and available biochemical information. M. luteus has one of the smallest genomes of actinobacteria with a circular chromosome of 2,501,097 base pairs and a GC content of 73%. The metabolic pathways required for biomass production in silico were determined based on earlier models of actinobacteria. The in silico network is used for metabolic comparison of M. luteus with other actinomycetes, and hence provides useful information for possible future biotechnological exploitation of this organism, e.g., for production of biofuels.     

Chemical and genomic evolution of enzyme-catalyzed reaction networks

There is a tendency that a unit of enzyme genes in an operon-like structure in the prokaryotic genome encodes enzymes that catalyze a series of consecutive reactions in a metabolic pathway. Our recent analysis shows that this and other genomic units correspond to chemical units reflecting chemical logic of organic reactions. From all known metabolic pathways in the KEGG database we identified chemical units, called reaction modules, as the conserved sequences of chemical structure transformation patterns of small molecules. The extracted patterns suggest co-evolution of genomic units and chemical units. While the core of the metabolic network may have evolved with mechanisms involving individual enzymes and reactions, its extension may have been driven by modular units of enzymes and reactions.     

Enzyme-specific profiles for genome annotation: PRIAM

The advent of fully sequenced genomes opens the ground for the reconstruction of metabolic pathways on the basis of the identification of enzyme-coding genes. Here we describe PRIAM, a method for automated enzyme detection in a fully sequenced genome, based on the classification of enzymes in the ENZYME database. PRIAM relies on sets of position-specific scoring matrices ('profiles') automatically tailored for each ENZYME entry. Automatically generated logical rules define which of these profiles is required in order to infer the presence of the corresponding enzyme in an organism. As an example, PRIAM was applied to identify potential metabolic pathways from the complete genome of the nitrogen-fixing bacterium Sinorhizobium meliloti. The results of this automated method were compared with the original genome annotation and visualised on KEGG graphs in order to facilitate the interpretation of metabolic pathways and to highlight potentially missing enzymes.