TSdb: A database of transporter substrates linking metabolic pathways and transporter systems on a genome scale via their shared substrates

TSdb (http://tsdb.cbi.pku.edu.cn) is the first manually curated central repository that stores formatted information on the substrates of transporters. In total, 37608 transporters with 15075 substrates from 884 organisms were curated from UniProt functional annotation. A unique feature of TSdb is that all the substrates are mapped to identifiers from the KEGG Ligand compound database. Thus, TSdb links current metabolic pathway schema with compound transporter systems via the shared compounds in the pathways. Furthermore, all the transporter substrates in TSdb are classified according to their biochemical properties, biological roles and subcellular localizations. In addition to the functional annotation of transporters, extensive compound annotation that includes inhibitor information from the KEGG Ligand and BRENDA databases has been integrated, making TSdb a useful source for the discovery of potential inhibitory mechanisms linking transporter substrates and metabolic enzymes. User-friendly web interfaces are designed for easy access, query and download of the data. Text and BLAST searches against all transporters in the database are provided. We will regularly update the substrate data with evidence from new publications.     

Reconstruction and In Silico Analysis of Metabolic Network for an Oleaginous Yeast,Yarrowia lipolytica

With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.     

Metabolic potential of the organic‐solvent tolerant Pseudomonas putida DOT‐T1E deduced from its annotated genome

Pseudomonas putida DOT‐T1E is an organic solvent tolerant strain capable of degrading aromatic hydrocarbons. Here we report the DOT‐T1E genomic sequence (6 394 153 bp) and its metabolic atlas based on the classification of enzyme activities. The genome encodes for at least 1751 enzymatic reactions that account for the known pattern of C, N, P and S utilization by this strain. Based on the potential of this strain to thrive in the presence of organic solvents and the subclasses of enzymes encoded in the genome, its metabolic map can be drawn and a number of potential biotransformation reactions can be deduced. This information may prove useful for adapting desired reactions to create value‐added products. This bioengineering potential may be realized via direct transformation of substrates, or may require genetic engineering to block an existing pathway, or to re‐organize operons and genes, as well as possibly requiring the recruitment of enzymes from other sources to achieve the desired transformation.     

Validation of metabolic pathway databases based on chemical substructure search

Metabolic pathway databases such as KEGG contain information on thousands of biochemical reactions drawn from the biomedical literature. Ensuring consistency of such large metabolic pathways is essential to their proper use. In this paper, we present a new method to determine consistency of an important class of biochemical reactions. Our method exploits the knowledge of the atomic rearrangement pattern in biochemical reactions, to reduce the automatic atom mapping problem to a series of chemical substructure searches between the substrate and the product of a biochemical reaction. As an illustrative application, we describe the exhaustive validation of a substantial portion from the latest release of the KEGG LIGAND database.     

MRAD: Metabolic reaction analysis database--an entity-relationship approach

The Metabolic Reaction Analysis Database (MRAD) is a relational database based on the Entity-Relationship (ER) model which combines information about organisms, biochemical pathways, reactions, enzymes, substrates, products and genes. It describes 244,596 genes in 79 organisms, 6,552 enzymes, and 3,552 reactions, 3,100 substrates, 2,866 products and 118 metabolic pathways. The MRAD graphical user interface allows for the identification of metabolic reactions which are similar and dissimilar in multiple organisms, reactions in a pathway which are missing in an organism and using any combination between one to six of the biological entities of organisms, genes, pathways, enzymes, substrates and products to determine metabolic reactions. MRAD provides a powerful and efficient tool for the construction of flux balance models for metabolic engineering applications.     

Carbon-fate maps for metabolic reactions

MOTIVATION: Stable isotope labeling of small-molecule metabolites (e.g. (13)C-labeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reactions, which can be difficult to collect in a useful form when considering a large number of enzymatic reactions. RESULTS: We report carbon-fate maps for 4605 enzyme-catalyzed reactions documented in the KEGG database. Every fate map has been manually checked for consistency with known reaction mechanisms. A map includes a standardized structure-based identifier for each reactant (namely, an InChI string); indices for carbon atoms that are uniquely derived from the metabolite identifiers; structural data, including an identification of homotopic and prochiral carbon atoms; and a bijective map relating the corresponding carbon atoms in substrates and products. Fate maps are defined using the BioNetGen language (BNGL), a formal model-specification language, which allows a set of maps to be automatically translated into isotopomer mass-balance equations. AVAILABILITY: The carbon-fate maps and software for visualizing the maps are freely available (http://cellsignaling.lanl.gov/FateMaps/).     

Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution

A new method for the mathematical analysis of large metabolic networks is presented. Based on the fact that the occurrence of a metabolic reaction generally requires the existence of other reactions providing its substrates, series of metabolic networks are constructed. In each step of the corresponding expansion process those reactions are incorporated whose substrates are made available by the networks of the previous generations. The method is applied to the set of all metabolic reactions included in the KEGG database. Starting with one or more seed compounds, the expansion results in a final network whose compounds define the scope of the seed. Scopes of all metabolic compounds are calculated and it is shown that large parts of cellular metabolism can be considered as the combined scope of simple building blocks. Analyses of various expansion processes reveal crucial metabolites whose incorporation allows for the increase in network complexity. Among these metabolites are common cofactors such as NAD⁺, ATP, and coenzyme A. We demonstrate that the outcome of network expansion is in general very robust against elimination of single or few reactions. There exist, however, crucial reactions whose elimination results in a dramatic reduction of scope sizes. It is hypothesized that the expansion process displays characteristics of the evolution of metabolism such as the temporal order of the emergence of metabolic pathways. [Reviewing Editor : Dr. David Pollock]     

Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition

Background  

Metabolic pathway is a highly regulated network consisting of many metabolic reactions involving substrates, enzymes, and products, where substrates can be transformed into products with particular catalytic enzymes. Since experimental determination of the network of substrate-enzyme-product triad (whether the substrate can be transformed into the product with a given enzyme) is both time-consuming and expensive, it would be very useful to develop a computational approach for predicting the network of substrate-enzyme-product triads.     

基于文献挖掘的巨大芽胞杆菌代谢网络模型的构建与分析

【目的】通过挖掘实验性文献,建立巨大芽胞杆菌事实型代谢网络模型,以详尽解析生理特性,优化其生理功能。【方法】从PubMed、Derwent Innovations Index、中国知网等公共文献(专利)数据库中获取与巨大芽胞杆菌(Bacillus megaterium)相关的实验性文献建立本地文献数据库。采用文献挖掘工具获取功能基因、酶、代谢物和生化反应等信息,以其为基础构建代谢网络粗模型,进一步借助KEGG等数据库修正以及Matlab程序的模拟得到精细模型(系统生物学标记语言的形式)。【结果】最终的精细模型共有292个生化反应、378个代谢物、220个酶和217个基因。以1.62 mmol/g cell/h的葡萄糖底物吸收速率为限制性条件,模拟的菌体比生长速率为0.089 h-1,略低于实验值0.11 h-1。此外,嘧啶代谢途径的单基因敲除模拟结果表明,准确率为90%。【结论】该代谢网络模型涵盖了中心代谢途径、维生素B12合成途径和氨基酸代谢途径,并在一定程度上反映了营养底物与基因对巨大芽胞杆菌生长性能的影响。     

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.     

Metabolic Pathfinding Using RPAIR Annotation

Metabolic databases contain information about thousands of small molecules and reactions, which can be represented as networks. In the context of metabolic reconstruction, pathways can be inferred by searching optimal paths in such networks. A recurrent problem is the presence of pool metabolites (e.g., water, energy carriers, and cofactors), which are connected to hundreds of reactions, thus establishing irrelevant shortcuts between nodes of the network. One solution to this problem relies on weighted networks to penalize highly connected compounds. A more refined solution takes the chemical structure of reactants into account in order to differentiate between side and main compounds of a reaction. Thanks to an intensive annotation effort at KEGG, decompositions of reactions into reactant pairs (RPAIR) categorized by their role (main, trans, cofac, ligase, and leave) are now available.The goal of this article is to evaluate the impact of RPAIR data on pathfinding in metabolic networks. To this end, we measure the impact of different parameters concerning the construction of the metabolic network: mapping of reactions and reactant pairs onto a graph, use of selected categories of reactant pairs, weighting schemes for compounds and reactions, removal of highly connected metabolites, and reaction directionality. In total, we tested 104 combinations of parameters and identified their optimal values for pathfinding on the basis of 55 reference pathways from three organisms.The best-performing metabolic network combines the biochemical knowledge encoded by KEGG RPAIR with a weighting scheme penalizing highly connected compounds. With this network, we could recover reference pathways from Escherichia coli with an average accuracy of 93% (32 pathways), from Saccharomyces cerevisiae with an average accuracy of 66% (11 pathways), and from humans with an average accuracy of 70% (12 pathways). Our pathfinding approach is available as part of the Network Analysis Tools.     

Ab initio reconstruction of metabolic pathways

We propose a new formulation for the problem of ab initio metabolic pathway reconstruction. Given a set of biochemical reactions together with their substrates and products, we consider the reactions as transfers of atoms between the chemical compounds and we look for successions of reactions transferring a maximal (or preset) number of atoms between a given source and sink compound. We state this problem as the one of finding a composition of partial injections that maximizes the image size. First, we study the theoretical complexity of this problem, state some related problems and then give a practical algorithm to solve them. Finally, we present two applications of this approach to the reconstruction of the tryptophan biosynthesis pathway and to the glycolysis.     

Modeling cholesterol metabolism by gene expression profiling in the hippocampus

An important part of the challenge of building models of biochemical reactions is determining reaction rate constants that transform substrates into products. We present a method to derive enzymatic kinetic values from mRNA expression levels for modeling biological networks without requiring further tuning. The core metabolic reactions of cholesterol in the brain, particularly in the hippocampus, were simulated. To build the model the baseline mRNA expression levels of genes involved in cholesterol metabolism were obtained from the Allen Mouse Brain Atlas. The model is capable of replicating the trends of relative cholesterol levels in Alzheimer's and Huntington's diseases; and reliably simulated SLOS, desmosterolosis, and Dhcr14/Lbr knockout studies. A sensitivity analysis correctly uncovers the Hmgcr, Idi2 and Fdft1 sites that regulate cholesterol homeostasis. Overall, our model and methodology can be used to pinpoint key reactions, which, upon manipulation, may predict altered cholesterol levels and reveal insights into potential drug therapy targets under diseased conditions.     

In vivo metabolite profiling as a means to identify uncharacterized lipase function: Recent success stories within the alpha beta hydrolase domain (ABHD) enzyme family

Genome sequencing efforts have identified many uncharacterized lipase/esterase enzymes that have potential to be drug targets for metabolic diseases such as obesity, diabetes, and atherosclerosis. However, sequence information and associated structural predictions provide only a loose framework for linking enzyme function to disease risk. We are now confronted with the challenge of functionally annotating a large number of uncharacterized lipases, with the goal of generating new therapies for metabolic diseases. This daunting challenge involves gathering not only sequence-driven predictions, but also more importantly structural, biochemical (substrates and products), and physiological data. At the center of such drug discovery efforts are accurately identifying physiologically relevant substrates and products of individual lipases, and determining whether newly identified substrates/products can modulate disease in appropriate preclinical animal model systems. This review describes the importance of coupling in vivo metabolite profiling to in vitro enzymology as a powerful means to assign lipase function in disease specific contexts using animal models. In particular, we highlight recent examples using this multidisciplinary approach to functionally annotate genes within the α/β hydrolase fold domain (ABHD) family of enzymes. These new discoveries within the ABHD enzyme family serve as powerful examples of linking novel lipase function to human disease. This article is part of a Special Issue entitled Tools to study lipid functions.     

Challenges to be faced in the reconstruction of metabolic networks from public databases

In the post-genomic era, the biochemical information for individual compounds, enzymes, reactions to be found within named organisms has become readily available. The well-known KEGG and BioCyc databases provide a comprehensive catalogue for this information and have thereby substantially aided the scientific community. Using these databases, the complement of enzymes present in a given organism can be determined and, in principle, used to reconstruct the metabolic network. However, such reconstructed networks contain numerous properties contradicting biological expectation. The metabolic networks for a number of organisms are reconstructed from KEGG and BioCyc databases, and features of these networks are related to properties of their originating database.     

Reconstruction of metabolic network in the bovine mammary gland tissue

Understanding bovine metabolism and its relationship with milk products is important in cow breeding. In the present work, the metabolic network in the mammary gland tissue of cattle was reconstructed with the available bovine genome information using several public datasets from NCBI, Uniprot, and KEGG. The network consisted of 1,743 metabolites named by KEGG compound numbers as nodes and 657 enzymes that catalyzed the corresponding reactions as edges. The characteristics of the network were analyzed. The top 20 hub metabolites were determined, and the mean path length was identified to be 6.52. Moreover, 11 key enzymes with significant changes in expression under the condition of mastitis were identified and analyzed by integrating the microarray expression data of normal and clinical mastitis. Aside from the GATM gene, 10 downregulated enzymes were detected in bovine with mastitis. In addition, many of the identified enzymes were involved in amino acid metabolisms or had a direct connection to amino acid metabolisms. These results indicate that mastitis could affect the expression of enzymes, which is vital in some amino acid metabolisms, resulting in the reduction of milk proteins. The present work provides information that may improve the understanding on bovine milk production and mastitis.     

The auxiliary substrate concept — an approach for overcoming limits of microbial performances

In biotechnological processes, fundamental performances of microorganisms are used. The economy of these processes is essentially determined by the efficiency, velocity (productivity) and quality of the products. Therefore it is a permanent task and challenge for basic and biotechnological research to seek out measures for improving the actually attained parameters. The auxiliary substrate concept supplics an approach. It is based on the fact that chemo-organo-heterotrophic substrates differ in the carbon: energy ratio, thus, growth yield is limited in energy and/or reducing power. It says that, by simultaneous utilization of physiologically similar substrates (mixed substrates), the growth yield increases. The substrates are to combine in such a way that with their simultaneous utilization a minimum of carbon is dissimilated merely for the purpose of the generation of biologically useful energy and/or reducing power. Since all chemo-organo-heterotrophic substrates are more or less energy-deficient, an increase in growth efficiency can be expected if the individual substrates of the mixture are assimilated more efficiently than the respective substrates alone. This may result, for instance, from an immediate assimilation of a substrate (according to the “manner of finished part construction”). An increased growth rate is rather the rule than the exception in mixed substrate utilization. In product syntheses the substrates are, depending on the concrete product and metabolic pathway, either energy-excess or energy-excess or energy-deficient. or, in other words, the processes are energy-generating or energy-consuming, respectively. If this is responsible for discrepancies between the possible yields determined by the carbon metabolism and the experimentally obtained yields, the discrepancies should be able to be decreased and the yields increased by mixing substrates. The substrates are to choose and combine so that, due to simultaneous utilization, the product formation process becomes energy neutral. As a rule, the enhanced efficiency is accompanied by an increased velocity. This does not only apply to syntheses, but also to degradation (and detoxification) reactions. Even supposedly inert compounds or persistent substances can be activated by simultaneous (co-)metabolization of another (an auxiliary substrate, victim substrate or co-substrate) and converted at a considerable rate. It is of interest for syntheses of products but in particular for degradation and decontamination of harmful and waste products in the environment that the residual concentrations of the substrates are smaller than those achieved if the compounds of a mixture are metabolized separately. The auxiliary substrate concept has proven to be fruitful, both for theoretical and practical questions. It was practically already being used before it was formulated (mixed substrate utilization, cometabolism). However, an abundance of regulatory and energetic aspects are waiting to be investigated in more detail.     

Production of saponin in fermentation process of Sanchi (Panax notoginseng) and biotransformation of saponin byBacillus subtilis

An antitumor compound ginsenoside Rh₄ was produced during the fermentation process of Sanchi (Panax notoginseng) byBacillus subtilis. Saponins ginsenosides Rh₁ and Re were transformed byB. subtilis and were produced two main transformed products. the transformed product of ginsenoside Rh₁ was determined to be 3-O-β-D-glucopyranosyl-6-O-β-D-glucopyranosyl-20(S)-protopanaxatriol, and the transformed product of ginsenoside Re had 162 atomic mass units (amu) greater than the substrate. Compard with the substrates, the transformed products had one more glucosyl moiety linked respectively, which indicated that glycosylion reaction occurred in the transformation process.