BioCyc Database Research Articles

Sustainable Use of Organic Seaweed Fertilizer Improves the Metagenomic Function of Microbial Communities in the Soil of Rice Plants

The frequent use of chemical fertilizers in agricultural practices has developed into a serious environmental concern which urgently needs a solution to restrain their use in agricultural systems. Hence, there is an urgent need to investigate potential organic fertilizers from various natural resources to decrease the use of chemical fertilizers. Seaweed is among the natural resources with potential sustainability value. Our previous work has shown the effectiveness of seaweed fertilizer for increasing plant growth and soil beneficial microbiota. This study aims to evaluate the functional genes present in the soil of rice plants treated with seaweed fertilization. It involves amendments with reduced concentrations of chemical fertilizer in three groups: CF (only chemical fertilizer), CFSF1 (50% dose of CF + seaweed fertilizer 1 ton/ha), and CFSF2 (50% dose of CF + seaweed fertilizer 2 ton/ha). The rice plants supplemented with CFSF1 and CFSF2 were taller and faster to mature compared to CF. In addition, the primary macronutrients nitrogen (N), phosphorus (P), and potassium (K) were also significantly higher in soil supplemented with SF. Our findings showed increased ammonia-oxidizing archaea Crenarchaeota abundance in increasing SF treatments. The PICRUSt analyses indicated enriched functional genes and proteins in relation to amino acid, nucleotide, protein, and carbohydrate metabolism based on the KEGG, BioCyc, and PFAM databases. The current outcomes enhanced our understanding regarding the importance of microbial community for soil quality. Furthermore, seaweed supplementation has shown improvement in soil fertility, which significantly increases rice plant growth and productivity.

A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications.

Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies.

Leveraging Curation Among Escherichia coli Pathway/Genome Databases Using Ortholog-Based Annotation Propagation.

Updating genome databases to reflect newly published molecular findings for an organism was hard enough when only a single strain of a given organism had been sequenced. With multiple sequenced strains now available for many organisms, the challenge has grown significantly because of the still-limited resources available for the manual curation that corrects errors and captures new knowledge. We have developed a method to automatically propagate multiple types of curated knowledge from genes and proteins in one genome database to their orthologs in uncurated databases for related strains, imposing several quality-control filters to reduce the chances of introducing errors. We have applied this method to propagate information from the highly curated EcoCyc database for Escherichia coli K–12 to databases for 480 other Escherichia coli strains in the BioCyc database collection. The increase in value and utility of the target databases after propagation is considerable. Target databases received updates for an average of 2,535 proteins each. In addition to widespread addition and regularization of gene and protein names, 97% of the target databases were improved by the addition of at least 200 new protein complexes, at least 800 new or updated reaction assignments, and at least 2,400 sets of GO annotations.

Identification of Potential Drug Targets in Helicobacter pylori Using In Silico Subtractive Proteomics Approaches and Their Possible Inhibition through Drug Repurposing.

The class 1 carcinogen, Helicobacter pylori, is one of the World Health Organization’s high priority pathogens for antimicrobial development. We used three subtractive proteomics approaches using protein pools retrieved from: chokepoint reactions in the BIOCYC database, the Kyoto Encyclopedia of Genes and Genomes, and the database of essential genes (DEG), to find putative drug targets and their inhibition by drug repurposing. The subtractive channels included non-homology to human proteome, essentiality analysis, sub-cellular localization prediction, conservation, lack of similarity to gut flora, druggability, and broad-spectrum activity. The minimum inhibitory concentration (MIC) of three selected ligands was determined to confirm anti-helicobacter activity. Seventeen protein targets were retrieved. They are involved in motility, cell wall biosynthesis, processing of environmental and genetic information, and synthesis and metabolism of secondary metabolites, amino acids, vitamins, and cofactors. The DEG protein pool approach was superior, as it retrieved all drug targets identified by the other two approaches. Binding ligands (n = 42) were mostly small non-antibiotic compounds. Citric, dipicolinic, and pyrophosphoric acid inhibited H. pylori at an MIC of 1.5–2.5 mg/mL. In conclusion, we identified potential drug targets in H. pylori, and repurposed their binding ligands as possible anti-helicobacter agents, saving time and effort required for the development of new antimicrobial compounds.

Lolium perenne apoplast metabolomics for identification of novel metabolites produced by the symbiotic fungus Epichloë festucae.

Summary Epichloë festucae is an endophytic fungus that forms a symbiotic association with Lolium perenne. Here we analysed how the metabolome of the ryegrass apoplast changed upon infection of this host with sexual and asexual isolates of E. festucae.A metabolite fingerprinting approach was used to analyse the metabolite composition of apoplastic wash fluid from uninfected and infected L. perenne. Metabolites enriched or depleted in one or both of these treatments were identified using a set of interactive tools. A genetic approach in combination with tandem MS was used to identify a novel product of a secondary metabolite gene cluster.Metabolites likely to be present in the apoplast were identified using marvis in combination with the BioCyc and KEGG databases, and an in‐house Epichloë metabolite database. We were able to identify the known endophyte‐specific metabolites, peramine and epichloëcyclins, as well as a large number of unknown markers.To determine whether these methods can be applied to the identification of novel Epichloë‐derived metabolites, we deleted a gene encoding a NRPS (lgsA) that is highly expressed in planta. Comparative MS analysis of apoplastic wash fluid from wild‐type‐ vs mutant‐infected plants identified a novel Leu/Ile glycoside metabolite present in the former.

Determining the Presence of Metabolic Pathways using Machine Learning Approach

The reconstruction of the metabolic network of an organism based on its genome sequence is a key challenge in systems biology. One of the strategies that can be used to address this problem is the prediction of the presence or the absence of a metabolic pathway from a reference database of known pathways. Although, such models have been constructed manually, obviously such a method cannot be used to cover thousands of genomes that has been sequenced. Therefore, more advanced techniques are needed for computational representation of metabolic networks. In this research, we have explored machine learning approach to determine the presence or the absent of metabolic pathway based on its annotated genome. We have built our own dataset of 4978 instances of pathways. The dataset consists of 1585 pathways with each having 20 different representations from 20 organisms. The pathways were obtained from the BioCyc Database Collection. The pathway dataset also consists of 20 features used to describe each pathway. In order to identify the suitable classifier, we have experimented five machine learning algorithms with and without applying feature selection methods, namely Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Logistic Regression. Our experiments have shown that Support Vector Machine is the best classifier with an accuracy of 96.9%, while the maximum accuracy reached by the previous work is 91.2%. Hence, adding more data to the pathway dataset can improve the performance of the machine learning classifiers.

Investigation of the underling mechanism of ketamine for antidepressant effects in treatment-refractory affective disorders via molecular profile analysis.

Ketamine elicits a rapid antidepressant effect in treatment-refractory affective disorders. The aim of the present study was to elucidate the underlying mechanism of this effect and to identify potential targets of ketamine for antidepressant effects. GSE73798 and GSE73799 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in hippocampus or striatum samples treated with ketamine, phencyclidyne or memantine compared with a saline or normal group at 1, 2, 4 and 8 h. The overlapping DEGs were the DEGs in both hippocampus and striatum samples. Kyoto Encyclopedia of Genes and Genomes and BioCyc databases were used to perform functional annotation and pathway analyses. Protein-protein interactions (PPIs) were predicted using Search Tool for the Retrieval of Interacting Genes/Proteins version 9.1 for the DEGs in the striatum samples treated with ketamine, phencyclidine or memantine compared with normal samples. Reverse transcription-quantitative polymerase chain reaction was performed to determine mRNA levels. Perilipin 4 (Plin4), serum/glucocorticoid regulated kinase 1 (Sgk1), kruppel like factor 2 (Klf2) and DDB1 and CUL4 associated factor 12 like 1 (Dcaf12l1) were the overlapping DEGs in the striatum samples treated with the three drugs at different time points. The mRNA expression levels of Plin4, Sgk1 and Klf2 were significantly higher (P<0.05), and the mRNA expression level of Dcaf12l1 was significantly lower in the striatum samples of the ketamine-treated group compared with the control group in an in vivo experiment. Both Sgk1 and Klf2 were enriched in the ‘forkhead box O (FoxO) signaling pathway’, and Sgk1 was additionally enriched in the ‘mechanistic target of rapamycin kinase (mTOR) signaling pathway’. PPI networks of DEGs in the striatum samples treated with ketamine, phencyclidine and memantine compared with normal samples were constructed, and Klf2 was involved in more pairs and was therefore a gene hub in the three networks. The four genes, Plin4, Sgk1, Klf2 and Dcaf12l1, were differentially expressed in all of the groups that treated with the three drugs and their expression levels were verified in in vivo experiments. The FoxO and mTOR signaling pathways may be involved in the underlying mechanism of the antidepressant effects of ketamine, and Plin4, Sgk1, Klf2 and Dcaf12l1 may be potential biomarkers for depression in N-methyl-D-aspartic acid receptor antagonist treatment.

BioCyc: A Genomic and Metabolic Web Portal with Multiple Omics Analytical Tools

BioCyc.org is a web portal that contains Pathway/Genome Databases for over 14,500 organisms, including 2400 human microbiome members. The BioCyc databases are unique in integrating a diverse range of data and providing a high level of curation for important microbes as well as model eukaryotic organisms and Homo sapiens. Each Pathway/Genome Database describes the genome and proteome of an organism, as well as its biochemical pathways and (for a small number of organisms) its regulatory network. BioCyc bioinformatics tools combine unparalleled breadth and user friendliness and include a unique set of visualization tools to speed comprehension of its extensive and complex data. BioCyc also includes MetaCyc, the largest collection of metabolic pathways and enzymes currently available, with over 2600 pathways. It contains the equivalent of about 9,400 textbook-pages of mini-review summaries for enzymes and pathways. The data in MetaCyc has been manually curated from over 58,000 published papers. Analysis tools in BioCyc include comparisons of pathways and genomes among multiple organisms, alignment of orthologous genes in a multi-genome browser, and searching for minimal-cost metabolic network routes that connect two substrates. Tools are also provided for visualizing omics data – such as metabolomics and gene expression data – on individual pathway diagrams, on personalized groups of multiple pathway diagrams called pathway collages, and on diagrams depicting the full metabolic network of an organism. A new visualization tool in BioCyc is the Omics Dashboard, an interactive tool for exploration and analysis of gene-expression and metabolomics datasets through a hierarchy of cellular systems and subsystems. This highly visual tool enables the user to survey their data at a very high level of abstraction, and to drill down to examine details at the pathway, subsystem, and gene levels. Patterns in the data can often be perceived quickly and easily. Another new tool is the MultiOmics Explainer, which helps researchers understand and interpret the results of their omics experiments in the context of what is known about an organism's metabolic and regulatory network. At this time this tool is not available online and requires installation of the software on a local computer. Another recent addition to BioCyc involves manual curation of databases for important pathogens. So far, the project includes Mycobacterium tuberculosis (H37Rv), Staphylococcus aureus (NCTC 8325) and Salmonella enterica (14028S) The BioCyc databases can be accessed through the BioCyc.org web site and can be downloaded. In addition, the Pathway Tools software can be downloaded and installed locally to create BioCyc-like databases for more genomes, curate existing databases, or perform additional analyses that are not available online. Access to MetaCyc and EcoCyc is unrestricted. Unlimited access to other databases requires a subscription. Support or Funding Information National Institute of General Medical Sciences of the National Institutes of Health (NIH) [GM080746, GM077678, GM75742] A portion of the initial display of the Omics Dashboard, showing the average gene expression levels of genes, at different time points, in pathways from different subsystems. Another example output of the Omics Dashboard, showing gene-expression profiles at the regulon level. A typical pathway diagram from a BioCyc database. The Multi-Genome Browser enables comparison of orthologous regions from different organisms. Genes in the Genome Browser are color-coded to indicate transcription units. Reaction diagrams provide color-coded atom mapping between reactants and products. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Computing Metabolic Routes in the Human Microbiome

ObjectiveDevelop a computational tool for computing metabolic transformations across multiple organisms within the human microbiome.BackgroundThe complexity of microbiomes and their metabolic networks makes it hard to understand their collective biochemical capabilities. We are enabling computational insights in the context of BioCyc.org, an extensive web portal for microbial genomics and metabolic pathways. BioCyc contains 11,000 microbial genomes including 929 organisms from the Human Microbiome Project. Two BioCyc databases are noteworthy as bacterial references: the EcoCyc database for Escherichia coli has been curated from 33,000 publications, and the BsubCyc database for Bacillus subtilis has been curated from 4,000 publications. BioCyc contains an extensive set of bioinformatics tools. Genome‐related tools include a genome browser, sequence searching and alignment, and extraction of sequence regions. Pathway‐related tools include a tool for navigating zoomable organism‐specific metabolic map diagrams that can be painted with metabolomics and gene‐expression data.Results ObtainedThe new BioCyc Multi‐Organism (metabolic) Route Search (MORS) tool enables the user to explore biochemical conversions among metabolites that are accomplished by multiple microbiome organisms. To specify a route search problem the user provides a starting and an ending metabolite of interest, and a set of organisms that provides the reactions.As the endpoint for one example route, we picked indoxyl sulfate, because it is implicated in toxicity among patients with kidney disease. We selected L‐tryptophan as a starting point because it is the known source of a microbial route to indoxyl sulfate. MORS found a known [1] route of 3 reaction steps, retaining 9 atoms from start to end. The first reaction can be catalyzed by 164 different microbes, whereas the last two reactions are catalyzed by Homo sapiens only. A second route of 9 reactions is also found, retaining 7 atoms. Although this route appears biochemically possible, it looks less plausible because the the required reactions are contributed from a larger set of organisms.For another example route, we wanted to see how microbes might be linked by autoinducers, which are compounds involved in quorum sensing. The start was S‐adenosyl‐L‐methionine, the known source for some autoinducers. As the end, we picked L‐homoserine lactone, which is a degradation product common to a class of autoinducers. We find several related routes, which all have two reactions, retaining 7 atoms. The differences between the routes are that the actual autoinducers are different, which are the products of the first reaction.MethodsThe MORS tool computes a set of optimal routes connecting the starting and ending compounds. Each route is a linear sequence of reactions that converts the starting compound to the ending compound. An optimal route minimizes the number of reactions used while maximizing the number of atoms from the starting compound that are incorporated into the ending compound. The set of reactions from which the routes are computed is the union of all reactions in the metabolic networks of the selected organisms ‐‐ all reactions in those organisms are assumed to be accessible, independent of transport considerations.ConclusionsThe MORS tool will be available in the Spring 2018 release of the BioCyc website.Support or Funding InformationThis work was funded by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number GM080746 to P.D.K. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Computing Metabolic Routes in the Human Microbiome

ObjectiveDevelop a computational tool for computing metabolic transformations across multiple organisms within the human microbiome.BackgroundThe complexity of microbiomes and their metabolic networks makes it hard to understand their collective biochemical capabilities. We are enabling computational insights in the context of BioCyc.org, an extensive web portal for microbial genomics and metabolic pathways. BioCyc contains 11,000 microbial genomes including 929 organisms from the Human Microbiome Project. Two BioCyc databases are noteworthy as bacterial references: the EcoCyc database for Escherichia coli has been curated from 33,000 publications, and the BsubCyc database for Bacillus subtilis has been curated from 4,000 publications. BioCyc contains an extensive set of bioinformatics tools. Genome‐related tools include a genome browser, sequence searching and alignment, and extraction of sequence regions. Pathway‐related tools include a tool for navigating zoomable organism‐specific metabolic map diagrams that can be painted with metabolomics and gene‐expression data.Results ObtainedThe new BioCyc Multi‐Organism (metabolic) Route Search (MORS) tool enables the user to explore biochemical conversions among metabolites that are accomplished by multiple microbiome organisms. To specify a route search problem the user provides a starting and an ending metabolite of interest, and a set of organisms that provides the reactions.As the endpoint for one example route, we picked indoxyl sulfate, because it is implicated in toxicity among patients with kidney disease. We selected L‐tryptophan as a starting point because it is the known source of a microbial route to indoxyl sulfate. MORS found a known [1] route of 3 reaction steps, retaining 9 atoms from start to end. The first reaction can be catalyzed by 164 different microbes, whereas the last two reactions are catalyzed by Homo sapiens only. A second route of 9 reactions is also found, retaining 7 atoms. Although this route appears biochemically possible, it looks less plausible because the the required reactions are contributed from a larger set of organisms.For another example route, we wanted to see how microbes might be linked by autoinducers, which are compounds involved in quorum sensing. The start was S‐adenosyl‐L‐methionine, the known source for some autoinducers. As the end, we picked L‐homoserine lactone, which is a degradation product common to a class of autoinducers. We find several related routes, which all have two reactions, retaining 7 atoms. The differences between the routes are that the actual autoinducers are different, which are the products of the first reaction.MethodsThe MORS tool computes a set of optimal routes connecting the starting and ending compounds. Each route is a linear sequence of reactions that converts the starting compound to the ending compound. An optimal route minimizes the number of reactions used while maximizing the number of atoms from the starting compound that are incorporated into the ending compound. The set of reactions from which the routes are computed is the union of all reactions in the metabolic networks of the selected organisms ‐‐ all reactions in those organisms are assumed to be accessible, independent of transport considerations.ConclusionsThe MORS tool will be available in the Spring 2018 release of the BioCyc website.Support or Funding InformationThis work was funded by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number GM080746 to P.D.K. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

ArthropodaCyc: a CycADS powered collection of BioCyc databases to analyse and compare metabolism of arthropods.

Arthropods interact with humans at different levels with highly beneficial roles (e.g. as pollinators), as well as with a negative impact for example as vectors of human or animal diseases, or as agricultural pests. Several arthropod genomes are available at present and many others will be sequenced in the near future in the context of the i5K initiative, offering opportunities for reconstructing, modelling and comparing their metabolic networks. In-depth analysis of these genomic data through metabolism reconstruction is expected to contribute to a better understanding of the biology of arthropods, thereby allowing the development of new strategies to control harmful species. In this context, we present here ArthropodaCyc, a dedicated BioCyc collection of databases using the Cyc annotation database system (CycADS), allowing researchers to perform reliable metabolism comparisons of fully sequenced arthropods genomes. Since the annotation quality is a key factor when performing such global genome comparisons, all proteins from the genomes included in the ArthropodaCyc database were re-annotated using several annotation tools and orthology information. All functional/domain annotation results and their sources were integrated in the databases for user access. Currently, ArthropodaCyc offers a centralized repository of metabolic pathways, protein sequence domains, Gene Ontology annotations as well as evolutionary information for 28 arthropod species. Such database collection allows metabolism analysis both with integrated tools and through extraction of data in formats suitable for systems biology studies. Database URL: http://arthropodacyc.cycadsys.org/

Gramene 2016: comparative plant genomics and pathway resources.

Gramene (http://www.gramene.org) is an online resource for comparative functional genomics in crops and model plant species. Its two main frameworks are genomes (collaboration with Ensembl Plants) and pathways (The Plant Reactome and archival BioCyc databases). Since our last NAR update, the database website adopted a new Drupal management platform. The genomes section features 39 fully assembled reference genomes that are integrated using ontology-based annotation and comparative analyses, and accessed through both visual and programmatic interfaces. Additional community data, such as genetic variation, expression and methylation, are also mapped for a subset of genomes. The Plant Reactome pathway portal (http://plantreactome.gramene.org) provides a reference resource for analyzing plant metabolic and regulatory pathways. In addition to ∼200 curated rice reference pathways, the portal hosts gene homology-based pathway projections for 33 plant species. Both the genome and pathway browsers interface with the EMBL-EBI's Expression Atlas to enable the projection of baseline and differential expression data from curated expression studies in plants. Gramene's archive website (http://archive.gramene.org) continues to provide previously reported resources on comparative maps, markers and QTL. To further aid our users, we have also introduced a live monthly educational webinar series and a Gramene YouTube channel carrying video tutorials.

Membranome: A Database of Single-Spanning Transmembrane Proteins

The Membranome database (http://membranome.org) has been created. The database is focused on structural models of single-spanning (bitopic) transmembrane (TM) proteins and their oligomeric complexes. Current version of Membranome collects data on bitopic proteins of six model organisms, Methanococcus jannaschii, Escherichia coli, Saccharomyces cerevisiae, Dictyostelium discoideum, Arabidopsis thaliana and Homo sapiens that represent different kingdoms of life. It provides comprehensive information on more than 4,500 bitopic proteins, including their three-dimensional (3D) structures, interactions, biological functions, topology, localizations, and classification. The structural data comprise available experimental structures and 3D models of TM alpha-helices that were generated using our new thermodynamic model of alpha-helix formation in the lipid bilayer approximated as an anisotropic solvent with predefined polarity profiles [1]. All bitopic proteins in the Membranome were classified into 15 functional classes, 660 superfamilies and 1076 families. Interactions between different bitopic proteins are included based on data from ConsensusPathDB and PDB. The database provides linking to amino acid sequences from Uniprot and HGNC, structural domains from Interpro and Pfam, protein-proteins interactions from STRING, experimental structures of water-soluble and transmembrane domains from PDB and OPM databases, and annotations in iHOP. Protein entries are also linked to corresponding biological pathways in KEGG, BioCyc and Reactome databases and small molecule metabolic pathways (from SMPDB). Membranome is intended to provide experimentally validated computational models of homo-dimers formed by TM alpha-helices of bitopic proteins.Reference:[1] I.D. Pogozheva, S. Tristram-Nagle, H.I. Mosberg, A.L. Lomize, Structural adaptations of proteins to different biological membranes, Biochim Biophys Acta, 1828 (2013) 2592-2608.

A computational platform to maintain and migrate manual functional annotations for BioCyc databases.

BackgroundBioCyc databases are an important resource for information on biological pathways and genomic data. Such databases represent the accumulation of biological data, some of which has been manually curated from literature. An essential feature of these databases is the continuing data integration as new knowledge is discovered. As functional annotations are improved, scalable methods are needed for curators to manage annotations without detailed knowledge of the specific design of the BioCyc database.ResultsWe have developed CycTools, a software tool which allows curators to maintain functional annotations in a model organism database. This tool builds on existing software to improve and simplify annotation data imports of user provided data into BioCyc databases. Additionally, CycTools automatically resolves synonyms and alternate identifiers contained within the database into the appropriate internal identifiers.ConclusionsAutomating steps in the manual data entry process can improve curation efforts for major biological databases. The functionality of CycTools is demonstrated by transferring GO term annotations from MaizeCyc to matching proteins in CornCyc, both maize metabolic pathway databases available at MaizeGDB, and by creating strain specific databases for metabolic engineering.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0115-1) contains supplementary material, which is available to authorized users.

CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes

We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER’s prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.

Deciphering plant–pathogen interactions applying metabolomics: principles and applications

The in-depth study of plant metabolic networks and their changes in response to biotic stimuli is an emerging field of metabolomics which has a great potential to provide new insights into plant physiology, plant–pathogen interactions, crop protection and breeding. In spite of the development of metabolomics in numerous applications, its application to the study of plant–pathogen interactions is still in its infancy. Here we describe an integrated methodology for the study of plant–pathogen interactions, ranging from experimental design and execution, to analyses of samples and data, and their biological interpretation. As a study case, the investigation of interactions in the pathosystem potato (Solanum tuberosum L.) and Rhizoctonia solani using metabolomics is presented. Chemical analyses were performed by integrating Fourier transform-ion cyclotron resonance/mass spectrometry (FT-ICR/MS) with gas chromatography/mass spectrometry (GC/MS). The use of two analytical platforms enabled the study of alterations in the content of infected plants for a large number of metabolites belonging to chemical groups with diverse physicochemical properties, such as amino acids, fatty acids, and carboxylic acids, as well as carbohydrates, and terpenoid and steroidal glycoalkaloids. The biological interpretation of data was performed using the powerful software Cytoscape whose role in metabolomics will be highlighted, and BioCyc databases. The principle behind such an approach is to reveal changes in the global metabolic network of the plant in response to pathogen invasion and subsequently to correlate those changes to gene expression.

Application of string similarity ratio and edit distance in automatic metabolite reconciliation comparing reconstructions and models

Increasing numbers of biochemical network models become available and reuse of these models is becoming more common. As a consequence, tools to compare models are needed. Comparison can be difficult because model builders often use different standards during reconstruction, metabolite formulas are not always indicated, IDs and names of metabolites are different, and models are stored in different formats (SBML, COBRA and others). Herein, a model comparison algorithm for SBML and COBRA format models is presented, called ModeRator. Precondition for correct matching of reactions is the comparison of the participating metabolites. ModeRator is based on the comparison of metabolite names as text strings. An automatic three level filtering approach is implemented in the software, which rejects pairs of potentially equal metabolites and builds an opinion about metabolite pairs with high similarity in metabolite names. ModeRator was applied to two test cases, comparing two models of each, E.coli and S.cerevisiae. Matches of the automatic mapping were manually inspected and compared with the automatic predictions. Automatic metabolite mapping of E.coli models (1314 and 1704 metabolites) comparing only identifiers revealed a high number of accordant metabolites. Both models originate from the same source (BioCyc database). No significant difference between automatic mapping and manual curation are observed. For the comparison of two S.cerevisiae models (679 and 1061 metabolites), three level filtration by metabolite name is used. The discrepancy between manual curated predictions and ModeRator predictions was 7%.

CycADS: an annotation database system to ease the development and update of BioCyc databases

In recent years, genomes from an increasing number of organisms have been sequenced, but their annotation remains a time-consuming process. The BioCyc databases offer a framework for the integrated analysis of metabolic networks. The Pathway tool software suite allows the automated construction of a database starting from an annotated genome, but it requires prior integration of all annotations into a specific summary file or into a GenBank file. To allow the easy creation and update of a BioCyc database starting from the multiple genome annotation resources available over time, we have developed an ad hoc data management system that we called Cyc Annotation Database System (CycADS). CycADS is centred on a specific database model and on a set of Java programs to import, filter and export relevant information. Data from GenBank and other annotation sources (including for example: KAAS, PRIAM, Blast2GO and PhylomeDB) are collected into a database to be subsequently filtered and extracted to generate a complete annotation file. This file is then used to build an enriched BioCyc database using the PathoLogic program of Pathway Tools. The CycADS pipeline for annotation management was used to build the AcypiCyc database for the pea aphid (Acyrthosiphon pisum) whose genome was recently sequenced. The AcypiCyc database webpage includes also, for comparative analyses, two other metabolic reconstruction BioCyc databases generated using CycADS: TricaCyc for Tribolium castaneum and DromeCyc for Drosophila melanogaster. Linked to its flexible design, CycADS offers a powerful software tool for the generation and regular updating of enriched BioCyc databases. The CycADS system is particularly suited for metabolic gene annotation and network reconstruction in newly sequenced genomes. Because of the uniform annotation used for metabolic network reconstruction, CycADS is particularly useful for comparative analysis of the metabolism of different organisms.Database URL: http://www.cycadsys.org

BioCyc: Microbial Genomes and Cellular Networks: The BioCyc system helps scientists navigate the greatly expanding information space of genomes and metabolic information

Genome sequencing capabilities and capacity are so advanced that the genomes of virtually all microbial species of interest are likely to be deciphered during the next decade. Improved means for interpreting and applying this sequence information enhance its value. The BioCyc database collection and website include data for more than 1,000 microbial genomes as well as tools for analyzing that information. These resources help users to address key questions such as: What are the functions of the upstream and downstream neighbors of a gene? How far apart are the genes, and are they likely to form an operon? What is the sequence of the gene and DNA segments near it? What metabolic pathway does a gene play a role in?

Beyond the genome (BTG) is a (PGDB) pathway genome database: HumanCyc

HumanCyc is a manually curated database of enzymatic reactions and metabolic pathways that can be used as a versatile reference resource or as a tool for analyzing ‘omics’ data sets in the context of metabolic pathways. The HumanCyc database was generated from the annotated genome using the Pathway Tools [1] software package written by SRI International (SRI). The software assists users in analyzing genomic data by predicting the metabolic network of an organism, presenting pathway and genome data in user-friendly and intuitive displays, and providing tools for missing enzyme identification, ‘omics’ data analyses and comparative studies. The HumanCyc website (http://humancyc.org/) and the downloadable HumanCyc software support a variety of query, visualization and analysis tools. Users can find and display existing metabolites, pathways and enzymes. Users can also define groups of metabolites or proteins and perform enrichment analysis of an ‘omics’ data set to determine over- represented substrates, pathways or enzymes. It is possible to color code a ‘zoomable’ human metabolic network diagram with metabolomic, proteomic or transcriptomic data for visual analysis of patterns in data sets. The software colors entities in the diagram to reflect their measured levels in useruploaded ‘omics’ data sets. By connecting metabolites to reactions to enzymes to genes, HumanCyc facilitates the integration of multiple ‘omics’ data types. It describes 255 human metabolic pathways, 3315 human enzymes, 1862 biochemical reactions, and 1359 human metabolites. HumanCyc is linked to human genome resources such as Ensembl, GeneCards, Entrez Gene, OMIM, PubChem and UniProt. Curation of the HumanCyc knowledge base is proceeding in conjunction with the Pharmacometabolomics Research Network (PMRN), a multi-year collaboration to develop tools and provide resources for the metabolomics community. To this end, we have focused on curation of information from the scientific literature concerning chemical compounds and enzymes of interest to the PMRN and the larger field of metabolomics, including pathways for metabolism of xenobiotics, nutrients and metabolites produced by the gut microbiome. HumanCyc is a component of the SRI maintained BioCyc databases, which number nearly 700, mostly prokaryotic and PGDBs. BioCyc databases contain 9000 compounds, including many produced by the human microbiome and plant dietary nutrients. Future work will include expansion of the BioCyc collection to keep pace with genome sequencing from the Human Microbiome Project and allow analysis of these Human Microbiome PGDBs and the impact of interspecies interactions on human metabolism.