Here, micrographite is used to study and dissect the epithelial ovarian cancer gene and miRNA transcriptome defining and validating a new regulatory circuit related to ovarian cancer histotype specificity.The production rate of gene expression data is nothing less than astounding. However, with the benefit of hindsight we can assert that, since we completely ignored the non-coding part of the transcriptome, we spent the last decade to study cell mechanisms having few data in our hands. In this scenario, microRNAs, which are key post-trascriptional regulators, deserve special attention. Given the state of knowledge about their biogenesis, mechanisms of action and the numerous experimentally validated target genes, miRNAs are also gradually appearing in the formal pathway representations such as KEGG and Reactome maps. However, the number of miRNAs annotated in pathway maps are very few and pathway analyses exploiting this new regulatory layer are still lacking. To fill these gaps, we present  Moreover, it has been increasingly clear that the integration of different Currently, miRNA and gene circuits are identified through the combination of binding prediction and expression correlation analyses . AlthougNotwithstanding the defective analysis procedures, since their discovery, miRNAs have been extensively studied and hundreds of target genes have been found. However, despite all these efforts, signaling pathways that are the formal description of biological circuits, contain very few miRNAs. This penalizes pathway analyses, widely used and essential approaches to enhance transcriptome measurements interpretations.micrographite, able to integrate pathway information with predicted and validated miRNA–target interactions and to perform integrated topological analyses of miRNA and gene expression profiles to identify miRNA–gene circuits.To overcome this limitation we developed a new computational pipeline, called micrographite is a helpful tool to guide the researcher in the interpretation of the expression measurements.The advantage of our approach is two-fold: (i) it performs integrated pathway analyses to better describe the cellular scenario shaped by the underlying expression data, (ii) it selects and refines predicted miRNA–target interactions exploiting the biological context. With these features, Here, we used our pipeline to study the regulatory circuits in the early stage of Epithelial Ovarian Cancer (EOC). EOC is the most common cause of death in gynecological diseases, with a 5-year survival rate virtually unchanged for the past 30 years . Despitegraphite, a Bioconductor package that we recently developed to store, manage and convert pathway annotations (from BioPax or KGML formats) into gene–gene networks. graphite is a pathway data interpreter that, following biologically driven rules, is able to solve the complexity of the pathway modules to generate interaction networks suitable for topological pathway analyses (graphite gathers pathway annotations from Kyoto Encyclopedia of Genes and Genomes (KEGG) . Two different batches of miRNA–target interactions are considered: the in silico predicted interactions and the validated ones. The validated miRNA–target interactions are derived from mirTarBase  , Reactoms (KEGG) , Nature s (KEGG)  and BiocrTarBase , miRecorrTarBase  and a marTarBase , miRWalkrTarBase  or miR2DrTarBase . As an erTarBase . For thiPct ≥ 0.8) and predicted miRNA–mRNA couples with a Pearson correlation coefficient |r| ≥ 0.4 and q-value ≤0.05. See Supplementary Material S1 for some statistics regarding the numbers of predicted and validated interactions included in the pipeline jointly with their overlap.Specifically in the ovarian cancer dataset we used TargetScan predictions  it selects pathways with covariance matrices or means significantly different between experimental conditions; and (ii) on these selected pathways, it identifies portions of the pathway  mostly associated with the phenotype. The identification of paths within a significantly altered pathway is one of the most innovative CliPPER features. Briefly, path identification is based on the graph decomposition into small-connected components, called cliques. Each clique is tested independently (according to the test on the means and/or concentration matrices) and then a significant level  for each clique is obtained. Following the Gaussian graphical model theory, maximal cliques can be used to reconstruct a junction tree. Junction tree is a hyper-tree having cliques as nodes and satisfying the running intersection property according to which, for any cliques C1 and C2 in the tree, every clique on the path connecting C1 and C2 contains C1 ∩ C2. Using the junction tree structure  we are able to identify and score all the paths in the graph. A path is a list of adjacent significant cliques . The score of a path is a function of all the P-values of the cliques contributing to the path ; higher the score, better the path. For more technical and methodological details see  selection of pathways – pathways are selected according to the significance levels obtained from the test on the mean or/and on concentration matrices of the whole pathway graphs  . To copes Figure , step 2;d Figure , step 3;s Figure , step 4 s Figure , step 5.micrographite is available as R functions under the AGPL-3 license. Code and guideline about micrographite pipeline is available at http://romualdi.bio.unipd.it/micrographite. The latest version of Bioconductor, graphite and CliPPER packages are required. micrographite can be used with validated and/or predicted miRNA–target interactions. Depending on the amount of expression data analyzed, it could take several hours on an entire pathway database. Specifically, using an Intel(R) Xeon(R) 3.30GH processor, the presented analysis on ovarian cancer data with 10 000 permutations, using validated and predicted miRNA–mRNA interactions and the KEGG pathway database took 20 hours.Permutation is used to estimate the significance levels of the test on the mean and on the concentration matrices either for the whole pathway or for the cliques. Under the null hypothesis the two groups have equal multivariate means (for the test on the means) and equal covariance matrices (for the test on the concentration matrices). Thus, using a classical permutational approach, we randomly permute samples across groups.et al.  were homogenized in an TissueLyser LT  and total ribonucleicacid (RNA) purified using RNeasy Mini Kit isolation system (Qiagen), following manufacturers protocols. Total RNA concentration and proteins contamination were determined by Nanodrop spectrophotometer . Only samples with a RNA integrity number (RIN) larger than six and a Nanodrop A260:280 ratio between 1.8 and 2.1 were further processed and aliquots stored at −80°C until use. Array experiments were performed using standard procedures as previously published by . Brieflyt-test were used to verify among groups mean differences; P-value ≤0.05 was considered statistically significant.Gene and miRNAs expression levels were validated by qRT-PCR on both training (array set) and validation set of patients. qRT-PCR have been performed using Sybr Green protocol  on an Applied Biosystems 7900HT instrument. Experiments were run in triplicate, using 384-well reaction plates in an automatic liquid handling station . Raw data was generated with Sodium dodecylsulphate (SDS) Relative Quantification software , data were normalized using the geometric mean of the four independent housekeeping controls . Two-sided Student's micrographite pipeline, briefly summarized in Figure graphite .Pathway analyses are manually annotated and derived by a long and careful process of curation produced by experts in the field. Thus, in However, since miRNA interactions are included in pathways only if the target gene is a pathway member, only a part of the collected interactions can be used. Respectively 1285, 1320, 1413 and 848 miRNA–target interactions are integrated in KEGG, Reactome, NCI and Biocarta pathways. In Table micrographite, we focused on the cell signaling characterization of mucinous histotypes. Mucinous ovarian carcinomas (MucEOC) is one of the four main subtypes of EOCs mostly diagnosed at early stages. MucEOCs develop almost always within the ovary and histologically it is indistinguishable from the mucinous non-ovarian carcinoma or the metastatic carcinoma with mucinous differentiation  , PI3K/AKT/mTOR signaling and by mutations or epigenetic losses of BRCA1/2, PTEN and TP53 ,23. EOC cancers) . The numcancers) . Moreovecancers) .Using EOC dataset, KEGG pathways were enriched by 1285 validated miRNA–target interactions and 63 TargetsScan predictions . TargetScan predictions were selected using a correlation-based cut-off (|r|≥0.4). We tried different correlation thresholds  in order to test the robustness of the approach. Our results show that the inclusion of a large number of miRNA–target interactions (e.g. using a loose threshold such as |r|≥0.2) gives worse results than including lower but well refined interactions. This is in agreement with the idea that the inclusion of noise in the graph reduces the efficiency of the method hiding real biological signals.micrographite step 2) reported in Table P-values ≤0.1 for both mean and variance tests were further considered for the follow analysis steps. The results at the pathway level are coherent with the biological knowledge characterizing mucinous subtype: the similarity of MucEOC with colorectal cancer, the involvement of the epidermal and transforming growth factors signals, such as the ErbB and the TGF-beta signaling and, moreover, the p53 signaling pathway. Also, the involvement of 'One carbon pool by folate' metabolic pathway is interesting in the light of the registered association between the high-risk of colorectal carcinoma and the folate depletion, which affects the mechanisms of DNA methylation, integrity and repair , contains many EOC and MucEOC related genes, such as miR-192 and miR-194 that have a mucEOC expression specificity. Interestingly, in this representation of the cell signaling it is clear that miR-192 and miR-194 act as bridges across multiple cellular signals of ovarian cancer interest. Specifically, miR-192 controls the signaling of RB1/E2Fs and, together with miR-194, the MDM2/TP53 signaling. Both the signaling paths, RB1/E2Fs and MDM2/TP53, are controllers of cell cycle that seems to be deeply affected as suggested by the presence in the mucEOC pathway of cyclins , cyclin-dependent kinase  and cyclin-dependent kinase inhibitors (CDKN2A) . The sigractions , suggestmicrographite pipeline using only gene expression data. The meta–pathway identified by the pipeline using only mRNA data is smaller and it is completely contained in the meta–pathway obtained using mRNA and miRNA datasets. The final best path obtained by mRNA data, is contained in the sixth path (ranked by max score) of the analysis using both mRNA and miRNAs . These findings further suggest that the inclusion of miRNAs improves and expands the gene analysis results. This is not a surprising conclusion at least for two reasons: from a methodological point of view, the more complete the pathways, the more accurate the analysis; from an applicative point of view, miRNAs are known to play a key role in tissue specificity and in regulation of cancer circuits  for a total of eight genes and five miRNAs. Specifically, the expression of miR-192/194 cluster is directly controlled by wild type TP53 that, enhancing their transcriptions, is able to arrest cell cycle . Among tmicrographite pipeline allows integrated analyses of gene and miRNA expression profiles within a pathway-based context. At our knowledge this is the first attempt in this direction. We applied successfully micrographite to dissect the complexity of regulatory networks in epithelial ovarian cancer. The circuit obtained conforms to the existing knowledge and adds new insights for the comprehension of the mucEOC subtype.The micrographite results allow the biologist to have a clear and more complete overview of cell behavior, this is fundamental in the understanding of cellular processes and to formulate new hypotheses during the interpretation of expression data.For researchers working with miRNAs, the advantages of our approach are several: (i) the possibility to integrate and (ii) analyze miRNA and mRNA expression profiles using pathway information and (iii) to biologically contextualize miRNA–mRNA validated and predicted interactions. The


Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools. Such results can be used in the discovery of new drug targets, protein-protein interactions or regulatory effects. In either case, putting new findings into context of already existing knowledge is essential. An important aspect to evaluate results of high-throughput experiments is access to pathway data within the scope of programming environments. There are several methods incorporating pathway data into these analyses either in a form of the gene sets [Easier access and decreased costs have lowered the entrance barrier for performing high-throughput experiments. However, analysis and interpretation of this data poses a challenge for biologists and bioinformaticians alike. This surge in generation of new data, both ene sets ,2 or as ene sets ,4,5. Furene sets . Additioene sets ,8,9.This manuscript aims to give an overview of software packages for the R Project for Statistical Computing that integrate pathway data for bioinformatic analysis within R. This manuscript focuses on provided pathway data, the extent of this data, and the integration of the R package with further analysis steps, as well as procedures for data visualization.In general, pathways can be divided into the groups of metabolic pathways, signaling pathways, gene regulatory networks and protein–protein interaction networks, however, mixtures and overlaps exist. Metabolic pathways represent chains of chemical reactions of small molecules, so called metabolites, which are catalyzed by specific proteins, called enzymes. Signaling pathways represent the communication within and between cells. Gene expression is the process of generating the functional cell product of the DNA sequence of a gene. Gene regulatory networks model the indirect regulation and interaction of genes. Further, protein–protein interaction (PPI) databases store computationally inferred or experimentally validated protein–protein interactions, which can be assembled to PPI networks. Certain design decisions have to be made to model any of the pathway types, mainly coming down to a trade-off between richness of detail and generalization. Several ways to encode pathway knowledge have been developed to represent the various pathway types. Apart from proprietary XML or database models developed by pathway providers, a number of standards have received broader attention. The KEGG Markup Language (KGML), developed for and used by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, is an example for a proprietary data model of a database provider to encode metabolic and signaling pathways . Howeverhttp://www.kegg.jp/kegg/docs/plea.html). On the other hand, pathway sketches and data can still be retrieved free of charge via the KEGG website and web services. Reactome [http://www.ebi.ac.uk/Tools/webservices/psicquic/registry/registry?action=STATUS) currently listing 28 web services available for querying databases for PSI MI-compatible data using the PSICQUIC querying language [A plethora of pathway databases are freely available on the web. Pathguide , an onliReactome  is an opReactome  is a colReactome  on the oReactome , who evalanguage .RCytoscape [http://projects.bigcat.unimaas.nl/pathvisiorpc/tutorial). Arguably, Cytoscape is currently the most popular tool for pathway editing, featuring over 150 plugins in the Cytoscape App store [During the last decade, a number of tools have been developed to create, edit and analyze pathways. Most notable among these are CellDesigner, PathVisio and Cytoscape. CellDesigner is heavily influenced by the Systems Biology Graphical Notation (SBGN)  and focuytoscape  is part pp store . An extepp store .The R Framework for Statistical Computing  has beenWithin this review R packages are evaluated according to five different aspects: The first aspect determines the source of integrated data; renowned metabolic or signaling pathway databases like KEGG or Reactome are more often integrated and available via several packages. Within the second aspect, the internal data model and its extent are described; this can range from supplying only gene sets of pathways, to undirected graphs and directed graphs with fully annotated edges. The next criterion will assert the dependence or interaction of the R package with external tools for tasks such as visualization or data import; for example Cytoscape  or the GRgraphviz, and 6 connector packages, for example XML and RJava, which provide access to external tools. The two most notable online repositories for R packages, CRAN  and BiocIn order to generate an overview of popular tools promoting the use of pathway data in R, the dependencies of and between these packages have been depicted in a dependency network in NCIgraph, tailored around the Pathway Interaction Database (PID) [RCytoscape to load pathways via Cytoscape to import arbitrary BioPAX data. The RCytoscape package [XMLRPC package. Furthermore, R graph objects can be loaded and visualized via Cytoscape. Another example is the R package gaggle, which is able to link to a Gaggle host and receive networks from other applications using the Gaggle framework for data exchange.The common sources of data for all reviewed R packages are online pathway databases, which allow users to download an export of their curated pathway data or access via a web service. All reviewed packages pursue one of three approaches to integrate pathway data into R: The first approach is that pathway data is retrieved via external tools, i.e., Gaggle  or Cytosse (PID)  of the N package  is able graphite package [CePa package [pathRender reads interaction lists from the cMAP data package and builds graphs from these interactions.The second approach is that pathway data is retrieved, parsed and curated in a more or less automatic manner by the package maintainers and subsequently distributed directly via the package or as separate download. This approach is applied by the  package , which i package , BioCart package , PID [19 package  and SPIK package . The sam package , which iKEGGgraph package parses KGML encoded data and supplies it as R objects package [Pathview package [KEGGgraph package and also features a download function, which is able to automatically retrieve KEGG pathways via the web. The SBMLR package [XML package. The package rsbml uses the system library libSBML [PSICQUIC [rBiopaxParser parses arbitrary BioPAX Level 2 and Level 3 databases using the XML package and represents them in R [Finally, the third approach is the ability of packages to parse data encoded in generic pathway formats and model these in an internal R representation. Naturally, this enables users to parse arbitrary pathway databases. The  package . The Pat package  loads KG package  parses S libSBML  to parsePSICQUIC  retrievehem in R .Another criterion for pathway data integration is the extent of data that is made available by individual packages. The simplest representation of a pathway in R is a gene set. Further, undirected or directed graphs can be used to represent a specific pathway. In order to integrate the pathway data into further analyses, the user has to ensure that the appropriate information about nodes, edges and edge direction is available. Depending on the package, pathway graphs might be available in a mechanistic fashion, including all biological processes, e.g., complex assembly, cellular transport, transcription or phosphorylation, or pathways might be transformed into a regulatory graph, only containing inhibiting or activating interactions. Furthermore, the data provided by the databases might be enriched by additional annotation data for genes or references to published evidence.pathview and KEGGgraph packages supply for every pathway a KEGGPathway object, which includes pathway information like title and organism, and a directed R graph object, which maintains the pathway topology and all essential attributes. Both metabolic and regulatory graph views can be extracted from the KEGGPathway object. Furthermore, pathway nodes in KGML can represent families of proteins, which the packages are able to expand and map to the corresponding set of single proteins. Cytoscape is a Java-based software platform for visualizing and analyzing networks and graphs, which can be extended by a large number of available plugins. RCytoscape retrieves networks from Cytoscape and represents them as R graph objects. The R object has directed or undirected edges and is annotated depending on the network within Cytoscape. The Gaggle framework consists of plugins for different software tools and enables dynamic data exchange via a host service, which connects the independent tools. Package gaggle can receive network data from other Gaggle applications and import these as graph objects. NCIgraph bases on the data retrieved via RCytoscape and offers further functionality to merge pathway nodes of PID data, which depends on a specific annotation by the NCI. Despite the similar naming, the packages providing SBML data, rsbml and SBMLR, use different approaches for their internal data model. SBMLR parses the pathway data into an internal model comprised of a set of lists. These are specifically tailored to represent reactants, products and the reaction rates for model simulations in systems biology. On the other hand, rsbml uses libSBML to parse an SBML model either as an R graph object or as a Document Object Model (DOM). Using convenience functions the annotation data within the DOM object can be queried and modified, the graph object can be extracted and used for further analyses. The graphite package offers its parsed data as pathway objects, which are imported from KGML- and BioPAX-encoded data and converted to R objects. These objects store information about the nodes and directed and undirected edges, as well as the data source, pathway name and species. The pathway objects can be converted into R graph objects. Similarly, the CePa package supplies pre-parsed interaction lists of its integrated databases and can generate directed igraph pathway graphs. The PSICQUIC package enables the users to retrieve PSI MI-compatible lists of molecular interactions from databases offering PSICQUIC web services. Afterwards these can be used to assemble networks or graphs from the retrieved interactions and annotations. The package rBiopaxParser parses arbitrary BioPAX-encoded data and provides biopax objects within R. These objects include all information of the original data within one data frame, similar to the triplets of the Resource Description Framework (RDF) model. The BioPAX data can be accessed and edited using convenience functions and pathways can be converted into R graph objects.The A number of packages for pathway integration rely on external tools, either for accessing, parsing and importing data or for mapping between various identifiers. Many packages depend on external tools for visualization purposes as well; however, these are described in rBiopaxParser package and the querying capabilities of PSICQUIC, the curl tool must be installed. The gaggle package for data exchange relies on Java and the rJava package for communication with other Gaggle applications. The R packages rBiopaxParser, KEGGgraph and SBMLR rely on the XML package and the libxml system library for parsing, similarly rsbml depends on libSBML. For data import the packages NCIgraph and RCytoscape depend on a running Cytoscape session to load networks. In order to enrich available annotation and map identifiers the packages graphite and pathview depend on the AnnotationDbi package, while KEGGgraph, PSICQUIC and rBiopaxParser depend on the biomaRt package and web service.Not all dependencies must be fulfilled to run the packages out of the box, however functionality might be crippled. In order to use the download functionality of the HTSanalyzeR [limma package [NEM [DDEPN [birta [BoolNet [Pathway analyses in its classical approach, the gene set enrichment analysis, allow bioinformaticians to test if a pathway is significantly regulated between groups of samples. Many different algorithms are available in R to perform gene set enrichment analyses, for example via package analyzeR  and CAME package ,49. More package , clipper package  and many package ,51,52,53 package . Differeage [NEM , DDEPN [M [DDEPN , birta [N [birta  and Bool[BoolNet . On the [BoolNet ,58.rsbml and SBMLR include wrappers for solving ODEs using the SBML ODE Solver Library SOSlib [deSolve [graphite package enables users to run several pathway analyses tools which take pathway topology into account for their testing procedures, for example clipper [CePa package integrates standard gene set enrichment and custom over-representation analyses published by Gu and colleagues [Almost all reviewed packages enable the user to access pathways within R as graph objects. If nodes in these graphs represent genes, a list of nodes can be used to perform gene set enrichment analyses. For more advanced algorithms the input has often to be reformatted to match the requirements of the specific implementation. Graphs might have to be either directed or undirected, edge weights must stay within certain limits and different edge types might or might not exist. In order to enhance the bioinformatic workflow several reviewed packages offer a kind of wrapper for further analyses. Graphs are automatically transformed into the right format and the analyses can be directly called from within the package. The packages y SOSlib  and the [deSolve  respecti clipper , SPIA [3 clipper . The CePlleagues .pathRender provides plotting capabilities with preset parameters to quickly retrieve visualization of graphs. RedeR [igraph with a Java-based graphical user interface similar to Cytoscape, especially focused on visualizing extremely large networks. CePa provides preset plotting parameters for its supplied igraph objects. The packages NCIgraph, KEGGgraph, pathview and rBiopaxParser enable the user to plot pathways via Rgraphviz. Package pathview additionally provides a function to export native KEGG representations as PNG image files. The graphite package, which uses RCytoscape for visualization, as well as RCytoscape itself, visualize graphs via an active Cytoscape session. RCytoscape offers a multitude of well-documented parameters and functions to visualize, layout and modify R graph objects using Cytoscape.There are several packages available which focus primarily on the visualization of R graph data. Package s. RedeR  is a powFurthermore, the user is free to extend and improve visualization, since all methods for visualization—Cytoscape, RedeR, Graphviz as well as common R plotting—allow for individual modifications of parameters, e.g., color, size of nodes, width of edges.Bioinformatics and systems biology are still rapidly evolving fields, and along with these many new and exciting software tools are becoming available. This speed as well as the interdisciplinary work makes it difficult for biologists and bioinformaticians alike to keep up with new developments. A positive aspect is the maturing of data sources for pathway knowledge: All mentioned database providers either offer exports abiding to the standards of SBML  or BioPARgraphviz package since version 2.1. This enables many differently flavored visualization options from within R.However, the internal data model of the reviewed packages is usually a combined use of the R graph class and additional, package-specific, tables or lists for further annotation data, for example for identifier mapping or edge and node types. None of the dependencies on external tools really restrict the use of the reviewed packages. The standalone versions of Cytoscape, RedeR and Gaggle are available via Java on all platforms and the Graphviz library has been integrated into the Assessing pathway analyses or network reconstructions probably pose the biggest challenge for users, due to the package-specific data modeling of pathways within R. This is partly due to the fact that the packages for further analyses have varying requirements on graphs or prior knowledge, for example, that graphs must be supplied as undirected graphs or directed acyclic graphs. However, these problems might be tackled in the future by providing standardized pathway classes, for example via Bioconductor, which can automate these transformations.RCytoscape and RedeR are mainly focused on providing general capabilities for data exchange and visualization to their users. On the other hand, packages which focus on providing pathway information, for example graphite, CePa, rBiopaxParser or KEGGgraph, provide plotting capabilities with many preset parameters and thus offer pre-formatted pathway visualization. This enables users to work with these packages and the provided pathway data out of the box.Concerning the visualization, it is not surprising that packages which mostly act as an interface and connect R and external tools such as All of the reviewed R packages have a clear focus and provide unique capabilities. However, the different aspects within this review should enable users to narrow down their choice of packages for pathway data within R, according to the desired data sources, pathway analyses options and possibilities for visualization.Although, this review is focused on pathway data within R, users may also choose to fully rely on other tools. The Cytoscape platform has a large community of users and numerous plugins in its new app store . These eProgramming in R leaves many options for interactions with different systems and services and near endless possibilities to perform and combine analyses and tools. However, compared to all-in-one tools like DAVID, these possibilities come at a price: Even with the emerging generic standards for data encoding like BioPAX, SBML or PSI MI, the availability of interfacing or libraries for various programming languages can be a limiting factor. Fortunately, new interfaces  and libr


In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases.Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted.http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.MINE databases are freely accessible for non-commercial use via user-friendly web-tools at The online version of this article (doi:10.1186/s13321-015-0087-1) contains supplementary material, which is available to authorized users. Metabolomics, the study of the population of small molecules in a cell, has drawn intense interest in fields from medicine to synthetic biology because it can provide a fine-grain representation of cellular state and activity –4. Of paThere is a growing consensus that many enzymes mediate undocumented side-reactions (known as promiscuous activities) as a result of exposure to diverse cellular metabolites , 17. TheEscherichia coli and yeast. The focus on endogenously present and organism-specific metabolites has been cited as critical to improving the confidence of compound matches +, [M+H]+, [M+Na]+, [M−H]− and [M+CH3COO]− adducts. The results of this validation are displayed in Table m/z. The KEGG MINE database annotated an additional 14% of compounds while maintaining a similar accuracy to the KEGG annotations. PubChem annotates a comparable number of these known compounds to the KEGG MINE but does so at the expense of returning a bin of candidates that is two orders of magnitude larger than the MINE. While the MINE database has a higher median number of structures per peak than the KEGG database, the number remains feasible to examine manually. The web interface facilitates this process by distinguishing compounds that are present in user specified KEGG genome reconstructions from those generated by computational means, hence allowing users to consider the most probable isomers first. Additionally, users may restrict structures to a range of partition coefficients or Kováts retention index values. Candidate structures can then be downloaded as a Microsoft Excel compatible CSV file for further review.As a demonstration of the potential of MINEs for annotation of accurate mass data, a diverse test set of 667 unique compounds was compiled from MassBank . The datE. coli knockout study analyzed by LC–MS. The protocols for sample extraction, data acquisition and post processing are available in the supplementary information. 493 distinct exact MS features were extracted, 30 of which were identified following a traditional annotation workflow using NIST MSPepsearch  lipid that we were not able to identify with our traditional workflow. LipidBlast  was usedhttps://github.com/JamesJeffryes/MINE-API. This API allows the databases to be integrated into existing candidate ranking algorithms and pipelines. Future versions of these databases will incorporate transformation rules for spontaneous chemical reactions of metabolites, and improved filtering and prioritization of candidate structures.In addition to the existing web tools, the underlying MINE databases are accessible through free, developer-friendly APIs. Clients are available for integration into Python, Perl and JavaScript frameworks at In addition to expanding the scope for the metabolome, the MINE framework also offers a pipeline for illuminating the synthesis and degradation of poorly annotated secondary metabolites. While applied very broadly to nearly all of metabolism in this study, BNICE expansions may be focused on a region of interest in the metabolic network by adjusting the starting compounds and permissible transformations in a manner similar to that recently demonstrated by Ridder et al. . These tHere we have presented Metabolic In silico Network Expansions (MINEs) that utilizes generalized biochemical transformations to propose structures for use in untargeted metabolomics. The resulting compounds are rarely found in PubChem but are structurally similar to natural products. We have demonstrated the utility of these databases for proposing correct metabolite structures that stymied a standard annotation workflow. MINE data are accessible without licensing restrictions for non-commercial users through a user-friendly web interface and API for developers in several common scripting languages.http://minedatabase.mcs.anl.gov and API clients are available at https://github.com/JamesJeffryes/MINE-API. There are no restrictions for Academic Use. Commercial users must obtain a license from Pathway Solutions Inc. (www.pathway.jp) and explicit permission from the authors.MINE databases are freely accessible at:


Motivation: Extensive drug treatment gene expression data have been generated in order to identify biomarkers that are predictive for toxicity or to classify compounds. However, such patterns are often highly variable across compounds and lack robustness. We and others have previously shown that supervised expression patterns based on pathway concepts rather than unsupervised patterns are more robust and can be used to assess toxicity for entire classes of drugs more reliably.Results: We have developed a database, ToxDB, for the analysis of the functional consequences of drug treatment at the pathway level. We have collected 2694 pathway concepts and computed numerical response scores of these pathways for 437 drugs and chemicals and 7464 different experimental conditions. ToxDB provides functionalities for exploring these pathway responses by offering tools for visualization and differential analysis allowing for comparisons of different treatment parameters and for linking this data with toxicity annotation and chemical information.Database URL:http://toxdb.molgen.mpg.de The assessment of toxicity of compounds such as drugs, industrial chemicals, cosmetics and food ingredients is an important aspect of research with implications for patient health, consumer protection and nutrition.In order to identify more reliable molecular predictors of toxicity huge amounts of toxicogenomics data have been generated worldwide, e.g. by the Japanese Toxicogenomics project , the US In this work, we take advantage of the pathway collection of ConsensusPathDB , a meta-ToxDB builds on three components: (i) a comprehensive collection of pathway concepts along with drug treatment microarray data, (ii) a numerical method to compute pathway responses from genome-scale expression data, (iii) a web interface that enables user interaction .Figure in vivo, liver and kidney cells, and in human hepatocytes  in human and rat tissues at different time points and with different drug dosages. The first study (Open TG-GATES) provides toxicity information on compounds tested in rat atocytes . The secatocytes . The 459We used custom cdf files for mapping oligonucleotide probes of the human and rat microarrays to respective genes . This reReplicate experiments for a treatment with a certain dosage and at a certain time-point along with the corresponding control replicate experiments were grouped and the raw data was normalized using the GC Robust Multiarray Average method.One2one: the rat gene has exactly one orthologous human gene, and the corresponding rat microarray value is assigned to that human gene.One2many: the rat gene has multiple orthologs in the human genome, and the corresponding rat microarray value is assigned to all human paralogs.In the case of human data, genes could directly be related to human pathways. In the case of rat data, genes were assigned to human pathways by orthology. We used the orthology mapping of the Ensembl Biomart repository . We limiThe pathway scoring method was previously developed by us in the context of discriminating classes of chemicals with respect to their carcinogenic hazard in stem cell-derived human hepatocytes . We defik is defined as a set of genes A pathway patht-test, Welch test or Wilcoxon’s rank sum test, for RNA-sequencing data it could be, for example DEXSeq and edgeR. In this study, we performed Student’s t-test for each case-control experiment.Suppose genome-wide case-control experiments are carried out with some material, e.g. human tissue, rat tissue, cell lines including replicates. In toxicology such experiments consist typically of several chemical treatments of the cells which are compared against the untreated control cells at matched time points. We apply the pre-processing described earlier and compute a statistical test for all case-control studies. The choice of the statistical test is dependent on the type of data and the corresponding model for the background distribution. Suitable test-procedures for microarray data could be, e.g. limma, Student’s j, a fold-change P-value This yields for each gene j by:We now compute a gene score t-test procedure can be computed even with very low sample sizes, it is clear that the test has higher power the more replicates are used. However, in most toxicogenomics studies sample sizes are rather low, in the range of two to five replicates, which may downsize the confidence of the significance computation. On the other hand, using the above procedure incorporates all expression data and avoids a statistical pre-selection of genes based on P-values. Instead, P-values are only used for additional weighting of expression fold-changes which seems a more appropriate approach in these cases.The gene score describes a weighted fold-change of the gene with respect to the particular treatment, whereby the weight is increasing with the significance of the fold-change. Although technically, Student’s Furthermore, it should be noted that gene scores do not distinguish between positive or negative gene expression regulation but rather reflect whether the gene is affected by the treatment or not.kj for pathway pathk and chemical j is defined as the average gene score of all genes assigned to the pathway:The pathway score pathij, and compute the relative response score (RPR):In order to make pathway scores comparable across different treatments, we divide each score by the median pathway score over all pathways, pathkj, reflect the strength of chemical dose . In the web interface the user is thus provided with the background distribution derived from the entirety of RPR scores when inspecting individual responses.It should be noted that the pathway scores are fairly robust across different subsets of data and, thus, that the distribution of all RPR scores can serve as a background distribution for judging significance of individual RPR scores (The backend of the ToxDB is composed of a PostgreSQL database (version 9.2.4) running on an Apache/2.4.4 (64-bit Unix) server. The frontend HTML is designed using Flask (version 0.10.1), a web framework for Python (version 3.4.1). Pathway data are currently based on release 31 of the ConsensusPathDB and will be updated regularly. Plots in the web interface are drawn using JavaScript and the Google Visualization tools.The ToxDB web interface provides functionalities that allow exploration of different aspects of drug treatment data.On the front page, users can do a comprehensive search of all drugs, pathways, and genes in the database. Singular words, logical operators as well as several specific IDs are accepted.t-test. This feature can be used for example when inferring pathways that are affected differentially by different sets of drugs, in different target organs or different species.Here, treatments can be compared by assigning them to groups and the differences of the two groups are quantified by Student’s Users can select from the list of available drugs. The resulting view shows chemical annotation of this compound as well as a menu where experimental conditions can be fixed, e.g. study, cell type, dosage and time point. After setting the conditions, the corresponding expression data are assessed and the resulting pathway concepts are shown ordered by their RPR scores. A slider can be used to specify the number of displayed pathways. By clicking on a specific pathway, users can switch from the pathway  to the gConversely, if the user is interested in a certain pathway, e.g. when evaluating a specific functional assay, a single pathway can be selected and the resulting response scores of this pathway are displayed across all compounds and treatments. Additionally, toxicity information from two independent reviews , 12 is oSupplementary Materials).Tables and plots resulting from the described tools are made available for download in various formats in order to use the data in further analyses. Additionally, we provide a download section for the use cases reported in this study .diXa warehouse for chemical safety information and for experimental data download .Toxicity information—for judging liver toxicity of the compounds we included two assignment procedures: the first was defined by the FDA using box readings and literature mining  and the ToxDB will be updated on a regular basis, approximately every six months. Updates will include pathway concepts from the ConsensusPathDB  as well In order to exemplify work with ToxDB we have conducted several use cases, in which we investigate the effects of different drug treatments on cellular pathway responses.In the first use case we investigated five drugs with known hepatotoxic or cardiotoxic effects, respectively. By investigating drug-induced gene and pathway responses in ToxDB we can confirm information on toxicity and modes of action derived from literature and other databases, like ChEMBL. These results encourage inspecting other, less well-studied drugs in ToxDB and their effects on genes and pathways.In the second use case we approach the problem of identification potential modes of action in the opposite way. Here, we start from certain disease-related pathways and infer the drugs that mostly affect the pathway. We focus on the ‘cytochrome P450 pathway’ and on ‘non-alcohilc fatty liver disease’, which both play a role in hepatotoxicity. We find that they are indeed mostly affected by drugs known to promote liver disease, e.g. rifampin . AlthougSupplementary Materials.Full details of the use cases are reported in the ToxDB is a resource that analyzes drug-induced gene expression changes at the pathway level. Although the current approach is focused on drug treatment data, the pathway analysis approach can be applied to more general scenarios where case-control studies are given . By elevating analyses from the gene to the pathway level it is possible to gain more general information on functional changes and more robust biomarkers what ultimately contributes to the improvement of drug development pipelines.Supplementary data are available at Database Online.


Related research article Mallmann J, Heckmann D, Bräutigam A, Lercher MJ, Weber APM, Westhoff P, Gowik U. 2014. The role of photorespiration during the evolution of C4 photosynthesis in the genus Flaveria. eLife3:e02478. doi: 10.7554/eLife.02478Image C4 plants (Fb and Ft) have higher levels of certain enzymes than plants that use other forms of photosynthesisUnderstanding the evolution of complex innovations remains one of the most challenging problems in biology . Insight4 photosynthesis is a well-studied, complex adaptation that has independently evolved over 60 times  levels are currently rising, the last 30 million years witnessed great declines in CO2, which has limited the efficiency of photosynthesis. Rubisco, the critical photosynthetic enzyme that catalyses the fixation of CO2 into carbohydrate, also reacts with oxygen when CO2 levels are low and temperatures are high. When this occurs, plants activate a process known as photorespiration, an energetically expensive set of reactions that—importantly for this story—release one molecule of CO2.Although atmospheric carbon dioxide  and are responsible for around 25% of global terrestrial photosynthesis .To investigate this, Mallmann, Heckmann et al. combined a mechanistic model of C4 enzyme activity are permitted in the model, key elements of the C4 cycle are favoured as the nitrogen-balancing pathway. What's more, this model predicts that with a C4 cycle established, increasing the activity of the enzymes results in a linear increase in biomass yield. Allowing for low levels of C4 enzyme activity is biologically reasonable, as these enzymes are routinely present in C3 leaves. Mallmann, Heckmann et al. support their model predictions with experimental gene expression data from a set of C3, C2, C4, and other C3-C4 intermediate types in the plant lineage Flaveria, which show elevated C4 cycle activity even in intermediates that are not using the enzymes to capture carbon.Remarkably, when low levels of C2 cycle is established, the evolution of a fully realized C4 process is fairly trivial. Once C4 enzymes are recruited to shuttle nitrogen back to the mesophyll, it is all but inevitable. This can explain in part why C4 has evolved such a startling number of times, and why many of these origins are highly clustered across the tree of life. Many C4 evolutionary clusters likely share an ancestor that had already acquired an elevated likelihood of evolving the pathway (In other words, once a C pathway .Figure 12 species are so rare relative to C4 species—C2 is likely to be a step along the trajectory with a relatively short evolutionary lifespan. At the same time, it raises the question of why a handful of C2 species are persistent—the C2Mollugo verticillata group may be up to 15 million years old (2 plants have solved their nitrogen problem a different way, thereby limiting their own evolutionary accessibility to C4 photosynthesis. If so, this highlights the key role of contingency in adaptation, and our growing power to understand and predict macroevolutionary processes.This may also explain why Cears old . A testa


At the same concentration (10 µM), both curcumin and hemin equivalently mitigated oxidative stress in H2O2-treated glomerular mesangial cells. The benefit of using hemin was derived from its agonistic effect on HO-1, providing relief from oxidative stress. Selective inhibition of HO-1 completely blocked the action of hemin but not that of curcumin, suggesting simultaneous multi-pathway intervention by curcumin. Curcumin also increased cellular autophagy levels, enhancing its protective effect; however, hemin had no effects. Based on the fact that the dysregulation of multiple pathways is implicated in the etiology of complex diseases, we proposed a feasible method for identifying multi-pathway drugs from compounds with validated targets. Our efforts will help identify multi-pathway agents capable of providing comprehensive protection against renal injuries.Typically, most nephropathies can be categorized as complex human diseases in which the cumulative effect of multiple minor genes, combined with environmental and lifestyle factors, determines the disease phenotype. Thus, multi-target drugs would be more likely to facilitate comprehensive renoprotection than single-target agents. In this study, functional chemical-protein association analysis was performed to retrieve multi-target drugs of high pathway wideness from the STITCH 3.1 database. Pathway wideness of a drug evaluated the efficiency of regulation of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in quantity. We identified nine experimentally validated renoprotectants that exerted remarkable impact on KEGG pathways by targeting a limited number of proteins. We selected curcumin as an illustrative compound to display the advantage of multi-pathway drugs on renoprotection. We compared curcumin with hemin, an agonist of heme oxygenase-1 (HO-1), which significantly affects only one KEGG pathway, porphyrin and chlorophyll metabolism (adjusted  Complex human diseases, also known as multi-gene diseases, are caused by the integration of the cumulative effects of genetic, environmental, and lifestyle factors. According to this definition, the majority of nephropathies could be described as complex. Recently, large-scale approaches such as gene microarray techniques have been applied to reveal the gene expression signatures of pathological renal cells and tissues Abundant evidence has shown that most drugs are associated with multiple proteins rather than specifically targeting only one protein in a lock and key manner 2O2-treated glomerular mesangial cells was also used to examine the pluralistic effects of the multi-pathway drug curcumin. Curcumin displays high PW, in comparison with another renoprotectant, hemin that is a specific agonist of heme oxygenase-1 (HO-1). In summary, we demonstrate a method that allows a meaningful search for multi-pathway drugs with the potential of comprehensive protecting against renal injuries.In the present study, we aimed to search for drugs with multi-pathway effects from STITCH 3.1, the world's largest database of well-defined and accepted chemical-protein associations p<0.05 In this study, chemicals that are associated with multiple proteins (n≥10) were retrieved from the STITCH 3.1 database. A confidence score of 0.900 was selected as the threshold A high PW value indicates that a given chemical is efficient in intervening signaling pathways as much as possible by virtue of a limited number of targets. To investigate whether the affected pathways were nephropathy-related, the Gene Prospector online tool in HuGE Navigator was applied to find literature-reported nephropathy-related genes 2O2 for 24 h.Hemin, zinc protoporphyrin (ZnPP), and curcumin were purchased from Sigma-Aldrich Co. (USA). The anti-LC3 antibodies were obtained from Cell Signaling Technology, Inc. (USA). A stable human mesangial cell line  was established by transfection and immortalization by the viral oncogene large T-SV40 of human mesangial cells isolated from normal human glomeruli 4 cells/well and allowed to adhere to the wells for 24 h before H2O2 administration and drug treatments. The media from each well was then removed and replaced with PBS solution containing 0.5 mg/mL MTT, and the plates were incubated at 37°C for 4 h. The remaining supernatant was then removed. DMSO (150 µL) was added to each well and mixed thoroughly to dissolve the formazan crystals formed. After incubation for 10 min, the cell viability was determined by measuring the absorbance of each well at 490 nm.The 3--2,5-diphenyltetrazolium bromide (MTT) assay was used to determine the cell viabilities based on the mitochondrial dehydrogenase activity. In brief, the cells were seeded in a 96-well plate at a density of 2.5×10p-nitroanilide to a yellow formazan product, p-nitroaniline. The detailed analysis procedure is described in the manufacturer's protocol . Caspase-3 activity, which was defined as the ratio of the p-nitroanilide content to the amount of total protein present, was determined by detecting the absorbance of p-nitroanilide at 405 nm by using a microliter plate reader .Caspase-3 activity was determined using a caspase-3 activity assay kit , which is based on the ability of caspase-3 to convert acetyl-Asp-Glu-Val-Asp 4 in each individual experiment.ROS levels in the cells were quantified using an ROS assay kit . The cells were harvested by trypsin and exposed to 10 µM DCFH-DA for 20 min at 37°C. A fluorescence characteristic was then observed when DCFH-DA was intracellularly oxidized to DCF. The labeled cells were washed twice in PBS and then subjected to cytometric analysis at an excitation wavelength of 488 nm and an emission wavelength of 525 nm . The ROS level was represented by the fluorescence intensity. The number of analyzed cells was 1×10Cellular proteins were extracted with lysis buffer containing 1% protease inhibitor solution. The protein concentrations were subsequently determined using a BCA protein assay kit . The proteins were then electrophoresed by 15% SDS-PAGE and transferred onto nitrocellulose membranes. The membranes were blocked with 5% non-fat dry milk in PBS for 2 h at room temperature and incubated with the primary antibodies at 4°C overnight. Afterward, the membranes were washed with PBS-0.1% Tween 20 and incubated with fluorescence-conjugated goat anti-rabbit IgG or goat anti-mouse IgG at a dilution ratio of 1∶10,000  at room temperature for 1 h. The western blot bands were quantified using the Odyssey infrared imaging system .t-test or one-way ANOVA followed by Tukey's multiple comparison test. Differences were considered statistically significant only if p<0.05.All of the data are expressed as means ± SEM. The data analysis was performed using student's By applying the double restrictions of a confidence score of 0.9 and target numbers of 10, we identified 734 multi-target chemicals from the chemical-protein association database STITCH 3.1 . The enddrug metabolism , neuroactive ligand-receptor interaction , and calcium signaling pathway . Targeting multiple proteins does not mean that one drug can affect a remarkable intervention on multiple signaling pathways (p>0.05). However, 593 chemicals were identified as multi-pathway chemicals because they significantly regulated more than one KEGG pathways. Many of these are clinical drugs and natural active compounds  and decreased autophagy with hemin (p<0.001). Comparatively, we did not detect any significant effect of hemin on LC3 protein expression in the normoxic setting, similar to ZnPP. Curcumin was shown to remarkably enhance the LC3 expression in normoxic glomerular mesangial cells (p<0.01).Additionally, we investigated the effect of curcumin and hemin on cellular autophagy in Hectively . The expThe regulation of multiple genes and proteins can lead to significant alterations in signaling pathways, which strongly suggests that multiple mechanisms underlie the progression of nephropathies Despite the fact that a stringent search condition was applied , the STITCH database retrieval identified more than 700 multi-target chemicals. Based on a the huge number of proteins expressed in the human body Our result indicates that the KEGG pathway “drug metabolism” was most susceptible to multi-target chemicals. This was not surprising when we consider the number of drugs that are metabolized by pathways such as the cytochrome P450 system By combining PW calculations and literature searches, we recognized nine multi-pathway renoprotectants, among which curcumin has been the most studied. Numerous renoprotective effects of curcumin have been noted in the literature, such as anti-inflammatory effects, anti-oxidative effects, and anti-apoptosis effects 2O2-induced cell injuries.An increased level of ROS is thought to be an essential factor for altered autophagy in cells In conclusion, we have established a feasible method to search for multi-pathway renoprotectants. In vitro studies have revealed the advantage of multi-pathway drugs in repairing injured renal cells. Nonetheless, more in vivo studies are needed to further evaluate the usefulness of multi-pathway drugs, not only in nephropathies but also in other complex human diseases.Table S1Multi-target chemicals included in this study.(XLS)Click here for additional data file.Table S2Association between chemicals and KEGG pathways.(XLS)Click here for additional data file.Table S3Nephropathy-related KEGG pathways.(XLS)Click here for additional data file.Table S4Evidence for multi-pathway renoprotectants of high pathway wideness.(XLS)Click here for additional data file.


Functional annotations of large plant genome projects mostly provide information on gene function and gene families based on the presence of protein domains and gene homology, but not necessarily in association with gene expression or metabolic and regulatory networks. These additional annotations are necessary to understand the physiology, development and adaptation of a plant and its interaction with the environment.RiceCyc is a metabolic pathway networks database for rice. It is a snapshot of the substrates, metabolites, enzymes, reactions and pathways of primary and intermediary metabolism in rice. RiceCyc version 3.3 features 316 pathways and 6,643 peptide-coding genes mapped to 2,103 enzyme-catalyzed and 87 protein-mediated transport reactions. The initial functional annotations of rice genes with InterPro, Gene Ontology, MetaCyc, and Enzyme Commission (EC) numbers were enriched with annotations provided by KEGG and Gramene databases. The pathway inferences and the network diagrams were first predicted based on MetaCyc reference networks and plant pathways from the Plant Metabolic Network, using the Pathologic module of Pathway Tools. This was enriched by manually adding metabolic pathways and gene functions specifically reported for rice. The RiceCyc database is hierarchically browsable from pathway diagrams to the associated genes, metabolites and chemical structures. Through the integrated tool OMICs Viewer, users can upload transcriptomic, proteomic and metabolomic data to visualize expression patterns in a virtual cell. RiceCyc, along with additional species-specific pathway databases hosted in the Gramene project, facilitates comparative pathway analysis.http://www.gramene.org/pathway/.Here we describe the RiceCyc network development and discuss its contribution to rice genome annotations. As a case study to demonstrate the use of RiceCyc network as a discovery environment we carried out an integrated bioinformatic analysis of rice metabolic genes that are differentially regulated under diurnal photoperiod and biotic stress treatments. The analysis of publicly available rice transcriptome datasets led to the hypothesis that the complete tryptophan biosynthesis and its dependent metabolic pathways including serotonin biosynthesis are induced by taxonomically diverse pathogens while also being under diurnal regulation. The RiceCyc database is available online for free access at The online version of this article (doi:10.1186/1939-8433-6-15) contains supplementary material, which is available to authorized users. Oryza sativa) continue to be the flagship plant models and share a significant fraction of reported literature on gene functions and phenotypes associated with plant development and metabolism. Therefore, newly sequenced plant genomes and transcriptomes depend heavily on rice and Arabidopsis genome annotations for projecting genome, gene product and pathway annotations. In the majority of cases gene product annotations involve InterPro . Through this database we present a snapshot of the substrates, metabolites, enzymes, reactions and pathways of primary and intermediary metabolism in rice. In this report we will provide database insights based on RiceCyc version 3.3. Beside discussing the development of RiceCyc we will demonstrate the capabilities of RiceCyc as a platform for integrating and analyzing multiple omics data sets in terms of knowledge discovery. To emphasize the latter, we present here the first detailed report on full-scale annotation and integrated analysis of the serotonin, auxin IAA (indole-3-acetic acid) and tryptophan biosynthesis pathways in terms of stress and diurnal regulation, using the RiceCyc platform and publicly available rice transcriptomic data sets.In order to respond to and survive environmental challenges, plants, as sessile organisms, have developed a multitude of anatomical, morphological, growth habit and developmental adaptations that are based on underlying genetic variation. Gene duplication and alternative splicing of gene transcripts, commonly seen in plant species, provide plasticity and “sub-functionalization” that can contribute to plants’ ability to endure external stresses with reproductive success Freeling . While cThe RiceCyc pathway database currently features 316 metabolic pathways, 2,103 enzymatic reactions, 87 transport reactions and 1,543 compounds and metabolites. 6,643 protein coding genes i.e. about 14% of the total ~47,894 protein coding and tRNA genes that are either known and/or predicted from rice   allows users to perform comparisons between any two or more species by selecting their choices and look for common or species-specific pathways, reactions, metabolites etc. Each pathway and reaction details page also provides a cross-species comparison option to compare details of the specific pathway or reaction between any two or more species  illustrates the biochemical pathway map within the context of the cell metabolome  for validating and mapping microarray probe IDs, gene names or gene symbols to a locus ID in user provided data files. If the gene IDs are missing in the data file but if there are mappings from the microarray platform specific probe ID to a specific gene, the tool regenerates a valid expression data file for expression data analysis within the OMICs Viewer. The ability to visualize the expression patterns in virtual cell-like simulations and projections on the pathways and reactions helps the user to pinpoint and interpret interesting expression patterns as compared to examining a tabulated text file or annotation and function enrichment data derived from tools such as Blast2GO and BinGO.The tool Cellular Overview   which connects from the ‘Modifications’ hyperlink to the curation webpage (http://gramene.org/pathway/ricecyc_deleted_pathways.html).Computationally predicted databases suffer both from false negatives and false positives. The primary reference pathway database, MetaCyc that we have used for creating RiceCyc only houses experimentally demonstrated pathways, enzymes, reactions and compounds listed in its reference library which are derived from several organisms including plants. We used taxonomic filtering options to filter out non-plant pathways. This was successful only if the reference library was flagged with the appropriate taxonomic designation. To further reduce the false positives escaping the taxonomic pruning we evaluated the projections against the curated list of plant pathways and reactions provided in the frame format by PlantCyc-PMN , BioPAX level-2 and 3, and SBML (Stromback and Lambrix ftp://ftp.gramene.org/pub/gramene/pathways/ricecyc/) and the installation of PathwayTools . The local database setup on desktops running windows, linux, mac or unix environments offer additional operability, such as, faster speed, the opportunity to update the database with proprietary data, and the ability to perform programmatic queries including comparing species wide cellular overviews of pathway networks from multiple species.The complete RiceCyc database is available online as well as for download from CCA1 is reported to control genes related to plant defense and growth hormone metabolism in Arabidopsis  genes. It was reported  and β (ASB) subunits  show synchronized cycling, with upregulation around 12 h and 36 h (dusk) . Similarly, the enzyme tryptophan synthase  fungal pathogen Magnaporthe grisea - causing rice blast disease and 3) by the angiosperm parasitic weed Striga hermonthica. The above pathogens induced the genes ASB1 and ASA2, PAT (LOC_Os03g03450), PAI, IGPS, TSA (LOC_Os07g08430) and TSB1. Together these genes cover all six reaction steps in the tryptophan biosynthesis pathway. Interestingly, all the above genes/enzymes also exhibit synchronized diurnal cycling as described previously  shows preferred induction after Striga and Xanthomonas treatments whereas ASB2, ASA1, and TSB Type-2 show preferred induction after Xanthomonas treatment.The tryptophan biosynthetic pathway is known for its role in providing precursors for plant defense-related secondary metabolic compounds and the pathway genes are known to be induced by pathogens and elicitors  is reported not to be induced by pathogens or elicitors . XA21 is a pattern recognition receptor kinase that mediates resistance to the Gram-negative bacterium Xanthomonas oryzae pv. oryzae  having the indole-3-glycerol phosphate lyase activity and tryptophan synthase β-subunit (TSB) are known to be induced by biotic and abiotic stresses in Arabidopsis  by X. oryzae in the current study suggests a specific role for the ‘type 2’ TSB, at least in rice.Tryptophan synthase α-subunit , a neurotransmitter is well known to play several key physiological and behavior-associated roles in animals. Serotonin, its precursor molecules and interactors such as their receptors and transport proteins, are also known to show diurnal modulation in animals. Interestingly the serotonin molecule is also known to be present in a wide range of plant species (Roshchina De novo synthesis of IAA in plants is complex and can occur via tryptophan (Trp)-dependent or Trp-independent pathways. To date, a complete de novo auxin biosynthetic pathway has not been well-defined for plants (Zhao nts Zhao . The gennts Zhao . TherefoArabidopsis tryptophan amino transferase 1 (TAA1) gene mutants defective in the conversion of L-tryptophan to IPA  in contrast to the currently described YUC route of IAA synthesis , LOC_Os01g47530 (OsMPK8), LOC_Os03g57220 (GIP1), LOC_Os05g07860 (SAB10) and LOC_Os11g36180 (XA21) were also components of the “100 protein interactors” that were reported to be the key regulators within the rice biotic and abiotic stress response network that have been identified through Y2H screening  through activation of defense response genes in the nucleus  interacts with anthranilate synthase-β subunits ASB1 and ASB2 that are components of the key regulatory reaction of the tryptophan biosynthetic pathway. In addition, SAB10 (LOC_Os05g07860), a component of the ethylene mediated abiotic stress response subnetwork in rice  and both α subunits of this key regulatory enzyme complex. Many of these interactions based on co-induction and expression were reported in rice and Arabidopsis and are consistent with our analysis . These amino acids are reported to provide a protective role during induced oxidative stress and play a critical role in the initiation of hypersensitive response and immunity in Arabidopsis  gene, which encodes a cytochrome P450 monooxygenase that bears tryptamine 5-hydroxylase activity. It catalyzes the conversion of tryptamine to serotonin. This is a plant-specific reaction that is not found in humans  annotations to the rice gene products based on gene-gene homology evaluated by Ensembl Compara datasets. The final step was curation and quality check, where a human expert would review and accept, modify or reject the computational predictions. The rice genes used for the current build are based on MSU6 rice genome annotations. However, in the future we will be updating them with RAP (Rice Annotation Project) identifiers and upcoming revised IRGSP  annotations for Oryza sativa ssp. japonica cultivar Nipponbare.The rice protein sequences were downloaded from Gramene’s Rice Ensembl genome. The proteins were submitted to an annotation workflow that included HMM-based annotations from InterPro, SMART, PRINT, Prosite and Pfam. The second round of annotations involved cellular localization prediction based on Predotar for plastid and mitochondria . Functional enrichment analysis using GO category over-representation was carried out using the network visualization program Cytoscape . It is primarily a genome-scale network based on predicted protein-protein interactions and co-expression.The analysis of diurnal gene expression was conducted as described in Filichkin, Breton et al. . The datAfter building the RiceCyc pathway database, it was hosted on a webserver for online use, following installation guidelines provided by the PTools software Additional file 2: Table S2: Gene expression values of diurnally cycling rice genes mapped to RiceCyc pathways. The headers of data columns indicate the diurnal sampling time point starting with the beginning of the light period (0 h). (XLSX 379 KB)Additional file 3: Figure S1: Expression pattern and over-represented metabolic processes of diurnally cycling genes. (A) Hierarchical clustering of gene expression pattern of diurnally cycling genes in rice, mapping to RiceCyc metabolic pathways. Mean centered expression levels are represented with green denoting downregulation, and red indicating upregulation. (B) Over-represented gene ontology categories of gene set represented in (A). (PPTX 505 KB)Additional file 4: Figure S2: Gene expression analysis of the genes associated with enzymes catalyzing IAA biosynthesis. (A) A zoom-in view of IAA biosynthetic pathway. (B) Heat map of expression patterns of all cycling genes in the pathway with the gene ID and reaction of the pathway. Colored circles are used to identify individual reactions. (C) Biotic and abiotic stress induction of tryptophan biosynthetic genes based on data compiled in Genevestigator for rice. Expression data available for perturbations were filtered for significance level  and fold change . (PPTX 221 KB)Additional file 5: Figure S4: Tissue type expression pattern of genes in tryptophan biosynthesis (A), serotonin biosynthesis (B), and IAA biosynthesis (C) from data compiled in Genevestigator. (PPTX 178 KB)Additional file 6: Figure S3: Rice tryptophan biosynthetic enzyme interactome based on RiceNet. Blue circles on the periphery of the interactome represent tryptophan biosynthetic enzymes. Green circles represent interacting proteins identified from RiceNet. Three of the five anthranilate phosphoribosyltransferase paralogs  did not share interactions with other tryptophan biosynthetic enzymes and clustered separately . (PPTX 2 MB)http://www.functionalnet.org/ricenet/) to find proteins that interact with tryptophan biosynthetic pathway enzymes. Nodes represent genes/proteins while edges represent interactions. Column-A is node/interactor-1, column-B is relationship (network edge) and Column-C is node/interactor-2. (XLSX 33 KB)Additional file 7: Table S3: Rice tryptophan biosynthetic enzyme interactors based on the RiceNet. We probed the rice probabilistic functional network, RiceNet (Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Authors’ original file for figure 4Authors’ original file for figure 5Authors’ original file for figure 6Below are the links to the authors’ original submitted files for images.


This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License.Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (Database URL:http://www.metabolicatlas.org. Metabolism comprises a large number of biochemical reactions, which are mostly catalyzed by enzymes. Metabolism results in production of essential biochemical compounds and Gibbs free energy to maintain homeostasis of cellular functions. Over the past few decades, more than 2200 human enzymatic reactions  have beeGenome-scale metabolic models (GEMs) provide comprehensive overview of the genotype to phenotype relationship in living cells and thereby provide a scaffold for interpretation of high throughput data in the context of metabolism . Metabolh-tGEMs have been widely used for data analysis, data integration and simulation to gain knowledge about cell type-specific mechanisms prior to whole organism understanding. Knowledge from model-based data analysis has provided new insight about human metabolism, especially in connection with medical research prior to discovery of new treatments or prevention strategies, e.g. to identify new therapeutic agents or novel diagnostic biomarkers , 13–15.Reconstruction of a h-tGEM is generally done by integration of multi-omic data, but organizing several data layers of complex data is a challenge. To deal with integration and organization of biological data for GEM reconstruction, there is a need for a database system that is developed for this particular purpose. In connection with reconstruction of cell type-specific models, we established an HMR database using MySQL . The HMRTo efficiently manage and utilize the h-tGEMs and related information, the human metabolic atlas (HMA) website was built as an online resource to organize and provide comprehensive human metabolic information as models and a database to support further specific analysis or modeling as well as to communicate with the wider research community. There are three major services available on the HMA. The Repository provides management of human GEMs with a crucial feature that enables keeping track of available models. Currently the repository contains 98 human cell type-specific GEMs including models for normal and cancer cell types, 2 curated models and 5 human-related microbial models available to download in system biology markup language (SBML) format for metabolic simulation. To enable exploration of the provided models, a web-based metabolic map visualization system named Atlas, provides a way to explore human tissue-specific metabolic information overlaid on KEGG metabolic pathway maps with an interactive user interface. Finally, the HMA provides Hreed, a reaction database that provides high quality of structured information of metabolic reactions and related data. Future plans include support for cloud-based system and data curation, data expansion and application programming interface (API) for facilitating more advanced capabilities.The HMA website was developed using the Ruby on Rails platform providing basic web application building blocks for the Repository, Hreed and Atlas development. The Repository was developed to provide downloadable sets of GEMs of specific human tissues and related organisms. The models are versioned based on cell types; therefore, the latest release of a specific cell type is the updated version of the same cell type model in the previous release.http://www.mongodb.org/) that was recently developed  database described in  is highleveloped . The DBMeveloped .The main objectives of the Hreed development were to reduce data redundancy, to maintain data integrity and to improve query efficiency. Uniqueness of data is necessary for integration of multi-level data and to achieve this, all data in the database must be internally consistent, but must also be consistent with external databases with clear identifiers. A systematic data identifier system was therefore developed for compounds, reaction data and cross-references; and all were tightly integrated in the conceptual data layer.Conventional chemical identifiers are normally specified by names, which provide ambiguous description of chemicals. To provide a unique identifier for chemical compounds, IUPAC developed a unique computer readable identifier of chemical compounds named International Chemical Identifier (InChI) and InChIKey. InChI is comprised several data layers specifically generated from a molecular structure diagram . As the One criteria of the Hreed design is to support data integration with other public databases. Different systems of data identifiers, usually only a number, are ambiguous and not suitable for building a rigorous database. To overcome these problems, the MIRIAM registry, the Minimum Information Required in the Annotation of Models registry, was used and also implemented in conceptual data layers. The MIRIAM registry is usually provided as unified resources name (URN) composed of three parts separated by colon sign (:). The first part is prefix, always urn:miriam, to specify the source of registry from MIRIAM. The second part is a name space depended on the data collection of identifier. The last part is the identifier itself. Besides providing global unique identifiers, MIRIAM also provides information for retrieving the original data URL from the original database, which is very useful for avoiding dead links .Hreed contains several kinds of data including human genes, transcripts, proteins, small molecules and reaction data as shown in The map-rendering engine was developed using a D3 JavaScript (D3JS) library to render metabolic maps from map coordinates into scalable vector graphic (SVG) on a web browser where data can be overlaid. D3JS also provides interactive features to the map. By the virtue of using JavaScript and SVG, Atlas is compatible with mainstream web browsers such as Firefox, Safari, Chrome, Opera and Internet Explorer, without any additional browser add-on requirement.The map coordinates were taken from KEGG Markup Language (KGML) file, which is the only well-defined data interchange format providing metabolic map with coordinates in a biology-friendly style. These properties of KEGG map facilitate a seamless way to integrate data with the automatic map drawing protocol, and have therefore been widely used for many applications . HoweverAtlas has an interactive interface for users to visualize a specific map and retrieved corresponding pathway information by using autocomplete textbox. Pathway list is retrieved directly from KEGG database every time Atlas is opened to ensure that Atlas has the most up to date pathway and map information: cross-references and coordinates. Overlaid information is mapped using related tissue information from HMR2.0 model and Hreed, and gene and cross-reference information retrieved from a KGML file.The first release of cell type-specific metabolic models was automatically constructed from a generic GEM and resulted in GEMs for 69 normal and 16 cancer cell types . To effiBesides enabling download of models in SBML format, users can easily access human reaction data from Hreed. Hreed was developed based on an object-oriented data model for storing standardized reactions, compounds and gene annotation information from deposited models. Hreed web interface can be used to easily retrieve reaction data from Hreed, by using specific keywords of related genes, proteins, compounds or cross-references. The query results are downloadable in JSON and Comma Separated Values (CSV) format which is more convenient for further downstream computational analysis. This database query system can also be used for reconstructing user-specified metabolic models.Atlas metabolic map visualization system provides a comparative view of tissue-specific metabolic networks on KEGG metabolic maps. These data overlaid maps can be used for observing the relationship and differentiation of metabolism for each cell type in a graphical way. The map can be opened by typing into the auto-fill input box or by clicking on the map name on other maps. Summary of the gene identifier for each cell type is shown in a bar chart under the map. Further information can be presented in a popup, upon clicking the gene identifier. To focus on the specific tissues or organ systems, overlaid information can be chosen from a tissue filter tree displayed to the left of the map.The repository is a GEM collection of specific human cells and human-related organisms. The models are publicly available in SBML format. This repository is continuously updated when new GEMs become available. The recent release provides 100 human tissue-specific models including normal, cancer and curated models and five human-related microbial GEMs as shown in INIT models are automatically generated GEMs for different human cell types by combining a generic GEM with transcriptomic and proteomic data. The models are continuously generated and updated, triggered by data updates and algorithm improvement updates. The first release models (r1) were generated for 69 different human normal cell types and for 16 human cancer cell types , whereasApart from INIT models, there are two manually curated models deposited in the GEM repository. The Adipocytes1890 model was curated by using 7340 adipocyte-associated genes from immunohistochemistry assays, resulting in a comprehensive and functional GEM . FurtherSeveral recent studies have shown that the metabolism of the gut microbiome has influence on overall human metabolism, and we therefore also started to collect GEMs for human gut microorganisms. In this version of the HMA, there are GEMs for five key species that are representatives of the human gut microbiome . These GHreed is a comprehensive information ecosystem that can support model reconstruction and data curation, and can also serve as a context-aware data query system. This database is an improved version of the HMR database presented earlier , and theInformation in Hreed can be accessed using the graphical data query interface or the database application programming interface. The graphical data query interface, as shown in For benchmarking Hreed, compound and reaction information from the global reconstruction human metabolic model, Recon version 2.02 , was useTo visualize and compare h-tGEMs among different cell types, a visualization system was implemented providing a better view of tissue-specific metabolic models. A web-based visualization system, called Atlas, was developed with an interactive interface representing information from the latest release of INIT normal models on KEGG metabolic maps . The AtlAtlas user interface allows users to interactively explore the map, component details and overlaid data by using simple mouse controls. By mouse scrolling, the user can zoom in and out. Details of map components including id, name, link and graphs of overlaid data will be shown in a balloon popup by a left mouse clicking on the component. By clicking on reactions, usually represented as boxes, a dialog box is displayed, with information of the reaction, related genes, proteins and pathways taken from KEGG and related tissue from the Hreed. Clicking on some text in the dialog box will link directly to major cross-referenced databases, Ensembl , UniProthttp://www.colorbrewer2.org.Boxes and lines are shaded in blue. Darker blue means that reaction exists in more tissue-specific model, than ones in lighter blue. Yellow means that reaction does not exist in any tissue-specific model. No color means that reaction does not exist in humans, as per information in KEGG map. Bluish green means that gene exists in corresponded tissue-specific model. Important information is overlaid on the map by using a colorblind-friendly palette suggested by Color Universal Design Organization and The HMA website has been developed as a comprehensive web resource to (i) provide draft h-tGEMs generated by the automatic algorithm INIT, (ii) provide simulation ready functional h-tGEMs which can be used as predictive models and scaffolds for generating personalized GEMs, (iii) provide tools and an environment for further community driven expansion and (iv) provide visualization for comparative analysis of differences in metabolism of specific cell types on a metabolic map.The direct use of GEMs as a flat file database as well as relational database to provide information of human metabolism is not efficient , 33. TheHreed was first propagated from HMR by an automatic script. To achieve the requirements of the database system, propagated information was standardized; especially, small molecules and gene annotation. Only small molecules from HMR that have or can map to InChI identifier were incorporated into Hreed. Also, gene annotations were converted to Ensemble gene id. After a new cycle of model reconstruction, the curated information from GEMs can be integrated back to the database. Due to the flexible data structure, the database can be expanded further for other data categories such as complex molecule, protein–protein interaction and genetic interaction without changing the physical data layer. Besides data structure expansion ability, the database APIs also allow developers to implement new functions, especially data integration and analysis, to the database system in the future.The Atlas is currently based on KEGG metabolism map, which is easy to automatically render in interactive graphical format in a web browser. To extend the capability of the Atlas, other custom maps from other popular reaction databases: Reactome , HumanCyCompared with the first release of the website containing only a GEM repository, the HMA has been updated with several improvements and new features, and we believe that the HMA web resource will be a valuable data exchange hub for the research community and facilitate new knowledge creation in the field of human metabolism.http://www.metabolicatlas.org.The HMA is publicly available on