Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG pathways created with CyKEGGParser makes systems biology analysis more sensitive and appropriate compared to original pathways.The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app ( However, the KEGG pathway database suffers from a number of limitations that reduce the adaptability of the pathways for automated analysis. These include inconsistencies in KGML files supplied with each pathway image, such as absence of event or entity labels , reversed directions for some associations, absence of some interactions, and inconsistent representation of compound interactions2. Additionally, some features of KEGG pathways such as protein complex nodes and node duplication, enhance graphical representation, but reduce their machine-readability. Another limitation concerns abstractions  used in pathway construction: (1) paralogous genes, not always occurring together in the same biological context, are grouped into single nodes, and (2) all the genes are assumed to be expressed and present in the same pathway. Additionally, the sources of information on interactions depicted in pathways differ in quality and the nature of interactions . Even accounting for these bottlenecks, the KEGG pathway database is still a highly valued resource, and we aimed to develop a tool that would make the best use of the information collected in it.The KEGG pathway database is a widely accepted source for biomolecular pathway maps and has long been considered as the gold standard for pathway-based analyses due to well-formatted human-readable maps supplemented with machine-readable XML files (KGML), quality of curation and comprehensivenesshttp://apps.cytoscape.org/apps/keggscape) for KEGG pathways visualization and data integration, and others. However, none of the available apps addresses inconsistencies in KGML files, and nor do they neither deal with abstractions of KEGG pathways. Herein, we describe CyKEGGParser app for Cytoscape 3 for KEGG pathway retrieval, visualization, adjustment for inconsistencies in computer-assisted manner, context-specific pathway generation, and exporting the pathways in KGML and BioPAX formats. CyKEGGParser is best suited for KEGG signaling pathways.There is a wide variety of software that manipulate on KEGG pathways, both standalone and Cytoscape 3 apps, such as KEGGscape . The pathway selection dialogue provides a list of all KEGG pathways and organisms, however, if pathway KGML does not exist in the database the user will receive a warning message.The input for parsing is KGML formatted files, either stored locally or downloaded from the web via REST-based KEGG API. The KEGG API can be used for individual downloads for academic use only; bulk download and non-academic usage requires a KEGG FTP subscription and license agreement .All the corrections performed on the network, as well as tuning and saving steps (described below) are tracked in separate log files . Since CyKEGGParser relies on protein-protein interactions (PPI), parsing of metabolic pathways is not always as accurate as it is for signaling pathways. However, if only protein-protein interactions are of concern and if the KGML file contains respective <relation/> entries, CyKEGGParser will parse metabolic pathways similar to signaling ones.KEGG metabolic pathways, along with <relation/> entries, which characterize protein-protein interaction networks , also contain <reaction/> entries, characterizing compound interactions  pathways according to specific biological context: particular tissue or cell type, and experimentally confirmed physical interactions.Tissue-specific tuning. Tissue-specific tuning is aimed at providing the user with the ability to modify the networks based on genes expressed in a chosen cell/tissue type. Gene expression data for tuning is derived from BioGPS (http://biogps.org/) experiments for human normal and cancer tissues, provided by GeneCards (www.genecards.org), or may be supplied by the user . Along with specifying the source of data, the user chooses the tissue and specifies gene expression threshold.The algorithm firstly clones the network preserving all the attributes, except for node and edge identifiers (those should be unique). Then it iterates over all the genes contained in the cloned network nodes, and removes the genes with expression values less than the specified threshold. If a node contains at least one gene that is expressed in current tissue, it remains in the network, otherwise it is removed. Nodes other than of type “gene” are preserved in the network.PPI based drill-down. In KEGG pathways, node entries represent groups of paralogous genes that have similar functions or interaction profiles1. The main incentive of PPI based pathway drill-down is to expand each node into its component genes and connect only those pairs of genes that have been shown to have true physical interactions. Together with tissue-specific tuning, this leads to generation of a “fine-tuned” network, in which all the components occur in the same biological context.http://string-db.org/), have been loaded in an internal MySQL database, located at the server of Bioinformatics Group of the Institute of Molecular Biology NAS RA (http://molbiol.sci.am/big/). The user can choose the source of interactions from the list of databases , as well as set interaction confidence score threshold, which is computed based on various evidence channels, adjusted for probability of randomly observing an interaction3. The interactions are manually updated in the local My-SQL database and the version of String used is mentioned on the Tuning dialogue.PPI data, retrieved from the String database .CyKEGGParser provides the functionality of saving the processed pathways back in valid KGML format, so that the modified pathways may be used outside of Cytoscape. All the modifications done to the network are saved in the attributes specific to KGML format. In addition, CyKEGGParser uses KEGGTranslatorImplementation:, Pathway tuning section), and compared pathway topologies in each case.We have taken KEGG B Cell Receptor Signaling Pathway as an example to demonstrate CyKEGGParser functionality and its applicability in pathway-based biology research. B cells are important players in humoral immunity, and their main function is dependent on the B Cell Receptor Signaling Pathway, which is initiated by antigen binding to B cell receptor. We have tuned the B Cell Receptor Signaling Pathway based on BioGPS tissue-specific gene expression data in CD19 B and CD4 T cells  were chosen and 0.8 confidence score threshold was set. Comparison of the PPI-tuned and the original networks showed that the node “VAV3…”, which contains three genes, VAV1, VAV2 and VAV3, was duplicated in the original pathway, but remained only in one place in the tuned network  in CD14 monocytes, Adipocytes, and Cardiac myocytes . B05 and B01 datasets were generated from the normal tissue gene expression data, and by randomly assigning two-fold changes to genes based on Bernoulli distribution with probabilities 0.5 (B05) and 0.1 (B01), respectively.Click here for additional data file.http://apps.cytoscape.org/apps/kgmlreader) and KEGGscape (http://apps.cytoscape.org/apps/keggscape), semi-automatic correction and tuning-based enhancement of pathway specificity are unique and valuable features of CyKEGGParser. With this functionality we aim to maximize the effectiveness and sensitivity of gene expression-based systems biology analyses based on KEGG pathways.We have developed CyKEGGParser app for Cytoscape 3 that allows for import, correction, visualization, and tuning of KEGG pathways. Although KGML-based pathway import in Cytoscape has also been addressed by KGMLReader  that keeps track issues with the KGML files, if this is the result of missing information in the KGML? 2: In the PPI drill-down option, a confidence score threshold is provided, but it would be useful to direct readers to more information about this score and how to select appropriate values. What version of STRING do the authors use? Is this data automatically updated in their internal MySQL database? 3: Currently, there seems to be a problem with the BioPAX export. I believe the problem is in the way KEGGTranslator is called in that lacks the --format option . If this is indeed the problem, it should be easy to fix. It would be good if the authors validated the resulting BioPAX output from their KGML edits to see if any other issues arise. http://biopax.baderlab.org/validator/check.htmlMissing ID cross-references (XRefs) is a likely error, but one that the authors might not be able to fix if this information is missing from the KGML file. 4: "ppi" in "Set ppi threshold" should be capitalized in the Protein-protein interaction Settings window of the "Pathway tuning settings". Gene Expression Settings" with no file the text appears as "No file selecte"There are some other minor issues in the dialog. Some of the panels do not seem large enough to accommodate the presented text. On the "CyKEGGParser seems stable and it performs the key function of helping users edit KEGG pathways, but it may require additional steps by users that want figures that mimic the aesthetics of the original KEGG diagrams .I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thank you for reviewing our paper and your comments, the points you have mentioned are valuable for the app and comprehensiveness of the paper. Here is the detailed response and the description of changes we have made: 1: Currently, the diagrams rendered in CyKEGGParser lack some elements of the original diagrams. Here is the link to the original "B Cell Receptor Signaling Pathway" http://www.genome.jp/kegg-bin/show_pathway?hsa04662The original KEGG diagram contains vertical lines indicating transmembrane proteins that are missing from the CyKEGGParser version. There is also a missing interaction between RAC1 and "Regulation of actin cytoskeleton". Are both of the elements missing from KGML or are there limitations in the capabilities of Cytoscape to render elements, such as the vertical membrane lines? Is there any resource (by the authors or others) that keeps track issues with the KGML files, if this is the result of missing information in the KGML? KEGG pathway map images, besides containing information about pathway nodes and their interactions, are also decorated with various graphical notations for visualization of spatial distribution of pathway components . Since a KGML file solely includes nodes, relations between genes (gene products), compounds and maps (in some metabolic pathways only) and reactions, it is not possible to reconstruct any graphical decoration from a KGML file.RAC1 and “Regulation of actin cytoskeleton”. In some cases, pathway map nodes are presented to indicate that some nodes and edges are a part of another pathway. In such cases, pathway nodes should not be linked to any node via interactions. In other cases, however, the link from a node to pathway node indicates functional relationship, which is not included in KGML files. In these cases, users can add these interactions manually and save them back in KGML format using CyKEGGParser’s functionality. The same applies to present for protein – pathway and compound – pathway interactions, such is the case ofUnfortunately, there is no resource that keeps track of the elements that are missing in KGML files, nor is it possible for the app to “guess” what is missing while parsing the actual KGML file.2: In the PPI drill-down option, a confidence score threshold is provided, but it would be useful to direct readers to more information about this score and how to select appropriate values. What version of STRING do the authors use? Is this data automatically updated in their internal MySQL database? To clarify for the meaning of the confidence score, we have added the following paragraph in the User Manual and directed the user to String database’s help page for more details:http://string-db.org/newstring_cgi/show_info_page.pl?UserId=z7Cu7ePjQXnI&sessionId=rD_f8EsHGmAQ.” “In String, the confidence score is derived by combining evidence about protein-protein interactions from various sources, adjusted for probability of randomly observing the interaction. More information about confidence score meanings and interaction sources can be found at the Help page of String database:In the manuscript body, we have added the following paragraph, where we refer to the String database paper:“The user can choose the source of interactions from the list of databases , as well as set interaction confidence score threshold, which is computed based on various evidence channels, adjusted for probability of randomly observing an interaction [3].”The local My-SQL database will be manually updated when new versions of String database are published. Currently, the String version 9.05 is used, and we are populating the database with interactions from version 9.1 at the moment. We have added a label on the tuning dialogue of the app, where the last updated version is seen, and added this information in the User Manual:“The interactions are manually updated in the local My-SQL database and the version of String used is mentioned on the Tuning dialogue.”3: Currently, there seems to be a problem with the BioPAX export. I believe the problem is in the way KEGGTranslator is called in that lacks the --format option . If this is indeed the problem, it should be easy to fix. It would be good if the authors validated the resulting BioPAX output from their KGML edits to see if any other issues arise. http://biopax.baderlab.org/validator/check.htmlMissing ID cross-references (XRefs) is a likely error, but one that the authors might not be able to fix if this information is missing from the KGML file. CyKEGGParser calls KEGGTranslator for BioPAX2 and BioPAX3 conversion, with --format BioPAX_level2 and --format BioPAX_level3 options applied, respectively. Could you, please, send us the log file, to see why the –– format option is missing?http://www.biomedcentral.com/content/pdf/1752-0509-7-15.pdf. As In some cases, however, KEGGTranslator’s output is not successfully validated with BioPAX validator, mainly the BioPAX level 2 format. Since CyKEGGParser relies solely on command line calls to KEGGTranslator, there is nothing we can about these cases. However, we have tested whether the successfully validating outputs are also validated after pathway edits performed with CyKEGGParser or by the user in Cytoscape environment. Before calling KEGGTranslator, CyKEGGParser checks for absence of fields required by BioPAX2 and BioPAX3 formats, and adds default values as needed. This ensures that the edits do not induce problems for BioPAX conversion and further validation. The process of saving in KGML format and translating this format into BioPAX is described in the User Manual as follows:KEGGTranslator performs a number of steps in order to retrieve the data missing in KGML files and come up with a valid BioPAX model, including completion of reactions, fixing invalid content of KGML entities and fetching cross-references, as described in“KGML format saving assures that all the attributes required for BioPAX translation are available. For nodes, these are “entry: id” and “entry: type” attributes: these are assigned the default values (the next maximum id in the network and “gene” respectively). Node color and coordinates are not required for format conversion, however, if those are missing in the attributes, they will be assigned the values they have in Cytoscape. For interactions, the required attribute is the “type” attribute. If this attribute is missing in the network, it is assigned a value based on source and target node types, as follows: if both nodes are of type “gene” the interaction is assigned the type “PPrel”, if either of the nodes is of type “compound”, the type “PCrel” is assigned, otherwise the algorithm looks at the required format: in case of KGML and BioPAX_level2, it will assign “maplink” type to those interactions, where one of the nodes is of type “Map”, and will give the default value “PPrel” otherwise; in case of BioPAX_level3 the interaction will be removed from the network, since the latter format does not allow for interactions between non-physical entities.”4: "ppi" in "Set ppi threshold" should be capitalized in the Protein-protein interaction Settings window of the "Pathway tuning settings". There are some other minor issues in the dialog. Some of the panels do not seem large enough to accommodate the presented text. On the "Gene Expression Settings" with no file the text appears as "No file selecte". We have changed “ppi” to “PPI” in the “Set ppi threshold” of the dialogue, and added more space to file selection label in the “Gene Expression Settings” tab.  CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows by Lilit Nersisyan, Rouben Samsonyan and Arsen Arakelyan presents a comprehensive software tool allowing to edit KEGG pathways based on additional information taken, e.g., from tissue expression data and/or protein interaction networks. The functionalities are well described and illustrated using selected pathways in the context of B-cell signalling.The articleIn my opinion, the impact of the contribution is high especially for researchers who are interested in different kinds of biological networks and particularly who aim at interpreting experimental results in terms pathway activities.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


TRAF6 and ATN1 may be promising biomarkers for the distinction between serrated and conventional CRC.The aim of this study was to identify the molecular events that distinguish serrated colorectal carcinoma (SCRC) from conventional colorectal carcinoma (CCRC) through differential gene expression, pathway and protein-protein interaction (PPI) network analysis. The GSE4045 and GSE8671 microarray datasets were downloaded from the Gene Expression Omnibus database. We identified the genes that are differentially expressed between SCRC and normal colon tissues, CCRC and healthy tissues, and between SCRC and CCRC using Student’s t-tests and Benjamini-Hochberg (BH) multiple testing corrections. The differentially expressed genes (DEGs) were then mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and their enrichment for specific pathways was investigated using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool with a significance threshold of 0.1. Analysis of the potential interactions between the protein products of 220 DEGs (between CCRC and SCRC) was performed by constructing a PPI network using data from the high performance RDF database (P<0.1). The interaction between pathways was also analyzed in CCRC based on the PPI network. Our study identified thousands of genes differentially expressed in SCRC and CCRC compared to healthy tissues. The DEGs in SCRC and CCRC were enriched in cell cycle, DNA replication, and base excision repair pathways. The proteasome pathway was significantly enriched in SCRC but not in CCRC after BH adjustment. The PPI network showed that tumour necrosis factor receptor-associated factor 6 (TRAF6) and atrophin 1 (ATN1) were the most central genes in the network, with respective degrees of node predicted at 90 and 88. In conclusion, the preoteasome pathway was shown to be specifically enriched in SCRC. Furthermore,  Colorectal carcinoma (CRC) is the third most commonly diagnosed cancer  and has K-ras mutation and loss of chromosome 1p  between SCRC and CCRC and between each of these subtypes of CRC and healthy tissues. Then, DEGs were analyzed for their enrichment in molecular pathways, and a protein-protein interaction (PPI) network was constructed to identify the potential interactions between the genes’ products. Overall, this study provided information that may be useful for the clinical classification of CRC.http://www.ncbi.nlm.nih.gov/geo/). The data were derived from the accession numbers GSE4045  normalization  with theIn order to explore expression patterns in the two CRC subtypes, we constructed a cancer global network. First, we downloaded the following 14 cancer-related pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database : colorecThe Database for Annotation, Visualization and Integrated Discovery (DAVID) online enrichment tool ,19 was aWe constructed a PPI network for 149 DEGs selected from the GSE4045 dataset  using the high performance RDF database (HPRD) . In the Significantly enriched pathways of DEGS were used to construct a pathway interaction network. We used the cumulative probability of the hypergeometric distribution model to identify whether any two pathways included a significant number of interactions (P<0.05). The cumulative probability formula was as follows:N represents the number of protein interactions in which all DEGs are involved; M represents the number of protein interactions which are related to genes in pathway 1 (there was at least one DEG in the interaction); n represents the number of protein interactions that are related to genes in pathway 2 (there was at least one DEG in the interaction); k represents the number of interactions between pathway 1 and 2 (there was at least one DEG in the interaction). If the cumulative probability was less than 0.1, two pathways were defined as interacting. We defined the degree as the number of interactions which one node had with other nodes.where In this study, we set two different significance thresholds to identify DEGs, listed in We constructed a network of 341 genes and 1,569 relation after identifying the DEGs which were enriched in the 14 cancer pathways . Then, iAfter a Fisher exact test, there were 26 upregulated genes in SCRC and 25 upregulated genes in CCRC when P<0.05. In addition, there were 13 genes which were both upregulated between the two CRC subtypes with P<0.05. There were 12 genes with a fold-change (FC) >1.4 and one gene with FC<0.7 in SCRC, and 1 gene with FC>1.4 and no gene with FC<0.7 in CCRC.In this study, 2,736 DEGs from CCRC and 2,123 DEGs from SCRC P<0.05;  were selRpn3 was different between SCRC and CCRC: the gene was found to be downregulated in SCRC . The proteasome pathway included 26 DEGs in SCRC, but was not enriched in CCRC. The proteasome pathway and the related DEGs are shown in  in SCRC  and upre in SCRC .There was no interactions among pathways involved in SCRC (data not shown). However, there was some interaction between significantly enriched pathways involved in CCRC . In the TRAF6, P=0.088453), atrophin 1 , integrin β1 , fragile X-related gene 2 , kinase γ  with degrees of node estimated at 90, 88, 62, 53 and 45, respectively (We constructed a PPI network for 149 DEGs involved in a total of 1,564 protein interactions. As shown in ectively .Serrated and conventional CRC are two subtypes of CRC. In this study, we attempted to identify the similarities and differences between these subtypes. DEGs were identified, and the enriched pathways in which these DEGs are involved were analyzed. We also constructed pathway interaction networks and PPI networks to further understand the molecular mechanisms underlying the pathogenesis of these two subtypes.Based on KEGG pathway enrichment analysis, significantly enriched pathways in SCRC and CCRC included cell cycle, DNA replication, and base excision repair. These results suggest that a number of pathways are common between SCRC and CCRC.in vitro (The proteasome pathway was the only pathway that was enriched in SCRC but not in CCRC after BH adjustment. Proteasome is a subcellular organelle that is distributed in the cytosol, nucleus, endoplasmic reticulum and lysosome of eukaryotic cells . Its maiin vitro , but it in vitro . Our resin vitro . In the p53 was previously reported to be detectable in the plasma and serum of patients with colorectal cancer or adenomas (AXIN2 cause colorectal cancer with defective mismatch repair by activating β-catenin or TCF signaling (In the CCRC pathway interaction network, we observed some interaction between pathways that was not observed in the SCRC pathway interaction network. The p53, cell cycle and mismatch repair pathways showed the highest degree of node. A mutation in adenomas . Moreoveadenomas . It was ignaling . Thus, oTRAF6, ATN1, ITGB1, FXR2 and IKBKG. TRAF6 had the highest degree of node. TRAF6 encodes a ubiquitin ligase. When activated, it can produce short protein chains. TRAF6 acts as a molecular switch, which allows activation of different signals. TGF with TRAF6 were reported to specifically activate the kinase TAK1 and other stress-activated kinases, leading to cell death (TRAF6 was shown to be an important oncogene for RAS-mediated oncogenesis in lung cancers (TRAF6 in CRC. ATN1 encodes a nuclear corepressor expressed in nervous tissue. It interacts with tumor suppressors to control planar polarity (ATN1 and cancer (TRAF6 and ATN1 may represent novel biomarkers, which are suitable for distinguishing SCRC and CCRC.In the PPI network, there were five hub genes, ll death . TRAF6 w cancers . Ubiquit cancers . There ipolarity . There ad cancer ,36. TherTRAF6 and ATN1, which showed the highest degree of node in the constructed PPI network. These results will be helpful for the understanding of the genesis and the specific therapy of SCRC and CCRC. However, further experiments are needed to confirm our results.In conclusion, we found a number of significantly enriched pathways in SCRC and CCRC, which revealed certain aspects of the pathogenesis of these two subtypes of CRC. We also found that the proteasome pathway is significantly enriched only in SCRC. In addition, we identified the genes


Reactome and WikiPathways are two of the most popular freely available databases for biological pathways. Reactome pathways are centrally curated with periodic input from selected domain experts. WikiPathways is a community-based platform where pathways are created and continually curated by any interested party. The nascent collaboration between WikiPathways and Reactome illustrates the mutual benefits of combining these two approaches. We created a format converter that converts Reactome pathways to the GPML format used in WikiPathways. In addition, we developed the ComplexViz plugin for PathVisio which simplifies looking up complex components. The plugin can also score the complexes on a pathway based on a user defined criterion. This score can then be visualized on the complex nodes using the visualization options provided by the plugin. Using the merged collection of curated and converted Reactome pathways, we demonstrate improved pathway coverage of relevant biological processes for the analysis of a previously described polycystic ovary syndrome gene expression dataset. Additionally, this conversion allows researchers to visualize their data on Reactome pathways using PathVisio’s advanced data visualization functionalities. WikiPathways benefits from the dedicated focus and attention provided to the content converted from Reactome and the wealth of semantic information about interactions. Reactome in turn benefits from the continuous community curation available on WikiPathways. The research community at large benefits from the availability of a larger set of pathways for analysis in PathVisio and Cytoscape. The pathway statistics results obtained from PathVisio are significantly better when using a larger set of candidate pathways for analysis. The conversion serves as a general model for integration of multiple pathway resources developed using different approaches. vs. diseased state. This helps researchers investigate a disease. For instance, the low expression of a certain gene might in turn lead to the low abundance of a certain protein which might prevent the breakdown of a certain metabolite, the accumulation of which contributes to disease progression. High throughput “omics” technologies produce vast quantities of biological measurement data. Biological pathways provide an intuitive knowledge-based scaffold for integrating these data WikiPathways and Reactome are two commonly used pathway databases. Reactome pathways are centrally curated with periodic input by domain experts, while WikiPathways is a community-based platform where pathways are created and continually curated by any interested party. As part of an ongoing collaboration between Reactome and WikiPathways, we have added the Reactome pathways to WikiPathways and made them available from the Reactome portal on WikiPathways. Here, we demonstrate how such an integration is advantageous to both the Reactome and WikiPathways communities and to the general research community at large.Biological pathways are descriptive diagrams that describe biological processes, i.e. interactions between genes, proteins, and metabolites. Pathways can therefore be used to integrate and visualize molecular measurements of genes, proteins, and metabolites in different biological conditions, e.g. healthy state  Pathway diagrams are a common way to represent a wealth of information about biological molecules, interactions and processes. Currently, the Pathguide collection lists 45 freely available pathway databases with human data, out of which only 14 provide the data in a machine readable format . Even feWikiPathways is an open, collaborative platform for drawing, curating, and sharing biological pathways, built using the same MediaWiki software underlying Wikipedia. WikiPathways leverages community curation to grow and maintain its pathway collection beyond the capabilities of an internal curation team. Anybody can register at WikiPathways to create new pathways and curate existing ones. WikiPathways provides a JavaScript-based viewer for interactively navigating and highlighting pathway elements and a Java based editor for creating and curating pathways. It makes use of BridgeDb web services  to proviLike WikiPathways, Reactome is an open-source, open access pathway database with a substantial collection of diverse pathway models , 20. HowThe integration of Reactome content in WikiPathways provides Reactome with the power of community curation and broader format availability, including the semantic format using the WikiPathways RDF generator . At the In this manuscript, we describe the newly developed format converter to convert Reactome content for inclusion in WikiPathways. The addition of the Reactome pathways to the analysis collection of pathways available from WikiPathways improves the coverage of gene ontology biological process terms of the analysis collection to 90%. The converted Reactome pathways can be analyzed with several new analysis tools, such as the pathway analysis tool PathVisio and network analysis tool Cytoscape. As a pedagogic example, we perform pathway analysis using a publicly available transcriptomics dataset and the combined collection of pathways from WikiPathways and Reactome.We developed a Java based format converter to convert Reactome pathways into the WikiPathways format see . The congene product, protein, RNA, metabolite, pathway, complex, and unknown. Gene product is the default data node that can be used for all products of genes such as transcripts, proteins, RNAs, and genes. By default, these are represented as open rectangular boxes with black labels and borders. The more specific data node types such as protein and RNA can be used in the specific cases instead of a generic gene product node. The protein node is visually the same as the gene product node while RNAs are represented in purple. The metabolite node represents metabolites, drugs, or other small molecules; it is represented in blue. The pathway data node is used to denote a connection to another pathway, and represented in green without a border. The complex data node represents two types of complexes either a set of proteins represented as a brown rounded rectangle or a set of interacting proteins represented by a brown hexagon. Data nodes of type unknown are represented the same as the generic gene product node. Interactions describe the relationship between two data nodes. Currently, two collections of interactions are available in the drawing palette: basic interactions and Molecular Interaction Map (MIM) interactions [A pathway in WikiPathways consists of data nodes, interactions, and graphical elements, e.g. cellular compartments. Data nodes can be of the following types: ractions . Arrows Reactome uses a comparable but graphically slightly different method to describe pathway content. In Reactome, the core unit of the data model is the reaction. Entities  participate in reactions. These reactions form a network of biological interactions and are grouped into pathways. Reactome uses the SBGN Process Description format  to draw Each element of the pathway can be annotated using database identifiers for data analysis and also annotated with literature references. As an example of the conversion, the Abacavir transport and metabolism pathway is shown here , exampleThe ComplexViz plugin enables the user to highlight the components on the bottom of the pathway belonging to the complex selected in the pathway diagram or vice versa.Gene Ontology (GO) terms provide structured vocabularies for annotating the molecular function, biological process, and cellular location of gene products in a highly systematic way . Here, wi.e. at least one gene in one of the pathways is annotated with the term). 11343 (71%) GO terms are covered by both collections. The curated collection of WikiPathways includes an additional 5% and the reactome_approved collection an additional 14% to bring the total coverage of GO terms up to 90%  is currently the most complete and comprehensive curated collection of human metabolites . Here, whttp://plantreactome.gramene.org/. Pathways for the species Oryza sativa, Zea mays, and Arabidopsis thaliana were converted. The pathways for rice are manually curated, the pathways for the other species are computationally inferred from the rice pathways. These pathways have been made available in the plant portal at WikiPathways [The Reactome converter was also used to convert plant pathways from the Plant Reactome database freely available at Pathways .The conversion of pathways from Reactome has contributed 431 new manually curated pathways to the WikiPathways analysis set. In addition to the 293 pathways originally available in the curated collection. This combined set of 724 pathways are now available for analysis from WikiPathways, essentially doubling the pathway quantity. To evaluate the effect of this content enrichment on pathway analysis of genomics datasets we performed pathway analysis with the combined set.et al investigates PCOS using granulosa cells of 40 women discordant for PCOS undergoing in vitro fertilization [arrayanalysis.org [arrayanalysis.org has been provided  is a common heterogeneous endocrine disorder characterized by irregular menses, hyperandrogenism, and polycystic ovaries . Its clilization . Granuloysis.org . All arrprovided . Statists module –43.The biological entities in pathways and their relationships can be represented as nodes and edges in abstract biological networks. This opens up a large variety of network analysis methods to further extend, analyze and visualize biological pathways.The incorporation of the WikiPathways and ReactomeFIViz apps in the Cytoscape framework allows further investigation of biological pathways using a wide variety of Cytoscape apps for network analysis and visualization. The visualization of an example Reactome pathway with both apps is provided .The WikiPathways project has developed a suite of pathway visualization and editing tools for users to view and edit pathways, and established a dynamic community to continuously crowd source updates and novel pathway content. The contents in Reactome are created by select domain experts in target fields of research with Reactome editorial staff. Including Reactome content has significantly expanded the coverage of pathway information at WikiPathways. Likewise, incorporating community edits from the WikiPathways versions of Reactome content significantly expands their pool of contributors, helping them produce more frequent updates and create links to outside databases. In the current implementation, we use a notification mechanism developed in WikiPathways to send edits from WikiPathways to Reactome. However, such an approach cannot be scaled up if many edits occur in the WikiPathways web site. We plan to develop a robust round-trip software approach in the Reactome curator tool so that edits in WikiPathways for Reactome pathways can be imported into the Reactome database easily. Such a tool will find new edits, and then present them to Reactome curators in graphical user interfaces so that curators can decide whether or not these edits should be committed into the Reactome database. We believe a true round-trip approach between Reactome and WikiPathways will benefit both projects, and set an example for other projects to collaborate with each other.The conversion of Reactome pathways to the GPML format enables the analysis and data visualization of Reactome pathways in PathVisio. PathVisio is a widely used pathway analysis software, preferred due to its excellent data visualization capabilities as demonstrated by its use in numerous academic publications –49. PathThe pathway analysis case study presented here with a transcriptomics dataset comparing women with normal ovulatory physiology with those with PCOS shows that addition of the Reactome pathway set clearly improves pathway analysis results. The list of top ten most affected pathways feature pathways from both the curated and reactome_approved collection of pathways from WikiPathways. More pathways appear from the reactome_approved collection, which is expected since the collection is manually curated. The reactome_approved collection adds 4417 new gene products and 500 new metabolites. However, the curated collection still contains 1414 unique gene products and 2360 unique metabolites. There are 3438 gene products and 325 metabolites in common between the two collections. Therefore, the conversion adds content without much overlap.The toll like receptor (TLR) cascades pathway, which shows up as the most changed pathway in this condition is from the Reactome collection. TLRs are an important family of pattern recognition receptors (PRR) involved in innate immunity. The innate immune system initiates an inflammatory response after recognizing pathogens by PRRs . EmerginThe availability of Reactome pathways in WikiPathways allows the analysis of the pathways with several new analysis tools. Besides the analysis of pathways in PathVisio, users can also use the WikiPathways app for Cytoscape to analyze the pathways as biological networks. While the ReactomeFIViz app focuses on functional gene interaction networks, the WikiPathways app creates a representative network of the pathway including metabolites and other pathway elements. Consequently the created network provides a new analysis tool that allows the integrated analysis of different omics datasets.A Java based format converter has been developed to convert pathways from the Reactome database to the WikiPathways format. Pathways in Reactome are stored in a relational database with their diagrams encoded in XML strings, while pathways in WikiPathways are stored as GPML, which is an XML based file format. In addition, both repositories have Java based APIs according to which the pathway files can be read and written. These internal data models of the two databases are used to read and write the pathways obtained from them. This allows the converter to remain flexible and backwards compatible as long as the data models themselves are. This also makes the converter stable through version updates of pathways as long the pathways are organized according to the same model. The conversion is done in the following steps: (i) Creating a GPML pathway and adding pathway attributes, (ii) Converting the pathway elements, and (iii) Annotating the pathway and pathway elements. These steps are described further below. The converter is open source and the code is available from the GitHub repository .A Reactome pathway combined with its rendering information is read from the database via the Reactome Java API . A new GEach entity in the Reactome pathway is converted to the corresponding WikiPathways element. In Reactome, the reactions known as hyper edges are modeled such that there is a backbone reaction to which the inputs, outputs, catalysts, activators, and inhibitors are connected. Each branch of the hyper edge  is converted into a GPML interaction and connected to a backbone interaction using anchors; this achieves the same SBGN compliant reaction view for all substrates, products, enzymes, activators, and inhibitors in GPML .Subsequently, the Reactome pathway object is mined for annotations for the different elements. Preferably, the proteins are annotated with UniProt identifiers and the metabolites with ChEBI identifiers, in absence of the preferred annotation Reactome identifiers are used. Interactions, Complexes, and Pathways are annotated with Reactome identifiers. All pathway elements are also annotated with literature references using PubMed identifiers.Human GO Terms were downloaded from UniProt-GOA . ScriptsThe newly developed plugin improves visualization of data on complexes and their components. The plugin can be installed in PathVisio using the plugin manager and adds a side panel “Components”. The top half of this panel displays the components of the complex that is clicked as a mini pathway diagram. Imported data is visualized both on the main pathway diagram and on the “Components” side panel containing the complex component diagram. Clicking on the buttons in the side panel next to the mini pathway diagram, displays the cross-references and expression data available for that data node on the bottom half of the panel. The plugin also adds the submenu item “Complex Visualization” to the Data menu. Clicking it opens a dialog box for setting visualization options for complexes. Three visualization options have been implemented. These methods allow changing the border color of complex and components, coloring complex nodes according to a calculated ratio, and drawing the complex label. Users can select a border color for complexes and their components to indicate which complex and components belong together. Complexes can be colored based on the percentage of complex components that qualify the user defined criterion. This percentage is calculated for all complexes on the pathway. Color gradients or rules can be used to visualize the score on the complexes. Text labels can be drawn on the Complexes after data has been visualized, the font and size of text of the label can be changed. The plugin is open source and the code is available from the GitHub repository . A detaiS1 TableAccess and data availability.(XLSX)Click here for additional data file.S2 Tablearrayanalysis.org.Obtained from the statistical analysis module of (XLSX)Click here for additional data file.S1 Fighttp://www.reactome.org/PathwayBrowser/#DIAGRAM=500792) and (b) Pathway view on WikiPathways (http://wikipathways.org/instance/WP1825).(a) Reactome View of GPCR ligand binding pathway ((PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(a) using the WikiPathways app and (b) using the ReactomeFIViz app.(PDF)Click here for additional data file.S1 Textarrayanalysis.org.Obtained from the Affymetrix quality control and pre-processing module of (PDF)Click here for additional data file.S2 TextThis guide describes the functionalities of the ComplexViz plugin and how it can be used in PathVisio.(PDF)Click here for additional data file.


Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia. Neurodegenerative dementia (ND) is a multi-faceted cognitive impairment that is progressive and irreversible due to deterioration of brain cells and their interconnections. It involves multiple cognitive deficits manifested by memory impairment and cognitive disturbances. The understanding of the genetic basis of ND has advanced in recent years, giving some insights into disease pathophysiology, but there are still major knowledge gaps in understanding the molecular mechanism underlying dementia. Dementia can be caused by a wide variety of diseases including more frequent pathologies such as Alzheimer’s disease, but also rare ones including Pick’s disease. Despite the high prevalence of dementia in the population, no drug treatments are available that can provide a cure. The two main classes of drugs available to treat Alzheimer’s disease, cholinesterase inhibitors and NMDA receptor antagonists, can only ameliorate the symptoms, or temporarily slow down the disease progressionet al. employed the network propagation and random walk method to predict DTset al. developed a graphlet-based measure of network topology to predict potential drug targets-omic data (from the molecular to the network level) to acquire a comprehensive coverage of biomedical properties relevant to drug discovery.The identification of novel drug targets (DTs) is of great importance for the development of new pharmaceutical products3567881112In this study, we present a novel integrative approach to predict potential new drug targets for dementia based on multi-relational association mining (MRAM), an advanced data mining technique able to manipulate heterogeneous data without any information loss. The diseases studied are: Frontotemporal dementia (FTD), Alzheimer disease (AD), Lewy bodies disease (LBD), Progressive supranuclear palsy (PSP), Corticobasal dementia (CBD), Pick’s disease, Prion disease, Huntington’s disease, and Amyotrophic lateral sclerosis-Parkinsonism/dementia complex. The investigation was based on the list of known dementia DTs curated inOur systems biology approach identified a series of potential novel DTs functionally associated to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the predicted DTs, numerous serine threonine kinases, such as DLG4 (PSD-95) and a G-protein coupled receptor, Bradykinin receptors 2, were highlighted which could be considered for the development of innovative therapeutic approaches for the treatment of dementia.Extraction of molecular targets of drugs in different phases of the drug discovery process ;Construction of a protein interaction network including the 1-step neighbors of DTs;Integration of heterogeneous data from multiple databases  and from the DrugBank databaseDrug molecular targets were obtained by collecting information from different pharmaceutical company websites, from a clinical trial database  in the i2d database. The final PIN of interest contained the DTs (nodes) and their direct interactions (edges). In this study, we considered one-step neighbors. The network was undirected and unweighted because we considered the binary interactions. We considered both topological relationships between the DTs and their network neighbors and functional data representing biological properties of the DTs.i) = {pj ∈ N |eij ∈ E}, where eij denotes an interaction connecting pi and pj, and E is the set of interactions. Degree centrality is one of the main measures used to study hubs of a network. We also considered articulation proteins. A protein is an articulation point in a network iff removing it (and the interactions through it) disconnects the network. These topological properties help elucidating the role of the DTs in the PIN.Regarding the topological data features, we calculated the number of a protein’s neighbors in our PIN, i.e. the degree centrality index of proteins formally defined as the cardinality of the set DC. Multi-relational data mining (MRDM) open the way for handling and mining data in multiple tables (relations) directly in a MRD2526The extracted data from GO, i2d, InterPro and Reactome are represented as relational tables in the Microsoft SQL server management system. Later one-to-many or many-to-many relationships among the tables were established. For example, many proteins may have the same degree centrality (one-to-many relationship) and on the other side a protein may belong to many Reactome pathways and one Reactome pathway has many proteins involved (many-to-many relationship). X ⇒ Y, where X and Y are disjoint itemsets X ∩ Y = ∅. The algorithm finds rules within a dataset based on two parameters, support and probability/confidence. Support is the occurrence frequency of the targeted item or itemset in a given dataset. Probability is the co-occurrence frequency of items in Y and X.The MRAM algorithm to explore data in multiple relational tables was employed by using the SQL Server 2012 Analysis Services (SSAS). The MRAM algorithm in the SSAS package uses optimization techniques to save space and make processing faster. Similar to traditional AM, MRAM handles data as items and group of items, called itemset. An association model consists of a series of itemsets and rules of the form (1) positive training examples as the set of known DTs, denoted Spos, (2) negative training examples as the set of non-DT selected randomly from the set of proteins which do not belong to Spos, denoted Sneg, and (3) a five-dimension vector representing degree centrality fdeg, articulation point fart, GO term fGO, Reactome pathway fpath, and protein domain fdom. The MRAM algorithm traverses the input dataset in multiple tables to find items that appear together in a case. The algorithm then groups into itemsets any associated items that have support greater than a threshold MINIMUM_SUPPORT. Probability is calculated for each rule and the algorithm restricts the number of rules based on a threshold parameter MINIMUM_ PROBABILITY. The resulting rules were used to infer new putative DTs.The inputs of the algorithm are We run the proposed method to predict putative DTs from the set of connector genes identified in our previous workThe newly predicted DTs were used to extract the most representative GO biological process terms . For identifying and visualizing enriched GO terms, we used GOrillaSpos consists of 221 DTs. Since there are no available negative examples (non-DTs), we randomly selected three sets of negative examples with different sizes  to build the set Sneg. The approach applied to different size examples proved to be scalable and stable. We obtained a network of 3,112 proteins and 6,541 interactions that is well connected with the average shortest path length equal to 3 and the average number of neighbors equal to 4. We obtained 44,433 GO terms, 11,738 InterPro domains, and 4,240 Reactome pathways related to the 3,122 proteins.221 DTs out of 268 known DTs have at least one interacting neighbor in our PIN. Therefore the positive example Sneg of 221, 500 and 1,000 negatives. In all experiments, MRAM performed better than the other methods. To evaluate the contribution of each data feature, we did several experiments by excluding the features one-by-one and then computing the likelihood lift. To evaluate the performance of our method, we computed the lift charts for MRAM, Decision Tree (D-Tree), Naïve Bayes (NB), and Neural Network (NN) methods. These charts show how the model performs for all states of the predictable attribute. probability equal to 1 in three columns: Probability, Importance, and Rule. For example, the rule “GO:0005887 = C:integral to plasma membrane, Degree = 10–77, REACT_111102 = Signal Transduction ⇒DT = Y” has probability = 1 and importance = 0.514. The rule shows that one protein has chance to be a DT if it is integral to the plasma membrane, is central with at least 10 interactions (up to 77 interacting proteins), and takes part in the signal transduction pathway. The probability describes how likely the result of a rule is to occur. The importance measures the significance of a rule. The importance of a rule is calculated by the log likelihood of the right-hand side of the rule, given the left-hand side of the rule. For example, in the rule A ==> B, MRAM calculates the ratio of cases with A and B over cases with B but without A, and then normalizes that ratio by using a logarithmic scale. Indeed a rule with high probability might be too general to provide useful information. The greater importance, the more significant the rule is. The top rules with probability = 1 and importance > 0.5 are presented in Functional enrichment analyses of GO biological process terms was performed for the list of predicted DTs, showing metabolic-related terms , cell surface receptor signaling pathways , immune response-related terms , apoptosis, and long-term memory .Pathway analysis revealed similar results as the GO, but in addition indicates Alzheimer disease-amyloid secretase pathway, type 2 Diabetes, and metabotropic glutamate receptor group I pathways as associated with the predicted DTs .In recent years, wealth of information has been produced on neurodegenerative dementia and particularly on Alzheimer’s disease, and the integration of these data to obtain novel knowledge is one of the big challenges in modern neurobiology. This investigation employed a data mining method on heterogeneous data including categorical free text (i.e. GO terms and pathways), numerical values , and boolean values (i.e. articulation protein). The data were managed in a multiple relational database for which classical AM methods do not provide a suitable platform. MRAM is the most recent approach which aims to overcome the difficulties in multi-relational data integration. It enables direct pattern extraction from multiple relations, without the necessity of transferring data to a single relation25A significant enrichment in Alzheimer disease-amyloid secretase pathway (Panther:P00003), the hallmark of Alzheimer’s diseaseInterestingly, an association with type 2 diabetes pathway (KEGG:hsa04930) was also evidenced. Type 2 Diabetes is a major risk factor for Alzheimer’s disease and dementia and the concept that Alzheimer’s is fundamentally a metabolic disease that results in progressive impairment in the brain’s capacity to utilize glucose and respond to insulin/insulin like growth factor stimulation has recently gained increasing support441747Considering the most enriched GO terms and pathway, a major role for MAPK (KEGG: hsa04010) is evident  and 7. TFinally, considering the degree index , DLG4  and BDKRB2, a G protein-coupled receptor of the kinin family were identified, but further studies are needed to confirm this finding and the druggability of the proposed targets.How to cite this article: Nguyen, T.-P. et al. Novel drug target identification for the treatment of dementia using multi-relational association mining. Sci. Rep.5, 11104; doi: 10.1038/srep11104 (2015).


Scarabaeine dung beetles are the dominant dung feeding group of insects and are widely used as model organisms in conservation, ecology and developmental biology. Due to the conflicts among 13 recently published phylogenies dealing with the higher-level relationships of dung beetles, the phylogeny of this lineage remains largely unresolved. In this study, we conduct rigorous phylogenetic analyses of dung beetles, based on an unprecedented taxon sample (110 taxa) and detailed investigation of morphology (205 characters). We provide the description of morphology and thoroughly illustrate the used characters. Along with parsimony, traditionally used in the analysis of morphological data, we also apply the Bayesian method with a novel approach that uses anatomy ontology for matrix partitioning. This approach allows for heterogeneity in evolutionary rates among characters from different anatomical regions. Anatomy ontology generates a number of parameter-partition schemes which we compare using Bayes factor. We also test the effect of inclusion of autapomorphies in the morphological analysis, which hitherto has not been examined. Generally, schemes with more parameters were favored in the Bayesian comparison suggesting that characters located on different body regions evolve at different rates and that partitioning of the data matrix using anatomy ontology is reasonable; however, trees from the parsimony and all the Bayesian analyses were quite consistent. The hypothesized phylogeny reveals many novel clades and provides additional support for some clades recovered in previous analyses. Our results provide a solid basis for a new classification of dung beetles, in which the taxonomic limits of the tribes Dichotomiini, Deltochilini and Coprini are restricted and many new tribes must be described. Based on the consistency of the phylogeny with biogeography, we speculate that dung beetles may have originated in the Mesozoic contrary to the traditional view pointing to a Cenozoic origin. Dung beetles of the subfamily Scarabaeinae are a well-known group of insects thanks to their exploitation of animal feces, a behavioral trait with a global impact on Earth’s ecosystems. With more than 6200 species in 267 genera and with an estimated 30–50% of the species still undescribed , dung beetles exhibit diversity comparable to the Aves (ca. 9.800 species ), as welOnthophagus, one of the most species-rich genera in animal kingdom, in which horns exhibit fascinating phenotypic diversity, has emerged as a model system in evolutionary developmental biology and ecological development 1) and itsense of  is well 1, recovered in the present study, and comprising the majority of Australasian genera, one African genus Pedaria, and a subclade formed of New Zealand and New Caledonia genera  [Prionocephale deplanate Lin, 1980 is described from the Upper Cretaceous of China. The original description of that fossil does not list any unambiguous characters for placing it within Scarabaeinae, which indicates that there are no reliable evidence to consider it a member of the subfamily. The latest study [Dung beetle origin is a controversial issue whose main dilemma lies in a Cenozoic versus Mesozoic age of origin , 60. TheSiberia) . The desSiberia)  as premast study  dating tst study .Besides the abovementioned fossils, there is an ichnofossil from the Nearctic Cretaceous representing a herbivorous dinosaur coprolite and associated dung filled tunnels . HoweverThe lack of a reliable dated phylogeny does not allow testing a Cenozoic and Mesozoic hypotheses; however, the present tree, if assumed correct, can be used to assess these competing evolutionary scenarios. Here, we discuss the potential implications for this debate, which derived from the biogeographical pattern of the present phylogeny. We consider long-distance dispersal, dispersal via land bridges, and vicariance as main processes that potentially could shape present distribution of Scarabaeinae. Undoubtedly, such approach is speculative as it is not based on a robust biogeographical analyses  but currently, due to the lack of other data, it is the only way to get an insight into dung beetle evolutionary history.1  and L (consisting of Old World and New World taxa) can only be explained by long-distance intercontinental dispersal events occurring between Africa and Australia as well as between Africa and S. America. We can rule out the dispersal via land bridges scenario as we lack evidence for it. Some members of Scarabaeinae, as Onthophagini, indeed, owe their cosmopolitan distribution to land bridge dispersal from Old World to New World and Australia through Asia as supported by phylogeny indicating that monophyletic Australasian and American clades derive from Asian ancestors  which potentially could host Scarabaeinae if they were able to reach them, lack any traces of native dung beetle species. The longest recorded dispersal refers to the presence of two endemic Scarabaeinae genera on volcanic island of Mauritius, which seemingly happened from Madagascar (distance between two is ca. 900 km). This fact suggests that even medium-distance dispersal is a rare event in dung beetles, and assigns a very low probability to intercontinental dispersal requiring a few thousand kilometers to cross.First, suppose, dung beetles evolved during the Cenozoic after the Gondwana breakup, then biogeographical composition of such clades as e.g., Auscestors  would fit well the vicariance hypothesis if they were correspondingly aligned with fragmentation of Gondwana. This alignment would imply minimal age for clades Aus1, B and dung beetle origin coinciding with the separation of Africa from Gondwana in Late Jurassic (160 MYA). Clades consisting of New World and Old World taxa  would be constrained to minimal age of the separation of Africa from South America, which remained connected until mid-Cretaceous (110–95 MYA). In addition, vicariance pattern can also explain the nested position of the clade composed of New Zealand + New Caledonia taxa within Aus1. This phylogenetic pattern is consistent with breakup of a land mass, comprising New Caledonia and New Zealand, from Gondwana at 80 MYA, and its subsequent split separating New Zealand and New Caledonia taxa at 40–30 MYA. Although the current phylogeny supports a vicariant pattern, such a scenario poses quite old minimal ages for the origin of dung beetles and diversification of their main lineages. This strict vicariance can be relaxed assuming that dung beetles originated in Mesozoic after Gondwana breakup, and then dispersed among fragments of Gondwana when they were not too far away from each other. For example, the African south margin broke from Antarctica, the part of Gondwana, at 160 MYA and started drifting slowly northward, remained separated from Antarctica by the Indian opening of 300–1000 km over Early Cretaceous. This assumption can reduce minimal ages for the origin of the dung beetles and their lineages to early—mid Cretaceous and at the same time, it still reserves a key role for Gondwana and its fragments in shaping current distribution of Scarabaeinae.However, if we assume a Mesozoic origin for the dung beetles it could explain their present biogeographic pattern by Gondwana vicariance. The nested position of African Although, the conclusion about Mesozoic origin is preliminary, since we lack comprehensive dated phylogeny of dung beetles, the present tree has higher likelihood of fitting this hypothesis. At the same time, we cannot, by far, rule out, the hypotheses of Cenozoic origin.Detailed examination of morphology coupled with parsimony and Bayesian analyses using ontology for matrix partitioning has shed new light on the evolution of dung beetles. Many clades in the present phylogeny are congruent with those in other existing molecular and morphological phylogenies while some clades suggest novel relationships that have not been hypothesized before. We can, to a certain extent, consider congruent clades among previous phylogenies as well-supported but the number of such clades is low and does not allow for the selection of any specific phylogeny as more reliable over the others. Our results are consistent with the results from different studies and provide further resolution for Deltochilini and Dichotomiini as well as the remaining Scarabaeinae tribes whose placement has been contentious. Therefore, the present study provides an integrative pattern of phylogenetic relationships in Scarabaeinae, many parts of which are backed up by previous works. The consistency of the current phylogeny with biogeography likely suggests a Mesozoic rather than Cenozoic age of the origin of dung beetles. However, this conclusion is speculative and has to be confirmed by future total evidence studies integrating morphological, molecular and fossil data to date the dung beetle tree of life. The resulting phylogeny demonstrates that the present classification of Scarabaeinae has to be substantially revised. The mapped synapomorphies can be used to define the new (sub)tribes and groups of genera; however, nomenclatural changes, at present, would be premature and more data, primarily molecular, are required to corroborate the findings presented here.The photo-based illustrations of endophallic sclerites in Figs. The aedeagus lies on its lateral side inside the abdomen , which cThe notes for the illustrations of the somatic characters Figs. –47 are pPhallobase, basal area dorsally: (0) with one tubercle on each side : (0) cylindrical; symmetrical  or sometimes feebly subsymmetrical  ventral portion reduced presenting large phallobase opening starting at apical margin and continually enlarging toward base; sclerotization of ventral portion  subtle, translucent  vennslucent .Phallobase, rectangular notch of basal margin, presence (rear view): (0) absent; (1) present  absent; (1) present  absent; both parameres symmetrical  pral Figs. , 21 e, fParameres, relatively wide, rounded apically (Macroderes—Xinidium groundplan): (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present  absent; (1) present.Parameres, short, rounded apically : (0) absent; (1) present : (0) absent; (1) present.Parameres shortened, basolateral plate projected: (0) absent; (1) present.Parameres long, beak-shaped : (0) absent; (1) present.Parameres extremely reduced: (0) absent; (1) present  absent; (1) present : (0) absent; (1) present  absent; (1) present.Axial sclerite presence: (0) absent; (1) present : (0) axial sclerite processed in several lobes and occupying basal part of aedeagal sac; basal part of aedeagal sac possess either solely A or A and BSc sclerites : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present  absent; (1) present : (0) absent; (1) present : (0) absent; (1) present  absent; (1) present  absent; (1) present : (0) absent; (1) present  absent  prent Figs. , 24 k, lNote. See note for character 22.SA sclerite tightly wrapped within axial sclerite; almost inseparable but recognizable : (0) absent ; (1) present : (0) absent; (1) present : (0) absent; (1) present  : (0) absent; (1) present : (0) absent; (1) present  absent; (1) present (0) absent; (1) present .FLP*, presence: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent  absent ; (1) present : (0) absent; (1) present  that also cannot be unequivocally scored with either state of present character. Due to this ambiguity, we score this character in Heliocopris with question mark.SRP sclerite represents flat lamella located along right side of aedeagal sack; SRP bears small ring structure apically (Epirinus—Neosisyphus ground plan): (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present  absent or very weakly sclerotized; (1) distinctly present : (0) absent; (1) present : (0) absent; (1) present : (0) absent; (1) present.FLP sclerite(s), presence: (0) absent . To avoid incorrect homology assumption, this character is coded with question mark in Gymnopleurus and Paragymnopleurus.FLP sclerite, small c-shaped, superior apical portion indistinctly sclerotized (Dichotomius groundplan): (0) absent; (1) present  absent; (1) present.FLP sclerite, big, long, comprising 2–3 distinct lobes located in the same plane: (0) absent; (1) presentBSc sclerite, semicircularly shaped, presence: (0) absent; (1) present  absent; (1) present  absent; (1) present.IAS2sclerite, presence : (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present ( present .Elytron with 10 distinctly visible striae: (0) absent; (1) present  absent; (1) present ( present .Elytron with 8 stria and/or traces of striation 8: (0) absent; (1) present  absent; (1) present ( present .Elytron with 10 stria and/or traces of striation: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  one  twone e.g., , 37e.Elytron, 8thstria largely reduced (Anomiopus—Scatonomus): (0) absent; (1) present  absent; (1) present  absent; (1) present : (0) absent; (1) present  visible only preapically: (0) absent; (1) present  relatively narrow; (1) large (1) large .Elytron, 7thand 8thstriae largely reduced and visible only preapically: (0) absent; (1) present  absent; (1) present  absent; (1) present  distinctly depressed: (0) absent; (1) present  absent; (1) present , inner one extends from apex to median portion of elytra, and closely located to (merged with) outer one extending from apex to base: (0) absent; (1) present  absent; (1) present  absent; (1) present.Wing, RA4significantly longer than RP1; RP1arcuate, not parallel to RA4: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present : (0) absent; (1) present : (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present.Wing, MPa vein presence: (0) either absent or very weakly developed  diseveloped .Galea, dorsal part covered with big spurs: (0) absent; (1) present  absent; (1) present  absent  absent .Galea and lacinia, galea strongly sclerotized, hook-like: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent  absent ; (1) pre) absent .Glossal flap thick; internal margin usually densely haired: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present ( present .Epipharynx with triangular deep notch anteriorly: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  mandibles with largely extended membranous incisor lobes  memor lobes .Right mandible, molar lobe enlarged, with big denticles: (0) absent; (1) present  absent; (1) present  absent; (1) present.Gula, emarginated anteriorly; emargination shagreened and/or haired: (0) absent; (1) present  gular ridges invaginated only posteriorly, forming longitudinal lamella  (ntorium) ; (1) gulntorium) .Eye, degree of development: (0) well-developed; (1) significantly reducedClypeus, ventral anterior area with ridges forming rectangular pattern that medially divided into two parts: (0) absent; (1) present  absent; (1) present  absent; (1) present.Clypeus, apical emargination with medial pit: (0) absent; (1) present ; eyes not exposed dorsally: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present : (0) absent; (1) present.Prothorax, basisternal furca: (0) absent; (1) present  absent; (1) present present.Prosternum, coxal depressions widened, closely approaching lateral margin of pronotum: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent/invisible; (1) present/visible.Protarsus in male: (0) absent; (1) present.Protarsus in female:(0) absent; (1) present.Protarsus, last tarsomere triangularly processed apically: (0) absent; (1) present.Protarsus, claws: (0) absent; (1) present.Protarsus, claws big, bifurcate preapically: (0) absent; (1) present  absent; (1) present.Pro-, meso-, and metatarsus and apex of meso- and metatibia, ventral surface, with short dense hairs: (0) absent; (1) present  absent; (1) present.Meso- and metatarsus with one fixed claw: (0) absent; (1) present.Metatarsus and mesotarsus with thick tarsomere and big claws: (0) absent; (1) present.Meso- and metatarsus, last tarsomere triangularly projected apically: (0) absent; (1) present  no visible spurs; (1) one; (2) two.Mesotibia, number of apical spurs: (0) no visible spurs; (1) one; (2) two.Metepisternum, apex of lateral surface modified, convex and/or depressed (sometimes with unclear keel): (0) absent; (1) present  cut into elytron by notching it: (0) absent; (1) present  more or less rectangular with apical part rounded: (0) absent; (1) present  absent; (1) present  only one lateral process; (1) with two processes—lateral, and rear directed backward  absent; (1) present  absMetasternum, frontal part raised and projected forward: (0) absent; (1) present  absent; (1) present.Metascutellum, small, truncate, with bunch of hairs apically: (0) absent; (1) present  abdomen soft; sternites separated from each other by membrane; 1st sternite well developed laterally  abdaterally .Abdomen, 7thsternite largely expanded and crowding out remain sternites along midline; remain sternites visible only laterally: (0) absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present  absent; (1) present ( present .Pygidium distinctly swollen: (0) absent; (1) present ( present .Pygidium with grooved line(s) and/or ring: (0) absent; (1) present  absent; (1) present ( present .S1 Document(TXT)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 FigValues above branches indicate posterior probabilities.(PDF)Click here for additional data file.S6 FigValues above branches indicate posterior probabilities.(PDF)Click here for additional data file.S7 FigValues above branches indicate posterior probabilities.(PDF)Click here for additional data file.S1 Matrix(NEX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


Previous studies have indicated that the downstream proteins in a key pathway can be potential drug targets and that the pathway can play an important role in the action of drugs. So pathways could be considered as targets of small molecules. A link map between small molecules and pathways was constructed using gene expression profile, pathways, and gene expression of cancer cell line intervened by small molecules and then we analysed the topological characteristics of the link map. Three link patterns were identified based on different drug discovery implications for breast, liver, and lung cancer. Furthermore, molecules that significantly targeted the same pathways tended to treat the same diseases. These results can provide a valuable reference for identifying drug candidates and targets in molecularly targeted therapy. Recently, with the development of molecular biology techniques, molecularly targeted therapy has been applied in clinical practice , 2. In cSmall molecules act by simultaneously participating in multiple biological processes and triggering a variety of changes that lead to diverse reactions. A phenotype is always caused by a series of complex molecular reactions. A pathway embodies complex interactions between small molecules and macromolecules, and most pathways are interrelated , 6. ThusBased upon the above considerations, we have proposed a novel method to detect the links between small molecules and pathways. First, differentially expressed genes related to diseases were identified and enriched into KEGG pathways. Next, molecules that target each pathway were identified by Connectivity Map. We further constructed a link map between the molecules and their target pathways and analyzed the topological features of the link map. Moreover, by applying this method to a chosen set of data, we identified three link patterns. We also found that if molecules significantly targeted the same pathways, then they tended to treat the same disease. Besides, we provide potential candidate for drug experiment by mining and predicting medicinal small molecules and their target pathway in in silico method. This application may provide valuable information to molecularly targeted therapy from a pathway-based perspective.http://www.ncbi.nlm.nih.gov/geo/, accession number GSE5364). In this study, we used three datasets including both tumour tissues and adjacent normal tissues . In addition, another dataset was added into each type of cancer to establish link map as well as to predict target relationship. The data included both tumour tissues and adjacent normal tissues /43/43, liver (GSE9166)/15/18, and lung (GSE7670)/27/27).The microarray data were downloaded from Gene Expression Omnibus (http://www.connectivitymap.org/cmap/) consisted of more than 7,000 gene expression profiles treated with 1,309 small molecules. These expression profiles represented about 6,000 instances, each of which comprised a treatment and vehicle pair. By comparing the expression pattern similarity of the input genes and the genes perturbed in Connectivity Map instances, a list of molecules related to the input genes would be identified.Connectivity Map . When it came to data used to predict target relationship, we manipulated DAVID to annotate pathways and carried out corresponding enrichment analysis. Then, statistically enriched pathways, which are potentially relevant to diseases, were obtained using hypergeometric test (P≤0.05). Differentially expressed genes were partitioned into enriched KEGG pathways. For each pathway, up- and downregulated probes corresponding to differential genes were input into Connectivity Map. The small molecules that were significantly related to a certain pathway were identified according to P value from the permuted results given by Connectivity Map. In this way, small molecules for all the enriched pathways could be found. Thus, we could construct an adjacency matrix between the small molecules and the pathways. The elements of the matrix were set to one when P ≤ 0.01; otherwise, they would be set to zero. Suppose the adjacency matrix was A. When Aij = 1, then that small molecule i significantly targets pathway j. A bipartite M-P network of molecules and pathways can be constructed based on the adjacency matrix. The nodes of the M-P network were small molecules and pathways, respectively. A link was placed between a molecule and a pathway if the molecule significantly targets the pathway. The M-P network was also called the M-P link map. The differentially expressed genes from GSE5364 were annotated into KEGG pathways using WebGestalt  . Their corresponding heatmaps are shown in Figures S3(a) and S4(a).We proposed a method to construct a link map between small molecules and pathways (M-P link map), as described in For the M-P link map in The degree of the pathways did not show a similar distribution to the degree distribution for small molecules. There were no significant differences in the number of pathways over various degrees. The average degree of the pathways was higher than that of the small molecules. Additionally, the degrees of the pathways were all larger than one. This result indicated that one pathway might not be specifically targeted by one small molecule and was consistent with the biological fact that one pathway might be targeted by distinct small molecules through different genes in the pathway. Based on the M-P link map, there are several link patterns that help reveal the intervention features of molecules on their target pathways. The first pattern is that a single molecule targets multiple pathways (sM-mP). Molecules from this pattern can intervene in multiple pathways. Most tumours involve the regulation of multiple genes and processes. There are usually many potential targets in these tumours . All celThe second link pattern is where a single molecule targets a single pathway robustly (sM-sP), which can be extracted from the original M-P link map using molecule-molecule links and pathway-pathway links (as described in The third link pattern is where multiple molecules target a single pathway (mM-sP). In this pattern, one pathway is targeted by many molecules that do not link to any other pathways. Molecules in this pattern have few side effects. Therefore, these small molecules may be candidate adjuvant drugs for anticancer agents. Additionally, parts of the molecules tend to have similar efficacy. For the first instance, there are a total of 31 small molecules that target the Wnt signaling pathway in breast cancer from GSE5364. There are eight molecules with degree one, which are shaded light slate blue in P < 0.001 [For some diseases, single-agent therapy may not be sufficiently effective . To incr < 0.001 . The res < 0.001 . Such smFrom the M-P link map, we found that there were 71 small molecules shared by three cancers from GSE5364. All molecules with degrees of five or more from the three cancers were included in this group. Of these 71 molecules, 58 were recorded in existing databases. Nine of these 58 molecules can be directly used as antitumour agents or directly impact tumour-related proteins. They are trichostatin A, geldanamycin, azacitidine, puromycin, streptozotocin, vorinostat, genistein, camptothecin, and sulfadimethoxine. The five common pathways are ECM-receptor interaction, focal adhesion, insulin signalling, cell communication, and Type II diabetes mellitus. The first three pathways are related to cell differentiation, proliferation, and apoptosis. Pathways appearing in only one cancer seldom show these characteristics. In liver cancer, for example, the majority of the pathways are involved in biological processes related to the metabolic functions of the liver. For instance, glycerophospholipid metabolism and glycerolipid metabolism are part of lipid metabolism, while fructose and mannose metabolism and pyruvate metabolism belong to carbohydrate metabolism, and glutathione metabolism metabolises abnormal amino acids. In particular, lipid metabolism and pyruvate metabolism are closely related to liver cancer –40.To validate the link relationships between small molecules and pathways, we developed the reliability analysis. We used three sets of data from GEO, that is, GSE15852, GSE9166, and GSE7670, respectively, to construct M-P link. As a consequence, we obtained M-P links corresponding to six sets of data. Actually, there were two sets of data in each type of cancer. Firstly, for the links between 571 small molecules and 47 pathways from our method for breast cancer dataset, we extracted known targeted genes for each small molecule using DrugBank database and identified whether these targeted genes were members of 47 pathways. If so, the predicted target relationship can be validated. In the results, the relationships of 15 small molecules targeting 25 pathways have been identified. As a validated example, genistein was identified by CTD database, which was an antineoplastic and antitumor agent by targeting leukocyte transendothelial migration . FurtherIn this study, we constructed a link map between small molecules and pathways with Connectivity Map database and analysed the topological properties of this link map. Pathways were regarded as drug targets, and we studied small molecules' effects on disease from the perspective of these pathways, which provides valuable information for molecularly targeted therapy.To detect pathways that were targeted by small molecules, disease-related differentially expressed genes are first enriched into pathways. In this way, differentially expressed genes were divided into various subsets based on KEGG pathways they participate in. Thus, it is possible to detect potential candidate small molecules from the perspective of the pathways that include differentially expressed genes. Although there is low reproducibility of differentially expressed genes between diverse data sources for the same kind of diseases, the expression correlation of those genes is high . This reThe proposed approach established the mapping between disease-related pathways and small molecules. After analysing three cancer datasets from GSE5364, we were able to describe specific characteristics of the link maps of small molecules with high degrees in the sM-mP pattern that were always used for cancer treatment. Most tumours involve the regulation of multiple genes and processes, and there are usually many potential targets for treating tumours . Thus, sAlthough there are common molecules and pathways for the three cancers, there is no identifiable overlap among the M-P links in our datasets. This result is not surprising. In the process of pathway enrichment analysis for the three datasets, diverse differentially expressed genes may be enriched into different subpathways in the same pathway. When the genes are input into Connectivity Map to detect significant molecules, different genes in the same pathways may link to distinct molecules. Thus, in different datasets, the same pathway may be targeted by different molecules. Thus, there may not be any overlap among the M-P links for the three cancers.Meanwhile, in order to validate the stability of the results, we added another set of data in each type of cancer to construct link maps. By comparing to the original data, we found a good repeatability. The number of repeat small molecules between two sets of breast cancer data is 402; when it comes to lung cancer and liver cancer, the numbers are 218 and 276, respectively. Axon guidance, which proved to be small molecules of tumor treatment in the previous analysis, was found in both sets of lung cancer data. In breast cancer data, we had found the meticrane targeting PPAR signaling pathway that had been reported before. Meanwhile, we found the known clomipramine targeting metabolism of xenobiotics by cytochrome P450 pathway in liver cancer.In the future, we can expand the candidate target pathways affected by small molecules through the integration of multiple cancer datasets. As the information on pathways and small molecules accumulates, more comprehensive results will be obtained. In our future work, we will expand the types of cancer and provide creative thinking for molecularly targeted therapy from the perspective of systematic biology. We will focus our creative thinking on further researching mechanisms of drug functions of validated small molecules and make a contribution to the development of clinical anticancer drugs. Besides, we are taking other types of targets of small molecules, namely, miRNA, to specify the target relationships between small molecules and pathways. We will explore the molecule-impacted target pathway from different perspectives.Figure S1: Visualisation of the M-P link map for liver cancer from GSE5364 and its degree distribution.Figure S2: Visualisation of the M-P link map for lung cancer from GSE5364 and its degree distribution.Figure S3: Hierarchical clustering in the M-P link map for liver cancer from GSE5364.Figure S4: Hierarchical clustering in the M-P link map for lung cancer from GSE5364.TABLE S1: The characteristics of molecules with the sM-mP pattern in liver cancer.TABLE S2: The characteristics of molecules with the sM-mP pattern in lung cancer.TABLE S3: Details of the validation analysis by comparing the relationship of small molecules targeting pathways to DrugBank and CTD databases for breast, liver and lung cancers.