miRNA regulation of genes associated with <i>diabetes mellitus</i>.

<p>Genes and miRNAs are visualised as grey circles and yellow rounded rectangles, respectively. The names of the genes and miRNAs are displayed on the nodes. The genes linked to the MeSH term <i>diabetes mellitus</i> were obtained using Gene2MeSH (<a href="http://gene2mesh.ncibi.org/" target="_blank">http://gene2mesh.ncibi.org/</a>). The molecular interactions between the genes were obtained from the STRING database and are visualised in black. The MTIs originate from microCosm (486), TargetScan (207), miRTarBase (5) and miRecords (2), and are coloured in blue, orange, red and purple, respectively.The overlap threshold function was applied to show only MTIs present in at least two RegINs.</p

Comparison of available Cytoscape apps.

<p>Overview of four Cytoscape apps that are most related to CyTargetLinker. This table provides a comparison of the availability and data used in the apps.</p

CyTargetLinker workflow.

<p><b>Step 1:</b> Four miRNAs known to be involved in prostate cancer <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082160#pone.0082160-Heneghan1" target="_blank">[27]</a> are visualised in a Cytoscape network (A). The miRNAs are annotated with miRBase accession numbers and ids. <b>Step 2:</b> The regulatory interaction networks (RegINs) harbouring miRNA-target interactions (MTIs), either validated (miRecords and miRTarBase) or predicted (microCosm and TargetScan), are downloaded from <a href="http://projects.bigcat.unimaas.nl/cytargetlinker/regins" target="_blank">http://projects.bigcat.unimaas.nl/cytargetlinker/regins</a>. <b>Step 3:</b> Known targets are integrated (B) after specifying the miRBase accession number column, the RegINs directory and the direction “<i>Add Targets</i>” in the CyTargetLinker dialog. In the resulting network miRNAs and target genes are defined as grey circles and pink hexagons, respectively. The predicted MTIs are visualised in orange (TargetScan: 4239) and blue (microCosm: 2800) and the validated MTIs in red (miRTarBase: 59) and purple (miRecords: 24), as shown in the control panel. <b>Step 4:</b> The hide/show and overlap threshold functions were used to visualise validated interactions exclusively or to show the overlap in MTI coverage. In the validated network only the MTIs in miRecords and miRTarBase are visualised by hiding MTIs in TargetScan and microCosm (C). In the overlap network the MTIs present in two or more RegINs are shown by setting the threshold to 2 (D).</p

Meta network of the ErbB signaling pathway containing miRNA, TF and drug regulation.

<p>The ErbB signaling pathway (<a href="http://wikipathways.org/index.php/Pathway:WP673" target="_blank">http://wikipathways.org/index.php/Pathway:WP673</a>) from WikiPathways was extended with TFs, miRNAs and drugs regulating the elements in the pathway. The nodes from the original pathway are coloured in grey, TFs in green, miRNAs in yellow and drugs in purple. 258 TF-gene interactions from ENCODE, 162 miRNA-target interactions from the two validated miRNA-target RegINs (miRTarBase and miRecords) and 138 drug-target interactions from DrugBank were added to the network. The edges are coloured based on the RegIN source, purple and orange for ENCODE (proximal and distal), blue and red for miRTarBase and miRecords, and green for DrugBank.</p

Regulatory interaction network files.

<p>Subset of the RegINs (regulatory interaction networks) available for download on the CyTargetLinker website. All RegIN networks support the following identifier systems: (i) for genes/proteins Ensembl, NCBI gene, UniProt, (ii) for miRNAs miRBase accession number and ids, and (iii) for drugs DrugBank. (* Redistribution of data not allowed, but RegIN can be created with our provided conversion script).</p

Extend a GeneMania network with TF data from the ENCODE project.

<p>A set of known DNA repair genes (grey circled nodes) were used as input for the GeneMania app in Cytoscape. Physical interactions (black) between the query genes were added by GeneMania. Every node in a GeneMania network has an attribute “Entrez gene identifier” which was used by the CyTargetLinker app to extend the network with TF-gene interactions from the ENCODE networks. The integration direction was selected as “<i>Add regulators</i>”, indicating that the app should look for interactions that target the input genes. The colours and shapes of the TFs are based on the provided TF family information in the ENCODE project <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082160#pone.0082160-Gerstein1" target="_blank">[3]</a>. The ENCODE project studied proximal and distal TF regulation which are indicated in this figure as blue and red edges.</p

Reactome from a WikiPathways Perspective

<div><p>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.</p></div

A comparative view of the Abacavir transport and metabolism pathway for Homo sapiens from Reactome Database (version 54).

(a) Reactome View of Abacavir transport and metabolism (Homo sapiens) [32] and (b) Pathway view on WikiPathways(WP2712_r83598) [33].</p

Mapping Reactome pathways elements to WikiPathways pathway elements.

<p>This diagram shows the symbols used to represent different biological entities in Reactome and the corresponding symbol used to represent the same biological entity in WikiPathways.</p

Data_Sheet_2_Biological Pathways Leading From ANGPTL8 to Diabetes Mellitus–A Co-expression Network Based Analysis.CSV

Angiopoietin like protein 8 (ANGPTL8) is a newly identified hormone with unique nature due to its ability to regulate both glucose and lipid metabolic pathways. It is characterized as an important molecular player of insulin induced nutrient storage and utilization pathway during fasting to re-feeding metabolic transition. Several studies have contributed to increase our knowledge regarding its function and mechanism of action. Moreover, its altered expression levels have been observed in Insulin Resistance, Diabetes Mellitus (Types I & II) and Non Alcohlic Fatty Liver Disease emphasizing its assessment as a drug target. However, there is still a great deal of information that remains to be investigated including its associated biological processes, partner proteins in these processes, its regulators and its association with metabolic pathogenesis. In the current study, the analysis of a transcriptomic data set was performed for functional assessment of ANGPTL8 in liver. Weighted Gene Co-expression Network Analysis coupled with pathway analysis tools was performed to identify genes that are significantly co-expressed with ANGPTL8 in liver and investigate their presence in biological pathways. Gene ontology term enrichment analysis was performed to select the gene ontology classes that over-represent the hepatic ANGPTL8-co-expressed genes. Moreover, the presence of diabetes linked SNPs within the genes set co-expressed with ANGPTL8 was investigated. The co-expressed genes of ANGPTL8 identified in this study (n = 460) provides narrowed down list of molecular targets which are either co-regulated with it and/or might be regulation partners at different levels of interaction. These results are coherent with previously demonstrated roles and regulators of ANGPTL8. Specifically, thirteen co-expressed genes (MAPK8, CYP3A4, PIK3R2, PIK3R4,PRKAB2, G6PC, MAP3K11, FLOT1, PIK3C2G, SHC1, SLC16A2, and RAPGEF1) are also present in the literature curated pathway of ANGPTL8 (WP39151). Moreover, the gene-SNP analysis of highly associated biological processes with ANGPTL8 revealed significant genetic signals associated to Diabetes Mellitus and similar phenotypic traits. It provides meaningful insights on the influencing genes involved and co-expressed in these pathways. Findings of this study have implications in functional characterization of ANGPTL8 with emphasis on the identified genes and pathways and their possible involvement in the pathogenesis of Diabetes Mellitus and Insulin Resistance.</p