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Review
. 2013 Dec;154(12):2586.e1-12.
doi: 10.1016/j.pain.2013.09.003. Epub 2013 Sep 11.

PainNetworks: A Web-Based Resource for the Visualisation of Pain-Related Genes in the Context of Their Network Associations

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Free PMC article
Review

PainNetworks: A Web-Based Resource for the Visualisation of Pain-Related Genes in the Context of Their Network Associations

James R Perkins et al. Pain. .
Free PMC article

Abstract

Hundreds of genes are proposed to contribute to nociception and pain perception. Historically, most studies of pain-related genes have examined them in isolation or alongside a handful of other genes. More recently the use of systems biology techniques has enabled us to study genes in the context of the biological pathways and networks in which they operate. Here we describe a Web-based resource, available at http://www.PainNetworks.org. It integrates interaction data from various public databases with information on known pain genes taken from several sources (eg, The Pain Genes Database) and allows the user to examine a gene (or set of genes) of interest alongside known interaction partners. This information is displayed by the resource in the form of a network. The user can enrich these networks by using data from pain-focused gene expression studies to highlight genes that change expression in a given experiment or pairs of genes showing correlated expression patterns across different experiments. Genes in the networks are annotated in several ways including biological function and drug binding. The Web site can be used to find out more about a gene of interest by looking at the function of its interaction partners. It can also be used to interpret the results of a functional genomics experiment by revealing putative novel pain-related genes that have similar expression patterns to known pain-related genes and by ranking genes according to their network connections with known pain genes. We expect this resource to grow over time and become a valuable asset to the pain community.

Keywords: Microarrays; Pain genes; Protein interaction network; Protein–protein interactions; Systems biology; Web-based resource.

Figures

Fig. 1
Fig. 1
Example uses of the site. The central panel, from which the red arrows protrude, shows the homepage of the site. The red circle highlights the search bar, where the user enters their gene(s) of interest, and any other search parameters (such as the type of interactions they want to bring back). They then click search and the site will bring back a network involving their search gene(s) and the proteins interacting with this gene(s) (ie, interactors), as well as any interactions between these interactors. Three examples are shown: searching with ATF3 (cyclic AMP-dependent transcription factor), with the nodes in the resulting network coloured based on microarray data (top), opioid receptors, with the resultant network clustered into smaller networks (bottom left), and angiotensin receptors, with results filtered so that only direct physical interactions are displayed (bottom right). Figs. 2–4 of the full article show these networks in more detail.
Fig. 1
Fig. 1
Using PainNetworks to reproduce the NGF-TrkA signalling pathway. (A) The original pathway diagram, adapted from http://pathwaymaps.com/maps/652/. (B) The pathway as reproduced by PainNetworks, created by querying the resource with the pathway members, restricting the results to only include experimentally validated direct physical interactions, and only showing interactions occurring between the query genes.
Fig. 2
Fig. 2
Using PainNetworks to reproduce the NRG1-ErbB signalling pathway. (A) The pathway as reproduced by PainNetworks, showing the interactions involving NRG1/ErbB2/ErbB3. The genes from the original pathway are shown in red. In order to create this network, PainNetworks was queried using NRG1, ErbB2, and ErbB3; results were filtered so that only interactions that include these genes were shown; interactions between the nonquery genes were removed to prevent the number of edges in the network from obscuring the image. (B) The original pathway, taken from . Note that only a part of the pathway is reproduced, in order to stop the network becoming too large and difficult to interpret. (C) A second reproduction of the pathway. In this network, only direct interactions between the query genes are shown, and results have been limited to include only direct physical interactions.
Fig. 3
Fig. 3
Network degree of all genes in PainNetworks with at least one known interaction. The x-axis shows the number of interactions that each gene is involved in. The y-axis shows the number of these interactions that are with pain genes. Black points = non-pain-related genes, red points = pain genes. This figure was generated using direct and indirect physical interaction data but not predicted interactions.
Fig. 4
Fig. 4
Looking for druggable proteins associated with opioid receptors. Building the network using all 4 opioid receptors and their interactors, as shown in panel (A), results in a large network. Different ways of reducing the network to make it more interpretable are presented and explained further in the text: (B) and (C) show the results of clustering the original network to find groups of genes that interact with each other more frequently than they interact with genes outside of the group. (C) Genes highlighted in red represent genes with known drug targets, according to DrugBank . This annotation is obtained from the “Network selection” tab. (D) Shows the network with indirect interactions removed, leaving only direct physical interactions.
Fig. 5
Fig. 5
Finding out more about angiotensin II type 2 receptors and their potential role in pain signalling. (A) Looking at direct and indirect interactors of angiotensin II receptor, type 2 gene (AGTR2). Note that the shaded circles represent pain-related genes. (B) The direct physical interactions of AGTR1, AGRT2, and AGTRAP. (C) The same network as shown in (B), but highlighting genes that are differentially expressed according to a gene expression microarray experiment of a model of neuropathic pain (spinal nerve transection). Red = increased expression following nerve injury; blue = decreased expression.
Fig. 6
Fig. 6
AMP-dependent transcription factor (ATF3) interactors that change in expression in a nerve injury model of pain. (A) The network returned by querying PainNetworks with ATF3, using default parameters. Pain-related genes, which in this case are obtained from the Pain Genes Database, are highlighted as grey in the network. (B) The genes that in L5 DRGs in the spinal nerve transection vs sham dataset are highlighted in the network by the addition of red or blue rings, for increased or decreased expression (respectively) in SNT compared to sham.
Fig. 7
Fig. 7
Using PainNetworks to analyse the results of a microarray experiment comparing L5-DRG following spinal nerve transection to L5 following sham surgery. (A) Clicking on the relevant experiment on the PainNetworks homepage returns a table of the differentially expressed genes from the experiment, alongside their log fold changes in expression. (B) The top 15 genes in terms of increased expression following spinal nerve transection are selected and used as query genes for the site. (C) The resulting network is displayed on the site, in the network panel. (D) The user can then zoom in on different areas in the network to look for relationships between the query genes and their interaction partners.

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