The Pain Genes Database: An interactive web browser of pain-related transgenic knockout studies
ABSTRACT The transgenic knockout mouse is one of the most important tools of modern biology, and commonly employed by pain researchers to examine the function of genes of interest. Over 400 papers, at a current rate of >60 papers per year, have been published to date describing a statistically significant behavioral pain "phenotype" resulting from the null mutation of a single gene. The standard literature review format is incapable of providing a sufficiently broad and up-to-date overview of the field. We have therefore constructed the Pain Genes Database, an interactive, web-based data browser designed to allow easy access to and analysis of the published pain-related phenotypes of mutant mice (over 200 different mutants at the date of submission). Manuscripts describing results of pain-relevant knockout studies were identified via Medline search. Manuscripts were included in the database if they described the testing of a spontaneous or genetically engineered mutant mouse with null expression of a single gene on a behavioral assay of acute or tonic nociception, injury- or stimulus-induced hypersensitivity (i.e., allodynia or hyperalgesia), or drug- or stress-induced inhibition of nociception (i.e., analgesia), and reported at least one statistically significant difference between the mutant mice and their simultaneously tested wildtype controls. The database features two levels of exploration, one allowing the identification of genes by name, acronym, genomic position or "summary" phenotype, and the other allowing in-depth browsing, paper-by-paper, of specific phenotypes and test parameters. Links to genetic databases and Medline abstracts are provided for each gene and paper. It is our intention to update the database continually based on weekly Medline searches. This database should provide pain researchers with a useful and easy-to-use tool for the generation of novel hypotheses regarding the roles of genes and their protein products in pain processing and modulation. It can be accessed at http://paingeneticslab.ca/4105/06_02_pain_genetics_database.asp (or by visiting paingeneticslab.ca and clicking on the "Pain Genes Db" link under "Resources").
- SourceAvailable from: Thomas J J Blanck
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- "Based on knockout studies, the predicted mRNA targets (shown in light brown) have already been associated with hypersensitivity in various pain models (http://www.jbldesign.com/jmogil/enter.html) (Lacroix-Fralish et al., 2007), including some in which alterations in mechanical and/or cold allodynia were found. These include the voltage dependent sodium channel SCN9A (Nassar et al., 2004), the sodium channel beta 2 subunit (SCN2B) (Pertin et al., 2005; Lopez-Santiago et al., 2006), the voltage-dependent calcium channels T-type (Choi et al., 2007; Lee et al., 2009; Chen et al., 2010), N-type (Hatakeyama et al., 2001; Kim et al., 2001; Saegusa et al., 2001) and P/Q-type (Luvisetto et al., 2006), the pacemaker channels HCN1 (Momin et al., 2008) and HCN2 (Emery et al., 2011), the ligand-gated, non-selective cation channel TRPV1 (Caterina et al., 2000; Wang et al., 2008) and the transient receptor potential channel TRPM8 (Bautista et al., 2007; Dhaka et al., 2007; Gentry et al., 2010; Knowlton et al., 2010). "
ABSTRACT: Peripheral nerve injury alters the expression of hundreds of proteins in dorsal root ganglia (DRG). Targeting some of these proteins has led to successful treatments for acute pain, but not for sustained postoperative neuropathic pain. The latter may require targeting multiple proteins. Since a single microRNA (miR) can affect the expression of multiple proteins, here, we describe an approach to identify chronic neuropathic pain-relevant miRs. We used two variants of the spared nerve injury (SNI): Sural-SNI and Tibial-SNI and found distinct pain phenotypes between the two. Both models induced strong mechanical allodynia, but only Sural-SNI rats maintained strong mechanical and cold allodynia, as previously reported. In contrast, we found that Tibial-SNI rats recovered from mechanical allodynia and never developed cold allodynia. Since both models involve nerve injury, we increased the probability of identifying differentially regulated miRs that correlated with the quality and magnitude of neuropathic pain and decreased the probability of detecting miRs that are solely involved in neuronal regeneration. We found seven such miRs in L3-L5 DRG. The expression of these miRs increased in Tibial-SNI. These miRs displayed a lower level of expression in Sural-SNI, with four having levels lower than those in sham animals. Bioinformatics analysis of how these miRs could affect the expression of some ion channels supports the view that, following a peripheral nerve injury, the increase of the 7 miRs may contribute to the recovery from neuropathic pain while the decrease of four of them may contribute to the development of chronic neuropathic pain. The approach used resulted in the identification of a small number of potentially neuropathic pain relevant miRs. Additional studies are required to investigate whether manipulating the expression of the identified miRs in primary sensory neurons can prevent or ameliorate chronic neuropathic pain following peripheral nerve injuriesFrontiers in Neuroscience 08/2014; 8:266. DOI:10.3389/fnins.2014.00266 · 3.66 Impact Factor
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- "The list of pain-related genes was downloaded from the Pain Gene Database website (http://www.jbldesign.com/jmogil/enter.html) . The list of AD-related genes was constructed from OMIM and KEGG database [15,16]. "
ABSTRACT: Background Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes. Results We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a “weighted literature score” based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer’s disease, a total of 1101 pain candidate genes and 2810 Alzheimer’s disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer’s disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools. Conclusions Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.BMC Bioinformatics 06/2014; 15(1):179. DOI:10.1186/1471-2105-15-179 · 2.58 Impact Factor
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- "The risk of developing this condition is dependent on the interaction between environmental and genetic factors. Multiple techniques are currently being employed to investigate the molecular basis of pain: transcriptional profiling of experimental models of persistent pain in rodent and human [14,20,33,40], the study of inbred rodent strains and genetically modified animals to understand the genetic basis of acute and chronic pain [1,22,34,66], and, more recently, human genetics, describing both high impact variants [31,64] and the use of gene association studies in experimental pain models and patient cohorts [15,56]. Systems biology (defined as “the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources …” ), provides a means to organise, integrate, and maximise the utility of these large disparate pain-related data sets . "
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.Pain 09/2013; 154(12). DOI:10.1016/j.pain.2013.09.003 · 5.21 Impact Factor