Raju HB, Englander Z, Capobianco E, Tsinoremas NF, Lerch JK. Identification of potential therapeutic targets in a model of neuropathic pain.
Front Genet 2014;
5:131. [PMID:
24904634 PMCID:
PMC4033210 DOI:
10.3389/fgene.2014.00131]
[Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Accepted: 04/24/2014] [Indexed: 01/18/2023] Open
Abstract
Neuropathic pain (NP) is caused by damage to the nervous system, resulting in dysfunction and aberrant pain. The cellular functions (e.g., peripheral neuron spinal cord innervation, neuronal excitability) associated with NP often develop over time and are likely associated with gene expression changes. Gene expression studies on the cells involved in NP (e.g., sensory dorsal root ganglion neurons) are publically available; the mining of these studies may enable the identification of novel targets and the subsequent development of therapies that are essential for improving quality of life for the millions of individuals suffering with NP. Here we analyzed a publically available microarray dataset (GSE30165) in order to identify new RNAs (e.g., messenger RNA (mRNA) isoforms and non-coding RNAs) underlying NP. GSE30165 profiled gene expression in dorsal root ganglion neurons (DRG) and in sciatic nerve (SN) after resection, a NP model. Gene ontological analysis shows enrichment for sensory and neuronal processes. Protein network analysis demonstrates DRG upregulated genes typical to an injury and NP response. Of the top changing genes, 34 and 36% are associated with more than one protein coding isoform in the DRG and SN, respectively. The majority of genes are receptor and enzymes. We identified 15 long non-coding RNAs (lncRNAs) targeting these genes in LNCipedia.org, an online comprehensive lncRNA database. These RNAs represent new therapeutic targets for preventing NP development and this approach demonstrates the feasibility of data reanalysis for their identification.
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