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Harnessing pain heterogeneity and RNA transcriptome to identify blood‐based pain biomarkers: a novel correlational study design and bioinformatics approach in a graded chronic constriction injury model
Author(s) -
Grace Peter M.,
Hurley Daniel,
Barratt Daniel T.,
Tsykin Anna,
Watkins Linda R.,
Rolan Paul E.,
Hutchinson Mark R.
Publication year - 2012
Publication title -
journal of neurochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.75
H-Index - 229
eISSN - 1471-4159
pISSN - 0022-3042
DOI - 10.1111/j.1471-4159.2012.07833.x
Subject(s) - transcriptome , biomarker , neuropathic pain , chronic pain , bioinformatics , nociception , medicine , biology , biomarker discovery , computational biology , gene , neuroscience , proteomics , gene expression , genetics , receptor
J. Neurochem . (2012) 122 , 976–994. Abstract A quantitative, peripherally accessible biomarker for neuropathic pain has great potential to improve clinical outcomes. Based on the premise that peripheral and central immunity contribute to neuropathic pain mechanisms, we hypothesized that biomarkers could be identified from the whole blood of adult male rats, by integrating graded chronic constriction injury (CCI), ipsilateral lumbar dorsal quadrant (iLDQ) and whole blood transcriptomes, and pathway analysis with pain behavior. Correlational bioinformatics identified a range of putative biomarker genes for allodynia intensity, many encoding for proteins with a recognized role in immune/nociceptive mechanisms. A selection of these genes was validated in a separate replication study. Pathway analysis of the iLDQ transcriptome identified Fcγ and Fcε signaling pathways, among others. This study is the first to employ the whole blood transcriptome to identify pain biomarker panels. The novel correlational bioinformatics, developed here, selected such putative biomarkers based on a correlation with pain behavior and formation of signaling pathways with iLDQ genes. Future studies may demonstrate the predictive ability of these biomarker genes across other models and additional variables.