Frontiers in Molecular NeuroscienceNeurosciencePeer-reviewed journal article
Transcriptome analysis of the human tibial nerve identifies sexually dimorphic expression of genes involved in pain, inflammation and neuro-immunity
Pradipta R. RayiD, Jawad Khan, Andi WangzhouiD, Diana Tavares‐FerreiraiD, Armen N. AkopianiD, Gregory O. DussoriD, Theodore John PriceiD
Abstract
Sex differences in gene expression are important contributors to normal physiology and mechanisms of disease. This is increasingly apparent in understanding and potentially treating chronic pain where molecular mechanisms driving sex differences in neuronal plasticity are giving new insight into why certain chronic pain disorders preferentially affect women versus men. Large transcriptomic resources are increasingly available and can be used to mine for sex differences and molecular insight using donor cohorts. We analyzed more than 250 human tibial nerve (hTN) transcriptomes from the GTex Consortium project to gain insight into sex-dependent gene expression in the peripheral nervous system (PNS). We discover 149 genes with sex differential expression. Many of the genes upregulated in men are associated with inflammation, and appear to be primarily expressed by glia or immune cells. In women, we find the differentially upregulated transcription factor SP4 that drives a regulatory program, and may impact sex differences in PNS physiology. Many of these 149 DE genes have some previous association with chronic pain but few of them have been explored thoroughly. Additionally, using clinical data in the GTex database, we identify a subset of differentially expressed (DE) genes in diseases associated with chronic pain, arthritis and type II diabetes. Our work identifies sexually dimorphic gene expression in the human PNS with implications for discovery of sex-specific pain mechanisms.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 46
- FWCI
- 5.50
- Field percentile
- top 3%
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