Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk

The impact of chronic pain on brain gene expression

Background

Chronic pain affects one fifth of American adults, contributing significant public health burden. Chronic pain mechanisms can be further understood through investigating brain gene expression.

Methods

We tested differentially expressed genes (DEGs) in chronic pain, migraine, lifetime fentanyl and oxymorphone use, and with chronic pain genetic risk in four brain regions (dACC, DLPFC, MeA, BLA) and imputed cell type expression data from 304 postmortem donors. We compared findings across traits and with independent transcriptomics resources, and performed gene-set enrichment.

Results

We identified two chronic pain DEGs: B4GALT and VEGFB in bulk dACC. We found over 2000 (primarily BLA microglia) chronic pain cell type DEGs. Findings were enriched for mouse microglia pain genes, and for hypoxia and immune response. Cross-trait DEG overlap was minimal.

Conclusions

Chronic pain-associated gene expression is heterogeneous across cell type, largely distinct from that in pain-related traits, and shows BLA microglia are a key cell type.

Introduction to a special issue on big data and pain

This special issue comprised 7 articles from leaders in the field that focus on "big pain data", the large datasets and the associated methods for data analysis that are currently emerging in pain research. This collection of articles highlights the power and potential as well as points of caution that multi-disciplinary research utilising big data and their associated methods and interpretations present for pain research.