Discovery of Novel Pain Regulators Through Integration of Cross‐Species High‐Throughput Data
Ying Chen, Akhilesh K. Bajpai, Nan Li, Jiahui Xiang, Angelina Wang, Qingqing Gu, Junpu Ruan, Ran Zhang, Gang Chen, Lu LuABSTRACT
Aims
Chronic pain is an impeding condition that affects day‐to‐day life and poses a substantial economic burden, surpassing many other health conditions. This study employs a cross‐species integrated approach to uncover novel pain mediators/regulators.
Methods
We used weighted gene coexpression network analysis to identify pain‐enriched gene module. Functional analysis and protein‐protein interaction (PPI) network analysis of the module genes were conducted. RNA sequencing compared pain model and control mice. PheWAS was performed to link genes to pain‐related GWAS traits. Finally, candidates were prioritized based on node degree, differential expression, GWAS associations, and phenotype correlations.
Results
A gene module significantly over‐enriched with the pain reference set was identified (referred to as “pain module”). Analysis revealed 141 pain module genes interacting with 46 pain reference genes in the PPI network, which included 88 differentially expressed genes. PheWAS analysis linked 53 of these genes to pain‐related GWAS traits. Expression correlation analysis identified Vdac1, Add2, Syt2, and Syt4 as significantly correlated with pain phenotypes across eight brain regions. NCAM1, VAMP2, SYT2, ADD2, and KCND3 were identified as top pain response/regulator genes.
Conclusion
The identified genes and molecular mechanisms may enhance understanding of pain pathways and contribute to better drug target identification.