Discovering genetically-supported drug targets for multisite chronic pain through multi-omics Mendelian randomization and single-cell RNA-sequencing.

Abstract

Background: Multisite chronic pain (MCP) is a highly prevalent disorder with substantial unmet therapeutic needs.

Methods: We conducted multi-omics Mendelian randomization and Bayesian colocalization to identify potential therapeutic targets for MCP. Summary-level data of gene expressions and protein abundance levels were obtained from corresponding quantitative trait loci studies, respectively. Summary-level data for MCP was leveraged from the UK Biobank. The transcriptome-wide association study (TWAS), Mendelian randomization, and Bayesian colocalization approaches were applied to investigate the potential causal effects of gene expressions and protein levels on MCP in both blood and brain tissues. Phenome-wide Mendelian randomization analysis (MR-PheWAS), single-cell sequencing data, protein-protein interaction (PPI), and reaction pathway analysis were further conducted to digging the underlying mechanisms.

Results: Our analysis identified and validated two plasma targets for MCP, namely KLC1 and LANCL1, at both gene expression levels and protein levels across multi-methodologies. Moreover, MR-PheWAS observed additional benefits associated with these two targets. Through analyses based on single-cell sequencing data, we identified critical cell types for KLC1, primarily megakaryocytes, and neurons, notably linked to the axon guidance pathway, while LANCL1 showed associations with B lymphocytes, neurons, and the electron transport pathway. In dorsal root ganglions, we identified enrichments of both LANCL1 and KLC1 in putative silent nociceptors. The effects are possibly mediated through axonal transport and the activation of NMDARs, supported by PPI and reaction pathway analysis.

Conclusions: Our multi-dimensional analysis suggests that genetically determined KLC1 and LANCL1 are causally linked to MCP risk, holding promise as appealing drug targets for MCP.