Peripheral immune cell-specific genes in Parkinson's disease uncovered by multi-omics with therapeutic implications

Abstract

Parkinson's disease (PD) is a complex neurodegenerative disorder with growing evidence suggests peripheral immunity plays a role in its pathogenesis. However, the specific peripheral immune cell types and gene expression profiles associated with PD remain unclear. In this study, we integrated single-cell expression quantitative trait loci (sc-eQTL) data from 14 immune cell types in the OneK1K cohort with large-scale genome-wide association study (GWAS) data for PD. Using Mendelian randomization (MR) and Bayesian colocalization analyses, we identified 28 immune-cell-specific eGenes with significant associations to PD risk, among which 24 showed strong or moderate evidence of shared genetic signals. Notable candidates included FDFT1, ARSA, CTSB, and HLA-DQA1, each displaying cell-type-specific associations in CD4⁺ T cells, CD8⁺ T cells, B cells, and monocytes. Replication using an independent sc-eQTL dataset from the DICE project confirmed consistent findings for several eGenes. Additional validation through peripheral blood single-cell RNA sequencing (scRNA-seq) revealed distinct expression patterns and significant changes in PD patients. Phenome-wide association studies (PheWAS) showed multiple associations with immune-related traits and minimal associations with unrelated traits, indicating a favorable safety profile for therapeutic targeting. Drug repurposing analysis identified several candidate compounds, including felodipine, amodiaquine, alprazolam, and tetrandrine, some of which are predicted to cross the blood-brain barrier. Molecular docking simulations further supported strong binding interactions between these compounds and PD-associated targets such as CTSB and ARSA. This integrative approach highlights key immune-cell-specific genes involved in PD and proposes several repurposable drugs with central nervous system potential, paving the way for more targeted therapeutic strategies.

Fig. 1. Overview of study design and analytical workflow.

a Summary of input data including sc-eQTL data from 14 immune cell types (OneK1K cohort) and GWAS summary statistics of PD. b Primary analyses including two-sample MR to infer causal associations, Bayesian colocalization to detect shared causal variants, and scRNA-seq of peripheral immunological features. c Follow-up analyses including PheWAS for pleiotropic effects, drug prediction from DSigDB, and molecular docking simulations.

Fig. 2

Distribution of immune cell-specific eGenes based on instrument count.

Fig. 3. Manhattan plot of MR associations across diverse immune cell types.

Each dot represents an eGene-cell type pair tested for causal association with PD risk. Red points indicate associations that passed the FDR threshold. Notable eGenes with significant associations across specific cell types are annotated.

Fig. 4. Forest plot of prioritized eGenes showing colocalization with PD.

Colocalization strength is indicated by color: red bars represent eGenes of tier 1 with strong evidence (PP.H4 > 80%), and blue bars indicate eGenes of tier 2 with moderate evidence (50% < PP.H4 ≤ 80%).

Fig. 5. Single-cell transcriptomic profiling of peripheral blood immune cells from HC and PD patients.

A UMAP projection of all cells from six samples, colored by sample identity. B UMAP projection colored by unsupervised clustering results. C Annotation of nine major immune cell types based on canonical marker genes. D Proportion of each major immune cell type in HC and PD groups. E Dot plot showing the expression levels and detection rates of genetically prioritized eGenes across major immune cell types. F Representative UMAP feature plots illustrating cell-type-specific expression patterns of HLA-DQA1 and CTSB. G Proportion of significantly upregulated and downregulated genes per immune cell type. H Cell-type-specific differential expression of prioritized eGenes.

Fig. 6. PheWAS analysis of prioritized eGenes by using AstraZeneca PheWAS Portal.

A ARSA. B NEIL2. C SPNS1. Dot color indicates phenotype category; triangle direction reflects effect size direction.

Fig. 7. Immune-related trait associations identified by SNP-level PheWAS in Open Targets Platform and FinnGen.

Sankey plots illustrating significant immune-related phenotypic associations for SNPs corresponding to prioritized eGenes. A Direct immune measurements, including lymphocyte count and measurement of C-reactive protein (CRP), cathepsin B (CTSB), leukocyte immunoglobulin-like receptor subfamily B member 4 (LILRB4), and collectin-12 (CL-12). B Immune-related diseases, including anti-citrullinated protein antibody-positive rheumatoid arthritis (ACPA + RA), polyarthropathies, seropositive rheumatoid arthritis (SPRA), coeliac disease (CD), ankylosing spondylitis (AS), subacute thyroiditis (SAT), spondyloarthritis (SpA), thyroiditis, psoriasis vulgaris (PV), juvenile arthritis (JIA). Cell types represent the source of the eGene, with links to the corresponding SNP and associated traits. *measurement. #count.

Fig. 8. Molecular docking interactions between BBB permeant candidate drugs and target proteins.

A Amodiaquine and CTSB. B Methadone hydrochloride and ARSA. C Alprazolam and CTSB. D Felodipine and CTSB. Left: protein-ligand complex; center: close-up of binding site; right: 2D interaction map highlighting key residues and binding types.