Evidence map›Paper›PMID 42344507›Full record

ArticleFrontiers in medicine2026

Peripheral B cell immune dysregulation genetically contributes to stage-dependent neuroinflammation and identifies priority therapeutic targets in Parkinson's disease: a computational integration of Mendelian randomization and single-cell transcriptomics.

Lu Zhang, Chengwei Fu, Ting Gao, Kezhen Yang, Gang Ouyang, Wenjun Chen

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Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lu ZhangDepartment of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chengwei FuDepartment of Acupuncture, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China.
Ting GaoDepartment of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Kezhen YangDepartment of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Gang OuyangDepartment of Acupuncture, Geriatric Hospital of Nanjing Medical University, Nanjing, China.
Wenjun ChenDepartment of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Parkinson's disease (PD) lacks reliable peripheral biomarkers and disease-modifying therapies. Although peripheral B-cell alterations have been observed in PD, whether genetically predicted B-cell gene expression contributes to PD risk and can nominate candidate therapeutic targets remains unclear. Methods: We established a computational framework integrating two-sample Mendelian randomization (MR) based on B-cell subtype-specific sc-eQTLs (OneK1K) and PD GWAS (IPDGC; 33,674 cases/449,056 controls), Bayesian colocalization with DICE and FinnGen replication, exploratory pseudobulk stage-associated analysis (GSE223138), B-cell subpopulation fine-mapping in 10,466 CD19 + cells (GSE194245), CellChat v2 communication inference, and druggability prioritization with molecular docking-based structural plausibility assessment. Results: Multi-layer computational evidence prioritized eight high-confidence MR causal genes: five risk-increasing genes (CD74, HLA-DRB1, IL2RA, BLK, BANK1) and three protective genes (CR1, PTPN22, FCRL3). Exploratory pseudobulk analysis suggested four tentative stage-associated expression patterns, with early-change genes (CD74, HLA-DRB1, IL2RA) showing expression differences detectable as early as PD-Early in a small exploratory cohort. Subpopulation analysis suggested enrichment in Naive B (BLK, BANK1) and Atypical B cells (CD74, HLA-DRB1, IL2RA, CR1), while acknowledging that these fine-grained assignments are not equivalent to MR exposure categories. CellChat analysis predicted that the MIF signaling pathway showed an inferred communication probability increasing from 0.15 in HC to 0.48 in PD-Late. Five-dimensional evidence convergence nominated CD74, IL2RA, and CR1 as candidate targets, with molecular docking suggesting structural plausibility for selected small-molecule interactions, including Dasatinib-BLK (-7.2 kcal/mol) and ISO-1-CD74 (-6.8 kcal/mol). Conclusion: These computational findings suggest that genetically influenced B-cell gene expression may contribute to peripheral immune dysregulation in PD and nominate CD74, IL2RA, and CR1 as candidate targets for future validation. The results should be interpreted as hypothesis-generating and require experimental and clinical confirmation.

Indexed as

B cellsdrug repurposingintercellular communicationMendelian randomizationneuroinflammationParkinson’s diseaseperipheral immunitysingle-cell eQTL

Identifiers

PMID42344507
PMCPMC13286758

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.