Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation

作     者：Dong, Qiu Wu, Jiayang Zhang, Huaguo Chen, Xinhui Xu, Xi Chen, Jifeng Shi, Changzheng Luo, Liangping Zhang, Dong 

作者机构：Jinan Univ Affiliated Hosp 1 Dept Bone & Joint Surg Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Med Imaging Ctr Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Guangzhou Key Lab Mol & Funct Imaging Clin Transla Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Dept Ultrasonog Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 5 Med Imaging Ctr Heyuan Guangdong Peoples R China 

出 版 物：《JOURNAL OF INFLAMMATION RESEARCH》 (J. Inflamm. Res.)

年 卷 期：2025年第18卷

页      面：637-652页

核心收录：

基　　金：National Natural Science Foundation of China [81971672, 82271943] Guangzhou Basic Research Foundation Guangdong Basic and Applied Basic Research Foundation [2022A1515110630] Guangzhou Science and Technology Projects [2023A03J0609] 

主　　题：rheumatoid arthritis dendritic cells single-cell sequencing bulk transcriptome explainable machine learning drug repositioning 

摘      要：Purpose: Immunometabolism is pivotal in rheumatoid arthritis (RA) pathogenesis, yet the intricacies of its pathological regulatory mechanisms remain poorly understood. This study explores the complex immunometabolic landscape of RA to identify potential therapeutic targets. Patients and Methods: We integrated genome-wide association study (GWAS) data involving 1,400 plasma metabolites, 731 immune cell traits, and RA outcomes from over 58,000 participants. Mendelian randomization (MR) and mediation analyses were applied to evaluate causal relationships among plasma metabolites, immune cells, and RA. We further analyzed single-cell and bulk transcriptomes to investigate differential gene expression, immune cell interactions, and relevant biological processes. Machine learning models identified hub genes, which were validated via quantitative real-time PCR (qRT-PCR). Then, potential small- molecule drugs were screened using the Connectivity Map (CMAP) and molecular docking. Finally, a phenome-wide association study (PheWAS) was conducted to evaluate potential side effects of drugs targeting the hub genes. Results: Causalities were found between six plasma metabolites, five immune cells and RA in genetically determined levels. Notably, DC mediated 18% of the protective effect of PE on RA. Autophagy-related scores were elevated in both RA and DC subsets in PE- associated biological processes. Through observation in the functional differences in cellular interactions between the identified clusters, DCs with high autophagy scores may process such as necroptosis and the activation of the Jak-STAT signaling pathway in contributing the pathogenesis of RA. Explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). CMAP, molecular docking, and PheWAS analysis further highlighted vismodegib as a promising therapeutic candidate. Conclusion: This study clarifies the key immunometabolic mechanisms in RA, pinpoi