Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation - Dong Q; Wu J; Zhang H; Chen X; Xu X; Chen J; Shi C; Luo L; Zhang D (Journal of Inflammation Research)
Qiu Dong,1,* Jiayang Wu,2,3,* Huaguo Zhang,4,* Xinhui Chen,2,3 Xi Xu,2,3 Jifeng Chen,3 Changzheng Shi,2,3,5 Liangping Luo,3,5 Dong Zhang2,3 1Department of Bone and Joint Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People’s Republic of China; 2Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People’s Republic of China; 3The Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People’s Republic of China; 4Department of Ultrasonography, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People’s Republic of China; 5Medical Imaging Centre, the Fifth Affiliated Hospital of Jinan University, Heyuan, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dong Zhang, Email lbzhangdong@126.comPurpose: 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, pinpointing promising paths for better prevention, diagnosis, and treatment.Keywords: rheumatoid arthritis, dendritic cells, single-cell sequencing, bulk transcriptome, explainable machine learning, drug repositioning