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

Aug 5, 2026

Immunometabolism is central to rheumatoid arthritis (RA) pathogenesis, yet the regulatory mechanisms remain incompletely defined. This study integrates GWAS-based plasma metabolites, immune cell traits, and RA outcomes from more than 58,000 participants using Mendelian randomization and mediation analyses. Single-cell and bulk transcriptome analyses characterize gene expression, immune-cell interactions, and processes. Explainable machine learning identifies hub genes, validated by qRT-PCR, while CMAP, molecular docking, and PheWAS prioritize vismodegib as a candidate therapy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

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Introduction

Patients and Methods

Results

Conclusion

Keywords

Journal of Inflammation Research downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Journal of Inflammation Research Open Access Full Text Article ORIGINAL RESEARCH Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation Qiu Dong 1 , *, Jiayang Wu 2 , 3 , *, Huaguo Zhang 4 , *, Xinhui Chen 2 , 3 , Xi Xu2 , 3 , Jifeng Chen 3 , Changzheng Shi2 , 3 , 5 , Liangping Luo 3 , 5 , Dong Zhang 2 , 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 work Correspondence: Dong Zhang, Email [lbzhangdong@126.com](lbzhangdong@126.com) | 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 smallmolecule 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 PEassociated 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 asa 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 | | --- | | Introduction
Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease characterized by persistent polyarticular inflammation, synovial hyperplasia, pain, joint swelling and stiffness.1 The global prevalence of RA continues to rise as the | Received: 15 November 2024 Accepted: 7 January 2025 Published: 16 January 2025 Journal of Inflammation Research 2025:18

FAQs

What datasets and analytical methods were used to map the immunometabolic landscape of RA?

The study integrates GWAS data covering 1,400 plasma metabolites, 731 immune cell traits, and RA outcomes from over 58,000 participants, using Mendelian randomization and mediation analyses. It also uses single-cell and bulk transcriptome analyses to explore differential gene expression and immune-cell interactions.

How were the key genes identified and validated?

Machine learning models identified hub genes via PPI network analysis and related bioinformatics steps. The selected hub genes were validated experimentally using quantitative real-time PCR (qRT-PCR).

How were therapeutic candidates and potential side effects evaluated?

Small-molecule candidates were screened using Connectivity Map (CMAP) and molecular docking. A phenome-wide association study (PheWAS) assessed potential side effects for drug targeting of the hub genes, highlighting vismodegib as a promising candidate.

Aug 5, 2026

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