Hu Shou-Ye, Jiang Feng, Song Hui-Miao, Wang Ya-Kang, Tian Wen, Wu Hao, Yao Shi, He Chang-Yi, Gao Hui-Wu, Yang Tie-Lin, Yang Zhi, Guo Yan
Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China.
Key Laboratory of Biomedical Information Engineering of Ministry of Education, Key Laboratory of Biology Multiomics and Diseases in Shaanxi Province Higher Education Institutions, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China.
Rheumatology (Oxford). 2025 May 1;64(5):2515-2524. doi: 10.1093/rheumatology/keae654.
This study aimed to address the lack of gene expression regulation data in synovial tissues and to identify genes associated with rheumatoid arthritis (RA) in the synovium, a primary target tissue for RA.
Gene expression prediction models were built for synovial tissue using matched genotype and gene expression data from 202 subjects. Using this model, we conducted a transcriptome-wide association study (TWAS), utilizing the largest rheumatoid arthritis (RA) genome-wide association study (GWAS) meta-analysis data (n = 276 020). Further analyses, including conditional and joint analysis, causal analysis, differential expression analysis and gene-set enrichment analysis, were conducted to deepen our understanding of genetic architecture and comorbidity aetiology of RA.
Our analysis identified eight genes associated with rheumatoid arthritis (RA), including three novel genes: TPRA1 (PTWAS = 9.59 × 10-6), HIP1 (PTWAS = 1.47 × 10-5) and RP11-73E17.2 (PTWAS = 3.32 × 10-7). These genes differed from those identified in previous TWAS studies using alternative tissues and may play a crucial role in the target synovial tissue. We found four genes exhibited significant causal relationships with RA and were differentially expressed in RA patients. Furthermore, we explored potential drug repurposing opportunities for these genes.
Our study is the first to model gene expression in synovial tissue, uncovering novel genetic determinants of rheumatoid arthritis (RA). This advancement not only deepens our understanding of RA's genetic architecture, but also offers promising avenues for targeted therapies and drug repurposing.
本研究旨在解决滑膜组织中基因表达调控数据的缺乏问题，并鉴定滑膜（类风湿关节炎（RA）的主要靶组织）中与RA相关的基因。
利用202名受试者的匹配基因型和基因表达数据，构建滑膜组织的基因表达预测模型。使用该模型，我们进行了全转录组关联研究（TWAS），利用最大规模的类风湿关节炎（RA）全基因组关联研究（GWAS）荟萃分析数据（n = 276020）。进行了包括条件和联合分析、因果分析、差异表达分析和基因集富集分析等进一步分析，以加深我们对RA的遗传结构和共病病因的理解。
我们的分析鉴定出8个与类风湿关节炎（RA）相关的基因，包括3个新基因：TPRA1（PTWAS = 9.59×10-6）、HIP1（PTWAS = 1.47×10-5）和RP11-73E17.2（PTWAS = 3.32×10-7）。这些基因与之前使用其他组织的TWAS研究中鉴定出的基因不同，可能在靶滑膜组织中起关键作用。我们发现4个基因与RA存在显著因果关系，且在RA患者中差异表达。此外，我们探索了这些基因潜在的药物再利用机会。
我们的研究首次对滑膜组织中的基因表达进行建模，揭示了类风湿关节炎（RA）新的遗传决定因素。这一进展不仅加深了我们对RA遗传结构的理解，还为靶向治疗和药物再利用提供了有前景的途径。
