Mechanism of RORα in Promoting Osteoarthritis through p53 Deubiquitination-Mediated Chondrocyte Ferroptosis
Description
This study elucidates the role of the RORα–p53 axis in osteoarthritis (OA) pathogenesis via chondrocyte ferroptosis. The transcriptomic analysis of OA chondrocyte revealed pronounced iron dysregulation and upregulated ferroptosis markers. In a mouse OA model, the iron chelator deferoxamine attenuated cartilage damage, confirming ferroptosis's pathogenic role. We found that p53 expression was elevated in OA, promoting chondrocyte ferroptosis, while its inhibition restored antioxidant defenses. Retinoic acid-related orphan receptor alpha (RORα) acted as an upstream regulator; its overexpression exacerbated OA, whereas chondrocyte-specific knockout ameliorated cartilage damage by suppressing ferroptosis. Mechanistically, RORα stabilizes p53 by recruiting the deubiquitinase HAUSP, amplifying the ferroptotic cascade. Our findings demonstrate that the RORα–p53 axis is a novel pathogenic mechanism driving ferroptosis-mediated cartilage degeneration in OA, representing a promising therapeutic target.
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Steps to reproduce
All analyses were performed using widely adopted open-source bioinformatics tools. Quality control of raw sequencing data was conducted using FastQC (v0.11.9). Adapter trimming and filtering of low-quality reads were performed using Trimmomatic (v0.39). Clean reads were aligned to the reference genome using HISAT2 (v2.2.1). Gene expression quantification was conducted based on read counts generated from aligned BAM files using featureCounts (v2.0.1). Differential expression analysis was performed using the DESeq2 package (v1.30.0) in the R environment (v4.1.0). Downstream functional enrichment analyses, including Gene Ontology (GO) and KEGG pathway analysis, were carried out using the clusterProfiler package (v4.0.0) in R. All analyses were performed using default parameters unless otherwise specified. No custom scripts are required to interpret the data files. Processed data files (e.g., count matrices and differential expression results) can be directly used with standard statistical or visualization tools in R or other compatible platforms.
Institutions
- Sun Yat-sen UniversityGuangdong, Guangzhou