Tendinopathy is mainly characterized by local pain, functional limitation and decreased athletic ability, which seriously affects the quality of life of patients and the career of athletes. Farrerol (FA), one of the main active compounds extracted from Rhododendron and plants in the Rhododendron family, has a wide range of pharmacological activities, such as immunomodulatory, anti‐inflammatory and antiviral effects. However, the effect of FA on tendinopathy is unclear. Here, we investigated the pharmacological effect and mechanism of FA in tendon injury through collagenase‐induced tendinopathy in vivo and RSL3‐induced tenocytes injury in vitro. The results showed that FA alleviated the infiltration of inflammatory cells, promoted tenogenesis and improved mechanical properties of the Achilles tendon in rats. In addition, ferroptosis inducer RSL3 inhibits the tenogenesis in vitro and in vivo, which accelerates the progression of tendinopathy. Moreover, FA effectively inhibited iron accumulation and alleviated ferroptosis in the Achilles tendon. Using in vitro experiments, we found that FA antagonized ferroptosis by reducing lipid peroxidation and iron accumulation in tenocytes. Finally, we found that glutathione peroxidase 4 silencing could block the protective effect of FA on ferroptosis of tenocytes. Therefore, the results of this study suggest that FA can relieve collagenase‐induced tendinopathy by inhibiting ferroptosis, and reveal that FA may be a potentially effective drug for the treatment of tendinopathy in the future.
Osteoarthritis (OA) is one of the most common diseases in the orthopedic clinic, characterized by progressive cartilage degradation. RNA-binding proteins (RBPs) are capable of binding to RNAs at transcription and translation levels, playing an important role in the pathogenesis of OA. This study aims to investigate the diagnosis values of RBP-related genes in OA. The RBPs were collected from previous studies, and the GSE114007 dataset (control = 18, OA = 20) was downloaded from the Gene Expression Omnibus (GEO) as the training cohort. Through various bioinformatical and machine learning methods, including genomic difference detection, protein-protein interaction network analyses, Lasso regression, univariate logistic regression, Boruta algorithm, and SVM-RFE, RNMT and RBM24 were identified and then included into the random forest (RF) diagnosis model. GSE117999 dataset (control = 10, OA = 10) and clinical samples collected from local hospital (control = 10, OA = 11) were used for external validation. The RF model was a promising tool to diagnose OA in the training dataset (area under curve [AUC] = 1.000, 95% confidence interval [CI] = 1.000-1.000), the GSE117999 cohort (AUC = 0.900, 95% CI = 0.769–1.000), and local samples (AUC = 0.759, 95% CI = 0.568–0.951). Besides, qPCR and Western Blotting experiments showed that RNMT (
P
< 0.05) and RBM24 (
P
< 0.01) were both down-regulated in CHON-001 cells with IL-1β treatment. In all, an RF model to diagnose OA based on RNMT and RBM24 in cartilage tissue was constructed, providing a promising clinical tool and possible cut-in points in molecular mechanism clarification.
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