変形性関節症におけるジスルフィドプトーシス:SLC2A3ダウンレギュレーションの役割と潜在的な治療的意義
Ruoyang Feng1, Xiadiye Tuerhong2, Yongsong Cai3
1Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, China; Key Laboratory of Pathogenesis and Precision Treatment of Arthritis, Xi'an, ShaanXi Province, China.
Objective:
Disulfidptosis is a newly recognized form of regulated cell death characterized by actin cytoskeleton collapse under disulfide stress. Recent studies suggest it may significantly contribute to osteoarthritis (OA), though its exact role in OA development remains unclear. This study aims to analyze the expression of disulfidptosis-related genes in OA, identifying potential biomarkers and candidate therapeutic leads.
Methods:
Transcriptomic data from six datasets related to OA and single-cell RNA sequencing (scRNA-seq) data were sourced from the GEO database. To identify differentially expressed genes (DEGs) associated with disulfidptosis, we employed robust rank aggregation. We then utilized functional enrichment analysis, constructed a protein-protein interaction network, and applied machine learning models to prioritize key genes of interest. A single-cell analysis was conducted to evaluate gene expression within specific subsets of chondrocytes. Additionally, we predicted drug-target binding affinity using the GraphDTA model and performed molecular docking studies to assess drug-to-target binding affinity. Finally, immunohistochemistry, RT-qPCR, and Western blotting were performed for experimental validation.
Results:
We identified several DEGs related to disulfidptosis, including SLC2A3, PDLIM1, and SLC3A2, which were found to be downregulated in OA tissues, particularly in pre-fibrocartilage chondrocyte subtypes. Our functional analysis indicated that these genes were enriched in pathways associated with cytoskeleton organization and the oxidative stress response. Furthermore, machine learning models demonstrated that these genes have significant diagnostic potential across various datasets. Using GraphDTA for drug prediction and molecular docking, we discovered that talaroflavone exhibits a strong binding affinity to SLC2A3, with highest predicted affinity (13.98) and a binding energy of -8.8 kcal/mol. Experimental validation confirmed the downregulation of SLC2A3 in OA-affected cartilage. Functional assays indicated that modulating SLC2A3 could alleviate cartilage matrix degradation and oxidative stress, suggesting a potential functional association between SLC2A3 and OA progression.
Conclusion:
This study providing functional evidence for a potential association between disulfidptosis-related genes and OA, while highlighting SLC2A3 as a potential functional regulator for further investigation. Additionally, was identified talaroflavone as a computationally predicted lead compound that warrants future experimental validation. These findings not only enhance our understanding of the molecular pathways underlying OA but also lay the groundwork for potential development of targeted therapeutic strategies.
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