Identification of Key Genes and Potential Mechanisms Based on the Autophagy Regulatory Network in Osteoclasts Using a Murine Osteoarthritis Model - Zhang H; Sun H; Zhang W; Xu Y; Geng D (Dove Medical Press, 2022)
Haifeng Zhang,* Houyi Sun,* Wei Zhang,* Yaozeng Xu, Dechun Geng Department of Orthopedics, the First Affiliated Hospital of Soochow University, Suzhou City, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dechun Geng; Yaozeng Xu, Department of Orthopedics, the First Affiliated Hospital of Soochow University, Suzhou City, People’s Republic of China, Tel +86 512-67780999 ; +86 512-67780249, Email szgengdc@163.com; xuyaozeng@163.comBackground: Osteoarthritis (OA) is a degenerative joint disease that acts as a major cause of early disability in the old population. However, the molecular mechanisms of autophagy in osteoclasts involved in OA remain unclear.Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) repository. The NCBI GEO2R and ScanGEO analysis tool were used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was predicted by the STRING website and visualized with Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed to enrich GO terms and signaling pathways using Metascape database. To predict LC3-interacting region (LIR) motif among these DEGs, the iLIR database was selected to assess specific short linear sequences. To obtain potential upstream miRNA targets of these DEGs, the mRNA-miRNA interaction networks were predicted by miRWalk database. The knee OA model was performed in mice, and autophagy related mRNAs of osteoclasts were identified. Experimental specimens were further verified with histopathological staining.Results: Becn1, Atg3, Atg12, Pik3c3, and Gabarapl2 were obtained as coexpressed differential genes. PPI network was constructed and deduced the other 60 related genes. GO and KEGG enrichment networks indicated that autophagy-animal, selective autophagy, and mitophagy mainly participated in autophagy regulation in osteoclasts. The possible LIR sequences were collected to predict motifs. The mRNA–miRNA interaction networks suggested that many miRNAs could regulate autophagy-related genes individually and collectively. The RT–PCR results suggested that these five genes were upregulated in the OA group. Histopathological staining revealed that osteoclasts were increased in subchondral bone, and higher expression of these DEGs in the OA group was compared to the sham group.Conclusion: Our results reveal that the role of autophagy in osteoclasts could be a regulatory mechanism in OA and that these autophagy-related genes might be targets for the intervention of OA disease.Keywords: osteoclasts, autophagy, osteoarthritis, bioinformatics, LIR, miRNA