Comparisons of gene expression between peripheral blood mononuclear cells and bone tissue in osteoporosis

Rc3h1 negatively regulates osteoclastogenesis by limiting energy metabolism

Rationale: Osteoclasts are giant bone-resorbing cells that need vigorous mitochondrial respiration to support their activation. Rc3h1, an RNA-binding protein, precisely governs the homeostasis of mRNA. However, the precise role of Rc3h1 in regulating iron metabolism and mitochondrial respiration in osteoclasts is not yet understood. Methods: We generated Rc3h1-deficient mice in osteoclast precursors and mature osteoclasts. The bone mass and osteoclast activity in bone tissues were evaluated. Moreover, we assessed the differentiation, bone resorption, iron content, and mitochondrial function of osteoclasts in vitro. In the end, the target gene of Rc3h1 and its role in mediating the effect of Rc3h1 on mitochondrial respiration in osteoclasts were further investigated. Results: Mice lacking Rc3h1 exhibit low bone mass. In addition, Rc3h1 deletion in osteoclasts significantly promotes osteoclast activation. Mechanistically, Rc3h1 post-transcriptionally represses the expression of transferrin receptor 1 (Tfr1), restricting iron absorption and mitochondrial respiration in osteoclasts. Inhibition of Tfr1 in Rc3h1-deficient osteoclasts diminishes excessive osteoclast formation and mitochondrial respiration. Conclusion: These findings suggest that Rc3h1 has a negative effect on osteoclast activation via limiting iron resorption and mitochondrial respiration. Finally, targeting the Rc3h1/Tfr1 axis might represent a potential therapeutic approach for bone-loss diseases.

Identification and functional analysis of genes mediating osteoclast-driven progression of osteoporosis

OBJECTIVE

The pathological mechanism of osteoporosis (OP) involves increased bone resorption mediated by osteoclasts and decreased bone formation mediated by osteoblasts, leading to an imbalance in bone homeostasis. Identifying key molecules in osteoclast-mediated OP progression is crucial for the prevention and treatment of OP.

METHODS

Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on the OP patient datasets from the GEO database. The results were intersected with the differential expression results from the osteoclast differentiation dataset to identify key genes. These key genes were then subjected to disease relevance analysis, and consensus clustering was performed on OP patient samples based on their expression profiles. The subgroups were analyzed for differences, followed by GO, KEGG, GSEA, and GSVA analyses, and immune infiltration. Finally, osteoclast differentiation model was constructed. After validating the success of the model using TRAP and F-actin staining, the differential expression of key genes was validated in vitro via Western blot.

RESULTS

CTRL, ARHGEF5, PPAP2C, VSIG2, and PBLD were identified as key genes. These genes exhibited strong disease relevance (AUC > 0.9). Functional enrichment results also indicated their close association with OP and osteoclast differentiation. In vitro differential expression validation showed that during osteoclast differentiation, CTRL was downregulated, while ARHGEF5, PPAP2C, VSIG2, and PBLD were upregulated, with all differences being statistically significant (P < 0.05).

DISCUSSION

Currently, there are no studies on the effects of these five genes on osteoclast differentiation. Therefore, it is meaningful to design in vivo and in vitro perturbation experiments to observe the impact of each gene on osteoclast differentiation and OP progression.

CONCLUSION

CTRL, ARHGEF5, PPAP2C, VSIG2, and PBLD show high potential as molecular targets for basic and clinical research in osteoclast-mediated OP.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning

BACKGROUND

Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia.

METHODS

Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated.

RESULTS

The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93).

CONCLUSION

We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.