The interplay between the human microbiome and the musculoskeletal system represents a burgeoning field of research with profound implications for understanding and treating musculoskeletal disorders. This review articulates the pivotal role of the microbiome in modulating bone health, highlighting the gut-bone axis as a critical nexus for potential therapeutic intervention. Through a meticulous analysis of recent clinical research, we underscore the microbiome’s influence on osteoporosis, sarcopenia, osteoarthritis, and rheumatoid arthritis, delineating both the direct and indirect mechanisms by which microbiota could impact musculoskeletal integrity and function. Our investigation reveals novel insights into the microbiota’s contribution to bone density regulation, hormone production, immune modulation, and nutrient absorption, laying the groundwork for innovative microbiome-based strategies in musculoskeletal disease management. Significantly, we identify the challenges hindering the translation of research into clinical practice, including the limitations of current microbial sequencing techniques and the need for standardized methodologies in microbiome studies. Furthermore, we highlight promising directions for future research, particularly in the realm of personalized medicine, where the microbiome’s variability offers unique opportunities for tailored treatment approaches. This review sets a new agenda for leveraging gut microbiota in the diagnosis, prevention, and treatment of musculoskeletal conditions, marking a pivotal step toward integrating microbiome science into clinical musculoskeletal care.

Purpose: Osteoarthritis (OA) is a disease of senescence and inflammation. Hedgehog’s role in OA mechanisms is unclear. This study combines Bulk RNA-seq and scRNA-seq to identify Hedgehog-associated genes in OA, investigating their impact on the pathogenesis of OA.Materials and methods: Download and merge eight bulk-RNA seq datasets from GEO, also obtain a scRNA-seq dataset for validation and analysis. Analyze Hedgehog pathway activity in OA using bulk-RNA seq datasets. Use ten machine learning algorithms to identify important Hedgehog-associated genes, validate predictive models. Perform GSEA to investigate functional implications of identified Hedgehog-associated genes. Assess immune infiltration in OA using Cibersort and MCP-counter algorithms. Utilize ConsensusClusterPlus package to identify Hedgehog-related subgroups. Conduct WGCNA to identify key modules enriched based on Hedgehog-related subgroups. Characterization of genes by methylation and GWAS analysis. Evaluate Hedgehog pathway activity, expression of hub genes, pseudotime, and cell communication, in OA chondrocytes using scRNA-seq dataset. Validate Hedgehog-associated gene expression levels through Real-time PCR analysis.Results: The activity of the Hedgehog pathway is significantly enhanced in OA. Additionally, nine important Hedgehog-associated genes have been identified, and the predictive models built using these genes demonstrate strong predictive capabilities. GSEA analysis indicates a significant positive correlation between all seven important Hedgehog-associated genes and lysosomes. Consensus clustering reveals the presence of two hedgehog-related subgroups. In Cluster 1, Hedgehog pathway activity is significantly upregulated and associated with inflammatory pathways. WGCNA identifies that genes in the blue module are most significantly correlated with Cluster 1 and Cluster 2, as well as being involved in extracellular matrix and collagen-related pathways. Single-cell analysis confirms the significant upregulation of the Hedgehog pathway in OA, along with expression changes observed in 5 genes during putative temporal progression. Cell communication analysis suggests an association between low-scoring chondrocytes and macrophages.Conclusion: The Hedgehog pathway is significantly activated in OA and is associated with the extracellular matrix and collagen proteins. It plays a role in regulating immune cells and immune responses.

Purpose: The aim of this study was to investigate the activation of immune-metabolic pathways in osteoarthritis (OA) and their role in disease progression. We employed differential expression analysis and Gene Set Enrichment AnalysisMaterials and methods: Gene set enrichment analysis (GSEA) to identify activated immune-metabolism pathways in OA. Subsequently, Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with OA and immune-metabolism scores, followed by enrichment analysis to reveal the functional characteristics of these modules. To identify immune-metabolism related differentially expressed genes (DEGs), we utilized seven machine learning methods, including lasso regression, random forest, bagging, gradient boosting machines (GBM), Xgboost-xgbLinear, Xgboost-xgbtree, and decision trees, to construct predictive models and validate their reliability. Based on the expression profiles of hub immune-metabolism related DEGs, we stratified OA patients into two immune-metabolism related subgroups and deeply investigated the differences in immune profiles, drug responses, functions, and pathways between these subgroups. Additionally, we analyzed the expression and pseudotime trajectories of hub immune-metabolism related DEGs at the single-cell level. Through genome-wide association studies (GWAS), we explored the mechanisms of action of hub immune-metabolism related DEGs. Finally, real-time polymerase chain reaction (RT-PCR) was utilized to verify the expression of hub immune-metabolism related DEGs.Results: Immune-metabolism related pathways were significantly activated during the development of OA. Thirteen central immune metabolism-related genes (CX3CR1, ADIPOQ, IL17RA, APOD, EGFR, SPP1, PLA2G2A, CXCL14, RARB, ADM, CX3CL1, TNFSF10, and MPO) were identified. Predictive modeling by constructing these genes has good predictive power for identifying OA. These genes are mainly associated with endothelial cells. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR results suggested that they were all significantly expressed in OA.Conclusion: Our findings indicate that immune metabolism plays a key role in the development of OA and provide new perspectives for future therapeutic strategies