The Role of Immunocyte Infiltration Regulatory Network Based on hdWGCNA and Single-Cell Bioinformatics Analysis in Intervertebral Disc Degeneration.

Immune infiltration plays a crucial role in intervertebral disc degeneration (IDD). In this study, we explored the immune microenvironment of IDD through single-cell bioinformatics analysis. Three single-cell datasets were integrated into this study. Nucleus pulposus cells (NPCs) were divided into subgroups based on characteristic genes, and the role of each subgroup in the IDD process was analyzed through pseudo-time trajectory analysis. The hub genes were obtained using hdWGCNA, further identified by bulk datasets and pseudo-time sequence. The expression of the hub genes defined the NPCs related to immune infiltration, and the interaction between these NPCs and immunocytes was explored. The NPCs were divided into four subgroups: reserve NPCs, HCL-NPCs, response NPCs, and support NPCs, which, respectively, dominate the four processes of IDD: non, mild, moderate, and severe degeneration. SPP1 and ICAM1 were identified as the nucleus pulposus immune infiltration hub genes. Macrophages and myelocytes played pro-inflammatory roles in the SPP1-ICAM both-up NPC group through the SPP1-CD44 pathway and ICAM1-ITGB2 ligand-receptor pathway, respectively. At the same time, both-up NPCs sought self-help inflammation remission from neutrophils through the ANXA1-FPR1 pathway. The systematic analysis of the differentiation and immune infiltration landscapes helps to understand IDD's overall development process. Our data suggest that SPP1 and ICAM1 may be new targets for the treatment of inflammatory infiltration in IDD.

Hyperforin improves matrix stiffness induced nucleus pulposus inflammatory degeneration by activating mitochondrial fission.

OBJECTIVE: The continuously increasing extracellular matrix stiffness during intervertebral disc degeneration promotes disease progression. In an attempt to obtain novel treatment methods, this study aims to investigate the changes in nucleus pulposus cells under the stimulation of a stiff microenvironment. DESIGN: RNA sequencing and metabolomics experiments were combined to evaluate the primary nucleus pulposus and screen key targets under mechanical biological stimulation. Additionally, small molecules work in vitro were used to confirm the target regulatory effect and investigate the mechanism. In vivo, treatment effects were validated using a rat caudal vertebrae compression model. RESULTS: Our research results revealed that by activating TRPC6, hyperforin, a herbaceous extract can rescue the inflammatory phenotype caused by the stiff microenvironment, hence reducing intervertebral disc degeneration (IDD). Mechanically, it activates mitochondrial fission to inhibit PFKFB3. CONCLUSION: In summary, this study reveals the important bridging role of TRPC6 between mechanical stiffness, metabolism, and inflammation in the context of nucleus pulposus degeneration. TRPC6 activation with hyperforin may become a promising treatment for IDD.

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis.

Background: Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model. Methods: Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration. Results: The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed. Conclusion: CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.