DNA demethylation of promoter region orchestrates SPI-1-induced ADAMTS-5 expression in articular cartilage of osteoarthritis mice.

Osteoarthritis (OA) is one of the most prevalent joint diseases in aged people and characterized by articular cartilage degeneration, synovial inflammation, and abnormal bone remodeling. Recent advances in OA research have clearly shown that OA development is associated with aberrant DNA methylation status of many OA-related genes. As one of most important cartilage degrading proteases in OA, a disintegrin and metalloproteinase with thrombospondin motifs subtype 5 (ADAMTS-5) is activated to mediate cartilage degradation in human OA and experimental murine OA models. The pathological factors and signaling pathways mediating ADAMTS-5 activation during OA development are not well defined and have been a focus of intense research. ADAMTS-5 promoter is featured by CpG islands. So far there have been no reports concerning the DNA methylation status in ADAMTS-5 promoter during OA development. In this study, we sought to investigate DNA methylation status in ADAMTS-5 promoter, the role of DNA methylation in ADAMTS-5 activation in OA, and the underlying mechanisms. The potential for anti-OA intervention therapy which is based on modulating DNA methylation is also explored. Our results showed that DNA methyltransferases 1 (Dnmt1) downregulation-associated ADAMTS-5 promoter demethylation played an important role in ADAMTS-5 activation in OA, which facilitated SPI-1 binding on ADAMTS-5 promoter to activate ADAMTS-5 expression. More importantly, OA pathological phenotype of mice was alleviated in response to Dnmt1-induced DNA methylation of ADAMTS-5 promoter. Our study will benefit not only for deeper insights into the functional role and regulation mechanisms of ADAMTS-5 in OA, but also for the discovery of disease-modifying OA drugs on the basis of ADAMTS-5 via modulating DNA methylation status.

Development of a Diagnostic Model for Differentiating Tuberculous Spondylitis and Pyogenic Spondylitis With MRI: A Multicenter Retrospective Observational Study.

STUDY DESIGN: Multicenter retrospective observational study. OBJECTIVE: This study aimed to distinguish tuberculous spondylitis (TS) from pyogenic spondylitis (PS) using magnetic resonance imaging (MRI). Further, a novel diagnostic model for differential diagnosis was developed. SUMMARY OF BACKGROUND DATA: TS and PS are the two most common spinal infections. Distinguishing between these types clinically is challenging. Delayed diagnosis can lead to deficits or kyphosis. Currently, there is a lack of radiology-based diagnostic models for TS and PS. METHODS: We obtained radiologic images from MRI imaging of patients with TS and PS and applied the least absolute shrinkage and selection operator regression to select the optimal features for a predictive model. Predictive models were built using multiple logistic regression analysis. Clinical utility was determined using decision curve analysis, and internal validation was performed using bootstrap resampling. RESULTS: A total of 201 patients with TS (n=105) or PS (n=96) were enrolled. We identified significant differences in MRI features between both groups. We found that noncontiguous multivertebral and single-vertebral body involvement were common in TS and PS, respectively. Vertebral bone lesions were more severe in the TS group than in the PS group (Z=-4.553, P <0.001). The patients in the TS group were also more prone to vertebral intraosseous, epidural, and paraspinal abscesses ( P <0.001). A total of 8 predictors were included in the diagnostic model. Analysis of the calibration curve and area under the receiver operating characteristic curve suggested that the model was well-calibrated with high prediction accuracy. CONCLUSIONS: This is the largest study comparing MRI features in TS and PS and the first to develop an MRI-based nomogram, which may help clinicians distinguish between TS and PS.

Comprehensive analysis of m6A RNA methylation modification patterns and the immune microenvironment in osteoarthritis.

Background: Osteoarthritis (OA) is the most common joint degenerative disease, and so far, there is no effective therapy to prevent or delay its development. Considerable attention is now being given to the impact of m6A RNA methylation modification on the disease immune regulation. However, much remains unknown about the function of m6A modification in OA. Methods: A total of 63 OA and 59 healthy samples were applied to comprehensively examine the m6A regulators mediated RNA methylation modification pattern in OA, and evaluate the impacts of distinct patterns on the characteristics of OA immune microenvironment, including immune infiltration cells, immune responses and human leukocyte antigen (HLAs) genes expression. In addition, we screened out the m6A phenotype-related genes and further explored their potential biological functions. At last, we verified the expression of key m6A regulators and their associations with immune cells, in vitro. Results: Most of m6A regulators was differentially expressed in OA samples compared to the normal tissues. Based on six hub-m6A regulators identified as abnormally expressed in OA samples, we developed a classifier to distinguish OA patients from healthy individuals. We noted that immune characteristics of OA were correlated with m6A regulators. For instance, YTHDF2 had a strongest significantly positive correlation with regulatory T cells (Tregs) and IGFBP2 was strongest negatively associated with dendritic cells (DCs), which were confirmed by the immunohistochemistry (IHC) staining. Two distinct m6A modification patterns were determined: pattern B had higher infiltrating immunocytes and more active immune responses than pattern A, and two patterns differed in the expression of HLA genes. We also identified 1,592 m6A phenotype-related genes that could mediate the OA synovitis and cartilage degradation by the PI3K-Akt signaling pathway. Quantitative real-time polymerase chain reaction (qRT-PCR) results indicated that IGFBP2 was significantly overexpressed, while YTHDF2 mRNA expression was decreased in OA samples, which was consistent with our findings. Conclusion: Our research proves the essential impact of m6A RNA methylation modification on the OA immune microenvironment, and helps to explain the regulatory mechanism behind it, which may open up a new direction for more precise immunotherapy of osteoarthritis.

Evaluation of Biomarkers and Immune Microenvironment of Osteoarthritis: Evidence From Omics Data and Machine Learning.

Objectives: This study aimed to identify novel biomarkers for osteoarthritis (OA) and explore potential pathological immune cell infiltration. Methods: We identified differentially expressed genes (DEGs) between OA and normal synovial tissues using the limma package in R, and performed enrichment analyses to understand the functions and enriched pathways of DEGs. Weighted gene co-expression network analysis (WGCNA) and distinct machine-learning algorithms were then used to identify hub modules and candidate biomarkers. We assessed the diagnostic value of the candidate biomarkers using receiver operating characteristic (ROC) analysis. We then used the CIBERSORT algorithm to analyze immune cell infiltration patterns, and the Wilcoxon test to screen out hub immune cells that might affect OA occurrence. Finally, the expression levels of hub biomarkers were confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Results: We identified 102 up-regulated genes and 110 down-regulated genes. The functional enrichment analysis results showed that DEGs are enriched mainly in immune response pathways. Combining the results of the algorithms and ROC analysis, we identified GUCA1A and NELL1 as potential diagnostic biomarkers for OA, and validated their diagnosibility using an external dataset. Construction of a TF-mRNA-miRNA network enabled prediction of potential candidate compounds targeting hub biomarkers. Immune cell infiltration analyses revealed the expression of hub biomarkers to be correlated with CD8 T cells, memory B cells, M0/M2 macrophages, resting mast cells and resting dendritic cells. qRT-PCR results showed both GUCA1A and NELL1 were significantly increased in OA samples (p < 0.01). All validations are consistent with the microarray hybridization, indicating that GUCA1A and NELL1 may be involved in the pathogenesis of OA. Conclusion: The findings suggest that GUCA1A and NELL1, closely related to OA occurrence and progression, represent new OA candidate markers, and that immune cell infiltration plays a significant role in the progression of OA.