New Insights into the Pro-Inflammatory and Osteoclastogenic Profile of Circulating Monocytes in Osteoarthritis Patients

Causal associations between circulating immune cells and osteoarthritis: A bidirectional mendelian randomization study

PURPOSE

Osteoarthritis (OA) is a common degenerative joint disease, with its etiology remaining poorly understood. Our study aims to explore the causal associations between immune cells and OA, with the goal of generating a new perspective for targeted intervention strategies.

METHODS

A bidirectional two-sample Mendelian randomization (MR) analysis was performed to estimate the causality between multiple circulating immune cells and different sites of OA. The immune cell traits analyzed included the counts of circulating white blood cells (WBC), lymphocytes, monocytes, neutrophils, eosinophils, and basophils, as well as certain subsets of T and B lymphocytes. The OA types included were OA at any site, knee OA, hip OA, spine OA, thumb OA, and hand OA. Inverse-variance weighted (IVW), MR-Egger, weight median and weight mode were used to evaluate causal effects, with IVW being the main analysis method. Sensitivity analyses were conducted to assess heterogeneity and pleiotropy.

RESULTS

Our findings indicated that resting regulatory T cell (Treg) absolute counts (AC) were causally associated with an increased risk for spine OA [odds ratio (OR), 1.051; 95 % confidence interval (CI), 1.018-1.086; P=0.0005, PFDR=0.0350], and spine OA showed a positive causal relationship with the neutrophils count (OR, 1.104; 95 %CI, 1.032-1.181; P=0.0039, PFDR=0.0233). Besides, OA at any site was correlated with a rise in circulating eosinophils count (OR, 1.05; 95 %CI, 1.021-1.079; P=0.0007, PFDR=0.0041), while knee OA was associated with decreased total WBC (OR, 0.945; 95 %CI, 0.912-0.979; P=0.0016, PFDR=0.0048) and monocytes counts (OR, 0.958; 95 %CI, 0.934-0.982; P=0.0007, PFDR = 0.0041). No evidence of heterogeneity or horizontal pleiotropy was detected.

CONCLUSIONS

Our study has demonstrated the causal associations between multiple immune cells and diverse joint OA. These results highlight the intricate interplay between immune cells and OA, suggesting potential targets for therapeutic interventions to manage disease progression and alleviate symptoms.

Innate Immunity and Synovitis: Key Players in Osteoarthritis Progression

Osteoarthritis (OA) is a chronic progressive disease of the joint. Although representing the most frequent cause of disability in the elderly, OA remains partly obscure in its pathogenic mechanisms and is still the orphan of resolutive therapies. The concept of what was once considered a "wear and tear" of articular cartilage is now that of an inflammation-related disease that affects over time the whole joint. The attention is increasingly focused on the synovium. Even from the earliest clinical stages, synovial inflammation (or synovitis) is a crucial factor involved in OA progression and a major player in pain onset. The release of inflammatory molecules in the synovium mediates disease progression and worsening of clinical features. The activation of synovial tissue-resident cells recalls innate immunity cells from the bloodstream, creating a proinflammatory milieu that fuels and maintains a damaging condition of low-grade inflammation in the joint. In such a context, cellular and molecular inflammatory behaviors in the synovium could be the primum movens of the structural and functional alterations of the whole joint. This paper focuses on and discusses the involvement of innate immunity cells in synovitis and their role in the progression of OA.

Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment

BACKGROUND

Osteoarthritis (OA) is a prevalent joint disorder characterized by cartilage degeneration and joint inflammation. Liquid-liquid phase separation (LLPS), a biophysical process involved in cellular organization, has recently gained attention in OA research. However, the relationship between LLPS and OA remains poorly understood.

METHODS

We analyzed gene expression data from the GSE48556 dataset to identify LLPS-related genes associated with OA. Differential expression analysis, enrichment analyses, and machine learning algorithms were employed to explore the functional significance of LLPS-related genes in OA and then construct a diagnostic model for OA. In addition, IL-1β as a pro-inflammatory factor to establish an in vitro OA model, and the protein expression levels of OA biomarkers were detected by western blot.

RESULTS

A total of 145 LLPS-related genes were screened in OA samples. Enrichment analyses revealed these genes were mainly enriched in mRNA metabolic processes, cytoplasmic granules, and insulin resistance. Four characteristic genes for OA were selected by using machine learning algorithms, including ADRB2, H3F3B, GNL3L, and PELO. These genes showed satisfactory diagnostic values. Furthermore, there were association between these biomarkers and immune cells, including T cells CD8 and monocytes. In vitro experiments showed that IL-1β stimulation significantly inhibited the cell viability of chondrocytes and enhanced the levels of pro-inflammatory factors, that could mimic the inflammatory state of OA. The expression levels of GNL3L and H3F3B proteins in IL-1β group were obviously lower than those in control group, while levels of ADRB2 and PELO were higher, which was consistent with the results of bioinformatics analysis.

CONCLUSION

Our study identifies LLPS-related genes as potential diagnostic biomarkers for OA. These findings provide insights into the molecular mechanisms underlying OA pathogenesis and offer opportunities for the development of novel therapeutic strategies.