A novel serological biomarker are associated with disease severity in patients with osteoarthritis

Therapeutic analysis of laser moxibustion for different KL graded knee osteoarthritis

BACKGROUND

Our previous studies showed that laser moxibustion may be effective in alleviating the symptoms of knee osteoarthritis. However, the therapeutic effect in patients with different Kellgren-Lawrence (KL) grades is still unclear. We aimed to compare the efficacy of laser moxibustion in the treatment of knee osteoarthritis with different KL grades.

METHODS

A total of 392 symptomatic KOA patients with different KL grades were randomly assigned to the laser treatment or sham laser control group (1:1). The patients received laser moxibustion treatment or sham treatment 3 times a week for 4 weeks. Outcomes were measured using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores and Visual Analog Scale (VAS) scores, and the primary outcome measurement was the change in WOMAC pain scores from baseline to week 4.

RESULTS

Among 392 randomized participants, 364 (92.86%) completed the trial. Participants with KL grades 2, 3, and 4 had significantly higher pain, functional, and total WOMAC scores than those with KL grade 1. Spearman correlation test results showed a positive correlation between KL grade and WOMAC pain, function, stiffness scores, and WOMAC total scores. That is, the higher the KL grade, the higher the WOMAC pain, function, stiffness, and WOMAC total scores. After 4 weeks of treatment, patients with KL grades 2 and 3 had significantly higher improvement scores in pain, function, and total scores than those with KL grade 1, whereas those with KL grade 2 had significantly higher improvement scores in stiffness than those with KL grade 1. Patients with KL grade 4 showed no significant effects after laser moxibustion treatment.

CONCLUSION

Laser moxibustion is effective for pain reduction and functional improvement in the treatment of KOA with KL grades 2 and 3.

Chondroprotective effects of Apolipoprotein D in knee osteoarthritis mice through the PI3K/AKT/mTOR signaling pathway

BACKGROUND

Because the pathophysiology of osteoarthritis (OA) has not been fully elucidated, targeted treatments are lacking. In this study, we assessed the role and underlying mechanism apolipoprotein D (APOD) on the development of OA.

METHODS

To establish an in vitro OA model, we extracted primary chondrocytes from the cartilage of C57BL/6 mice and stimulated the chondrocytes with IL-1β. After APOD intervention or incubation with an overexpressing plasmid, we detected inflammatory-related markers using RT-qPCR, Western blotting, and ELISA. To detect apoptosis and autophagy-related markers, we used flow cytometry, immunofluorescence, and transmission electron microscopy (TEM). Finally, we measured the level of oxidative stress. We also used RNA-seq to identify the APOD-regulated downstream signaling pathways. We used an in vivo mice OA model of the anterior cruciate ligament transection (ACLT) and administered intra-articular adenovirus overexpressing APOD. To examine cartilage damage severity, we used immunohistochemical analysis (IHC), micro-CT, scanning electron microscopy (SEM), and Safranin O-fast green staining.

RESULTS

Our results showed that APOD inhibited chondrocyte inflammation, degeneration, and apoptosis induced by IL-1β. Additionally, APOD reversed autophagy inhibition and oxidative stress and also blocked activation of the PI3K/AKT/mTOR signaling pathway induced by IL-1β. Finally, overexpression of the APOD gene through adenovirus was sufficient to mitigate OA progression.

CONCLUSIONS

Our findings revealed that APOD had a chondroprotective role in OA progression by the PI3K/AKT/mTOR signaling pathway.

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis

OBJECTIVE

To elucidate potential molecular mechanisms differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) through a bioinformatics analysis of differentially expressed genes (DEGs) in patient synovial cells, aiming to provide new insights for clinical treatment strategies.

MATERIALS AND METHODS

Gene expression datasets GSE1919, GSE82107, and GSE77298 were downloaded from the Gene Expression Omnibus (GEO) database to serve as the training groups, with GSE55235 being used as the validation dataset. The OA and RA data from the GSE1919 dataset were merged with the standardized data from GSE82107 and GSE77298, followed by batch effect removal to obtain the merged datasets of differential expressed genes (DEGs) for OA and RA. Intersection analysis was conducted on the DEGs between the two conditions to identify commonly upregulated and downregulated DEGs. Enrichment analysis was then performed on these common co-expressed DEGs, and a protein-protein interaction (PPI) network was constructed to identify hub genes. These hub genes were further analyzed using the GENEMANIA online platform and subjected to enrichment analysis. Subsequent validation analysis was conducted using the GSE55235 dataset.

RESULTS

The analysis of differentially expressed genes in the synovial cells from patients with Osteoarthritis (OA) and Rheumatoid Arthritis (RA), compared to a control group (individuals without OA or RA), revealed significant changes in gene expression patterns. Specifically, the genes APOD, FASN, and SCD were observed to have lower expression levels in the synovial cells of both OA and RA patients, indicating downregulation within the pathological context of these diseases. In contrast, the SDC1 gene was found to be upregulated, displaying higher expression levels in the synovial cells of OA and RA patients compared to normal controls.Additionally, a noteworthy observation was the downregulation of the transcription factor PPARG in the synovial cells of patients with OA and RA. The decrease in expression levels of PPARG further validates the alteration in lipid metabolism and inflammatory processes associated with the pathogenesis of OA and RA. These findings underscore the significance of these genes and the transcription factor not only as biomarkers for differential diagnosis between OA and RA but also as potential targets for therapeutic interventions aimed at modulating their expression to counteract disease progression.

CONCLUSION

The outcomes of this investigation reveal the existence of potentially shared molecular mechanisms within Osteoarthritis (OA) and Rheumatoid Arthritis (RA). The identification of APOD, FASN, SDC1, TNFSF11 as key target genes, along with their downstream transcription factor PPARG, highlights common potential factors implicated in both diseases. A deeper examination and exploration of these findings could pave the way for new candidate targets and directions in therapeutic research aimed at treating both OA and RA. This study underscores the significance of leveraging bioinformatics approaches to unravel complex disease mechanisms, offering a promising avenue for the development of more effective and targeted treatments.

Risk of metabolic abnormalities in osteoarthritis: a new perspective to understand its pathological mechanisms

Although aging has traditionally been viewed as the most important risk factor for osteoarthritis (OA), an increasing amount of epidemiological evidence has highlighted the association between metabolic abnormalities and OA, particularly in younger individuals. Metabolic abnormalities, such as obesity and type II diabetes, are strongly linked to OA, and they affect both weight-bearing and non-weight-bearing joints, thus suggesting that the pathogenesis of OA is more complicated than the mechanical stress induced by overweight. This review aims to explore the recent advances in research on the relationship between metabolic abnormalities and OA risk, including the impact of abnormal glucose and lipid metabolism, the potential pathogenesis and targeted therapeutic strategies.

Proteomic profiling of human menisci from mild joint degeneration and end-stage osteoarthritis versus healthy controls

Objective

To gain new insight into the molecular changes of the meniscus by comparing the proteome profiles of healthy controls with mild degeneration and end-stage osteoarthritis (OA).

Method

We obtained tissue plugs from lateral and medial menisci of 37 individuals (central part of the posterior horn) classified as healthy (n ​= ​12), mild signs of joint damage (n ​= ​13) and end-stage OA (n ​= ​12). The protein profile was analysed by nano-liquid chromatography-mass spectrometry using data-independent acquisition and quantified by Spectronaut. Linear-mixed effects modelling was applied to extract the between-group comparisons.

Results

A similar protein profile was observed for the mild group as compared to healthy controls while the most different group was end-stage OA mainly for the medial compartment. When a pattern of gradual change in protein levels from healthy to end-stage OA was required, a 42-proteins panel was identified, suggesting a potential role in OA development. The levels of QSOX1 were lower and G6PD higher in the mild group following the proposed protein abundance pattern. Qualitative protein changes suggest lower levels of CYTL1 as a potential biomarker of early joint degradation.

Conclusion

For future targeted proteomic approaches, we propose a candidate panel of 42 proteins based on gradually altered meniscal posterior horn protein abundance patterns associated with joint degradation.

A two-gene random forest model to diagnose osteoarthritis based on RNA-binding protein-related genes in knee cartilage tissue

Osteoarthritis (OA) is one of the most common diseases in the orthopedic clinic, characterized by progressive cartilage degradation. RNA-binding proteins (RBPs) are capable of binding to RNAs at transcription and translation levels, playing an important role in the pathogenesis of OA. This study aims to investigate the diagnosis values of RBP-related genes in OA. The RBPs were collected from previous studies, and the GSE114007 dataset (control = 18, OA = 20) was downloaded from the Gene Expression Omnibus (GEO) as the training cohort. Through various bioinformatical and machine learning methods, including genomic difference detection, protein-protein interaction network analyses, Lasso regression, univariate logistic regression, Boruta algorithm, and SVM-RFE, RNMT and RBM24 were identified and then included into the random forest (RF) diagnosis model. GSE117999 dataset (control = 10, OA = 10) and clinical samples collected from local hospital (control = 10, OA = 11) were used for external validation. The RF model was a promising tool to diagnose OA in the training dataset (area under curve [AUC] = 1.000, 95% confidence interval [CI] = 1.000-1.000), the GSE117999 cohort (AUC = 0.900, 95% CI = 0.769-1.000), and local samples (AUC = 0.759, 95% CI = 0.568-0.951). Besides, qPCR and Western Blotting experiments showed that RNMT (P < 0.05) and RBM24 (P < 0.01) were both down-regulated in CHON-001 cells with IL-1β treatment. In all, an RF model to diagnose OA based on RNMT and RBM24 in cartilage tissue was constructed, providing a promising clinical tool and possible cut-in points in molecular mechanism clarification.