Identification of abnormally methylated-differentially expressed genes and pathways in osteoarthritis: a comprehensive bioinformatic study

Disease-Associated Signatures Persist in Extracellular Vesicles from Reprogrammed Cells of Osteoarthritis Patients

Osteoarthritis (OA) is a prevalent joint disorder that lacks effective therapies to halt cartilage degeneration. Mesenchymal stromal cell (MSC)-derived small extracellular vesicles (sEVs) are being investigated as promising chondroprotective agents. Compared to primary MSCs, induced pluripotent stem cell (iPSC)-derived MSCs (MLCs) offer superior scalability and enhanced paracrine activity. The aim of this study was to explore the feasibility of using autologous MLC-derived sEVs as a potential therapeutic strategy for OA through the analysis of their protein cargo. iPSCs from an OA patient and a healthy donor were differentiated into MLCs. sEVs were isolated from these MLCs and characterized, with a particular focus on their protein cargo. Both iPSC lines were successfully differentiated into MLCs, which secreted sEVs with comparable size distributions and yields. The analysis of differentially expressed proteins revealed a high abundance of proteins associated with OA pathology and cartilage degradation in sEVs from OA MLCs compared to those from healthy MLCs. The persistence of OA-associated protein signatures in autologous MLC-derived sEVs may limit their therapeutic efficacy. These findings underscore the importance of carefully evaluating disease-specific protein profiles in sEVs for regenerative applications.

Integrative analyses of mendelian randomization and bioinformatics reveal casual relationship and genetic links between COVID-19 and knee osteoarthritis

BACKGROUND

Clinical and epidemiological analyses have found an association between coronavirus disease 2019 (COVID-19) and knee osteoarthritis (KOA). Infection with COVID-19 may increase the risk of developing KOA.

OBJECTIVES

This study aimed to investigate the potential causal relationship between COVID-19 and KOA using Mendelian randomization (MR) and to explore the underlying mechanisms through a systematic bioinformatics approach.

METHODS

Our investigation focused on exploring the potential causal relationship between COVID-19, acute upper respiratory tract infection (URTI) and KOA utilizing a bidirectional MR approach. Additionally, we conducted differential gene expression analysis using public datasets related to these three conditions. Subsequent analyses, including transcriptional regulation analysis, immune cell infiltration analysis, single-cell analysis, and druggability evaluation, were performed to explore potential mechanisms and prioritize therapeutic targets.

RESULTS

The results indicate that COVID-19 has a one-way impact on KOA, while URTI does not play a causal role in this association. Ribosomal dysfunction may serve as an intermediate factor connecting COVID-19 with KOA. Specifically, COVID-19 has the potential to influence the metabolic processes of the extracellular matrix, potentially impacting the joint homeostasis. A specific group of genes (COL10A1, BGN, COL3A1, COMP, ACAN, THBS2, COL5A1, COL16A1, COL5A2) has been identified as a shared transcriptomic signature in response to KOA with COVID-19. Imatinib, Adiponectin, Myricetin, Tranexamic acid, and Chenodeoxycholic acid are potential drugs for the treatment of KOA patients with COVID-19.

CONCLUSIONS

This study uniquely combines Mendelian randomization and bioinformatics tools to explore the possibility of a causal relationship and genetic association between COVID-19 and KOA. These findings are expected to provide novel perspectives on the underlying biological mechanisms that link COVID-19 and KOA.

Single-cell profiling uncovers synovial fibroblast subpopulations associated with chondrocyte injury in osteoarthritis

Background

Chondrocytes and synovial cells participate in the pathogenesis of osteoarthritis (OA). Nonetheless, the interactions and correlations between OA synovial cells and chondrocytes remain unclear. This study aims to elucidate the interactions and correlations between OA synovial cells and chondrocytes, so as to deepen understanding of OA pathogenesis.

Methods

Single-cell sequencing analysis was employed to analyze clusters of synovial and chondrocyte cells within the OA dataset. Through cell interaction analysis, the potential interactions between these two cell types were further explored. Differential gene expression analysis was used to examine the differences among synovial-related cell clusters.

Results

The study identified specific characteristics of synovial fibroblasts through single-cell sequencing analysis. Subsequent cell interaction analysis revealed interactions and correlations between synovial fibroblast clusters and cell clusters in both damaged and non-damaged cartilages. CILP+ fibroblasts showed significant interactions with non-damaged chondrocytes, while POSTN+ fibroblasts exhibited significant interactions with damaged chondrocytes. Furthermore, differential gene expression analysis revealed that genes such as PRELP, CLU, COMP, TNFRSF12A, INHBA, CILP, and SERPINE2, were significantly upregulated in CILP+ fibroblasts. These genes are involved in promoting cell proliferation, inhibiting inflammatory pathways, and stabilizing cell structure, thereby exerting reparative and protective effects on chondrocytes. In contrast, COL6A3, COL6A1, COL1A2, COL1A1, COL3A1, TGF-β1, MMP2, AEBP1, SPARC, FNDC1, and POSTN were upregulated in POSTN+ fibroblasts. These genes may contribute to chondrocyte damage and further degeneration by promoting chondrocyte catabolism, driving inflammation, activating inflammatory pathways, and facilitating chondrocyte apoptosis and destruction.

Conclusion

Our study elucidated the interactions and correlations between OA synovial cells and chondrocytes. CILP+ synovial fibroblasts may exert reparative and protective effects on chondrocytes of patients with OA by promoting cell proliferation, inhibiting inflammation, and stabilizing cellular structures, thereby potentially mitigating the progression of cartilage lesions in affected patients. In contrast, POSTN+ synovial fibroblasts may exacerbate chondrocyte deterioration in patients with OA by enhancing degradation, inflammation, and apoptosis, thereby exacerbating cartilage lesions. Investigating the underlying molecular mechanisms between OA synovial cells and chondrocytes refines the understanding of OA pathogenesis and provides valuable insights for the clinical diagnosis and treatment of OA.

A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation

BACKGROUND

Lactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA).

METHODS

Lactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes.

RESULTS

Through the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05).

CONCLUSIONS

A lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.

ITGB1 alleviates osteoarthritis by inhibiting cartilage inflammation and apoptosis via activating cAMP pathway

OBJECTIVE

We aimed to screen novel biomarkers for osteoarthritis (OA) using bioinformatic methods and explore its regulatory mechanism in OA development.

METHODS

Differentially expressed genes were screened out from GSE98918 and GSE82107 datasets. Protein-protein interaction network and enrichment analysis were employed to search for hub gene and regulatory pathway. Hematoxylin-eosin, Safranin O-Fast green staining, and immunohistochemistry were performed to assess pathological damage. TNF-α, IL-1β, and IL-6 concentrations were determined by enzyme-linked immunosorbent assay. Real-time quantitative PCR was applied to verify expression of hub genes in OA model. The expression of key protein and pathway proteins was determined by western blot. Furthermore, Cell Counting Kit-8 and flow cytometry were conducted to explore the role of hub gene in chondrocytes.

RESULTS

We identified 6 hub genes of OA, including ITGB1, COL5A1, COL1A1, THBS2, LAMA1, and COL12A1, with high prediction value. ITGB1 was screened as a pivotal regulator of OA and cAMP pathway was selected as the key regulatory pathway. ITGB1 was down-regulated in OA model. ITGB1 overexpression attenuated pathological damage and apoptosis in OA rats with the reduced levels of TNF-α, IL-1β and IL-6. ITGB1 overexpression activated cAMP pathway in vivo and vitro models. In vitro model, ITGB1 overexpression promoted cell viability, while inhibited apoptosis. ITGB1 overexpression also caused a decrease of TNF-α, IL-1β, and IL-6 concentrations. cAMP pathway inhibitor reversed the positive effect of ITGB1 on OA cell model.

CONCLUSION

ITGB1 is a novel biomarker for OA, which inhibits OA development by activating the cAMP pathway.

Identification and analysis of RNA-5-methylcytosine-related key genes in osteoarthritis

BACKGROUND

5-methylcytosine (m5C) modification is widely associated with many biological and pathological processes. However, knowledge of m5C modification in osteoarthritis (OA) remains lacking. Thus, our study aimed to identify common m5C features in OA.

RESULTS

In the present study, we identified 1395 differentially methylated genes (DMGs) and 1673 differentially expressed genes (DEGs) using methylated RNA immunoprecipitation next-generation sequencing (MeRIP-seq) and RNA-sequencing. A co-expression analysis of DMGs and DEGs showed that the expression of 133 genes was significantly affected by m5C methylation. A protein-protein interaction network of the 133 genes was constructed using the STRING database, and the cytoHubba plug-in of Cytoscape was used to hub genes were screen out 11 hub genes, including MMP14, VTN, COL15A1, COL6A2, SPARC, COL5A1, COL6A3, COL6A1, COL8A2, ADAMTS2 and COL7A1. The Pathway enrichment analysis by the ClueGO and CluePedia plugins in Cytoscape showed that the hub genes were significantly enriched in collagen degradation and extracellular matrix degradation.

CONCLUSIONS

Our study indicated that m5C modification might play an important role in OA pathogenesis, and the present study provides worthwhile insight into identifying m5C-related therapeutic targets in OA.

A comprehensive analysis of biomarkers associated with synovitis and chondrocyte apoptosis in osteoarthritis

Introduction

Osteoarthritis (OA) is a chronic disease with high morbidity and disability rates whose molecular mechanism remains unclear. This study sought to identify OA markers associated with synovitis and cartilage apoptosis by bioinformatics analysis.

Methods

A total of five gene-expression profiles were selected from the Gene Expression Omnibus database. We combined the GEO with the GeneCards database and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses; then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to identify the characteristic genes, and a predictive risk score was established. We used the uniform manifold approximation and projection (UMAP) method to identify subtypes of OA patients, while the CytoHubba algorithm and GOSemSim R package were used to screen out hub genes. Next, an immunological assessment was performed using single-sample gene set enrichment analysis and CIBERSORTx.

Results

A total of 56OA-related differential genes were selected, and 10 characteristic genes were identified by the LASSO algorithm. OA samples were classified into cluster 1 and cluster 2 subtypes byUMAP, and the clustering results showed that the characteristic genes were significantly different between these groups. MYOC, CYP4B1, P2RY14, ADIPOQ, PLIN1, MFAP5, and LYVE1 were highly expressed in cluster 2, and ANKHLRC15, CEMIP, GPR88, CSN1S1, TAC1, and SPP1 were highly expressed in cluster 1. Protein-protein interaction network analysis showed that MMP9, COL1A, and IGF1 were high nodes, and the differential genes affected the IL-17 pathway and tumor necrosis factor pathway. The GOSemSim R package showed that ADIPOQ, COL1A, and SPP1 are closely related to the function of 31 hub genes. In addition, it was determined that mmp9 and Fos interact with multiple transcription factors, and the ssGSEA and CIBERSORTx algorithms revealed significant differences in immune infiltration between the two OA subtypes. Finally, a qPCR experiment was performed to explore the important genes in rat cartilage and synovium tissues; the qPCR results showed that COL1A and IL-17A were both highly expressed in synovitis tissues and cartilage tissues of OA rats, which is consistent with the predicted results.

Discussion

In the future, common therapeutic targets might be found forsimultaneous remissions of both phenotypes of OA.

Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and osteoarthritis

A growing of evidence has showed that patients with osteoarthritis (OA) had a higher coronavirus 2019 (COVID-19) infection rate and a poorer prognosis after infected it. Additionally, scientists have also discovered that COVID-19 infection might cause pathological changes in the musculoskeletal system. However, its mechanism is still not fully elucidated. This study aims to further explore the sharing pathogenesis of patients with both OA and COVID-19 infection and find candidate drugs. Gene expression profiles of OA (GSE51588) and COVID-19 (GSE147507) were obtained from the Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) for both OA and COVID-19 were identified and several hub genes were extracted from them. Then gene and pathway enrichment analysis of the DEGs were performed; protein-protein interaction (PPI) network, transcription factor (TF)-gene regulatory network, TF-miRNA regulatory network and gene-disease association network were constructed based on the DEGs and hub genes. Finally, we predicted several candidate molecular drugs related to hub genes using DSigDB database. The receiver operating characteristic curve (ROC) was applied to evaluate the accuracy of hub genes in the diagnosis of both OA and COVID-19. In total, 83 overlapping DEGs were identified and selected for subsequent analyses. CXCR4, EGR2, ENO1, FASN, GATA6, HIST1H3H, HIST1H4H, HIST1H4I, HIST1H4K, MTHFD2, PDK1, TUBA4A, TUBB1 and TUBB3 were screened out as hub genes, and some showed preferable values as diagnostic markers for both OA and COVID-19. Several candidate molecular drugs, which are related to the hug genes, were identified. These sharing pathways and hub genes may provide new ideas for further mechanistic studies and guide more individual-based effective treatments for OA patients with COVID-19 infection.

Genetic biomarkers in osteoarthritis: a quick overview

Osteoarthritis (OA) is a chronic musculoskeletal disease with a polygenic and heterogeneous nature. In addition, when clinical manifestations appear, the evolution of the disease is usually already irreversible. Therefore, the efforts on OA research are focused mainly on the discovery of therapeutic targets and reliable biomarkers that permit the early identification of different OA-related parameters such as diagnosis, prognosis, or phenotype identification. To date, potential candidate protein biomarkers have been associated with different aspects of the disease; however, there is currently no gold standard. In this sense, genomic data could act as complementary biomarkers of diagnosis and prognosis or even help to identify therapeutic targets of the disease. In this review, we will describe the most recent advances in genetic biomarkers in OA over the past three years.

Males and Females Have Distinct Molecular Events in the Articular Cartilage during Knee Osteoarthritis

Osteoarthritis (OA) is a major public health challenge that imposes a remarkable burden on the affected individuals and the healthcare system. Based on the clinical observation, males and females have different prevalence rates and severity levels of OA. Thus, sex-based differences may play essential roles in OA's prognosis and treatment outcomes. To date, the comprehensive understanding of the relationship between sex and OA is still largely lacking. In the current study, we analyzed a published transcriptome dataset of knee articular cartilage (GSE114007) from 18 healthy (five females, 13 males) and 20 OA (11 females, nine males) donors to provide a slight insight into this important but complex issue. First, comparing female healthy cartilage samples with those of males revealed 36 differential expression genes (DEGs), indicating the fundamental sex-related differences at the molecular level. Meanwhile, 923 DEGs were distinguished between OA and healthy female cartilage, which can be enriched to 15 Reactome pathways. On the other hand, when comparing OA and healthy male cartilage, there are only 419 DEGs were identified, and only six pathways were enriched against the Reactome database. The different signaling response to OA in the male and female cartilage was further enforced by recognizing 50 genes with significantly different OA-responsive expression fold changes in males and females. Particularly, 14 Reactome pathways, such as "Extracellular matrix organization", "Collagen biosynthesis and modifying enzymes", "Dissolution of fibrin clot", and "Platelet Aggregation (Plug formation)", can be noted from these 50 sex-dependent OA-responsive genes. Overall, the current study explores the Sex as a Biological Variable (SABV) at the transcriptomic level in the knee articular cartilage in both healthy status and OA event, which could help predict the differential OA prognosis and treatment outcome of males and female patients.

Proteomic Analysis of Synovial Fibroblasts and Articular Chondrocytes Co-Cultures Reveals Valuable VIP-Modulated Inflammatory and Degradative Proteins in Osteoarthritis

Osteoarthritis (OA) is the most common musculoskeletal disorder causing a great disability and a reduction in the quality of life. In OA, articular chondrocytes (AC) and synovial fibroblasts (SF) release innate-derived immune mediators that initiate and perpetuate inflammation, inducing cartilage extracellular matrix (ECM) degradation. Given the lack of therapies for the treatment of OA, in this study, we explore biomarkers that enable the development of new therapeutical approaches. We analyze the set of secreted proteins in AC and SF co-cultures by stable isotope labeling with amino acids (SILAC). We describe, for the first time, 115 proteins detected in SF-AC co-cultures stimulated by fibronectin fragments (Fn-fs). We also study the role of the vasoactive intestinal peptide (VIP) in this secretome, providing new proteins involved in the main events of OA, confirmed by ELISA and multiplex analyses. VIP decreases proteins involved in the inflammatory process (CHI3L1, PTX3), complement activation (C1r, C3), and cartilage ECM degradation (DCN, CTSB and MMP2), key events in the initiation and progression of OA. Our results support the anti-inflammatory and anti-catabolic properties of VIP in rheumatic diseases and provide potential new targets for OA treatment.