Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value.

METHODS

OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs.

RESULTS

We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated.

CONCLUSIONS

Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.

Comparison of inflammatory molecular mechanisms between osteoarthritis and rheumatoid arthritis via gene microarrays

Osteoarthritis (OA) and rheumatoid arthritis (RA) treatment requires exact arthritis type diagnosis. We compared inflammatory molecular mechanisms between OA and RA to introduce reliable molecular biomarkers. The GSE55235 and GSE100786 microarray datasets were acquired from the GEO. Data preprocessing and differential expression analysis were conducted in OA and RA groups and their control groups applying GEO2R. Differentially expressed genes (DEGs) with a |LogFC|>1 and adj. p<0.05 were determined. Gene ontology (GO) and signaling pathway analysis were done utilizing PANTHER and Enrichr. The suitability of gene expression alterations as biomarkers was tested using the receiver operating characteristic (ROC) curve analysis. We found 2129 DEGs between the OA and control groups and 2494 DEGs between the RA and control groups. GO on the DEGs showed enrichment in binding, cellular processes, and cellular anatomical entities in molecular functions, biological processes, and cellular components, respectively. Enrichr found the cell differentiation pathways of Th1 and Th2 only in RA. The ROC curve analysis indicated HLA-DQA1 downregulation and MAPK8IP3 upregulation as reliable biomarkers to discriminate RA from OA in peripheral blood and bone marrow samples, respectively. We found more DEGs in patients with OA than those with RA and determined inflammatory pathways and genes unique to RA as reliable biomarkers to discriminate RA from OA. Gene expression alterations associated with Th1 and Th2 cell differentiation pathways, including HLA-DQA1 downregulation and MAPK8IP3 upregulation, could be novel molecular biomarkers to diagnose RA.

Elucidation of the Key Therapeutic Targets and Potential Mechanisms of Marmesine against Knee Osteoarthritis via Network Pharmacological Analysis and Molecular Docking

Background

Marmesine, a major active ingredient isolated from Radix Angelicae biseratae (Duhuo), has been reported to have multiple pharmacological activities. However, its therapeutic effects against knee osteoarthritis (OA) remain poorly investigated. The present study is aimed at uncovering the core targets and signaling pathways of marmesine against osteoarthritis using a combined method of bioinformatics and network pharmacology.

Methods

We utilized SwissTargetPrediction and PharmMapper to collect the potential targets of marmesine. OA-related differentially expressed genes (DEGs) were identified from GSE98918 dataset. Then, the intersection genes between DEGs and candidate genes of marmesine were subjected to protein-protein interaction (PPI) network construction and functional enrichment analysis. The core targets were verified using the molecular docking technology.

Results

A total of 320 marmesine-related genes and 5649 DEGs and 60 ingredient-disease targets between them were identified. The results of functional enrichment analyses revealed that response to oxygen levels, neuroinflammatory response, PI3K-Akt signaling pathway, MAPK signaling pathway, FoxO signaling pathway, and osteoclast differentiation was identified as the potential mechanisms of marmesine against OA. EGFR, CASP3, MMP9, PPARG, and MAPK1 served as hub genes regulated by marmesine in the treatment of OA, and the molecular docking further verified the results.

Conclusion

Marmesine exerts the therapeutic effects against OA through multitarget and multipathways, in which EGFR, CASP3, MMP9, PPARG, and MAPK1 might be hub genes. Our research indicated that the combination of bioinformatics and network pharmacology could serve as an effective approach for investigating the potential mechanisms of natural product.

Gene Expression Microarray Data Identify Hub Genes Involved in Osteoarthritis

The present study was performed to explore the underlying molecular mechanisms and screen hub genes of osteoarthritis (OA) via bioinformatics analysis. In total, twenty-five OA synovial tissue samples and 25 normal synovial tissue samples were derived from three datasets, namely, GSE55457, GSE55235, and GSE1919, and were used to identify the differentially expressed genes (DEGs) of OA by R language. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Venn diagram was built to show the potential hub genes identified in all three datasets. The STRING database was used for constructing the protein–protein interaction (PPI) networks and submodules of DEGs. We identified 507 upregulated and 620 downregulated genes. Upregulated DEGs were significantly involved in immune response, MHC class II receptor activity, and presented in the extracellular region, while downregulated DEGs were mainly enriched in response to organic substances, extracellular region parts, and cadmium ion binding. Results of KEGG analysis indicated that the upregulated DEGs mainly existed in cell adhesion molecules (CAMs), while downregulated DEGs were significantly involved in the MAPK signaling pathway. A total of eighteen intersection genes were identified across the three datasets. These include Nell-1, ATF3, RhoB, STC1, and VEGFA. In addition, 10 hub genes including CXCL12, CXCL8, CCL20, and CCL4 were found in the PPI network and module construction. Identification of DEGs and hub genes associated with OA may be helpful for revealing the molecular mechanisms of OA and further promotes the development of relevant biomarkers and drug targets.

Miya Improves Osteoarthritis Characteristics via the Gut-Muscle-Joint Axis According to Multi-Omics Analyses

Background: The gut microbiota is associated with osteoarthritis (OA) progression. Miya (MY) is a product made from Clostridium butyricum, a member of gut microbiota. This study was conducted to investigate the effects of MY on OA and its underlying mechanisms.

Methods: An OA rat model was established, and MY was used to treat the rats for 4 weeks. Knee joint samples from the rats were stained with hematoxylin-eosin, and fecal samples from the OA and OA+MY groups were subjected to 16S rDNA sequencing and metabolomic analysis. The contents of succinate dehydrogenase and muscle glycogen in the tibia muscle were determined, and related genes and proteins were detected using quantitative reverse transcription polymerase chain reaction and western blotting.

Results: Hematoxylin and eosin staining showed that treatment with MY alleviated the symptoms of OA. According to the sequencing results, MY significantly increased the Chao1, Shannon, and Pielou evenness values compared to those in the untreated group. At the genus level, the abundances of Prevotella, Ruminococcus, Desulfovibrio, Shigella, Helicobacter, and Streptococcus were higher in the OA group, whereas Lactobacillus, Oscillospira, Clostridium, and Coprococcus were enriched after MY treatment. Metabolomic analysis revealed 395 differentially expressed metabolites. Additionally, MY treatment significantly increased the succinate dehydrogenase and muscle glycogen contents in the muscle caused by OA (p > 0.05). Finally, AMPK, Tfam, Myod, Ldh, Chrna1, Chrnd, Rapsyn, and Agrin were significantly downregulated in the muscles of OA mice, whereas Lcad, Mcad, and IL-1β were upregulated; MY significantly reversed these trends induced by OA.

Conclusions: MY may promote the repair of joint damage and protect against OA via the gut-muscle-joint axis.

Potential biomarkers that discriminate rheumatoid arthritis and osteoarthritis based on the analysis and validation of datasets

Background

Rheumatoid arthritis (RA) and osteoarthritis (OA) share some similar arthritic symptoms, but different mechanisms underlie the pathogenesis of these two diseases. Analysis of differentially expressed molecules in rheumatoid arthritis and osteoarthritis may assist in improving diagnosis and treatment strategies in clinical practice.

Methods

Microarray and RNA-seq data were acquired from the gene expression omnibus database. Differentially expressed genes (DEGs) were identified using Bioconductor packages. Receiver operating characteristic curves were plotted to assess performance. Gene ontology enrichment analysis was conducted using the clusterProfiler application. During validation, synovial fluid was harvested from patients who had undergone in-hospital joint replacement, in which the expression of proteins was measured using enzyme-linked immunosorbent assays.

Results

Compared with OA samples, RA samples showed 14 genes to be upregulated and 3 to be downregulated. Gene ontology analysis indicated that DEGs principally included molecules responsible for the regulation of a synovial tissue inflammatory response. Seven genes displayed a good discriminatory power with an AUC higher than 0.90. ADAMDEC1 was the biomarker that most clearly discriminated RA from OA in the database, exhibiting an AUC of 0.999, a sensitivity of 100%, and a specificity of 97.8%. Following validation, the expression levels of ADAMDEC1 in the synovial fluid from RA patients were significantly higher than those in the synovial fluid from OA patients (P < 0.05). At the cut-off value of 1957 pg/mL, ADAMDEC1 expression in the synovial fluid discriminated RA from OA with an AUC of 0.951, a specificity of 88.6%, and a sensitivity of 92.9%.

Conclusion

The differential expression of genes in RA compared with OA indicates potential targets for molecular diagnosis and treatment. The presence of ADAMDEC1 in synovial fluid is a good biomarker of RA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05277-x.

Exploring the Mystery of Osteoarthritis using Bioinformatics Analysis of Cartilage Tissue

BACKGROUND

Osteoarthritis (OA) is a kind of chronic disease relating to joints, which seriously affectsthe daily life activities of the elderly and can also lead to disability. However, the pathogenesis of OA is still unclear, which leads to limited treatment and the therapeutic effect far from people's expectations. This study aims to filter out key genes in the pathogenesis of OA and explore their potential role in the occurrence and development of OA.

METHODS

The dataset of GSE117999 was obtained and analyzed in order to identify the differentially expressed genes (DEGs), hub genes and key genes. We also identified potential miRNAs which may play a major role in the pathogenesis of OA, and verified their difference in OA by real-time quantitative PCR (RT-qPCR). DGldb was found to serve as an indicator to identify drugs with potential therapeutic effects on key genes and Receiver Operating Characteristic (ROC) analysis was used for identifying underlying biomarkers of OA.

RESULTS

We identified ten key genes, including MDM2, RB1, EGFR, ESR1, UBE2E3, WWP1, BCL2, OAS2, TYMS and MSH2. Then, we identified hsa-mir-3613-3p, hsa-mir-548e-5p and hsamir- 5692a to be potentially related to key genes. In addition, RT-qPCR confirmed the differential expression of identified genes in mouse cartilage with or without OA. We then identified Etoposide and Everolimus, which were potentially specific to the most key genes. Finally, we speculated that ESR1 might be a potential biomarker of OA.

CONCLUSION

In this study, potential key genes related to OA and their biological functions were identified, and their potential application value in the diagnosis and treatment of OA has been demonstrated, which will help us to improve the therapeutic effect of OA.

Identification and development of a novel 5-gene diagnostic model based on immune infiltration analysis of osteoarthritis

Background

Osteoarthritis (OA), which is due to the progressive loss and degeneration of articular cartilage, is the leading cause of disability worldwide. Therefore, it is of great significance to explore OA biomarkers for the prevention, diagnosis, and treatment of OA.

Methods and materials

The GSE129147, GSE57218, GSE51588, GSE117999, and GSE98918 datasets with normal and OA samples were downloaded from the Gene Expression Omnibus (GEO) database. The GSE117999 and GSE98918 datasets were integrated, and immune infiltration was evaluated. The differentially expressed genes (DEGs) were analyzed using the limma package in R, and weighted gene co-expression network analysis (WGCNA) was used to explore the co-expression genes and co-expression modules. The co-expression module genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and hub genes were identified by the degree, MNC, closeness, and MCC algorithms. The hub genes were used to construct a diagnostic model based on support vector machines.

Results

The Immune Score in the OA samples was significantly higher than in the normal samples, and a total of 2313 DEGs were identified. Through WGCNA, we found that the yellow module was significantly positively correlated with the OA samples and Immune Score and negatively correlated with the normal samples. The 142 DEGs of the yellow module were related to biological processes such as regulation of inflammatory response, positive regulation of inflammatory response, blood vessel morphogenesis, endothelial cell migration, and humoral immune response. The intersections of the genes obtained by the 4 algorithms resulted in 5 final hub genes, and the diagnostic model constructed with these 5 genes showed good performance in the training and validation cohorts.

Conclusions

The 5-gene diagnostic model can be used to diagnose OA and guide clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03183-9.

Recent Advances in Pharmacological Intervention of Osteoarthritis: A Biological Aspect

Osteoarthritis (OA) is a degenerative joint disease in the musculoskeletal system with a relatively high incidence and disability rate in the elderly. It is characterized by the degradation of articular cartilage, inflammation of the synovial membrane, and abnormal structure in the periarticular and subchondral bones. Although progress has been made in uncovering the molecular mechanism, the etiology of OA is still complicated and unclear. Nevertheless, there is no treatment method that can effectively prevent or reverse the deterioration of cartilage and bone structure. In recent years, in the field of pharmacology, research focus has shifted to disease prevention and early treatment rather than disease modification in OA. Biologic agents become more and more attractive as their direct or indirect intervention effects on the initiation or development of OA. In this review, we will discuss a wide spectrum of biologic agents ranging from DNA, noncoding RNA, exosome, platelet-rich plasma (PRP), to protein. We searched for key words such as OA, DNA, gene, RNA, exosome, PRP, protein, and so on. From the pharmacological aspect, stem cell therapy is a very special technique, which is not included in this review. The literatures ranging from January 2016 to August 2021 were included and summarized. In this review, we aim to help readers have a complete and precise understanding of the current pharmacological research progress in the intervention of OA from the biological aspect and provide an indication for the future translational studies.

Identification of differentially expressed genes in synovial tissue of osteoarthritis based on a more robust integrative analysis method

OBJECTIVE

This study aimed to identify osteoarthritis (OA) related genes based on microarray data in synovium with a more robust integrative analysis method.

METHODS

Four series GSE55457, GSE12021, GSE55235, and GSE55584 (36 OA and 29 normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) of GSE55457, GSE12021, and GSE55235 were identified using the LIMMA package. Overlapping DEGs from the intersection of the three series were detected. Simultaneously, samples in the four series were pooled to identify DEGs with integrated analysis using the Sva package.

RESULTS

In total, 74 overlapping DEGs and 242 DEGs by integrating four series were detected. Based on them, 70 common DEGs were used to construct a protein-protein interaction (PPI) network, involving 61 nodes and 206 edges. Also, three gene modules and five hub genes, named JUN, IL6, VEGFA, MYC, and EGR1, were identified.

CONCLUSIONS

Seventy DEGs were finally identified with a more robust integrative analysis method. JUN, IL6, VEGFA, MYC, and EGR1 were identified as hub genes in the development of OA. Key Points • 76 overlapping DEGs were detected from the intersection of DEGs in GSE55457, GSE12021, and GSE55235. • 242 DEGs were identified by integrating four series using Sva package. • 72 common DEGs were finally identified based on the overlapping DEGs and the integrated DEGs. • JUN, IL6, VEGFA, MYC, and EGR1 were identified as hub genes in the development of OA.

Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

BACKGROUND

This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms.

METHODS

Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD).

RESULTS

A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including "Lysosome," "Pathways in cancer," "Wnt signaling pathway," "ECM-receptor interaction" and "Focal adhesion" in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified.

CONCLUSION

In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

MicroRNA-126 Attenuates the Effect of Chemokine CXCL8 on Proliferation, Migration, Apoptosis, and MAPK-Dependent Signaling Activity of Vascular Endothelial Cells Cultured in a Medium with High Glucose Concentration

We studied the mechanisms by which microRNA-126 regulates proliferation and migration of human umbilical vein endothelial cells (HUVEC) cultured in a medium with high glucose concentration and treated with chemokine CXCL8. Cell proliferation, apoptosis, and migration were analyzed by the CCK-8 assay, Annexin V-PI staining, and Transwell assay, respectively. The ratios of p-ERK/ERK, p-P38/P38, p-JNK/JNK were determined by ELISA. HUVEC cells cultured in the presence of high glucose concentration (30 mmol/ml) and treated with CXCL8 (50 ng/ml) demonstrated more intensive proliferation, migration, and p-ERK/ERK, p-P38/P38, and p-JNK/JNK ratios and significantly lower apoptosis rate than control cells (high glucose, no treatment) and cells treated with CXCL8 and transfected with microRNA-126-mimic. Thus, microRNA-126 regulates proliferation and migration of HUVEC cells cultured in the presence of high glucose concentrations and treated with CXCL8 through inhibition of MAPK signaling pathway.

Identification of the Key Role of NF-κB Signaling Pathway in the Treatment of Osteoarthritis With Bushen Zhuangjin Decoction, a Verification Based on Network Pharmacology Approach

This study aimed to identify whether the NF-κB signaling pathway plays a key role in the treatment of osteoarthritis (OA) with Bushen Zhuangjin Decoction (BZD) based on a typical network pharmacology approach (NPA). Four sequential experiments were performed: 1) conventional high-performance liquid chromatography (HPLC), 2) preliminary observation of the therapeutic effects of BZD, 3) NPA using three OA-related gene expression profiles, and 4) verification of the key pathway identified by NPA. Only one HPLC-verified compound (paeoniflorin) was identified from the candidate compounds discovered by NPA. The genes verified in the preliminary observation were also identified by NPA. NPA identified a key role for the NF-κB signaling pathway in the treatment of OA with BZD, which was confirmed by conventional western blot analysis. This study identified and verified NF-κB signaling pathway as the most important inflammatory signaling pathway involved in the mechanisms of BZD for treating OA by comparing the NPA results with conventional methods. Our findings also indicate that NPA is a powerful tool for exploring the molecular targets of complex herbal formulations, such as BZD.

The relationship between synovial inflammation, structural pathology, and pain in post-traumatic osteoarthritis: differential effect of stem cell and hyaluronan treatment

Background

Synovitis is implicated in the severity and progression of pain and structural pathology of osteoarthritis (OA). Increases in inflammatory or immune cell subpopulations including macrophages and lymphocytes have been reported in OA synovium, but how the particular subpopulations influence symptomatic or structural OA disease progression is unclear. Two therapies, hyaluronan (HA) and mesenchymal stem cells (MSCs), have demonstrated efficacy in some clinical settings: HA acting as device to improve joint function and provide pain relief, while MSCs may have immunomodulatory and disease-modifying effects. We used these agents to investigate whether changes in pain sensitization or structural damage were linked to modulation of the synovial inflammatory response in post-traumatic OA.

Methods

Skeletally mature C57BL6 male mice underwent medial-meniscal destabilisation (DMM) surgery followed by intra-articular injection of saline, a hyaluronan hexadecylamide derivative (Hymovis), bone marrow-derived stem cells (MSCs), or MSC + Hymovis. We quantified the progression of OA-related cartilage, subchondral bone and synovial histopathology, and associated pain sensitization (tactile allodynia). Synovial lymphocytes, monocyte/macrophages and their subpopulations were quantified by fluorescent-activated cell sorting (FACS), and the expression of key inflammatory mediators and catabolic enzyme genes quantified by real-time polymerase chain reaction (PCR).

Results

MSC but not Hymovis significantly reduced late-stage (12-week post-DMM) cartilage proteoglycan loss and structural damage. Allodynia was initially reduced by both treatments but significantly better at 8 and 12 weeks by Hymovis. Chondroprotection by MSCs was not associated with specific changes in synovial inflammatory cell populations but rather regulation of post-injury synovial Adamts4, Adamts5, Mmp3, and Mmp9 expression. Reduced acute post-injury allodynia with all treatments coincided with decreased synovial macrophage and T cell numbers, while longer-term effect on pain sensitization with Hymovis was associated with increased M2c macrophages.

Conclusions

This therapeutic study in mice demonstrated a poor correlation between cartilage, bone or synovium (histo)pathology, and pain sensitization. Changes in the specific synovial inflammatory cell subpopulations may be associated with chronic OA pain sensitization, and a novel target for symptomatic treatment.