Analysis of critical molecules and signaling pathways in osteoarthritis and rheumatoid arthritis

MicroRNAs-mediated regulation pathways in rheumatic diseases

Rheumatoid arthritis (RA) and ankylosing spondylitis (AS) are two common rheumatic disorders marked by persistent inflammatory joint disease. Patients with RA have osteodestructive symptoms, but those with AS have osteoproliferative manifestations. Ligaments, joints, tendons, bones, and muscles are all affected by rheumatic disorders. In recent years, many epigenetic factors contributing to the pathogenesis of rheumatoid disorders have been studied. MicroRNAs (miRNAs) are small, non-coding RNA molecules implicated as potential therapeutic targets or biomarkers in rheumatic diseases. MiRNAs play a critical role in the modulation of bone homeostasis and joint remodeling by controlling fibroblast-like synoviocytes (FLSs), chondrocytes, and osteocytes. Several miRNAs have been shown to be dysregulated in rheumatic diseases, including miR-10a, 16, 17, 18a, 19, 20a, 21, 27a, 29a, 34a, 103a, 125b, 132, 137, 143, 145, 146a, 155, 192, 203, 221, 222, 301a, 346, and 548a.The major molecular pathways governed by miRNAs in these cells are Wnt, bone-morphogenic protein (BMP), nuclear factor (NF)-κB, receptor activator of NF-κB (RANK)-RANK ligand (RANKL), and macrophage colony-stimulating factor (M-CSF) receptor pathway. This review aimed to provide an overview of the most important signaling pathways controlled by miRNAs in rheumatic diseases.

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.

Identification of differentially expressed genes and pathways crosstalk analysis in Rheumatoid and Osteoarthritis using next-generation sequencing and protein-protein networks

Osteoarthritis occurs when protective cartilage of bones worn out. Similarlty, cartilage damage occurs mainly in the pannus cartilage in rheumatoid arthritis. It is a potentially debilitating condition, affecting women two to three times more often than men. The cause and prognosis of rheumatoid and osteoarthritis are still poorly known. However, advances in the study of disease pathogenesis have encouraged the creation of new therapeutics with improved outcomes. The purpose of this study is to investigate the differentially expressed genes potentially involved in dysregulated rheumatoid arthritis (RA) and their association to other types of arthritis, including osteoarthritis (OA). Complete RNAs were isolated for RNA expression profiling using next-generation sequencing from human primary cultured normal and RA chondrocytes. From RNA sequencing results 250 differentially expressed genes were identified using bioinformatics analysis, of which 32 were found to be significantly playing role in RA pathogenesis and its associated diseases. Molecular ontologies of the identified genes showed they are connected to Innate immune response, Protein phosphorylation, Transcription initiation from RNA polymerase II promoter, Immune response, Neoplasms of bones, as well as osteorthritis, and Rheumatoid arthritis. Among the identified genes, TRAF1, TRAF2, BAMP, STX11, MEOX2, AES, REL, FHL3, PNMA1, SGTA, LZTS2, SIAH2, PNMA1, and TFCP2 were found to be highly enriched in the protein-protein interaction network. The significant cross talks were found in Hypertrophic cardiomyopathy, Small cell lung cancer, Proteasome, p53 signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, Small cell lung cancer, SNARE interactions in vesicular transport, RIG-I-like receptor signaling pathway, and Hypertrophic cardiomyopathy pathways. The results offer new opportunities for target gene control in RA and OA cartilage destruction.

Investigation of Alpinia calcarata constituent interactions with molecular targets of rheumatoid arthritis: docking, molecular dynamics, and network approach

Rheumatoid arthritis (RA) is a systemic autoimmune disorder that commonly affects multiple joints of the body. Currently, there is no permanent cure to the disease, but it can be managed with several potent drugs that cause serious side effects on prolonged use. Traditional remedies are considered promising for the treatment of several diseases, particularly chronic conditions, because they have lower side effects compared to synthetic drugs. In folklore, the rhizome of Alpinia calcarata Roscoe (Zingiberaceae) is used as a major ingredient of herbal formulations to treat RA. Phytoconstituents reported in A. calcarata rhizomes are diterpenoids, sesquiterpenoid, flavonoids, phytosterol, and volatile oils. The present study is intended to understand the molecular-level interaction of phytoconstituents present in A. calcarata rhizomes with RA molecular targets using computational approaches. A total of 30 phytoconstituents reported from the plant were used to carry out docking with 36 known targets of RA. Based on the docking results, 4 flavonoids were found to be strongly interacting with the RA targets. Further, molecular dynamics simulation confirmed stable interaction of quercetin with 6 targets (JAK3, SYK, MMP2, TLR8, IRAK1, and JAK1), galangin with 2 targets (IRAK1 and JAK1), and kaempferol (IRAK1) with one target of RA. Moreover, the presence of these three flavonoids was confirmed in the A. calcarata rhizome extract using LC-MS analysis. The computational study suggests that flavonoids present in A. calcarata rhizome may be responsible for RA modulatory activity. Particularly, quercetin and galangin could be potential development candidates for the treatment of RA. Investigation of Alpinia calcarata constituent interactions with molecular targets of rheumatoid arthritis: docking, molecular dynamics, and network approach.

Association of CCL2 Gene Variants with Osteoarthritis

BACKGROUND AND AIMS

The chemokine (C-C motif) Ligand 2 (CCL2)/CCR2 signaling was associated with macrophage accumulation, synovitis and cartilage damage in a mouse osteoarthritis (OA) model.

MATERIALS AND METHODS

Here a case-control study in a Chinese Han population was conducted to investigate the possible association between the CCL2 gene polymorphism and risk of OA. DNA was extracted from 367 primary knee OA patients and 303 healthy controls. Then CCL2 gene polymorphisms were determined using a standard polymerase chain reaction restriction fragment length polymorphism. Plasma CCL2 levels were measured by using sandwich enzyme-linked immunosorbent assay (ELISA).

RESULTS

It was found the CCL2 gene rs1024611 and rs4586 polymorphisms significantly increased the risk of OA. Stratified analyses showed the risk of OA might be increased by rs1024611 polymorphism in males and non-drinkers, and was increased by rs4586 polymorphism among smokers and drinkers. The CC genotype of rs4586 polymorphism was significantly correlated with the increased CCL2 level compared to TT genotype.

CONCLUSIONS

In conclusion, CCL2 gene polymorphisms (rs1024611 and rs4586) confer susceptibility to OA and may be potential markers for early diagnosis of OA.

Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes

Background

Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis.

Methods

The raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system.

Results

A total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).

Distribution of abnormal IgG glycosylation patterns from rheumatoid arthritis and osteoarthritis patients by MALDI-TOF-MSn

IgG glycosylation differs in rheumatoid arthritis (RA) and osteoarthritis (OA), which should contribute to their pathogenesis research and diagnosis.

Cytokine-induced interleukin-1 receptor antagonist protein expression in genetically engineered equine mesenchymal stem cells for osteoarthritis treatment

BACKGROUND

A combination of tissue engineering methods employing mesenchymal stem cells (MSCs) together with gene transfer takes advantage of innovative strategies and highlights a new approach for targeting osteoarthritis (OA) and other cartilage defects. Furthermore, the development of systems allowing tunable transgene expression as regulated by natural disease-induced substances is highly desirable.

METHODS

Bone marrow-derived equine MSCs were transduced with a lentiviral vector expressing interleukin-1 receptor antagonist (IL-1Ra) gene under the control of an inducible nuclear factor-kappa B-responsive promoter and IL-1Ra production upon pro-inflammatory cytokine stimulation [tumor necrosis factor (TNF)α, interleukin (IL)-1β] was analysed. To assess the biological activity of the IL-1Ra protein that was produced and the therapeutic effect of IL-1Ra-expressing MSCs (MSC/IL-1Ra), cytokine-based two- and three-dimensional in vitro models of osteoarthritis using equine chondrocytes were established and quantitative real-time polymerase chain reaction (PCR) analysis was used to measure the gene expression of aggrecan, collagen IIA1, interleukin-1β, interleukin-6, interleukin-8, matrix metalloproteinase-1 and matrix metalloproteinase-13.

RESULTS

A dose-dependent increase in IL-1Ra expression was found in MSC/IL-1Ra cells upon TNFα administration, whereas stimulation using IL-1β did not lead to IL-1Ra production above the basal level observed in nonstimulated cells as a result of the existing feedback loop. Repeated cycles of induction allowed on/off modulation of transgene expression. In vitro analyses revealed that IL-1Ra protein present in the conditioned medium from MSC/IL-1Ra cells blocks OA onset in cytokine-treated equine chondrocytes and co-cultivation of MSC/IL-1Ra cells with osteoarthritic spheroids alleviates the severity of the osteoarthritic changes.

CONCLUSIONS

Thus, pro-inflammatory cytokine induced IL-1Ra protein expression from genetically modified MSCs might represent a promising strategy for osteoarthritis treatment.

Association of CCL13 levels in serum and synovial fluid with the radiographic severity of knee osteoarthritis

OBJECTIVE

CCL13, a recently identified CC chemokine, plays an important role in the process of joint destruction, which is considered a common cause for osteoarthritis (OA). This study aims to examine the relation of CCL13 levels in serum and synovial fluid (SF) with the radiographic severity of OA.

METHODS

CCL13 levels in serum and SF were evaluated using enzyme-linked immunosorbent assay method in 240 patients with knee OA and 134 control subjects. The progression of OA was classified using the Kellgren-Lawrence (KL) system by evaluating x-ray changes observed in anteroposterior knee radiography.

RESULTS

Knee OA patients had higher levels of serum CCL13 compared with control subjects. Knee OA patients with KL grade 4 showed significantly elevated CCL13 levels in serum and SF compared with those with KL grades 2 and 3. Knee OA patients with KL grade 3 had significantly higher SF levels of CCL13 compared with those with KL grade 2. CCL13 levels in serum and SF of knee OA patients were significantly correlated with disease severity evaluated by KL grading criteria.

CONCLUSIONS

CCL13 levels in serum and SF were correlated with the radiographic severity of OA. CCL13 levels in serum and SF may serve as a biomarker for the progression of OA.

Identifying genes related with rheumatoid arthritis via system biology analysis

Rheumatoid arthritis (RA) is a chronic, inflammatory joint disease that mainly attacks synovial joints. However, the underlying systematic relationship among different genes and biological processes involved in the pathogenesis are still unclear. By analyzing and comparing the transcriptional profiles from RA, OA (osteoarthritis) patients as well as ND (normal donors) with bioinformatics methods, we tend to uncover the potential molecular networks and critical genes which play important roles in RA and OA development. Initially, hierarchical clustering was performed to classify the overall transcriptional profiles. Differentially expressed genes (DEGs) between ND and RA and OA patients were identified. Furthermore, PPI networks were constructed, functional modules were extracted, and functional annotation was also applied. Our functional analysis identifies 22 biological processes and 2 KEGG pathways enriched in the commonly-regulated gene set. However, we found that number of set of genes differentially expressed genes only between RA and ND reaches up to 244, indicating this gene set may specifically accounts for processing to disease of RA. Additionally, 142 biological processes and 19 KEGG pathways are over-represented by these 244 genes. Meanwhile, although another 21 genes were differentially expressed only in OA and ND, no biological process nor pathway is over-represented by them.

Differential co-expression analysis of rheumatoid arthritis with microarray data

The aim of the present study was to investigate the underlying molecular mechanisms of rheumatoid arthritis (RA) using microarray expression profiles from osteoarthritis and RA patients, to improve diagnosis and treatment strategies for the condition. The gene expression profile of GSE27390 was downloaded from Gene Expression Omnibus, including 19 samples from patients with RA (n=9) or osteoarthritis (n=10). Firstly, the differentially expressed genes (DEGs) were obtained with the thresholds of |logFC|>1.0 and P<0.05, using the t‑test method in LIMMA package. Then, differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) were screened with q<0.25 by the differential coexpression analysis and differential regulation analysis of gene expression microarray data package. Secondly, pathway enrichment analysis for DCGs was performed by the Database for Annotation, Visualization and Integrated Discovery and the DCLs associated with RA were selected by comparing the obtained DCLs with known transcription factor (TF)-targets in the TRANSFAC database. Finally, the obtained TFs were mapped to the known TF-targets to construct the network using cytoscape software. A total of 1755 DEGs, 457 DCGs and 101988 DCLs were achieved and there were 20 TFs in the obtained six TF-target relations (STAT3-TNF, PBX1‑PLAU, SOCS3-STAT3, GATA1-ETS2, ETS1-ICAM4 and CEBPE‑GATA1) and 457 DCGs. A number of TF-target relations in the constructed network were not within DCLs when the TF and target gene were DCGs. The identified TFs may have an important role in the pathogenesis of RA and have the potential to be used as biomarkers for the development of novel diagnostic and therapeutic strategies for RA.

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

INTRODUCTION

Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers.

METHODS

Three multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl's statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.

RESULTS

The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for 'RA'), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways.

CONCLUSION

First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

Arthritic periosteal tissue from joint replacement surgery: a novel, autologous source of stem cells

The overarching aim of this study is to assess the feasibility of using periosteal tissue from the femoral neck of arthritic hip joints, usually discarded in the normal course of hip replacement surgery, as an autologous source of stem cells. In addition, the study aims to characterize intrinsic differences between periosteum-derived cell (PDC) populations, isolated via either enzymatic digestion or a migration assay, including their proliferative capacity, surface marker expression, and multipotency, relative to commercially available human bone marrow-derived stromal cells (BMSCs) cultured under identical conditions. Commercial BMSCs and PDCs were characterized in vitro, using a growth assay, flow cytometry, as well as assay of Oil Red O, alizarin red, and Safranin O/Fast Green staining after respective culture in adipo-, osteo-, and chondrogenic media. Based on these outcome measures, PDCs exhibited proliferation rate, morphology, surface receptor expression, and multipotency similar to those of BMSCs. No significant correlation was observed between outcome measures and donor age or diagnosis (osteoarthritis [OA] and rheumatoid arthritis [RA], respectively), a profound finding given recent rheumatological studies indicating that OA and RA share not only common biomarkers and molecular mechanisms but also common pathophysiology, ultimately resulting in the need for joint replacement. Furthermore, PDCs isolated via enzymatic digestion and migration assay showed subtle differences in surface marker expression but otherwise no significant differences in proliferation or multipotency; the observed differences in surface marker expression may indicate potential effects of isolation method on the population of cells isolated and/or the behavior of the respective isolated cell populations. This study demonstrates, for the first time to our knowledge, the feasibility of using arthritic tissue resected during hip replacement as a source of autologous stem cells. In sum, periosteum tissue that is resected with the femoral neck in replacing the hip represents an unprecedented and, to date, unstudied source of stem cells from OA and RA patients. Follow-up studies will determine the degree to which this new, autologous source of stem cells can be banked for future use.