Osteoarthritis Samples Research Articles

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

To facilitate drug discovery and development for the treatment of osteoporosis. With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

The role of m5C RNA methylation regulators in the diagnosis and immune microenvironment of osteoarthritis

The 5-methylcytosine (m5C) is a common post-transcriptional RNA methylation modification and is involved in the pathological process of many diseases. However, little is known about the role of m5C in osteoarthritis (OA). OA gene data and the corresponding information were downloaded from the Gene Expression Omnibus database. Based on 36 m5C regulators, we constructed the landscape and diagnostic model for OA. Later, two m5C modification patterns were identified, and functional analyses were performed to evaluate whether these patterns were related to endoplasmic reticulum (ER) stress and mitochondrial autophagy. We further comprehensively analyzed the immune cell infiltration characteristics in different modification patterns in OA. We also established the post-transcriptional regulatory networks and drug-gene networks. Our findings suggested that m5C regulators were differentially expressed between OA and normal samples and could serve as novel biomarkers for the diagnosis of OA. Besides, m5C regulators may be involved in regulating ER stress, mitochondrial autophagy, and immune infiltration in OA. The m5C modification can influence the sensitivity to drugs and the potential post-transcriptional regulatory mechanisms might provide promising targets.

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

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). 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. 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). 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.

Exosomes-Shuttled lncRNA SNHG7 by Bone Marrow Mesenchymal Stem Cells Alleviates Osteoarthritis Through Targeting miR-485-5p/FSP1 Axis-Mediated Chondrocytes Ferroptosis and Inflammation.

Osteoarthritis (OA), a degenerative joint disorder, is a major reason of disability in adults. Accumulating evidences have proved that bone marrow mesenchymal stem cells (BMSCs)-carried exosomes play a significant therapeutic effect on OA. However, the precise regulatory network remains unknown. OA and normal cartilage samples were acquired from patients, and chondrocytes were exposed to IL-1β to conduct a cellular OA model. Exosomes prepared from BMSCs were identified using nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM). Cell viability was determined with CCK-8 assay. Inflammatory injury was assessed by LDH and inflammatory factors (TNF-α and IL-6) using corresponding ELISA kits, respectively. Ferroptosis was evaluated by GSH, MDA and iron levels using corresponding kits, and ROS level with DCFH-DA. The expressions of genes/proteins were determined with RT-qPCR/western bolt. RNA immunoprecipitation and luciferase activity assay were conducted for testing the interactions of small nucleolar RNA host gene 7 (SNHG7)/ferroptosis suppressor protein 1 (FSP1) and miR-485-5p. The expressions of SNHG7 and FSP1 were both reduced in IL-1β-induced chondrocytes and OA cartilage tissues, and there was a positive correlation between them in clinical level. Moreover, SNHG7 was enriched in BMSCs-derived exosomes (BMSCs-Exos) and could be internalized by chondrocytes. Functional analysis illustrated that BMSCs-Exos administration repressed inflammatory injury, oxidative stress and ferroptosis in IL-1β-induced chondrocytes, while these changes were reinforced when SNHG7 was overexpressed in BMSCs-Exos. Notably, FSP1 silencing in chondrocytes abolished the beneficial effects mediated by exosomal SNHG7. Exosomal SNHG7 released from BMSCs inhibited inflammation and ferroptosis in IL-1β-induced chondrocytes through miR-485-5p/FSP1 axis. This work suggested that BMSCs-derived exosomal SNHG7 would be a prospective target for OA treatment.

Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis.

The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles of OA cartilage GSE114007 and GSE57218 were obtained from the Gene Expression Omnibus. And the limma method was used to detect differentially expressed genes (DEGs). Second, the biological functions of the DEGs in OA were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Wayne plots were employed to visualize the differentially expressed mitochondrial genes (MDEGs) in OA. Subsequently, the LASSO and SVM-RFE algorithms were employed to elucidate potential OA signature genes within the set of MDEGs. As a result, GRPEL and MTFP1 were identified as signature genes. Notably, GRPEL1 exhibited low expression levels in OA samples from both experimental and test group datasets, demonstrating high diagnostic efficacy. Furthermore, RT-qPCR analysis confirmed the reduced expression of Grpel1 in an in vitro OA model. Lastly, ssGSEA analysis revealed alterations in the infiltration abundance of several immune cells in OA cartilage tissue, which exhibited correlation with GRPEL1 expression. Altogether, this study has revealed that GRPEL1 functions as a novel and significant diagnostic indicator for OA by employing two machine learning methodologies. Furthermore, these findings provide fresh perspectives on potential targeted therapeutic interventions in the future.

Transcriptome combined with single cell to explore hypoxia-related biomarkers in osteoarthritis

Osteoarthritis (OA) is a prevalent degenerative condition among the elderly on a global scale. Research has demonstrated that hypoxia can promote chondrocyte apoptosis and autophagy leading to OA. Hence, it was vital to screen the hypoxia related biomarkers in OA. We introduced transcriptome data to screen out differentially expressed genes (DEGs) in GSE114007 and GSE57218 (OA samples vs control samples). We performed differential expression analysis in key annotated cell to obtain differentially expressed marker genes at the single-cell level (GSE169454). Venn diagram was executed to identify hypoxia related differentially expressed genes (HR-DEGs) associated with OA. Further, feature genes were obtained through the application of least absolute shrinkage and selection operator (LASSO) regression and the Random Forest (RF) algorithm. Receiver operating characteristic (ROC) and expression level analysis were used to identify hypoxia related biomarkers in OA. We further performed immune infiltration and gene set enrichment analysis (GSEA) based on hypoxia related biomarkers. Finally, we analyzed the expression of biomarkers in single-cell level. We identified 2351 DEGs associated with OA. At the single-cell level, 242 differentially expressed marker genes were obtained. 12 HR-DEGs were retained venn diagram. Subsequently, three hypoxia related biomarkers (ADM, DDIT3 and MAFF) were identified. Moreover, we got 15 significantly different immune cells. Finally, we found a lower expression of ADM, DDIT3 and MAFF in OA group compared to the control group in ECs. Overall, we obtained three hypoxia related biomarkers (ADM, DDIT3 and MAFF) associated with OA, which established a theoretical basis for addressing OA.

Evaluation of Chondral Defect Repair Using Human Fibronectin Adhesion Assay-Derived Chondroprogenitors Suspended in Lyophilized Fetal Collagen Scaffold: An Ex Vivo Osteochondral Unit Model Study.

Chondral defect repair is challenging due to a scarcity of reparative cells and the need to fill a large surface area, compounded by the absence of self-healing mechanisms. Fibronectin adhesion assay-derived chondroprogenitors (FAA-CPs) have emerged as a promising alternative with enhanced chondrogenic ability and reduced hypertrophy. De-cellularized bio-scaffolds are reported to act as extracellular matrix, mimicking the structural and functional characteristics of native tissue, thereby facilitating cell attachment and differentiation. This study primarily assessed the synergistic effect of FAA-CPs suspended in fetal cartilage-derived collagen-containing scaffolds in repairing chondral defects. The de-cellularized and lyophilized fetal collagen was prepared from the tibio-femoral joint of a 36 + 4-week gestational age fetus. FAA-CPs were isolated from osteoarthritic cartilage samples (n = 3) and characterized. In ex vivo analysis, FAA-CPs at a density of 1 × 106 cells were suspended in the lyophilized scaffold and placed into the chondral defects created in the Osteochondral Units and harvested on the 35th day for histological examination. The lyophilized scaffold of de-cellularized fetal cartilage with FAA-CPs demonstrated effective healing of the critical size chondral defect. This was evidenced by a uniform distribution of cells, a well-organized collagen-fibrillar network, complete filling of the defect with alignment to the surface, and favorable integration with the adjacent cartilage. However, these effects were less pronounced in the plain scaffold control group and no demonstrable repair observed in the empty defect group. This study suggests the synergistic potential of FAA-CPs and collagen scaffold for chondral repair which needs to be further explored for clinical therapy. The online version contains supplementary material available at 10.1007/s43465-024-01192-6.

A significant difference of synovial mast cells in synovium from rotator cuff arthropathy compared to rotator cuff tears: A histological pilot study

ObjectiveAim of the present study was to compare the presence of Mast Cells (MCs) in synovial samples from gleno-humeral osteoarthritis (OA) and from control group. MethodsSynovial tissue samples were obtained during arthroplasty from 23 patients with gleno-humeral OA due to rotator cuff arthropathy (RCA) and from 20 patients without OA, constituting OA group and control group respectively. Before surgery self-reported pain was assessed using VAS score and OSS was used to value functional ability. Shoulder radiograph (Antero-posterior, Y-view and Grashey views) was evaluated by musculoskeletal radiologist and graded according to modified Samilson-Prieto classification.Synovial tissue, obtained during arthroplasty and arthroscopic procedure, was prepared to immunohistochemical analysis with anti-CD31 and anti-CD117 antibodies, to detect respectively endothelial cells and MCs at 40x magnification. Synovitis scores have been assessed. Under the control of the image processing system the distribution and the total number of vessels and MCs were determined. ResultsThe numbers of MCs and the area fraction (20x magnification) occupied by them were significantly higher in OA samples than in control tissue. The synovitis score was higher in OA patients with a positive correlation. Vessels number and area fraction were higher in OA patients than in controls. Analysis of MC number in relation to clinical data indicated positive correlation with the VAS score. ConclusionsThe distribution of MCs on synovium significantly differ between OA and control groups. Despite the design of the study could not conclude the cause-effect relationship, the presence of MCs might have role in OA pathogenesis. Level of evidenceHistological study.

Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM.

Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation. In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset. Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets. Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.

TPX2 upregulates MMP13 to promote the progression of lipopolysaccharide-induced osteoarthritis.

This study seeks to identify potential clinical biomarkers for osteoarthritis (OA) using bioinformatics and investigate OA mechanisms through cellular assays. Differentially Expressed Genes (DEGs) from GSE52042 (four OA samples, four control samples) were screened and analyzed with protein-protein interaction (PPI) analysis. Overlapping genes in GSE52042 and GSE206848 (seven OA samples, and seven control samples) were identified and evaluated using Gene Set Enrichment Analysis (GSEA) and clinical diagnostic value analysis to determine the hub gene. Finally, whether and how the hub gene impacts LPS-induced OA progression was explored by in vitro experiments, including Western blotting (WB), co-immunoprecipitation (Co-IP), flow cytometry, etc. Bioinformatics analysis of DEGs (142 up-regulated and 171 down-regulated) in GSE52042 identified two overlapping genes (U2AF2, TPX2) that exhibit significant clinical diagnostic value. These genes are up-regulated in OA samples from both GSE52042 and GSE206848 datasets. Notably, TPX2, which AUC = 0.873 was identified as the hub gene. In vitro experiments have demonstrated that silencing TPX2 can alleviate damage to chondrocytes induced by lipopolysaccharide (LPS). Furthermore, there is a protein interaction between TPX2 and MMP13 in OA. Excessive MMP13 can attenuate the effects of TPX2 knockdown on LPS-induced changes in OA protein expression, cell growth, and apoptosis. In conclusion, our findings shed light on the molecular mechanisms of OA and suggested TPX2 as a potential therapeutic target. TPX2 could promote the progression of LPS-induced OA by up-regulating the expression of MMP13, which provides some implications for clinical research.

SLAMF8 as a potential biomarker for rheumatoid arthritis identified by comparing peripheral blood mononuclear cells, fibroblast‐like synoviocytes, and synovial tissue using bioinformatics analysis

AbstractBackgroundRheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease. Its pathogenesis is not fully understood, and early diagnosis is challenging owing to the lack of effective biomarkers. This study aimed to analyze different samples to identify potential biomarkers and therapeutic targets.MethodsMicroarray datasets of RA, osteoarthritis (OA), and healthy control (HC) were downloaded from the Gene Expression Omnibus database. R software was used to identify differentially expressed genes (DEGs), which were visualized using volcano and heat maps. Venn diagrams, principal component analysis, gene set enrichment analysis, gene ontology, and Kyoto Encyclopedia of genes and genomes were used to analyze the data. A protein–protein interaction network was constructed, and synovial tissues from patients with RA and OA were collected for verification using the collagen‐induced arthritis mouse model.ResultsMore DEGs were found in synovial tissues than in peripheral blood mononuclear cells or fibroblast‐like synoviocytes. Principal component analysis revealed significant differences between the RA and OA samples, highlighting the unique advantages of synovial tissue. Enrichment analysis revealed that metabolic and cytokine signaling pathways play crucial roles in the development of RA. Further analysis of the four synovial datasets identified 54 DEGs, of which signaling lymphocytic activation molecule family (SLAMF) 8 was identified as the key molecule. SLAMF8 levels were increased in the synovial tissue of patients with RA compared to those of patients with OA (0.38 ± 0.19 vs. 12.40 ± 1.66), and SLAMF8 levels were similarly elevated in collagen‐induced arthritis model mice compared with those in the healthy mice (1.13 ± 0.47 vs. 9.05 ± 2.52).ConclusionsThis study established the unique advantages of synovial tissue for RA research and identified metabolic and cytokine signaling pathways as important for RA development. Thus, SLAMF8 may be a potential therapeutic target for RA.

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging.

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.

Uncovering the shared neuro-immune-related regulatory mechanisms between spinal cord injury and osteoarthritis

Adults with spinal cord injury (SCI), a destructive neurological injury, have a significantly higher incidence of osteoarthritis (OA), a highly prevalent chronic joint disorder. This study aimed to dissect the neuroimmune-related regulatory mechanisms of SCI and OA using bioinformatics analysis. Using microarray data from the Gene Expression Omnibus database, differentially expressed genes (DEGs) were screened between SCI and sham samples and between OA and control samples. Common DEGs were used to construct a protein-protein interaction (PPI) network. Weighted gene co-expression network analysis (WGCNA) was used to mine SCI- and OA-related modules. Shared miRNAs were identified, and target genes were predicted using the Human MicroRNA Disease Database (HMDD) database. A miRNA-gene-pathway regulatory network was constructed with overlapping genes, miRNAs, and significantly enriched pathways. Finally, the expression of the identified genes and miRNAs was verified using RT-qPCR. In both the SCI and OA groups, 185 common DEGs were identified, and three hub clusters were obtained from the PPI network. WGCNA revealed three SCI-related modules and two OA-related modules. There were 43 overlapping genes between the PPI network clusters and the WGCNA network modules. Seventeen miRNAs shared between patients with SCI and OA were identified. A regulatory network consisting of five genes, six miRNAs, and six signaling pathways was constructed. Upregulation of CD44, TGFBR1, CCR5, and IGF1, while lower levels of miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p in both SCI and OA were successfully verified using RT-qPCR. Our study suggests that a miRNA-gene-pathway network is implicated in the neuroimmune-related regulatory mechanisms of SCI and OA. CD44, TGFBR1, CCR5, and IGF1, and their related miRNAs (miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p) may serve as promising biomarkers and candidate therapeutic targets for SCI and OA.

Comparative assessment of chondral defect repair using migratory chondroprogenitors suspended in either gelled or freeze-dried platelet-rich plasma: An in vitro and ex vivo human osteochondral unit model study

BackgroundChondroprogenitors, with enhanced chondrogenic potential, have emerged to be a promising alternative for cell-based therapy in cartilage repair. Platelet-rich plasma (PRP), widely used for intra-articular treatment, has a short half-life. Freeze-dried PRP (FD-PRP), with an extended half-life and retained growth factors, is gaining attention. This study compares the efficacy of Migratory Chondroprogenitors (MCPs) in gelled PRP and FD-PRP using in-vitro and ex-vivo models, assessing FD-PRP as a potential off-the-shelf option for effective cartilage repair. MethodologyMCPs were isolated from osteoarthritic cartilage samples (n = 3), characterized through FACS and RT-PCR. For in-vitro analysis, cells were loaded into gelled PRP and FD-PRP scaffolds at a density of 1x106 cells per scaffold. Trilineage differentiation studies and live-dead assays were conducted on MCPs using Calcein AM/Propidium Homodimer-1. In ex-vivo analysis, MCPs of the same density were added to Osteochondral Units (OCU) with chondral defects containing PRP gel and FD-PRP scaffolds, harvested on the 15th and 35th days for histological examination. Controls included cell-free scaffolds. ResultsOur in-vitro analysis demonstrates the robust viability of MCPs in both scaffolds, with no discernible impact on their differentiation capacity. Ex-vivo analysis of the OCU for cartilage repair showed that the chondrogenic potential characterized by the accumulation of extracellular matrix containing glycosaminoglycans and collagen type II production (with no alteration in collagen type X), was observed to be better with the gel PRP and the gel PRP containing MCP groups. ConclusionsThese findings support the preference for gel PRP as a superior synergistic scaffold for chondroprogenitor delivery.

Identification and validation of immune-related genes in osteoarthritic synovial fibroblasts

ObjectiveOA was generally considered as a non-inflammatory disease dominated by articular cartilage degeneration. However, the role of synovitis in OA pathogenesis has received increasing attention. Recent studies support that OA patients have a pro-inflammatory/catabolic synovial environment similar to RA patients, promoting the occurrence and development of OA. Therefore, we investigated the co-immune-related genes and pathways of OA and RA to explore whether part of the pathogenesis of RA synovitis can be used to explain OA synovitis. MethodsData of GSE29746 and GSE12021 were downloaded from the Gene Expression Omnibus (GEO) database. Compared with control group, differentially expressed genes (DEGs) of OA and RA groups were screened separately by R software, Venny website was used to screen co-DEGs. Metascape was used to screen the common enriched terms and pathways between OA and RA. STRING website and Cytoscape software were used to map protein–protein interaction (PPI) networks and screen co-hub genes. GSE29746 was selected as the test dataset, and GSE12021 as the validation dataset for validate the co-hub genes. The results were validated by western blotting (WB) and real-time quantitative polymerase chain reaction (qPCR) of clinical synovial samples. ResultsWe identified 573 OA-related DEGs, 148 RA-related DEGs, and 52 co-DEGs, revealing 14 common enriched terms, most of which were related to immune inflammation. IL7R was the only upregulated co-hub gene between OA and RA in the PPI network, consistent with the validation dataset. IL7R was highly expressed in clinical osteoarthritic synovial samples (P < 0.001). ConclusionOur findings suggested that IL7R is a critical co-DEG in OA and RA and confirmed the involvement of immune inflammation in disease pathogenesis. Furthermore, it confirms the role of IL7R in synovial inflammation in RA and OA synovitis and provides evidence for further investigation of OA immune inflammation.

Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis.

Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.

RNA-binding proteins potentially regulate the alternative splicing of apoptotic genes during knee osteoarthritis progression

BackgroundAlternative splicing (AS) is a principal mode of genetic regulation and one of the most widely used mechanisms to generate structurally and functionally distinct mRNA and protein variants. Dysregulation of AS may result in aberrant transcription and protein products, leading to the emergence of human diseases. Although considered important for regulating gene expression, genome-wide AS dysregulation, underlying mechanisms, and clinical relevance in knee osteoarthritis (OA) remain unelucidated. Therefore, in this study, we elucidated and validated AS events and their regulatory mechanisms during OA progression.ResultsIn this study, we identified differentially expressed genes between human OA and healthy meniscus samples. Among them, the OA-associated genes were primarily enriched in biological pathways such as extracellular matrix organization and ossification. The predominant OA-associated regulated AS (RAS) events were found to be involved in apoptosis during OA development. The expression of the apoptosis-related gene BCL2L13, XAF1, and NF2 were significantly different between OA and healthy meniscus samples. The construction of a covariation network of RNA-binding proteins (RBPs) and RAS genes revealed that differentially expressed RBP genes LAMA2 and CUL4B may regulate the apoptotic genes XAF1 and BCL2L13 to undergo AS events during OA progression. Finally, RT-qPCR revealed that CUL4B expression was significantly higher in OA meniscus samples than in normal controls and that the AS ratio of XAF1 was significantly different between control and OA samples; these findings were consistent with their expected expression and regulatory relationships.ConclusionsDifferentially expressed RBPs may regulate the AS of apoptotic genes during knee OA progression. XAF1 and its regulator, CUL4B, may serve as novel biomarkers and potential therapeutic targets for this disease.

CDKN1A regulation on chondrogenic differentiation of human chondrocytes in osteoarthritis through single-cell and bulk sequencing analysis

ObjectiveChondrocyte death is the hallmark of cartilage degeneration during osteoarthritis (OA). However, the specific pathogenesis of cell death in OA chondrocytes has not been elucidated. This study aims to validate the role of CDKN1A, a key programmed cell death (PCD)-related gene, in chondrogenic differentiation using a combination of single-cell and bulk sequencing approaches. DesignOA-related RNA-seq data (GSE114007, GSE55235, GSE152805) were downloaded from Gene Expression Omnibus database. PCD-related genes were obtained from GeneCards database. RNA-seq was performed to annotate the cell types in OA and control samples. Differentially expressed genes (DEGs) among those cell types (scRNA-DEGs) were screened. A nomogram of OA was constructed based on the featured genes, and potential drugs targeting the featured genes were predicted. The presence of key genes was confirmed using Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), Western blot (WB), and immunohistochemistry (IHC). Micromass culture and Alcian blue staining were used to determine the effect of CDKN1A on chondrogenesis. ResultsSix cell types, namely HomC, HTC, RepC, preFC, FC, and RegC, were annotated in scRNA-seq data. Five featured genes (JUN, CDKN1A, HMGB2, DDIT3, and DDIT4) were screened by multiple biological information analysis methods. TAXOTERE had the highest ability to dock with DDIT3. Functional analysis indicated that CDKN1A was enriched in processes related to collagen catabolism and acts as a positive regulator of autophagy. Additionally, CDKN1A was found to be associated with several KEGG pathways, including those involved in acute myeloid leukemia and autoimmune thyroid disease. CDKN1A was confirmed down-regulated in the joint tissues of OA mouse model and OA model cell. Inhibiting the expression of CDKN1A can significantly suppress the differentiation of OA chondrocytes. ConclusionOur findings highlight the critical role of CDKN1A in promoting cartilage formation in both in vivo and in vitro and suggest its potential as a therapeutic target for OA treatment.

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

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

Soluble signal inhibitory receptor on leukocytes-1 reflects disease activity and assists diagnosis of patients with rheumatoid arthritis

BackgroundSIRL-1, an immunosuppressive receptor encoded by the VSTM1 gene, has recently been linked to rheumatoid arthritis (RA) due to its association with activated polymorphonuclear neutrophils (PMNs). Considering that the activated PMNs play a crucial role in the pathogenesis of rheumatoid arthritis (RA), we aimed to measure the levels of soluble SIRL-1, investigating whether they add value to RA in the clinical diagnosis. MethodsUtilizing an enzyme-linked immunosorbent assay, the concentration of sSIRL-1 was measured in serum samples from cohort 1 diagnosed with RA (n = 96), gout (n = 54), osteoarthritis (n = 47), healthy controls (n = 86) and synovial fluid samples from OA (n = 8) and RA (n = 8) patients, respectively. Additionally, an external validation in cohort 2 (n = 156) comprising various inflammatory diseases was employed. ResultsThe study revealed a distinctive upregulation of sSIRL-1 in the serum of RA compared to HC and other arthralgia diseases (p < 0.0001), which also displayed a significant elevation in synovial fluid from RA compared to OA (p < 0.05). Notably, sSIRL-1 levels exhibited a significant decrease in patients who achieved disease remission (p < 0.05). Furthermore, the diagnostic accuracy of RA was enhanced when sSIRL-1 was combined with anti-CCP and RF, yielding an impressive AUC value of 0.950. ConclusionThe expression pattern of sSIRL-1 in RA, coupled with its correlation with disease activity, underscores its potential clinical utility for both diagnosis and disease monitoring in RA patients. This study offers valuable insights into the evolving diagnostic landscape of RA.