Functional Tissue Analysis Reveals Successful Cryopreservation of Human Osteoarthritic Synovium

Identification of osteoarthritis-associated chondrocyte subpopulations and key gene-regulating drugs based on multi-omics analysis

The mechanism by which chondrocytes respond to mechanical stress in joints significantly affects the balance and function of cartilage. This study aims to characterize osteoarthritis-associated chondrocyte subpopulations and key gene targets for regulatory drugs. To begin, single-cell and transcriptome datasets were obtained from the Gene Expression Omnibus (GEO) database. Cell communication and pseudo-temporal analysis, as well as High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA), were conducted on the single-cell data to identify key chondrocyte subtypes and module genes. Subsequently, Consensus Cluster Plus analysis was utilized to identify distinct disease subgroups within the osteoarthritis (OA) training dataset based on the key module genes. Furthermore, differential gene expression analysis and GO/KEGG pathway enrichment analysis were performed on the identified subgroups. To screen for hub genes associated with OA, a combination of 10 machine learning algorithms and 113 algorithm compositions was integrated. Additionally, the immune and pathway scores of the training dataset samples were evaluated using the ESTIMATE, MCP-counter, and ssGSEA algorithms to establish the relationship between the hub genes and immune and pathways. Following this, a network depicting the interaction between the hub genes and transcription factors was constructed based on the Network Analyst database. Moreover, the hub genes were subjected to drug prediction and molecular docking using the RNAactDrug database and AutoDockTools. Finally, real-time fluorescence quantitative PCR (RT-qPCR) was employed to detect the expression of hub genes in the plasma samples collected from osteoarthritis patients and healthy adults. In the OA sample, there is a significant increase in the proportion of prehypertrophic chondrocytes (preHTC), particularly in subgroups 6, 7, and 9. We defined these subgroups as OA_PreHTC subgroups. The OA_PreHTC subgroup exhibits a higher communication intensity with proliferative-related pathways such as ANGPTL and TGF-β. Furthermore, two OA disease subgroups were identified in the training set samples. This led to the identification of 411 differentially expressed genes (DEGs) related to osteoarthritis, 2485 DEGs among subgroups, as well as 238 intersecting genes and 5 hub genes (MMP13, FAM26F, CHI3L1, TAC1, and CKS2). RT-qPCR results indicate significant differences in the expression levels of five hub genes and their related TFs in the clinical blood samples of OA patients compared to the healthy control group (NC). Moreover, these five hub genes are positively associated with inflammatory pathways such as TNF-α, JAK-STAT3, and inflammatory response, while being negatively associated with proliferation pathways like WNT and KRAS. Additionally, the five hub genes are positively associated with neutrophils, activated CD4 T cell, gamma delta T cell, and regulatory T cell, while being negatively associated with CD56dim natural killer cell and Type 17T helper cell. Molecular docking results reveal that CAY10603, Tenulin, T0901317, and Nonactin exhibit high binding activity to CHI3L1, suggesting their potential as therapeutic drugs for OA. The OA_PreHTC subgroups plays a crucial role in the occurrence and development of osteoarthritis (OA). Five hub genes may exert their effects on OA through interactions with PreHTC cells, other chondrocytes, and immune cells, playing a role in inhibiting cell proliferation and stimulating inflammation, thus having high diagnostic value for OA. Additionally, CAY10603, Tenulin, T0901317, and Nonactin have potential therapeutic effects for OA patients.

ER O-glycosylation in synovial fibroblasts drives cartilage degradation

How arthritic synovial fibroblasts (SFs) activate cartilage ECM degradation remains unclear. GALNT enzymes initiate O-glycosylation in the Golgi; when relocated to the ER, their activity stimulates ECM degradation. Here, we show that in human rheumatoid and osteoarthritic synovial SFs, GALNTs are relocated to the ER. In an RA mouse model, GALNTs relocation occurs shortly before arthritis symptoms and abates as the animal recovers. An ER GALNTs inhibitor prevents cartilage ECM degradation in vitro and expression of this chimeric protein in SFs results in the protection of cartilage. One of the ER targets of GALNTs is the resident protein Calnexin, which is exported to the cell surface of arthritic SFs. Calnexin participates in matrix degradation by reducing ECM disulfide bonds. Anti-Calnexin antibodies block ECM degradation and protect animals from RA. In sum, ER O-glycosylation is a key switch in arthritic SFs and glycosylated surface Calnexin could be a therapeutic target.

Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study

Introduction

Knee osteoarthritis (KOA) is a degenerative joint disease characterized by the progressive deterioration of cartilage and synovial inflammation. A critical mechanism in the pathogenesis of KOA is impaired efferocytosis in synovial tissue. The present study aimed to identify and validate key efferocytosis-related genes (EFRGs) in KOA synovial tissue by using comprehensive bioinformatics and machine learning approaches.

Methods

We integrated three datasets (GSE55235, GSE55457, and GSE12021) from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) associated with efferocytosis and performed weighted gene co-expression network analysis. Subsequently, we utilized univariate logistic regression analysis, least absolute shrinkage and selection operator regression, support vector machine, and random forest algorithms to further refine these genes. The results were then inputted into multivariate logistic regression analysis to construct a diagnostic nomogram. Public datasets and quantitative real-time PCR experiments were employed for validation. Additionally, immune infiltration analysis was conducted with CIBERSORT using the combined datasets.

Results

Analysis of the intersection between DEGs and EFRGs identified 12 KOA-related efferocytosis DEGs. Further refinement through machine learning algorithms and multivariate logistic regression revealed UCP2, CX3CR1, and CEBPB as hub genes. Immune infiltration analysis demonstrated significant correlations between immune cell components and the expression levels of these hub genes. Validation using independent datasets and experimental approaches confirmed the robustness of these findings.

Conclusions

This study successfully identified three hub genes (UCP2, CX3CR1, and CEBPB) with significant expression alterations in KOA, demonstrating high diagnostic potential and close associations with impaired efferocytosis. These targets may modulate synovial efferocytosis-related immune processes, offering novel therapeutic avenues for KOA intervention.

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.

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning

Background

Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples.

Methods

We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers.

Results

In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set.

Conclusion

We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

iPSC-derived NK cells expressing high-affinity IgG Fc receptor fusion CD64/16A to mediate flexible, multi-tumor antigen targeting for lymphoma

Introduction

NK cells can mediate tumor cell killing by natural cytotoxicity and by antibody-dependent cell-mediated cytotoxicity (ADCC), an anti-tumor mechanism mediated through the IgG Fc receptor CD16A (FcγRIIIA). CD16A polymorphisms conferring increased affinity for IgG positively correlate with clinical outcomes during monoclonal antibody therapy for lymphoma, linking increased binding affinity with increased therapeutic potential via ADCC. We have previously reported on the FcγR fusion CD64/16A consisting of the extracellular region of CD64 (FcγRI), a high-affinity Fc receptor normally expressed by myeloid cells, and the transmembrane/cytoplasmic regions of CD16A, to create a highly potent and novel activating fusion receptor. Here, we evaluate the therapeutic potential of engineered induced pluripotent stem cell (iPSC)-derived NK (iNK) cells expressing CD64/16A as an "off-the-shelf", antibody-armed cellular therapy product with multi-antigen targeting potential.

Methods

iNK cells were generated from iPSCs engineered to express CD64/16A and an interleukin (IL)-15/IL-15Rα fusion (IL-15RF) protein for cytokine independence. iNK cells and peripheral blood NK cells were expanded using irradiated K562-mbIL21-41BBL feeder cells to examine in in vitro and in vivo assays using the Raji lymphoma cell line. ADCC was evaluated in real-time by IncuCyte assays and using a xenograft mouse model with high circulating levels of human IgG.

Results

Our data show that CD64/16A expressing iNK cells can mediate potent anti-tumor activity against human B cell lymphoma. In particular, (i) under suboptimal conditions, including low antibody concentrations and low effector-to-target ratios, iNK-CD64/16A cells mediate ADCC, (ii) iNK-CD64/16A cells can be pre-loaded with tumor-targeting antibodies (arming) to elicit ADCC, (iii) armed iNK-CD64/16A cells can be repurposed with additional antibodies to target new tumor antigens, and (iv) cryopreserved, armed iNK-CD64/16A are capable of sustained ADCC in a tumor xenograft model under saturating levels of human IgG.

Discussion

iNK-CD64/16A cells allow for a flexible use of antibodies (antibody arming and antibody targeting), and an "off-the-shelf" platform for multi-antigen recognition to overcome limitations of adoptive cell therapies expressing fixed antigen receptors leading to cancer relapse due to antigen escape variants.

Targeting the senescence-related genes MAPK12 and FOS to alleviate osteoarthritis

Background

The mechanism by which chondrocyte senescence aggravate OA progression has not yet been well elucidated. The aim of this study was to investigate the chondrocyte senescence related gene biosignatures in OA, and to analyze on the underlying mechanisms of senescence in OA.

Materials and methods

We intersected osteoarthritis dataset GSE82107 from GEO database and senescence dataset from CellAge database of human senescence-associated genes based on genetic manipulations experiments plus gene expression profilin, and screened out 4 overlapping genes. The hub genes were verified in vitro and in human OA cartilage tissues by qRT-PCR. We further confirmed the function of mitogen-activated protein kinase 12 (MAPK12) and Fos proto-oncogene (FOS) in OA in vitro and in vivo by qRT-PCR, western blotting, Edu staining, immunofluorescence, SA-β-gal staining, HE, IHC, von frey test, and hot plate.

Results

1458 downregulated and 218 upregulated DEGs were determined from GSE82107, and 279 human senescence-associated genes were downloaded from CellAge database. After intersection assay, we screened out 4 overlapping genes, of which FOS, CYR61 and TNFSF15 were upregulated, MAPK12 was downregulated. The expression of MAPK12 was obviously downregulated, whereas the expression profiles of FOS, CYR61 and TNFSF15 were remarkedly upregulated in H2O2- or IL-1β-stimulated C28/I2 cells, human OA cartilage tissues, and knee cartilage of aging mice. Furthermore, both MAPK12 over-expression and FOS knock-down can promote cell proliferation and cartilage anabolism, inhibit cell senescence and cartilage catabolism, relieve joint pain in H2O2- or IL-1β-stimulated C28/I2 cells and mouse primary chondrocytes, destabilization of the medial meniscus (DMM) mice.

Conclusion

This study explored that MAPK12 and FOS are involved in the occurrence and development of OA through modulating chondrocyte senescence. They might be biomarkers of OA chondrocyte senescence, and provides some evidence as subsequent possible therapeutic targets for OA.

The translational potential of this article

The translation potential of this article is that we revealed MAPK12 and FOS can effectively alleviate OA by regulating chondrocyte senescence, and thus provided potential therapeutic targets for prevention or treatment of OA in the future.

Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study

Background

Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA.

Methods

We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of CRTAM in RA. In addition, comparisons of diagnostic efficacy and biological roles among CRTAM and other classic biomarkers of RA were also performed.

Results

Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including CRTAM, PTTG1IP, ITGB2, and MMP13, were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of CRTAM could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including MMP3, S100A8, S100A9, IL6, COMP, LAG3, and ENTPD1. Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes.

Conclusion

CRTAM, PTTG1IP, ITGB2, and MMP13 were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, CRTAM might fuel cancer progression through immune signals, especially among RA patients.

Identification of potential pathogenic genes related to osteoporosis and osteoarthritis

BACKGROUND

Osteoarthritis (OA) and osteoporosis (OS) are the most common orthopedic diseases.

OBJECTIVE

To identify important genes as biomarkers for the pathogenesis of OA and OS.

METHODS

Microarray data for OA and OS were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the OA and healthy control groups and between the OS and healthy control groups were identified using the Limma software package. Overlapping hub DEGs were selected using MCC, MNC, DEGREE, and EPC. Weighted gene co-expression network analysis (WGCNA) was used to mine OA- and OS-related modules. Shared hub DEGs were identified, human microRNA disease database was used to screen microRNAs associated with OA and OS, and an miRNA-target gene network was constructed. Finally, the expression of shared hub DEGs was evaluated.

RESULTS

A total of 104 overlapping DEGs were identified in both the OA and OS groups, which were mainly related to inflammatory biological processes, such as the Akt and TNF signaling pathways Forty-six hub DEGs were identified using MCC, MNC, DEGREE, and EPC modules using different algorithms. Seven modules with 392 genes that highly correlated with disease were identified in the WGCNA. Furthermore, 10 shared hub DEGs were identified between the OA and OS groups, including OGN, FAP, COL6A3, THBS4, IGFBP2, LRRC15, DDR2, RND3, EFNB2, and CD48. A network consisting of 8 shared hub DEGs and 55 miRNAs was constructed. Furthermore, CD48 was significantly upregulated in the OA and OS groups, whereas EFNB2, DR2, COL6A3, and RND3 were significantly downregulated in OA and OS. Other hub DEGs were significantly upregulated in OA and downregulated in OS.

CONCLUSIONS

The ten genes may be promising biomarkers for modulating the development of both OA and OS.

Integrated single-cell and bulk RNA sequencing analysis identified pyroptosis-related signature for diagnosis and prognosis in osteoarthritis

Osteoarthritis (OA), a degenerative disease of the joints, has one of the highest disability rates worldwide. This study investigates the role of pyroptosis-related genes in osteoarthritis and their expression in different chondrocyte subtypes at the individual cell level. Using OA-related datasets for single-cell RNA sequencing and RNA-seq, the study identified PRDEGs and DEGs and conducted Cox regression analysis to identify independent prognostic factors for OA. CASP6, NOD1, and PYCARD were found to be prognostic factors. Combined Weighted Gene Correlation Network Analysis with PPI network, a total of 15 hub genes related to pyroptosis were involved in the notch and oxidative phosphorylation pathways, which could serve as biomarkers for the diagnosis and prognosis of OA patients. The study also explored the heterogeneity of chondrocytes between OA and normal samples, identifying 19 single-cell subpopulation marker genes that were significantly different among 7 chondrocyte cell clusters. AGT, CTSD, CYBC, and THYS1 were expressed differentially among different cell subpopulations, which were associated with cartilage development and metabolism. These findings provide valuable insights into the molecular mechanisms underlying OA and could facilitate the development of new therapeutic strategies for this debilitating disease.

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.

Molecular crosstalk between articular cartilage, meniscus, synovium, and subchondral bone in osteoarthritis

AIMS

Osteoarthritis (OA) is a common degenerative joint disease worldwide, which is characterized by articular cartilage lesions. With more understanding of the disease, OA is considered to be a disorder of the whole joint. However, molecular communication within and between tissues during the disease process is still unclear. In this study, we used transcriptome data to reveal crosstalk between different tissues in OA.

METHODS

We used four groups of transcription profiles acquired from the Gene Expression Omnibus database, including articular cartilage, meniscus, synovium, and subchondral bone, to screen differentially expressed genes during OA. Potential crosstalk between tissues was depicted by ligand-receptor pairs.

RESULTS

During OA, there were 626, 97, 1,060, and 2,330 differentially expressed genes in articular cartilage, meniscus, synovium, and subchondral bone, respectively. Gene Ontology enrichment revealed that these genes were enriched in extracellular matrix and structure organization, ossification, neutrophil degranulation, and activation at different degrees. Through ligand-receptor pairing and proteome of OA synovial fluid, we predicted ligand-receptor interactions and constructed a crosstalk atlas of the whole joint. Several interactions were reproduced by transwell experiment in chondrocytes and synovial cells, including TNC-NT5E, TNC-SDC4, FN1-ITGA5, and FN1-NT5E. After lipopolysaccharide (LPS) or interleukin (IL)-1β stimulation, the ligand expression of chondrocytes and synovial cells was upregulated, and corresponding receptors of co-culture cells were also upregulated.

CONCLUSION

Each tissue displayed a different expression pattern in transcriptome, demonstrating their specific roles in OA. We highlighted tissue molecular crosstalk through ligand-receptor pairs in OA pathophysiology, and generated a crosstalk atlas. Strategies to interfere with these candidate ligands and receptors may help to discover molecular targets for future OA therapy.Cite this article: Bone Joint Res 2022;11(12):862-872.

Cartilage debris and osteoarthritis risk factors influence gene expression in the synovium in end stage osteoarthritis

BACKGROUND

Gene expression in healthy synovium remains poorly characterised. Thus, synovial functional activity changes associated with osteoarthritis (OA) are difficult to define. This study sought to identify differentially expressed genes (DEG) of end-stage OA and assess the influence of OA risk factors on these DEG.

METHODS

Anonymised patient clinical data and x-ray images were analysed. Osteoarthritic and non-osteoarthritic patients with soft tissue or traumatic knee injuries were matched for body mass index (BMI) and sex. Tissue samples were partitioned for immunocytochemistry (IHC) and microarray analysis. Multiple bioinformatics applications were utilised to determine changes in functional and canonical pathway activation.

RESULTS

Age, disease-modifying injections and hypertension were confounding factors between patient groups. Inflammation was present in all tissues. Cartilage debris and inflammatory aggregates were noted in many osteoarthritic patient tissues. IHC and expression analyses revealed upregulation of synoviolin 1 (SYVN1) in osteoarthritic synovium. Significant differential expression was noted in 2084 genes. Osteoarthritic synovium displayed a significant upregulation of 95% of DEG coding for proteins, relative to non-osteoarthritic synovium tissues. Unfolded protein response (UPR)-related genes were upregulated in osteoarthritic synovium; gene expression of molecules within many canonical pathways including protein ubiquitination and UPR pathways was modified by BMI and sex.

CONCLUSIONS

The synovium of all three pathologies exhibited elements of an inflammatory response. Cartilage debris, age, BMI and sex influence DEG of osteoarthritic synovium. UPR pathway is the top deregulated canonical pathway identified in osteoarthritic synovium regardless of BMI and sex, while typical OA-associated inflammatory and matrix gene responses were minimal.

Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates

The molecular mechanisms of osteoarthritis, the most common chronic disease, remain unexplained. This study aimed to use bioinformatic methods to identify the key biomarkers and immune infiltration in osteoarthritis. Gene expression profiles (GSE55235, GSE55457, GSE77298, and GSE82107) were selected from the Gene Expression Omnibus database. A protein-protein interaction network was created, and functional enrichment analysis and genomic enrichment analysis were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) databases. Immune cell infiltration between osteoarthritic tissues and control tissues was analyzed using the CIBERSORT method. Identify immune patterns using the ConsensusClusterPlus package in R software using a consistent clustering approach. Molecular biological investigations were performed to discover the important genes in cartilage cells. A total of 105 differentially expressed genes were identified. Differentially expressed genes were enriched in immunological response, chemokine-mediated signaling pathway, and inflammatory response revealed by the analysis of GO and KEGG databases. Two distinct immune patterns (ClusterA and ClusterB) were identified using the ConsensusClusterPlus. Cluster A patients had significantly lower resting dendritic cells, M2 macrophages, resting mast cells, activated natural killer cells and regulatory T cells than Cluster B patients. The expression levels of TCA1, TLR7, MMP9, CXCL10, CXCL13, HLA-DRA, and ADIPOQSPP1 were significantly higher in the IL-1β-induced group than in the osteoarthritis group in an in vitro qPCR experiment. Explaining the differences in immune infiltration between osteoarthritic tissues and normal tissues will contribute to the understanding of the development of osteoarthritis.

Local inhibition of TGF-β1 signaling improves Th17/Treg balance but not joint pathology during experimental arthritis

TGF-β1 is an important growth factor to promote the differentiation of T helper 17 (Th17) and regulatory T cells (Treg). The potential of TGF-β1 as therapeutic target in T cell-mediated diseases like rheumatoid arthritis (RA) is unclear. We investigated the effect of TGF-β1 inhibition on murine Th17 differentiation in vitro, on human RA synovial explants ex vivo, and on the development of experimental arthritis in vivo. Murine splenocytes were differentiated into Th17 cells, and the effect of the TGF-βRI inhibitor SB-505124 was studied. Synovial biopsies were cultured in the presence or absence of SB-505124. Experimental arthritis was induced in C57Bl6 mice and treated daily with SB-505124. Flow cytometry analysis was performed to measure different T cell subsets. Histological sections were analysed to determine joint inflammation and destruction. SB-505124 potently reduced murine Th17 differentiation by decreasing Il17a and Rorc gene expression and IL-17 protein production. SB-505124 significantly suppressed IL-6 production by synovial explants. In vivo, SB-505124 reduced Th17 numbers, while increased numbers of Tregs were observed. Despite this skewed Th17/Treg balance, SB-505124 treatment did not result in suppression of joint inflammation and destruction. Blocking TGF-β1 signalling suppresses Th17 differentiation and improves the Th17/Treg balance. However, local SB-505124 treatment does not suppress experimental arthritis.

Identification of Key Genes and Pathways in Osteoarthritis via Bioinformatic Tools: An Updated Analysis

OBJECTIVE

Osteoarthritis (OA) is a severe and common degenerative disease; however, the exact pathology of OA is undefined. Our study is designed to investigate the underlying molecular mechanism of OA with bioinformatic tools.

DESIGN

Three updated GEO datasets: GSE55235, GSE55457, and GSE82107 were selected for data analyzing. R software was utilized to screen and confirm the candidate differentially expressed genes in the development of OA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway were performed to identify the enriched GO terms and signaling pathways. Protein and protein interaction (PPI) models were built to observe the connected relationship among each potential protein.

RESULTS

A total of 113 upregulated genes and 161 downregulated genes were found by integrating 3 datasets. GO enrichment indicated that cell differentiation, cellular response to starvation, and negative regulation of phosphorylation were important biological processes. KEGG enrichment indicated that FoxO, IL-17 signaling pathways, and osteoclast differentiation mainly participated in the progression of OA. Combining the molecular function and PPI results, ubiquitylation was identified as a pivotal bioactive reaction involved in OA.

CONCLUSION

Our study provided updated candidate genes and pathways of OA, which may benefit further research and treatment for OA.

Targeting of fibroblast activation protein in rheumatoid arthritis patients: imaging and ex vivo photodynamic therapy

Objective

Activated synovial fibroblasts are key effector cells in RA. Selectively depleting these based upon their expression of fibroblast activation protein (FAP) is an attractive therapeutic approach. Here we introduce FAP imaging of inflamed joints using ⁶⁸Ga-FAPI-04 in a RA patient, and aim to assess feasibility of anti-FAP targeted photodynamic therapy (FAP-tPDT) ex vivo using 28H1-IRDye700DX on RA synovial explants.

Methods

Remnant synovial tissue from RA patients was processed into 6 mm biopsies and, from several patients, into primary fibroblast cell cultures. Both were treated using FAP-tPDT. Cell viability was measured in fibroblast cultures and biopsies were evaluated for histological markers of cell damage. Selectivity of the effect of FAP-tPDT was assessed using flow cytometry on primary fibroblasts and co-cultured macrophages. Additionally, one RA patient intravenously received ⁶⁸Ga-FAPI-04 and was scanned using PET/CT imaging.

Results

In the RA patient, FAPI-04 PET imaging showed high accumulation of the tracer in arthritic joints with very low background signal. In vitro, FAP-tPDT induced cell death in primary RA synovial fibroblasts in a light dose-dependent manner. An upregulation of cell damage markers was observed in the synovial biopsies after FAP-tPDT. No significant effects of FAP-tPDT were noted on macrophages after FAP-tPDT of neighbouring fibroblasts.

Conclusion

In this study the feasibility of selective FAP-tPDT in synovium of rheumatoid arthritis patients ex vivo is demonstrated. Furthermore, this study provides the first indication that FAP-targeted PET/CT can be used to image arthritic joints, an important step towards application of FAP-tPDT as a targeted locoregional therapy for RA.

Screening of osteoarthritis diagnostic markers based on immune-related genes and immune infiltration

Osteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology.

OATargets: a knowledge base of genes associated with osteoarthritis joint damage in animals

OBJECTIVES

To collate the genes experimentally modulated in animal models of osteoarthritis (OA) and compare these data with OA transcriptomics data to identify potential therapeutic targets.

METHODS

PubMed searches were conducted to identify publications describing gene modulations in animal models. Analysed gene expression data were retrieved from the SkeletalVis database of analysed skeletal microarray and RNA-Seq expression data. A network diffusion approach was used to predict new genes associated with OA joint damage.

RESULTS

A total of 459 genes were identified as having been modulated in animal models of OA, with ageing and post-traumatic (surgical) models the most prominent. Ninety-eight of the 143 genes (69%) genetically modulated more than once had a consistent effect on OA joint damage severity. Several discrepancies between different studies were identified, providing lessons on interpretation of these data. We used the data collected along with OA gene expression data to expand existing annotations and prioritise the most promising therapeutic targets, which we validated using the latest reported associations. We constructed an online database OATargets to allow researchers to explore the collated data and integrate it with existing OA and skeletal gene expression data.

CONCLUSIONS

We present a comprehensive survey and online resource for understanding gene regulation of animal model OA pathogenesis.

Screening and verification of hub genes involved in osteoarthritis using bioinformatics

Osteoarthritis (OA) is one of the most common causes of disability and its development is associated with numerous factors. A major challenge in the treatment of OA is the lack of early diagnosis. In the present study, a bioinformatics method was employed to filter key genes that may be responsible for the pathogenesis of OA. From the Gene Expression Omnibus database, the datasets GSE55457, GSE12021 and GSE55325 were downloaded, which comprised 59 samples. Of these, 30 samples were from patients diagnosed with osteoarthritis and 29 were normal. Differentially expressed genes (DEGs) were obtained by downloading and analyzing the original data using bioinformatics. The Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathways were analyzed using the Database for Annotation, Visualization and Integrated Discovery online database. Protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/proteins online database. BSCL2 lipid droplet biogenesis associated, seipin, FOS-like 2, activator protein-1 transcription factor subunit (FOSL2), cyclin-dependent kinase inhibitor 1A (CDKN1A) and kinectin 1 (KTN1) genes were identified as key genes by using Cytoscape software. Functional enrichment revealed that the DEGs were mainly accumulated in the ErbB, MAPK and PI3K-Akt pathways. Reverse transcription-quantitative PCR analysis confirmed a significant reduction in the expression levels of FOSL2, CDKN1A and KTN1 in OA samples. These genes have the potential to become novel diagnostic and therapeutic targets for OA.

Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold-change | >1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

microRNA-9 might be a novel protective factor for osteoarthritis patients

BACKGROUND

The study aimed to identify the targeting genes and miRNAs using the microarray expression profile dataset for Osteoarthritis (OA) patients. Differentially expressed genes (DEGs) between OA and control samples were identified using Bayes method of limma package. Subsequently, a protein-protein interaction (PPI) network was constructed. miRNAs and transcription factor (TFs) based on DEGs in PPI network were identified using Webgestalt and ENCODE, respectively. Finally, MCODE, Gene Ontology (GO) function, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. The expressions of several DEGs and predicted miRNAs in OA rats were detected by RT-PCR.

RESULTS

A total of 594 DEGs were identified. In PPI network, there were 313 upregulated DEGs and 22 downregulated DEGs. Besides, the regulatory relationships included 467 upregulated interactions and 85 downregulated interactions (miR-124A → QKI and MAP 1B) between miRNA and DEGs in PPI network. The module from downregulated DEGs-TFs-miRNA networks was mainly enriched to low-density lipoprotein particle clearance, response to linoleic acid, and small molecule metabolic process BP terms. Moreover, QKI, MAP 1B mRNA and miR-9 expressions were significantly reduced in OA rats.

CONCLUSION

miR-9 might be a protective factor for OA patients via inhibiting proliferation and differentiation of cartilage progenitor cells. miR-124A might play an important role in progression of OA through targeting QKI and MAP 1B.

The Immune Cell Landscape in Different Anatomical Structures of Knee in Osteoarthritis: A Gene Expression-Based Study

Background

Immunological mechanisms play a vital role in the pathogenesis of knee osteoarthritis (KOA). Moreover, the immune phenotype is a relevant prognostic factor in various immune-related diseases. In this study, we used CIBERSORT for deconvolution of global gene expression data to define the immune cell landscape of different structures of knee in osteoarthritis. Methods and Findings. By applying CIBERSORT, we assessed the relative proportions of immune cells in 76 samples of knee cartilage, 146 samples of knee synovial tissue, 40 samples of meniscus, and 50 samples of knee subchondral bone. Enumeration and activation status of 22 immune cell subtypes were provided by the obtained immune cell profiles. In synovial tissues, the differences in proportions of plasma cells, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and eosinophils between normal tissues and osteoarthritic tissues were statistically significant (P < 0.05). The area under the curve was relatively large in resting mast cells, dendritic cells, and M2 macrophages in receiver operating characteristic analyses. In subchondral bones, the differences in proportions of resting master cells and neutrophils between normal tissues and osteoarthritic tissues were statistically significant (P < 0.05). In subchondral bones, the proportions of immune cells, from the principle component analyses, displayed distinct group-bias clustering. Resting mast cells and T cell CD8 were the major component of first component. Moreover, we revealed the potential interaction between immune cells. There was almost no infiltration of immune cells in the meniscus and cartilage of the knee joint.

Conclusions

The immune cell composition in KOA differed substantially from that of healthy joint tissue, while it also differed in different anatomical structures of the knee. Meanwhile, activated mast cells were mainly associated with high immune cell infiltration in OA. Furthermore, we speculate M2 macrophages in synovium and mast cells in subchondral bone may play an important role in the pathogenesis of OA.

RETRACTED: Human bone mesenchymal stem cells-derived exosomes overexpressing microRNA-26a-5p alleviate osteoarthritis via down-regulation of PTGS2

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Concern was raised about the reliability of the Western blot results in Figures 2E, 3D and F, 4B, E+G, 5D+I, and 6D+F, which appear to have a similar phenotype as contained in many other publications, detailed here: https://pubpeer.com/publications/73C0A79F5EDF9ECC9818CE2D9B2A09; and here: https://docs.google.com/spreadsheets/d/1r0MyIYpagBc58BRF9c3luWNlCX8VUvUuPyYYXzxWvgY/edit#gid=262337249. The provenance of the flow cytometry data in Figure 5A was also questioned, as it appeared to have histograms that were hand drawn. The journal requested the corresponding author comment on these concerns and provide the raw data. The authors did not respond to this request and therefore the Editor-in-Chief decided to retract the article.

Identification of pathways and genes associated with synovitis in osteoarthritis using bioinformatics analyses

Synovitis in osteoarthritis (OA) is a very common condition. However, its underlying mechanism is still not well understood. This study aimed to explore the molecular mechanisms of synovitis in OA. The gene expression profile GSE82107 (downloaded from the Gene Expression Omnibus database) included 10 synovial tissues of the OA patients and 7 synovial tissues of healthy people. Subsequently, differentially expressed gene (DEG) analysis, GO (gene ontology) enrichment analysis, pathway analysis, pathway network analysis, and gene signal network analysis were performed using Gene-Cloud of Biotechnology Information (GCBI). A total of 1,941 DEGs consisting of 1,471 upregulated genes and 470 downregulated genes were determined. Genes such as PSMG3, LRP12 MIA-RAB4B, ETHE1, SFXN1, DAZAP1, RABEP2, and C9orf16 were significantly regulated in synovitis of OA. In particular, the MAPK signalling pathway, apoptosis, and pathways in cancer played the most important roles in the pathway network. The relationships between these pathways were also analysed. Genes such as NRAS, SPHK2, FOS, CXCR4, PLD1, GNAI2, and PLA2G4F were strongly implicated in synovitis of OA. In summary, this study indicated that several molecular mechanisms were implicated in the development and progression of synovitis in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic advances.

Integrated analysis of key mRNAs and lncRNAs in osteoarthritis

Osteoarthritis (OA) is the most common type of arthritis, observed mainly in the population aged >65 years. However, the mechanism underlying the development and progression of OA has remained largely elusive. The present study aimed to identify differentially expressed mRNAs and lncRNAs in OA. By analyzing the GSE48556 and GSE82107 datasets, a total of 202 up- and 434 downregulated mRNAs were identified in OA. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that differently expressed genes were mainly involved in regulating antigen processing and presentation, interspecies interaction between organisms, immune response, transcription and signal transduction. In addition, a series of long non-coding (lnc)RNAs were differently expressed in OA. To provide novel data on the molecular mechanisms and functional roles of these lncRNAs in OA, a co-expression analysis was performed, which revealed that the dysregulated lncRNAs were associated with transcription, signal transduction, immune response and cell adhesion. In addition, certain key genes in protein-protein interaction networks were identified. The present study provided useful information for exploring potential candidate biomarkers for the diagnosis and prognosis of OA, as well as novel drug targets.

Long-term results of a randomized, controlled, double-blind study of low-level laser therapy before exercises in knee osteoarthritis: laser and exercises in knee osteoarthritis

OBJECTIVES

To assess the long-term effects of low-level laser therapy (LLLT), in combination with strengthening exercises in patients with osteoarthritis of the knee.

DESIGN

Follow-up results at three and six months in a previously published randomized, double-blind, placebo-controlled trial.

SETTING

Specialist Rehabilitation Services.

SUBJECTS

Forty participants of both genders, aged 50-75 years with knee osteoarthritis grade 2-4 on Kellgren-Lawrence scale.

INTERVENTION

The LLLT group received 10 LLLT treatments with invisible infrared laser (904 nm, 3 Joules/point) over three weeks followed by an eight-week supervised strengthening exercise program. The placebo LLLT group received identical treatment, but the infrared laser output was disabled.

MAIN MEASURES

Pain on a visual analogue scale, paracetamol consumption, and osteoarthritis severity measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Lequesne Index.

RESULTS

The new data obtained during the follow-up period showed that all outcomes remained stable and there were no significant differences between the groups at three and six months. However, daily consumption of rescue analgesics (paracetamol) was significantly lower in the LLLT group throughout the follow-up period, ending at a group difference of 0.45 vs. 3.40 units ( P < 0.001) at six months follow-up. We conclude that within the limitations of this small study, the previously reported improvement after LLLT plus exercise was maintained for a period of six months.

CONCLUSION

We find that the immediate post-intervention improvements from LLLT plus strengthening exercises were maintained for six months.