Aims. Lumbar spinal stenosis (LSS) is a common skeletal system disease that has been partly attributed to genetic variation. However, the correlation between genetic variation and pathological changes in LSS is insufficient, and it is difficult to provide a reference for the early diagnosis and treatment of the disease. Methods. We conducted a transcriptome-wide association study (TWAS) of spinal canal stenosis by integrating genome-wide association study summary statistics (including 661 cases and 178,065 controls) derived from Biobank Japan, and pre-computed gene expression weights of skeletal muscle and whole blood implemented in FUSION software. To verify the TWAS results, the candidate genes were furthered compared with messenger RNA (mRNA) expression profiles of LSS to screen for common genes. Finally, Metascape software was used to perform enrichment analysis of the candidate genes and common genes. Results. TWAS identified 295 genes with permutation p-values < 0.05 for skeletal muscle and 79 genes associated for the whole blood, such as RCHY1 (PTWAS = 0.001). Those genes were enriched in 112 gene ontology (GO) terms and five Kyoto Encyclopedia of Genes and Genomes pathways, such as ‘chemical carcinogenesis - reactive oxygen species’ (LogP value = −2.139). Further comparing the TWAS significant genes with the differentially expressed genes identified by mRNA expression profiles of LSS found 18 overlapped genes, such as interleukin 15 receptor subunit alpha (IL15RA) (PTWAS = 0.040, PmRNA = 0.010). Moreover, 71 common GO terms were detected for the enrichment results of TWAS and mRNA expression profiles, such as negative regulation of cell differentiation (LogP value = −2.811). Conclusion. This study revealed the genetic mechanism behind the pathological changes in LSS, and may provide novel insights for the early diagnosis and intervention of LSS. Cite this article: Bone Joint Res 2023;12(6):387–396

Aims. Degenerative cervical spondylosis (DCS) is a common musculoskeletal disease that encompasses a wide range of progressive degenerative changes and affects all components of the cervical spine. DCS imposes very large social and economic burdens. However, its genetic basis remains elusive. Methods. Predicted whole-blood and skeletal muscle gene expression and genome-wide association study (GWAS) data from a DCS database were integrated, and functional summary-based imputation (FUSION) software was used on the integrated data. A transcriptome-wide association study (TWAS) was conducted using FUSION software to assess the association between predicted gene expression and DCS risk. The TWAS-identified genes were verified via comparison with differentially expressed genes (DEGs) in DCS RNA expression profiles in the Gene Expression Omnibus (GEO) (Accession Number: GSE153761). The Functional Mapping and Annotation (FUMA) tool for genome-wide association studies and Meta tools were used for gene functional enrichment and annotation analysis. Results. The TWAS detected 420 DCS genes with p < 0.05 in skeletal muscle, such as ribosomal protein S15A (RPS15A) (PTWAS = 0.001), and 110 genes in whole blood, such as selectin L (SELL) (PTWAS = 0.001). Comparison with the DCS RNA expression profile identified 12 common genes, including Apelin Receptor (APLNR) (PTWAS = 0.001, PDEG = 0.025). In total, 148 DCS-enriched Gene Ontology (GO) terms were identified, such as mast cell degranulation (GO:0043303); 15 DCS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, such as the sphingolipid signalling pathway (ko04071). Nine terms, such as degradation of the extracellular matrix (R-HSA-1474228), were common to the TWAS enrichment results and the RNA expression profile. Conclusion. Our results identify putative susceptibility genes; these findings provide new ideas for exploration of the genetic mechanism of DCS development and new targets for preclinical intervention and clinical treatment. Cite this article: Bone Joint Res 2023;12(1):80–90

Aims. This study aimed, through bioinformatics analysis and in vitro experiment validation, to identify the key extracellular proteins of intervertebral disc degeneration (IDD). Methods. The gene expression profile of GSE23130 was downloaded from the Gene Expression Omnibus (GEO) database. Extracellular protein-differentially expressed genes (EP-DEGs) were screened by protein annotation databases, and we used Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the functions and pathways of EP-DEGs. STRING and Cytoscape were used to construct protein-protein interaction (PPI) networks and identify hub EP-DEGs. NetworkAnalyst was used to analyze transcription factors (TFs) and microRNAs (miRNAs) that regulate hub EP-DEGs. A search of the Drug Signatures Database (DSigDB) for hub EP-DEGs revealed multiple drug molecules and drug-target interactions. Results. A total of 56 EP-DEGs were identified in the differential expression analysis. EP-DEGs were enriched in the extracellular structure organization, ageing, collagen-activated signalling pathway, PI3K-Akt signalling pathway, and AGE-RAGE signalling pathway. PPI network analysis showed that the top ten hub EP-DEGs are closely related to IDD. Correlation analysis also demonstrated a significant correlation between the ten hub EP-DEGs (p＜0.05), which were selected to construct TF–gene interaction and TF–miRNA coregulatory networks. In addition, ten candidate drugs were screened for the treatment of IDD. Conclusion. The findings clarify the roles of extracellular proteins in IDD and highlight their potential as promising novel therapeutic targets. Cite this article: Bone Joint Res 2023;12(9):522–535

Aims. Osteoporosis (OP) is a metabolic bone disease, characterized by a decrease in bone mineral density (BMD). However, the research of regulatory variants has been limited for BMD. In this study, we aimed to explore novel regulatory genetic variants associated with BMD. Methods. We conducted an integrative analysis of BMD genome-wide association study (GWAS) and regulatory single nucleotide polymorphism (rSNP) annotation information. Firstly, the discovery GWAS dataset and replication GWAS dataset were integrated with rSNP annotation database to obtain BMD associated SNP regulatory elements and SNP regulatory element-target gene (E-G) pairs, respectively. Then, the common genes were further subjected to HumanNet v2 to explore the biological effects. Results. Through discovery and replication integrative analysis for BMD GWAS and rSNP annotation database, we identified 36 common BMD-associated genes for BMD irrespective of regulatory elements, such as FAM3C (p. discovery GWAS. = 1.21 × 10. -25. , p. replication GWAS. = 1.80 × 10. -12. ), CCDC170 (p. discovery GWAS. = 1.23 × 10. -11. , p. replication GWAS. = 3.22 × 10. -9. ), and SOX6 (p. discovery GWAS. = 4.41 × 10. -15. , p. replication GWAS. = 6.57 × 10. -14. ). Then, for the 36 common target genes, multiple gene ontology (GO) terms were detected for BMD such as positive regulation of cartilage development (p = 9.27 × 10. -3. ) and positive regulation of chondrocyte differentiation (p = 9.27 × 10. -3. ). Conclusion. We explored the potential roles of rSNP in the genetic mechanisms of BMD and identified multiple candidate genes. Our study results support the implication of regulatory genetic variants in the development of OP. Cite this article: Bone Joint Res 2023;12(2):147–154

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

Aims. To explore the novel molecular mechanisms of histone deacetylase 4 (HDAC4) in chondrocytes via RNA sequencing (RNA-seq) analysis. Methods. Empty adenovirus (EP) and a HDAC4 overexpression adenovirus were transfected into cultured human chondrocytes. The cell survival rate was examined by real-time cell analysis (RTCA) and EdU and flow cytometry assays. Cell biofunction was detected by Western blotting. The expression profiles of messenger RNAs (mRNAs) in the EP and HDAC4 transfection groups were assessed using whole-transcriptome sequencing (RNA-seq). Volcano plot, Gene Ontology, and pathway analyses were performed to identify differentially expressed genes (DEGs). For verification of the results, the A289E/S246/467/632 A sites of HDAC4 were mutated to enhance the function of HDAC4 by increasing HDAC4 expression in the nucleus. RNA-seq was performed to identify the molecular mechanism of HDAC4 in chondrocytes. Finally, the top ten DEGs associated with ribosomes were verified by quantitative polymerase chain reaction (QPCR) in chondrocytes, and the top gene was verified both in vitro and in vivo. Results. HDAC4 markedly improved the survival rate and biofunction of chondrocytes. RNA-seq analysis of the EP and HDAC4 groups showed that HDAC4 induced 2,668 significant gene expression changes in chondrocytes (1,483 genes upregulated and 1,185 genes downregulated, p < 0.05), and ribosomes exhibited especially large increases. The results were confirmed by RNA-seq of the EP versus mutated HDAC4 groups and the validations in vitro and in vivo. Conclusion. The enhanced ribosome pathway plays a key role in the mechanism by which HDAC4 improves the survival rate and biofunction of chondrocytes. Cite this article: Bone Joint Res 2023;12(7):433–446

Aims. This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of immune infiltration in synovial tissue. Methods. The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed. Results. A total of 88 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were significantly enriched in leucocyte migration and interleukin (IL)-17 signalling pathways. Disease ontology (DO) indicated that DEGs were mostly enriched in rheumatoid arthritis. Six hub genes including FosB proto-oncogene, AP-1 transcription factor subunit (FOSB); C-X-C motif chemokine ligand 2 (CXCL2); CXCL8; IL-6; Jun proto-oncogene, AP-1 transcription factor subunit (JUN); and Activating transcription factor 3 (ATF3) were identified and verified by GEO datasets. ATF3 (area under the curve = 0.975) turned out to be a potential biomarker for the diagnosis of early OA. Several infiltrating immune cells varied significantly between early-stage OA and end-stage OA, such as resting NK cells (p = 0.016), resting dendritic cells (p = 0.043), and plasma cells (p = 0.043). Additionally, ATF3 was significantly correlated with resting NK cells (p = 0.034), resting dendritic cells (p = 0.026), and regulatory T cells (Tregs, p = 0.018). Conclusion. ATF3 may be a potential diagnostic marker for early diagnosis and treatment of OA, and immune cell infiltration provides new perspectives for understanding the mechanism during OA progression. Cite this article: Bone Joint Res 2022;11(9):679–689

Aims. This study aimed to explore the role of small colony variants (SCVs) of Staphylococcus aureus in intraosseous invasion and colonization in patients with periprosthetic joint infection (PJI). Methods. A PJI diagnosis was made according to the MusculoSkeletal Infection Society (MSIS) for PJI. Bone and tissue samples were collected intraoperatively and the intracellular invasion and intraosseous colonization were detected. Transcriptomics of PJI samples were analyzed and verified by polymerase chain reaction (PCR). Results. SCVs can be isolated from samples collected from chronic PJIs intraoperatively. Transmission electron microscopy (TEM) and immunofluorescence (IF) showed that there was more S. aureus in bone samples collected from chronic PJIs, but much less in bone samples from acute PJIs, providing a potential mechanism of PJI. Immunofluorescence results showed that SCVs of S. aureus were more likely to invade osteoblasts in vitro. Furthermore, TEM and IF also demonstrated that SCVs of S. aureus were more likely to invade and colonize in vivo. Cluster analysis and principal component analysis (PCA) showed that there were substantial differences in gene expression profiles between chronic and acute PJI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these differentially expressed genes were enriched to chemokine-related signal pathways. PCR also verified these results. Conclusion. Our study has shown that the S. aureus SCVs have a greater ability to invade and colonize in bone, resulting in S. aureus remaining in bone tissues long-term, thus explaining the pathogenesis of chronic PJI. Cite this article: Bone Joint Res 2022;11(12):843–853

Aims. Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA. Methods. First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used unsupervised clustering to divide OA into three subtypes. The gene ontology and pathway enrichment of three subtypes were analyzed. CIBERSORT was used to evaluate the infiltration of immune cells in different subtypes. Finally, OA-related genes were obtained from the Molecular Signatures Database for validation, and diagnostic markers were screened according to clinical characteristics. Quantitative reverse transcription polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of markers. Results. C1 subtype is mainly concentrated in the development of skeletal muscle organs, C2 lies in metabolic process and immune response, and C3 in pyroptosis and cell death process. Therefore, we divided OA into three subtypes: bone remodelling subtype (C1), immune metabolism subtype (C2), and cartilage degradation subtype (C3). The number of macrophage M0 and activated mast cells of C2 subtype was significantly higher than those of the other two subtypes. COL2A1 has significant differences in different subtypes. The expression of COL2A1 is related to age, and trafficking protein particle complex subunit 2 is related to the sex of OA patients. Conclusion. This study linked different tissues with gene expression profiles, revealing different molecular subtypes of patients with knee OA. The relationship between clinical characteristics and OA-related genes was also studied, which provides a new concept for the diagnosis and treatment of OA. Cite this article: Bone Joint Res 2023;12(12):702–711

Aims. The association of auraptene (AUR), a 7-geranyloxycoumarin, on osteoporosis and its potential pathway was predicted by network pharmacology and confirmed in experimental osteoporotic mice. Methods. The network of AUR was constructed and a potential pathway predicted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) terms enrichment. Female ovariectomized (OVX) Institute of Cancer Research mice were intraperitoneally injected with 0.01, 0.1, and 1 mM AUR for four weeks. The bone mineral density (BMD) level was measured by dual-energy X-ray absorptiometry. The bone microstructure was determined by histomorphological changes in the femora. In addition, biochemical analysis of the serum and assessment of the messenger RNA (mRNA) levels of osteoclastic markers were performed. Results. In total, 65.93% of the genes of the AUR network matched with osteoporosis-related genes. Osteoclast differentiation was predicted to be a potential pathway of AUR in osteoporosis. Based on the network pharmacology, the BMD and bone mineral content levels were significantly (p < 0.05) increased in the whole body, femur, tibia, and lumbar spine by AUR. AUR normalized the bone microstructure and the serum alkaline phosphatase (ALP), bone-specific alkaline phosphatase (bALP), osteocalcin, and calcium in comparison with the OVX group. In addition, AUR treatment reduced TRAP-positive osteoclasts and receptor activator of nuclear factor kappa-B ligand (RANKL). +. nuclear factor of activated T cells 1 (NFATc1). +. expression in the femoral body. Moreover, the expressions of initiators for osteoclastic resorption and bone matrix degradation were significantly (p < 0.05) regulated by AUR in the lumbar spine of the osteoporotic mice. Conclusion. AUR ameliorated bone loss by downregulating the RANKL/NFATc1 pathway, resulting in improvement of osteoporosis. In conclusion, AUR might be an ameliorative cure that alleviates bone loss in osteoporosis via inhibition of osteoclastic activity. Cite this article: Bone Joint Res 2022;11(5):304–316

Aims. We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Methods. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature. Results. We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples. Conclusion. The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring. Cite this article: Bone Joint Res 2022;11(8):548–560

Aims. Developmental dysplasia of the hip (DDH) is a complex musculoskeletal disease that occurs mostly in children. This study aimed to investigate the molecular changes in the hip joint capsule of patients with DDH. Methods. High-throughput sequencing was used to identify genes that were differentially expressed in hip joint capsules between healthy controls and DDH patients. Biological assays including cell cycle, viability, apoptosis, immunofluorescence, reverse transcription polymerase chain reaction (RT-PCR), and western blotting were performed to determine the roles of the differentially expressed genes in DDH pathology. Results. More than 1,000 genes were differentially expressed in hip joint capsules between healthy controls and DDH. Both gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that extracellular matrix (ECM) modifications, muscle system processes, and cell proliferation were markedly influenced by the differentially expressed genes. Expression of Collagen Type I Alpha 1 Chain (COL1A1), COL3A1, matrix metalloproteinase-1 (MMP1), MMP3, MMP9, and MMP13 was downregulated in DDH, with the loss of collagen fibres in the joint capsule. Expression of transforming growth factor beta 1 (TGF-β1) was downregulated, while that of TGF-β2, Mothers against decapentaplegic homolog 3 (SMAD3), and WNT11 were upregulated in DDH, and alpha smooth muscle actin (αSMA), a key myofibroblast marker, showed marginal increase. In vitro studies showed that fibroblast proliferation was suppressed in DDH, which was associated with cell cycle arrest in G0/G1 and G2/M phases. Cell cycle regulators including Cyclin B1 (CCNB1), Cyclin E2 (CCNE2), Cyclin A2 (CCNA2), Cyclin-dependent kinase 1 (CDK1), E2F1, cell division cycle 6 (CDC6), and CDC7 were downregulated in DDH. Conclusion. DDH is associated with the loss of collagen fibres and fibroblasts, which may cause loose joint capsule formation. However, the degree of differentiation of fibroblasts to myofibroblasts needs further study. Cite this article: Bone Joint Res 2021;10(9):558–570

Objectives. Osteoporosis is a chronic disease. The aim of this study was to identify key genes in osteoporosis. Methods. Microarray data sets GSE56815 and GSE56814, comprising 67 osteoporosis blood samples and 62 control blood samples, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in osteoporosis using Limma package (3.2.1) and Meta-MA packages. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify biological functions. Furthermore, the transcriptional regulatory network was established between the top 20 DEGs and transcriptional factors using the UCSC ENCODE Genome Browser. Receiver operating characteristic (ROC) analysis was applied to investigate the diagnostic value of several DEGs. Results. A total of 1320 DEGs were obtained, of which 855 were up-regulated and 465 were down-regulated. These differentially expressed genes were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, mainly associated with gene expression and osteoclast differentiation. In the transcriptional regulatory network, there were 6038 interactions pairs involving 88 transcriptional factors. In addition, the quantitative reverse transcriptase-polymerase chain reaction result validated the expression of several genes (VPS35, FCGR2A, TBCA, HIRA, TYROBP, and JUND). Finally, ROC analyses showed that VPS35, HIRA, PHF20 and NFKB2 had a significant diagnostic value for osteoporosis. Conclusion. Genes such as VPS35, FCGR2A, TBCA, HIRA, TYROBP, JUND, PHF20, NFKB2, RPL35A and BICD2 may be considered to be potential pathogenic genes of osteoporosis and may be useful for further study of the mechanisms underlying osteoporosis. Cite this article: B. Xia, Y. Li, J. Zhou, B. Tian, L. Feng. Identification of potential pathogenic genes associated with osteoporosis. Bone Joint Res 2017;6:640–648. DOI: 10.1302/2046-3758.612.BJR-2017-0102.R1

Aims. Osteoarthritis (OA) is the most prevalent joint disease. However, the specific and definitive genetic mechanisms of OA are still unclear. Methods. Tissue-related transcriptome-wide association studies (TWAS) of hip OA and knee OA were performed utilizing the genome-wide association study (GWAS) data of hip OA and knee OA (including 2,396 hospital-diagnosed hip OA patients versus 9,593 controls, and 4,462 hospital-diagnosed knee OA patients versus 17,885 controls) and gene expression reference to skeletal muscle and blood. The OA-associated genes identified by TWAS were further compared with the differentially expressed genes detected by the messenger RNA (mRNA) expression profiles of hip OA and knee OA. Functional enrichment and annotation analysis of identified genes was performed by the DAVID and FUMAGWAS tools. Results. We detected 33 common genes, eight common gene ontology (GO) terms, and one common pathway for hip OA, such as calcium and integrin-binding protein 1 (CIB1) (PTWAS = 0.025, FCmRNA = -1.575 for skeletal muscle), adrenomedullin (ADM) (PTWAS = 0.022, FCmRNA = -4.644 for blood), Golgi apparatus (PTWAS <0.001, PmRNA = 0.012 for blood), and phosphatidylinositol 3' -kinase-protein kinase B (PI3K-Akt) signalling pathway (PTWAS = 0.033, PmRNA = 0.005 for blood). For knee OA, we detected 24 common genes, eight common GO terms, and two common pathways, such as histocompatibility complex, class II, DR beta 1 (HLA-DRB1) (PTWAS = 0.040, FCmRNA = 4.062 for skeletal muscle), Follistatin-like 1 (FSTL1) (PTWAS = 0.048, FCmRNA = 3.000 for blood), cytoplasm (PTWAS < 0.001, PmRNA = 0.005 for blood), and complement and coagulation cascades (PTWAS = 0.017, PmRNA = 0.001 for skeletal muscle). Conclusion. We identified a group of OA-associated genes and pathways, providing novel clues for understanding the genetic mechanism of OA. Cite this article:Bone Joint Res. 2020;9(3):130–138

Objectives. The aim of this study was to provide a comprehensive understanding of alterations in messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in cartilage affected by osteoarthritis (OA). Methods. The expression profiles of mRNAs, lncRNAs, and circRNAs in OA cartilage were assessed using whole-transcriptome sequencing. Bioinformatics analyses included prediction and reannotation of novel lncRNAs and circRNAs, their classification, and their placement into subgroups. Gene ontology and pathway analysis were performed to identify differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed circRNAs (DECs). We focused on the overlap of DEGs and targets of DELs previously identified in seven high-throughput studies. The top ten DELs were verified by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in articular chondrocytes, both in vitro and in vivo. Results. In total, 739 mRNAs, 1152 lncRNAs, and 42 circRNAs were found to be differentially expressed in OA cartilage tissue. Among these, we identified 18 overlapping DEGs and targets of DELs, and the top ten DELs were screened by expression profile analysis as candidate OA-related genes. WISP2, ATF3, and CHI3L1 were significantly increased in both normal versus OA tissues and normal versus interleukin (IL)-1β-induced OA-like cell models, while ADAM12, PRELP, and ASPN were shown to be significantly decreased. Among the identified DELs, we observed higher expression of ENST00000453554 and MSTRG.99593.3, and lower expression of MSTRG.44186.2 and NONHSAT186094.1 in normal versus OA cells and tissues. Conclusion. This study revealed expression patterns of coding and noncoding RNAs in OA cartilage, which added sets of genes and noncoding RNAs to the list of candidate diagnostic biomarkers and therapeutic agents for OA patients. Cite this article: H. Li, H. H. Yang, Z. G. Sun, H. B. Tang, J. K. Min. Whole-transcriptome sequencing of knee joint cartilage from osteoarthritis patients. Bone Joint Res 2019;8:290–303. DOI: 10.1302/2046-3758.87.BJR-2018-0297.R1

Objectives. The molecular mechanism of rheumatoid arthritis (RA) remains elusive. We conducted a protein-protein interaction network-based integrative analysis of genome-wide association studies (GWAS) and gene expression profiles of RA. Methods. We first performed a dense search of RA-associated gene modules by integrating a large GWAS meta-analysis dataset (containing 5539 RA patients and 20 169 healthy controls), protein interaction network and gene expression profiles of RA synovium and peripheral blood mononuclear cells (PBMCs). Gene ontology (GO) enrichment analysis was conducted by DAVID. The protein association networks of gene modules were generated by STRING. Results. For RA synovium, the top-ranked gene module is HLA-A, containing TAP2, HLA-A, HLA-C, TAPBP and LILRB1 genes. For RA PBMCs, the top-ranked gene module is GRB7, consisting of HLA-DRB5, HLA-DRA, GRB7, CD63 and KIT genes. Functional enrichment analysis identified three significant GO terms for RA synovium, including antigen processing and presentation of peptide antigen via major histocompatibility complex class I (false discovery rate (FDR) = 4.86 × 10 – 4), antigen processing and presentation of peptide antigen (FDR = 2.33 × 10 – 3) and eukaryotic translation initiation factor 4F complex (FDR = 2.52 × 10 – 2). Conclusion. This study reported several RA-associated gene modules and their functional association networks. Cite this article: X. Xiao, J. Hao, Y. Wen, W. Wang, X. Guo, F. Zhang. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: an analysis. Bone Joint Res 2016;5:314–319. DOI: 10.1302/2046-3758.57.2000502

Aim. Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage. Patients and Methods. Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results. We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032). Conclusion. We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA. Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1

Objectives. The aim of this study was to identify key pathological genes in osteoarthritis (OA). Methods. We searched and downloaded mRNA expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) of joint synovial tissues from OA and normal individuals. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses were used to assess the function of identified DEGs. The protein-protein interaction (PPI) network and transcriptional factors (TFs) regulatory network were used to further explore the function of identified DEGs. The quantitative real-time polymerase chain reaction (qRT-PCR) was applied to validate the result of bioinformatics analysis. Electronic validation was performed to verify the expression of selected DEGs. The diagnosis value of identified DEGs was accessed by receiver operating characteristic (ROC) analysis. Results. A total of 1085 DEGs were identified. KEGG pathway analysis displayed that Wnt was a significantly enriched signalling pathway. Some hub genes with high interactions such as USP46, CPVL, FKBP5, FOSL2, GADD45B, PTGS1, and ZNF423 were identified in the PPI and TFs network. The results of qRT-PCR showed that GADD45B, ADAMTS1, and TFAM were down-regulated in joint synovial tissues of OA, which was consistent with the bioinformatics analysis. The expression levels of USP46, CPVL, FOSL2, and PTGS1 in electronic validation were compatible with the bio-informatics result. CPVL and TFAM had a potential diagnostic value for OA based on the ROC analysis. Conclusion. The deregulated genes including USP46, CPVL, FKBP5, FOSL2, GADD45B, PTGS1, ZNF423, ADAMTS1, and TFAM might be involved in the pathology of OA. Cite this article: X. Zhang, Y. Bu, B. Zhu, Q. Zhao, Z. Lv, B. Li, J. Liu. Global transcriptome analysis to identify critical genes involved in the pathology of osteoarthritis. Bone Joint Res 2018;7:298–307. DOI: 10.1302/2046-3758.74.BJR-2017-0245.R1

Aims

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

Aims

Rotator cuff tear (RCT) is the leading cause of shoulder pain, primarily associated with age-related tendon degeneration. This study aimed to elucidate the potential differential gene expressions in tendons across different age groups, and to investigate their roles in tendon degeneration.