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Bioinformatics analysis of differentially expressed genes in subchondral bone in early experimental osteoarthritis using microarray data.

ABSTRACT: BACKGROUND:Osteoarthritis (OA) is the most common arthritic disease in humans, affecting the majority of individuals over 65 years of age. The aim of this study is to identify the gene expression profile specific to subchondral bone in OA by comparing the different expression profiles in experimental and sham-operation groups. METHODS:Gene expression profile GSE30322 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by limma package. And Database for Annotation, Visualization and Integrated Discovery (DAVID) databases were further used to identify the potential gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, a protein-protein interaction (PPI) network was constructed and significant modules were extracted. RESULTS:Totally, 588 DEGs were identified including 199 upregulated DEGs and 389 downregulated DEGs screened in OA and sham-operation. GO showed that DEGs were significantly enhanced for ribosomal subunit export from nucleus and molting cycle. KEGG pathway analysis revealed that target genes were enriched in thiamine metabolism. CONCLUSION:These key candidate DEGs that affect the progression of OA, and these genes might serve as potential therapeutic targets for OA.

SUBMITTER: Wang Z

PROVIDER: S-EPMC7414553 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Bioinformatics analysis of differentially expressed genes in subchondral bone in early experimental osteoarthritis using microarray data.

Wang Zhao Z Ji Yong Y Bao Hong-Wei HW

Journal of orthopaedic surgery and research 20200808 1

<h4>Background</h4>Osteoarthritis (OA) is the most common arthritic disease in humans, affecting the majority of individuals over 65 years of age. The aim of this study is to identify the gene expression profile specific to subchondral bone in OA by comparing the different expression profiles in experimental and sham-operation groups.<h4>Methods</h4>Gene expression profile GSE30322 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained ...[more]

PMID: 32771051

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Project description:Osteoarthritis (OA) is a complex degenerative joint disease, which is not only a cartilage but also a bone disease. A better understanding of the early molecular mechanism changes of subchondral bone in vivo may contribute to elucidating the pathogenesis of OA. We used microarray technology to investigate the time-course molecular changes of subchondral bone just beneath damaged cartilage in early stage of experimental osteoarthritis, and found 2,234 differentially expressed (DE) genes at 1 week, 1,944 at 2 weeks and 1,517 at 4 weeks postsurgery.Further analysis of dysregulated genes indicated that subchondral bone remodeling occurred sequentially and in a time-dependent manner at the gene expression level. Some known dysregulated genes suspected roles in influencing bone development or bone remodeling, such as Alp, Igf1, Tgf M-NM-21, Postn, Mmp3, Tnfsf11, Acp5, Bmp5, Aspn and Ihh, were confirmed by real-time PCR, and results indicated that our microarray data could accurately reflect gene expression patterns of early OA. Subsequently, to validate the results of our microarray analysis at protein level, immunohistochemistry staining was introduced to investigate the translational level of genes Mmp3 and Aspn in tissue sections, and results showed that the level of Mmp3 protein expression was totally matched the results of microarray and real-time PCR analysis. Nevertheless, the expression of Aspn protein was not observed differentially expressed at any time point. Ninety 10-week-old male Sprague-Dawley rats, weighing 300-325g, were used in the study. Animals were equally divided into two groups: experimental group (E-Group) and sham-operated group (S-Group). The E-Group rats underwent open surgery, involved in both medial meniscectomy and medial collateral ligament (MCL) transaction with micro-scissors. The S-Group rats were carried out with a sham operation, via a similar incision, without operations of the medial meniscus and the medial collateral ligament.Animals were killed at 1, 2, and 4 weeks postsurgery, and 15 animals were put into use per-timepoint in each treatment group. 5 animals were used for histological analysis and immunohistochemistry, and others were used for microarray study and Real-time polymerase chain reaction (PCR) analysis equally at each timepoint.

Project description:Osteoarthritis (OA) is the most prevalent chronic degenerative disease that affects the health of the elderly. The present study aimed to identify significant genes involved in OA via bioinformatics analysis. A gene expression dataset (GSE104793) was downloaded from the Gene Expression Omnibus. Bioinformatics analysis was then performed in order to identify differentially expressed genes (DEGs) between untreated chondrocytes and chondrocytes cultured with interleukin-1β (IL-1β) for 24 h. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using Metascape. A protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes. Gene set enrichment analysis (GSEA) was performed using GSEA software. Furthermore, chondrocytes were extracted and treated with IL-1β (10 ng/ml) for 24 h, and reverse-transcription quantitative PCR was used to confirm differential expression of hub genes. Patient samples were also collected to verify the bioinformatic analysis results. Based on the cut-off criteria used for determination of the DEGs, a total of 844 DEGs, including 498 upregulated and 346 downregulated DEGs, were identified. The DEGs were mainly enriched in the GO terms and KEGG pathways ‘inflammatory response’, ‘negative regulation of cell proliferation’, ‘ossification’, ‘taxis’, ‘blood vessel morphogenesis’, ‘extracellular structure organization’, ‘mitotic cell cycle process’ and ‘TNF signaling pathway’. The majority of the PCR results, namely the differential expression of kininogen 2, complement C3, cyclin B1, cell division cycle 20, cyclin A2, 1-phosphatidylinositol 4-kinase, BUB1 mitotic checkpoint serine/threonine kinase, kinesin family member 11, cyclin B2 and BUB1 mitotic checkpoint serine/threonine kinase B were consistent with the bioinformatics results. Collectively, the present observations provided a regulation network of IL-1β-stimulated chondrocytes, which may provide potential targets of OA therapy.

Project description:BackgroundBatch effects due to sample preparation or array variation (type, charge, and/or platform) may influence the results of microarray experiments and thus mask and/or confound true biological differences. Of the published approaches for batch correction, the algorithm "Combating Batch Effects When Combining Batches of Gene Expression Microarray Data" (ComBat) appears to be most suitable for small sample sizes and multiple batches.MethodsSynovial fibroblasts (SFB; purity > 98%) were obtained from rheumatoid arthritis (RA) and osteoarthritis (OA) patients (n = 6 each) and stimulated with TNF-α or TGF-β1 for 0, 1, 2, 4, or 12 hours. Gene expression was analyzed using Affymetrix Human Genome U133 Plus 2.0 chips, an alternative chip definition file, and normalization by Robust Multi-Array Analysis (RMA). Data were batch-corrected for different acquiry dates using ComBat and the efficacy of the correction was validated using hierarchical clustering.ResultsIn contrast to the hierarchical clustering dendrogram before batch correction, in which RA and OA patients clustered randomly, batch correction led to a clear separation of RA and OA. Strikingly, this applied not only to the 0 hour time point (i.e., before stimulation with TNF-α/TGF-β1), but also to all time points following stimulation except for the late 12 hour time point. Batch-corrected data then allowed the identification of differentially expressed genes discriminating between RA and OA. Batch correction only marginally modified the original data, as demonstrated by preservation of the main Gene Ontology (GO) categories of interest, and by minimally changed mean expression levels (maximal change 4.087%) or variances for all genes of interest. Eight genes from the GO category "extracellular matrix structural constituent" (5 different collagens, biglycan, and tubulointerstitial nephritis antigen-like 1) were differentially expressed between RA and OA (RA > OA), both constitutively at time point 0, and at all time points following stimulation with either TNF-α or TGF-β1.ConclusionBatch correction appears to be an extremely valuable tool to eliminate non-biological batch effects, and allows the identification of genes discriminating between different joint diseases. RA-SFB show an upregulated expression of extracellular matrix components, both constitutively following isolation from the synovial membrane and upon stimulation with disease-relevant cytokines or growth factors, suggesting an "imprinted" alteration of their phenotype.

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Bioinformatics analysis of differentially expressed genes in subchondral bone in early experimental osteoarthritis using microarray data.

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Bioinformatics analysis of differentially expressed genes in subchondral bone in early experimental osteoarthritis using microarray data.

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