RNA seq study of synovial biopsies from early osteoarthritis and early rheumatoid arthritis patients

Abstract

Purpose: Osteoarthritis (OA) and Rheumatoid arthritis (RA) are two types of most common arthritis worldwide. Although both OA and RA are long-lasting diseases and accompanied by swelling and pain at the joints of hands, wrists and knees, OA usually occurs when the cartilage at the end of the bones wearing out and the bones rub against each other; while RA occurs when the immune system mistakenly attacks the joints and is usually defined as an autoimmune disease. For the purpose of clinical practice, it is important to accurately diagnose and treat OA and RA; however, the pathological mechanisms of both diseases remain to be further revealed. The aim of our present study is to investigate the potential biomarkers that specifically related to these two types of diseases. We analyzed the gene expression profile of OA and RA by high throughput sequencing from samples isolated from joint synovial biopsies that collected from patients and healthy controls. Doing this will provide insight into the underlying genetic basis of these diseases. Methods: First of all, GSE89408 was downloaded from GEO (Gene Expression Omnibus) database, which includes 107 synovial biopsies samples from early (<12 Months) untreated OA (n=22) and RA (n=57) patients, and 28 healthy controls. Patients with RA and OA were diagnosed and classified according to the 1987 revised American College of Rheumatology (ACR) criteria and the criteria of Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association, respectively. GPL11154 Illumina HiSeq 2000 (Homo sapiens) was used for detection. Data preprocessing was performed by R affy package (http://www.bioconductor.org/packages/release/bioc/html/affy.html). Differentially expressed genes (DEGs, false discovery rate<0.05, Fold Change>1) between OA and Control, RA and Control, OA and RA were identified by GENEVESTIGATOR software (https://genevestigator.com/gv/). The false discovery rate (FDR) was obtained with the Benjamini-Hochberg (BH) method based on the raw P-values. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) network of the DEGs were obtained from Cytoscape 3.6.0 and GCBI (https://www.gcbi.com.cn/gclib/html/index). Results: The average age of 28 healthy donors was 35.2-year-old (y) (range, 20-66 y), including 14 females (50%) and 14 males (50%). The average age of OA donors was 49 y (range, 19-69 y), including 13 females (59.1%) and 9 males (40.9%). The average age of RA donors was 55.9 y (range, 25-89 y), including 33 females (57.9%) and 24 males (42.1%). A total of 519 DEGs (375 downregulated and 144 upregulated DEGs), 3998 DEGs (2034 downregulated and 1964 upregulated DEGs), and 891 DEGs (618 downregulated and 273 upregulated DEGs) were identified between OA and control, RA and control, OA and RA patients, respectively (Figure 1, A-C). Seventy-one DEGs with pathologically significance between OA and RA patients were identified (Figure 1, D). These 71 DEGs were enriched in biological processes, such as oxidative phosphorylation, response to oxygen, and cell migration, and mainly related to Oxidative phosphorylation and Toll-like receptor signaling pathways. The PPIs of the 71 DEGs from OA and RA showed that IL-8 and TOP2A were the key proteins in the network (Figure 2). Conclusions: Our results indicate that the clinical pathological difference between OA and RA might be detected in the biological process of oxidative phosphorylation, as well as in the signaling pathways related to Oxidative phosphorylation and Toll-like receptors. In addition, IL-8 and TOP2A might be the key proteins that regulate Oxidative phosphorylation and Toll-like receptor signaling pathways in mediating the difference between OA and RA. We will further validate our findings in future studies through well-designed and well-controlled experiments.View Large Image Figure ViewerDownload Hi-res image Download (PPT)