Integrative analysis of biomarkers and therapeutic targets in synovium of patients with osteoarthritis by multiple microarrays

doi:10.12307/2021.041

Abstract

Abstract: BACKGROUND: Osteoarthritis is one of the most common chronic diseases in the old adults, and currently there is no effective treatment.
OBJECTIVE: To identify differentially expressed genes and related signaling pathways in synovial tissue of osteoarthritis, elucidate the pathogenesis of osteoarthritis and seek for effective drug targets.
METHODS: Ten related data sets were collected from GEO database, including 120 synovial tissue samples from patients with osteoarthritis and 85 samples from normal subjects, and the differentially expressed genes were identified. Genetic ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on these differentially expressed genes. The protein-protein interaction network of differentially expressed genes was constructed using Cytoscape software, and module analysis was performed to screen key genes. Through the Drugbank database, approved drugs for osteoarthritis were screened, and the target genes for these drugs were mined in the Drug Gene Interaction database.
RESULTS AND CONCLUSION: A total of 25 differentially expressed genes were identified, including 20 up-regulated differentially expressed genes (SPP1, matrix metalloproteinase 1, matrix metalloproteinase 9, etc.) and 5 down-regulated differentially expressed genes (APOD, FKBP5, ZBTB16, etc.). GO functional enrichment analysis showed that these differentially expressed genes were mainly involved in the metabolism of extracellular matrix (e.g., collagen catabolism, extracellular matrix catabolism, collagen metabolism, extracellular structure, and tissue and protein metabolism). KEGG enrichment analysis showed that the differentially expressed genes were mainly concentrated in the “interleukin-17 signaling pathway” and “rheumatoid arthritis.” Through the Drugbank database and the Drug Gene Interaction database, a total of 50 approved osteoarthritis drugs and their corresponding 209 target genes were identified. SPP1, matrix metalloproteinase 1 and matrix metalloproteinase 9 were the intersection genes of the differentially expressed genes and the target genes of the osteoarthritis drug chondroitin sulfate, triamcinolone and celecoxib, and glucosamine. To conclude, the pathogenesis of osteoarthritis may be related to the degradation of extracellular matrix induced by the genes expressed in the synovial membrane. SPP1, matrix metalloproteinase 1 and matrix metalloproteinase 9 may be effective molecular targets for the treatment of osteoarthritis.

中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程

Key words: soft tissue, synovium, osteoarthritis, microarray chip, gene, signaling pathway, cell, target

CLC Number:

Liu Jinfu, Zeng Ping, Nong Jiao, Fan Siqi, Feng Chengqin, Huang Jiaxing. Integrative analysis of biomarkers and therapeutic targets in synovium of patients with osteoarthritis by multiple microarrays[J]. Chinese Journal of Tissue Engineering Research, 2021, 25(23): 3690-3696.

Figures/Tables 10

References

[1] MOBASHERI A, BATT M. An update on the pathophysiology of osteoarthritis. Ann Phys Rehabil Med. 2016;59(5-6):333-339.
[2] 章晓云,张驰,宋世雷,等.基于网络药理学和蛋白模块分析淫羊藿治疗骨关节炎的作用与机制[J].中国组织工程研究,2020,24(17): 2660-2666.
[3] KUBOSCH EJ, LANG G, FURST D, et al. The Potential for Synovium-derived Stem Cells in Cartilage Repair. Curr Stem Cell Res Ther. 2018; 13(3):174-184.
[4] MATHIESSEN A, CONAGHAN PG. Synovitis in osteoarthritis: current understanding with therapeutic implications. Arthritis Res Ther. 2017; 19(1):18.
[5] LI Z, ZHONG L, DU Z, et al. Network analyses of differentially expressed genes in osteoarthritis to identify hub genes. BioMed Res Int. 2019; 2019:8340573.
[6] LI M, ZHI L, ZHANG Z, et al. Identification of potential target genes associated with the pathogenesis of osteoarthritis using microarray based analysis. Mol Med Rep. 2017;16(3):2799-806.
[7] UNGETHUEM U, HAEUPL T, WITT H, et al. Molecular signatures and new candidates to target the pathogenesis of rheumatoid arthritis. Physiol Genomics. 2010;42a(4):267-282.
[8] FINIS K, SÜLTMANN H, RUSCHHAUPT M, et al. Analysis of pigmented villonodular synovitis with genome-wide complementary DNA microarray and tissue array technology reveals insight into potential novel therapeutic approaches. Arthritis Rheum. 2006;54(3):1009-1019.
[9] HUBER R, HUMMERT C, GAUSMANN U, et al. Identification of intra-group, inter-individual, and gene-specific variances in mRNA expression profiles in the rheumatoid arthritis synovial membrane. Arthritis Res Ther. 2008;10(4):R98.
[10] WANG Q, ROZELLE AL, LEPUS CM, et al. Identification of a central role for complement in osteoarthritis. Nat Med. 2011;17(12):1674-1679.
[11] THOMAS GP, DUAN R, PETTIT AR, et al. Expression profiling in spondyloarthropathy synovial biopsies highlights changes in expression of inflammatory genes in conjunction with tissue remodelling genes. BMC Musculoskelet Disorders. 2013;14:354.
[12] LAMBERT C, DUBUC JE, MONTELL E, et al. Gene expression pattern of cells from inflamed and normal areas of osteoarthritis synovial membrane. Arthritis Rheumatol. 2014;66(4):960-968.
[13] WOETZEL D, HUBER R, KUPFER P, et al. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation. Arthritis Res Ther. 2014;16(2):R84.
[14] BROEREN MG, DE VRIES M, BENNINK MB, et al. Functional tissue analysis reveals successful cryopreservation of human osteoarthritic synovium. PLoS One. 2016;11(11):e0167076.
[15] GUO Y, WALSH AM, FEARON U, et al. CD40L-dependent pathway is active at various stages of rheumatoid arthritis disease progression. J Immunol. 2017;198(11):4490-4501.
[16] GREEN GH, DIGGLE PJ. On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure. Stat Appl Genet Mol Biol. 2007;6:Article27.
[17] RAUDVERE U, KOLBERG L, KUZMIN I, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47(W1):W191-W198.
[18] SZKLARCZYK D, FRANCESCHINI A, WYDER S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic acids res. 2015;43(Database issue):D447-D452.
[19] WISHART DS, FEUNANG YD, GUO AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018; 46(D1):D1074-D1082.
[20] RHODES DR, CHINNAIYAN AM. Bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers. Ann N Y Acad Sci. 2004;1020:32-40.
[21] ZHANG H, LIEW CC, MARSHALL KW. Microarray analysis reveals the involvement of beta-2 microglobulin (B2M) in human osteoarthritis. Osteoarthritis Cartilage. 2002;10(12):950-960.
[22] DEL REY MJ, USATEGUI A, IZQUIERDO E, et al. Transcriptome analysis reveals specific changes in osteoarthritis synovial fibroblasts. Ann Rheum Dis. 2012;71(2):275-280.
[23] BIRCH HL. Extracellular Matrix and Ageing. Sub-cellular biochemistry. 2018;90:169-190.
[24] SHI Y, HU X, CHENG J, et al. A small molecule promotes cartilage extracellular matrix generation and inhibits osteoarthritis development. Nat Commun. 2019;10(1):1914.
[25] VAN DER KRAAN PM, VAN DEN BERG WB. Chondrocyte hypertrophy and osteoarthritis: role in initiation and progression of cartilage degeneration? Osteoarthritis Cartilage. 2012;20(3):223-232.
[26] FAVERO M, BELLUZZI E, TRISOLINO G, et al. Inflammatory molecules produced by meniscus and synovium in early and end-stage osteoarthritis: a coculture study. J Cell Physiol. 2019;234(7): 11176-11187.
[27] PASTOUREAU PC, CHOMEL AC, BONNET J. Evidence of early subchondral bone changes in the meniscectomized guinea pig. A densitometric study using dual-energy X-ray absorptiometry subregional analysis. Osteoarthritis Cartilage. 1999;7(5):466-473.
[28] YAMADA K, HEALEY R, AMIEL D, et al. Subchondral bone of the human knee joint in aging and osteoarthritis. Osteoarthritis Cartilage. 2002;10(5):360-369.
[29] IWAKURA Y, ISHIGAME H, SAIJO S, et al. Functional specialization of interleukin-17 family members. Immunity. 2011;34(2):149-162.
[30] KAPOOR M, MARTEL-PELLETIER J, LAJEUNESSE D, et al. Role of proinflammatory cytokines in the pathophysiology of osteoarthritis. Nat Rev Rheumatol. 2011;7(1):33-42.
[31] CAI L, YIN JP, STAROVASNIK MA, et al. Pathways by which interleukin 17 induces articular cartilage breakdown in vitro and in vivo. Cytokine. 2001;16(1):10-21.
[32] LUBBERTS E, JOOSTEN LA, VAN DE LOO FA, et al. Reduction of interleukin-17-induced inhibition of chondrocyte proteoglycan synthesis in intact murine articular cartilage by interleukin-4. Arthritis Rheum. 2000;43(6):1300-1306.
[33] DE LANGE-BROKAAR BJ, IOAN-FACSINAY A, VAN OSCH GJ, et al. Synovial inflammation, immune cells and their cytokines in osteoarthritis: a review. Osteoarthritis Cartilage. 2012;20(12):1484-99.
[34] BROUWERS H, VON HEGEDUS J, TOES R, et al. Lipid mediators of inflammation in rheumatoid arthritis and osteoarthritis. Best Pract Res Clin Rheumatol. 2015;29(6):741-755.
[35] MALEMUD CJ. Matrix Metalloproteinases and Synovial Joint Pathology. Prog Mol Biol Transl Sci. 2017;148:305-325.
[36] YAMAGA M, TSUJI K, MIYATAKE K, et al. Osteopontin level in synovial fluid is associated with the severity of joint pain and cartilage degradation after anterior cruciate ligament rupture. PLoS One. 2012; 7(11):e49014.
[37] HONSAWEK S, TANAVALEE A, SAKDINAKIATTIKOON M, et al. Correlation of plasma and synovial fluid osteopontin with disease severity in knee osteoarthritis. Clin Biochem. 2009;42(9):808-812.
[38] WANG Q, WANG W, ZHANG F, et al. NEAT1/miR-181c Regulates Osteopontin (OPN)-Mediated Synoviocyte Proliferation in Osteoarthritis. J Cell Biochem. 2017;118(11):3775-3784.
[39] XU G, NIE H, LI N, et al. Role of osteopontin in amplification and perpetuation of rheumatoid synovitis. J Clin Invest. 2005;115(4): 1060-1067.
[40] CHENG C, GAO S, LEI G. Association of osteopontin with osteoarthritis. Rheumatol Int. 2014;34(12):1627-1631.
[41] THORSON C, GALICIA K, BURLESON A, et al. Matrix Metalloproteinases and Their Inhibitors and Proteoglycan 4 in Patients Undergoing Total Joint Arthroplasty. Clin Appl Thromb Hemost. 2019;25: 1076029619828113.
[42] Eo SH, Choi SY, Kim SJ. PEP-1-SIRT2-induced matrix metalloproteinase-1 and -13 modulates type II collagen expression via ERK signaling in rabbit articular chondrocytes. Exp Cell Res. 2016; 348(2):201-208.
[43] PELLETIER JP, RAYNAULD JP, CARON J, et al. Decrease in serum level of matrix metalloproteinases is predictive of the disease-modifying effect of osteoarthritis drugs assessed by quantitative MRI in patients with knee osteoarthritis. Ann Rheum Dise. 2010;69(12):2095-2101.
[44] WYATT LA, NWOSU LN, WILSON D, et al. Molecular expression patterns in the synovium and their association with advanced symptomatic knee osteoarthritis. Osteoarthritis Cartilage. 2019;27(4):667-675.
[45] BURRAGE PS, MIX KS, BRINCKERHOFF CE. Matrix metalloproteinases: role in arthritis. Front Biosci. 2006;11:529-543.
[46] NAGASE H, WOESSNER JF JR. Matrix metalloproteinases. J Biol Chem. 1999;274(31):21491-21494.
[47] FLANNERY CR. MMPs and ADAMTSs: functional studies. Front Biosci. 2006;11:544-569.
[48] LI H, WANG D, YUAN Y, et al. New insights on the MMP-13 regulatory network in the pathogenesis of early osteoarthritis. Arthritis Res Ther. 2017;19(1):248.