Gene Expression Analysis before and after Treatment with Adalimumab in Patients with Ankylosing Spondylitis Identifies Molecular Pathways Associated with Response to Therapy

Marzia Dolcino, Elisa Tinazzi, Andrea Pelosi, Giuseppe Patuzzo, Francesca Moretta, Claudio Lunardi, Antonio Puccetti, Marzia Dolcino, Elisa Tinazzi, Andrea Pelosi, Giuseppe Patuzzo, Francesca Moretta, Claudio Lunardi, Antonio Puccetti

Abstract

The etiology of Ankylosing spondylitis (AS) is still unknown and the identification of the involved molecular pathogenetic pathways is a current challenge in the study of the disease. Adalimumab (ADA), an anti-tumor necrosis factor (TNF)-alpha agent, is used in the treatment of AS. We aimed at identifying pathogenetic pathways modified by ADA in patients with a good response to the treatment. Gene expression analysis of Peripheral Blood Cells (PBC) from six responders and four not responder patients was performed before and after treatment. Differentially expressed genes (DEGs) were submitted to functional enrichment analysis and network analysis, followed by modules selection. Most of the DEGs were involved in signaling pathways and in immune response. We identified three modules that were mostly impacted by ADA therapy and included genes involved in mitogen activated protein (MAP) kinase, wingless related integration site (Wnt), fibroblast growth factor (FGF) receptor, and Toll-like receptor (TCR) signaling. A separate analysis showed that a higher percentage of DEGs was modified by ADA in responders (44%) compared to non-responders (12%). Moreover, only in the responder group, TNF, Wnt, TLRs and type I interferon signaling were corrected by the treatment. We hypothesize that these pathways are strongly associated to AS pathogenesis and that they might be considered as possible targets of new drugs in the treatment of AS.

Keywords: Adalimumab (ADA); Ankylosing spondylitis; gene module; gene-array; protein-protein interaction (PPI) network.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Real time (RT)-PCR of some modulated genes in AS patients. Genes selected for validation were IL6ST, TNFRSF25, TNFSF8 and STAT1. The transcripts of the selected genes were increased in AS samples when compared to healthy donors. Relative expression levels were calculated for each sample after normalization against the housekeeping gene GAPDH. Experiments have been conducted in triplicates. Similar results were obtained using the housekeeping genes18s rRNA and beta-actin.
Figure 2
Figure 2
Network analysis of differentially expressed genes (DEGs) in AS patients. (A) Protein-protein interaction (PPI) network of DEGs; (B) Degree Sorted Circle Layout of the PPI network. Nodes are ordered around a circle based on their degree of connectivity (number of edges). Nodes belonging to modules are highlighted in red; (C) Modules originated from the interaction network.
Figure 3
Figure 3
Protein-protein interaction (PPI) network of DEGs in AS patients after treatment.

References

    1. Stolwijk C., Essers I., van Tubergen A., Boonen A., Bazelier M.T., de Bruin M.L., de Vries F. The epidemiology of extra-articular manifestations in ankylosing spondylitis: A population-based matched cohort study. Ann. Rheum. Dis. 2015;74:1373–1378. doi: 10.1136/annrheumdis-2014-205253.
    1. Reveille J.D. An update on the contribution of the MHC to as susceptibility. Clin. Rheumatol. 2014;33:749–757. doi: 10.1007/s10067-014-2662-7.
    1. Braun J., van den Berg R., Baraliakos X., Boehm H., Burgos-Vargas R., Collantes-Estevez E., Dagfinrud H., Dijkmans B., Dougados M., Emery P., et al. 2010 update of the ASAS/EULAR recommendations for the management of ankylosing spondylitis. Ann. Rheum. Dis. 2011;70:896–904. doi: 10.1136/ard.2011.151027.
    1. Ward M.M., Deodhar A., Akl E.A., Lui A., Ermann J., Gensler L.S., Smith J.A., Borenstein D., Hiratzka J., Weiss P.F., et al. American college of rheumatology/spondylitis association of america/spondyloarthritis research and treatment network 2015 recommendations for the treatment of ankylosing spondylitis and nonradiographic axial spondyloarthritis. Arthritis Rheumatol. 2016;68:282–298. doi: 10.1002/art.39298.
    1. Sieper J., oddubnyy D. New evidence on the management of spondyloarthritis. Nat. Rev. Rheumatol. 2016;12:282–295. doi: 10.1038/nrrheum.2016.42.
    1. Batliwalla F.M., Baechler E.C., Xiao X., Li W., Balasubramanian S., Khalili H., Damle A., Ortmann W.A., Perrone A., Kantor A.B., et al. Peripheral blood gene expression profiling in rheumatoid arthritis. Genes Immun. 2005;6:388–397. doi: 10.1038/sj.gene.6364209.
    1. Baechler E.C., Batliwalla F.M., Karypis G., Gaffney P.M., Ortmann W.A., Espe K.J., Shark K.B., Grande W.J., Hughes K.M., Kapur V., et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl. Acad. Sci. USA. 2003;100:2610–2615. doi: 10.1073/pnas.0337679100.
    1. Dolcino M., Ottria A., Barbieri A., Patuzzo G., Tinazzi E., Argentino G., Beri R., Lunardi C., Puccetti A. Gene expression profiling in peripheral blood cells and synovial membranes of patients with psoriatic arthritis. PLoS ONE. 2015;10:e0128262. doi: 10.1371/journal.pone.0128262.
    1. Oliveira R.D., Fontana V., Junta C.M., Marques M.M., Macedo C., Rassi D.M., Passos G.A., Donadi E.A., Louzada-Junior P. Differential gene expression profiles may differentiate responder and nonresponder patients with rheumatoid arthritis for methotrexate (MTX) monotherapy and MTX plus tumor necrosis factor inhibitor combined therapy. J. Rheumatol. 2012;39:1524–1532. doi: 10.3899/jrheum.120092.
    1. Kim T.H., Choi S.J., Lee Y.H., Song G.G., Ji J.D. Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets. Joint Bone Spine. 2014;81:325–330. doi: 10.1016/j.jbspin.2014.01.013.
    1. Van der Linden S., Valkenburg H.A., Cats A. Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria. Arthritis Rheum. 1984;27:361–368. doi: 10.1002/art.1780270401.
    1. Raychaudhuri S.P., Deodhar A. The classification and diagnostic criteria of ankylosing spondylitis. J. Autoimmun. 2014;48–49:128–133. doi: 10.1016/j.jaut.2014.01.015.
    1. Godfrin-Valnet M., Prati C., Puyraveau M., Toussirot E., Letho-Gyselink H., Wendling D. Evaluation of spondylarthritis activity by patients and physicians: ASDAS, BASDAI, PASS, and flares in 200 patients. Joint Bone Spine. 2013;80:393–398. doi: 10.1016/j.jbspin.2013.01.003.
    1. Mi H., Thomas P. Panther pathway: An ontology-based pathway database coupled with data analysis tools. Methods Mol. Biol. 2009;563:123–140.
    1. Franceschini A., Szklarczyk D., Frankild S., Kuhn M., Simonovic M., Roth A., Lin J., Minguez P., Bork P., von Mering C., et al. STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013;41:D808–D815. doi: 10.1093/nar/gks1094.
    1. Jensen L.J., Kuhn M., Stark M., Chaffron S., Creevey C., Muller J., Doerks T., Julien P., Roth A., Simonovic M., et al. STRING 8—A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37:D412–D416. doi: 10.1093/nar/gkn760.
    1. Bader G.D., Hogue C.W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003;4:2. doi: 10.1186/1471-2105-4-2.
    1. Cline M.S., Smoot M., Cerami E., Kuchinsky A., Landys N., Workman C., Christmas R., Avila-Campilo I., Creech M., Gross B., et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2007;2:2366–2382. doi: 10.1038/nprot.2007.324.
    1. Dolcino M., Cozzani E., Riva S., Parodi A., Tinazzi E., Lunardi C., Puccetti A. Gene expression profiling in dermatitis herpetiformis skin lesions. Clin. Dev. Immunol. 2012;2012:198956. doi: 10.1155/2012/198956.
    1. Tinazzi E., Dolcino M., Puccetti A., Rigo A., Beri R., Valenti M.T., Corrocher R., Lunardi C. Gene expression profiling in circulating endothelial cells from systemic sclerosis patients shows an altered control of apoptosis and angiogenesis that is modified by Iloprost infusion. Arthritis Res. Ther. 2010;12:R131. doi: 10.1186/ar3069.
    1. Dolcino M., Patuzzo G., Barbieri A., Tinazzi E., Rizzi M., Beri R., Argentino G., Ottria A., Lunardi C., Puccetti A. Gene expression profiling in peripheral blood mononuclear cells of patients with common variable immunodeficiency: Modulation of adaptive immune response following intravenous immunoglobulin therapy. PLoS ONE. 2014;9:e97571. doi: 10.1371/journal.pone.0097571.
    1. Heid C.A., Stevens J., Livak K.J., Williams P.M. Real time quantitative PCR. Genome Res. 1996;6:986–994. doi: 10.1101/gr.6.10.986.
    1. Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001;25:402–408. doi: 10.1006/meth.2001.1262.
    1. Mahmoudi M., Aslani S., Nicknam M.H., Karami J., Jamshidi A.R. New insights toward the pathogenesis of ankylosing spondylitis; genetic variations and epigenetic modifications. Mod. Rheumatol. 2016;27:198–209. doi: 10.1080/14397595.2016.1206174.
    1. Gao R., Sun W., Chen Y., Su Y., Wang C., Dong L. Elevated serum levels of soluble CD30 in ankylosing spondylitis patients and its association with disease severity-related parameters. BioMed Res. Int. 2015;2015:617282. doi: 10.1155/2015/617282.
    1. Barbieri A., Dolcino M., Tinazzi E., Rigo A., Argentino G., Patuzzo G., Ottria A., Beri R., Puccetti A., Lunardi C. Characterization of CD30/CD30L(+) cells in peripheral blood and synovial fluid of patients with rheumatoid arthritis. J. Immunol. Res. 2015;2015:729654. doi: 10.1155/2015/729654.
    1. Lee E.J., Lee E.J., Chung Y.H., Song D.H., Hong S., Lee C.K., Yoo B., Kim T.H., Park Y.S., Kim S.H., et al. High level of interleukin-32 gamma in the joint of ankylosing spondylitis is associated with osteoblast differentiation. Arthritis Res. Ther. 2015;17:350. doi: 10.1186/s13075-015-0870-4.
    1. Rother N., van der Vlag J. Disturbed T-cell signaling and altered Th17 and regulatory T-cell subsets in the pathogenesis of systemic lupus erythematosus. Front. Immunol. 2015;6:610. doi: 10.3389/fimmu.2015.00610.
    1. Lubberts E. The IL-23-IL-17 axis in inflammatory arthritis. Nat. Rev. Rheumatol. 2015;11:415–429. doi: 10.1038/nrrheum.2015.53.
    1. Diani M., Altomare G., Reali E. T helper cell subsets in clinical manifestations of psoriasis. J. Immunol. Res. 2016;2016:7692024. doi: 10.1155/2016/7692024.
    1. Gracey E., Qaiyum Z., Almaghlouth I., Lawson D., Karki S., Avvaru N., Zhang Z., Yao Y., Ranganathan V., Baglaenko Y., et al. IL-7 primes IL-17 in mucosal-associated invariant T (mait) cells, which contribute to the Th17-axis in ankylosing spondylitis. Ann. Rheum. Dis. 2016;75:2124–2432. doi: 10.1136/annrheumdis-2015-208902.
    1. Xie W., Zhou L., Li S., Hui T., Chen D. Wnt/beta-catenin signaling plays a key role in the development of spondyloarthritis. Ann. N. Y. Acad. Sci. 2016;1364:25–31. doi: 10.1111/nyas.12968.
    1. Smith J.A. Update on ankylosing spondylitis: Current concepts in pathogenesis. Curr. Allergy Asthma Rep. 2015;15:489. doi: 10.1007/s11882-014-0489-6.
    1. Meylan F., Davidson T.S., Kahle E., Kinder M., Acharya K., Jankovic D., Bundoc V., Hodges M., Shevach E.M., Keane-Myers A., et al. The TNF-family receptor DR3 is essential for diverse T-cell-mediated inflammatory diseases. Immunity. 2008;29:79–89. doi: 10.1016/j.immuni.2008.04.021.
    1. Bull M.J., Williams A.S., Mecklenburgh Z., Calder C.J., Twohig J.P., Elford C., Evans B.A., Rowley T.F., Slebioda T.J., Taraban V.Y., et al. The death receptor 3-TNF-like protein 1a pathway drives adverse bone pathology in inflammatory arthritis. J. Exp. Med. 2008;205:2457–2464. doi: 10.1084/jem.20072378.
    1. Liu W., Wu Y.H., Zhang L., Liu X.Y., Xue B., Wang Y., Liu B., Jiang Q., Kwang H.W., Wu D.J. Elevated serum levels of IL-6 and IL-17 may associate with the development of ankylosing spondylitis. Int. J. Clin. Exp. Med. 2015;8:17362–17376.
    1. Braem K., Luyten F.P., Lories R.J. Blocking p38 signaling inhibits chondrogenesis in vitro but not ankylosis in a model of ankylosing spondylitis in vivo. Ann. Rheum. Dis. 2012;71:722–728. doi: 10.1136/annrheumdis-2011-200377.
    1. Studer R.K., Bergman R., Stubbs T., Decker K. Chondrocyte response to growth factors is modulated by p38 mitogen-activated protein kinase inhibition. Arthritis Res. Ther. 2004;6:R56–R64. doi: 10.1186/ar1022.
    1. Assassi S., Reveille J.D., Arnett F.C., Weisman M.H., Ward M.M., Agarwal S.K., Gourh P., Bhula J., Sharif R., Sampat K., et al. Whole-blood gene expression profiling in ankylosing spondylitis shows upregulation of Toll-like receptor 4 and 5. J. Rheumatol. 2011;38:87–98. doi: 10.3899/jrheum.100469.
    1. Tan F.K., Farheen K. The potential importance of Toll-like receptors in ankylosing spondylitis. Int. J. Clin. Rheumtol. 2011;6:649–654. doi: 10.2217/ijr.11.61.
    1. Krug H.E. Fibroblasts from mice with progessive ankylosis proliferate excessively in response to transforming growth factor-beta 1. J. Investig. Med. 1998;46:134–139.
    1. Travis M.A., Sheppard D. Tgf-beta activation and function in immunity. Annu. Rev. Immunol. 2014;32:51–82. doi: 10.1146/annurev-immunol-032713-120257.
    1. Chen K., Wei X.Z., Zhu X.D., Bai Y.S., Chen Y., Wang C.F., Chen Z.Q., Li M. Whole-blood gene expression profiling in ankylosing spondylitis identifies novel candidate genes that may contribute to the inflammatory and tissue-destructive disease aspects. Cell. Immunol. 2013;286:59–64. doi: 10.1016/j.cellimm.2013.10.009.
    1. Gordon R.A., Grigoriev G., Lee A., Kalliolias G.D., Ivashkiv L.B. The interferon signature and stat1 expression in rheumatoid arthritis synovial fluid macrophages are induced by tumor necrosis factor alpha and counter-regulated by the synovial fluid microenvironment. Arthritis Rheum. 2012;64:3119–3128. doi: 10.1002/art.34544.
    1. Nzeusseu Toukap A., Galant C., Theate I., Maudoux A.L., Lories R.J., Houssiau F.A., Lauwerys B.R. Identification of distinct gene expression profiles in the synovium of patients with systemic lupus erythematosus. Arthritis Rheum. 2007;56:1579–1588. doi: 10.1002/art.22578.
    1. Thurlings R.M., Boumans M., Tekstra J., van Roon J.A., Vos K., van Westing D.M., van Baarsen L.G., Bos C., Kirou K.A., Gerlag D.M., et al. Relationship between the type I Interferon signature and the response to rituximab in rheumatoid arthritis patients. Arthritis Rheum. 2010;62:3607–3614. doi: 10.1002/art.27702.
    1. Raterman H.G., Vosslamber S., de Ridder S., Nurmohamed M.T., Lems W.F., Boers M., van de Wiel M., Dijkmans B.A., Verweij C.L., Voskuyl A.E. The Interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients. Arthritis Res. Ther. 2012;14:R95. doi: 10.1186/ar3819.
    1. Moschella F., Torelli G.F., Valentini M., Urbani F., Buccione C., Petrucci M.T., Natalino F., Belardelli F., Foa R., Proietti E. Cyclophosphamide induces a type I Interferon-associated sterile inflammatory response signature in cancer patients’ blood cells: Implications for cancer chemoimmunotherapy. Clin. Cancer Res. 2013;19:4249–4261. doi: 10.1158/1078-0432.CCR-12-3666.
    1. Maria N.I., Brkic Z., Waris M., van Helden-Meeuwsen C.G., Heezen K., van de Merwe J.P., van Daele P.L., Dalm V.A., Drexhage H.A., Versnel M.A. Mxa as a clinically applicable biomarker for identifying systemic Interferon type I in primary sjogren’s syndrome. Ann. Rheum. Dis. 2014;73:1052–1059. doi: 10.1136/annrheumdis-2012-202552.
    1. Caignard G., Lucas-Hourani M., Dhondt K.P., Labernardiere J.L., Petit T., Jacob Y., Horvat B., Tangy F., Vidalain P.O. The v protein of tioman virus is incapable of blocking type I Interferon signaling in human cells. PLoS ONE. 2013;8:e53881. doi: 10.1371/journal.pone.0053881.
    1. Ambrosi A., Espinosa A., Wahren-Herlenius M. Il-17: A new actor in IFN-driven systemic autoimmune diseases. Eur. J. Immunol. 2012;42:2274–2284. doi: 10.1002/eji.201242653.
    1. Axtell R.C., de Jong B.A., Boniface K., van der Voort L.F., Bhat R., De Sarno P., Naves R., Han M., Zhong F., Castellanos J.G., et al. T helper type 1 and 17 cells determine efficacy of Interferon-beta in multiple sclerosis and experimental encephalomyelitis. Nat. Med. 2010;16:406–412. doi: 10.1038/nm.2110.
    1. Brkic Z., Corneth O.B., van Helden-Meeuwsen C.G., Dolhain R.J., Maria N.I., Paulissen S.M., Davelaar N., van Hamburg J.P., van Daele P.L., Dalm V.A., et al. T-helper 17 cell cytokines and Interferon type I: Partners in crime in systemic lupus erythematosus? Arthritis Res. Ther. 2014;16:R62. doi: 10.1186/ar4499.
    1. O’Shea J.J., Plenge R. Jak and STAT signaling molecules in immunoregulation and immune-mediated disease. Immunity. 2012;36:542–550. doi: 10.1016/j.immuni.2012.03.014.
    1. Zhu Z.Q., Tang J.S., Cao X.J. Transcriptome network analysis reveals potential candidate genes for ankylosing spondylitis. Eur. Rev. Med. Pharmacol. Sci. 2013;17:3178–3185.
    1. Beyer C., Schett G. Pharmacotherapy: Concepts of pathogenesis and emerging treatments. Novel targets in bone and cartilage. Best Pract. Res. Clin. Rheumatol. 2010;24:489–496. doi: 10.1016/j.berh.2010.03.001.
    1. Nakayamada S., Kurose H., Saito K., Mogami A., Tanaka Y. Small GTP-binding protein Rho-mediated signaling promotes proliferation of rheumatoid synovial fibroblasts. Arthritis Res. Ther. 2005;7:R476–R484. doi: 10.1186/ar1694.
    1. Xu W., Liang C.G., Li Y.F., Ji Y.H., Qiu W.J., Tang X.Z. Involvement of Notch1/Hes signaling pathway in ankylosing spondylitis. Int. J. Clin. Exp. Pathol. 2015;8:2737–2745.

Source: PubMed

Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

3
Abonner