Original Article | Published:

Transcriptome analysis of ankylosing spondylitis patients before and after TNF-α inhibitor therapy reveals the pathways affected

Genes and Immunity volume 18, pages 184190 (2017) | Download Citation

Abstract

Tumor necrosis factor-α (TNF-α) inhibitors are highly effective in suppressing inflammation in ankylosing spondylitis (AS) patients, and operate by suppression of TFN-α and downstream immunological pathways. To determine the mechanisms of action of TNF-α inhibitors in AS patients, we used transcriptomic and bioinformatic approaches on peripheral blood mononuclear cells from AS patients pre and post treatment. We found 656 differentially expressed genes, including the genome-wide significant AS-associated genes, IL6R, NOTCH1, IL10, CXCR2 and TNFRSF1A. A distinctive gene expression profile was found between male and female patients, mainly because of sex chromosome-linked genes and interleukin 17 receptor C, potentially accounting for the differences in clinical manifestation and treatment response between the genders. In addition to immune and inflammation regulatory pathways, like intestinal immune network for IgA production, cytokine–cytokine receptor interaction, Ras signaling pathway, allograft rejection and hematopoietic cell lineage, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses revealed that infection-associated pathways (influenza A and toxoplasmosis) and metabolism-associated pathways were involved in response to TNF-α inhibitor treatment, providing insight into the mechanism of TNF-α inhibitors.

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References

  1. 1.

    , , , , . Impact of ankylosing spondylitis on work and family life: comparisons with the US population. Arthritis Rheum 2008; 59: 497–503.

  2. 2.

    , , , , , et al. Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nat Genet 2013; 45: 730–738.

  3. 3.

    , . Overestimation of the prevalence of ankylosing spondylitis in the Berlin study: comment on the article by Braun et al. Arthritis Rheum 2005; 52: 4049–4050.

  4. 4.

    , , , , , . Epidemiology of spondyloarthritis in the People's Republic of China: review of the literature and commentary. Semin Arthritis Rheum 2007; 37: 39–47.

  5. 5.

    , , , , , et al. Spondyloarthropathies in Japan: nationwide questionnaire survey performed by the Japan Ankylosing Spondylitis Society. J Rheumatol 2001; 28: 554–559.

  6. 6.

    , , , , , . Ankylosing spondylitis in West Africans—evidence for a non-HLA-B27 protective effect. Ann Rheum Dis 1997; 56: 68–70.

  7. 7.

    , , , , , et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat Genet 2016; 48: 510–518.

  8. 8.

    . Major histocompatibility genes and ankylosing spondylitis. Best Pract Res Clin Rheumatol 2006; 20: 601–609.

  9. 9.

    , , . Genetics of ankylosing spondylitis-insights into pathogenesis. Nat Rev Rheumatol 2015; 12: 81–91.

  10. 10.

    , , , , , et al. Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects. Arthritis Res Ther 2011; 13: 1–8.

  11. 11.

    , , , , , et al. 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.

  12. 12.

    , , . Transcriptome network analysis reveals potential candidate genes for ankylosing spondylitis. Eur Rev Med Pharmacol Sci 2013; 17: 3178–3185.

  13. 13.

    , , . Rheumatoid arthritis and ankylosing spondylitis - pathology of acute inflammation. Clin Exp Rheumatol 2009; 27 (4 Suppl 55): 1216–1218.

  14. 14.

    , , , , , et al. 2010 Update of the international ASAS recommendations for the use of anti-TNF agents in patients with axial spondyloarthritis. Ann Rheum Dis 2011; 70: 905–908.

  15. 15.

    . Efficacy of TNFα blockers in patients with ankylosing spondylitis and non-radiographic axial spondyloarthritis: a meta-analysis. Ann Rheum Dis 2014; 74: 1241–1248.

  16. 16.

    , , , , . Ankylosing spondylitis assessment group preliminary definition of short-term improvement in ankylosing spondylitis. Arthritis Rheum 2001; 44: 279.1–279.

  17. 17.

    , , , , , et al. Long-term efficiency of infliximab in patients with ankylosing spondylitis: real life data confirm the potential for dose reduction. RMD Open 2016; 2: e000272.

  18. 18.

    , , , , , et al. Clinical outcomes associated with switching or discontinuation from anti-TNF inhibitors for nonmedical reasons. Clin Ther 2017; 39: 849–862 e6.

  19. 19.

    , , , . Cytokine correlates of clinical response patterns to infliximab treatment of ankylosing spondylitis. Ann Rheum Dis 2004; 63: 84–87.

  20. 20.

    , , , , . Transmembrane TNF-alpha: structure, function and interaction with anti-TNF agents. Rheumatology (Oxford) 2010; 49: 1215–1228.

  21. 21.

    . Use of RNA sequencing to evaluate rheumatic disease patients. Arthritis Res Ther 2015; 17: 167.

  22. 22.

    , , , , . Gene expression profiling reveals a downregulation in immune-associated genes in patients with AS. Ann Rheum Dis 2010; 69: 1724–1729.

  23. 23.

    , , , , , et al. Identification of RGS1 as a candidate biomarker for undifferentiated spondylarthritis by genome-wide expression profiling and real-time polymerase chain reaction. Arthritis Rheum 2009; 60: 3269–3279.

  24. 24.

    , , , , , et al. Insights in to the pathogenesis of axial spondyloarthropathy based on gene expression profiles. Arthritis Res Ther 2009; 11: R168.

  25. 25.

    , , , , , 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.

  26. 26.

    , , , , , et al. Whole blood transcriptional profiling in ankylosing spondylitis identifies novel candidate genes that might contribute to the inflammatory and tissue-destructive disease aspects. Arthritis Res Ther 2011; 13: R57.

  27. 27.

    , , , , , . Changes in gene expression profiles of the hip joint ligament of patients with ankylosing spondylitis revealed by DNA chip. Clin Rheumatol 2012; 31: 1479–1491.

  28. 28.

    , , , , , et al. Expression profiling in spondyloarthropathy synovial biopsies highlights changes in expression of inflammatory genes in conjunction with tissue remodelling genes. BMC Musculoskelet Disord 2013; 14: 354.

  29. 29.

    , , , , , et al. Aberrant expression of shared master-key genes contributes to the immunopathogenesis in patients with juvenile spondyloarthritis. PLoS ONE 2014; 9: e115416.

  30. 30.

    Regulatory RNAs underlying genetic associations in ankylosing spondylitis. PhD Thesis, University of Queensland, Australia 2015.

  31. 31.

    , , , , , et al. 2012 update of the 2008 American College of Rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis. Arthritis Care Res (Hoboken) 2012; 64: 625–639.

  32. 32.

    , , , . In vivo effect of anti-TNF agent (etanercept) in reactivation of latent toxoplasmosis. J Parasit Dis 2016; 40: 1459–1465.

  33. 33.

    , , , , , et al. Interleukin 10 polymorphisms in ankylosing spondylitis. Genes Immun 2003; 4: 74–76.

  34. 34.

    , , , , , et al. Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility. Nat Genet 2011; 43: 761–767.

  35. 35.

    , , , , , et al. Interleukin‐17–positive mast cells contribute to synovial inflammation in spondylarthritis. Arthritis Rheum 2012; 64: 99–109.

  36. 36.

    , , . Interleukin-17 and type 17 helper T cells. N Engl J Med 2009; 361: 888–898.

  37. 37.

    , , . Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria. Arthritis Rheum 1984; 27: 361–368.

  38. 38.

    , , , , , . A new approach to defining disease status in ankylosing spondylitis: the Bath Ankylosing Spondylitis Disease Activity Index. J Rheumatol 1994; 21: 2286–2291.

  39. 39.

    , , , , , et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013; 29: 15–21.

  40. 40.

    , , . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550.

  41. 41.

    , , , , , et al. STRING v9. 1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013; 41: D808–D815.

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Acknowledgements

We would like to thank the participating patients for taking part in this study. The study was supported by grants from the Science and Technology Project of Wenzhou (No. Y20160028). MAB is funded by a National Health and Medical Research Council Senior Principal Research Fellowship.

Author information

Affiliations

  1. Rheumatology Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    • X B Wang
  2. Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology (QUT) at Translational Research Institute, Brisbane, Australia

    • X B Wang
    • , J J Ellis
    • , D J Pennisi
    • , X Song
    • , K Hollis
    • , L A Bradbury
    • , Z Li
    • , T J Kenna
    •  & M A Brown
  3. Cancer and Molecular Medicine Program, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Queensland, Australia

    • J Batra
  4. Australian Prostate Cancer Research Centre–Queensland, Translational Research Institute, Woolloongabba, Queensland, Australia

    • J Batra
  5. Centre for Precision Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    • M A Brown

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The authors declare no conflict of interest.

Corresponding author

Correspondence to M A Brown.

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DOI

https://doi.org/10.1038/gene.2017.19

Supplementary Information accompanies this paper on Genes and Immunity website (http://www.nature.com/gene)

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