Rheumatology International

, Volume 33, Issue 1, pp 129–137 | Cite as

Gene expression patterns in peripheral blood cells associated with radiographic severity in African Americans with early rheumatoid arthritis

  • Richard J. Reynolds
  • Xiangqin Cui
  • Laura K. Vaughan
  • David T. Redden
  • Zenoria Causey
  • Elizabeth Perkins
  • Tishi Shah
  • Laura B. Hughes
  • CLEAR Investigators
  • Aarti Damle
  • Marlena Kern
  • Peter K. Gregersen
  • Martin R. Johnson
  • S. Louis BridgesJr.
Original Article

Abstract

Gene expression profiling may be used to stratify patients by disease severity to test the hypothesis that variable disease outcome has a genetic component. In order to define unique expression signatures in African American rheumatoid arthritis (RA) patients with severe erosive disease, we undertook a gene expression study using samples of RNA from peripheral blood mononuclear cells (PBMCs). RNA from baseline PBMC samples of 96 African American RA patients with early RA (<2 years disease duration) was hybridized to cDNA probes of the Illumina Human HT-V3 expression array. Expression analyses were performed using the ca. 25,000 cDNA probes, and then expression levels were compared to the total number of erosions in radiographs of the hands and feet at baseline and 36 months. Using a false discovery rate cutoff of Q = 0.30, 1,138 genes at baseline and 680 genes at 36 months significantly correlated with total erosions. No evidence of a signal differentiating disease progression, or change in erosion scores between baseline and 36 months, was found. Further analyses demonstrated that the differential gene expression signature was localized to the patients with the most erosive disease (>10 erosions). Ingenuity Pathway Analysis demonstrated that genes with fold change greater than 1.5 implicated immune pathways such as CTLA signaling in cytotoxic T lymphocytes. These results demonstrate that CLEAR patients with early RA having the most severe erosive disease, as compared to more mild cases (<10 erosions), may be characterized by a set of differentially expressed genes that represent biological pathways with relevance to autoimmune disease.

Keywords

Genome-wide gene expression Sharp/van der Heijde Pathway analysis CLEAR ABCoN 

Notes

Acknowledgments

The authors kindly thank the CLEAR investigators: Moreland, Conn, Smith, Callahan, Jonas, Brasington, Howard. This research was supported by NIH 2P60 AR048095-06 (RP Kimberly, P.I.) Multidisciplinary Clinical Research Center Project 3: Predictors of Rheumatoid Arthritis Severity in African Americans; and NIH N01-AR-6-2278 (SLB, PI) Continuation of the Consortium for the Longitudinal Evaluation of African Americans with Early Rheumatoid Arthritis (CLEAR) Registry. Support also provided by the UAB Center for Clinical and Translational Science through the NIH National Center for Research Resources as part of its Clinical and Translational Science Award Program (5UL1RR025777-03, 5KL2RR025776-03, 5TL1RR025775-03). RJR was supported in part by NIH K01- AR060848 and LKV by K01-DK080188. The authors gratefully acknowledge the kind willingness of the CLEAR study participants whose PBMCs were used for this study.

References

  1. 1.
    Scherer H, Dörner T, Burmester G (2010) Patient-tailored therapy in rheumatoid arthritis: an editorial review. Curr Opin Rheumatol 22:237–245PubMedCrossRefGoogle Scholar
  2. 2.
    Gregersen P, Silver J, Winchester R (1987) The shared epitope hypothesis. An approach to understanding the molecular genetics of susceptibility to rheumatoid arthritis. Arthritis Rheum 30:1205–1213PubMedCrossRefGoogle Scholar
  3. 3.
    Coenen M, Gregersen P (2009) Rheumatoid arthritis: a view of the current genetic landscape. Genes Immun 10:101–111PubMedCrossRefGoogle Scholar
  4. 4.
    Ding B, Padyukov L, Lundström E et al (2009) Different patterns of associations with anti-citrullinated protein antibody-positive and anti-citrullinated protein antibody-negative rheumatoid arthritis in the extended major histocompatibility complex region. Arthritis Rheum 60:30–38PubMedCrossRefGoogle Scholar
  5. 5.
    Stahl EA, Raychaudhuri S, Remmers EF et al (2010) Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet 42:508–514PubMedCrossRefGoogle Scholar
  6. 6.
    Weyand CM, Hicok KC, Conn DL, Goronzy JJ (1992) The influence of HLA-DRB1 genes on disease severity in rheumatoid arthritis. Ann Intern Med 117:801–806PubMedGoogle Scholar
  7. 7.
    Plant D, Thomson W, Lunt M et al (2011) The role of rheumatoid arthritis genetic susceptibility markers in the prediction of erosive disease in patients with early inflammatory polyarthritis: results from the Norfolk Arthritis Register. Rheumatology 50:78–84PubMedCrossRefGoogle Scholar
  8. 8.
    Brown DA, Moore J, Johnen H et al (2007) Serum macrophage inhibitory cytokine 1 in rheumatoid arthritis: a potential marker of erosive joint destruction. Arthritis Rheum 56:753–764PubMedCrossRefGoogle Scholar
  9. 9.
    Edwards CJ, Feldman JL, Beech J et al (2007) Molecular profile of peripheral blood mononuclear cells from patients with rheumatoid arthritis. Mol Med 13:40–58PubMedCrossRefGoogle Scholar
  10. 10.
    Olsen NJ, Moore JH, Aune TM (2004) Gene expression signatures for autoimmune disease in peripheral blood mononuclear cells. Arthritis Res Ther 6:120–128PubMedCrossRefGoogle Scholar
  11. 11.
    Olsen N, Sokka T, Seehorn C et al (2004) A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells. Ann Rheum Dis 63:1387–1392PubMedCrossRefGoogle Scholar
  12. 12.
    Lequerré T, Bansard C, Vittecoq O et al (2009) Early and long-standing rheumatoid arthritis: distinct molecular signatures identified by gene-expression profiling in synovia. Arthritis Res Ther 11:R99PubMedCrossRefGoogle Scholar
  13. 13.
    Batliwalla FM, Baechler EC, Xiao X et al (2005) Peripheral blood gene expression profiling in rheumatoid arthritis. Genes Immun 6:388–397PubMedCrossRefGoogle Scholar
  14. 14.
    Junta CM, Sandrin-Garcia P, Fachin-Saltoratto AL et al (2009) Differential gene expression of peripheral blood mononuclear cells from rheumatoid arthritis patients may discriminate immunogenetic, pathogenic and treatment features. Immunology 127:365–372PubMedCrossRefGoogle Scholar
  15. 15.
    Lindberg J, Wijbrandts C, van Baarsen L et al. (2010) The Gene Expression Profile in the Synovium as a Predictor of the Clinical Response to Infliximab Treatment in Rheumatoid Arthritis. Plos One 5. doi:10.1371/journal.pone.0011310
  16. 16.
    Lequerré T, Gauthier-Jauneau A, Bansard C et al (2006) Gene profiling in white blood cells predicts infliximab responsiveness in rheumatoid arthritis. Arthritis Res Ther 8:R105PubMedCrossRefGoogle Scholar
  17. 17.
    Liu C, Batliwalla F, Li W et al (2008) Genome-wide association scan identifies candidate polymorphisms associated with differential response to anti-TNF treatment in rheumatoid arthritis. Mol Med 14:575–581PubMedGoogle Scholar
  18. 18.
    Bridges SL, Causey ZL, Burgos PI et al (2010) Radiographic severity of rheumatoid arthritis in African Americans: results from a multicenter observational study. Arthritis Care Res 62:624–631CrossRefGoogle Scholar
  19. 19.
    Bolstad B, Irizarry R, Astrand M, Speed T (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193PubMedCrossRefGoogle Scholar
  20. 20.
    Cui X, Hwang JT, Qiu J, Blades NJ, Churchill GA (2005) Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 6:59–75PubMedCrossRefGoogle Scholar
  21. 21.
    Yang H, Churchill G (2007) Estimating p-values in small microarray experiments. Bioinformatics 23:38–43PubMedCrossRefGoogle Scholar
  22. 22.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Roy Stat Soc Ser B Methodol 57:289–300Google Scholar
  23. 23.
    Wu H, Kerr MK, Cui X, Churchill GA (2003) MAANOVA, a software package for the analysis of spotted cDNA microarray experiments. In: Parmigiani G, Garret ES, Irizarry RA, Zeger SL (eds) The analysis of gene expressions data: an overview of methods and software. Springer, New YorkGoogle Scholar
  24. 24.
    Nam D, Kim SY (2008) Gene-set approach for expression pattern analysis. Brief Bioinform 9:189–197PubMedCrossRefGoogle Scholar
  25. 25.
    Huang dW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13CrossRefGoogle Scholar
  26. 26.
    Plenge RM, Padyukov L, Remmers EF et al (2005) Replication of putative candidate-gene associations with rheumatoid arthritis in >4,000 samples from North America and Sweden: association of susceptibility with PTPN22, CTLA4, and PADI4. Am J Hum Genet 77:1044–1060PubMedCrossRefGoogle Scholar
  27. 27.
    Hughes LB, Reynolds RJ, Brown EE et al (2010) Most common single-nucleotide polymorphisms associated with rheumatoid arthritis in persons of European ancestry confer risk of rheumatoid arthritis in African Americans. Arthritis Rheum 62:3547–3553PubMedCrossRefGoogle Scholar
  28. 28.
    Kremer JM, Westhovens R, Leon M et al (2003) Treatment of rheumatoid arthritis by selective inhibition of T-cell activation with fusion protein CTLA4Ig. N Engl J Med 349:1907–1915PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Richard J. Reynolds
    • 1
  • Xiangqin Cui
    • 2
  • Laura K. Vaughan
    • 2
  • David T. Redden
    • 3
  • Zenoria Causey
    • 1
  • Elizabeth Perkins
    • 1
  • Tishi Shah
    • 1
  • Laura B. Hughes
    • 1
  • CLEAR Investigators
  • Aarti Damle
    • 4
  • Marlena Kern
    • 4
  • Peter K. Gregersen
    • 4
  • Martin R. Johnson
    • 5
  • S. Louis BridgesJr.
    • 1
  1. 1.Division of Clinical Immunology and Rheumatology, Department of MedicineUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Section on Statistical Genetics, Department of BiostatisticsUniversity of Alabama at BirminghamBirminghamUSA
  3. 3.Department of BiostatisticsUniversity of Alabama at BirminghamBirminghamUSA
  4. 4.Robert S. Boas Center for Genomics and Human GeneticsManhassetUSA
  5. 5.Department of Pharmacology and ToxicologyUniversity of Alabama at BirminghamBirminghamUSA

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