Whole blood expression profiling from the TREAT trial: insights for the pathogenesis of polyarticular juvenile idiopathic arthritis

Kaiyu Jiang, Laiping Wong, Ashley D Sawle, M Barton Frank, Yanmin Chen, Carol A Wallace, James N Jarvis, Kaiyu Jiang, Laiping Wong, Ashley D Sawle, M Barton Frank, Yanmin Chen, Carol A Wallace, James N Jarvis

Abstract

Background: The Trial of Early Aggressive Therapy in Juvenile Idiopathic Arthritis (TREAT trial) was accompanied by a once-in-a-generation sample collection for translational research. In this paper, we report the results of whole blood gene expression analyses and genomic data-mining designed to cast light on the immunopathogenesis of polyarticular juvenile idiopathic arthritis (JIA).

Methods: TREAT samples and samples from an independent cohort were analyzed on Affymetrix microarrays and compared to healthy controls. Data from the independent cohort were used to validate the TREAT data. Pathways analysis was used to characterize gene expression profiles. Furthermore, we correlated differential gene expression with new information about functional regulatory elements within the genome to develop models of aberrant gene expression in JIA.

Results: There was a strong concordance in gene expression between TREAT samples and the independent cohort. In addition, rheumatoid factor (RF)-positive and RF-negative patients showed only small differences on whole blood expression profiles. Analysis of the combined samples showed 158 genes represented by 176 probes that showed differential expression between TREAT subjects at baseline and healthy controls. None of the differentially expressed genes were encoded within linkage disequilibrium blocks containing single nucleotide polymorphisms known to be associated with risk for JIA. Functional analysis of these genes showed functional associations with multiple processes associated with innate and adaptive immunity, and appeared to reflect overall suppression of STAT1-3/interferon response factor-mediated pathways.

Conclusions: Despite their limitations, whole blood expression profiles clearly distinguish children with polyarticular JIA from healthy controls. Whole blood expression profiles identify several immunologic pathways of biologic relevance that will need to be pursued in homogeneous cell populations in order to clarify mechanisms of pathogenesis.

Trial registration: ClinicalTrials.gov registry #NCT00443430 , originally registered 2 March 2007 and last updated 30 May 2013.

Keywords: Gene expression; Juvenile idiopathic arthritis; Microarray; Pathogenesis; Whole blood.

Figures

Fig. 1
Fig. 1
Correlation between gene expression of probes between Oklahama (OK) and TREAT data. Four observations are shown; first, at x = y, probes correlate well; second, with y < 8, probes with low expression in the Oklahoma data but a range of expression in the TREAT data; third, when x < 8, probes with low expression in the TREAT data but a range of expression in the Oklahoma data; and, lastly, random scatters of probes in between Oklahoma and TREAT data
Fig. 2
Fig. 2
Distribution of probe qualities in TREAT data (left) and Oklahoma data (OK; right)
Fig. 3
Fig. 3
Correlation of probes between TREAT and Oklahoma (OK) for (top left) good probes with good quality in both datasets and (top right) bad quality probes in both datasets. Bottom left, good probes in TREAT but bad in Oklahoma; bottom right, good probes in Oklahoma but bad in TREAT. Using probes that are high quality in both datasets improves correlation between the two datasets
Fig. 4
Fig. 4
Mechanistic network derived from differential gene expression analysis of upstream regulators of differentially expressed genes using IPA and comparing untreated to healthy controls. Nodes in orange reflect predicted activation, while those in blue are predicted to be inhibited based on the patterns of differential gene expression. Upstream regulators CXCL8 (a) and CSF3 (b) show an activation pattern
Fig. 5
Fig. 5
Mechanistic network derived from differential gene expression analysis of upstream regulators of differentially expressed genes using IPA and comparing untreated JIA to healthy controls. Nodes in orange reflect predicted activation, while those in blue are predicted to be inhibited based on the patterns of differential gene expression. CD3–T-cell receptor (TCR) activation is predicted from the pattern of gene expression
Fig. 6
Fig. 6
Mechanistic network derived from differential gene expression analysis using IPA and comparing untreated JIA to healthy controls. Nodes in orange reflect predicted activation, while those in blue are predicted to be inhibited based on the patterns of differential gene expression. Suppression of IRF1 (a) and IRF7 (b) regulated networks is predicted from this analysis
Fig. 7
Fig. 7
Mechanistic network derived from differential gene expression analysis using IPA and comparing untreated JIA to healthy controls. Nodes in orange reflect predicted activation, while those in blue are predicted to be inhibited based on the patterns of differential gene expression. Suppression of TLR9-regulated networks is predicted from this analysis

References

    1. Wallace CA, Giannini EH, Spalding SJ, et al. Trial of early aggressive therapy in polyarticular juvenile idiopathic arthritis. Arthritis Rheum. 2012;64:2012–21. doi: 10.1002/art.34343.
    1. Jiang K, Sawle AD, Frank MB, et al. Whole blood gene expression profiling predicts therapeutic response at six months in patients with polyarticular juvenile idiopathic arthritis. Arthritis Rheumatol. 2014;66:1363–71. doi: 10.1002/art.38341.
    1. Liu J, Walter E, Stenger D, Thach D. Effects of globin mRNA reduction methods on gene expression profiles from whole blood. J Mol Diagn. 2006;8:551–8. doi: 10.2353/jmoldx.2006.060021.
    1. Chai V, Vassilakos A, Lee Y, Wright JA, Young AH. Optimization of the PAXgene blood RNA extraction system for gene expression analysis of clinical samples. J Clin Lab Anal. 2005;19:182–8. doi: 10.1002/jcla.20075.
    1. Du N, Jiang K, Sawle AD, et al. Dynamic tracking of functional gene modules in treated juvenile idiopathic arthritis. Genome Med. 2015;7:109. doi: 10.1186/s13073-015-0227-2.
    1. Petty RE, Southwood TR, Manners P, et al. International League of Associations for Rheumatology classification of juvenile idiopathic arthritis: second revision, Edmonton, 2001. J Rheumatol. 2004;31:390–2.
    1. Cobb JP, Mindrinos MN, Miller-Graziano C, et al. Application of genome-wide expression analysis to human health and disease. Proc Natl Acad Sci U S A. 2005;102:4801–6. doi: 10.1073/pnas.0409768102.
    1. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24:1547–8. doi: 10.1093/bioinformatics/btn224.
    1. Lin SM, Du P, Huber W, Kibbe WA. Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res. 2008;36:e11. doi: 10.1093/nar/gkm1075.
    1. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.
    1. Calvano SE, Xiao W, Richards DR, et al. A network-based analysis of systemic inflammation in humans. Nature. 2005;437:1032–7. doi: 10.1038/nature03985.
    1. Ficenec D, Osborne M, Pradines J, et al. Computational knowledge integration in biopharmaceutical research. Brief Bioinform. 2003;4:260–78. doi: 10.1093/bib/4.3.260.
    1. Hinks A, Cobb J, Marion MC, et al. Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis. Nat Genet. 2013;45:664–9. doi: 10.1038/ng.2614.
    1. Hersh AO, Prahalad S. Immunogenetics of juvenile idiopathic arthritis: a comprehensive review. J Autoimmun. 2015;64:113–24. doi: 10.1016/j.jaut.2015.08.002.
    1. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2. doi: 10.1093/bioinformatics/btq033.
    1. Bhardwaj RS, Eblenkamp M, Berndt T, Tietze L, Klosterhalfen B. Role of HSP70i in regulation of biomaterial-induced activation of human monocytes-derived macrophages in culture. J Mater Sci Mater Med. 2001;12:97–106. doi: 10.1023/A:1008974524580.
    1. Xu H, Lai W, Zhang Y, et al. Tumor-associated macrophage-derived IL-6 and IL-8 enhance invasive activity of LoVo cells induced by PRL-3 in a KCNN4 channel-dependent manner. BMC Cancer. 2014;14:330. doi: 10.1186/1471-2407-14-330.
    1. Teofoli P, Lotti T. Cytokines, fibrinolysis and vasculitis. Int Angiol. 1995;14:125–9.
    1. Jarvis JN, Petty HR, Tang Y, et al. Evidence for chronic, peripheral activation of neutrophils in polyarticular juvenile rheumatoid arthritis. Arthritis Res Ther. 2006;8:R154. doi: 10.1186/ar2048.
    1. Grom AA, Hirsch R. T-cell and T-cell receptor abnormalities in the immunopathogenesis of juvenile rheumatoid arthritis. Curr Opin Rheumatol. 2000;12:420–4. doi: 10.1097/00002281-200009000-00012.
    1. Jiang K, Sun X, Chen Y, Shen Y, Jarvis JN. RNA sequencing from human neutrophils reveals distinct transcriptional differences associated with chronic inflammatory states. BMC Med Genomics. 2015;8:55. doi: 10.1186/s12920-015-0128-7.
    1. Pohlers D, Beyer A, Koczan D, et al. Constitutive upregulation of the transforming growth factor-beta pathway in rheumatoid arthritis synovial fibroblasts. Arthritis Res Ther. 2007;9:R59. doi: 10.1186/ar2217.
    1. Knowlton N, Jiang K, Frank MB, et al. The meaning of clinical remission in polyarticular juvenile idiopathic arthritis: gene expression profiling in peripheral blood mononuclear cells identifies distinct disease states. Arthritis Rheum. 2009;60:892–900. doi: 10.1002/art.24298.
    1. Jarvis JN, Jiang K, Frank MB, et al. Gene expression profiling in neutrophils from children with polyarticular juvenile idiopathic arthritis. Arthritis Rheum. 2009;60:1488–95. doi: 10.1002/art.24450.
    1. Beauvillain C, Delneste Y, Scotet M, et al. Neutrophils efficiently cross-prime naive T cells in vivo. Blood. 2007;110:2965–73. doi: 10.1182/blood-2006-12-063826.
    1. Puga I, Cols M, Barra CM, et al. B cell-helper neutrophils stimulate the diversification and production of immunoglobulin in the marginal zone of the spleen. Nat Immunol. 2012;13:170–80. doi: 10.1038/ni.2194.
    1. Sun J, Dodd H, Moser EK, Sharma R, Braciale TJ. CD4+ T cell help and innate-derived IL-27 induce Blimp-1-dependent IL-10 production by antiviral CTLs. Nat Immunol. 2011;12:327–34. doi: 10.1038/ni.1996.
    1. Hock BD, Taylor KG, Cross NB, et al. Effect of activated human polymorphonuclear leucocytes on T lymphocyte proliferation and viability. Immunology. 2012;137:249–58. doi: 10.1111/imm.12004.
    1. Harwood NE, Barral P, Batista FD. Neutrophils—the unexpected helpers of B-cell activation. EMBO Rep. 2012;13:93–4. doi: 10.1038/embor.2011.259.
    1. Odobasic D, Kitching AR, Yang Y, et al. Neutrophil myeloperoxidase regulates T-cell-driven tissue inflammation in mice by inhibiting dendritic cell function. Blood. 2013;121:4195–204. doi: 10.1182/blood-2012-09-456483.
    1. Jiang K, Frank M, Chen Y, Osban J, Jarvis JN. Genomic characterization of remission in juvenile idiopathic arthritis. Arthritis Res Ther. 2013;15:R100. doi: 10.1186/ar4280.
    1. Jiang K, Zhu L, Buck MJ, et al. Disease-associated single-nucleotide polymorphisms from noncoding regions in juvenile idiopathic arthritis are located within or adjacent to functional genomic elements of human neutrophils and CD4+ T cells. Arthritis Rheumatol. 2015;67:1966–77. doi: 10.1002/art.39135.
    1. Gibson G, Powell JE, Marigorta UM. Expression quantitative trait locus analysis for translational medicine. Genome Med. 2015;7:60. doi: 10.1186/s13073-015-0186-7.
    1. Yasuda K, Richez C, Uccellini MB, et al. Requirement for DNA CpG content in TLR9-dependent dendritic cell activation induced by DNA-containing immune complexes. J Immunol. 2009;183:3109–17. doi: 10.4049/jimmunol.0900399.
    1. Kline JN. Eat dirt: CpG DNA and immunomodulation of asthma. Proc Am Thorac Soc. 2007;4:283–8. doi: 10.1513/pats.200701-019AW.
    1. Klinman DM. Adjuvant activity of CpG oligodeoxynucleotides. Int Rev Immunol. 2006;25:135–54. doi: 10.1080/08830180600743057.
    1. Rosa R, Melisi D, Damiano V, et al. Toll-like receptor 9 agonist IMO cooperates with cetuximab in K-ras mutant colorectal and pancreatic cancers. Clin Cancer Res. 2011;17:6531–41. doi: 10.1158/1078-0432.CCR-10-3376.
    1. Berger R, Fiegl H, Goebel G, et al. Toll-like receptor 9 expression in breast and ovarian cancer is associated with poorly differentiated tumors. Cancer Sci. 2010;101:1059–66. doi: 10.1111/j.1349-7006.2010.01491.x.
    1. Vaisanen MR, Vaisanen T, Jukkola-Vuorinen A, et al. Expression of toll-like receptor-9 is increased in poorly differentiated prostate tumors. Prostate. 2010;70:817–24. doi: 10.1002/pros.21115.
    1. Ronkainen H, Hirvikoski P, Kauppila S, et al. Absent Toll-like receptor-9 expression predicts poor prognosis in renal cell carcinoma. J Exp Clin Cancer Res. 2011;30:84. doi: 10.1186/1756-9966-30-84.
    1. Takala H, Kauppila JH, Soini Y, et al. Toll-like receptor 9 is a novel biomarker for esophageal squamous cell dysplasia and squamous cell carcinoma progression. J Innate Immun. 2011;3:631–8. doi: 10.1159/000329115.
    1. Katz SJ, Russell AS. Re-evaluation of antimalarials in treating rheumatic diseases: re-appreciation and insights into new mechanisms of action. Curr Opin Rheumatol. 2011;23:278–81. doi: 10.1097/BOR.0b013e32834456bf.
    1. Brewer EJ, Giannini EH, Kuzmina N, Alekseev L. Penicillamine and hydroxychloroquine in the treatment of severe juvenile rheumatoid arthritis. Results of the USA-USSR double-blind placebo-controlled trial. N Engl J Med. 1986;314:1269–76. doi: 10.1056/NEJM198605153142001.
    1. Stroncek DF, Clay ME, Smith J, et al. Composition of peripheral blood progenitor cell components collected from healthy donors. Transfusion. 1997;37:411–7. doi: 10.1046/j.1537-2995.1997.37497265342.x.
    1. Field LA, Jordan RM, Hadix JA, et al. Functional identity of genes detectable in expression profiling assays following globin mRNA reduction of peripheral blood samples. Clin Biochem. 2007;40:499–502. doi: 10.1016/j.clinbiochem.2007.01.004.
    1. Weinstein JS, Lezon-Geyda K, Maksimova Y, et al. Global transcriptome analysis and enhancer landscape of human primary T follicular helper and T effector lymphocytes. Blood. 2014;124:3719–29. doi: 10.1182/blood-2014-06-582700.
    1. Lin H, Joehanes R, Pilling LC, et al. Whole blood gene expression and interleukin-6 levels. Genomics. 2014;104:490–5. doi: 10.1016/j.ygeno.2014.10.003.
    1. Powell JE, Henders AK, McRae AF, et al. Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent. Genome Res. 2012;22:456–66. doi: 10.1101/gr.126540.111.
    1. Xiao W, Mindrinos MN, Seok J, et al. A genomic storm in critically injured humans. J Exp Med. 2011;208:2581–90. doi: 10.1084/jem.20111354.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever