Issue

Vol. 64 No. 10: Issue 10

The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.

Meta-analysis of gene expression profiles of peripheral blood cells in systemic lupus erythematosus

https://doi.org/10.14715/cmb/2018.64.10.7
Sang-Cheol Bae
Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
Young Ho Lee
Department of Rheumatology, Korea University College of Medicine, Seoul, Korea

Corresponding Author(s) : Young Ho Lee

lyhcgh@korea.ac.kr

Cellular and Molecular Biology, Vol. 64 No. 10: Issue 10

Abstract

Our purpose was to identify differentially expressed (DE) genes and biological processes associated with gene expression changes in systemic lupus erythematosus (SLE). We performed a meta-analysis using the INMEX program (integrative meta-analysis of expression data) on publicly available microarray Genetic Expression Omnibus (GEO) datasets of peripheral blood mononuclear cells (PBMCs) of SLE patients and healthy controls. We performed Gene Ontology (GO) enrichment analysis by using hypergeometric tests. In total, five comparisons (2 B cells, 2 CD4 T cells, and 1 myeloid cell) from two GEO datasets containing 51 cases and 46 controls were included in the meta-analysis. We identified 483 genes consistently differentially expressed in SLE (260 up-regulated and 223 down-regulated). The up-regulated gene with the lowest P-value (P-value = 7.33E-10) was TAP1 (transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)). The up-regulated gene with the largest effect size (ES = 2.7799, P-value = 8.28E-06) was STAT1 (signal transducer and activator of transcription 1, 91 kDa). The down-regulated gene with the lowest P-value (P-value = 2.53E-06) was EIF3F (eukaryotic translation initiation factor 3, subunit F), and the down-regulated gene with the largest ES (ES = −1.8543, P-value = 8.56E-06) was FBL (fibrillarin). The most significant enrichment among 317 GO categories was the type I interferon (IFN)-mediated signaling pathway with P = 1.93E-20. Other significant GO categories included cellular response to type I IFN (P = 1.93E-20) and response to type I IFN (P = 2.86E-20). Our meta-analysis demonstrates that up-regulated genes mediate IFN-regulated and cytokine-mediate signaling pathways, innate immune response, and antigen processing and presentation. These results provide insights into molecular mechanisms associated to the pathophysiology of SLE.

Keywords

SLE Gene expression Meta-analysis.
Bae, S.-C., & Lee, Y. H. (2018). Meta-analysis of gene expression profiles of peripheral blood cells in systemic lupus erythematosus. Cellular and Molecular Biology, 64(10), 40–49. https://doi.org/10.14715/cmb/2018.64.10.7
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Lee YH & Nath SK. Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis. Hum Genet 2005; 118:434-43.
  2. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286:531-7.
  3. Jones C, Simpson P, Mackay A & Lakhani SR. Expression profiling using cDNA microarrays. Methods Mol Med 2006; 120:403-14.
  4. Ramasamy A, Mondry A, Holmes CC & Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 2008; 5:e184.
  5. Siddiqui AS, Delaney AD, Schnerch A, Griffith OL, Jones SJ & Marra MA. Sequence biases in large scale gene expression profiling data. Nucleic Acids Res 2006; 34:e83.
  6. Cahan P, Rovegno F, Mooney D, Newman JC, St Laurent G, 3rd & McCaffrey TA. Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization. Gene 2007; 401:12-8.
  7. Rung J & Brazma A. Reuse of public genome-wide gene expression data. Nat Rev Genet 2013; 14:89-99.
  8. Griffith OL, Melck A, Jones SJ & Wiseman SM. Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers. J Clin Oncol 2006; 24:5043-51.
  9. Choi SJ, Rho YH, Ji JD, Song GG & Lee YH. Genome scan meta-analysis of rheumatoid arthritis. Rheumatology (Oxford) 2006; 45:166-70.
  10. Lee YH, Rho YH, Choi SJ, Ji JD & Song GG. Ankylosing spondylitis susceptibility loci defined by genome-search meta-analysis. J Hum Genet 2005; 50:453-9.
  11. Lee YH, Rho YH, Choi SJ, Ji JD & Song GG. Meta-analysis of genome-wide linkage studies for bone mineral density. J Hum Genet 2006; 51:480-6.
  12. Xia J, Fjell CD, Mayer ML, Pena OM, Wishart DS & Hancock RE. INMEX--a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res 2013; 41:W63-W70.
  13. Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, et al. NCBI GEO: archive for functional genomics data sets--10 years on. Nucleic Acids Res 2011; 39:D1005-10.
  14. Bolstad BM, Irizarry RA, Astrand M & Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19:185-93.
  15. Choi JK, Yu U, Kim S & Yoo OJ. Combining multiple microarray studies and modeling interstudy variation. Bioinformatics 2003; 19 Suppl 1:i84-90.
  16. Egger M, Smith GD & Phillips AN. Meta-analysis: principles and procedures. BMJ 1997; 315:1533-7.
  17. DerSimonian R & Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7:177-88.
  18. Falcon S & Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics 2007; 23:257-8.
  19. Lee HM, Sugino H, Aoki C & Nishimoto N. Underexpression of mitochondrial-DNA encoded ATP synthesis-related genes and DNA repair genes in systemic lupus erythematosus. Arthritis Res Ther 2011; 13:R63.
  20. Garaud JC, Schickel JN, Blaison G, Knapp AM, Dembele D, Ruer-Laventie J, et al. B cell signature during inactive systemic lupus is heterogeneous: toward a biological dissection of lupus. PLoS One 2011; 6:e23900.
  21. Gromme M & Neefjes J. Antigen degradation or presentation by MHC class I molecules via classical and non-classical pathways. Mol Immunol 2002; 39:181-202.
  22. Hale MB, Krutzik PO, Samra SS, Crane JM & Nolan GP. Stage dependent aberrant regulation of cytokine-STAT signaling in murine systemic lupus erythematosus. PLoS One 2009; 4:e6756.
  23. Pelzel C, Begitt A, Wenta N & Vinkemeier U. Evidence against a role for beta-arrestin1 in STAT1 dephosphorylation and the inhibition of interferon-gamma signaling. Mol Cell 2013; 50:149-56.
  24. Huang CM, Hang LW, Chen CL, Wu JY & Tsai FJ. Polymorphisms of TAP1 transporter genes in Chinese patients with systemic lupus erythematosus in Taiwan. Rheumatol Int 2004; 24:130-2.
  25. Kanemitsu S, Ihara K, Saifddin A, Otsuka T, Takeuchi T, Nagayama J, et al. A functional polymorphism in fas (CD95/APO-1) gene promoter associated with systemic lupus erythematosus. J Rheumatol 2002; 29:1183-8.
  26. Namjou B, Sestak AL, Armstrong DL, Zidovetzki R, Kelly JA, Jacob N, et al. High-density genotyping of STAT4 reveals multiple haplotypic associations with systemic lupus erythematosus in different racial groups. Arthritis Rheum 2009; 60:1085-95.
  27. Bachmann M, Ulziibat S, Hardle L, Pfeilschifter J & Muhl H. IFNalpha converts IL-22 into a cytokine efficiently activating STAT1 and its downstream targets. Biochem Pharmacol 2013; 85:396-403.
  28. Arasappan D, Tong W, Mummaneni P, Fang H & Amur S. Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells. BMC Med 2011; 9:65.
  29. Qing X & Putterman C. Gene expression profiling in the study of the pathogenesis of systemic lupus erythematosus. Autoimmun Rev 2004; 3:505-9.
  30. Aringer M, Gunther C & Lee-Kirsch MA. Innate immune processes in lupus erythematosus. Clin Immunol 2013; 147:216-22.
Read More
Author biographies is not available.
Crossref
Scopus
Google Scholar
Europe PMC
Download this PDF file
PDF
Statistic
Read Counter : 867 Download : 430