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Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis

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Abstract

Multiple sclerosis (MS) is the most prevalent demyelinating disease and the principal cause of neurological disability in young adults. Recent microarray gene expression profiling studies have identified several genetic variants contributing to the complex pathogenesis of MS, however, expressional and functional studies are still required to further understand its molecular mechanism. The present study aimed to analyze the molecular mechanism of MS using microarray analysis combined with bioinformatics techniques. We downloaded the gene expression profile of MS from Gene Expression Omnibus (GEO) and analysed the microarray data using the differentially coexpressed genes (DCGs) and links package in R and Database for Annotation, Visualization and Integrated Discovery. The regulatory impact factor (RIF) algorithm was used to measure the impact factor of transcription factor. A total of 1,297 DCGs between MS patients and healthy controls were identified. Functional annotation indicated that these DCGs were associated with immune and neurological functions. Furthermore, the RIF result suggested that IKZF1, BACH1, CEBPB, EGR1, FOS may play central regulatory roles in controlling gene expression in the pathogenesis of MS. Our findings confirm the presence of multiple molecular alterations in MS and indicate the possibility for identifying prognostic factors associated with MS pathogenesis.

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References

  1. Compston A, Coles A (2008) Multiple sclerosis. Lancet 372(9648):1502–1517. doi:10.1016/S0140-6736(08)61620-7

    Article  PubMed  CAS  Google Scholar 

  2. Rosati G (2001) The prevalence of multiple sclerosis in the world: an update. Neurol Sci 22(2):117–139

    Article  PubMed  CAS  Google Scholar 

  3. Fontoura P, Garren H (2010) Multiple sclerosis therapies: molecular mechanisms and future. Results Probl Cell Differ 51:259–285. doi:10.1007/400_2010_36

    Article  PubMed  CAS  Google Scholar 

  4. Smolders J (2011) Vitamin D and multiple sclerosis: correlation, causality, and controversy. Autoimmune Dis 2011:629538. doi:10.4061/2011/629538

    Google Scholar 

  5. van der Mei IA, Ponsonby AL, Dwyer T, Blizzard L, Simmons R, Taylor BV, Butzkueven H, Kilpatrick T (2003) Past exposure to sun, skin phenotype, and risk of multiple sclerosis: case–control study. BMJ 327(7410):316. doi:10.1136/bmj.327.7410.316327/7410/316

    Article  PubMed  Google Scholar 

  6. Hafler DA, Compston A, Sawcer S, Lander ES, Daly MJ, De Jager PL, de Bakker PI, Gabriel SB, Mirel DB, Ivinson AJ, Pericak-Vance MA, Gregory SG, Rioux JD, McCauley JL, Haines JL, Barcellos LF, Cree B, Oksenberg JR, Hauser SL (2007) Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357(9):851–862. doi:10.1056/NEJMoa073493

    Article  PubMed  CAS  Google Scholar 

  7. Schonrock LM, Gawlowski G, Bruck W (2000) Interleukin-6 expression in human multiple sclerosis lesions. Neurosci Lett 294(1):45–48

    Article  PubMed  CAS  Google Scholar 

  8. Lock C, Hermans G, Pedotti R, Brendolan A, Schadt E, Garren H, Langer-Gould A, Strober S, Cannella B, Allard J, Klonowski P, Austin A, Lad N, Kaminski N, Galli SJ, Oksenberg JR, Raine CS, Heller R, Steinman L (2002) Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat Med 8(5):500–508. doi:10.1038/nm0502-500nm0502-500

    Article  PubMed  CAS  Google Scholar 

  9. Chabas D, Baranzini SE, Mitchell D, Bernard CC, Rittling SR, Denhardt DT, Sobel RA, Lock C, Karpuj M, Pedotti R, Heller R, Oksenberg JR, Steinman L (2001) The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science 294(5547):1731–1735. doi:10.1126/science.1062960294/5547/1731

    Article  PubMed  CAS  Google Scholar 

  10. John GR, Shankar SL, Shafit-Zagardo B, Massimi A, Lee SC, Raine CS, Brosnan CF (2002) Multiple sclerosis: re-expression of a developmental pathway that restricts oligodendrocyte maturation. Nat Med 8(10):1115–1121. doi:10.1038/nm781nm781

    Article  PubMed  CAS  Google Scholar 

  11. Lim CK, Brew BJ, Sundaram G, Guillemin GJ (2010) Understanding the roles of the kynurenine pathway in multiple sclerosis progression. Int J Tryptophan Res 3:157–167

    PubMed  CAS  Google Scholar 

  12. Kanehisa M (2002) The KEGG database. Novartis Found Symp 247:91–101 (discussion 101–103, 119–128, 244–152)

    Article  PubMed  CAS  Google Scholar 

  13. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  PubMed  CAS  Google Scholar 

  14. Carter SL, Brechbuhler CM, Griffin M, Bond AT (2004) Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 20(14):2242–2250. doi:10.1093/bioinformatics/bth234bth234

    Article  PubMed  CAS  Google Scholar 

  15. Choi JK, Yu U, Yoo OJ, Kim S (2005) Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21(24):4348–4355. doi:10.1093/bioinformatics/bti722

    Article  PubMed  CAS  Google Scholar 

  16. de la Fuente A (2010) From ‘differential expression’ to ‘differential networking’—identification of dysfunctional regulatory networks in diseases. Trends Genet 26(7):326–333. doi:10.1016/j.tig.2010.05.001

    Article  PubMed  Google Scholar 

  17. Yu H, Liu BH, Ye ZQ, Li C, Li YX, Li YY (2011) Link-based quantitative methods to identify differentially coexpressed genes and gene pairs. BMC Bioinform 12:315. doi:10.1186/1471-2105-12-315

    Article  Google Scholar 

  18. Liu BH, Yu H, Tu K, Li C, Li YX, Li YY (2010) DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data. Bioinformatics 26(20):2637–2638. doi:10.1093/bioinformatics/btq471

    Article  PubMed  CAS  Google Scholar 

  19. Team RDC (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

  20. Benjamini YHY (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300

    Google Scholar 

  21. da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. doi:10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  22. Reverter A, Hudson NJ, Nagaraj SH, Perez-Enciso M, Dalrymple BP (2010) Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics 26(7):896–904. doi:10.1093/bioinformatics/btq051

    Article  PubMed  CAS  Google Scholar 

  23. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. doi:10.1101/gr.123930313/11/2498

    Article  PubMed  CAS  Google Scholar 

  24. Hauser SL, Oksenberg JR (2006) The neurobiology of multiple sclerosis: genes, inflammation, and neurodegeneration. Neuron 52(1):61–76. doi:10.1016/j.neuron.2006.09.011

    Article  PubMed  CAS  Google Scholar 

  25. Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, Wu W, Uitdehaag BM, Kappos L, Polman CH, Matthews PM, Hauser SL, Gibson RA, Oksenberg JR, Barnes MR (2009) Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 18(11):2078–2090. doi:10.1093/hmg/ddp120

    Article  PubMed  CAS  Google Scholar 

  26. Cunninghame Graham DS, Morris DL, Bhangale TR, Criswell LA, Syvanen AC, Ronnblom L, Behrens TW, Graham RR, Vyse TJ (2011) Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with systemic lupus erythematosus. PLoS Genet 7(10):e1002341. doi:10.1371/journal.pgen.1002341

    Article  PubMed  CAS  Google Scholar 

  27. Kastner P, Chan S (2011) Role of Ikaros in T-cell acute lymphoblastic leukemia. World J Biol Chem 2(6):108–114. doi:10.4331/wjbc.v2.i6.108

    Article  PubMed  Google Scholar 

  28. Payne KJ, Dovat S (2011) Ikaros and tumor suppression in acute lymphoblastic leukemia. Crit Rev Oncog 16(1–2):3–12

    Article  PubMed  Google Scholar 

  29. Vassalli P (1992) The pathophysiology of tumor necrosis factors. Annu Rev Immunol 10:411–452. doi:10.1146/annurev.iy.10.040192.002211

    Article  PubMed  CAS  Google Scholar 

  30. Tyor WR, Glass JD, Baumrind N, McArthur JC, Griffin JW, Becker PS, Griffin DE (1993) Cytokine expression of macrophages in HIV-1-associated vacuolar myelopathy. Neurology 43(5):1002–1009

    Article  PubMed  CAS  Google Scholar 

  31. Raine CS (1995) Multiple sclerosis: TNF revisited, with promise. Nat Med 1(3):211–214

    Article  PubMed  CAS  Google Scholar 

  32. Tan J, Town T, Paris D, Placzek A, Parker T, Crawford F, Yu H, Humphrey J, Mullan M (1999) Activation of microglial cells by the CD40 pathway: relevance to multiple sclerosis. J Neuroimmunol 97(1–2):77–85

    Article  PubMed  CAS  Google Scholar 

  33. Tan J, Town T, Saxe M, Paris D, Wu Y, Mullan M (1999) Ligation of microglial CD40 results in p44/42 mitogen-activated protein kinase-dependent TNF-alpha production that is opposed by TGF-beta 1 and IL-10. J Immunol 163(12):6614–6621

    PubMed  CAS  Google Scholar 

  34. Jana M, Dasgupta S, Liu X, Pahan K (2002) Regulation of tumor necrosis factor-alpha expression by CD40 ligation in BV-2 microglial cells. J Neurochem 80(1):197–206

    Article  PubMed  CAS  Google Scholar 

  35. Mycko MP, Papoian R, Boschert U, Raine CS, Selmaj KW (2004) Microarray gene expression profiling of chronic active and inactive lesions in multiple sclerosis. Clin Neurol Neurosurg 106(3):223–229. doi:10.1016/j.clineuro.2004.02.019

    Article  PubMed  Google Scholar 

  36. Antony JM, Ellestad KK, Hammond R, Imaizumi K, Mallet F, Warren KG, Power C (2007) The human endogenous retrovirus envelope glycoprotein, syncytin-1, regulates neuroinflammation and its receptor expression in multiple sclerosis: a role for endoplasmic reticulum chaperones in astrocytes. J Immunol 179(2):1210–1224

    PubMed  CAS  Google Scholar 

  37. Yu JS, Hayashi T, Seboun E, Sklar RM, Doolittle TH, Hauser SL (1991) Fos RNA accumulation in multiple sclerosis white matter tissue. J Neurol Sci 103(2):209–215

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

This study was supported by National Natural Science Foundation of China, Yangtai Guan (81230027), National Natural Science Foundation of China, Mingyuan Liu (81200924) and Natural Science Foundation of Shanghai Province, China, Mingyuan Liu (12ZR1427400).

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Authors and Affiliations

  1. Department of Neurology, Changhai Hospital, 168 Changhai Road, Shanghai, 200433, China

    Mingyuan Liu, Xiaojun Hou, Ping Zhang, Yong Hao, Yiting Yang, Xiongfeng Wu, Desheng Zhu & Yangtai Guan

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  1. Mingyuan Liu
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  2. Xiaojun Hou
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Correspondence to Yangtai Guan.

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Mingyuan Liu and Xiaojun Hou should regard as the co-first authors.

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Liu, M., Hou, X., Zhang, P. et al. Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis. Mol Biol Rep 40, 3731–3737 (2013). https://doi.org/10.1007/s11033-012-2449-3

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