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Common genetic variation within miR-146a predicts disease onset and relapse in multiple sclerosis

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Abstract

Despite extensive studies focusing on the changes in expression of microRNAs (miRNAs) in multiple sclerosis (MS) compared to healthy controls, few studies have evaluated the association of genetic variants of miRNAs with MS clinical course. We investigated whether a functional polymorphism in the MS associated miR-146a gene predicted clinical course (hazard of conversion to MS and of relapse, and annualized change in disability), using a longitudinal cohort study of persons with a first demyelinating event followed up to their 5-year review. We found the genotype (GC+CC) of rs2910164 predicted relapse compared with the GG genotype (HR=2.09 (95% CI 1.42, 3.06), p=0.0001), as well as a near-significant (p=0.07) association with MS conversion risk. Moreover, we found a significant additive interaction between rs2910164 and baseline anti-EBNA-1 IgG titers predicting risk of conversion to MS (relative excess risk due to interaction [RERI] 2.39, p=0.00002) and of relapse (RERI 1.20, p=0.006). Supporting these results, similar results were seen for the other EBV-correlated variables: anti-EBNA-2 IgG titers and past history of infectious mononucleosis. There was no association of rs2910164 genotype for disability progression. Our findings provide evidence for miR-146a and EBV infection in modulating MS clinical course.

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References

  1. Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP (2008) The impact of microRNAs on protein output. Nature 455(7209):64–71. https://doi.org/10.1038/nature07242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhou Y, Simpson S Jr, Holloway AF, Charlesworth J, van der Mei I, Taylor BV (2014) The potential role of epigenetic modifications in the heritability of multiple sclerosis. Mult Scler 20(2):135–140. https://doi.org/10.1177/1352458514520911

    Article  CAS  PubMed  Google Scholar 

  3. Liu G, Abraham E (2013) MicroRNAs in immune response and macrophage polarization. Arterioscler Thromb Vasc Biol 33(2):170–177. https://doi.org/10.1161/ATVBAHA.112.300068

    Article  PubMed  PubMed Central  Google Scholar 

  4. Junker A, Krumbholz M, Eisele S, Mohan H, Augstein F, Bittner R, Lassmann H, Wekerle H, Hohlfeld R, Meinl E (2009) MicroRNA profiling of multiple sclerosis lesions identifies modulators of the regulatory protein CD47. Brain 132(Pt 12):3342–3352. https://doi.org/10.1093/brain/awp300

    Article  PubMed  Google Scholar 

  5. Fenoglio C, Cantoni C, De Riz M, Ridolfi E, Cortini F, Serpente M, Villa C, Comi C, Monaco F, Mellesi L, Valzelli S, Bresolin N, Galimberti D, Scarpini E (2011) Expression and genetic analysis of miRNAs involved in CD4+ cell activation in patients with multiple sclerosis. Neurosci Lett 504(1):9–12. https://doi.org/10.1016/j.neulet.2011.08.021

    Article  CAS  PubMed  Google Scholar 

  6. Waschbisch A, Atiya M, Linker RA, Potapov S, Schwab S, Derfuss T (2011) Glatiramer acetate treatment normalizes deregulated microRNA expression in relapsing remitting multiple sclerosis. PLoS One 6(9):e24604. https://doi.org/10.1371/journal.pone.0024604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Godshalk SE, Bhaduri-McIntosh S, Slack FJ (2008) Epstein-Barr virus-mediated dysregulation of human microRNA expression. Cell Cycle 7(22):3595–3600

    Article  CAS  PubMed  Google Scholar 

  8. Vrijens K, Bollati V, Nawrot TS (2015) MicroRNAs as potential signatures of environmental exposure or effect: a systematic review. Environ Health Perspect. https://doi.org/10.1289/ehp.1408459

  9. Ascherio A, Munger KL (2007) Environmental risk factors for multiple sclerosis. Part I: The role of infection. Ann Neurol 61(4):288–299. https://doi.org/10.1002/ana.21117

    Article  PubMed  Google Scholar 

  10. Ascherio A, Munger KL (2007) Environmental risk factors for multiple sclerosis. Part II: Noninfectious factors. Ann Neurol 61(6):504–513. https://doi.org/10.1002/ana.21141

    Article  CAS  PubMed  Google Scholar 

  11. Gong J, Tong Y, Zhang HM, Wang K, Hu T, Shan G, Sun J, Guo AY (2012) Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis. Hum Mutat 33(1):254–263. https://doi.org/10.1002/humu.21641

    Article  CAS  PubMed  Google Scholar 

  12. Lucas R, Ponsonby AL, Dear K, Taylor B, Dwyer T, McMichael A, Valery P, van der Mei A, Williams D, Pender M, Kilpatrick T, Chapman C, Coulthard A (2009) Associations between silicone skin cast score, cumulative sun exposure and other factors in the Ausimmune Study: a multicentre Australian study. Cancer Epidemiol Biomark Prev 18(11): 2887–2894

  13. Strautins K, Tschochner M, James I, Choo L, Dunn DS, Pedrini M, Kermode A, Carroll W, Nolan D (2014) Combining HLA-DR risk alleles and anti-Epstein-Barr virus antibody profiles to stratify multiple sclerosis risk. Mult Scler 20(3):286–294. https://doi.org/10.1177/1352458513498829

    Article  CAS  PubMed  Google Scholar 

  14. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, Lublin FD, Metz LM, McFarland HF, O'Connor PW, Sandberg-Wollheim M, Thompson AJ, Weinshenker BG, Wolinsky JS (2005) Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald criteria”. Ann Neurol 58(6):840–846

    Article  PubMed  Google Scholar 

  15. Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33(11):1444–1452

    Article  CAS  PubMed  Google Scholar 

  16. Prentice RL, Williams BJ, Peterson AV (1981) On the regression analysis of multivariate failure time data. Biometrika 68(2):373–379

    Article  Google Scholar 

  17. Bashinskaya VV, Kulakova OG, Boyko AN, Favorov AV, Favorova OO (2015) A review of genome-wide association studies for multiple sclerosis: classical and hypothesis-driven approaches. Hum Genet 134(11–12):1143–1162. https://doi.org/10.1007/s00439-015-1601-2

    Article  CAS  PubMed  Google Scholar 

  18. Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de la Chapelle A (2008) Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A 105(20):7269–7274. https://doi.org/10.1073/pnas.0802682105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li Y, Du C, Wang W, Ma G, Cui L, Zhou H, Tao H, Yao L, Zhao B, Li K (2015) Genetic association of MiR-146a with multiple sclerosis susceptibility in the Chinese population. Cell Physiol Biochem 35(1):281–291. https://doi.org/10.1159/000369695

    Article  CAS  PubMed  Google Scholar 

  20. Khan D, Dai R, Karpuzoglu E, Ahmed SA (2010) Estrogen increases, whereas IL-27 and IFN-gamma decrease, splenocyte IL-17 production in WT mice. Eur J Immunol 40(9):2549–2556. https://doi.org/10.1002/eji.201040303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dai R, Phillips RA, Zhang Y, Khan D, Crasta O, Ahmed SA (2008) Suppression of LPS-induced interferon-gamma and nitric oxide in splenic lymphocytes by select estrogen-regulated microRNAs: a novel mechanism of immune modulation. Blood 112(12):4591–4597. https://doi.org/10.1182/blood-2008-04-152488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Park R, Lee WJ, Ji JD (2016) Association between the three functional miR-146a single-nucleotide polymorphisms, rs2910164, rs57095329, and rs2431697, and autoimmune disease susceptibility: a meta-analysis. Autoimmunity:1–8. https://doi.org/10.3109/08916934.2016.1171854

  23. Chan EK, Ceribelli A, Satoh M (2013) MicroRNA-146a in autoimmunity and innate immune responses. Ann Rheum Dis 72(Suppl 2):ii90–ii95. https://doi.org/10.1136/annrheumdis-2012-202203

    Article  CAS  PubMed  Google Scholar 

  24. Baumjohann D, Ansel KM (2013) MicroRNA-mediated regulation of T helper cell differentiation and plasticity. Nat Rev Immunol 13(9):666–678. https://doi.org/10.1038/nri3494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Pender MP, Burrows SR (2014) Epstein-Barr virus and multiple sclerosis: potential opportunities for immunotherapy. Clin Transl Immunology 3(10):e27. https://doi.org/10.1038/cti.2014.25

    Article  PubMed  PubMed Central  Google Scholar 

  26. Zhou Y, Zhu G, Charlesworth JC, Simpson S Jr, Rubicz R, Goring HH, Patsopoulos NA, Laverty C, Wu F, Henders A, Ellis JJ, van der Mei I, Montgomery GW, Blangero J, Curran JE, Johnson MP, Martin NG, Nyholt DR, Taylor BV, consortium AN (2016) Genetic loci for Epstein-Barr virus nuclear antigen-1 are associated with risk of multiple sclerosis. Mult Scler. https://doi.org/10.1177/1352458515626598

  27. Disanto G, Hall C, Lucas R, Ponsonby AL, Berlanga-Taylor AJ, Giovannoni G, Ramagopalan SV, Ausimmune Investigator G (2013) Assessing interactions between HLA-DRB1*15 and infectious mononucleosis on the risk of multiple sclerosis. Mult Scler 19(10):1355–1358. https://doi.org/10.1177/1352458513477231

    Article  PubMed  Google Scholar 

  28. Sundqvist E, Sundstrom P, Linden M, Hedstrom AK, Aloisi F, Hillert J, Kockum I, Alfredsson L, Olsson T (2012) Lack of replication of interaction between EBNA1 IgG and smoking in risk for multiple sclerosis. Neurology 79(13):1363–1368. https://doi.org/10.1212/WNL.0b013e31826c1ab7

    Article  CAS  PubMed  Google Scholar 

  29. Hedstrom AK, Bomfim IL, Barcellos LF, Briggs F, Schaefer C, Kockum I, Olsson T, Alfredsson L (2014) Interaction between passive smoking and two HLA genes with regard to multiple sclerosis risk. Int J Epidemiol 43(6):1791–1798. https://doi.org/10.1093/ije/dyu195

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The members of the AUSLONG investigators group are as follows: Robyn M Lucas (National Centre for Epidemiology and Population Health, Canberra); Keith Dear (Duke Kunshan University, Kunshan, China); Anne-Louise Ponsonby and Terry Dwyer (Murdoch Children’s Research Institute, Melbourne, Australia); Ingrid van der Mei, Leigh Blizzard, Steve Simpson, Jr., and Bruce V Taylor (Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia); Simon Broadley (School of Medicine, Griffith University, Gold Coast Campus, Australia); Trevor Kilpatrick (Centre for Neurosciences, Department of Anatomy and Neuroscience, University of Melbourne, Melbourne, Australia); David Williams and Jeanette Lechner-Scott (University of Newcastle, Newcastle, Australia); Cameron Shaw and Caron Chapman (Barwon Health, Geelong, Australia); Alan Coulthard (University of Queensland, Brisbane, Australia); and Patricia Valery (QIMR Berghofer Medical Research Institute, Brisbane, Australia).

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Author notes

  1. Yuan Zhou and Ming Chen contributed equally to this manuscript.

Authors and Affiliations

  1. Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street TAS, Hobart, 7000, Australia

    Yuan Zhou, Steve Simpson Jr, Jac C. Charlesworth, Nicholas Blackburn, Ingrid van der Mei & Bruce V Taylor

  2. Department of Clinical Laboratory, Xinqiao Hospital, Third Military Medical University, Chongqing, China

    Ming Chen

  3. Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia

    Steve Simpson Jr

  4. National Centre for Epidemiology and Population Health, Research School of Population Health, The Australian National University, Canberra, Australia

    Robyn M. Lucas

  5. Murdoch Children’s Research Institute, University of Melbourne, Melbourne, Australia

    Anne-Louise Ponsonby

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  1. Yuan Zhou
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Consortia

Ausimmune/AUSLONG investigators group

  • Robyn M Lucas
  • , Keith Dear
  • , Anne-Louise Ponsonby
  • , Terry Dwyer
  • , Ingrid van der Mei
  • , Leigh Blizzard
  • , Steve Simpson Jr.
  • , Bruce V Taylor
  • , Simon Broadley
  • , Trevor Kilpatrick
  • , David Williams
  • , Jeanette Lechner-Scott
  • , Cameron Shaw
  • , Caron Chapman
  • , Alan Coulthard
  •  & Patricia Valery

Corresponding author

Correspondence to Bruce V Taylor.

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Zhou, Y., Chen, M., Simpson, S. et al. Common genetic variation within miR-146a predicts disease onset and relapse in multiple sclerosis. Neurol Sci 39, 297–304 (2018). https://doi.org/10.1007/s10072-017-3177-1

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  • DOI: https://doi.org/10.1007/s10072-017-3177-1

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