Susceptibility variants in the CD58 gene locus point to a role of microRNA-548ac in the pathogenesis of multiple sclerosis

Mutation Research - Reviews in Mutation Research

Volumn 763, Issue , 2015, Pages 161-167

  Hecker, Michael ^a   Fitzner, Brit ^a   Blaschke, Jana ^a   Blaschke, Paul ^a   Zettl, Uwe Klaus ^a  

^a UNIVERSITY OF ROSTOCK   (Germany)

Author keywords

Genetic risk; MicroRNA; Multiple sclerosis; Single nucleotide polymorphisms; Transposable element

Indexed keywords

LYMPHOCYTE FUNCTION ASSOCIATED ANTIGEN 3; MICRORNA; MICRORNA 548AC; UNCLASSIFIED DRUG; DGCR8 PROTEIN, HUMAN; DROSHA PROTEIN, HUMAN; MIRN548 MICRORNA, HUMAN; RIBONUCLEASE III; RNA BINDING PROTEIN; TRANSPOSON;

B LYMPHOCYTE ACTIVATION; CELL MATURATION; CELL PROLIFERATION; CHROMATIN ASSEMBLY AND DISASSEMBLY; CONTROLLED CLINICAL TRIAL (TOPIC); CYTOKINE PRODUCTION; DISEASE ACTIVITY; DISEASE COURSE; GENE FREQUENCY; GENE LINKAGE DISEQUILIBRIUM; GENE LOCUS; GENETIC VARIABILITY; HAPLOTYPE; HEREDITY; HUMAN; IMMUNE RESPONSE; META ANALYSIS (TOPIC); MULTIPLE SCLEROSIS; PLASMACYTOID DENDRITIC CELL; PRIORITY JOURNAL; QUANTITATIVE TRAIT LOCUS; REVIEW; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE POLYMORPHISM; T LYMPHOCYTE; ANIMAL; CHEMISTRY; GENETIC ASSOCIATION; GENETIC PREDISPOSITION; GENETICS; INTRON; METABOLISM; MOLECULAR EVOLUTION; PATHOLOGY; PRIMATE; TRANSPOSON;

PRIMATES;

ANIMALS; ANTIGENS, CD58; DNA TRANSPOSABLE ELEMENTS; EVOLUTION, MOLECULAR; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENETIC VARIATION; HUMANS; INTRONS; LINKAGE DISEQUILIBRIUM; MICRORNAS; MULTIPLE SCLEROSIS; POLYMORPHISM, SINGLE NUCLEOTIDE; PRIMATES; RIBONUCLEASE III; RNA-BINDING PROTEINS;

EID: 84924973524     PISSN: 13835742     EISSN: 13882139     Source Type: Journal    
DOI: 10.1016/j.mrrev.2014.10.002     Document Type: Review

References (70)

1. Compston A., Coles A. Multiple sclerosis. Lancet 2008, 372:1502-1517.
2. Katz Sand I.B., Lublin F.D. Diagnosis and differential diagnosis of multiple sclerosis. Continuum (Minneap. Minn.) 2013, 19:922-943.
3. Sospedra M., Martin R. Immunology of multiple sclerosis. Annu. Rev. Immunol. 2005, 23:683-747.
4. Wingerchuk D.M., Carter J.L. Multiple sclerosis: current and emerging disease-modifying therapies and treatment strategies. Mayo Clin. Proc. 2014, 89:225-240.
5. Haghikia A., et al. Therapies for multiple sclerosis: translational achievements and outstanding needs. Trends Mol. Med. 2013, 19:309-319.
6. Ascherio A., et al. Vitamin D and multiple sclerosis. Lancet Neurol. 2010, 9:599-612.
7. Lucas R.M., et al. Epstein-Barr virus and multiple sclerosis. J. Neurol. Neurosurg. Psychiatr. 2011, 82:1142-1148.
8. Hedström A.K., et al. Smoking and multiple sclerosis susceptibility. Eur. J. Epidemiol. 2013, 28:867-874.
9. O'Gorman C., et al. Modelling genetic susceptibility to multiple sclerosis with family data. Neuroepidemiology 2013, 40:1-12.
10. Sawcer S., et al. Multiple sclerosis genetics. Lancet Neurol. 2014, 13:700-709.
11. Sawcer S., et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011, 476:214-219.
12. Hafler D.A., et al. Risk alleles for multiple sclerosis identified by a genomewide study. N. Engl. J. Med. 2007, 357:851-862.
13. Beecham A.H., et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 2013, 45:1353-1360.
14. Westerlind H., et al. Modest familial risks for multiple sclerosis: a registry-based study of the population of Sweden. Brain 2014, 137:770-778.
15. Zhang X., et al. Laying a solid foundation for Manhattan-'setting the functional basis for the post-GWAS era'. Trends Genet. 2014, 30:140-149.
16. Gregory S.G., et al. Interleukin 7 receptor alpha chain (IL7R) shows allelic and functional association with multiple sclerosis. Nat. Genet. 2007, 39:1083-1091.
17. Patsopoulos N.A., et al. Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann. Neurol. 2011, 70:897-912.
18. D'Netto M.J., et al. Risk alleles for multiple sclerosis in multiplex families. Neurology 2009, 72:1984-1988.
19. Hecker M., et al. CD58 and multiple sclerosis: genetic association, gene expression and prognostic value. Neurology 2014, 82(Suppl. 10). P6.156.
20. Milo R., Miller A. Revised diagnostic criteria of multiple sclerosis. Autoimmun. Rev. 2014, 13:518-524.
21. Lublin F.D., et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 2014, 83:278-286.
22. Altshuler D.M., et al. Integrating common and rare genetic variation in diverse human populations. Nature 2010, 467:52-58.
23. G. Zhang et al., Single-locus genetic association analysis by ordinal tests, in: C. Rao et al. (Eds.), Handbook of Statistics: Bioinformatics in Human Health and Heredity, North-Holland, 2012, pp. 309-338.
24. De Jager P.L., et al. The role of the CD58 locus in multiple sclerosis. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:5264-5269.
25. Handel A.E., et al. The effect of single nucleotide polymorphisms from genome wide association studies in multiple sclerosis on gene expression. PLoS One 2010, 5:e10142.
26. Hundeshagen A., et al. Elevated type I interferon-like activity in a subset of multiple sclerosis patients: molecular basis and clinical relevance. J. Neuroinflammation 2012, 9:140.
27. Westra H.J., et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 2013, 45:1238-1243.
28. Mowry E.M., et al. Multiple sclerosis susceptibility genes: associations with relapse severity and recovery. PLoS One 2013, 8:e75416.
29. Mowry E.M., et al. Association of multiple sclerosis susceptibility variants and early attack location in the CNS. PLoS One 2013, 8:e75565.
30. Jensen C.J., et al. Multiple sclerosis susceptibility-associated SNPs do not influence disease severity measures in a cohort of Australian MS patients. PLoS One 2010, 5:e10003.
31. Kaizuka Y., et al. The coreceptor CD2 uses plasma membrane microdomains to transduce signals in T cells. J. Cell Biol. 2009, 185:521-534.
32. Ariel O., et al. Signal transduction by CD58: the transmembrane isoform transmits signals outside lipid rafts independently of the GPI-anchored isoform. Cell. Signal. 2009, 21:1100-1108.
33. Mestas J., Hughes C.C. Endothelial cell costimulation of T cell activation through CD58-CD2 interactions involves lipid raft aggregation. J. Immunol. 2001, 167:4378-4385.
34. Kim E.O., et al. Homotypic cell to cell cross-talk among human natural killer cells reveals differential and overlapping roles of 2B4 and CD2. J. Biol. Chem. 2010, 285:41755-41764.
35. Jima D.D., et al. Deep sequencing of the small RNA transcriptome of normal and malignant human B cells identifies hundreds of novel microRNAs. Blood 2010, 116:e118-e127.
36. Abecasis G.R., et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012, 491:56-65.
37. Wallich R., et al. Gene structure, promoter characterization, and basis for alternative mRNA splicing of the human CD58 gene. J. Immunol. 1998, 160:2862-2871.
38. Kozomara A., Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014, 42:D68-D73.
39. Ameres S.L., Zamore P.D. Diversifying microRNA sequence and function. Nat. Rev. Mol. Cell Biol. 2013, 14:475-488.
40. Ha M., Kim V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 2014, 15:509-524.
41. Angerstein C., et al. Integration of MicroRNA databases to study MicroRNAs associated with multiple sclerosis. Mol. Neurobiol. 2012, 45:520-535.
42. Guerau-de-Arellano M., et al. miRNA profiling for biomarker discovery in multiple sclerosis: from microarray to deep sequencing. J. Neuroimmunol. 2012, 248:32-39.
43. Ma X., et al. Expression, regulation and function of microRNAs in multiple sclerosis. Int. J. Med. Sci. 2014, 11:810-818.
44. Hecker M., et al. MicroRNA expression changes during interferon-beta treatment in the peripheral blood of multiple sclerosis patients. Int. J. Mol. Sci. 2013, 14:16087-16110.
45. Waschbisch A., et al. Glatiramer acetate treatment normalizes deregulated microRNA expression in relapsing remitting multiple sclerosis. PLoS One 2011, 6:e24604.
46. Piriyapongsa J., Jordan I.K. A family of human microRNA genes from miniature inverted-repeat transposable elements. PLoS One 2007, 2:e203.
47. Yuan Z., et al. MicroRNA genes derived from repetitive elements and expanded by segmental duplication events in mammalian genomes. PLoS One 2011, 6:e17666.
48. Ardlie K.G., et al. Patterns of linkage disequilibrium in the human genome. Nat. Rev. Genet. 2002, 3:299-309.
49. Cartault F., et al. Mutation in a primate-conserved retrotransposon reveals a noncoding RNA as a mediator of infantile encephalopathy. Proc. Natl. Acad. Sci. U.S.A. 2012, 109:4980-4985.
50. Varani G., McClain W.H. The G×U wobble base pair. A fundamental building block of RNA structure crucial to RNA function in diverse biological systems. EMBO Rep. 2000, 1:18-23.
51. Sun G., et al. SNPs in human miRNA genes affect biogenesis and function. RNA 2009, 9:1640-1651.
52. Auyeung V.C., et al. Beyond secondary structure: primary-sequence determinants license pri-miRNA hairpins for processing. Cell 2013, 152:844-858.
53. Neilsen C.T., et al. IsomiRs-the overlooked repertoire in the dynamic microRNAome. Trends Genet. 2012, 28:544-549.
54. Mix E., et al. Animal models of multiple sclerosis-potentials and limitations. Prog. Neurobiol. 2010, 92:386-404.
55. Patejdl R., Zettl U.K. Animal models in neurology: drawbacks and opportunities. Curr. Pharm. Des. 2012, 18:4443-4452.
56. Kuchen S., et al. Regulation of microRNA expression and abundance during lymphopoiesis. Immunity 2010, 32:828-839.
57. Su A.I., et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. U.S.A. 2004, 101:6062-6067.
58. Wu C., et al. BioGPS and MyGene.info: organizing online, gene-centric information. Nucleic Acids Res. 2013, 41:D561-D565.
59. L.S. Young et al., EBV gene expression and regulation, in: A. Arvin et al. (Eds.), Human Herpesviruses: Biology, Therapy, and Immunoprophylaxis, Cambridge University Press, 2007, pp. 461-489.
60. Megyola C., et al. Identification of a sub-population of B cells that proliferates after infection with Epstein-Barr virus. Virol. J. 2011, 8:84.
61. Schwab N., et al. An imbalance of two functionally and phenotypically different subsets of plasmacytoid dendritic cells characterizes the dysfunctional immune regulation in multiple sclerosis. J. Immunol. 2010, 184:5368-5374.
62. Russo A., Potenza N. Antiviral effects of human microRNAs and conservation of their target sites. FEBS Lett. 2011, 585:2551-2555.
63. Liang T., et al. Genome-wide analysis of mir-548 gene family reveals evolutionary and functional implications. J. Biomed. Biotechnol. 2012, 2012:679563.
64. Thomson D.W., et al. Experimental strategies for microRNA target identification. Nucleic Acids Res. 2011, 39:6845-6853.
65. Bulik-Sullivan B., et al. Prioritization of genetic variants in the microRNA regulome as functional candidates in genome-wide association studies. Hum. Mutat. 2013, 34:1049-1056.
66. Kim J.Y., et al. CD58 polymorphisms associated with the risk of neuromyelitis optica in a Korean population. BMC Neurol. 2014, 14:57.
67. Jarius S., et al. Neuromyelitis optica: clinical features, immunopathogenesis and treatment. Clin. Exp. Immunol. 2014, 176:149-164.
68. Kumar V., et al. Immunochip SNP array identifies novel genetic variants conferring susceptibility to candidaemia. Nat. Commun. 2014, 5:4675.
69. Ryckman K.K., et al. Host genetic factors and vaccine-induced immunity to HBV infection: haplotype analysis. PLoS One 2010, 5:e12273.
70. Larkin M.A., et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007, 23:2947-2948.