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

Overview of attention for article published in Neurological Sciences, November 2017
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Title
Common genetic variation within miR-146a predicts disease onset and relapse in multiple sclerosis
Published in
Neurological Sciences, November 2017
DOI 10.1007/s10072-017-3177-1
Pubmed ID
Authors

Yuan Zhou, Ming Chen, Steve Simpson, Robyn M. Lucas, Jac C. Charlesworth, Nicholas Blackburn, Ingrid van der Mei, Anne-Louise Ponsonby, Ausimmune/AUSLONG investigators group, Bruce V Taylor

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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Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 17%
Student > Bachelor 6 13%
Student > Ph. D. Student 5 11%
Other 3 7%
Student > Doctoral Student 3 7%
Other 5 11%
Unknown 16 35%
Readers by discipline Count As %
Neuroscience 6 13%
Medicine and Dentistry 6 13%
Biochemistry, Genetics and Molecular Biology 5 11%
Agricultural and Biological Sciences 2 4%
Immunology and Microbiology 2 4%
Other 6 13%
Unknown 19 41%