• Int J Mol Sci · Aug 2013

    MicroRNA expression changes during interferon-beta treatment in the peripheral blood of multiple sclerosis patients.

    • Michael Hecker, Madhan Thamilarasan, Dirk Koczan, Ina Schröder, Kristin Flechtner, Sherry Freiesleben, Georg Füllen, Hans-Jürgen Thiesen, and Uwe Klaus Zettl.
    • Steinbeis Transfer Center for Proteome Analysis, Schillingallee 68, 18057 Rostock, Germany. michael.hecker@rocketmail.com
    • Int J Mol Sci. 2013 Aug 5; 14 (8): 16087-110.

    AbstractMicroRNAs (miRNAs) are small non-coding RNA molecules acting as post-transcriptional regulators of gene expression. They are involved in many biological processes, and their dysregulation is implicated in various diseases, including multiple sclerosis (MS). Interferon-beta (IFN-beta) is widely used as a first-line immunomodulatory treatment of MS patients. Here, we present the first longitudinal study on the miRNA expression changes in response to IFN-beta therapy. Peripheral blood mononuclear cells (PBMC) were obtained before treatment initiation as well as after two days, four days, and one month, from patients with clinically isolated syndrome (CIS) and patients with relapsing-remitting MS (RRMS). We measured the expression of 651 mature miRNAs and about 19,000 mRNAs in parallel using real-time PCR arrays and Affymetrix microarrays. We observed that the up-regulation of IFN-beta-responsive genes is accompanied by a down-regulation of several miRNAs, including members of the mir-29 family. These differentially expressed miRNAs were found to be associated with apoptotic processes and IFN feedback loops. A network of miRNA-mRNA target interactions was constructed by integrating the information from different databases. Our results suggest that miRNA-mediated regulation plays an important role in the mechanisms of action of IFN-beta, not only in the treatment of MS but also in normal immune responses. miRNA expression levels in the blood may serve as a biomarker of the biological effects of IFN-beta therapy that may predict individual disease activity and progression.

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