Computational identification and experimental validation of microRNAs binding to the Alzheimer-related gene ADAM10 - Augustin Regina; Endres Kristina; Reinhardt Sven; Kuhn Peer-Hendrik; Lichtenthaler Stefan F; Hansen Jens; Wurst Wolfgang; Trümbach Dietrich (BMC, 2012)
Abstract Background MicroRNAs (miRNAs) are post-transcriptional regulators involved in numerous biological processes including the pathogenesis of Alzheimer's disease (AD). A key gene of AD, ADAM10, controls the proteolytic processing of APP and the formation of the amyloid plaques and is known to be regulated by miRNA in hepatic cancer cell lines. To predict miRNAs regulating ADAM10 expression concerning AD, we developed a computational approach. Methods MiRNA binding sites in the human ADAM10 3' untranslated region were predicted using the RNA22, RNAhybrid and miRanda programs and ranked by specific selection criteria with respect to AD such as differential regulation in AD patients and tissue-specific expression. Furthermore, target genes of miR-103, miR-107 and miR-1306 were derived from six publicly available miRNA target site prediction databases. Only target genes predicted in at least four out of six databases in the case of miR-103 and miR-107 were compared to genes listed in the AlzGene database including genes possibly involved in AD. In addition, the target genes were used for Gene Ontology analysis and literature mining. Finally, we used a luciferase assay to verify the potential effect of these three miRNAs on ADAM10 3'UTR in SH-SY5Y cells. Results Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%). Conclusions Our approach shows the requirement of incorporating specific, disease-associated selection criteria into the prediction process to reduce the amount of false positive predictions. In summary, our method identified three miRNAs strongly suggested to be involved in AD, which possibly regulate ADAM10 expression and hence offer possibilities for the development of therapeutic treatments of AD.