| Summary: | <p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are post-transcriptional regulators involved in numerous biological processes including the pathogenesis of Alzheimer’s disease (AD). A key gene of AD, <it>ADAM10</it>, controls the proteolytic processing of <it>APP</it> and the formation of the amyloid plaques and is known to be regulated by miRNA in hepatic cancer cell lines. To predict miRNAs regulating <it>ADAM10</it> expression concerning AD, we developed a computational approach.</p> <p>Methods</p> <p>MiRNA binding sites in the human <it>ADAM10</it> 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 <it>miR-103</it>, <it>miR-107</it> and <it>miR-1306</it> 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 <it>miR-103</it> and <it>miR-107</it> 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 <it>ADAM10</it> 3'UTR in SH-SY5Y cells.</p> <p>Results</p> <p>Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (<it>miR-103</it>, <it>miR-107</it>, <it>miR-1306</it>) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of <it>miR-103</it> (<it>p</it>-value = 0.0065) and <it>miR-107</it> (<it>p</it>-value = 0.0009) showed significant overlap with the AlzGene database except for <it>miR-1306</it>. Interactions between <it>miR-103</it> and <it>miR-107</it> to genes were revealed playing a role in processes leading to AD. <it>ADAM10</it> expression in the reporter assay was reduced by <it>miR-1306</it> (28%), <it>miR-103</it> (45%) and <it>miR-107</it> (52%).</p> <p>Conclusions</p> <p>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 <it>ADAM10</it> expression and hence offer possibilities for the development of therapeutic treatments of AD.</p>
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