Prediction of miRNA-mRNA associations in Alzheimer’s disease mice using network topology

Abstract

BackgroundLittle is known about the relationship between miRNA and mRNA expression in Alzheimer’s disease (AD) at early- or late-symptomatic stages. Sequence-based target prediction algorithms and anti-correlation profiles have been applied to predict miRNA targets using omics data, but this approach often leads to false positive predictions. Here, we applied the joint profiling analysis of mRNA and miRNA expression levels to Tg6799 AD model mice at 4 and 8 months of age using a network topology-based method. We constructed gene regulatory networks and used the PageRank algorithm to predict significant interactions between miRNA and mRNA.ResultsIn total, 8 cluster modules were predicted by the transcriptome data for co-expression networks of AD pathology. In total, 54 miRNAs were identified as being differentially expressed in AD. Among these, 50 significant miRNA-mRNA interactions were predicted by integrating sequence target prediction, expression analysis, and the PageRank algorithm. We identified a set of miRNA-mRNA interactions that were changed in the hippocampus of Tg6799 AD model mice. We determined the expression levels of several candidate genes and miRNA. For functional validation in primary cultured neurons from Tg6799 mice (MT) and littermate (LM) controls, the overexpression of ARRDC3 enhanced PPP1R3C expression. ARRDC3 overexpression showed the tendency to decrease the expression of miR139-5p and miR3470a in both LM and MT primary cells. Pathological environment created by Aβ treatment increased the gene expression of PPP1R3C and Sfpq but did not significantly alter the expression of miR139-5p or miR3470a. Aβ treatment increased the promoter activity of ARRDC3 gene in LM primary cells but not in MT primary cells.ConclusionsOur results demonstrate AD-specific changes in the miRNA regulatory system as well as the relationship between the expression levels of miRNAs and their targets in the hippocampus of Tg6799 mice. These data help further our understanding of the function and mechanism of various miRNAs and their target genes in the molecular pathology of AD.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-644) contains supplementary material, which is available to authorized users.

MicroRNA‐139‐5p negatively regulates NR2A‐containing NMDA receptor in the rat pilocarpine model and patients with temporal lobe epilepsy

“…19 Recently, miR-139-5p expression was found to be significantly down-regulated in several neurologic disorders including TLE, 10,11 ischemic brain damage, 20 glioblastomas, 21 and Alzheimer's disease (AD). 22 Given the critical role of miRNAs in TLE, we hypothesize that up-regulation of the miR-139-5p expression level in the rat pilocarpine model following NMDA-receptor blockade, may contribute to modulate NMDA receptors to control SE following pilocarpine injections. In this study, we sought to evaluate the effect of miR-139-5p on NMDAreceptor subunits NR2A and NR2B following pilocarpineinduced SE.…”

Downregulation of microRNA-448 improves isoflurane-induced learning and memory impairment in rats

“…The association between miR448 and caspase3 in neuron apoptosis caused by isoflurane remains to be fully elucidated. However, Noh et al (40) demonstrated that caspase-3 was highly expressed following miR-448 overexpression in mice with Alzheimer's disease. In the present study, caspase-3 protein was downregulated while Bcl-x was upregulated by the miR-448 inhibitor in isofluranetreated neurons (Fig.…”

miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets

“…To assess the performance of miRCat2, we ran it on multiple organisms and benchmarked the results against other commonly used miRNA detection tools, miRCat (version srna-workbenchV3.2), miRDeep2 (version miRDeep2.0.0.7), miRPlant (version miRPlant_V5) and miReap (version mireap_0.2). The organisms we considered are Danio rerio ( Cifuentes et al , 2010 ), Homo sapiens ( Shin et al , 2010 ; Somel et al , 2010 ; Vaz et al , 2010 ; Hou et al , 2011 ; Friedländer et al , 2014 ; Kim et al , 2016 ), Mus musculus ( Bosson et al , 2014 ; Groenendyk et al , 2014 ; Noh et al , 2014 ; Modzelewski et al , 2015 ; Meng et al , 2015 ), Caenorhabditis elegans ( Garcia-Segura et al , 2015 ), Drosophila melanogaster ( Lee et al , 2014 ), Heliconius melpomene ( Surridge et al , 2011 ), Xenopus laevis ( Ahmed et al , 2015 ) (animal datasets), Solanum lycopersicum ( Lopez-Gomollon et al , 2012 ; Kravchik et al , 2014 ), Glycine max ( Curtin et al , 2016 ) and Arabidopsis thaliana ( Wang et al , 2011 ) (plant datasets). We have downloaded these datasets from the GEO ( Barrett et al , 2013 ) and SRA ( Leinonen et al , 2011 ) databases.…”