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Open Access Highly Accessed Research article

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

Haneul Noh1, Charny Park2, Soojun Park3, Young Seek Lee1, Soo Young Cho456* and Hyemyung Seo1*

Author Affiliations

1 Department of Molecular & Life Sciences, Hanyang University, 1271 Sa-dong, Sangrok-gu, Ansan, Gyeonggi-do, South Korea

2 Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 120-750, Korea

3 Bio-Medical IT Convergence Research Department, ETRI, 218 Gajeong-ro, Yusoeng-gu, Daejeon 305-700, Korea

4 MRC Harwell, Mammalian Genetics Unit, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0RD, UK

5 Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX Institute, Seoul National University, Seoul 151-742, Korea

6 Laboratory of Developmental Biology and Genomics, College of Veterinary Medicine, Research Institute for Veterinary Science BK21, Program for Veterinary Science, Seoul National University, Seoul 151-742, Korea

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BMC Genomics 2014, 15:644  doi:10.1186/1471-2164-15-644

Published: 3 August 2014

Abstract

Background

Little 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.

Results

In 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.

Conclusions

Our 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.

Keywords:
Alzheimer’s disease; microRNA; Transcriptome; Network module