Molecular network analysis of human microRNA targetome: from cancers to Alzheimer's disease

Abstract

MicroRNAs (miRNAs), a class of endogenous small noncoding RNAs, mediate posttranscriptional regulation of protein-coding genes by binding chiefly to the 3' untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation. A single miRNA concurrently downregulates hundreds of target mRNAs designated "targetome", and thereby fine-tunes gene expression involved in diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. Recently, we characterized the molecular network of the whole human miRNA targetome by using bioinformatics tools for analyzing molecular interactions on the comprehensive knowledgebase. We found that the miRNA targetome regulated by an individual miRNA generally constitutes the biological network of functionally-associated molecules in human cells, closely linked to pathological events involved in cancers and neurodegenerative diseases. We also identified a collaborative regulation of gene expression by transcription factors and miRNAs in cancer-associated miRNA targetome networks. This review focuses on the workflow of molecular network analysis of miRNA targetome in silico. We applied the workflow to two representative datasets, composed of miRNA expression profiling of adult T cell leukemia (ATL) and Alzheimer's disease (AD), retrieved from Gene Expression Omnibus (GEO) repository. The results supported the view that miRNAs act as a central regulator of both oncogenesis and neurodegeneration.

Figure 1

The workflow of molecular network analysis of microRNA targetome. First, differentially expressed miRNAs (DEMs) among distinct samples and experimental conditions are extracted from microRNA expression profiling datasets based on microarray, qPCR, and next-generation sequencing (NGS) experiments by the standard statistical evaluation. Next, predicted targets and/or validated targets for DEMs are obtained by using target prediction programs, such as TargetScan, PicTar, MicroCosm and Diana-microT 3.0, or by searching them on databases of experimentally validated targets, such as miRTarBase, miRWalk, and miRecords. The expression of DEM targets in the cells and tissues examined is verified by searching them on UniGene, BioGPS, and HPRD. Molecular networks and pathways relevant to DEM targets are identified by using pathway analysis tools, such as KEGG, IPA, and KeyMolnet. Finally, the functionally inverse relationship between miRNAome and targetome is validated by loss-of-function or gain-of-function experiments in an in vitro and/or in vivo model.

Figure 2

The whole human microRNAome plays a specialized role in oncogenesis. Among 1,223 miRNAs of the whole human miRNAome, Diana-microT 3.0 identified the set of reliable targets from 273 miRNAs. Among them, KeyMolnet extracted molecular networks from 232 miRNAs. The generated network was compared side by side with human canonical networks of the KeyMolnet library, composed of 430 pathways, 885 diseases, and 208 pathological events to identify the canonical network showing the most statistically significant contribution to the extracted network. After top three pathways, diseases, and pathological events were individually totalized, the cumulated numbers of top 10 of (a) pathway, (b) disease, and (c) pathological event categories are expressed as a bar graph. The figure is cited from our study [17].

Figure 3

MicroRNA targetome suggests aberrant upregulation of cell cycle regulators in AD brains. The targets for the set of miRNAs downregulated in AD brains were identified by searching them on the miRTarBase [18]. The expression of targets in the human brain was verified by searching them on UniGene. Overall, 852 theoretically upregulated target genes for the set of miRNAs downregulated in AD brains were imported into the Core Analysis tool of IPA. The canonical pathway defined by “Cyclins and Cell Cycle Regulation” showing a significant relationship with the targetome (p = 2.18E-19) is shown. The red nodes represent cell cycle regulators theoretically upregulated in AD brains.

Figure 4

Differentially expressed microRNAs separate the cluster of ATL cells from normal CD4⁺T cells. We studied the dataset GSE31629 that contains miRNA expression profiling of peripheral blood mononuclear cells (PBMC) derived form ATL patients (n = 40) and CD4⁺ T cells from healthy control subjects (n = 22). Hierarchical clustering analysis of the set of 4 upregulated and 24 downregulated miRNAs in ATL cells versus normal CD4⁺ T cells separated the cluster of ATL samples from the cluster of normal CD4⁺ T cells. Hierarchical clustering analysis was performed by using Cluster 3.0 ( bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster) and TreeView 1.1.5r2 ( sourceforge.net/projects/jtreeview).

Figure 5

MicroRNA targetome suggests aberrant upregulation of T cell receptor signaling pathway molecules in ATL cells. The set of 932 targets for 24 downregluated miRNAs in ATL cells versus normal CD4⁺ T cells (GSE31629) were imported into the Functional Annotation tool of DAVID to identify relevant KEGG pathways. The T-cell receptor (TCR) signaling pathway (hsa04660) (p = 1.31E-5) is shown. The orange nodes represent the genes theoretically upregulated in ATL cells.

Figure 6

MicroRNA targetome shows a significant relationship with infection and cancer-related network in ATL cells. The set of 932 targets for 24 downregluated miRNAs in ATL cells versus normal CD4⁺ T cells (GSE31629) were imported into the Core Analysis tool of IPA in the display setting of 70 molecules per network. The first rank molecular network defined by “Infectious Disease, Protein Synthesis, Cancer” (p = 1.00E-82) is shown in the subcellular location layout. The red nodes represent the genes theoretically upregulated in ATL cells.

Figure 7

Differentially expressed microRNAs separate the cluster of AD brains from normal controls. We studied the dataset GSE16759 that contains miRNA expression profiling of the parietal cortex of AD patients (n = 4) and age-matched normal controls (NC) (n = 4). The Braak staging for AD pathology ranking from 0 to VI is shown in individual cases. Hierarchical clustering analysis of the set of 16 upregulated and 22 downregulated miRNAs in AD versus NC separated the cluster of AD samples from the cluster of NC, except for one NC sample classified as an intermediate between AD and NC groups. Hierarchical clustering analysis was performed by using Cluster 3.0 and TreeView 1.1.5r2.

Figure 8

MicroRNA targetome suggests aberrant upregulation of cell cycle regulators in AD brains. The set of 662 targets for 22 downregluated miRNAs in AD brains versus normal control brains (GSE16759) were imported into the Functional Annotation tool of DAVID to identify relevant KEGG pathways. The cell cycle pathway (hsa04110) (p = 1.51E-5) is shown. The orange nodes represent the genes theoretically upregulated in AD brains.