- Open Access
Integrated analyses to reconstruct microRNA-mediated regulatory networks in mouse liver using high-throughput profiling
© Hsu et al.; licensee BioMed Central Ltd. 2015
- Published: 21 January 2015
MicroRNAs (miRNAs) simultaneously target many transcripts through partial complementarity binding, and have emerged as a key type of post-transcriptional regulator for gene expression. How miRNA accomplishes its pleiotropic effects largely depends on its expression and its target repertoire. Previous studies discovered thousands of miRNAs and numerous miRNA target genes mainly through computation and prediction methods which produced high rates of false positive prediction. The development of Argonaute cross-linked immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) provides a system to effectively determine miRNA target genes. Likewise, the accuracy of dissecting the transcriptional regulation of miRNA genes has been greatly improved by chromatin immunoprecipitation of the transcription factors coupled with sequencing (ChIP-Seq). Elucidation of the miRNA target repertoire will provide an in-depth understanding of the functional roles of microRNA pathways. To reliably reconstruct a miRNA-mediated regulatory network, we established a computational framework using publicly available, sequence-based transcription factor-miRNA databases, including ChIPBase and TransmiR for the TF-miRNA interactions, along with miRNA-target databases, including miRTarBase, TarBase and starBase, for the miRNA-target interactions. We applied the computational framework to elucidate the miRNA-mediated regulatory network in the Mir122a -/- mouse model, which has an altered transcriptome and progressive liver disease.
We applied our computational framework to the expression profiles of miRNA/mRNA of Mir122a -/- mutant mice and wild-type mice. The miRNA-mediated network involves 40 curated TFs contributing to the aberrant expression of 65 miRNAs and 723 curated miRNA target genes, of which 56% was found in the differentially-expressed genes of Mir122a -- mice. Hence, the regulatory network disclosed previously-known and also many previously-unidentified miRNA-mediated regulations in mutant mice. Moreover, we demonstrate that loss of imprinting at the chromosome 12qF1 region is associated with miRNA overexpression in human hepatocellular carcinoma and stem cells, suggesting initiation of precancerous changes in young mice deficient in miR-122. A group of 9 miRNAs was found to share miR-122 target genes, indicating synergy between miRNAs and target genes by way of multiplicity and cooperativity.
The study provides significant insight into miRNA-mediated regulatory networks. Based on experimentally verified data, this network is highly reliable and effective in revealing previously-undetermined disease-associated molecular mechanisms. This computational framework can be applied to explore the significant TF-miRNA-miRNA target interactions in any complex biological systems with high degrees of confidence.
- Regulatory Network
- Ingenuity Pathway Analysis
- miRNA Expression Profile
- CTCF Binding Site
- Ingenuity Pathway Analysis Analysis
Since its first discovery in Caenorhabditis elegans [1–3], microRNA (miRNA) has been seen as a key regulator of gene expression . miRNAs are RNA molecules ranging in size from 21 to 23 nucleotides that down-regulate genes by guiding Argonaute (AGO) proteins to form miRNA-induced silencing complexes (miRISC) leading to translational repression or mRNA degradation . Currently, there are 2588 mature human miRNAs (1881 precursors) and 1915 mature mouse miRNAs (1193 precursors) (miRBase 21, http://www.mirbase.org/). miRNAs are regulated in a developmental stage-and tissue-specific fashion [6, 7] and are known to participate in diverse biological functions [8–12]. The dysregulation of miRNAs has been correlated with the pathogenesis of various human diseases such as cancer . Cumulative evidence has shown that, similar to protein-coding genes, miRNA genes primarily fall under transcriptional and epigenetic regulation . With the advent of bioinformatics and high-throughput technology, several TF-miRNA regulatory network databases have been constructed. CircuitsDB performed human and mouse TF-miRNA Feed-Forward regulatory Loops (FFLs) by predicting the TF promoter and miRNA-target region . Wang et al.  curated studies containing approximate 5000 transcription factor-miRNA regulations to create the TransmiR database. Yang et al.  integrated the high throughput ChIP-Seq datasets to provide a web-based tool called ChIPBase to help detect transcription factor binding sites (TFBSs) for non-coding RNAs including miRNAs.
The power of miRNA-target interactions (MTIs) lies in their multiplicity and cooperativity. For nearly a decade, MTIs were mostly obtained by way of computation and prediction. Numerous bioinformatics tools were generated that were based primarily on miRNA seed region complementarity with genes, free energy of miRNA-RNA duplexes, and conservation of target sites across the species . Widely used MTIs prediction tools include TargetScan , miRanda , PicTar  and many others. The pace of identification of relevant MTIs is rather slow, mostly due to the high rate of false-positive predictions of miRNA binding sites. Rapid progress in high throughput screening technologies such as microarrays, small RNA sequencing (sRNA-Seq) and RNA sequencing (RNA-Seq) can expedite MTI prediction. The most exciting breakthrough in recent years, however, is a method which allows for the direct collection of AGO-miRNA-mRNA complexes by Argonaute cross-linked immunoprecipitation coupled with high-throughput sequencing. This experimental approach has advanced our understanding of MTI interactions from prediction-based knowledge to a level that is empirically-and genome wide-based. Representative technologies include CLIP-Seq (cross-linking and immunoprecipitation sequencing)  and CLASH sequencing (crosslinking, ligation, and sequencing of hybrids)  which can directly detect miRNA target sites. Meanwhile, development of the web servers, dChip-GemiNI , MAGIA [23, 24], and mirConnX  provides additional tools for constructing TF-miRNA regulatory networks through integrating gene and miRNA expression profiles with target prediction. As a proof-of-concept demonstration, the high-throughput techniques of CLIP-Seq for miRNA-target interaction and ChIP-Seq for TF studies were found to accelerate discovery of TF-miRNA-gene regulatory networks in human pancreatic cancer early this year .
While experimental-based databases have become more popular for building gene regulatory networks, most reconstructed TF-miRNA-gene regulation networks are still computation/prediction-based. We expect an analysis platform integrating high-throughput datasets from ChIP-Seq for TF-miRNA network and CLIP-Seq and CLASH for miRNA-Target networks could be used to effectively explore significant TF-miRNA-MTIs interactions in any complex biological system with improved confidence.
We applied this integrated analysis platform to the Mir122a knockout mouse (Mir122a -/- ) model of liver disease. MicroRNA-122 (miR-122) is a highly abundant, developmental-regulated, liver-specific miRNA. It plays a pivotal role in maintaining lipid metabolism homeostasis [27, 28] and tumor suppression in the liver [29, 30]. Mir122a knockout mice (Mir122a -/- ) develop temporally-controlled steatohepatitis, fibrosis and hepatocellular carcinoma (HCC), a path similar to the disease progression in humans [31, 32]. A striking feature of this mouse model is that miR-122a profoundly modulates the liver transcriptome. The pathway disturbances that might drive cancer initiation and progression are found in young Mir122a -/- mice (2-months old). To date we have detected the expression of 79 experimentally-verified miR-122 targets, which represents only 8.9% (79/886) of the differentially expressed genes (DEGs)  in Mir122a -/- livers. Since miR-122 represents ~70% of the liver miRNAs, the imbalance of the miRNA homeostasis in Mir122a -/- liver can give rise to liver damage. A systematic, genome-wide investigation of the miRNA-mediated regulatory networks will provide important insights into the molecular mechanisms by which miR-122 modulates liver transcriptome and disease.
Differential expression of miRNAs in Mir122a -/- mice
List of differentially expressed miRNAs common to both OpenArray analysis and miRNA-Seq.
Potential TF-miRNA regulatory networks in Mir122a -/- mice
The appearance of higher numbers of connections in some hub-TFs and hub-DEmiRs suggests that these hub-TFs may play an essential role in the regulation of multiple DEmiRs, and that the hub-DEmiRs could potentially be regulated by multiple TFs in a combinatorial mode. For example, spleen focus forming virus (SFFV) proviral integration oncogene (Spi1) and CCCTC-binding factor (Ctcf) are the top two hub-TFs (Figure 2). The expression level of both Spi1 and Ctcf is moderately increased (1.3-1.4 fold) in Mir122a -/- mice. Ctcf can perhaps regulate 42 DEmiRs including 32 UPmiRs and 10 DNmiRs. Most of the Ctcf-UPmiRs (94%) are located in Chr12qF1 locus. Spi1 could potentially regulate 23 DEmiRs (15 UPmiRs with 12 miRNAs in Chr12qF1 and 8 DNmiRs). Our analysis also suggests that 6 DEmiRs, miR-17-5p, miR-486b-5p, miR-19a-3p, miR-484, miR-199a-5p and miR-182-5p, are likely to be co-regulated by both Ctcf and Spi1. Further experimentation is needed to support these initial findings. It is interesting to note that Spi1-UPmiRs and Ctcf-UPmiRs are respectively clustered within different regions of Chr12qF1, namely the Meg3-Rian region and Mirg region.
Network analysis of curated miRNA target genes
Statistics for curated miRNA-target interactions.
Number of curated targets
Number of DE miRNA target genes in Mir122a -/- mice
The complex interplay of miRNAs and target genes is accomplished through multiplicity and cooperativity. We identified 9 DEmiRs that can simultaneously regulate 39 miR-122 targets (Table S4.1, S4.2). A priori., we reasoned that in Mir122a -/- livers, elevated expression of Klf6, a miR-122 target gene , is a simple response to the loss of miR-122a; nonetheless, elevated expression of Klf6 is likely to be the sum of a cascade of events involving the loss of miR-122a and the down regulation of 5 other DNmiRs (miR-17-5, -31, 19a, 93-5p and 144-3p). Our analysis supports the conclusion that miRNAs and transcription factors are two major classes of gene regulators using similar control principles, namely one-to-many and many-to-one relationships between the regulator and the target genes .
Functional analysis of 204 down-regulated target genes of UPmiRs and 295 up-regulated target genes of DNmiRs in IPA "Diseases and Functions".
Diseases and Functions
Cellular Growth and Proliferation
Connective Tissue Disorders
RNA Post-Transcriptional Modification
Nervous System Development and Function
Cell-To-Cell Signaling and Interaction
Cell Death and Survival
Cellular Assembly and Organization
Total number of UPmiR-targets: 57
Total number of DNmiR-targets: 168
Function analysis of 204 down-regulated target genes of UPmiRs and 295 up-regulated target genes of DNmiRs in IPA "Canonical Pathways".
Ingenuity Canonical Pathways
Superpathway of Cholesterol Biosynthesis
Glycogen Biosynthesis II (from UDP-D-Glucose)
Pyrimidine Deoxyribonucleotides De Novo Biosynthesis I
Regulation of the Epithelial-Mesenchymal Transition Pathway
Leukocyte Extravasation Signaling
Axonal Guidance Signaling
Molecular Mechanisms of Cancer
NRF2-mediated Oxidative Stress Response
Actin Cytoskeleton Signaling
Hepatic Fibrosis/Hepatic Stellate Cell Activation
Protein Kinase A Signaling
Regulation of Actin-based Motility by Rho
Cell Cycle: G2/M DNA Damage Checkpoint Regulation
Total number of UPmiR-targets: 29
Total number of DNmiR-targets: 76
In this case study we analyzed the miRNA-mediated regulatory network in Mir122a -/- mouse livers and identified a complex interplay of TF-miRNA and miRNA-targets by way of multiplicity and cooperativity. Using similar control principles, TF and miRNA can jointly fine-tune the target gene expression. The results of this study showed that altered expression of miRNAs in miR-122a-deficient livers had far-reaching effects on liver homeostasis. Previously we demonstrated that fibrotic and tumor signature genes were induced early in Mir122a deficient livers . The current study provided additional new evidence to reveal that the interplay between differentially expressed miRNAs and their target genes not only contributes greatly to the properties we previously characterized but also raised new activities to assist the process of cell transformation of Mir122a -/- hepatocytes. A general theme of DEmiR-target interaction is that UPmiRs-targets are involved in loss-of-function activities while DNmiRs-targets contribute to gain-of-function activities.
In the absence of the abundant liver-specific miR-122a, the altered expression of DEmiRs did not reinstate the normal physiological functions of miR-122a, but rather expedited the hepatocyte de-differentiation. Several lines of evidence supported our observation. (1) DEmiRs synergistically heighten the adverse effect of miR-122a deficiency. We found a group of 9 DNmiRs that can potentially target 39 miR-122a target genes. These 9 DNmiRs are miR-144-3p, 17-5p, 93-5p, 322-5p, 19a-3p, 31-5p, 145a-5p, 335-5p and 345-5p (Tables S4.1 and S4.2). miR-144-3p and miR-17-5p can respectively recognize 19 and 17 miR-122a targets. Upregulation of the miR-122a-co-targets contribute to altered cell adhesion, cytoskeleton organization, protein phosphorylation, ion transport, glucose catabolic process and liver fibrosis. (2) Induced expression of Chr12qF1 miRNAs was detected in mutant hepatocytes. The imprinted Dlk1-Dio3 gene cluster is located on mouse chromosome 12 (12qF1) and human chromosome14 (14q32.2). Concurrent overexpression of Chr12qF1 miRNAs is regarded as the miRNA signature of the stem cell property [35, 36] and overexpression of 14q32.2 miRNAs has been detected in cancer stem cells and in aggressive types of human HCC  as well as in other cancer types . (3) Upregulation of several UPmiRs has been reported in HCC despite the fact the 2-month old Mir122a -/- livers are free of tumors. Elevated expression of miR-182 , miR-429 , miR-199a, 199a*, 200a, and 200b was positively and significantly correlated to the progression of liver fibrosis . These results reinforce our previous findings that dysregulation of liver functions begins in young Mir122a -/- mice .
In Mir122a -/- livers, overexpression of several classic imprinted genes H19, Igf2, Meg3, Mirg and Rian signals for altered epigenetic regulation. How miR-122 influences epigenetic regulation of DEGs is not clear. It has been reported that many miRNA targets are involved in gene regulation at the epigenetic level . From the curated miRNA-target interactions, several enzymes affecting methylation of DNA or histones were found to be the target genes of UPmiRs. Tet methylcytosine dioxygenase 2 (Tet2), lysine (K)-specific demethylase 7A (Kdm7a) and jumonji domain containing 1C (Jmjd1c, a H3K9 demethylase) are respectively targeted by 8, 9 and 5 UPmiRs (Table S3.2). Whether reduced expression of these enzymes is linked to epigenetic regulation of DEGs in Mir122a -/- mice requires further experimental demonstration. It is worth mentioning that CTCF, an insulator-and chromatin loop-associated protein as well as an epigenetic regulator, is a direct target of miR-122 .
TF-DEmiR interactions revealed that Ctcf, a hub-TF, potentially can regulate 42 DEmiRs including 32 UPmiRs and 10 DNmiRs. A great proportion of the Ctcf-UPmiRs (94%) is located in the Chr12qF1 locus. Recently, CTCF was found to be involved in the regulation of miR-125b1, miR-375, and the miR-290 cluster in breast cancer cells and stem cells . DNA methylation seems to play a role in the CTCF regulation of miRNAs since CTCF binding sites tend to be mapped to the hypomethylated regions . Our initial methylome data also showed that the Ctcf binding sites in the Chr12qF1 locus are less methylated in Mir122a -/- than in the WT liver (data not shown).
We applied our computational framework to the expression profiles of the miRNA/mRNA from Mir122a -/- mutant mice and wild-type mice and found a miRNA-mediated network involving 40 curated TFs regulating the aberrant expression of 65 miRNAs and 723 curated miRNA target genes, of which 56% was found in the differentially expressed genes of Mir122a -/- mice. This regulatory network disclosed previously known as well as many previously unidentified miRNA-mediated regulations in mutant mice. Remarkably, we demonstrate that loss of imprinting at 12qF1 is associated with miRNA overexpression in human HCC and stem cells, suggesting initiation of precancerous changes in young mice deficient in miR-122. In addition, a group of 9 miRNAs was found to share miR-122 target genes, indicating synergy between miRNAs and target genes by way of multiplicity and cooperativity. These findings have a potentially significant impact on our understanding of miRNA-mediated regulatory networks. By collectively utilizing the experimentally verified data, this computational framework is highly reliable, effective and is suitable for exploring significant TF-miRNA-miRNA target interactions in any complex biological system.
Computational framework for reconstructing the miRNA-mediated regulatory network
Figure 1 presents an overview of the proposed computational framework to reconstruct the miRNA regulatory network. We obtained mouse TF-miRNA regulation information from TransmiR and ChIPBase and those regulations related to differentially expressed miRNAs in the livers of Mir122a -/- and wild type mice were selected. The differentially expressed miRNAs were determined by expression profiles using the OpenArray system and small RNA-Seq. The differentially expressed genes were identified from the expression profiles using gene chips. Moreover, we integrated experimental miRNA-target gene interactions from miRTarBase, TarBase and starBase. For each miRNA-target interaction, miRNA and its target gene with inverse expression level were selected for further reconstruction of the miRNA-mediated regulatory network.
Curated TF-miRNA interactions and construction of the TF-miRNA regulatory network
To reconstruct the high confidence TF-miRNA regulatory network in Mir122a -/- mice, we integrated two experimentally verified resources, TransmiR (version 1.2)  and ChIPBase (release 1.1) , to support the regulations in TF-miRNA. TransmiR is the TF-miRNA interaction database, and contains 735 manually curated TF-miRNA regulatory interactions. ChIPBase provides the transcriptional regulation not only for the protein-coding genes, but also for non-coding RNA (including miRNA) from ChIP-Seq data. The miRNA promoter region used in this study is defined as a 5 kb upstream region and 1 kb downstream region. Transcription factor binding sites (TFBSs) from ChIP-Seq data on each miRNA promoter provide empirical evidence of TF-miRNA interactions. Table S1 summarizes the 8,697 curated TF-miRNA interactions between 73 TFs and 964 miRNAs in mouse livers. The TF-miRNA regulatory network was constructed based on the interactions between differentially expressed miRNAs and their experimentally verified TFs. We used Cytoscape (version 3.1.1) [45–48] to visualize the TF-miRNA regulatory network.
Curated miRNA-target interactions and construction of the miRNA-target regulatory network
To construct the miRNA-mediated regulatory network in Mir122a -/- mice, we collected datasets of experimentally validated miRNA-target interactions (MTIs) from miRTarBase , TarBase  and starBase . The miRTarBase release 4.5 dataset http://mirtarbase.mbc.nctu.edu.tw contains more than fifty thousand miRNA-target interactions, obtained by manually surveying the relevant literature using textual data mining tools to systematically filter research articles related to functional studies of miRNAs. Generally, the collected MTIs have been validated experimentally by 3'UTR-reporter assay, western blotting, microarray analysis and by next-generation sequencing experiments. TarBase (version 6) is the other data source for obtaining the experimentally validated MTIs. This work not only integrated the MTIs from miRTarBase and TarBase, we also collected MTIs from Argonaute CLIP-Seq data. starBase is a database used for deciphering miRNA-target interactions accumulated from 108 CLIP-Seq data from 37 studies. For this study, we retrieved MTIs only from data derived from mouse tissue. Table S1 summarizes the relationships among the 95,364 curated MTIs between 329 miRNAs and 7,001 target genes.
To construct the miRNA-target regulatory network, we gathered the DEmiRs and DEGs respectively from the miRNA expression profiles (OpenArray and small RNA-Seq) and microarray datasets. We identified the transcriptional regulation and the post-transcription regulation of DEmiRs from experimentally verified resources. An inverse relationship in expression was often found between miRNAs and their target genes [52–55], thus we focused on those inversely correlated pairs of DEmiRs and the curated targets. For each up-regulated miRNA, the curated target genes are down-regulated and vice versa. These interactions are imported to Cytoscape for visualizing the miRNA-target regulatory network.
Functional and pathway analysis using Ingenuity Pathway Analysis (IPA)
To discuss the TF-miRNA regulatory network, we analyzed the target genes of differentially expressed miRNAs using Ingenuity Pathways Analysis 2014 (IPA Summer Release 2014, Ingenuity Systems, http://www.ingenuity.com). IPA analysis identified biological functions and canonical pathways related to the targets of up-and down-regulated miRNAs and enabled comparison across multiple analyses with varying conditions. A threshold was set at p value 0.05 which was calculated by Fisher's exact test. Causal Network Analysis provides the causal connections between diseases, genes and networks of upstream regulators .
RNA isolation, gene expression, and high-density oligonucleotide microarray analysis
Total RNA was isolated from the liver samples of wild-type mice and Mir122a -/- mice using the Trizol reagent (Invitrogen) according to the manufacturer's protocol. The microarray hybridizations were performed using total RNA prepared from the liver samples of three wild-type mice and four Mir122a -/- mice at an age of 2-months. GeneChip Mouse Genome 430 2.0 Affymetrix oligonucleotide Gene Chips (Affymetrix) were analyzed at the Microarray & Gene Expression Analysis Core Facility (VGH-YM Genome Center, National Yang-Ming University) according to the Affymetrix protocols. All the data files are presented in compliance with MIAMI guidelines and can be accessed online at the Gene Expression Omnibus (series accession numbers GSE27713 ). A total of 2315 differentially expressed genes was identified in the Mir122a -/- mice, 1,448 genes up-regulated (a fold change of ≥ 1.5) and 867 genes down-regulated (a fold change of ≤ 0.66).
miRNA Profiling on high-throughput OpenArray™ system
MiRNA expression profiling of the liver sample from one wild-type mouse and one Mir122a -/- mouse (both 2-months old) was performed using TaqMan Rodent MicroRNA Array v2.0 (Applied Biosystems) according to the manufacturer's instructions. This array panel enables the simultaneous quantification of the expression of 750 rodent (mouse and rat) well-characterized mature miRNAs. All the mature miRNA names are mapped to the name recorded in miRBase V20. Expression data were processed using OpenArray™ Real-Time qPCR Analysis Software. The data were further analyzed in HTqPCR package from Bioconductor (v2.1.2) in R 2.23. Data were quantile normalized and duplicates averaged using U6 rRNA as an endogenous control. Undetermined miRNAs or those with a Ct value below 15 or greater than 35 across all samples were removed from subsequent analysis. We set the cut-off for the up-regulated miRNAs with ≥ 1.5 fold change, or ≤ 0.66 for down-regulated miRNAs.
Small RNA-Seq data analysis
The same liver samples that were analysed by OpenArray™ system were further subjected to small RNA sequencing. The sequencing libraries for miRNA-Seq were prepared using TruSeq Small RNA Sample Preparation Kit (Illumina) according to the manufacturer's instruction. One microgram of total RNA with an RNA integrity number (RIN) greater than 8.0 was ligated sequentially to 3' and 5' adaptors. Subsequently, the ligation product was reverse transcribed followed by PCR for 11 cycles to enrich miRNAs that have adapter sequences on both ends. The amplified cDNA construct was size fractionated (145-160 bp) and purified by 6% Novex TBE PAGE gel (Invitrogen). The miRNA library was quantified by qPCR and the size distribution was validated on a 2100 Bioanalyzer (Agilent) by a High Sensitivity DNA chip. The miRNA library was sequenced on a HiSeq 2000 (Illumina) by single end sequencing with a 50 bp read length.
Raw sequencing reads were quality pre-processed using the FASTX-Toolkit  version 0.0.13 as follows. First, the Illumina 3' adapter sequences 'TGGAATTCTCGGGTGCCAAGG' were removed. The reads were then trimmed according to their quality values based on the Phred quality score. We set a Phred quality score of 20 as the cut-off value. We obtained the small RNA reads if the reads were longer than 18 nucleotides and shorter than 30 nucleotides. We then used the ncPRO-seq  package (version 1.5.1), a bowtie-based  read alignment tool for the annotation reads, to confirm the read distribution in the reference genome. Only reads that mapped a maximum of two mismatches and 20 locations in the genome were used (bowtie parameter: -v2 -a -m20 --best --strata --nomaqround -f -y). Five main databases were employed to annotate the small RNA distribution: the UCSC reference genome (mm10), miRBase v20 [60, 61], UCSC refGene 06-Apr-2014, RFam v11.0 , and UCSC repeatMasks (mm10). To quantify the miRNA profiles, we used miRDeep2 [63, 64] package (version 184.108.40.206), another bowtie-based alignment tool. Figure S1 shows that more than 65% of small RNA reads were miRNAs, indicating that our small RNA reads were highly enriched for miRNAs. The miRNAs were identified as significantly differentially expressed as compared to those in normal livers if the fold change was greater than or equal to 1.5 (up-regulated miRNAs) or the fold change was less than or equal to 0.66 (down-regulated miRNAs).
We acknowledge the High-throughput Genome Analysis Core Facility of National Core Facility Program for Biotechnology, Taiwan (NSC 102-2319-B-010-001) and the Microarray & Gene Expression Analysis Core Facility of the VGH National Yang-Ming University Genome Research Center (VYMGC) for their technical support. This work was supported by a grant from the National Science Council (NSC 101-2321-B-010-004), a grant from the Ministry of Education, Aim for the Top University Plan (102AC-T502) to A.-P.T. This work was also supported in part by grant from Taiwan MOST I-RiCE Program (UST-UCSD International Center of Excellence in Advanced Bio-engineering, MOST 102-2911-I-009-101).
This article has been published as part of BMC Genomics Volume 16 Supplement 2, 2015: Selected articles from the Thirteenth Asia Pacific Bioinformatics Conference (APBC 2015): Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/16/S2
- Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993, 75 (5): 843-854. 10.1016/0092-8674(93)90529-Y.View ArticlePubMedGoogle Scholar
- Wightman B, Ha I, Ruvkun G: Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell. 1993, 75 (5): 855-862. 10.1016/0092-8674(93)90530-4.View ArticlePubMedGoogle Scholar
- Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G: The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000, 403 (6772): 901-906. 10.1038/35002607.View ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Huntzinger E, Izaurralde E: Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nature reviews Genetics. 2011, 12 (2): 99-110. 10.1038/nrg2936.View ArticlePubMedGoogle Scholar
- Miska EA: How microRNAs control cell division, differentiation and death. Current opinion in genetics & development. 2005, 15 (5): 563-568. 10.1016/j.gde.2005.08.005.View ArticleGoogle Scholar
- Sood P, Krek A, Zavolan M, Macino G, Rajewsky N: Cell-type-specific signatures of microRNAs on target mRNA expression. Proceedings of the National Academy of Sciences of the United States of America. 2006, 103 (8): 2746-2751. 10.1073/pnas.0511045103.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE, Hammond SM, Conlon FL, Wang DZ: The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nature genetics. 2006, 38 (2): 228-233. 10.1038/ng1725.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao Y, Samal E, Srivastava D: Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature. 2005, 436 (7048): 214-220. 10.1038/nature03817.View ArticlePubMedGoogle Scholar
- Cui Q, Yu Z, Purisima EO, Wang E: Principles of microRNA regulation of a human cellular signaling network. Molecular systems biology. 2006, 2: 46-PubMed CentralView ArticlePubMedGoogle Scholar
- Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, Macdonald PE, Pfeffer S, Tuschl T, Rajewsky N, Rorsman P, et al: A pancreatic islet-specific microRNA regulates insulin secretion. Nature. 2004, 432 (7014): 226-230. 10.1038/nature03076.View ArticlePubMedGoogle Scholar
- Xu C, Lu Y, Pan Z, Chu W, Luo X, Lin H, Xiao J, Shan H, Wang Z, Yang B: The muscle-specific microRNAs miR-1 and miR-133 produce opposing effects on apoptosis by targeting HSP60, HSP70 and caspase-9 in cardiomyocytes. Journal of cell science. 2007, 120 (Pt 17): 3045-3052.View ArticlePubMedGoogle Scholar
- Esteller M: Non-coding RNAs in human disease. Nature reviews Genetics. 2011, 12 (12): 861-874. 10.1038/nrg3074.View ArticlePubMedGoogle Scholar
- Wang J, Lu M, Qiu C, Cui Q: TransmiR: a transcription factor-microRNA regulation database. Nucleic acids research. 2010, D119-122. 38 DatabaseGoogle Scholar
- Friard O, Re A, Taverna D, De Bortoli M, Cora D: CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse. BMC bioinformatics. 2010, 11: 435-10.1186/1471-2105-11-435.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang JH, Li JH, Jiang S, Zhou H, Qu LH: ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic acids research. 2013, D177-187. 41 DatabaseGoogle Scholar
- Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS: MicroRNA targets in Drosophila. Genome biology. 2003, 5 (1): R1-10.1186/gb-2003-5-1-r1.PubMed CentralView ArticlePubMedGoogle Scholar
- Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP: Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nature structural & molecular biology. 2011, 18 (10): 1139-1146. 10.1038/nsmb.2115.View ArticleGoogle Scholar
- Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, et al: Combinatorial microRNA target predictions. Nature genetics. 2005, 37 (5): 495-500. 10.1038/ng1536.View ArticlePubMedGoogle Scholar
- Chi SW, Zang JB, Mele A, Darnell RB: Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009, 460 (7254): 479-486.PubMed CentralPubMedGoogle Scholar
- Helwak A, Kudla G, Dudnakova T, Tollervey D: Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell. 2013, 153 (3): 654-665. 10.1016/j.cell.2013.03.043.PubMed CentralView ArticlePubMedGoogle Scholar
- Yan Z, Shah PK, Amin SB, Samur MK, Huang N, Wang X, Misra V, Ji H, Gabuzda D, Li C: Integrative analysis of gene and miRNA expression profiles with transcription factor-miRNA feed-forward loops identifies regulators in human cancers. Nucleic acids research. 2012, 40 (17): e135-10.1093/nar/gks395.PubMed CentralView ArticlePubMedGoogle Scholar
- Sales G, Coppe A, Bisognin A, Biasiolo M, Bortoluzzi S, Romualdi C: MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic acids research. 2010, W352-359. 38 Web ServerGoogle Scholar
- Bisognin A, Sales G, Coppe A, Bortoluzzi S, Romualdi C: MAGIA(2): from miRNA and genes expression data integrative analysis to microRNA-transcription factor mixed regulatory circuits (2012 update). Nucleic acids research. 2012, W13-21. 40 Web ServerGoogle Scholar
- Huang GT, Athanassiou C, Benos PV: mirConnX: condition-specific mRNA-microRNA network integrator. Nucleic acids research. 2011, W416-423. 39 Web ServerGoogle Scholar
- Ye S, Yang L, Zhao X, Song W, Wang W, Zheng S: Bioinformatics Method to Predict Two Regulation Mechanism: TF-miRNA-mRNA and lncRNA-miRNA-mRNA in Pancreatic Cancer. Cell biochemistry and biophysics. 2014Google Scholar
- Esau C, Davis S, Murray SF, Yu XX, Pandey SK, Pear M, Watts L, Booten SL, Graham M, McKay R, et al: miR-122 regulation of lipid metabolism revealed by in vivo antisense targeting. Cell metabolism. 2006, 3 (2): 87-98. 10.1016/j.cmet.2006.01.005.View ArticlePubMedGoogle Scholar
- Krutzfeldt J, Rajewsky N, Braich R, Rajeev KG, Tuschl T, Manoharan M, Stoffel M: Silencing of microRNAs in vivo with 'antagomirs'. Nature. 2005, 438 (7068): 685-689. 10.1038/nature04303.View ArticlePubMedGoogle Scholar
- Kutay H, Bai S, Datta J, Motiwala T, Pogribny I, Frankel W, Jacob ST, Ghoshal K: Downregulation of miR-122 in the rodent and human hepatocellular carcinomas. Journal of cellular biochemistry. 2006, 99 (3): 671-678. 10.1002/jcb.20982.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsai WC, Hsu PW, Lai TC, Chau GY, Lin CW, Chen CM, Lin CD, Liao YL, Wang JL, Chau YP, et al: MicroRNA-122, a tumor suppressor microRNA that regulates intrahepatic metastasis of hepatocellular carcinoma. Hepatology. 2009, 49 (5): 1571-1582. 10.1002/hep.22806.View ArticlePubMedGoogle Scholar
- Hsu SH, Wang B, Kota J, Yu J, Costinean S, Kutay H, Yu L, Bai S, La Perle K, Chivukula RR, et al: Essential metabolic, anti-inflammatory, and anti-tumorigenic functions of miR-122 in liver. The Journal of clinical investigation. 2012, 122 (8): 2871-2883. 10.1172/JCI63539.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsai WC, Hsu SD, Hsu CS, Lai TC, Chen SJ, Shen R, Huang Y, Chen HC, Lee CH, Tsai TF, et al: MicroRNA-122 plays a critical role in liver homeostasis and hepatocarcinogenesis. The Journal of clinical investigation. 2012, 122 (8): 2884-2897. 10.1172/JCI63455.PubMed CentralView ArticlePubMedGoogle Scholar
- Xu H, He JH, Xiao ZD, Zhang QQ, Chen YQ, Zhou H, Qu LH: Liver-enriched transcription factors regulate microRNA-122 that targets CUTL1 during liver development. Hepatology. 2010, 52 (4): 1431-1442. 10.1002/hep.23818.View ArticlePubMedGoogle Scholar
- Csak T, Bala S, Lippai D, Satishchandran A, Catalano D, Kodys K, Szabo G: microRNA-122 regulates hypoxia-inducible factor-1 and vimentin in hepatocytes and correlates with fibrosis in diet-induced steatohepatitis. Liver international: official journal of the International Association for the Study of the Liver. 2014Google Scholar
- Stadtfeld M, Apostolou E, Akutsu H, Fukuda A, Follett P, Natesan S, Kono T, Shioda T, Hochedlinger K: Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells. Nature. 2010, 465 (7295): 175-181. 10.1038/nature09017.PubMed CentralView ArticlePubMedGoogle Scholar
- Valdmanis PN, Roy-Chaudhuri B, Kim HK, Sayles LC, Zheng Y, Chuang CH, Caswell DR, Chu K, Zhang Y, Winslow MM, et al: Upregulation of the microRNA cluster at the Dlk1-Dio3 locus in lung adenocarcinoma. Oncogene. 2013Google Scholar
- Luk JM, Burchard J, Zhang C, Liu AM, Wong KF, Shek FH, Lee NP, Fan ST, Poon RT, Ivanovska I, et al: DLK1-DIO3 genomic imprinted microRNA cluster at 14q32.2 defines a stemlike subtype of hepatocellular carcinoma associated with poor survival. The Journal of biological chemistry. 2011, 286 (35): 30706-30713. 10.1074/jbc.M111.229831.PubMed CentralView ArticlePubMedGoogle Scholar
- Manodoro F, Marzec J, Chaplin T, Miraki-Moud F, Moravcsik E, Jovanovic JV, Wang J, Iqbal S, Taussig D, Grimwade D, et al: Loss of imprinting at the 14q32 domain is associated with microRNA overexpression in acute promyelocytic leukemia. Blood. 2014, 123 (13): 2066-2074. 10.1182/blood-2012-12-469833.View ArticlePubMedGoogle Scholar
- Wang J, Li J, Shen J, Wang C, Yang L, Zhang X: MicroRNA-182 downregulates metastasis suppressor 1 and contributes to metastasis of hepatocellular carcinoma. BMC cancer. 2012, 12: 227-10.1186/1471-2407-12-227.PubMed CentralView ArticlePubMedGoogle Scholar
- Li L, Tang J, Zhang B, Yang W, Liugao M, Wang R, Tan Y, Fan J, Chang Y, Fu J, et al: Epigenetic modification of MiR-429 promotes liver tumour-initiating cell properties by targeting Rb binding protein 4. Gut. 2014Google Scholar
- Murakami Y, Toyoda H, Tanaka M, Kuroda M, Harada Y, Matsuda F, Tajima A, Kosaka N, Ochiya T, Shimotohno K: The progression of liver fibrosis is related with overexpression of the miR-199 and 200 families. PloS one. 2011, 6 (1): e16081-10.1371/journal.pone.0016081.PubMed CentralView ArticlePubMedGoogle Scholar
- Gruber AJ, Zavolan M: Modulation of epigenetic regulators and cell fate decisions by miRNAs. Epigenomics. 2013, 5 (6): 671-683. 10.2217/epi.13.65.View ArticlePubMedGoogle Scholar
- Saito Y, Saito H: Role of CTCF in the regulation of microRNA expression. Frontiers in genetics. 2012, 3: 186-PubMed CentralPubMedGoogle Scholar
- Mukhopadhyay R, Yu W, Whitehead J, Xu J, Lezcano M, Pack S, Kanduri C, Kanduri M, Ginjala V, Vostrov A, et al: The binding sites for the chromatin insulator protein CTCF map to DNA methylation-free domains genome-wide. Genome research. 2004, 14 (8): 1594-1602. 10.1101/gr.2408304.PubMed CentralView ArticlePubMedGoogle Scholar
- Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, et al: Integration of biological networks and gene expression data using Cytoscape. Nature protocols. 2007, 2 (10): 2366-2382. 10.1038/nprot.2007.324.PubMed CentralView ArticlePubMedGoogle Scholar
- Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T: A travel guide to Cytoscape plugins. Nature methods. 2012, 9 (11): 1069-1076. 10.1038/nmeth.2212.PubMed CentralView ArticlePubMedGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003, 13 (11): 2498-2504. 10.1101/gr.1239303.PubMed CentralView ArticlePubMedGoogle Scholar
- Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011, 27 (3): 431-432. 10.1093/bioinformatics/btq675.PubMed CentralView ArticlePubMedGoogle Scholar
- Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, Chou CH, Chu CF, Huang HY, Lin CM, Ho SY, et al: miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic acids research. 2014, D78-85. 42 DatabaseGoogle Scholar
- Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic acids research. 2012, D222-229. 40 DatabaseGoogle Scholar
- Li JH, Liu S, Zhou H, Qu LH, Yang JH: starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic acids research. 2014, D92-97. 42 DatabaseGoogle Scholar
- Sarachana T, Zhou R, Chen G, Manji HK, Hu VW: Investigation of post-transcriptional gene regulatory networks associated with autism spectrum disorders by microRNA expression profiling of lymphoblastoid cell lines. Genome medicine. 2010, 2 (4): 23-10.1186/gm144.PubMed CentralView ArticlePubMedGoogle Scholar
- Xiao Y, Ping Y, Fan H, Xu C, Guan J, Zhao H, Li Y, Lv Y, Jin Y, Wang L, et al: Identifying dysfunctional miRNA-mRNA regulatory modules by inverse activation, cofunction, and high interconnection of target genes: a case study of glioblastoma. Neuro-oncology. 2013, 15 (7): 818-828. 10.1093/neuonc/not018.PubMed CentralView ArticlePubMedGoogle Scholar
- Bandyopadhyay S, Mitra R: TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics. 2009, 25 (20): 2625-2631. 10.1093/bioinformatics/btp503.View ArticlePubMedGoogle Scholar
- Li XH, Qu JQ, Yi H, Zhang PF, Yi HM, Wan XX, He QY, Ye X, Yuan L, Zhu JF, et al: Integrated analysis of differential miRNA and mRNA expression profiles in human radioresistant and radiosensitive nasopharyngeal carcinoma cells. PloS one. 2014, 9 (1): e87767-10.1371/journal.pone.0087767.PubMed CentralView ArticlePubMedGoogle Scholar
- Kramer A, Green J, Pollard J, Tugendreich S: Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014, 30 (4): 523-530. 10.1093/bioinformatics/btt703.PubMed CentralView ArticlePubMedGoogle Scholar
- FASTX-Toolkit: FASTQ/A short-reads pre-processing tools. [http://hannonlab.cshl.edu/fastx_toolkit/]
- Chen CJ, Servant N, Toedling J, Sarazin A, Marchais A, Duvernois-Berthet E, Cognat V, Colot V, Voinnet O, Heard E, et al: ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data. Bioinformatics. 2012, 28 (23): 3147-3149. 10.1093/bioinformatics/bts587.View ArticlePubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome biology. 2009, 10 (3): R25-10.1186/gb-2009-10-3-r25.PubMed CentralView ArticlePubMedGoogle Scholar
- Griffiths-Jones S: The microRNA Registry. Nucleic acids research. 2004, D109-111. 32 DatabaseGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic acids research. 2014, D68-73. 42 DatabaseGoogle Scholar
- Burge SW, Daub J, Eberhardt R, Tate J, Barquist L, Nawrocki EP, Eddy SR, Gardner PP, Bateman A: Rfam 11.0: 10 years of RNA families. Nucleic acids research. 2013, D226-232. 41 DatabaseGoogle Scholar
- Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N: Discovering microRNAs from deep sequencing data using miRDeep. Nature biotechnology. 2008, 26 (4): 407-415. 10.1038/nbt1394.View ArticlePubMedGoogle Scholar
- Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic acids research. 2012, 40 (1): 37-52. 10.1093/nar/gkr688.PubMed CentralView ArticlePubMedGoogle Scholar
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