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Identification of microRNA-regulated gene networks by expression analysis of target genes.

ABSTRACT: MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGF? pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGF? signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.

SUBMITTER: Gennarino VA

PROVIDER: S-EPMC3371699 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Identification of microRNA-regulated gene networks by expression analysis of target genes.

Gennarino Vincenzo Alessandro VA D'Angelo Giovanni G Dharmalingam Gopuraja G Fernandez Serena S Russolillo Giorgio G Sanges Remo R Mutarelli Margherita M Belcastro Vincenzo V Ballabio Andrea A Verde Pasquale P Sardiello Marco M Banfi Sandro S

Genome research 20120224 6

MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns ...[more]

PMID: 22345618

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Project description:ObjectiveThe aim of the study was to find the key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct miRNA-target gene-TF regulatory networks to investigate the underlying molecular mechanism in colorectal adenoma (CRA).MethodsFour mRNA expression datasets and one miRNA expression dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were identified between CRA and normal samples. Moreover, functional enrichment analysis for DEGs was carried out utilizing the Cytoscape-plugin, known as ClueGO. These DEGs were mapped to STRING database to construct a protein-protein interaction (PPI) network. Then, a miRNA-target gene regulatory network was established to screen key DEMs. In addition, similar workflow of the analyses were also performed comparing the CRC samples with CRA ones to screen key DEMs. Finally, miRNA-target gene-TF regulatory networks were constructed for these key DEMs using iRegulon plug-in in Cytoscape.ResultsWe identified 514 DEGs and 167 DEMs in CRA samples compared to healthy samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in several terms and pathways, such as regulation of cell migration and bile secretion pathway. A PPI network was constructed including 325 nodes as well as 890 edges. A total of 59 DEGs and 65 DEMs were identified in CRC samples compared to CRA ones. In addition, Two key DEMs in CRA samples compared to healthy samples were identified, such as hsa-miR-34a and hsa-miR-96. One key DEM, hsa-miR-29c, which was identified when we compared the differentially expressed molecules found in the comparison CRA versus normal samples to the ones obtained in the comparison CRC versus CRA, was also identified in CRC samples compared to CRA ones. The miRNA-target gene-TF regulatory networks for these key miRNAs included two TFs, one TF and five TFs, respectively.ConclusionThese identified key genes, miRNA, TFs and miRNA-target gene-TF regulatory networks associated with CRA, to a certain degree, may provide some hints to enable us to better understand the underlying pathogenesis of CRA.

Project description:RationalePlasma low-density lipoprotein cholesterol (plasma LDL-C), vascular endothelial cells and peripheral blood mononuclear cells (PBMCs), particularly monocytes, play key roles in initiating atherosclerosis, the primary cause of cardiovascular disease (CVD). Although the mechanisms underlying development of atherosclerosis are not well understood, LDL-C is known to influence expression of endothelial microRNAs (miRNAs) and gene-targets of miRNAs to promote cell senescence. However, the impact of LDL-C on expression of PBMC miRNAs and miRNA targeted genes in response to an atherogenic diet is not known. In this study, we used unbiased methods to identify coordinately responsive PBMC miRNA- gene networks that differ between low and high LDL-C baboons when fed a high-cholesterol, high-fat (HCHF) diet.Methods and resultsUsing RNA Seq, we quantified PBMC mRNAs and miRNAs from half-sib baboons discordant for LDL-C plasma concentrations (low LDL-C, n = 3; high LDL-C, n = 3) before and after a 7-week HCHF diet challenge. For low LDL-C baboons, 626 genes exhibited significant change in expression (255 down-regulated, 371 up-regulated) in response to the HCHF diet, and for high LDL-C baboons 379 genes exhibited significant change in expression (162 down-regulated, 217 up-regulated) in response to the HCHF diet. We identified 494 miRNAs identical to human miRNAs and 47 novel miRNAs. Fifty miRNAs were differentially expressed in low LDL-C baboons (21 up- and 29 down-regulated) and 20 in high LDL-C baboons (11 up- and 9 down-regulated) in response to the HCHF diet. Among the differentially expressed miRNAs were miR-221/222 and miR-34a-3p, which were down-regulated, and miR-148a/b-5p, which was up-regulated. In addition, gene-targets of these miRNAs, VEGFA, MAML3, SPARC, and DMGDH, were inversely expressed and are central hub genes in networks and signaling pathways that differ between low and high LDL-C baboon HCHF diet response.ConclusionsWe have identified coordinately regulated HCHF diet-responsive PBMC miRNA-gene networks that differ between baboons discordant for LDL-C concentrations. Our findings provide potential insights into molecular mechanisms underlying initiation of atherosclerosis where LDL-C concentrations influence expression of specific miRNAs, which in turn regulate expression of genes that play roles in initiation of lesions.

Project description:BackgroundMicroRNAs (miRNAs) mediate posttranscriptional regulation of protein-coding genes by binding to the 3' untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation, depending on the degree of sequence complementarity. In general, a single miRNA concurrently downregulates hundreds of target mRNAs. Thus, miRNAs play a key role in fine-tuning of diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. However, it remains to be fully elucidated whether a set of miRNA target genes regulated by an individual miRNA in the whole human microRNAome generally constitute the biological network of functionally-associated molecules or simply reflect a random set of functionally-independent genes.MethodsThe complete set of human miRNAs was downloaded from miRBase Release 16. We explored target genes of individual miRNA by using the Diana-microT 3.0 target prediction program, and selected the genes with the miTG score ? 20 as the set of highly reliable targets. Then, Entrez Gene IDs of miRNA target genes were uploaded onto KeyMolnet, a tool for analyzing molecular interactions on the comprehensive knowledgebase by the neighboring network-search algorithm. The generated network, compared side by side with human canonical networks of the KeyMolnet library, composed of 430 pathways, 885 diseases, and 208 pathological events, enabled us to identify the canonical network with the most significant relevance to the extracted network.ResultsAmong 1,223 human miRNAs examined, Diana-microT 3.0 predicted reliable targets from 273 miRNAs. Among them, KeyMolnet successfully extracted molecular networks from 232 miRNAs. The most relevant pathway is transcriptional regulation by transcription factors RB/E2F, the disease is adult T cell lymphoma/leukemia, and the pathological event is cancer.ConclusionThe predicted targets derived from approximately 20% of all human miRNAs constructed biologically meaningful molecular networks, supporting the view that a set of miRNA targets regulated by a single miRNA generally constitute the biological network of functionally-associated molecules in human cells.

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Identification of microRNA-regulated gene networks by expression analysis of target genes.

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Identification of microRNA-regulated gene networks by expression analysis of target genes.

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