Article

Krek A, Grun D, Poy MN, et al. Combinatorial microRNA target predictions

The Rockefeller University, New York, New York, United States
Nature Genetics (Impact Factor: 29.35). 06/2005; 37(5):495-500. DOI: 10.1038/ng1536
Source: PubMed

ABSTRACT

MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.

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    • "The TargetScan algorithm[24]and PicTar[25]was used to predict miR-196a targets. Sus scrofa genes are not involved in the current version of TargetScan and PicTar, and, therefore, the prediction was based on the mRNA–miRNA interactions of Homo sapiens. "
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    ABSTRACT: MicroRNAs (miRNAs) are a class of small non-coding RNA molecules, which play important roles in animals by targeting mRNA transcripts for translational repression. Recent studies have demonstrated that miRNAs are involved in regulation of adipocyte development. The expression of miR-196a in different porcine tissues and developing fat tissues was detected, and gene ontology (GO) term enrichment was then used to predict the expression profiles and potential biological roles of miR-196a in swine. To further verify the roles of miR-196a in porcine adipocyte development, a recombinant adenovirus encoding miR-196a gene (Ad-miR-196a) was constructed and used to study the effect of miR-196a on preadipocyte proliferation and differentiation. Here, our data demonstrate that miR-196a displays a tissue-specific expression pattern and has comprehensive biological roles in swine, especially in adipose development. In addition, overexpression of miR-196a had no effect on preadipocyte proliferation, but induced preadipocyte differentiation by increasing expression of adipocyte specific markers, lipid accumulation and triglyceride content. These data represent the first demonstration of miR-196a expression profiles and roles in swine, thereby providing valuable insight into the functions of miR-196a in adipocyte biology.
    Preview · Article · Jan 2016 · Genes
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    • "This dataset contains a total of 11,161 genes regulated by 6101 miRNAs (grouped in 153 conserved miRNA families). We also used another dataset of predicted miRNA target genes obtained from PicTar [32]. The latter dataset includes 6,243 genes regulated by 168 miRNAs. "
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    ABSTRACT: It has been previously suggested that microRNAs (miRNAs) have a tendency to regulate the important components of biological networks. The goal of the present study was to systematically test if one can establish a relationship between miRNA targets and the important components of biological networks (including human protein-protein interaction network, signaling network and metabolic network). For this analysis, we have studied the attack robustness of these networks. It has been previously shown that deletion of network vertices in descending order of their importance (e.g., in decreasing order of vertex degrees) can affect the network structure much more considerably. In the current study, we introduced three miRNA-based measures of importance: "miRNA count" (i.e., the number of miRNAs that regulate a given network component); average adjacent miRNA count, "AAmiC" (i.e., the average number of miRNAs regulating the targeted components adjacent to a given component); and total adjacent miRNA count, "TAmiC" (i.e., the total number of miRNAs regulating the targeted components adjacent to a given component). Our results suggest that "miRNA count" is only marginally capable of locating the important components of the networks, while TAmiC was the most relevant measure. By comparing TAmiC with the classical centrality measures (which are solely based on the network structure) when simultaneously removing vertices, we show that this measure is correlated to degree and betweenness centrality measures, while its performance is generally better than that of closeness and eigenvector centrality measures. The results of this study suggest that TAmiC which represents a measure based on both network structure and biological knowledge, can successfully determine the important network components indicating that miRNA regulation and network robustness are related. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Full-text · Article · Dec 2015 · Computers in Biology and Medicine
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    • "The candidate proteins were determined in silico from scores of TargetScan (Friedman et al., 2008), PicTar (Krek et al., 2005) and DIANA (Maragkakis et al., 2009). From the three computational algorithms, a combined precision f(t) was calculated from each precision score, P i . "
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    ABSTRACT: Valproic acid (VPA) is an anti-convulsant drug that is recently shown to have neuroregenerative therapeutic actions. In this study, we investigate the underlying molecular mechanism of VPA and its effects on Bdnf transcription through microRNAs (miRNAs) and their corresponding target proteins. Using in silico algorithms, we predicted from our miRNA microarray and iTRAQ data that miR-124 is likely to target at guanine nucleotide binding protein alpha inhibitor 1 (GNAI1), an adenylate cyclase inhibitor. With the reduction of GNAI1 mediated by VPA, the cAMP is enhanced to increase Bdnf expression. The levels of GNAI1 protein and Bdnf mRNA can be manipulated with either miR-124 mimic or inhibitor. In summary, we have identified a novel molecular mechanism of VPA that induces miR-124 to repress GNAI1. The implication of miR-124→GNAI1→BDNF pathway with valproic acid treatment suggests that we could repurpose an old drug, valproic acid, as a clinical application to elevate neurotrophin levels in treating neurodegenerative diseases.
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