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


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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    • "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.
    Computers in Biology and Medicine 12/2015; 63. DOI:10.1016/j.compbiomed.2015.05.010 · 1.24 Impact Factor
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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.
    Neurochemistry International 10/2015; DOI:10.1016/j.neuint.2015.10.010 · 3.09 Impact Factor
    • "As stated earlier an inhibiting action of miR-375 on murine adiponectin receptor 2 (ADIPOR2) could be already decribed [Krek et al., 2005]. Using searchable algorithms (TargetScan, PicTar, miRanda, Tarbase), the human ADIPOR2 could also be identified as a putative target of miR-375. "
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    ABSTRACT: Late-onset hypogonadism (LOH), defined as a combination of low serum testosterone (T) levels in combination with clinical signs and symptoms of androgen deficiency in ageing men, is nowadays a well-characterized disease. Testosterone therapy in males affected by hypogonadism leads to a significant decrease of fat mass. In humans, the exact molecular mechanism of T effects on inhibition of adipogenesis is still unknown. We hypothesized that specific microRNAs could be regulated by androgens which might cause an inhibition of adipogenic differentiation. To confirm this hypothesis, human mesenchymal stem cells and a preadipocyte cell line were differentiated into mature adipocytes and in parallel treated with testosterone and dihydrotestosterone. The expression level of miR-375 was upregulated during adipogenic differentiation and downregulated after androgen treatment. Furthermore, we could show that after androgen treatment the decreased expression of miR-375 led to increased expression levels of adiponectin receptor 2 (ADIPOR2) compared to untreated adipocytes. Moreover, inhibition of miR-375 also mediated a decreased adipogenic differentiation and increased ADIPOR2 expression levels. In summary, we identified miR-375 as an androgen regulated microRNA, which could play an important role for understanding the mechanism of the increase in visceral fat mass and the associated insulin resistance caused by testosterone deficiency. Copyright © 2015. Published by Elsevier Ireland Ltd.
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