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      Principles of microRNA regulation of a human cellular signaling network

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          Abstract

          MicroRNAs (miRNAs) are endogenous ∼22-nucleotide RNAs, which suppress gene expression by selectively binding to the 3′-noncoding region of specific messenger RNAs through base-pairing. Given the diversity and abundance of miRNA targets, miRNAs appear to functionally interact with various components of many cellular networks. By analyzing the interactions between miRNAs and a human cellular signaling network, we found that miRNAs predominantly target positive regulatory motifs, highly connected scaffolds and most downstream network components such as signaling transcription factors, but less frequently target negative regulatory motifs, common components of basic cellular machines and most upstream network components such as ligands. In addition, when an adaptor has potential to recruit more downstream components, these components are more frequently targeted by miRNAs. This work uncovers the principles of miRNA regulation of signal transduction networks and implies a potential function of miRNAs for facilitating robust transitions of cellular response to extracellular signals and maintaining cellular homeostasis.

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          Most cited references17

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          Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

          MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.
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            Network motifs in the transcriptional regulation network of Escherichia coli

            Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.
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              Transcriptional regulatory networks in Saccharomyces cerevisiae.

              We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                1744-4292
                2006
                12 September 2006
                : 2
                : 46
                Affiliations
                [1 ]Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, Montreal, Quebec, Canada
                [2 ]Mammalian Cell Genetics Group, Biotechnology Research Institute, National Research Council Canada, Montreal, Quebec, Canada
                Author notes
                [a ]Computational Chemistry and Biology Group, Biotechnology Research Institute, National Research Council Canada, 6100 Royalmount, Montreal, Quebec, Canada H4P 2R2. E-mail: edwin.wang@ 123456cnrc-nrc.gc.ca
                [*]

                These authors contributed equally to this work

                Article
                msb4100089
                10.1038/msb4100089
                1681519
                16969338
                3f243fef-cdaa-43ac-93f1-ee730264962e
                Copyright © 2006, EMBO and Nature Publishing Group
                History
                : 21 March 2006
                : 3 July 2006
                Page count
                Pages: 1
                Categories
                Report

                Quantitative & Systems biology
                microrna targets,signaling network,signal transduction,microrna,regulation

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