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      Clustered microRNAs' coordination in regulating protein-protein interaction network

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          Abstract

          Background

          MicroRNAs (miRNAs), a growing class of small RNAs with crucial regulatory roles at the post-transcriptional level, are usually found to be clustered on chromosomes. However, with the exception of a few individual cases, so far little is known about the functional consequence of this conserved clustering of miRNA loci. In animal genomes such clusters often contain non-homologous miRNA genes. One hypothesis to explain this heterogeneity suggests that clustered miRNAs are functionally related by virtue of co-targeting downstream pathways.

          Results

          Integrating of miRNA cluster information with protein protein interaction (PPI) network data, our research supports the hypothesis of the functional coordination of clustered miRNAs and links it to the topological features of miRNAs' targets in PPI network. Specifically, our results demonstrate that clustered miRNAs jointly regulate proteins in close proximity of the PPI network. The possibility that two proteins yield to this coordinated regulation is negatively correlated with their distance in PPI network. Guided by the knowledge of this preference, we found several network communities enriched with target genes of miRNA clusters. In addition, our results demonstrate that the variance of this propensity can also partly be explained by protein's connectivity and miRNA's conservation.

          Conclusion

          In summary, this work supports the hypothesis of intra-cluster coordination and investigates the extent of this coordination.

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          Most cited references32

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          Identification of mammalian microRNA host genes and transcription units.

          To derive a global perspective on the transcription of microRNAs (miRNAs) in mammals, we annotated the genomic position and context of this class of noncoding RNAs (ncRNAs) in the human and mouse genomes. Of the 232 known mammalian miRNAs, we found that 161 overlap with 123 defined transcription units (TUs). We identified miRNAs within introns of 90 protein-coding genes with a broad spectrum of molecular functions, and in both introns and exons of 66 mRNA-like noncoding RNAs (mlncRNAs). In addition, novel families of miRNAs based on host gene identity were identified. The transcription patterns of all miRNA host genes were curated from a variety of sources illustrating spatial, temporal, and physiological regulation of miRNA expression. These findings strongly suggest that miRNAs are transcribed in parallel with their host transcripts, and that the two different transcription classes of miRNAs ('exonic' and 'intronic') identified here may require slightly different mechanisms of biogenesis.
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            Finding and evaluating community structure in networks.

            We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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              Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes.

              MicroRNAs (miRNAs) are short endogenous RNAs known to post-transcriptionally repress gene expression in animals and plants. A microarray profiling survey revealed the expression patterns of 175 human miRNAs across 24 different human organs. Our results show that proximal pairs of miRNAs are generally coexpressed. In addition, an abrupt transition in the correlation between pairs of expressed miRNAs occurs at a distance of 50 kb, implying that miRNAs separated by <50 kb typically derive from a common transcript. Some microRNAs are within the introns of host genes. Intronic miRNAs are usually coordinately expressed with their host gene mRNA, implying that they also generally derive from a common transcript, and that in situ analyses of host gene expression can be used to probe the spatial and temporal localization of intronic miRNAs.
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                Author and article information

                Journal
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2009
                26 June 2009
                : 3
                : 65
                Affiliations
                [1 ]Bioinformatics Group, Center for Advanced Computing Research, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China
                [2 ]Graduate School of Chinese Academy of Sciences, Beijing, PR China
                [3 ]Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, PR China
                Article
                1752-0509-3-65
                10.1186/1752-0509-3-65
                2714305
                19558649
                084a726c-d893-4b5d-b8a9-0d672c9c8500
                Copyright © 2009 Yuan et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 November 2008
                : 26 June 2009
                Categories
                Research Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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