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      Genome mapping and expression analyses of human intronic noncoding RNAs reveal tissue-specific patterns and enrichment in genes related to regulation of transcription

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          Abstract

          An analysis of the expression of 7,135 human totally intronic noncoding RNA transcripts plus the corresponding protein-coding genes using oligonucleotide arrays has identified diverse intronic RNA expression patterns, pointing to distinct regulatory roles.

          Abstract

          Background

          RNAs transcribed from intronic regions of genes are involved in a number of processes related to post-transcriptional control of gene expression. However, the complement of human genes in which introns are transcribed, and the number of intronic transcriptional units and their tissue expression patterns are not known.

          Results

          A survey of mRNA and EST public databases revealed more than 55,000 totally intronic noncoding (TIN) RNAs transcribed from the introns of 74% of all unique RefSeq genes. Guided by this information, we designed an oligoarray platform containing sense and antisense probes for each of 7,135 randomly selected TIN transcripts plus the corresponding protein-coding genes. We identified exonic and intronic tissue-specific expression signatures for human liver, prostate and kidney. The most highly expressed antisense TIN RNAs were transcribed from introns of protein-coding genes significantly enriched ( p = 0.002 to 0.022) in the 'Regulation of transcription' Gene Ontology category. RNA polymerase II inhibition resulted in increased expression of a fraction of intronic RNAs in cell cultures, suggesting that other RNA polymerases may be involved in their biosynthesis. Members of a subset of intronic and protein-coding signatures transcribed from the same genomic loci have correlated expression patterns, suggesting that intronic RNAs regulate the abundance or the pattern of exon usage in protein-coding messages.

          Conclusion

          We have identified diverse intronic RNA expression patterns, pointing to distinct regulatory roles. This gene-oriented approach, using a combined intron-exon oligoarray, should permit further comparative analysis of intronic transcription under various physiological and pathological conditions, thus advancing current knowledge about the biological functions of these noncoding RNAs.

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          Most cited references48

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

            T. Golub (1999)
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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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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2007
                26 March 2007
                : 8
                : 3
                : R43
                Affiliations
                [1 ]Departamento de Bioquimica, Instituto de Quimica, Universidade de São Paulo, 05508-900 São Paulo, SP, Brazil
                Article
                gb-2007-8-3-r43
                10.1186/gb-2007-8-3-r43
                1868932
                17386095
                37ef32fb-e4ee-49e2-8c25-94448832be60
                Copyright © 2007 Nakaya 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
                : 17 October 2006
                : 17 January 2007
                : 26 March 2007
                Categories
                Research

                Genetics
                Genetics

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