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      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin

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          Abstract

          Background

          Although analysis pipelines have been developed to use RNA-seq to identify long non-coding RNAs (lncRNAs), inference of their biological and pathological relevance remains a challenge. As a result, most transcriptome studies of autoimmune disease have only assessed protein-coding transcripts.

          Results

          We used RNA-seq data from 99 lesional psoriatic, 27 uninvolved psoriatic, and 90 normal skin biopsies, and applied computational approaches to identify and characterize expressed lncRNAs. We detect 2,942 previously annotated and 1,080 novel lncRNAs which are expected to be skin specific. Notably, over 40% of the novel lncRNAs are differentially expressed and the proportions of differentially expressed transcripts among protein-coding mRNAs and previously-annotated lncRNAs are lower in psoriasis lesions versus uninvolved or normal skin. We find that many lncRNAs, in particular those that are differentially expressed, are co-expressed with genes involved in immune related functions, and that novel lncRNAs are enriched for localization in the epidermal differentiation complex. We also identify distinct tissue-specific expression patterns and epigenetic profiles for novel lncRNAs, some of which are shown to be regulated by cytokine treatment in cultured human keratinocytes.

          Conclusions

          Together, our results implicate many lncRNAs in the immunopathogenesis of psoriasis, and our results provide a resource for lncRNA studies in other autoimmune diseases.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-014-0570-4) contains supplementary material, which is available to authorized users.

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          Most cited references36

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Coexpression analysis of human genes across many microarray data sets.

            We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function. Copyright 2004 Cold Spring Harbor Laboratory Press
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              The Genetic Signatures of Noncoding RNAs

              The majority of the genome in animals and plants is transcribed in a developmentally regulated manner to produce large numbers of non–protein-coding RNAs (ncRNAs), whose incidence increases with developmental complexity. There is growing evidence that these transcripts are functional, particularly in the regulation of epigenetic processes, leading to the suggestion that they compose a hitherto hidden layer of genomic programming in humans and other complex organisms. However, to date, very few have been identified in genetic screens. Here I show that this is explicable by an historic emphasis, both phenotypically and technically, on mutations in protein-coding sequences, and by presumptions about the nature of regulatory mutations. Most variations in regulatory sequences produce relatively subtle phenotypic changes, in contrast to mutations in protein-coding sequences that frequently cause catastrophic component failure. Until recently, most mapping projects have focused on protein-coding sequences, and the limited number of identified regulatory mutations have been interpreted as affecting conventional cis-acting promoter and enhancer elements, although these regions are often themselves transcribed. Moreover, ncRNA-directed regulatory circuits underpin most, if not all, complex genetic phenomena in eukaryotes, including RNA interference-related processes such as transcriptional and post-transcriptional gene silencing, position effect variegation, hybrid dysgenesis, chromosome dosage compensation, parental imprinting and allelic exclusion, paramutation, and possibly transvection and transinduction. The next frontier is the identification and functional characterization of the myriad sequence variations that influence quantitative traits, disease susceptibility, and other complex characteristics, which are being shown by genome-wide association studies to lie mostly in noncoding, presumably regulatory, regions. There is every possibility that many of these variations will alter the interactions between regulatory RNAs and their targets, a prospect that should be borne in mind in future functional analyses.
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                Author and article information

                Contributors
                tsoi.teen@gmail.com
                mkiyer@med.umich.edu
                pstuart@umich.edu
                wswindel@med.umich.edu
                johanng@med.umich.edu
                ttejasvi@med.umich.edu
                mksarkar@med.umich.edu
                bingshan.li@vanderbilt.edu
                junding@umich.edu
                voorhees@umich.edu
                hmkang@umich.edu
                rnair@umich.edu
                arul@umich.edu
                goncalo@umich.edu
                jelder@umich.edu
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                30 January 2015
                30 January 2015
                2015
                : 16
                : 1
                : 24
                Affiliations
                [ ]Department of Biostatistics, Center for Statistical Genetics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029 USA
                [ ]Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI USA
                [ ]Department of Dermatology, University of Michigan, Ann Arbor, MI USA
                [ ]Ann Arbor Veterans Affairs Hospital, University of Michigan, Ann Arbor, MI USA
                [ ]Department of Molecular Physiology and Biophysics, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN USA
                [ ]Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD USA
                [ ]Department of Pathology, University of Michigan Medical School, Ann Arbor, MI USA
                [ ]Department of Urology, University of Michigan Medical School, Ann Arbor, MI USA
                [ ]University of Michigan Medical School, 7412 Medical Sciences Building 1, 1301 E. Catherine, Ann Arbor, MI 48109-5675 USA
                Article
                570
                10.1186/s13059-014-0570-4
                4311508
                25723451
                aa7a8cb7-82d1-4443-ae82-8cd81cc1cbf5
                © Tsoi et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 September 2014
                : 11 December 2014
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Genetics
                Genetics

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