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      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms

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          Abstract

          To increase our understanding of psoriasis, we utilized RNA-seq to assay the transcriptomes of lesional psoriatic and normal skin. We sequenced polyadenylated RNA-derived cDNAs from 92 psoriatic and 82 normal punch biopsies, generating an average of ~38 million single-end 80-bp reads per sample. Comparison of 42 samples examined by both RNA-seq and microarray revealed marked differences in sensitivity, with transcripts identified only by RNA-seq having much lower expression than those also identified by microarray. RNA-seq identified many more differentially expressed transcripts enriched in immune system processes. Weighted gene co-expression network analysis (WGCNA) revealed multiple modules of coordinately expressed epidermal differentiation genes, overlapping significantly with genes regulated by the long non-coding RNA TINCR, its target gene, staufen-1 ( STAU1), the p63 target gene ZNF750, and its target KLF4. Other coordinately expressed modules were enriched for lymphoid and/or myeloid signature transcripts and genes induced by IL-17 in keratinocytes. Dermally-expressed genes were significantly down-regulated in psoriatic biopsies, most likely due to expansion of the epidermal compartment. These results demonstrate the power of WGCNA to elucidate gene regulatory circuits in psoriasis, and emphasize the influence of tissue architecture in both differential expression and co-expression analysis.

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          Most cited references 80

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Mapping and quantifying mammalian transcriptomes by RNA-Seq.

            We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.
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              RNA-Seq: a revolutionary tool for transcriptomics.

              RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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                Author and article information

                Journal
                0426720
                4839
                J Invest Dermatol
                J. Invest. Dermatol.
                The Journal of investigative dermatology
                0022-202X
                1523-1747
                21 February 2014
                17 January 2014
                July 2014
                01 January 2015
                : 134
                : 7
                : 1828-1838
                Affiliations
                [1 ]Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
                [2 ]Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
                [3 ]Department of Dermatology, University of Michigan, Ann Arbor, MI, USA
                [4 ]Ann Arbor Veterans Affairs Hospital, University of Michigan, Ann Arbor, MI, USA
                [5 ]Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
                Author notes
                []Corresponding authors: James T. Elder, 7412 Medical Sciences Building 1, University of Michigan Medical School, 1301 E. Catherine, Ann Arbor, MI 48109-5675, USA, phone (734) 647-8070, jelder@ 123456umich.edu , Goncalo R. Abecasis, Department of Biostatistics, School of Public Health, M4614 SPH I, University of Michigan, Box 2029, Ann Arbor, MI 48109-2029, USA, phone (734) 763-4901, goncalo@ 123456umich.edu
                [*]

                These authors contributed equally to this work.

                Article
                NIHMS556360
                10.1038/jid.2014.28
                4057954
                24441097
                Categories
                Article

                Dermatology

                skin, inflammation, immunology, cytokine, dermatology, psoriasis, transcriptome, network analysis

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