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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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          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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            The biological functions of T helper 17 cell effector cytokines in inflammation.

            T helper 17 (Th17) cells belong to a recently identified T helper subset, in addition to the traditional Th1 and Th2 subsets. These cells are characterized as preferential producers of interleukin-17A (IL-17A), IL-17F, IL-21, and IL-22. Th17 cells and their effector cytokines mediate host defensive mechanisms to various infections, especially extracellular bacteria infections, and are involved in the pathogenesis of many autoimmune diseases. The receptors for IL-17 and IL-22 are broadly expressed on various epithelial tissues. The effector cytokines of Th17 cells, therefore, mediate the crucial crosstalk between immune system and tissues, and play indispensable roles in tissue immunity.
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              Computational methods for transcriptome annotation and quantification using RNA-seq.

              High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.
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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
                3ef27b9a-1214-493f-8de6-4dd2812ae900
                History
                Categories
                Article

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

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