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      Epigenetic alterations in skin homing CD4 +CLA + T cells of atopic dermatitis patients

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          Abstract

          T cells expressing the cutaneous lymphocyte antigen (CLA) mediate pathogenic inflammation in atopic dermatitis (AD). The molecular alterations contributing to their dysregulation remain unclear. With the aim to elucidate putative altered pathways in AD we profiled DNA methylation levels and miRNA expression in sorted T cell populations (CD4 +, CD4 +CD45RA + naïve, CD4 +CLA +, and CD8 +) from adult AD patients and healthy controls (HC). Skin homing CD4 +CLA + T cells from AD patients showed significant differences in DNA methylation in 40 genes compared to HC ( p < 0.05). Reduced DNA methylation levels in the upstream region of the interleukin-13 gene ( IL13) in CD4 +CLA + T cells from AD patients correlated with increased IL13 mRNA expression in these cells. Sixteen miRNAs showed differential expression in CD4 +CLA + T cells from AD patients targeting genes in 202 biological processes ( p < 0.05). An integrated network analysis of miRNAs and CpG sites identified two communities of strongly interconnected regulatory elements with strong antagonistic behaviours that recapitulated the differences between AD patients and HC. Functional analysis of the genes linked to these communities revealed their association with key cytokine signaling pathways, MAP kinase signaling and protein ubiquitination. Our findings support that epigenetic mechanisms play a role in the pathogenesis of AD by affecting inflammatory signaling molecules in skin homing CD4 +CLA + T cells and uncover putative molecules participating in AD pathways.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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              REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

              Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
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                Author and article information

                Contributors
                nacevedoc@unicartagena.edu.co
                annika.scheynius@ki.se
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 October 2020
                22 October 2020
                2020
                : 10
                : 18020
                Affiliations
                [1 ]GRID grid.416648.9, ISNI 0000 0000 8986 2221, Department of Clinical Science and Education, Karolinska Institutet, and Sachs’ Children and Youth Hospital, , Södersjukhuset, ; 118 83 Stockholm, Sweden
                [2 ]GRID grid.10548.38, ISNI 0000 0004 1936 9377, National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, , Stockholm University, ; 10691 Stockholm, Sweden
                [3 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Biosciences and Nutrition, , Karolinska Institutet, ; Stockholm, Sweden
                [4 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Medicine Solna, Translational Immunology Unit, , Karolinska Institutet, ; Stockholm, Sweden
                [5 ]GRID grid.24381.3c, ISNI 0000 0000 9241 5705, Dermatology and Venereology Unit, , Karolinska University Hospital, ; Stockholm, Sweden
                [6 ]GRID grid.502801.e, ISNI 0000 0001 2314 6254, Faculty of Medicine and Health Technology, , Tampere University, ; Tampere, Finland
                [7 ]GRID grid.502801.e, ISNI 0000 0001 2314 6254, Institute of Biosciences and Medical Technologies (BioMediTech), , Tampere, University, ; Tampere, Finland
                [8 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Institute of Biotechnology, , University of Helsinki, ; Helsinki, Finland
                [9 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Women’s and Children’s Health, , Karolinska Institutet, ; Stockholm, Sweden
                [10 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Science for Life Laboratory, , Karolinska Institutet, ; Stockholm, Sweden
                [11 ]GRID grid.412885.2, ISNI 0000 0004 0486 624X, Present Address: Institute for Immunological Research, , University of Cartagena, ; Cartagena, Colombia
                Article
                74798
                10.1038/s41598-020-74798-z
                7582180
                33093567
                704804f9-4c4e-4852-8b70-33133474c07e
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 June 2020
                : 15 September 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100013493, Ellen, Walter and Lennart Hesselman Foundation for Scientific Research;
                Funded by: FundRef http://dx.doi.org/10.13039/501100001729, Stiftelsen för Strategisk Forskning;
                Award ID: RBc08-0027
                Funded by: Karolinska Institute
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                immunology,adaptive immunity,cytokines,gene regulation in immune cells,inflammation,lymphocytes,immunological disorders,skin diseases,epigenetics,genome,gene ontology,gene regulatory networks,microarrays

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