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      Normality sensing licenses local T cells for innate-like tissue surveillance

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          Abstract

          The increasing implication of lymphocytes in general physiology and immune surveillance outside of infection poses the question of how their antigen receptors might be involved. Here, we show that macromolecular aggregates of intraepidermal γδ T cell antigen receptors (TCRs) in the mouse skin aligned with and depended on Skint1, a butyrophilin-like (BTNL) protein expressed by differentiated keratinocytes (KCs) at steady state. Interruption of TCR-mediated ‘normality sensing’ had no impact on γδ T cell numbers but altered their signature phenotype, while the epidermal barrier function was compromised. In addition to the regulation of steady-state physiology, normality sensing licensed intraepidermal T cells to respond rapidly to subsequent tissue perturbation by using innate tumor necrosis factor (TNF) superfamily receptors. Thus, interfering with Skint1-dependent interactions between local γδ T cells and KCs at steady state increased the susceptibility to ultraviolet B radiation (UVR)-induced DNA damage and inflammation, two cancer-disposing factors.

          Abstract

          Hayday and colleagues show that sustained Skint1-dependent interactions between murine intraepidermal γδ T cells and keratinocytes are required to maintain the homeostatic barrier function and phenotype of the intraepidermal γδ T cells, including their preparedness to respond appropriately to epidermal challenges.

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          Most cited references68

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                adrian.hayday@kcl.ac.uk
                Journal
                Nat Immunol
                Nat Immunol
                Nature Immunology
                Nature Publishing Group US (New York )
                1529-2908
                1529-2916
                14 February 2022
                14 February 2022
                2022
                : 23
                : 3
                : 411-422
                Affiliations
                [1 ]GRID grid.451388.3, ISNI 0000 0004 1795 1830, The Francis Crick Institute, ; London, UK
                [2 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Peter Gorer Department of Immunobiology, , King’s College London, ; London, UK
                [3 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Monoclonal Antibody Core Facility, , Helmholtz Zentrum München, ; Neuherberg, Germany
                [4 ]GRID grid.417834.d, Present Address: Institute of Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, , Federal Research Institute for Animal Health, ; Greifswald-Insel Riems, Germany
                Author information
                http://orcid.org/0000-0003-0961-8846
                http://orcid.org/0000-0002-3981-367X
                http://orcid.org/0000-0002-9495-5793
                Article
                1124
                10.1038/s41590-021-01124-8
                8901436
                35165446
                104f2910-e83f-469d-9441-7854325559f5
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 July 2021
                : 20 December 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 106292/Z/14/Z
                Award ID: FC001093
                Award ID: 100156/Z/12/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000289, Cancer Research UK (CRUK);
                Award ID: FC001093
                Award Recipient :
                Funded by: Medical Research Council (FC001093) Cancer Research UK-King’s and City of London Cancer Centre
                Funded by: FundRef https://doi.org/10.13039/100000884, Cancer Research Institute (CRI);
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                Immunology
                immunological surveillance,gammadelta t cells,lymphocyte activation
                Immunology
                immunological surveillance, gammadelta t cells, lymphocyte activation

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