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      Multiomics analysis couples mRNA turnover and translational control of glutamine metabolism to the differentiation of the activated CD4 + T cell

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          Abstract

          The ZFP36 family of RNA-binding proteins acts post-transcriptionally to repress translation and promote RNA decay. Studies of genes and pathways regulated by the ZFP36 family in CD4 + T cells have focussed largely on cytokines, but their impact on metabolic reprogramming and differentiation is unclear. Using CD4 + T cells lacking Zfp36 and Zfp36l1, we combined the quantification of mRNA transcription, stability, abundance and translation with crosslinking immunoprecipitation and metabolic profiling to determine how they regulate T cell metabolism and differentiation. Our results suggest that ZFP36 and ZFP36L1 act directly to limit the expression of genes driving anabolic processes by two distinct routes: by targeting transcription factors and by targeting transcripts encoding rate-limiting enzymes. These enzymes span numerous metabolic pathways including glycolysis, one-carbon metabolism and glutaminolysis. Direct binding and repression of transcripts encoding glutamine transporter SLC38A2 correlated with increased cellular glutamine content in ZFP36/ZFP36L1-deficient T cells. Increased conversion of glutamine to α-ketoglutarate in these cells was consistent with direct binding of ZFP36/ZFP36L1 to Gls (encoding glutaminase) and Glud1 (encoding glutamate dehydrogenase). We propose that ZFP36 and ZFP36L1 as well as glutamine and α-ketoglutarate are limiting factors for the acquisition of the cytotoxic CD4 + T cell fate. Our data implicate ZFP36 and ZFP36L1 in limiting glutamine anaplerosis and differentiation of activated CD4 + T cells, likely mediated by direct binding to transcripts of critical genes that drive these processes.

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
                louise.matheson@babraham.ac.uk
                martin.turner@babraham.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 November 2022
                16 November 2022
                2022
                : 12
                : 19657
                Affiliations
                [1 ]GRID grid.418195.0, ISNI 0000 0001 0694 2777, Immunology Programme, , The Babraham Institute, Babraham Research Campus, ; Cambridge, CB22 3AT UK
                [2 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, , IBR, University of Birmingham, ; Edgbaston, Birmingham, B15 2TT UK
                [3 ]GRID grid.5335.0, ISNI 0000000121885934, Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, , University of Cambridge, ; Cambridge, CB2 0AW UK
                [4 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, Institute of Metabolism and Systems Research, , University of Birmingham, ; Birmingham, UK
                [5 ]Present Address: IONTAS, The Works, Unity Campus, Cambridge, CB22 3EF UK
                [6 ]Present Address: Nature Reviews Rheumatology, The Campus, 4 Crinan Street, London, N1 9XW UK
                [7 ]GRID grid.418236.a, ISNI 0000 0001 2162 0389, Present Address: Immunology Research Unit, , GlaxoSmithKline, ; Gunnels Wood Road, Stevenage, SG1 2NY Herts UK
                [8 ]GRID grid.418236.a, ISNI 0000 0001 2162 0389, Present Address: Bioanalysis, Immunogenicity and Biomarkers (BIB), , IVIVT, GSK, ; Stevenage, SG1 2NY UK
                [9 ]Present Address: Discovery Biology, Discovery Science, R&D, AstraZeneca, Cambridge, UK
                [10 ]GRID grid.15781.3a, ISNI 0000 0001 0723 035X, Present Address: Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), Inserm UMR1291, CNRS UMR5051, University Paul Sabatier, CHU Purpan, ; BP3028, 31024 Toulouse, France
                Article
                24132
                10.1038/s41598-022-24132-6
                9669047
                36385275
                cc212aad-1a4b-43fb-82f7-f42afa7cd102
                © 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 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
                : 15 July 2022
                : 10 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BBS/E/B/000C0427
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                cd4-positive t cells,gene regulation in immune cells,transcriptomics,rna-binding proteins,metabolomics

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