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      Type I Interferon Transcriptional Network Regulates Expression of Coinhibitory Receptors in Human T cells

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          Abstract

          While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. Type 1 interferon (IFN-I) modulates T cell immunity in viral infection, autoimmunity, and cancer, and may facilitate induction of T cell exhaustion in chronic viral infection. Here we show that IFN-I regulates co-inhibitory receptor expression on human T cells, inducing PD-1/TIM-3/LAG-3 while surprisingly inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses enabled the construction of dynamic transcriptional regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors on human primary T cells revealed unique regulators that control expression of co-inhibitory receptors. We found that the dynamic IFN-I response in vitro closely mirrored T cell features with IFN-I linked acute SARS-CoV-2 infection in human, with high LAG3 and decreased TIGIT expression. Finally, our gene regulatory network identified SP140 as a key regulator for differential LAG3 and TIGIT expression, which were validated at the level of protein expression. The construction of IFN-I regulatory networks with identification of unique transcription factors controlling co-inhibitory receptor expression may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and autoimmunity.

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          Most cited references54

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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 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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                Author and article information

                Contributors
                Journal
                Res Sq
                ResearchSquare
                Research Square
                American Journal Experts
                08 June 2021
                : rs.3.rs-133494
                Affiliations
                Yale University
                Yale University School of Medicine
                Tel Aviv University
                Yale University
                Yale University
                Yale University
                Department of Immunobiology, Yale University School of Medicine; Department of Neurology, Yale University School of Medicine
                Yale University
                Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine
                Yale University
                Tel Aviv University
                Harvard University
                Author notes

                Contributions

                T.S.S., A.M., V.K.K., and D.A.H. conceptualized the study. T.S.S. performed in vitro experiments with the help of H.A.S., M.C., and P-P.A.; T.S.S. prepared sequencing libraries and M.R.L. processed bulk RNA-seq data alignment; S.D. and A.M. performed computational analysis to construct gene regulatory network with input from T.S.S.; J.C.S. performed scRNA-seq analysis on COVID-19 data with the help of T.S.S., A.U., and N.K.; T.S.S., S.D., J.C.S., A.M., and D.A.H. wrote the manuscript with input from all authors; T.S.S., A.M., V.K.K. and D.A.H. supervised the overall study.

                Author information
                http://orcid.org/0000-0002-9806-2642
                http://orcid.org/0000-0003-2962-6253
                http://orcid.org/0000-0001-5917-4601
                Article
                10.21203/rs.3.rs-133494
                10.21203/rs.3.rs-133494/v1
                8202434
                34127967
                1bb34be7-b856-4b24-9c7c-bf1a01dd1a76

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

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                t cell immunity,immunology,coinhibitory receptors,type 1 interferon

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