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      T-BET and EOMES Accelerate and Enhance Functional Differentiation of Human Natural Killer Cells

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          Abstract

          T-bet and Eomes are transcription factors that are known to be important in maturation and function of murine natural killer (NK) cells. Reduced T-BET and EOMES expression results in dysfunctional NK cells and failure to control tumor growth. In contrast to mice, the current knowledge on the role of T-BET and EOMES in human NK cells is rudimentary. Here, we ectopically expressed either T-BET or EOMES in human hematopoietic progenitor cells. Combined transcriptome, chromatin accessibility and protein expression analyses revealed that T-BET or EOMES epigenetically represses hematopoietic stem cell quiescence and non-NK lineage differentiation genes, while activating an NK cell-specific transcriptome and thereby drastically accelerating NK cell differentiation. In this model, the effects of T-BET and EOMES are largely overlapping, yet EOMES shows a superior role in early NK cell maturation and induces faster NK receptor and enhanced CD16 expression. T-BET particularly controls transcription of terminal maturation markers and epigenetically controls strong induction of KIR expression. Finally, NK cells generated upon T-BET or EOMES overexpression display improved functionality, including increased IFN-γ production and killing, and especially EOMES overexpression NK cells have enhanced antibody-dependent cellular cytotoxicity. Our findings reveal novel insights on the regulatory role of T-BET and EOMES in human NK cell maturation and function, which is essential to further understand human NK cell biology and to optimize adoptive NK cell therapies.

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          Most cited references77

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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
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                24 September 2021
                2021
                : 12
                : 732511
                Affiliations
                [1] 1 Laboratory of Experimental Immunology, Department of Diagnostic Sciences, Ghent University , Ghent, Belgium
                [2] 2 Cancer Research Institute Ghent (CRIG) , Ghent, Belgium
                [3] 3 Department of Biomolecular Medicine, Ghent University , Ghent, Belgium
                [4] 4 Laboratory of Immunobiology, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, K.U. Leuven , Leuven, Belgium
                [5] 5 Laboratory of Pharmaceutical Biotechnology, Department of Pharmaceutics, Ghent University , Ghent, Belgium
                Author notes

                Edited by: Marina Cella, Washington University School of Medicine in St. Louis, United States

                Reviewed by: Domenico Mavilio, University of Milan, Italy; Nick Huntington, Monash University, Australia

                *Correspondence: Georges Leclercq, georges.leclercq@ 123456ugent.be

                This article was submitted to NK and Innate Lymphoid Cell Biology, a section of the journal Frontiers in Immunology

                †Present address: Sylvie Taveirne, Orionis Biosciences, Ghent, Belgium

                Article
                10.3389/fimmu.2021.732511
                8497824
                34630413
                50a615ef-d5a6-4052-97b9-cd56b3eae684
                Copyright © 2021 Kiekens, Van Loocke, Taveirne, Wahlen, Persyn, Van Ammel, De Vos, Matthys, Van Nieuwerburgh, Taghon, Van Vlierberghe, Vandekerckhove and Leclercq

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 June 2021
                : 27 August 2021
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 77, Pages: 17, Words: 10249
                Funding
                Funded by: Fonds Wetenschappelijk Onderzoek , doi 10.13039/501100003130;
                Award ID: G.0444.17N , 1S29317N, 12N4515N, 1S45317N
                Categories
                Immunology
                Original Research

                Immunology
                human nk cells,transcription factors,t-bet,eomes,cd16 expression,antibody-dependent cellular cytotoxicity,nk cell biology,nk cell therapy

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