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      T-bet and Eomes instruct the development of two distinct natural killer cell lineages in the liver and in the bone marrow

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          Abstract

          Mutually exclusive expression of T-bet and Eomes drives the development of distinct NK cell lineages with complementary functions.

          Abstract

          Trail +DX5 Eomes natural killer (NK) cells arise in the mouse fetal liver and persist in the adult liver. Their relationships with Trail DX5 + NK cells remain controversial. We generated a novel Eomes-GFP reporter murine model to address this question. We found that Eomes NK cells are not precursors of classical Eomes + NK cells but rather constitute a distinct lineage of innate lymphoid cells. Eomes NK cells are strictly dependent on both T-bet and IL-15, similarly to NKT cells. We observed that, in the liver, expression of T-bet in progenitors represses Eomes expression and the development of Eomes + NK cells. Reciprocally, the bone marrow (BM) microenvironment restricts T-bet expression in developing NK cells. Ectopic expression of T-bet forces the development of Eomes NK cells, demonstrating that repression of T-bet is essential for the development of Eomes + NK cells. Gene profile analyses show that Eomes NK cells share part of their transcriptional program with NKT cells, including genes involved in liver homing and NK cell receptors. Moreover, Eomes NK cells produce a broad range of cytokines, including IL-2 and TNF in vitro and in vivo, during immune responses against vaccinia virus. Thus, mutually exclusive expression of T-bet and Eomes drives the development of different NK cell lineages with complementary functions.

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          Variance stabilization applied to microarray data calibration and to the quantification of differential expression.

          We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.
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            Maturation of mouse NK cells is a 4-stage developmental program.

            Surface density of CD27 and CD11b subdivides mouse natural killer (NK) cells into 4 subsets: CD11b(low)CD27(low), CD11b(low)CD27(high), CD11b(high)CD27(high), and CD11b(high)CD27(low). To determine the developmental relationship between these 4 subsets, we used several complementary approaches. First, we took advantage of NDE transgenic mice that express enhanced green fluorescent protein (EGFP) and diphtheria toxin receptor specifically in NK cells. Diphtheria toxin injection leads to a transient depletion of NK cells, allowing the monitoring of the phenotype of developing EGFP+ NK cells after diphtheria toxin injection. Second, we evaluated the overall proximity between NK-cell subsets based on their global gene profile. Third, we compared the proliferative capacity of NK-cell subsets at steady state or during replenishment of the NK-cell pool. Fourth, we performed adoptive transfers of EGFP+ NK cell subsets from NDE mice into unirradiated mice and followed the fate of transferred cells. The results of these various experiments collectively support a 4-stage model of NK-cell maturation CD11b(low)CD27(low) --> CD11b(low)CD27(high) --> CD11b(high)CD27(high) --> CD11b(high)CD27(low). This developmental program appears to be associated with a progressive acquisition of NK-cell effector functions.
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              Sphingosine-1-phosphate and lymphocyte egress from lymphoid organs.

              Much has been learned about how cells enter lymphoid tissues. But how do they leave? Sphingosine-1-phosphate (S1P) has emerged over the past decade as a central mediator of lymphocyte egress. In this review, we summarize the current understanding of how S1P promotes exit from the secondary lymphoid organs and thymus. We review what is known about additional requirements for emigration and summarize the mostly distinct requirements for exit from the bone marrow. Egress from lymphoid organs is limited during immune responses, and we examine how this regulation works. There is accumulating evidence for roles of S1P in directing immune cell behavior within lymphoid tissues. How such actions can fit together with the egress-promoting role of S1P is discussed. Finally, we examine current understanding of how FTY720, a drug that targets S1P receptors and is approved for the treatment of multiple sclerosis, causes immune suppression.
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                Author and article information

                Journal
                J Exp Med
                J. Exp. Med
                jem
                jem
                The Journal of Experimental Medicine
                The Rockefeller University Press
                0022-1007
                1540-9538
                10 March 2014
                : 211
                : 3
                : 563-577
                Affiliations
                [1 ]CIRI, International Center for Infectiology Research, Université de Lyon ; [2 ]Institut National de la Santé et de la Recherche Médicale, U1111 ; [3 ]Ecole Normale Supérieure de Lyon ; [4 ]Université Lyon 1, Centre International de Recherche en Infectiologie ; and [5 ]Centre National de la Recherche Scientifique, UMR5308, 69007 Lyon, France
                [6 ]Laboratoire d’Immunologie, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, 69495 Pierre-Bénite, France
                [7 ]Howard Hughes Medical Institute and [8 ]Immunology Program, Memorial Sloan-Kettering Cancer Center, New York, NY 10065
                [9 ]Department of Nephrology, Division of Clinical Medicine ; [10 ]Anatomy and Embryology, Division of Biomedical Science ; and [11 ]Institute for Integrative Sleep Medicine (WPI-IIIS), Faculty of Medicine, University of Tsukuba, Ibaraki 305-8575, Japan
                [12 ]Institute of Immunology, Hannover Medical School, 30625 Hannover, Germany
                [13 ]AltraBio SAS, 69007 Lyon, France
                Author notes
                CORRESPONDENCE Thierry Walzer: Thierry.walzer@ 123456inserm.fr

                C. Daussy, F. Faure, and K. Mayol contributed equally to this paper.

                Article
                20131560
                10.1084/jem.20131560
                3949572
                24516120
                b07f1254-3453-4826-8526-bf540801a677
                © 2014 Daussy et al.

                This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 3.0 Unported license, as described at http://creativecommons.org/licenses/by-nc-sa/3.0/).

                History
                : 23 July 2013
                : 23 January 2014
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                Medicine
                Medicine

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