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      Antigen-loaded MR1 tetramers define T cell receptor heterogeneity in mucosal-associated invariant T cells

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          Abstract

          Generation of antigen-loaded MR1 tetramers that specifically stain MAIT cells identifies heterogeneity in phenotypes and TCR repertoires in humans and mice.

          Abstract

          Mucosal-associated invariant T cells (MAIT cells) express a semi-invariant T cell receptor (TCR) α-chain, TRAV1-2–TRAJ33, and are activated by vitamin B metabolites bound by the major histocompatibility complex (MHC)–related class I–like molecule, MR1. Understanding MAIT cell biology has been restrained by the lack of reagents to specifically identify and characterize these cells. Furthermore, the use of surrogate markers may misrepresent the MAIT cell population. We show that modified human MR1 tetramers loaded with the potent MAIT cell ligand, reduced 6-hydroxymethyl-8- d-ribityllumazine (rRL-6-CH 2OH), specifically detect all human MAIT cells. Tetramer + MAIT subsets were predominantly CD8 + or CD4 CD8 , although a small subset of CD4 + MAIT cells was also detected. Notably, most human CD8 + MAIT cells were CD8α +CD8β −/lo, implying predominant expression of CD8αα homodimers. Tetramer-sorted MAIT cells displayed a T H1 cytokine phenotype upon antigen-specific activation. Similarly, mouse MR1–rRL-6-CH 2OH tetramers detected CD4 +, CD4 CD8 and CD8 + MAIT cells in Vα19 transgenic mice. Both human and mouse MAIT cells expressed a broad TCR-β repertoire, and although the majority of human MAIT cells expressed TRAV1-2–TRAJ33, some expressed TRAJ12 or TRAJ20 genes in conjunction with TRAV1-2. Accordingly, MR1 tetramers allow precise phenotypic characterization of human and mouse MAIT cells and revealed unanticipated TCR heterogeneity in this population.

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          Most cited references42

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          Phenotypic analysis of antigen-specific T lymphocytes.

          Identification and characterization of antigen-specific T lymphocytes during the course of an immune response is tedious and indirect. To address this problem, the peptide-major histocompatability complex (MHC) ligand for a given population of T cells was multimerized to make soluble peptide-MHC tetramers. Tetramers of human lymphocyte antigen A2 that were complexed with two different human immunodeficiency virus (HIV)-derived peptides or with a peptide derived from influenza A matrix protein bound to peptide-specific cytotoxic T cells in vitro and to T cells from the blood of HIV-infected individuals. In general, tetramer binding correlated well with cytotoxicity assays. This approach should be useful in the analysis of T cells specific for infectious agents, tumors, and autoantigens.
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            CD1d-restricted and TCR-mediated activation of valpha14 NKT cells by glycosylceramides.

            Natural killer T (NKT) lymphocytes express an invariant T cell antigen receptor (TCR) encoded by the Valpha14 and Jalpha281 gene segments. A glycosylceramide-containing alpha-anomeric sugar with a longer fatty acyl chain (C26) and sphingosine base (C18) was identified as a ligand for this TCR. Glycosylceramide-mediated proliferative responses of Valpha14 NKT cells were abrogated by treatment with chloroquine-concanamycin A or by monoclonal antibodies against CD1d/Vbeta8, CD40/CD40L, or B7/CTLA-4/CD28, but not by interference with the function of a transporter-associated protein. Thus, this lymphocyte shares distinct recognition systems with either T or NK cells.
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              The integration of macromolecular diffraction data.

              The objective of any modern data-processing program is to produce from a set of diffraction images a set of indices (hkls) with their associated intensities (and estimates of their uncertainties), together with an accurate estimate of the crystal unit-cell parameters. This procedure should not only be reliable, but should involve an absolute minimum of user intervention. The process can be conveniently divided into three stages. The first (autoindexing) determines the unit-cell parameters and the orientation of the crystal. The unit-cell parameters may indicate the likely Laue group of the crystal. The second step is to refine the initial estimate of the unit-cell parameters and also the crystal mosaicity using a procedure known as post-refinement. The third step is to integrate the images, which consists of predicting the positions of the Bragg reflections on each image and obtaining an estimate of the intensity of each reflection and its uncertainty. This is carried out while simultaneously refining various detector and crystal parameters. Basic features of the algorithms employed for each of these three separate steps are described, principally with reference to the program MOSFLM.
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                Author and article information

                Journal
                J Exp Med
                J. Exp. Med
                jem
                The Journal of Experimental Medicine
                The Rockefeller University Press
                0022-1007
                1540-9538
                21 October 2013
                : 210
                : 11
                : 2305-2320
                Affiliations
                [1 ]Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity ; and [2 ]Department of Anatomy and Neuroscience; The University of Melbourne, Parkville, Victoria 3010, Australia
                [3 ]Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO 63110
                [4 ]Cancer Immunology Research Program, Research Division, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia
                [5 ]Department of Biochemistry and Molecular Biology, School of Biomedical Sciences ; and [6 ]Australian Research Council Centre of Excellence in Structural and Functional Microbial Genomics; Monash University, Clayton, Victoria 3800, Australia
                [7 ]Division of Chemistry and Structural Biology, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
                [8 ]Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff CF14 4XN, Wales, UK
                Author notes
                CORRESPONDENCE Lars Kjer-Nielsen: lkn@ 123456unimelb.edu.au OR James McCluskey: jamesm1@ 123456unimelb.edu OR Jamie Rossjohn: jamie.rossjohn@ 123456monash.edu

                R. Reantragoon and A.J. Corbett contributed equally to this paper.

                J. Rossjohn, J. McCluskey, and L. Kjer-Nielsen contributed equally to this paper.

                Article
                20130958
                10.1084/jem.20130958
                3804952
                24101382
                b1a43052-ec59-43f0-ad32-461ed656f2d9
                © 2013 Reantragoon 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
                : 9 May 2013
                : 28 August 2013
                Categories
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                Medicine
                Medicine

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