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      Exploring the pre-immune landscape of antigen-specific T cells

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          Abstract

          Background

          Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals.

          Methods

          We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity.

          Results

          Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules.

          Conclusions

          Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology.

          Electronic supplementary material

          The online version of this article (10.1186/s13073-018-0577-7) contains supplementary material, which is available to authorized users.

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          Most cited references40

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          Identifying specificity groups in the T cell receptor repertoire

          T cell receptor (TCR) sequences are very diverse, with many more possible sequence combinations than T cells in any one individual. Here we define the minimal requirements for TCR antigen specificity, through an analysis of TCR sequences using a panel of peptide and major histocompatibility complex (pMHC)-tetramer-sorted cells and structural data. From this analysis we developed an algorithm that we term GLIPH (grouping of lymphocyte interactions by paratope hotspots) to cluster TCRs with a high probability of sharing specificity owing to both conserved motifs and global similarity of complementarity-determining region 3 (CDR3) sequences. We show that GLIPH can reliably group TCRs of common specificity from different donors, and that conserved CDR3 motifs help to define the TCR clusters that are often contact points with the antigenic peptides. As an independent validation, we analysed 5,711 TCRβ chain sequences from reactive CD4 T cells from 22 individuals with latent Mycobacterium tuberculosis infection. We found 141 TCR specificity groups, including 16 distinct groups containing TCRs from multiple individuals. These TCR groups typically shared HLA alleles, allowing prediction of the likely HLA restriction, and a large number of M. tuberculosis T cell epitopes enabled us to identify pMHC ligands for all five of the groups tested. Mutagenesis and de novo TCR design confirmed that the GLIPH-identified motifs were critical and sufficient for shared-antigen recognition. Thus the GLIPH algorithm can analyse large numbers of TCR sequences and define TCR specificity groups shared by TCRs and individuals, which should greatly accelerate the analysis of T cell responses and expedite the identification of specific ligands.
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            Quantifiable predictive features define epitope-specific T cell receptor repertoires

            T cells are defined by a heterodimeric surface receptor, the T cell receptor (TCR), that mediates recognition of pathogen-associated epitopes through interactions with peptide and major histocompatibility complexes (pMHCs). TCRs are generated by genomic rearrangement of the germline TCR locus, a process termed V(D)J recombination, that has the potential to generate marked diversity of TCRs (estimated to range from 1015 (ref. 1) to as high as 1061 (ref. 2) possible receptors). Despite this potential diversity, TCRs from T cells that recognize the same pMHC epitope often share conserved sequence features, suggesting that it may be possible to predictively model epitope specificity. Here we report the in-depth characterization of ten epitope-specific TCR repertoires of CD8+ T cells from mice and humans, representing over 4,600 in-frame single-cell-derived TCRαβ sequence pairs from 110 subjects. We developed analytical tools to characterize these epitope-specific repertoires: a distance measure on the space of TCRs that permits clustering and visualization, a robust repertoire diversity metric that accommodates the low number of paired public receptors observed when compared to single-chain analyses, and a distance-based classifier that can assign previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Our analyses demonstrate that each epitope-specific repertoire contains a clustered group of receptors that share core sequence similarities, together with a dispersed set of diverse ‘outlier’ sequences. By identifying shared motifs in core sequences, we were able to highlight key conserved residues driving essential elements of TCR recognition. These analyses provide insights into the generalizable, underlying features of epitope-specific repertoires and adaptive immune recognition.
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              T cell antigen receptor recognition of antigen-presenting molecules.

              The Major Histocompatibility Complex (MHC) locus encodes classical MHC class I and MHC class II molecules and nonclassical MHC-I molecules. The architecture of these molecules is ideally suited to capture and present an array of peptide antigens (Ags). In addition, the CD1 family members and MR1 are MHC class I-like molecules that bind lipid-based Ags and vitamin B precursors, respectively. These Ag-bound molecules are subsequently recognized by T cell antigen receptors (TCRs) expressed on the surface of T lymphocytes. Structural and associated functional studies have been highly informative in providing insight into these interactions, which are crucial to immunity, and how they can lead to aberrant T cell reactivity. Investigators have determined over thirty unique TCR-peptide-MHC-I complex structures and twenty unique TCR-peptide-MHC-II complex structures. These investigations have shown a broad consensus in docking geometry and provided insight into MHC restriction. Structural studies on TCR-mediated recognition of lipid and metabolite Ags have been mostly confined to TCRs from innate-like natural killer T cells and mucosal-associated invariant T cells, respectively. These studies revealed clear differences between TCR-lipid-CD1, TCR-metabolite-MR1, and TCR-peptide-MHC recognition. Accordingly, TCRs show remarkable structural and biological versatility in engaging different classes of Ag that are presented by polymorphic and monomorphic Ag-presenting molecules of the immune system.
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                Author and article information

                Contributors
                mikhail.shugay@gmail.com
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                25 August 2018
                25 August 2018
                2018
                : 10
                : 68
                Affiliations
                [1 ]Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia
                [2 ]ISNI 0000 0001 0807 5670, GRID grid.5600.3, Division of Infection and Immunity, , Cardiff University School of Medicine, ; Cardiff, UK
                [3 ]ISNI 0000 0000 9559 0613, GRID grid.78028.35, Department of Molecular Technologies, , Pirogov Russian National Research Medical University, ; Moscow, Russia
                [4 ]ISNI 0000 0004 0555 3608, GRID grid.454320.4, Center for Data-Intensive Biomedicine and Biotechnology, , Skoltech, ; Moscow, Russia
                [5 ]ISNI 0000 0004 0494 4180, GRID grid.454751.6, Central European Institute of Technology, CEITEC, ; Brno, Czech Republic
                [6 ]ISNI 0000 0001 0807 5670, GRID grid.5600.3, Systems Immunity Research Institute, , Cardiff University School of Medicine, ; Cardiff, UK
                Author information
                http://orcid.org/0000-0001-7826-7942
                Article
                577
                10.1186/s13073-018-0577-7
                6109350
                30144804
                53a6169a-a9ed-4c92-8936-cf95f96bd637
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 May 2018
                : 6 August 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006769, Russian Science Foundation;
                Award ID: 17-15-01495
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Molecular medicine
                antigen,immune repertoire,immunogenicity,t cell receptor
                Molecular medicine
                antigen, immune repertoire, immunogenicity, t cell receptor

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