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      T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection

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          Abstract

          Antigen-specific, MHC-restricted αβ T cells are necessary for protective immunity against Mycobacterium tuberculosis, but the ability to broadly study these responses has been limited. In the present study, we used single-cell and bulk T cell receptor (TCR) sequencing and the GLIPH2 algorithm to analyze M. tuberculosis-specific sequences in two longitudinal cohorts, comprising 166 individuals with M. tuberculosis infection who progressed to either tuberculosis ( n = 48) or controlled infection ( n = 118). We found 24 T cell groups with similar TCR-β sequences, predicted by GLIPH2 to have common TCR specificities, which were associated with control of infection ( n = 17), and others that were associated with progression to disease ( n = 7). Using a genome-wide M. tuberculosis antigen screen, we identified peptides targeted by T cell similarity groups enriched either in controllers or in progressors. We propose that antigens recognized by T cell similarity groups associated with control of infection can be considered as high-priority targets for future vaccine development.

          Abstract

          Analysis of peripheral mycobacteria-reactive CD4 + T cell receptor sequences from individuals infected with Mycobacterium tuberculosis shows a high degree of overlap between progressors and controllers, but points to some distinct clonotypes that are enriched in either group.

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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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              Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence.

              Countless millions of people have died from tuberculosis, a chronic infectious disease caused by the tubercle bacillus. The complete genome sequence of the best-characterized strain of Mycobacterium tuberculosis, H37Rv, has been determined and analysed in order to improve our understanding of the biology of this slow-growing pathogen and to help the conception of new prophylactic and therapeutic interventions. The genome comprises 4,411,529 base pairs, contains around 4,000 genes, and has a very high guanine + cytosine content that is reflected in the biased amino-acid content of the proteins. M. tuberculosis differs radically from other bacteria in that a very large portion of its coding capacity is devoted to the production of enzymes involved in lipogenesis and lipolysis, and to two new families of glycine-rich proteins with a repetitive structure that may represent a source of antigenic variation.
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                Author and article information

                Contributors
                thomas.scriba@uct.ac.za
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                5 January 2023
                5 January 2023
                2023
                : 29
                : 1
                : 258-269
                Affiliations
                [1 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, and Division of Immunology, Department of Pathology, , University of Cape Town, ; Cape Town, South Africa
                [2 ]GRID grid.168010.e, ISNI 0000000419368956, Institute for Immunity, Transplantation and Infection, , Stanford University School of Medicine, ; Stanford, CA USA
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Human Immune Monitoring Center, , Stanford University, ; Stanford, CA USA
                [4 ]GRID grid.488675.0, ISNI 0000 0004 8337 9561, Africa Health Research Institute, ; Durban, South Africa
                [5 ]GRID grid.16463.36, ISNI 0000 0001 0723 4123, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, , University of KwaZulu-Natal, ; Durban, South Africa
                [6 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Infection and Immunity, , University College London, ; London, UK
                [7 ]GRID grid.168645.8, ISNI 0000 0001 0742 0364, Department of Microbiology and Physiological Systems, , University of Massachusetts Chan Medical School, ; Worcester, MA USA
                [8 ]GRID grid.418159.0, ISNI 0000 0004 0491 2699, Max Planck Institute for Infection Biology, ; Berlin, Germany
                [9 ]GRID grid.516369.e, Max Planck Institute for Multidisciplinary Sciences, ; Göttingen, Germany
                [10 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Hagler Institute for Advanced Study, Texas A&M University, ; College Station, TX USA
                [11 ]GRID grid.11956.3a, ISNI 0000 0001 2214 904X, DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research; Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, , Stellenbosch University, ; Cape Town, South Africa
                [12 ]GRID grid.168010.e, ISNI 0000000419368956, Howard Hughes Medical Institute, , Stanford University, ; Stanford, CA USA
                [13 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Microbiology and Immunology, , Stanford University School of Medicine, ; Stanford, CA USA
                [14 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, , University of Cape Town, ; Cape Town, South Africa
                [15 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, School of Public Health and Family Medicine, , University of Cape Town, ; Cape Town, South Africa
                [16 ]GRID grid.5650.6, ISNI 0000000404654431, KNCV Tuberculosis Foundation The Hague Amsterdam Institute of Global Health and Development, Academic Medical Centre, ; Amsterdam, the Netherlands
                [17 ]GRID grid.432518.9, ISNI 0000 0004 0628 1165, Aeras, ; Rockville, MA USA
                [18 ]GRID grid.11956.3a, ISNI 0000 0001 2214 904X, DST-NRF Centre of Excellence for Biomedical TB Research and MRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, , Stellenbosch University, ; Tygerberg, South Africa
                [19 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Infectious Diseases, , Leiden University Medical Centre, ; Leiden, the Netherlands
                [20 ]GRID grid.67105.35, ISNI 0000 0001 2164 3847, Tuberculosis Research Unit, Department of Medicine, , Case Western Reserve University School of Medicine and University Hospitals Case Medical Center, ; Cleveland, OH USA
                [21 ]GRID grid.11194.3c, ISNI 0000 0004 0620 0548, Department of Medicine and Department of Microbiology, College of Health Sciences, , Faculty of Medicine, Makerere University, ; Kampala, Uganda
                [22 ]GRID grid.418159.0, ISNI 0000 0004 0491 2699, Department of Immunology, , Max Planck Institute for Infection Biology, ; Berlin, Germany
                [23 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, , London School of Hygiene and Tropical Medicine, ; London, UK
                [24 ]Karonga Prevention Study, Chilumba, Malawi
                [25 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, , University of Cape Town, ; Cape Town, South Africa
                [26 ]GRID grid.452387.f, ISNI 0000 0001 0508 7211, Ethiopian Health and Nutrition Research Institute, ; Addis Ababa, Ethiopia
                [27 ]GRID grid.7692.a, ISNI 0000000090126352, University Medical Centre, ; Utrecht, the Netherlands
                [28 ]GRID grid.418720.8, ISNI 0000 0000 4319 4715, Armauer Hansen Research Institute, ; Addis Ababa, Ethiopia
                [29 ]GRID grid.415063.5, ISNI 0000 0004 0606 294X, Vaccines and Immunity Theme, , Medical Research Council Unit, ; Fajara, The Gambia
                [30 ]GRID grid.6203.7, ISNI 0000 0004 0417 4147, Department of Infectious Disease Immunology, , Statens Serum Institute, ; Copenhagen, Denmark
                [31 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Microbiology and Immunology, , Stanford University, ; Stanford, CA USA
                Author information
                http://orcid.org/0000-0003-2538-6467
                http://orcid.org/0000-0002-3374-6699
                http://orcid.org/0000-0001-9866-8268
                http://orcid.org/0000-0003-2487-125X
                http://orcid.org/0000-0003-3491-1809
                http://orcid.org/0000-0001-6868-657X
                Article
                2110
                10.1038/s41591-022-02110-9
                9873565
                36604540
                a358fbd1-980e-43ca-8e4c-05e49f3e0604
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 October 2021
                : 25 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000865, Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation);
                Award ID: OPP1066265
                Award ID: OPP1023483
                Award ID: OPP1065330
                Award ID: OPP1113682 Davis
                Award ID: OPP1137006
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000308, Carnegie Corporation of New York (CCNY);
                Funded by: FundRef https://doi.org/10.13039/100000011, Howard Hughes Medical Institute (HHMI);
                Award ID: Davis
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 210662/Z/18/Z
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2023

                Medicine
                tuberculosis,immunological surveillance,infection,t-cell receptor
                Medicine
                tuberculosis, immunological surveillance, infection, t-cell receptor

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