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      TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs

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          Abstract

          T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes – groups of biochemically similar TCRs – that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate [FDR]<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

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          Most cited references43

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          NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

          Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.
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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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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                30 November 2021
                2021
                30 November 2021
                : 10
                : e68605
                Affiliations
                [1 ] Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center Seattle United States
                [2 ] Department of Immunology, St Jude Children's Research Hospital Memphis United States
                [3 ] Department of Industrial Engineering and Management, Ben-Gurion University of the Negev Be'er Sheva Israel
                [4 ] Department of Immunology, St Jude Children's Research Hospital Memphis United States
                [5 ] St Jude Children's Research Hospital Memphis United States
                [6 ] Shraga Segal Department of Microbiology and Immunology, Ben-Gurion University of the Negev Be'er Sheva United States
                [7 ] Fred Hutchinson Cancer Research Center Seattle United States
                University College London United Kingdom
                École Normale Supérieure France
                University College London United Kingdom
                University College London United Kingdom
                University College London United Kingdom
                University College London United Kingdom
                Author information
                https://orcid.org/0000-0002-1652-4023
                https://orcid.org/0000-0002-0224-6464
                https://orcid.org/0000-0001-7627-2166
                Article
                68605
                10.7554/eLife.68605
                8631793
                34845983
                d76ba375-9ce2-43d8-8660-2bf24a9b50e1
                © 2021, Mayer-Blackwell et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 20 March 2021
                : 11 November 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: AI136514-03
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: ORIP S10OD028685
                Award Recipient :
                The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Computational and Systems Biology
                Immunology and Inflammation
                Custom metadata
                Distance-based TCR analysis enables grouping of biochemically similar clonotypes into meta-clonotypes that have increased publicity, and therefore statistical power, for population-level detection of antigen-specific T cells in infection and vaccination.

                Life sciences
                t cell receptor,immune repertoire,sars-cov-2,software,biomarkers,human
                Life sciences
                t cell receptor, immune repertoire, sars-cov-2, software, biomarkers, human

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