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Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization.

Sun, Y; Best, K; Cinelli, M; Heather, JM; Reich-Zeliger, S; Shifrut, E; Friedman, N; ... Chain, B; + view all (2017) Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization. Front Immunol , 8 , Article 430. 10.3389/fimmu.2017.00430. Green open access

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Abstract

T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3β sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3β sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors.

Type: Article
Title: Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization.
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fimmu.2017.00430
Publisher version: https://doi.org/10.3389/fimmu.2017.00430
Language: English
Additional information: © 2017 Sun, Best, Cinelli, Heather, Reich-Zeliger, Shifrut, Friedman, Shawe-Taylor and Chain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: CDR3, T cell receptor, machine learning, ovalbumin, repertoire analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1555503
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