Specificity, privacy, and degeneracy in the CD4 T cell receptor repertoire following immunization

Yuxin Sun, Katharine Best, Mattia Cinelli, James M. Heather, Shlomit Reich-Zeliger, Eric Shifrut, Nir Friedman, John Shawe-Taylor, Benny Chain

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number430
JournalFrontiers in Immunology
Volume8
Issue numberAPR
DOIs
StatePublished - 13 Apr 2017
Externally publishedYes

Keywords

  • CDR3
  • Machine learning
  • Ovalbumin
  • Repertoire analysis
  • T cell receptor

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