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Nature. 2017 Jul 6;547(7661):94-98. doi: 10.1038/nature22976. Epub 2017 Jun 21.

Identifying specificity groups in the T cell receptor repertoire.

Author information

1
Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, California 94305, USA.
2
Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA.
3
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305, USA.
4
Department of Surgery, Stanford University School of Medicine, Stanford, California 94305, USA.
5
Human Immune Monitoring Center, Stanford University School of Medicine, Stanford, California 94305, USA.
6
Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA.
7
PSM Biotechnology, University of San Francisco, California 94305, USA.
8
La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, La Jolla, California 92037, USA.
9
Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA.
10
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.
11
The Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California 94305, USA.

Abstract

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.

PMID:
28636589
PMCID:
PMC5794212
DOI:
10.1038/nature22976
[Indexed for MEDLINE]
Free PMC Article

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