The discriminatory power of the T cell receptor

AbstractT cells use their T-cell receptors (TCRs) to discriminate between lower-affinity self and higher-affinity non-self pMHC antigens. The strength of this discrimination and the mechanisms that produce it remain controversial. Although a large number of mouse and human TCRs have now been characterised, they have not been used to precisely quantitate discrimination. Here, we systematically quantify the discrimination of TCRs using a discrimination power (α). Early influential studies on three mouse TCRs suggested that discrimination was nearly perfect (α ~ 9.0). In striking contrast, our analysis of published data on other mouse and human TCRs, and more recent data on the original mouse TCRs, produced significantly lower discrimination (α = 2.0). Although not perfect, the discriminatory power of TCR was greater than that of conventional receptors such as cytokine receptors, GPCRs, RTKs, and CARs (α ≤ 1). The revised discriminatory power of the TCR is readily explained by a kinetic proofreading mechanisms, and accounts for the ability of low affinity self-antigens to stimulate autoimmune and anti-tumour T cell responses.

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The discriminatory power of the T cell receptor

eLife ◽

10.7554/elife.67092 ◽

2021 ◽

Vol 10 ◽

Author(s):

Johannes Pettmann ◽

Anna Huhn ◽

Enas Abu Shah ◽

Mikhail A Kutuzov ◽

Daniel B Wilson ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

T Cell Receptors ◽

Cell Receptor ◽

Discriminatory Power ◽

Factors Affecting ◽

Surface Receptors ◽

Human T Cells ◽

Conventional Cell ◽

Kinetic Proofreading

T cells use their T-cell receptors (TCRs) to discriminate between lower-affinity self and higher-affinity non-self pMHC antigens. Although the discriminatory power of the TCR is widely believed to be near-perfect, technical difficulties have hampered efforts to precisely quantify it. Here, we describe a method for measuring very low TCR/pMHC affinities, and use it to measure the discriminatory power of the TCR, and the factors affecting it. We find that TCR discrimination, although enhanced compared with conventional cell-surface receptors, is imperfect: primary human T cells can respond to pMHC with affinities as low as KD ~1 mM. The kinetic proofreading mechanism fit our data, providing the first estimates of both the time delay (2.8 s) and number of biochemical steps (2.67) that are consistent with the extraordinary sensitivity of antigen recognition. Our findings explain why self pMHC frequently induce autoimmune diseases and anti-tumour responses, and suggest ways to modify TCR discrimination.

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A Framework to Identify Antigen-Expanded T Cell Receptor Clusters Within Complex Repertoires

Frontiers in Immunology ◽

10.3389/fimmu.2021.735584 ◽

2021 ◽

Vol 12 ◽

Author(s):

Valentina Ceglia ◽

Erin J. Kelley ◽

Annalee S. Boyle ◽

Sandra Zurawski ◽

Heather L. Mead ◽

...

Keyword(s):

T Cell ◽

T Cell Receptor ◽

T Cell Responses ◽

Cell Receptor ◽

Antigen Specificity ◽

Protein Antigens ◽

Cell Responses ◽

Tcr Repertoire ◽

Receptor Clusters ◽

Theoretical Sensitivity

Common approaches for monitoring T cell responses are limited in their multiplexity and sensitivity. In contrast, deep sequencing of the T Cell Receptor (TCR) repertoire provides a global view that is limited only in terms of theoretical sensitivity due to the depth of available sampling; however, the assignment of antigen specificities within TCR repertoires has become a bottleneck. This study combines antigen-driven expansion, deep TCR sequencing, and a novel analysis framework to show that homologous ‘Clusters of Expanded TCRs (CETs)’ can be confidently identified without cell isolation, and assigned to antigen against a background of non-specific clones. We show that clonotypes within each CET respond to the same epitope, and that protein antigens stimulate multiple CETs reactive to constituent peptides. Finally, we demonstrate the personalized assignment of antigen-specificity to rare clones within fully-diverse uncultured repertoires. The method presented here may be used to monitor T cell responses to vaccination and immunotherapy with high fidelity.

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TCR Transgenic Mice: A Valuable Tool for Studying Viral Immunopathogenesis Mechanisms

International Journal of Molecular Sciences ◽

10.3390/ijms21249690 ◽

2020 ◽

Vol 21 (24) ◽

pp. 9690

Author(s):

Yong-Bin Cho ◽

In-Gu Lee ◽

Yong-Hyun Joo ◽

So-Hee Hong ◽

Young-Jin Seo

Keyword(s):

T Cell ◽

Immune Responses ◽

T Cell Receptor ◽

Global Economy ◽

Viral Infections ◽

Cell Receptor ◽

Cell Responses ◽

Significant Burden ◽

Underlying Mechanisms ◽

Remarkable Progress

Viral infectious diseases are a significant burden on public health and the global economy, and new viral threats emerge continuously. Since CD4+ and CD8+ T cell responses are essential to eliminating viruses, it is important to understand the underlying mechanisms of anti-viral T cell-mediated immunopathogenesis during viral infections. Remarkable progress in transgenic (Tg) techniques has enabled scientists to more readily understand the mechanisms of viral pathogenesis. T cell receptor (TCR) Tg mice are extremely useful in studying T cell-mediated immune responses because the majority of T cells in these mice express specific TCRs for partner antigens. In this review, we discuss the important studies utilizing TCR Tg mice to unveil underlying mechanisms of T cell-mediated immunopathogenesis during viral infections.

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The Hepatitis C Virus Core Protein Modulates T Cell Responses by Inducing Spontaneous and Altering T-cell Receptor-triggered Ca2+Oscillations

Journal of Biological Chemistry ◽

10.1074/jbc.m300185200 ◽

2003 ◽

Vol 278 (21) ◽

pp. 18877-18883 ◽

Cited By ~ 44

Author(s):

Anders Bergqvist ◽

Sara Sundström ◽

Lina Y. Dimberg ◽

Erik Gylfe ◽

Maria G. Masucci

Keyword(s):

Hepatitis C Virus ◽

Hepatitis C ◽

T Cell ◽

T Cell Receptor ◽

Core Protein ◽

T Cell Responses ◽

Cell Receptor ◽

Cell Responses ◽

Virus Core

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CD1b-restricted GEM T cell responses are modulated byMycobacterium tuberculosismycolic acid meromycolate chains

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1708252114 ◽

2017 ◽

Vol 114 (51) ◽

pp. E10956-E10964 ◽

Cited By ~ 22

Author(s):

Andrew Chancellor ◽

Anna S. Tocheva ◽

Chris Cave-Ayland ◽

Liku Tezera ◽

Andrew White ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

T Cell Receptor ◽

T Cell Responses ◽

Cell Receptor ◽

Head Group ◽

Computational Simulations ◽

Functional Responses ◽

Cell Responses ◽

Group Movement

Tuberculosis (TB), caused byMycobacterium tuberculosis, remains a major human pandemic. Germline-encoded mycolyl lipid-reactive (GEM) T cells are donor-unrestricted and recognize CD1b-presented mycobacterial mycolates. However, the molecular requirements governing mycolate antigenicity for the GEM T cell receptor (TCR) remain poorly understood. Here, we demonstrate CD1b expression in TB granulomas and reveal a central role for meromycolate chains in influencing GEM-TCR activity. Meromycolate fine structure influences T cell responses in TB-exposed individuals, and meromycolate alterations modulate functional responses by GEM-TCRs. Computational simulations suggest that meromycolate chain dynamics regulate mycolate head group movement, thereby modulating GEM-TCR activity. Our findings have significant implications for the design of future vaccines that target GEM T cells.

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T-cell-receptor cross-recognition and strategies to select safe T-cell receptors for clinical translation

Immuno-Oncology Technology ◽

10.1016/j.iotech.2019.06.003 ◽

2019 ◽

Vol 2 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Amalie Kai Bentzen ◽

Sine Reker Hadrup

Keyword(s):

T Cell ◽

T Cell Receptor ◽

T Cell Receptors ◽

Cell Receptor ◽

Clinical Translation ◽

Cell Receptors

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Developing and validating an approach for diagnosing and prognosticating cancer from biochemical motifs in T-cell receptors.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e15260 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e15260-e15260

Author(s):

Jared L Ostmeyer ◽

Lindsay G Cowell ◽

Scott Christley

Keyword(s):

Amino Acid ◽

T Cell ◽

T Cell Receptor ◽

T Cell Receptors ◽

Ovarian Tissue ◽

Cell Receptor ◽

Healthy Tissue ◽

Cervical Lesions ◽

Cell Receptors ◽

Biochemical Features

e15260 Background: Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate cancer by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Methods: Our goal is to develop a method to diagnose and prognosticate cancer using sequenced T-cell receptors. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a machine learning based approach. Each predictive model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Results: Using a patient-holdout cross-validation, we fit predictive models to distinguish: 1. colorectal tumors from healthy tissue matched controls with 93% accuracy, 2. breast tumors from healthy tissue matched controls with 94% accuracy, 3. ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort) 4. and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. Conclusions: Immune repertoires can be used to diagnose and prognosticate cancer.

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