Abstract

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.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1, 2, 4, S3, and S7 and Table S1 and S2.

Article and author information

Author details

  1. Johannes Pettmann

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1979-8943
  2. Anna Huhn

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  3. Enas Abu Shah

    Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5033-8171
  4. Mikhail A Kutuzov

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3386-4350
  5. Daniel B Wilson

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  6. Michael L Dustin

    Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
    Competing interests
    Michael L Dustin, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4983-6389
  7. Simon J Davis

    Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  8. P Anton van der Merwe

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9902-6590
  9. Omer Dushek

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
    For correspondence
    omer.dushek@path.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5847-5226

Funding

Wellcome Trust (207537/Z/17/Z)

  • Johannes Pettmann
  • Anna Huhn
  • Enas Abu Shah
  • Mikhail A Kutuzov
  • Daniel B Wilson
  • Omer Dushek

Wellcome Trust (098274/Z/12/Z)

  • Simon J Davis

Wellcome Trust (100262Z/12/Z)

  • Michael L Dustin

Wellcome Trust (203737/Z/16/Z)

  • Johannes Pettmann

UCB-Oxford Post-doctoral Fellowship

  • Enas Abu Shah

National Science Foundation Division of Mathematical Sciences USA (NSF-DMS 1902854)

  • Daniel B Wilson

Edward Penley Abraham Trust Studentship

  • Anna Huhn

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Ethical approval was provided by the Medical Sciences Inter-divisional Research Ethics Committee (IDREC) at the University of Oxford (R51997/RE001).

Copyright

© 2021, Pettmann et al.

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

Metrics

  • 8,843
    views
  • 1,294
    downloads
  • 75
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Johannes Pettmann
  2. Anna Huhn
  3. Enas Abu Shah
  4. Mikhail A Kutuzov
  5. Daniel B Wilson
  6. Michael L Dustin
  7. Simon J Davis
  8. P Anton van der Merwe
  9. Omer Dushek
(2021)
The discriminatory power of the T cell receptor
eLife 10:e67092.
https://doi.org/10.7554/eLife.67092

Share this article

https://doi.org/10.7554/eLife.67092

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.