Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity

  1. William S DeWitt
  2. Anajane Smith
  3. Gary Schoch
  4. John A Hansen
  5. Frederick A Matsen
  6. Philip Bradley  Is a corresponding author
  1. Fred Hutchinson Cancer Research Center, United States

Abstract

The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.

Data availability

Data and analysis scripts needed to reproduce the findings of this study have been deposited in the Zenodo database (doi:10.5281/zenodo.1248193).

The following previously published data sets were used

Article and author information

Author details

  1. William S DeWitt

    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6802-9139
  2. Anajane Smith

    Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Gary Schoch

    Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. John A Hansen

    Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Frederick A Matsen

    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0607-6025
  6. Philip Bradley

    Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    For correspondence
    pbradley@fredhutch.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0224-6464

Funding

National Institutes of Health (CA015704)

  • Anajane Smith
  • Gary Schoch
  • John A Hansen
  • Frederick A Matsen
  • Philip Bradley

Fred Hutchinson Cancer Research Center (Salary support)

  • Philip Bradley

National Institutes of Health (R01-HL105914)

  • Anajane Smith
  • Gary Schoch
  • John A Hansen

National Institutes of Health (R01-GM113246)

  • Frederick A Matsen

National Institutes of Health (U19-AI117891)

  • Frederick A Matsen

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

Reviewing Editor

  1. Aleksandra M Walczak, École Normale Supérieure, France

Ethics

Human subjects: All samples were collected and analyzed, and informed consent and consent to publish were obtained, according to research protocols approved by the Fred Hutchinson Cancer Research Center (FHCRC) Institutional Review Board.

Version history

  1. Received: May 14, 2018
  2. Accepted: August 21, 2018
  3. Accepted Manuscript published: August 28, 2018 (version 1)
  4. Version of Record published: September 28, 2018 (version 2)

Copyright

© 2018, DeWitt 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

  • 7,793
    views
  • 1,267
    downloads
  • 110
    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. William S DeWitt
  2. Anajane Smith
  3. Gary Schoch
  4. John A Hansen
  5. Frederick A Matsen
  6. Philip Bradley
(2018)
Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity
eLife 7:e38358.
https://doi.org/10.7554/eLife.38358

Share this article

https://doi.org/10.7554/eLife.38358

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Ryan T Bell, Harutyun Sahakyan ... Eugene V Koonin
    Research Article

    A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.

    1. Computational and Systems Biology
    Skander Kazdaghli, Iordanis Kerenidis ... Philip Teare
    Research Article

    Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream classification. Here we propose novel imputation methods based on determinantal point processes (DPP) that enhance popular techniques such as the multivariate imputation by chained equations and MissForest. Their advantages are twofold: improving the quality of the imputed data demonstrated by increased accuracy of the downstream classification and providing deterministic and reliable imputations that remove the variance from the classification results. We experimentally demonstrate the advantages of our methods by performing extensive imputations on synthetic and real clinical data. We also perform quantum hardware experiments by applying the quantum circuits for DPP sampling since such quantum algorithms provide a computational advantage with respect to classical ones. We demonstrate competitive results with up to 10 qubits for small-scale imputation tasks on a state-of-the-art IBM quantum processor. Our classical and quantum methods improve the effectiveness and robustness of clinical data prediction modeling by providing better and more reliable data imputations. These improvements can add significant value in settings demanding high precision, such as in pharmaceutical drug trials where our approach can provide higher confidence in the predictions made.