Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunity’s success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we...

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Bibliographic Details
Published in:PLoS genetics Vol. 19; no. 2; p. e1010652
Main Authors: Ruiz Ortega, María, Spisak, Natanael, Mora, Thierry, Walczak, Aleksandra M.
Format: Journal Article
Language:English
Published: United States Public Library of Science 24.02.2023
Public Library of Science (PLoS)
Subjects:
Adaptive immunity
Analysis
Antigens
B cells
Biology and life sciences
Cell membranes
Cell receptors
Coronaviruses
COVID-19
Health aspects
Humans
Learning models (Stochastic processes)
Life Sciences
Lymphocytes
Lymphocytes T
Medicine and Health Sciences
Receptor mechanisms
Receptors, Antigen, T-Cell
Research and Analysis Methods
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
T cell receptors
T cells
T-Lymphocytes
Vaccines
France
ISSN:1553-7404, 1553-7390, 1553-7404
Online Access:Get full text
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Summary:Adaptive immunity’s success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we show many of these public receptors are shared by chance in healthy individuals. This predictable overlap is driven not only by biases in the random generation process of receptors, as previously reported, but also by their common functional selection. However, the model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong specific antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnostic tool for predicting SARS-CoV-2 status from repertoire data.
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The authors have declared that no competing interests exist.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1010652