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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Vydáno v:	PLoS genetics Ročník 19; číslo 2; s. e1010652
Hlavní autoři:	Ruiz Ortega, María, Spisak, Natanael, Mora, Thierry, Walczak, Aleksandra M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 24.02.2023 Public Library of Science (PLoS)
Témata:	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
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Shrnutí:	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.
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7404 1553-7390 1553-7404
DOI:	10.1371/journal.pgen.1010652