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Review

Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses

1
Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
2
St. Jude Graduate School of Biomedical Sciences, Memphis, TN 38105, USA
3
Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK
4
T-6, Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Elizabeth M. Anderson and Sigrid Gouma
Viruses 2021, 13(9), 1786; https://doi.org/10.3390/v13091786
Received: 31 July 2021 / Revised: 28 August 2021 / Accepted: 2 September 2021 / Published: 7 September 2021
(This article belongs to the Special Issue Pre-existing Immunity Effects on Viral Infections and Vaccinations)
If viral strains are sufficiently similar in their immunodominant epitopes, then populations of cross-reactive T cells may be boosted by exposure to one strain and provide protection against infection by another at a later date. This type of pre-existing immunity may be important in the adaptive immune response to influenza and to coronaviruses. Patterns of recognition of epitopes by T cell clonotypes (a set of cells sharing the same T cell receptor) are represented as edges on a bipartite network. We describe different methods of constructing bipartite networks that exhibit cross-reactivity, and the dynamics of the T cell repertoire in conditions of homeostasis, infection and re-infection. Cross-reactivity may arise simply by chance, or because immunodominant epitopes of different strains are structurally similar. We introduce a circular space of epitopes, so that T cell cross-reactivity is a quantitative measure of the overlap between clonotypes that recognize similar (that is, close in epitope space) epitopes. View Full-Text
Keywords: cross-reactivity; pre-existing immunity; heterologous infection; mathematical modeling; competition process; bipartite network cross-reactivity; pre-existing immunity; heterologous infection; mathematical modeling; competition process; bipartite network
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MDPI and ACS Style

Gaevert, J.A.; Luque Duque, D.; Lythe, G.; Molina-París, C.; Thomas, P.G. Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses. Viruses 2021, 13, 1786. https://doi.org/10.3390/v13091786

AMA Style

Gaevert JA, Luque Duque D, Lythe G, Molina-París C, Thomas PG. Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses. Viruses. 2021; 13(9):1786. https://doi.org/10.3390/v13091786

Chicago/Turabian Style

Gaevert, Jessica Ann, Daniel Luque Duque, Grant Lythe, Carmen Molina-París, and Paul Glyndwr Thomas. 2021. "Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses" Viruses 13, no. 9: 1786. https://doi.org/10.3390/v13091786

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