Abstract

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.

Highlights

  • Of particular relevance in this instance is the potential role played by pre-existing immunity in the form of cross-reactive T cells or B cells generated during previous common coronavirus infections [11,20,21]

  • Since we were interested in the population dynamics of the T cell repertoire, we focused on parameters that describe behaviors that directly affect the populations of cells

  • While much has yet to be determined about the nature of T cell cross-reactivity, it is clear that cross-reactive CD8+ T cells play a role in the immune response to respiratory infections, those that an individual can experience repeatedly during a life time

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. T cell cross-reactivity and pre-existing immunity, and to propose our own mathematical models of cross-reactivity in an immune system faced with heterologous viral infections. We focus on cross-reactive CD8+ T cells in the context of influenza and coronavirus infections Both Andrew Sewell and Don Mason laid out the argument that T cells must be cross-reactive because the number of possible pMHCs that the immune system could encounter far exceeds the number of potential T cells in a given host [1,13]. Of particular relevance in this instance is the potential role played by pre-existing immunity in the form of cross-reactive T cells or B cells generated during previous common coronavirus infections [11,20,21]. In addition to reproducing observations and fitting models to real-life datasets, we can use mathematical models to propose and test new hypotheses, and test these against new biological data (see Figure 3)

T Cell Cross-Reactivity in the Context of Influenza Viruses
T Cell Cross-Reactivity in the Context of Coronaviruses Infection
Modeling T Cell Cross-Reactivity with a Bipartite Recognition Network
Constructing the TCR-pMHC Recognition Network
VDP Sampling for Unfocused Cross-Reactivity
VDP Sampling for Focused Cross-Reactivity
Dynamics of T Cells during a Viral Infection
Modeling with an Example
Death Events
Homeostatic Division-Events
Infection-Induced Differentiation and Division Events
Stochastic Model of T Cell Dynamics in Homeostasis and during Infection
Dynamics of T Cell Responses and Cross-Reactivity
Modeling T Cell Cross-Reactivity with a Distance in Epitope Space
Findings
Discussion and Conclusions
Full Text
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