Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses

Area of Science:

• Immunology
• Computational Biology
• Virology

Background:

• Cross-reactive T cells can provide protection against different viral strains.
• Pre-existing immunity is vital for adaptive immune responses to influenza and coronaviruses.
• T cell recognition patterns can be modeled using bipartite networks.

Purpose of the Study:

• To model T cell cross-reactivity using bipartite networks.
• To explore the dynamics of T cell repertoires during infection and re-infection.
• To quantify T cell cross-reactivity based on epitope similarity.

Main Methods:

• Constructing bipartite networks to represent T cell epitope recognition.
• Analyzing T cell repertoire dynamics under various conditions (homeostasis, infection, re-infection).
• Introducing a circular epitope space to measure cross-reactivity quantitatively.

Main Results:

• Bipartite networks can effectively model T cell cross-reactivity.
• Structural similarity of epitopes drives cross-reactivity, alongside chance occurrences.
• T cell cross-reactivity is a quantitative measure of epitope overlap within the defined space.

Conclusions:

• T cell cross-reactivity is a significant factor in adaptive immunity against viruses like influenza and coronaviruses.
• Bipartite network modeling provides a framework for understanding T cell repertoire dynamics.
• Quantifying epitope overlap enhances our understanding of cross-protective immune responses.