A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses

Abstract

T cell recognition of SARS-CoV-2 antigens after vaccination and/or natural infection has played a central role in resolving SARS-CoV-2 infections and generating adaptive immune memory. However, the clinical impact of SARS-CoV-2-specific T cell responses is variable and the mechanisms underlying T cell interaction with target antigens are not fully understood. This is especially true given the virus’ rapid evolution, which leads to new variants with immune escape capacity. In this study, we used the Omicron variant as a model organism and took a systems approach to evaluate the impact of mutations on CD8+ T cell immunogenicity. We computed an ‘immunogenicity potential’ score for each SARS-CoV-2 peptide antigen from the ancestral strain and Omicron, capturing both antigen presentation and T cell recognition probabilities. By comparing ancestral vs. Omicron immunogenicity scores, we reveal a divergent and heterogeneous landscape of impact for CD8+ T cell recognition of mutated targets in Omicron variants. While T cell recognition of Omicron peptides is broadly preserved, we observed mutated peptides with deteriorated immunogenicity that may assist breakthrough infection in some individuals. We then combined our scoring scheme with anin-silicomutagenesis, to characterise the position- and residue-specific theoretical mutational impact on immunogenicity. While we predict many escape trajectories from the theoretical landscape of substitutions, our study suggests that Omicron mutations in T cell epitopes did not develop under cell-mediated pressure. Our study provides a generalisable platform for fostering a deeper understanding of existing and novel variant impact on antigen-specific vaccine- and/or infection-induced T cell immunity.

A robust deep learning platform to predict CD8+ T-cell epitopes

“…We showed that the out-of-distribution detection module is well-suited for the 'needle in a haystack' problem by identifying those predicted negative with low confidence. In another study, we used TRAP to investigate the immune escape potential of SARS-CoV-2 variants 64 . By combining TRAP with in silico mutagenesis, we evaluated the extent to which all possible theoretical single point mutations can give rise to variants of concern and be detrimental to T-cell immunity.…”