Systematic analysis of SARS-CoV-2 Omicron subvariants' impact on B and T cell epitopes.

Autor: Al Khalaf R; Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milano, Italia., Bernasconi A; Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milano, Italia., Pinoli P; Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milano, Italia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Sep 19; Vol. 19 (9), pp. e0307873. Date of Electronic Publication: 2024 Sep 19 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0307873
Abstrakt: Introduction: Epitopes are specific structures in antigens that are recognized by the immune system. They are widely used in the context of immunology-related applications, such as vaccine development, drug design, and diagnosis / treatment / prevention of disease. The SARS-CoV-2 virus has represented the main point of interest within the viral and genomic surveillance community in the last four years. Its ability to mutate and acquire new characteristics while it reorganizes into new variants has been analyzed from many perspectives. Understanding how epitopes are impacted by mutations that accumulate on the protein level cannot be underrated.
Methods: With a focus on Omicron-named SARS-CoV-2 lineages, including the last WHO-designated Variants of Interest, we propose a workflow for data retrieval, integration, and analysis pipeline for conducting a database-wide study on the impact of lineages' characterizing mutations on all T cell and B cell linear epitopes collected in the Immune Epitope Database (IEDB) for SARS-CoV-2.
Results: Our workflow allows us to showcase novel qualitative and quantitative results on 1) coverage of viral proteins by deposited epitopes; 2) distribution of epitopes that are mutated across Omicron variants; 3) distribution of Omicron characterizing mutations across epitopes. Results are discussed based on the type of epitope, the response frequency of the assays, and the sample size. Our proposed workflow can be reproduced at any point in time, given updated variant characterizations and epitopes from IEDB, thereby guaranteeing to observe a quantitative landscape of mutations' impact on demand.
Conclusion: A big data-driven analysis such as the one provided here can inform the next genomic surveillance policies in combatting SARS-CoV-2 and future epidemic viruses.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Al Khalaf et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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