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Comprehensive analysis of nasal IgA antibodies induced by intranasal administration of the SARS-CoV-2 spike protein (preprint)
biorxiv; 2023.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2023.04.10.536311
ABSTRACT
Intranasal vaccination is an attractive strategy for preventing COVID-19 disease as it stimulates the production of multimeric secretory immunoglobulin A (IgAs), the predominant antibody isotype in the mucosal immune system, at the target site of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry. Currently, the evaluation of intranasal vaccine efficacy is based on the measurement of polyclonal antibody titers in nasal lavage fluid. However, how individual multimeric secretory IgA protects the mucosa from SARS-CoV-2 infection remains to be elucidated. To understand the precise contribution and molecular nature of multimeric secretory IgAs induced by intranasal vaccines, we developed 99 monoclonal IgAs from nasal mucosa and 114 monoclonal IgAs or IgGs from nonmucosal tissues of mice that were intranasally immunized with the SARS-CoV-2 spike protein. The nonmucosal IgAs exhibited shared origins and both common and unique somatic mutations with the related nasal IgA clones, indicating that the antigen-specific plasma cells in the nonmucosal tissues originated from B cells stimulated at the nasal mucosa. Comparing the spike protein binding reactivity, angiotensin-converting enzyme-2-blocking and SARS-CoV-2 virus neutralization of monomeric and multimeric IgA pairs recognizing different epitopes showed that even nonneutralizing monomeric IgA, which represents 70% of the nasal IgA repertoire, can protect against SARS-CoV-2 infection when expressed as multimeric secretory IgAs. Our investigation is the first to demonstrate the function of nasal IgAs at the monoclonal level, showing that nasal immunization can provide effective immunity against SARS-CoV-2 by inducing multimeric secretory IgAs at the target site of virus infection.

Full text: Available Collection: Preprints Database: bioRxiv Language: English Year: 2023 Document Type: Preprint

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Full text: Available Collection: Preprints Database: bioRxiv Language: English Year: 2023 Document Type: Preprint