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COVID-19 adenoviral vector vaccination elicits a robust memory B cell response with the capacity to recognize Omicron BA.2 and BA.5 variants (preprint)
biorxiv; 2023.
Preprint
Dans Anglais
| bioRxiv | ID: ppzbmed-10.1101.2023.02.28.530547
ABSTRACT
Following the COVID-19 pandemic caused by SARS-CoV-2, novel vaccines have successfully reduced severe disease and death. Despite eliciting lower antibody responses, adenoviral vector vaccines are nearly as effective as mRNA vaccines. Therefore, protection against severe disease may be mediated by immune memory cells. We here evaluated plasma antibody and memory B cells (Bmem) targeting the Spike receptor binding domain (RBD) elicited by the adenoviral vector vaccine ChAdOx1 (AstraZeneca), their capacity to bind Omicron subvariants, and compared this to the response elicited by the mRNA vaccine BNT162b2 (Pfizer-BioNTech). Whole blood was sampled from 31 healthy adults pre-vaccination, and four weeks after dose one and dose two of ChAdOx1. Neutralizing antibodies (NAb) against SARS-CoV-2 were quantified at each timepoint. Recombinant RBDs of the Wuhan-Hu-1 (WH1), Delta, BA.2, and BA.5 variants were produced for ELISA-based quantification of plasma IgG and incorporated separately into fluorescent tetramers for flow cytometric identification of RBD-specific Bmem. NAb and RBD-specific IgG levels were over eight times lower following ChAdOx1 vaccination than BNT162b2. In ChAdOx1-vaccinated individuals, median plasma IgG recognition of BA.2 and BA.5 as a proportion of WH1-specific IgG was 26% and 17%, respectively. All donors generated resting RBD-specific Bmem, which were boosted after the second dose of ChAdOx1, and were similar in number to those produced by BNT162b2. The second dose of ChAdOx1 boosted Bmem that recognized VoC, and 37% and 39% of WH1-specific Bmem recognized BA.2 and BA.5, respectively. These data uncover mechanisms by which ChAdOx1 elicits immune memory to confer effective protection against severe COVID-19.
Texte intégral:
Disponible
Collection:
Preprints
Base de données:
bioRxiv
langue:
Anglais
Année:
2023
Type de document:
Preprint
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