Back

Fc-gamma receptor-dependent antibody effector functions are required for vaccine protection against infection by antigenic variants of SARS-CoV-2

Mackin, S. R.; Desai, P.; Whitener, B. M.; Karl, C. E.; Liu, M.; Baric, R. S.; Edwards, D. K.; Chicz, T. M.; McNamara, R. P.; Alter, G.; Diamond, M. S.

2022-11-29 microbiology
10.1101/2022.11.27.518117 bioRxiv
Show abstract

Emerging SARS-CoV-2 variants with antigenic changes in the spike protein are neutralized less efficiently by serum antibodies elicited by legacy vaccines against the ancestral Wuhan-1 virus. Nonetheless, these vaccines, including mRNA-1273 and BNT162b2, retained their ability to protect against severe disease and death, suggesting that other aspects of immunity control infection in the lung. Although vaccine-elicited antibodies can bind Fc gamma receptors (Fc{gamma}Rs) and mediate effector functions against SARS-CoV-2 variants, and this property correlates with improved clinical COVID-19 outcome, a causal relationship between Fc effector functions and vaccine-mediated protection against infection has not been established. Here, using passive and active immunization approaches in wild-type and Fc-gamma receptor (Fc{gamma}R) KO mice, we determined the requirement for Fc effector functions to protect against SARS-CoV-2 infection. The antiviral activity of passively transferred immune serum was lost against multiple SARS-CoV-2 strains in mice lacking expression of activating Fc{gamma}Rs, especially murine Fc{gamma}R III (CD16), or depleted of alveolar macrophages. After immunization with the preclinical mRNA-1273 vaccine, protection against Omicron BA.5 infection in the respiratory tract also was lost in mice lacking Fc{gamma}R III. Our passive and active immunization studies in mice suggest that Fc-Fc{gamma}R engagement and alveolar macrophages are required for vaccine-induced antibody-mediated protection against infection by antigenically changed SARS-CoV-2 variants, including Omicron strains.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.