Artikel ; Online: An intranasal influenza virus-vectored vaccine prevents SARS-CoV-2 replication in respiratory tissues of mice and hamsters
Nature Communications, Vol 14, Iss 1, Pp 1-
2023 Band 12
Abstract: Abstract Current available vaccines for COVID-19 are effective in reducing severe diseases and deaths caused by SARS-CoV-2 infection but less optimal in preventing infection. Next-generation vaccines which are able to induce mucosal immunity in the upper ...
Abstract | Abstract Current available vaccines for COVID-19 are effective in reducing severe diseases and deaths caused by SARS-CoV-2 infection but less optimal in preventing infection. Next-generation vaccines which are able to induce mucosal immunity in the upper respiratory to prevent or reduce infections caused by highly transmissible variants of SARS-CoV-2 are urgently needed. We have developed an intranasal vaccine candidate based on a live attenuated influenza virus (LAIV) with a deleted NS1 gene that encodes cell surface expression of the receptor-binding-domain (RBD) of the SARS-CoV-2 spike protein, designated DelNS1-RBD4N-DAF. Immune responses and protection against virus challenge following intranasal administration of DelNS1-RBD4N-DAF vaccines were analyzed in mice and compared with intramuscular injection of the BioNTech BNT162b2 mRNA vaccine in hamsters. DelNS1-RBD4N-DAF LAIVs induced high levels of neutralizing antibodies against various SARS-CoV-2 variants in mice and hamsters and stimulated robust T cell responses in mice. Notably, vaccination with DelNS1-RBD4N-DAF LAIVs, but not BNT162b2 mRNA, prevented replication of SARS-CoV-2 variants, including Delta and Omicron BA.2, in the respiratory tissues of animals. The DelNS1-RBD4N-DAF LAIV system warrants further evaluation in humans for the control of SARS-CoV-2 transmission and, more significantly, for creating dual function vaccines against both influenza and COVID-19 for use in annual vaccination strategies. |
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Schlagwörter | Science ; Q |
Thema/Rubrik (Code) | 570 |
Sprache | Englisch |
Erscheinungsdatum | 2023-04-01T00:00:00Z |
Verlag | Nature Portfolio |
Dokumenttyp | Artikel ; Online |
Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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