Back

Towards the development of a mucosal vectored vaccine against enterovirus D68

Zimina, A.; Viktorova, E.; Kouiavskaia, D.; Chumakov, K.; Rosenfeld, A. B.; Belov, G.

2025-12-12 microbiology
10.64898/2025.12.12.693937 bioRxiv
Show abstract

Enterovirus D68 (EVD68) is an emerging pathogen associated with neurological complications, including temporary and permanent paralysis, and lethality. Currently, no EVD68-specific vaccines or antivirals are available. Here, we used a Newcastle disease virus (NDV) vector to develop a live mucosal vaccine that produces EVD68 virus-like particles (VLPs) and evaluated its immunogenicity in a murine model. We made constructs producing VLPs of the prototype EVD68 strain Fermon and the USA/2018-23088 (Ohio-2018) strain from a recent outbreak in the USA. Our results demonstrate robust production and proper processing of EVD68 capsid proteins upon co-expression with the viral protease 3CD. Intranasally-immunized animals effectively seroconverted to NDV antigens, indicating successful replication of the vectors. Antibodies that could bind both the Fermon and Ohio-2018 virions were observed in the sera of mice immunized with either viral vector. At least some animals also demonstrated a strong respiratory mucosal IgA response against EVD68 capsid proteins. These data confirm the immunogenicity of EVD68 proteins expressed from a viral vector in the respiratory tract. Yet neither humoral nor mucosal antibodies were protective against EVD68 infection in cell culture. Analysis of cells infected with recombinant NDVs by electron microscopy indicated structural differences between bona fide EVD68 virions and VLPs, suggesting that the lack of neutralizing antibody response is likely due to antigenic disparity between the EVD68 virions and empty VLPs and/or limited stability of VLPs within the murine respiratory tract. These results have important implications for the development of VLP-based vaccines against enteroviruses.

Published in Virology (predicted rank #5) · training set

Matching journals

The top 5 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.