Login / Signup

Immunogenicity of Mumps Virus Genotype G Vaccine Candidates in Jeryl Lynn-Immunized Mice.

Kelsey BriggsCara KirbyAshley C BeavisJames ZengelPreetish PatilChristian SauderBiao He
Published in: Journal of virology (2022)
Mumps virus (MuV) causes a highly contagious human disease characterized by the enlargement of the parotid glands. In severe cases, mumps can lead to neurological complications such as aseptic meningitis and encephalitis. Vaccination with the attenuated Jeryl Lynn (JL) MuV vaccine has dramatically reduced the incidence of MuV infection. Recently, large outbreaks have occurred in vaccinated populations. The vaccine strain JL was generated from genotype A, while most current circulating strains belong to genotype G. In this study, we examined the immunogenicity and longevity of genotype G-based vaccines. We found that our recombinant genotype G-based vaccines provide robust neutralizing titers toward genotype G for up to 1 year in mice. In addition, we demonstrated that a third dose of a genotype G-based vaccine following two doses of JL immunization significantly increases neutralizing titers toward the genotype G strain. Our data suggest that after two doses of JL vaccination, which most people have received, a third dose of a genotype G-based vaccine can generate immunity against a genotype G strain. IMPORTANCE At present, most individuals have received two doses of the measles, mumps, and rubella (MMR) vaccine, which contains genotype A mumps vaccine. One hurdle in developing a new mumps vaccine against circulating genotype G virus is whether the new genotype G vaccine can generate immunity in humans that are immunized against genotype A virus. This work demonstrates that a novel genotype G-based vaccine can be effective in animals which received two doses of genotype A-based vaccine, suggesting that the lead genotype G vaccine may induce anti-G immunity in humans who have received two doses of the current vaccine, providing support for testing this vaccine in humans.
Keyphrases
  • type diabetes
  • escherichia coli
  • machine learning
  • metabolic syndrome
  • endothelial cells
  • big data