Cell-Adapted Mutations and Antigenic Diversity of Influenza B Viruses in Missouri, 2019-2020 Season

Seasonal influenza vaccine performance and the potential benefits of mRNA vaccines

Influenza remains a public health threat, partly due to suboptimal effectiveness of vaccines. One factor impacting vaccine effectiveness is strain mismatch, occurring when vaccines no longer match circulating strains due to antigenic drift or the incorporation of inadvertent (eg, egg-adaptive) mutations during vaccine manufacturing. In this review, we summarize the evidence for antigenic drift of circulating viruses and/or egg-adaptive mutations occurring in vaccine strains during the 2011-2020 influenza seasons. Evidence suggests that antigenic drift led to vaccine mismatch during four seasons and that egg-adaptive mutations caused vaccine mismatch during six seasons. These findings highlight the need for alternative vaccine development platforms. Recently, vaccines based on mRNA technology have demonstrated efficacy against SARS-CoV-2 and respiratory syncytial virus and are under clinical evaluation for seasonal influenza. We discuss the potential for mRNA vaccines to address strain mismatch, as well as new multi-component strategies using the mRNA platform to improve vaccine effectiveness.

MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production

Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.

Antigenic characterization of influenza and SARS-CoV-2 viruses

Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.