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.

Predicting the Antigenic Relationship of Foot-and-Mouth Disease Virus for Vaccine Selection Through a Computational Model

Foot-and-mouth disease virus (FMDV) is an antigenic-variable RNA virus that is responsible for the recurrence of foot-and-mouth disease in livestock and can be prevented and controlled using a vaccine with broad-spectrum protection. Current anti-genicity evaluation methods, which involve animal immunity experiments and serum preparation, are unable to fulfill the needs of high-throughput antigenicity measurements. This study designed an antigenicity scoring model to rapidly predict the antigenicity of FMDV. Antigenic-dominant sites were initially determined on the VP1 protein, a position-specific scoring matrix and physical chemical indexes were integrated to generate antigenicity descriptors. Independent tests showed a high accuracy of 0.848 and an AUC value of 0.889, indicating the good performance of the model in antigenicity measurement. When applying this model to historical data, annual antigenicity coverage of widely used vaccine strains was successfully evaluated, this was also supported by previous experiments. Furthermore, the utility of this model was extended to select potential broad-spectrum vaccines among 1,201 historical non-redundant strains to recommend potential univalent, bivalent and trivalent vaccine candidates. The results suggested that the computational model designed in this study could be used for the high-throughput antigenicity measurement of FMDV and could aid in vaccine development for preventing FMDV epidemics.

Foot and mouth disease vaccine strain selection: current approaches and future perspectives

INTRODUCTION

Lack of cross protection between foot and mouth disease (FMD) virus (FMDV) serotypes as well as incomplete protection between some subtypes of FMDV affect the application of vaccine in the field. Further, the emergence of new variant FMD viruses periodically makes the existing vaccine inefficient. Consequently, periodical vaccine strain selection either by in vivo methods or in vitro methods become an essential requirement to enable utilization of appropriate and efficient vaccines.

AREAS COVERED

Here we describe the cross reactivity of the existing vaccines with the global pool of circulating viruses and the putative selected vaccine strains for targeting protection against the two major circulating serotype O and A FMD viruses for East Africa, the Middle East, South Asia and South East Asia.

EXPERT COMMENTARY

Although in vivo cross protection studies are more appropriate methods for vaccine matching and selection than in vitro neutralization test or ELISA, in the face of an outbreak both in vivo and in vitro methods of vaccine matching are not easy, and time consuming. The FMDV capsid contains all the immunogenic epitopes, and therefore vaccine strain prediction models using both capsid sequence and serology data will likely replace existing tools in the future.

A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures

Rapid determination of the antigenicity of influenza A virus could help identify the antigenic variants in time. Currently, there is a lack of computational models for predicting antigenic variants of some common hemagglutinin (HA) subtypes of influenza A viruses. By means of sequence analysis, we demonstrate here that multiple HA subtypes of influenza A virus undergo similar mutation patterns of HA1 protein (the immunogenic part of HA). Further analysis on the antigenic variation of influenza A virus H1N1, H3N2 and H5N1 showed that the amino acid residues' contribution to antigenic variation highly differed in these subtypes, while the regional bands, defined based on their distance to the top of HA1, played conserved roles in antigenic variation of these subtypes. Moreover, the computational models for predicting antigenic variants based on regional bands performed much better in the testing HA subtype than those did based on amino acid residues. Therefore, a universal computational model, named PREDAV-FluA, was built based on the regional bands to predict the antigenic variants for all HA subtypes of influenza A viruses. The model achieved an accuracy of 0.77 when tested with avian influenza H9N2 viruses. It may help for rapid identification of antigenic variants in influenza surveillance.

Global Foot-and-Mouth Disease Research Update and Gap Analysis: 3 - Vaccines

This study assessed research knowledge gaps in the field of FMDV (foot-and-mouth disease virus) vaccines. The study took the form of a literature review (2011-15) combined with research updates collected in 2014 from 33 institutes from across the world. Findings were used to identify priority areas for future FMD vaccine research. Vaccines play a vital role in FMD control, used both to limit the spread of the virus during epidemics in FMD-free countries and as the mainstay of disease management in endemic regions, particularly where sanitary controls are difficult to apply. Improvements in the performance or cost-effectiveness of FMD vaccines will allow more widespread and efficient disease control. FMD vaccines have changed little in recent decades, typically produced by inactivation of whole virus, the quantity and stability of the intact viral capsids in the final preparation being key for immunogenicity. However, these are exciting times and several promising novel FMD vaccine candidates have recently been developed. This includes the first FMD vaccine licensed for manufacture and use in the USA; this adenovirus-vectored FMD vaccine causes in vivo expression of viral capsids in vaccinated animals. Another promising vaccine candidate comprises stabilized empty FMDV capsids produced in vitro in a baculovirus expression system. Recombinant technologies are also being developed to improve otherwise conventionally produced inactivated vaccines, for example, by creating a chimeric vaccine virus to increase capsid stability and by inserting sequences into the vaccine virus for desired antigen expression. Other important areas of ongoing research include enhanced adjuvants, vaccine quality control procedures and predicting vaccine protection from immune correlates, thus reducing dependency on animal challenge studies. Globally, the degree of independent vaccine evaluation is highly variable, and this is essential for vaccine quality. Previously neglected, the importance of evaluating vaccination programme effectiveness and impact is increasingly being recognized.