Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

TL;DR

This paper presents a method to predict influenza virus antigenic properties from genetic data using phylogenetic analysis, enabling better forecasting of virus evolution and providing a visualization tool for real-time tracking.

Contribution

It introduces a novel approach to map antigenic differences onto phylogenetic trees, allowing sequence-based antigenicity prediction and future population forecasting.

Findings

Antigenic differences correlate well with phylogenetic paths.

Sequence-based models can predict antigenic phenotypes accurately.

A web application visualizes antigenic evolution in real-time.

Abstract

Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and re-infect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA) and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser based application that visualizes antigenic data on a continuously updated phylogeny.