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

Abstract

Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect 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 A(H3N2) 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 browserbased application that visualizes antigenic data on a continuously updated phylogeny.evolution | antigenic distance | phylogenetic tree

Evolution-informed forecasting of seasonal influenza A (H3N2)

“…The compartmental model did not include age structure (44) or social structure (45), proper inclusion of which might help to correct the underestimation of incidence peaks in this study. The evolutionary indices mainly consider antigenic change based on mutations in HA; these measures could be improved by further knowledge of antigenic phenotype (46, 47), including other viral segments like protein neuraminidase (NA) (48). Also, other factors affecting the fitness of the virus could be considered (14, 49), including receptor binding ability related to cell entry and transmission (50).…”

“…One key limitation identified in our work is the low and variable number of sequences outside the transmission season, when this information would be most critical. Improvements to the general idea presented here will result from current efforts on purely evolutionary forecasting, which can provide better means to quantify antigenic change of the virus (13, 14, 17, 40, 46, 47), and to lengthen lead times further by concatenating evolutionary and evo-epidemiological prediction. Similarly, increased understanding of the virus’ genotype-phenotype map will also further inform this kind of effort.…”

Delineation of the Genera Haemoproteus and Plasmodium Using RNA-Seq and Multi-gene Phylogenetics

“…In this aspect, phylogenetics is key in determining how we research these pathogens. Parasites sharing a recent common ancestor offer alternative avenues of researching and understanding species infecting humans (Grech et al 2006; Lefèvre et al 2007; Parker et al 2015; Neher et al 2016). Lefèvre et al (2007) discussed the use of a mouse-parasite model system in Grech et al (2006).…”

Population effect of influenza vaccination under co-circulation of non-vaccine variants and the case for a bivalent A/H3N2 vaccine component

“…When there is evidence (either based on epidemiological data and/or on the analysis of the evolution of the influenza virus, e.g. [32,33]) that major circulation on multiple, reasonably distinct influenza strains is likely during the upcoming season, our work stresses the benefit of including multiple strains within an influenza subtype, especially influenza A/H3N2, in a vaccine.…”