Abstract
Determining phenotype from genetic data is a fundamental challenge. Identification of emerging antigenic variants among circulating influenza viruses is critical to the vaccine virus selection process, with vaccine effectiveness maximized when constituents are antigenically similar to circulating viruses. Hemagglutination inhibition (HI) assay data are commonly used to assess influenza antigenicity. Here, sequence and 3-D structural information of hemagglutinin (HA) glycoproteins were analyzed together with corresponding HI assay data for former seasonal influenza A(H1N1) virus isolates (1997–2009) and reference viruses. The models developed identify and quantify the impact of eighteen amino acid substitutions on the antigenicity of HA, two of which were responsible for major transitions in antigenic phenotype. We used reverse genetics to demonstrate the causal effect on antigenicity for a subset of these substitutions. Information on the impact of substitutions allowed us to predict antigenic phenotypes of emerging viruses directly from HA gene sequence data and accuracy was doubled by including all substitutions causing antigenic changes over a model incorporating only the substitutions with the largest impact. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine emerging techniques that predict the evolution of virus populations from one year to the next, leading to stronger theoretical foundations for selection of candidate vaccine viruses. These techniques have great potential to be extended to other antigenically variable pathogens.
Highlights
Antigenic evolution of human influenza A viruses is characterized by rapid drift, with structural changes in antigenic epitopes allowing the virus to escape existing immunity
Using assays to attribute antigenic variation to amino acid sequence changes we identify substitutions that contribute to antigenic drift and quantify their impact
We show that substitutions identified as low-impact are a critical component of virus antigenic evolution and by including these, as well as the high-impact substitutions often focused on, the accuracy of predicting antigenic phenotypes of emerging viruses from genotype is doubled
Summary
Antigenic evolution of human influenza A viruses is characterized by rapid drift, with structural changes in antigenic epitopes allowing the virus to escape existing immunity. The continually evolving antigenic phenotype of influenza A viruses presents an ongoing challenge for vaccine virus selection, as effectiveness is greatest when vaccine components are antigenically similar to circulating viruses. For each substitution the associated change in log HI titer, relative to Neth or Neth Δ130, were partitioned into antigenic (ΔHA) and non-antigenic (ΔHN) components. The range of antigenic effects of K141E, ΔK130, E153K and D187N amino acid substitutions, measured against the panel of antisera, were consistent with predictions from the modeling. The range in antigenic impact (ΔHA) measured using individual antisera is shown in Fig 3 and the mean observed titers averaged across four repeats are shown in S4 Table and as a heat-map in S2 Fig. Across all substitutions we observed a mean error in our predictions of 0.14 antigenic units
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