Abstract
ABSTRACTThe antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic data set of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Errors between predicted and measured distances of uncharacterized strains were 0.35, 0.61, 1.69, and 0.13 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine by using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen.IMPORTANCE Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift. To overcome this, we developed nonlinear regression models that predict antigenic phenotype from genetic sequence data by training the model on hemagglutination inhibition assay results. We used these models to predict antigenic phenotype for previously uncharacterized IAV, ranked the importance of genetic features for antigenic phenotype, and experimentally validated our predictions. Our model predicted virus antigenic characteristics from genetic sequence data and provides a rapid and accurate method linking genetic sequence data to antigenic characteristics. This approach also provides support for public health by identifying viruses that are antigenically advanced from strains used as pandemic preparedness candidate vaccine viruses.
Highlights
The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential
In this study, a model was developed to computationally estimate antigenic distances between different IAVs in swine based on amino acid sequence using nonlinear machine learning methods
Predicting antigenic distances from genetic sequence data can identify strains that require further antigenic characterization, reduce the number of hemagglutination inhibition (HI) assays required to describe circulating antigenic diversity, and aid in the selection of candidate strains for vaccines when genetic diversity surveilled in the field does not have an adequate antigenic match in current vaccine formulations
Summary
The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. In the United States, swine IAV is monitored by the U.S Department of Agriculture (USDA) in collaboration with regional veterinary diagnostic laboratories in the National Animal Health Laboratory Network [7] These data are synthesized primarily using phylogenetic analysis [7, 8], but there is no coordinated effort to characterize the phenotypic differences between circulating viruses [9]. Machine learning methods were used to model the antigenic properties of IAV in swine and predict the antigenic distance between different strains using HA sequences Modeling methods, such as the ones we present, are able to overcome the prohibitive costs and logistical challenges associated with large-scale phenotypic characterization. Understanding how genetic diversity, and which amino acids within the HA gene are the most important, can allow for the simulation of the antigenic evolution of swine IAV and make predictions about the persistence and circulation of future IAV strains
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