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Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses.

ABSTRACT: Distinguishing mutations that determine an organism's phenotype from (near-) neutral 'hitchhikers' is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring 'antigenic trees' for the major viral surface protein hemagglutinin. In the antigenic tree, antigenic weights are assigned to all tree branches, which allows us to resolve the antigenic impact of the associated amino acid changes. Our technique predicted antigenic distances with comparable accuracy to antigenic cartography. Additionally, it identified both known and novel sites, and amino acid changes with antigenic impact in the evolution of influenza A (H3N2) viruses from 1968 to 2003. The technique can also be applied for inference of 'phenotype trees' and genotype-phenotype relationships from other types of pairwise phenotype distances.

SUBMITTER: Steinbruck L

PROVIDER: S-EPMC3330098 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses.

Steinbrück Lars L McHardy Alice Carolyn AC

PLoS computational biology 20120419 4

Distinguishing mutations that determine an organism's phenotype from (near-) neutral 'hitchhikers' is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring 'antigenic trees' for the major viral surface pr ...[more]

PMID: 22532796

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Project description:BACKGROUND: In pandemic and epidemic forms, avian and human influenza viruses often cause significant damage to human society and economics. Gradually accumulated mutations on hemagglutinin (HA) cause immunologically distinct circulating strains, which lead to the antigenic drift (named as antigenic variants). The "antigenic variants" often requires a new vaccine to be formulated before each annual epidemic. Mapping the genetic evolution to the antigenic drift of influenza viruses is an emergent issue to public health and vaccine development RESULTS: We developed a method for identifying antigenic critical amino acid positions, rules, and co-mutated positions for antigenic variants. The information gain (IG) and the entropy are used to measure the score of an amino acid position on hemagglutinin (HA) for discriminating between antigenic variants and similar viruses. A position with high IG and entropy implied that this position is highly correlated to an antigenic drift. Nineteen positions with high IG and high genetic diversity are identified as antigenic critical positions on the HA proteins. Most of these antigenic critical positions are located on five epitopes or on the surface based on the HA structure. Based on IG values and entropies of these 19 positions on the HA, the decision tree was applied to create a rule-based model and to identify rules for predicting antigenic variants of a given two HA sequences which are often a vaccine strain and a circulating strain. The predicting accuracies of this model on two sets, which consist of a training set (181 hemagglutination inhibition (HI) assays) and an independent test set (31,878 HI assays), are 91.2% and 96.2% respectively. CONCLUSION: Our method is able to identify critical positions, rules, and co-mutated positions on HA for predicting the antigenic variants. The information gains and the entropies of HA positions provide insight to the antigenic drift and co-evolution positions for influenza seasons. We believe that our method is robust and is potential useful for studying influenza virus evolution and vaccine development.

Project description:Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development, though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharacterised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.

Dataset Information

Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses.

Publications

Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses.

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