Inference of genotype-phenotype relationships in the antigenic evolution of human influenza A (H3N2) viruses.
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ABSTRACT:
SUBMITTER: Steinbruck L
PROVIDER: S-EPMC3330098 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC2648776 | biostudies-literature
Project description:In the early 1970s, a human influenza A/Port Chalmers/1/73 (H3N2)-like virus colonized the European swine population. Analyses of swine influenza A (H3N2) viruses isolated in The Netherlands and Belgium revealed that in the early 1990s, antigenic drift had occurred, away from A/Port Chalmers/1/73, the strain commonly used in influenza vaccines for pigs. Here we show that Italian swine influenza A (H3N2) viruses displayed antigenic and genetic changes similar to those observed in Northern European viruses in the same period. We used antigenic cartography methods for quantitative analyses of the antigenic evolution of European swine H3N2 viruses and observed a clustered virus evolution as seen for human viruses. Although the antigenic drift of swine and human H3N2 viruses has followed distinct evolutionary paths, potential cluster-differentiating amino acid substitutions in the influenza virus surface protein hemagglutinin (HA) were in part the same. The antigenic evolution of swine viruses occurred at a rate approximately six times slower than the rate in human viruses, even though the rates of genetic evolution of the HA at the nucleotide and amino acid level were similar for human and swine H3N2 viruses. Continuous monitoring of antigenic changes is recommended to give a first indication as to whether vaccine strains may need updating. Our data suggest that humoral immunity in the population plays a smaller role in the evolutionary selection processes of swine H3N2 viruses than in human H3N2 viruses.
| S-EPMC1866135 | biostudies-literature
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.
| S-EPMC10079231 | biostudies-literature
Project description:The genetic basis of antigenic drift of human A/H3N2 influenza virus is crucial to understanding the constraints of influenza evolution and determinants of vaccine escape. Amino acid changes at only seven positions near the receptor binding site of the surface hemagglutinin protein have been shown to be responsible for the major antigenic changes for over forty years. Experimental structures of HA are now available for the majority of the observed antigenic clusters of A/H3N2. An analysis of the HA structures of these viruses reveals the likely consequences of these mutations on the structure of HA and thus, provides a structural basis for the antigenic changes seen in human influenza viruses.
| S-EPMC10144855 | biostudies-literature
Project description:Current inactivated influenza vaccines provide protection when vaccine antigens and circulating viruses share a high degree of similarity in hemagglutinin protein. Five antigenic sites in the hemagglutinin protein have been proposed, and 131 amino acid positions have been identified in the five antigenic sites. In addition, 20, 18, and 32 amino acid positions in the hemagglutinin protein have been identified as mouse monoclonal antibody-binding sites, positively selected codons, and substantially diverse codons, respectively. We investigated these amino acid positions for predicting antigenic variants of influenza A/H3N2 viruses in ferrets. Results indicate that the model based on the number of amino acid changes in the five antigenic sites is best for predicting antigenic variants (agreement = 83%). The methods described in this study could be applied to predict vaccine-induced cross-reactive antibody responses in humans, which may further improve the selection of vaccine strains.
| S-EPMC3320420 | biostudies-literature
Project description:BackgroundThe human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is driven by the simultaneous mutations at antigenic sites. Here, we analyze simultaneous or co-occurring mutations in the HA protein of human influenza A/H3N2, A/H1N1 and B viruses to predict potential mutations, characterizing the antigenic evolution.MethodsWe obtain the rules of mutation co-occurrence using association rule mining after extracting HA1 sequences and detect co-mutation sites under strong selective pressure. Then we predict the potential drifts with specific mutations of the viruses based on the rules and compare the results with the "observed" mutations in different years.ResultsThe sites under frequent mutations are in antigenic regions (epitopes) or receptor binding sites.ConclusionsOur study demonstrates the co-occurring site mutations obtained by rule mining can capture the evolution of influenza viruses, and confirms that cooperative interactions among sites of HA1 protein drive the influenza antigenic evolution.
| S-EPMC5260787 | biostudies-literature
Project description:As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.
| S-EPMC8683081 | biostudies-literature
Project description:BACKGROUND: In circulating influenza viruses, gradually accumulated mutations on the glycoprotein hemagglutinin (HA), which interacts with infectivity-neutralizing antibodies, lead to the escape of immune system (called antigenic drift). The antibody recognition is highly correlated to the conformation change on the antigenic sites (epitopes), which locate on HA surface. To quantify a changed epitope for escaping from neutralizing antibodies is the basis for the antigenic drift and vaccine development. RESULTS: We have developed an epitope-based method to identify the antigenic drift of influenza A utilizing the conformation changes on epitopes. A changed epitope, an antigenic site on HA with an accumulated conformation change to escape from neutralizing antibody, can be considered as a "key feature" for representing the antigenic drift. According to hemagglutination inhibition (HI) assays and HA/antibody complex structures, we statistically measured the conformation change of an epitope by considering the number of critical position mutations with high genetic diversity and antigenic scores. Experimental results show that two critical position mutations can induce the conformation change of an epitope to escape from the antibody recognition. Among five epitopes of HA, epitopes A and B, which are near to the receptor binding site, play a key role for neutralizing antibodies. In addition, two changed epitopes often drive the antigenic drift and can explain the selections of 24 WHO vaccine strains. CONCLUSIONS: Our method is able to quantify the changed epitopes on HA for predicting the antigenic variants and providing biological insights to the vaccine updates. We believe that our method is robust and useful for studying influenza virus evolution and vaccine development.
| S-EPMC3044287 | biostudies-literature
Project description:The demonstrated link between the emergence of H3N2 variant (H3N2v) influenza A viruses (IAVs) and swine exposure at agricultural fairs has raised concerns about the human health risk posed by IAV-infected swine. Understanding the antigenic profiles of IAVs circulating in pigs at agricultural fairs is critical to developing effective prevention and control strategies. Here, 68 H3N2 IAV isolates recovered from pigs at Ohio fairs (2009 to 2011) were antigenically characterized. These isolates were compared with other H3 IAVs recovered from commercial swine, wild birds, and canines, along with human seasonal and variant H3N2 IAVs. Antigenic cartography demonstrated that H3N2 IAV isolates from Ohio fairs could be divided into two antigenic groups: (i) the 2009 fair isolates and (ii) the 2010 and 2011 fair isolates. These same two antigenic clusters have also been observed in commercial swine populations in recent years. Human H3N2v isolates from 2010 and 2011 are antigenically clustered with swine-origin IAVs from the same time period. The isolates recovered from pigs at fairs did not cross-react with ferret antisera produced against the human seasonal H3N2 IAVs circulating during the past decade, raising the question of the degree of immunity that the human population has to swine-origin H3N2 IAVs. Our results demonstrate that H3N2 IAVs infecting pigs at fairs and H3N2v isolates were antigenically similar to the IAVs circulating in commercial swine, demonstrating that exhibition swine can function as a bridge between commercial swine and the human population.
| S-EPMC3700273 | biostudies-literature
Project description:An accurate assessment of antigenic similarity between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic similarity measure that accounts for specific changes in strains that are of epidemiological importance in influenza. Empirically grounded statistical models best achieve this. In this study, an interpretable machine-learning model was developed using distinguishing features of antigenic variants to analyze antigenic similarity. The features comprised of cluster information, amino acid sequences located in known antigenic and receptor-binding sites of influenza A (H3N2). In order to assess validity of parameters, accuracy and relevance of model to vaccine effectiveness, the model was applied to influenza A (H3N2) viruses due to their abundant genetic data and epidemiological relevance to influenza surveillance. An application of the model revealed that all model parameters were statistically significant to determining antigenic similarity between strains. Furthermore, upon evaluating the model for predicting antigenic similarity between strains, it achieved 95% area under Receiver Operating Characteristic curve (AUC), 94% accuracy, 76% precision, 97% specificity, 68% sensitivity and a diagnostic odds ratio (DOR) of 83.19. Above all, the model was found to be strongly related to influenza vaccine effectiveness to indicate the correlation between vaccine effectiveness and antigenic similarity between vaccine and circulating strains in an epidemic. The study predicts probabilities of antigenic similarity and estimates changes in strains that lead to antigenic variants. A successful application of the methods presented in this study would complement the global efforts in influenza surveillance.
| S-EPMC8605065 | biostudies-literature