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Title: Inference of Genotype–Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses}
Article:
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.
Introduction:
Influenza viruses are responsible for ∼500, 000 deaths annually and are a substantial threat to human health[1]. Besides seasonal infections caused by human viruses, four major pandemics over the last 100 years have resulted in ∼50 million deaths worldwide[2]–[4]. The viruses are classified into three genera( A, B, C), all from the Orthomyxoviridae family, which comprises single-stranded, negative sense RNA viruses. Influenza A and B viruses evolve rapidly and continuously accumulate amino acid changes in the antibody-binding( epitope) sites of the surface proteins, resulting in changes in antigenicity. Thus, novel ‘antigenic types’ regularly appear and rise to predominance, causing worldwide epidemics despite existing vaccination programs[5],[6]. Influenza A viruses are further categorized into subtypes based on the composition of their surface proteins, hemagglutinin( H or HA) and neuraminidase( N or NA). In the human population, the subtypes H1N1 and H3N2 are currently circulating[7]. Both global population structure and geographic migration patterns are known to influence the evolution of H3N2. Russell et al. suggested East–Southeast Asia to serve as a global reservoir, from which seasonal epidemics in temperate zones are seeded[8]. Other regions, such as China or USA, might serve as seeding regions, too, and migration from and to other tropical regions than East-Southeast Asia is thought to have a significant influence on the global dynamics[9],[10]. To monitor genetic and antigenic changes, the World Health Organization( WHO) runs a global surveillance program[11]. Quantification of viral antigenic phenotypes is done with the hemagglutination inhibition( HI) assay, which measures the ability of an antiserum to inhibit the agglutination of red blood cells by a viral antigen[12]. Antigenic cartography, involving multidimensional scaling of log-normalized HI titers, subsequently generates an accurate low-dimensional representation of the antigenic distances between antigen–antiserum pairs[5],[13]. If a novel antigenic type with increasing prevalence is detected, the vaccine composition, consisting of two strains of influenza A( H3N2 and H1N1) and one strain of influenza B, is updated to include an antigenically closer match. Antigenic cartography of influenza A( H3N2) isolates from 1968 to 2003 revealed that antigenic types circulate for 3. 3 years, on average, in worldwide epidemics before being replaced by a successor[5]. A comparison of antigenic and genetic maps showed that, the antigenic impact of genetic changes varies, depending on the nature of the amino acids exchanged, their structural positioning and epistatic interactions with other sites. Subsequent studies have incorporated both antigenic and genetic data for predicting antigenically novel strains[14]–[16]. Additionally, many groups have investigated the influence of sequence positions and sequence variation on viral evolution, based on different computational criteria[17]–[24]. Even though the general principles governing the antigenic evolution of influenza A viruses are well studied, computational methods for directly determining the antigenic impact of individual amino acid exchanges do not yet exist. Such analyses currently require time- and cost-intensive experimental characterization of mutant viruses[5]. On the other end of the spectrum, antigenic cartography allows identification of ‘cluster difference substitutions’, comprising all near-conserved changes that distinguish consecutive antigenic clusters. We describe a method for the inference of ‘antigenic trees’, which is based on a least-squares optimization( LSO) procedure of fitting pairwise antigenic distances onto an evolutionary tree for the major antigenic determinant of influenza A. It is a computational method allowing for a more fine-grained resolution of the antigenic impact of individual changes than antigenic cartography without time- and cost-intensive experiments. Application to HA sequences and serological data from human influenza A( H3N2) viral isolates from 1968 to 2003 determined the antigenic impact of all branch-associated amino acid changes for this time period. Our technique identified known antigenic types and the amino acid changes associated with the type transitions. For sufficiently resolved branches, the antigenic impact of individual exchanges could be quantified. The method furthermore found known and novel key HA sites and changes in antigenic evolution.
Discussion:
The antigenic impact of amino acid substitutions in the antigenic evolution of influenza A viruses can reliably be determined by time- and cost-intensive experimental analysis. As an alternative, we present a computational technique for inferring the antigenic impact of amino acid changes. Our method determines antigenic branch lengths for a given tree topology by fitting pairwise antigenic distances between isolates onto the tree with LSO. For inference of the tree, any state-of-the-art method can be used. A comparison between maximum likelihood, maximum parsimony and neighbor-joining trees showed that all resulted in similar prediction errors( leave-one-out absolute prediction error: 0. 86, 0. 87 and 0. 87 antigenic units, respectively; correlation between predicted and measured by Pearson's correlation coefficient was 0. 86 for all three methods). The antigenic impact of the branch-associated amino acid changes is determined by reconstructing the branch-associated amino acid changes with maximum likelihood[40]; other techniques, such as maximum parsimony or Bayesian reconstruction, could also be used[41],[42]. A comparison between maximum likelihood and maximum parsimony ancestral character state reconstruction showed that these differed only in minor aspects, with the maximum likelihood reconstruction being an intermediate between accelerated and delayed transition in case of ties with maximum parsimony reconstruction. However, we did observe that more trunk branches were not assigned changes based on maximum likelihood reconstruction, which decreased the interpretability of antigenic weights in some cases. We studied the antigenic evolution of the influenza A( H3N2) virus from 1968 to 2003 with antigenic trees inferred from data described in Smith et al.( 2004)[5]. This allowed us to identify areas and branches in the tree corresponding to known antigenic types and transitions between these types. Analysis of antigenic weights identified seven sites in the HA1 domain of HA that were repeatedly associated with high antigenic impact. Additionally, our method identified five amino acid changes with high antigenic weights at several places in the antigenic tree. The sites and substitutions identified by our method may be of particular relevance for influenza A( H3N2) virus antigenic evolution, which has not been described before. For six of the seven positions found by site-directed mutagenesis to defining antigenic clusters for the 35 year time period( Koel et al.; Antigenic evolution of influenza A( H3N2) virus is dictated by 7 residues in the hemagglutinin protein; 2nd International Influenza Meeting, Münster; 2011), changes with high antigenic weights were identified with our technique, thus further supporting their relevance for influenza A( H3N2) evolution. The additional sites detected by our method could be more relevant for genetic and antigenic variations between viral strains in our data set not resulting in antigenic cluster transitions. These were not analyzed by Koel et al., who characterized antigenic differences of prototype viruses with the amino acid consensus sequences of the antigenic clusters. As the dataset covers 35 years of viral evolution with a relatively small number of isolates, not all substitutions could be resolved to individual branches and their individual antigenic impacts inferred. A denser sampling of data points would allow a more precise decoding of the genotype–antigenicity relationships, as viral isolates were unevenly sampled across the 35 years. The median number of viral isolates available per year between 1989 and 1997 was 15, whereas for the remaining years only three isolates per year were sampled( median). This unequal sampling is reflected in resolution of mutations to specific branches. Between 1989 and 1997, 19% of the branches with assigned changes carry three or more changes, whereas for the other years this is the case for 37% of the branches. Our method allows inference of genotype to phenotype relationships from genetic sequences and associated pairwise phenotypic distances between individuals of a population or different taxa. We demonstrated the usefulness of this technique for analyzing the antigenic impact of amino acid changes in the evolution of human influenza A. An application of our method could be in influenza A virus surveillance. Here, it could be used to identify isolates and associated changes with large antigenic impact, which need to be identified for vaccine strain updates prior to an antigenic type transitions[43]. However, our method is not restricted to the analysis of influenza viruses or antigenic distance information but can be applied to the study of any system, be it within or across species, where homologous genetic sequences and associated pairwise phenotype distances are available. The software is available upon request from the authors.
The molecular evolution of any organism is described by changes in the genotype resulting from genetic drift or selection to maintain or establish fitness under the given environmental conditions . Identification of phenotype-defining changes and their distinction from ( near- ) neutral ( ‘hitchhikers’ ) ones is a fundamental challenge in genome research . The standard approach involves time- and cost-intensive mutation experiments , which are typically low throughput , due to their experimental nature . We have developed a computational method for the inference of phenotypic impact of genotypic changes that is applicable to any system , within or across species , where homologous genetic sequences and associated pairwise phenotype distances are available . We demonstrate the accuracy of our method by application to the human influenza A ( H3N2 ) virus . This exemplary system is of particular interest , as recognizing changes in the antigenic phenotype and a viral strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines . We accurately identified known sites and amino acid changes with antigenic impact over 35 years of evolution , and provide further details on individual antigenically relevant changes in the evolution of influenza A ( H3N2 ) viruses .