Identifying potential immunodominant positions and predicting antigenic variants of influenza A/H3N2 viruses

Mapping antibody footprints using binding profiles

The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb's binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch-an epitope prediction method that is based on a mAb's binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50-60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.

Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning

The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance in silico, based on genetic sequence data, and identify differences in specific amino acid sites associated with antigenic differences between viruses. First, we obtained antigenic distance estimates derived from serum neutralization assays cross-reacting PRRSV monospecific antisera with virus isolates from 27 PRRSV1 viruses circulating in Europe. Antigenic distances were weakly to moderately associated with ectodomain amino acid distance for open reading frames (ORFs) 2 to 4 (ρ < 0.2) and ORF5 (ρ = 0.3), respectively. Dividing the antigenic distance values at the median, we then categorized the sera-virus pairs into two levels: low and high antigenic distance (dissimilarity). In the machine learning models, we used amino acid distances in the ectodomains of ORFs 2 to 5 and site-wise amino acid differences between the viruses as potential predictors of antigenic dissimilarity. Using mixed-effect gradient boosting models, we estimated the antigenic distance (high versus low) between serum-virus pairs with an accuracy of 81% (95% confidence interval, 76 to 85%); sensitivity and specificity were 86% and 75%, respectively. We demonstrate that using sequence data we can estimate antigenic distance and potential cross-protection between PRRSV1 strains. IMPORTANCE Understanding cross-protection between cocirculating PRRSV1 strains is crucial to reducing losses associated with PRRS outbreaks on farms. While experimental studies to determine cross-protection are instrumental, these in vivo studies are not always practical or timely for the many cocirculating and emerging PRRSV strains. In this study, we demonstrate the ability to rapidly estimate potential immunologic cross-reaction between different PRRSV1 strains in silico using sequence data routinely collected by production systems. These models can provide fast turn-around information crucial for improving PRRS management decisions such as selecting vaccines/live virus inoculation to be used on farms and assessing the risk of outbreaks by emerging strains on farms previously exposed to certain PRRSV strains and vaccine development among others.

A statistical analysis of antigenic similarity among influenza A (H3N2) viruses

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.

Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin

The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in Southeast Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.

Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks

The rapid evolution of influenza A viruses poses a great challenge to vaccine development. Analytical and machine learning models have been applied to facilitate the process of antigenicity determination. In this study, we designed deep convolutional neural networks (CNNs) to predict Influenza antigenicity. Our model is the first that systematically analyzed 566 amino acid properties and 141 amino acid substitution matrices for their predictability. We then optimized the structure of the CNNs using particle swarm optimization. The optimal neural networks outperform other predictive models with a blind validation accuracy of 95.8%. Further, we applied our model to vaccine recommendations in the period 1997 to 2011 and contrasted the performance of previous vaccine recommendations using traditional experimental approaches. The results show that our model outperforms the WHO recommendation and other existing models and could potentially improve the vaccine recommendation process. Our results show that WHO often selects virus strains with small variation from year to year and learns slowly and recovers once coverage dips very low. In contrast, the influenza strains selected via our CNN model can differ quite drastically from year to year and exhibit consistently good coverage. In summary, we have designed a comprehensive computational pipeline for optimizing a CNN in the modeling of Influenza A antigenicity and vaccine recommendation. It is more cost and time-effective when compared to traditional hemagglutination inhibition assay analysis. The modeling framework is flexible and can be adopted to study other type of viruses.

Models for predicting the evolution of influenza to inform vaccine strain selection

Influenza vaccine composition is reviewed before every flu season because influenza viruses constantly evolve through antigenic changes. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System monitor the antigenic phenotypes of circulating viruses all year round. Vaccine strains are selected in anticipation of the upcoming influenza season to allow adequate time for production. A mismatch between vaccine strains and predominant strains in the flu season can significantly reduce vaccine effectiveness. Models for predicting the evolution of influenza based on the relationship of genetic mutations and antigenic characteristics of circulating viruses may inform vaccine strain selection decisions. We review the literature on state-of-the-art tools and prediction methodologies utilized in modeling the evolution of influenza to inform vaccine strain selection. We then discuss areas that are open for improvement and need further research.

Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2

The rapid and consistent mutation of influenza requires frequent evaluation of antigenicity variation among newly emerged strains, during which several in-silico methods have been reported to facilitate the assays. In this paper, we designed a structure-based antigenicity scoring model instead of those sequence-based previously published. Protein structural context was adopted to derive the antigenicity-dominant positions, as well as the physic-chemical change of local micro-environment in correlation with antigenicity change. Then a position specific scoring matrix (PSSM) profile and local environmental change over above positions were integrated to predict the antigenicity variance. Independent testing showed a high accuracy of 0.875, and sensitivity of 0.986, with a significant ability to discover antigenic-escaping strains. When applying this model to the historical data, global and regional antigenic drift events can be successfully detected. Furthermore, two well-known vaccine failure events were clearly suggested. Therefore, this structure-context model may be particularly useful to identify those to-be-failed vaccine strains, in addition to suggest potential new vaccine strains.

Predicting the Mutating Distribution at Antigenic Sites of the Influenza Virus

Mutations of the influenza virus lead to antigenic changes that cause recurrent epidemics and vaccine resistance. Preventive measures would benefit greatly from the ability to predict the potential distribution of new antigenic sites in future strains. By leveraging the extensive historical records of HA sequences for 90 years, we designed a computational model to simulate the dynamic evolution of antigenic sites in A/H1N1. With templates of antigenic sequences, the model can effectively predict the potential distribution of future antigenic mutants. Validation on 10932 HA sequences from the last 16 years showing that the mutated antigenic sites of over 94% of reported strains fell in our predicted profile. Meanwhile, our model can successfully capture 96% of antigenic sites in those dominant epitopes. Similar results are observed on the complete set of H3N2 historical data, supporting the general applicability of our model to multiple sub-types of influenza. Our results suggest that the mutational profile of future antigenic sites can be predicted based on historical evolutionary traces despite the widespread, random mutations in influenza. Coupled with closely monitored sequence data from influenza surveillance networks, our method can help to forecast changes in viral antigenicity for seasonal flu and inform public health interventions.

Determination of antigenicity-altering patches on the major surface protein of human influenza A/H3N2 viruses

Human influenza viruses are rapidly evolving RNA viruses that cause short-term respiratory infections with substantial morbidity and mortality in annual epidemics. Uncovering the general principles of viral coevolution with human hosts is important for pathogen surveillance and vaccine design. Protein regions are an appropriate model for the interactions between two macromolecules, but the currently used epitope definition for the major antigen of influenza viruses, namely hemagglutinin, is very broad. Here, we combined genetic, evolutionary, antigenic, and structural information to determine the most relevant regions of the hemagglutinin of human influenza A/H3N2 viruses for interaction with human immunoglobulins. We estimated the antigenic weights of amino acid changes at individual sites from hemagglutination inhibition data using antigenic tree inference followed by spatial clustering of antigenicity-altering protein sites on the protein structure. This approach determined six relevant areas (patches) for antigenic variation that had a key role in the past antigenic evolution of the viruses. Previous transitions between successive predominating antigenic types of H3N2 viruses always included amino acid changes in either the first or second antigenic patch. Interestingly, there was only partial overlap between the antigenic patches and the patches under strong positive selection. Therefore, besides alterations of antigenicity, other interactions with the host may shape the evolution of human influenza A/H3N2 viruses.

Selecting vaccine strains for H3N2 human influenza A virus

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenicity was unknown. Here the antigenic model for estimating the degree of antigenic difference (antigenic distance) between amino acid sequences of HA1 was integrated into the process of selecting vaccine strains for H3N2 human influenza A virus. When the effectiveness of a potential vaccine strain for a target season was evaluated retrospectively using the average antigenic distance between the strain and the epidemic viruses sampled in the target season, the most effective vaccine strain was identified mostly in the season one year before the target season (pre-target season). Effectiveness of actual vaccines appeared to be lower than that of the strains randomly chosen in the pre-target season on average. It was recommended to replace the vaccine strain for every target season with the strain having the smallest average antigenic distance to the others in the pre-target season. The procedure of selecting vaccine strains for future epidemic seasons described in the present study was implemented in the influenza virus forecasting system (INFLUCAST) (http://www.nsc.nagoya-cu.ac.jp/~yossuzuk/influcast.html).

Computational Identification of Antigenicity-Associated Sites in the Hemagglutinin Protein of A/H1N1 Seasonal Influenza Virus

The antigenic variability of influenza viruses has always made influenza vaccine development challenging. The punctuated nature of antigenic drift of influenza virus suggests that a relatively small number of genetic changes or combinations of genetic changes may drive changes in antigenic phenotype. The present study aimed to identify antigenicity-associated sites in the hemagglutinin protein of A/H1N1 seasonal influenza virus using computational approaches. Random Forest Regression (RFR) and Support Vector Regression based on Recursive Feature Elimination (SVR-RFE) were applied to H1N1 seasonal influenza viruses and used to analyze the associations between amino acid changes in the HA1 polypeptide and antigenic variation based on hemagglutination-inhibition (HI) assay data. Twenty-three and twenty antigenicity-associated sites were identified by RFR and SVR-RFE, respectively, by considering the joint effects of amino acid residues on antigenic drift. Our proposed approaches were further validated with the H3N2 dataset. The prediction models developed in this study can quantitatively predict antigenic differences with high prediction accuracy based only on HA1 sequences. Application of the study results can increase understanding of H1N1 seasonal influenza virus antigenic evolution and accelerate the selection of vaccine strains.

Predictability of antigenic evolution for H3N2 human influenza A virus

Influenza A virus continues to pose a threat to public health. Since this virus can evolve escape mutants rapidly, it is desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately, they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. Here a theoretical method was introduced to predict the antigenic evolution of H3N2 human influenza A virus by evaluating de novo mutations through estimating the antigenic distance. The antigenic distance defined with the hemagglutination inhibition (HI) titer was estimated with antigenic models taking into account the volume, isoelectric point, relative solvent accessibility, and distances from receptor-binding sites (RBS) and N-linked glycosylation sites (NGS) for amino acids in hemagglutinin 1 (HA1). When the best model with the optimized parameter values was used to predict the antigenic evolution for the dominant strains, the prediction accuracy was relatively low. However, there appeared to be an overall tendency that the amino acid sites with larger potential net effect on antigenicity were more likely to evolve and the amino acid changes with larger potential effect were more likely to take place, suggesting that natural selection may operate to enhance the antigenic evolution of H3N2 human influenza A virus.

Computational prediction of vaccine strains for human influenza A (H3N2) viruses

Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.

Genetic and antigenic typing of seasonal influenza virus breakthrough cases from a 2008-2009 vaccine efficacy trial

Estimations of the effectiveness of vaccines against seasonal influenza virus are guided by comparisons of the antigenicities between influenza virus isolates from clinical breakthrough cases with strains included in a vaccine. This study examined whether the prediction of antigenicity using a sequence analysis of the hemagglutinin (HA) gene-encoded HA1 domain is a simpler alternative to using the conventional hemagglutination inhibition (HI) assay, which requires influenza virus culturing. Specimens were taken from breakthrough cases that occurred in a trivalent influenza virus vaccine efficacy trial involving >43,000 participants during the 2008-2009 season. A total of 498 influenza viruses were successfully subtyped as A(H3N2) (380 viruses), A(H1N1) (29 viruses), B(Yamagata) (23 viruses), and B(Victoria) (66 viruses) from 603 PCR- or culture-confirmed specimens. Unlike the B strains, most A(H3N2) (377 viruses) and all A(H1N1) viruses were classified as homologous to the respective vaccine strains based on their HA1 domain nucleic acid sequence. HI titers relative to the respective vaccine strains and PCR subtyping were determined for 48% (182/380) of A(H3N2) and 86% (25/29) of A(H1N1) viruses. Eighty-four percent of the A(H3N2) and A(H1N1) viruses classified as homologous by sequence were matched to the respective vaccine strains by HI testing. However, these homologous A(H3N2) and A(H1N1) viruses displayed a wide range of relative HI titers. Therefore, although PCR is a sensitive diagnostic method for confirming influenza virus cases, HA1 sequence analysis appeared to be of limited value in accurately predicting antigenicity; hence, it may be inappropriate to classify clinical specimens as homologous or heterologous to the vaccine strain for estimating vaccine efficacy in a prospective clinical trial.

Emergence of a highly pathogenic avian influenza virus from a low-pathogenic progenitor

Avian influenza (AI) viruses of the H7 subtype have the potential to evolve into highly pathogenic (HP) viruses that represent a major economic problem for the poultry industry and a threat to global health. However, the emergence of HPAI viruses from low-pathogenic (LPAI) progenitor viruses currently is poorly understood. To investigate the origin and evolution of one of the most important avian influenza epidemics described in Europe, we investigated the evolutionary and spatial dynamics of the entire genome of 109 H7N1 (46 LPAI and 63 HPAI) viruses collected during Italian H7N1 outbreaks between March 1999 and February 2001. Phylogenetic analysis revealed that the LPAI and HPAI epidemics shared a single ancestor, that the HPAI strains evolved from the LPAI viruses in the absence of reassortment, and that there was a parallel emergence of mutations among HPAI and later LPAI lineages. Notably, an ultradeep-sequencing analysis demonstrated that some of the amino acid changes characterizing the HPAI virus cluster were already present with low frequency within several individual viral populations from the beginning of the LPAI H7N1 epidemic. A Bayesian phylogeographic analysis revealed stronger spatial structure during the LPAI outbreak, reflecting the more rapid spread of the virus following the emergence of HPAI. The data generated in this study provide the most complete evolutionary and phylogeographic analysis of epidemiologically intertwined high- and low-pathogenicity viruses undertaken to date and highlight the importance of implementing prompt eradication measures against LPAI to prevent the appearance of viruses with fitness advantages and unpredictable pathogenic properties.

IMPORTANCE

The Italian H7 AI epidemic of 1999 to 2001 was one of the most important AI outbreaks described in Europe. H7 viruses have the ability to evolve into HP forms from LP precursors, although the mechanisms underlying this evolutionary transition are only poorly understood. We combined epidemiological information, whole-genome sequence data, and ultradeep sequencing approaches to provide the most complete characterization of the evolution of HPAI from LPAI viruses undertaken to date. Our analysis revealed that the LPAI viruses were the direct ancestors of the HPAI strains and identified low-frequency minority variants with HPAI mutations that were present in the LPAI samples. Spatial analysis provided key information for the design of effective control strategies for AI at both local and global scales. Overall, this work highlights the importance of implementing rapid eradication measures to prevent the emergence of novel influenza viruses with severe pathogenic properties.

Antigenic analyses of highly pathogenic avian influenza a viruses

In response to the ongoing threat to animal and human health posed by HPAI endemic in poultry, Asia (H5N1) and North America (H7N3) have revived efforts to reduce pandemic risk by disease control at the source and improved pandemic vaccines. Discovery of conserved neutralization epitopes in the HA, which mediate broad protection within and across HA subtypes have changed the paradigm of "broadly reactive" or "universal" vaccine design. Development of such vaccines would benefit from comparative antigenic analysis of viruses with increasing divergence within (and between) HA subtypes. A review of recent work to define the antigenic properties of HPAI viruses revealed data generated through an array of experimental approaches. This information has supported diagnostics and vaccine development for animal and human health. Further harmonization of analytical methods is needed to determine the antigenic relationships among multiple lineages of rapidly evolving HPAI viruses.

The antigenic evolution of influenza: drift or thrift?

It is commonly assumed that antibody responses against the influenza virus are polarized in the following manner: strong antibody responses are directed at highly variable antigenic epitopes, which consequently undergo 'antigenic drift', while weak antibody responses develop against conserved epitopes. As the highly variable epitopes are in a constant state of flux, current antibody-based vaccine strategies are focused on the conserved epitopes in the expectation that they will provide some level of clinical protection after appropriate boosting. Here, we use a theoretical model to suggest the existence of epitopes of low variability, which elicit a high degree of both clinical and transmission-blocking immunity. We show that several epidemiological features of influenza and its serological and molecular profiles are consistent with this model of 'antigenic thrift', and that identifying the protective epitopes of low variability predicted by this model could offer a more viable alternative to regularly update the influenza vaccine than exploiting responses to weakly immunogenic conserved regions.

Retrospective swine influenza serological surveillance in the four highest pig density provinces of Thailand before the introduction of the 2009 pandemic Influenza A virus subtype H1N1 using various antibody detection assays

Genetic characterization of the hemagglutinin gene of the 6 selected Thai Swine influenza virus (SIV) isolates (4 H1 and 2 H3 isolates) used in the establishment of a hemagglutination inhibition (HI) assay was analyzed. Based on the phylogenetic analysis, Thai SIVs could be divided into 3 clusters of the H1 viruses (clusters I and II belonging to classical swine H1α, and cluster III belonging to classical swine H1γ), and 2 clusters of the H3 viruses both belonging to human-like 1970s. The serological results indicated that swH1N1-06 (H1 cluster I) is a suitable representative SIV for the HI test antigen to detect H1 SIV-specific antibodies in the Thai swine population, while both swH3N2-05 and swH3N2-07 should be used for Thai H3 SIV-specific antibody detection. The HI test results of swine sera collected from pigs in the 4 highest pig population provinces of Thailand indicated that the percentage of pigs seropositive to swH3N2-07 was highest compared to swH1N1-06, swH1N1-09, and swH3N2-05 (85.4%, 50.1%, 18.6%, and 15.8%, respectively). It should be noted that countries lacking SIV genetic information should be concerned with determining the most suitable HI test antigens to use when performing the tests due to the genetic variation and limited cross-reaction of SIVs. The results of the current study demonstrated that HI tests should be implemented with the suitable field strains as the representative test antigen to ascertain accurate SIV serostatus in Thailand and that test antigens should be genetically analyzed and compared with circulating strains regularly.

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

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.

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.

Mapping of H3N2 influenza antigenic evolution in China reveals a strategy for vaccine strain recommendation

One of the primary efforts in influenza vaccine strain recommendation is to monitor through gene sequencing the viral surface protein haemagglutinin (HA) variants that lead to viral antigenic changes. Here we have developed a computational method, denoted as PREDAC, to predict antigenic clusters of influenza A (H3N2) viruses with high accuracy from viral HA sequences. Application of PREDAC to large-scale HA sequence data of H3N2 viruses isolated from diverse regions of Mainland China identified 17 antigenic clusters that have dominated for at least one season between 1968 and 2010. By tracking the dynamics of the dominant antigenic clusters, we not only find that dominant antigenic clusters change more frequently in China than in the United States/Europe, but also characterize the antigenic patterns of seasonal H3N2 viruses within China. Furthermore, we demonstrate that the coupling of large-scale HA sequencing with PREDAC can significantly improve vaccine strain recommendation for China.

Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence – by controlling for phylogenetic structure – for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease.

New strains of viruses arise continually. Consequently, predicting when past exposure to closely related strains will protect against infection by novel strains is central to understanding the dynamics of a broad range of the world's most important infectious diseases. While previous research has developed valuable tools for describing the observed antigenic landscapes, our ability to predict cross-protection between different viral strains depends almost entirely on cumbersome and expensive live animal work, often restricted to model species rather than the natural host. The development of computer-based approaches to the estimation of cross-protection from viral sequence data would be hugely valuable, and our study represents a significant step towards this research goal.

Allele dynamics plots for the study of evolutionary dynamics in viral populations

Phylodynamic techniques combine epidemiological and genetic information to analyze the evolutionary and spatiotemporal dynamics of rapidly evolving pathogens, such as influenza A or human immunodeficiency viruses. We introduce ‘allele dynamics plots’ (AD plots) as a method for visualizing the evolutionary dynamics of a gene in a population. Using AD plots, we propose how to identify the alleles that are likely to be subject to directional selection. We analyze the method’s merits with a detailed study of the evolutionary dynamics of seasonal influenza A viruses. AD plots for the major surface protein of seasonal influenza A (H3N2) and the 2009 swine-origin influenza A (H1N1) viruses show the succession of substitutions that became fixed in the evolution of the two viral populations. They also allow the early identification of those viral strains that later rise to predominance, which is important for the problem of vaccine strain selection. In summary, we describe a technique that reveals the evolutionary dynamics of a rapidly evolving population and allows us to identify alleles and associated genetic changes that might be under directional selection. The method can be applied for the study of influenza A viruses and other rapidly evolving species or viruses.

Statistical assessment of biosimilar products

Biological products or medicines are therapeutic agents that are produced using a living system or organism. Access to these life-saving biological products is limited because of their expensive costs. Patents on the early biological products will soon expire in the next few years. This allows other biopharmaceutical/biotech companies to manufacture the generic versions of the biological products, which are referred to as follow-on biological products by the U.S. Food and Drug Administration (FDA) or as biosimilar medicinal products by the European Medicine Agency (EMEA) of the European Union (EU). Competition of cost-effective follow-on biological products with equivalent efficacy and safety can cut down the costs and hence increase patients' access to the much-needed biological pharmaceuticals. Unlike for the conventional pharmaceuticals of small molecules, the complexity and heterogeneity of the molecular structure, complicated manufacturing process, different analytical methods, and possibility of severe immunogenicity reactions make evaluation of equivalence (similarity) between the biosimilar products and their corresponding innovator product a great challenge for both the scientific community and regulatory agencies. In this paper, we provide an overview of the current regulatory requirements for approval of biosimilar products. A review of current criteria for evaluation of bioequivalence for the traditional chemical generic products is provided. A detailed description of the differences between the biosimilar and chemical generic products is given with respect to size and structure, immunogenicity, product quality attributed, and manufacturing processes. In addition, statistical considerations including design criteria, fundamental biosimilar assumptions, and statistical methods are proposed. The possibility of using genomic data in evaluation of biosimilar products is also explored.

Fine antigenic variation within H5N1 influenza virus hemagglutinin's antigenic sites defined by yeast cell surface display

Fifteen strains of mAb specific for HA of the A/Hong Kong/482/97 (H5N1) influenza virus were generated. The HA antigenic sites of the human A/Hong Kong/482/97 (H5N1) influenza virus were defined by using yeast cell surface-displaying system and anti-H5 HA mAb. Evolution analysis of H5 HA identified residues that exhibit diversifying selection in the antigenic sites and demonstrated surprising differences between residue variation of H5 HA and H3 HA. A conserved neutralizing epitope in the H5 HA protein recognized by mAb H5M9 was found using viruses isolated from 1997-2006. Seven single amino acid substitutions were introduced into the HA antigenic sites, respectively, and the alteration of antigenicity was assessed. The structure obtained by homology-modeling and molecular dynamic methods showed that a subtle substitution at residue 124 propagates throughout its nearby loop (152-159). We discuss how the structural changes caused by point mutation might explain the altered antigenicity of the HA protein. The results demonstrate the existence of immunodominant positions in the H5 HA protein, alteration of these residues might improve the immunogenicity of vaccine strains.

ATIVS: analytical tool for influenza virus surveillance

The WHO Global Influenza Surveillance Network has routinely performed genetic and antigenic analyses of human influenza viruses to monitor influenza activity. Although these analyses provide supporting data for the selection of vaccine strains, it seems desirable to have user-friendly tools to visualize the antigenic evolution of influenza viruses for the purpose of surveillance. To meet this need, we have developed a web server, ATIVS (Analytical Tool for Influenza Virus Surveillance), for analyzing serological data of all influenza viruses and hemagglutinin sequence data of human influenza A/H3N2 viruses so as to generate antigenic maps for influenza surveillance and vaccine strain selection. Functionalities are described and examples are provided to illustrate its usefulness and performance. The ATIVS web server is available at http://influenza.nhri.org.tw/ATIVS/.

Correlating novel variable and conserved motifs in the Hemagglutinin protein with significant biological functions

Background

Variations in the influenza Hemagglutinin protein contributes to antigenic drift resulting in decreased efficiency of seasonal influenza vaccines and escape from host immune response. We performed an in silico study to determine characteristics of novel variable and conserved motifs in the Hemagglutinin protein from previously reported H3N2 strains isolated from Hong Kong from 1968–1999 to predict viral motifs involved in significant biological functions.

Results

14 MEME blocks were generated and comparative analysis of the MEME blocks identified blocks 1, 2, 3 and 7 to correlate with several biological functions. Analysis of the different Hemagglutinin sequences elucidated that the single block 7 has the highest frequency of amino acid substitution and the highest number of co-mutating pairs. MEME 2 showed intermediate variability and MEME 1 was the most conserved. Interestingly, MEME blocks 2 and 7 had the highest incidence of potential post-translational modifications sites including phosphorylation sites, ASN glycosylation motifs and N-myristylation sites. Similarly, these 2 blocks overlap with previously identified antigenic sites and receptor binding sites.

Conclusion

Our study identifies motifs in the Hemagglutinin protein with different amino acid substitution frequencies over a 31 years period, and derives relevant functional characteristics by correlation of these motifs with potential post-translational modifications sites, antigenic and receptor binding sites.

Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus

MOTIVATION

Continual and accumulated mutations in hemagglutinin (HA) protein of influenza A virus generate novel antigenic strains that cause annual epidemics.

RESULTS

We propose a model by incorporating scoring and regression methods to predict antigenic variants. Based on collected sequences of influenza A/H3N2 viruses isolated between 1971 and 2002, our model can be used to accurately predict the antigenic variants in 1999-2004 (agreement rate = 91.67%). Twenty amino acid positions identified in our model contribute significantly to antigenic difference and are potential immunodominant positions.