A Novel Sequence-Based Antigenic Distance Measure for H1N1, with Application to Vaccine Effectiveness and the Selection of Vaccine Strains

H1N1 influenza causes substantial seasonal illness and was the subtype of the 2009 influenza pandemic. Precise measures of antigenic distance between the vaccine and circulating virus strains help researchers design influenza vaccines with high vaccine effectiveness. We here introduce a sequence-based method to predict vaccine effectiveness in humans. Historical epidemiological data show that this sequence-based method is as predictive of vaccine effectiveness as hemagglutination inhibition (HI) assay data from ferret animal model studies. Interestingly, the expected vaccine effectiveness is greater against H1N1 than H3N2, suggesting a stronger immune response against H1N1 than H3N2. The evolution rate of hemagglutinin in H1N1 is also shown to be greater than that in H3N2, presumably due to greater immune selection pressure.Comment: 26 pages, 7 figures, 2 tables, supplemen

Antigenic and genetic evolution of contemporary swine H1 influenza viruses in the United States

Several lineages of influenza A viruses (IAV) currently circulate in North American pigs. Genetic diversity is further increased by transmission of IAV between swine and humans and subsequent evolution. Here, we characterized the genetic and antigenic evolution of contemporary swine H1N1 and H1N2 viruses representing clusters H1-α (1A.1), H1-β (1A.2), H1pdm (1A.3.3.2), H1-γ (1A.3.3.3), H1-δ1 (1B.2.2), and H1-δ2 (1B.2.1) currently circulating in pigs in the United States. The δ1-viruses diversified into two new genetic clades, H1-δ1a (1B.2.2.1) and H1-δ1b (1B.2.2.2), which were also antigenically distinct from the earlier H1-δ1-viruses. Further characterization revealed that a few key amino acid changes were associated with antigenic divergence in these groups. The continued genetic and antigenic evolution of contemporary H1 viruses might lead to loss of vaccine cross-protection that could lead to significant economic impact to the swine industry, and represents a challenge to public health initiatives that attempt to minimize swine-to-human IAV transmission

A Computational Framework for Influenza Antigenic Cartography

Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap

Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies

Quantifying the genetic basis of antigenic variation among human influenza A viruses

Influenza viruses are a major cause of morbidity and mortality worldwide, with seasonal epidemics of influenza resulting in around three to five million cases of severe illness globally each year. The evolution of influenza A viruses is characterised by rapid antigenic drift, which allows mutant viruses to evade host immunity acquired to previously circulating viruses. Antigenic variation is observed across a wide range of infectious organisms and can circumvent long-lasting immunity in hosts leading to repeated infection or non-clearance. Influenza A viruses can often be effectively combatted by the immune system and vaccines also exist to protect at-risk individuals, limiting the burden of disease. However, the effectiveness of the vaccine depends on constituents being antigenically similar to circulating viruses. Antigenic drift of influenza viruses therefore requires a global surveillance system responsible for the antigenic characterisation of circulating viruses. The identification of emerging antigenic variants is critical to the vaccine virus selection process and in addition experts must anticipate which viruses are likely to predominate in forthcoming epidemic seasons. Mutations to B-cell epitopes on the surface of haemagglutinin (HA) that facilitate escape from neutralising antibodies play a key role in influenza antigenic drift. Consequently the haemagglutination inhibition (HI) assay, which measures HA cross-reactivity, is commonly used to approximate antigenic phenotype. In this thesis, I investigate the genetic basis of antigenic variation among human influenza A viruses through analysis of HI data collected in recent decades and associated HA gene sequence data. In Chapter 2, I use phylogenetic methods and antigenic cartography to characterise the genetic and antigenic variation among the viruses studied and evaluate the usefulness of these methods for epitope identification. In Chapter 3, I extend a model developed to investigate antigenic differences among foot-and-mouth disease (FMD) viruses to former seasonal A(H1N1) viruses. By attributing variation in HI titre to amino acid differences between viruses, while accounting for phylogenetic relationships, I identify substitutions that have driven the antigenic evolution of the virus. Reverse genetics was then used to validate model predictions experimentally. In Chapter 4, I further extend the model and investigate the genetic drivers of antigenic drift among A(H3N2) viruses, comparing model results with published HI data generated using mutant recombinant viruses. In Chapter 5, I explore the power of the identified genetic determinants for predicting antigenic relationships among A(H1N1) and A(H3N2) viruses. Specifically I show that sequence-based models can be used to estimate the antigenicity of emerging viruses directly from their sequence and that by including substitutions of smaller antigenic impact, in addition to the high-impact substitutions that are often focused on, predictions were improved. I also demonstrate the versatility of these methods by extending this sequence-based approach to predict antigenic relationships among viruses of three serotypes of FMD virus. Determining phenotype from genotype is a fundamental challenge for virus research. It is of particular interest in the case of the antigenic evolution of influenza viruses, given the need to continually track changes in the virus population, anticipate which viruses will predominate in future seasons, and select vaccine viruses. Collectively, the results I present demonstrate an enhanced quantitative understanding of the molecular genetic basis of the adaptive phenotype of influenza viruses. The ability to quantify the phenotypic impact of specific amino acid substitutions should help to refine methods that predict, from genotype, the fitness and evolutionary success of influenza viruses from one season to the next, strengthening the theoretical foundations for vaccine virus selection. The techniques presented also have great potential to be extended to other antigenically variable pathogens and to elucidate the genetic basis of their antigenic variation

The Impact of Receptor Binding Avidity and Immune History on the Antigenic Determination of Influenza A Viruses

Most humans are repeatedly infected with new strains of influenza throughout their lifetime even though protective neutralizing antibodies against the viral hemagglutinin (HA) are generated after both natural infection and vaccination. This observed lack of protection against variant strains is largely attributed to a process termed `antigenic drift\u27, where accumulating mutations in HA quickly abrogate recognition by antibodies elicited by earlier strains. Consequently, current influenza vaccines must be updated frequently in an attempt to match the antigenic profiles of vaccine strains to those of circulating strains. However, the existing process of antigenic determination is imperfect: it fails to consider the effects of receptor binding avidity in the interpretation of hemagglutination inhibition (HAI) assays or the effects of pre-exposure history on how a novel virus is viewed antigenically by an altered immune system. Here, we designed a series of experiments to address these issues. First, we computationally modeled how variation in receptor binding avidity could affect the antigenic characterization of historic H3N2 strains and experimentally demonstrated that single point mutations in HA can skew HAI titers without actually affecting antibody binding. Additionally, using the same H3N2 system, we showed that a single amino acid mutation can significantly alter the immunodominance of the anti-HA antibody response. We then completed a series of studies to determine how immune history influences the specificity of antibody repertoires. In examining patient serology, we found that the specificity of the human antibody response against the 2009 pandemic H1N1 virus (pH1N1) was highly correlated with pre-exposure history to different seasonal H1N1 (sH1N1) strains during childhood. Using a ferret model, we demonstrate that the anti-pH1N1 antibody response can be shifted to highly conserved epitopes on HA when the animals were primed with sH1N1s that are otherwise antigenically distinct. Collectively, our studies demonstrate that accounting for receptor binding avidity and factors that alter antibody repertoires will improve influenza vaccination strategies in the future

Computational and Theoretical Analysis of Influenza Virus Evolution and Immune System Dynamics

Influenza causes annual global epidemics and severe morbidity and mortality. The influenza virus evolves to escape from immune system antibodies that bind to it. The immune system produces influenza virus specific antibodies by VDJ recombination and somatic hypermutation. In this dissertation, we analyze the mechanism of influenza virus evolution and immune system dynamics using theoretical modeling and computational simulation. The first half of this thesis discusses influenza virus evolution. The epidemiological data inspires a novel sequence-based antigenic distance measure for subtypes H1N1 and H3N2 virus, which are superior to the conventional measure using hemagglutination inhibition assay. Historical influenza sequences show that the selective pressure increases charge in immunodominant epitopes of the H3 hemagglutinin influenza protein. Statistical mechanics and high-performance computing technology predict fixation tendencies of the H3N2 influenza virus by free energy calculation. We introduce the notion of entropy from physics and informatics to identify the epitope regions of H1-subtype influenza A with application to vaccine efficacy. We also use entropy to quantify selection and diversity in viruses with application to the hemagglutinin of H3N2 influenza. Using the bacterial E. coli as a model, we show the evidence for recombination contributing to the evolution of extended spectrum β-lactamases (ES-BLs) in clinical isolates. A guinea pig experiment supports the discussion on influenza virus evolution. The second half of the thesis discusses immune system dynamics. We design a two-scale model to describe correlation in B cell VDJ usage of zebrafish. We also introduce a dynamical system to model original antigenic sin in influenza. This dissertation aims to help researchers understand the interaction between influenza virus and the immune system with a quantitative approach

Characterizing Emerging Canine H3 Influenza Viruses.

The continual emergence of novel influenza A strains from non-human hosts requires constant vigilance and the need for ongoing research to identify strains that may pose a human public health risk. Since 1999, canine H3 influenza A viruses (CIVs) have caused many thousands or millions of respiratory infections in dogs in the United States. While no human infections with CIVs have been reported to date, these viruses could pose a zoonotic risk. In these studies, the National Institutes of Allergy and Infectious Diseases (NIAID) Centers of Excellence for Influenza Research and Surveillance (CEIRS) network collaboratively demonstrated that CIVs replicated in some primary human cells and transmitted effectively in mammalian models. While people born after 1970 had little or no pre-existing humoral immunity against CIVs, the viruses were sensitive to existing antivirals and we identified a panel of H3 cross-reactive human monoclonal antibodies (hmAbs) that could have prophylactic and/or therapeutic value. Our data predict these CIVs posed a low risk to humans. Importantly, we showed that the CEIRS network could work together to provide basic research information important for characterizing emerging influenza viruses, although there were valuable lessons learned