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

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation

Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model

Understanding how genetic changes allow emerging virus strains to escape the protection afforded by vaccination is vital for the maintenance of effective vaccines. We use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the influenza A(H1N1) virus through the prediction of haemagglutination inhibition (HI) titre: pairwise measures of the antigenic similarity of virus strains. We propose a sparse hierarchical Bayesian model that can deal with the pairwise structure and inherent experimental variability in the H1N1 data through the introduction of latent variables. The latent variables represent the underlying HI titre measurement of any given pair of virus strains and help to account for the fact that, for any HI titre measurement between the same pair of virus strains, the difference in the viral sequence remains the same. Through accurately representing the structure of the H1N1 data, the model can select virus sites which are antigenic, while its latent structure achieves the computational efficiency that is required to deal with large virus sequence data, as typically available for the influenza virus. In addition to the latent variable model, we also propose a new method, the block‐integrated widely applicable information criterion biWAIC, for selecting between competing models. We show how this enables us to select the random effects effectively when used with the model proposed and we apply both methods to an A(H1N1) data set

Predicting the Antigenic Evolution of Influenza Viruses with Application to Vaccination Strategy

Seasonal influenza viruses cause substantial worldwide mortality and morbidity every year. The evolution of these viruses comprise unique systems for studying natural evolutionary processes in real-time. Host immune systems recognise pathogens based on binding affinities between host antibodies and pathogen antigens. Pathogens with similar antibody binding are said to be antigenically similar. Seasonal influenza viruses evolve antigenically over a timescale observable by humans. Vertebrate immune responses also adapt rapidly, such that a single infection usually leaves a host protected for life against antigenically similar strains. In humans this imposes natural selection for antigenic novelty in wild virus populations. Influenza vaccines contain virus antigen which elicit the production of antibodies that protect against antigenically similar strains. After major antigenic evolution in wild viruses, vaccines must be updated to remain effective. I use a simple model to show that even in best case scenarios current influenza vaccination strategies cannot completely avoid antigenic mismatch. I then present the first study that quantifies the link between degree of antigenic mismatch and vaccine effectiveness. Knowledge of the molecular variation responsible for major antigenic change in natural influenza viruses has improved greatly in the last decade. I review this work, and present the application of a computational approach from the field of quantitative genetics to this problem. Subsequently, I show that patterns in biophysical features of substitutions responsible for major antigenic change are far from random, begging the question: how predictable are these amino acid substitutions? I answer this question by developing a computational framework to rank candidate substitutions by their biophysical similarity to substitutions responsible for major antigenic change and show that predictions can be made that are substantially better than chance selections. Finally, I discuss the application of these rankings to advanced influenza vaccination strategies based on the principle of immunity management. I expand on how this work should be the basis for further investigation into the mechanisms that govern different components of influenza virus fitness and ultimately the antigenic evolution of seasonal influenza viruses

Modelling the Epidemiological Dynamics of Seasonal Influenza Viruses at Local Scales

Seasonal influenza viruses are a substantial source of disease burden globally, causing epidemics across all climatic regions. Through error-prone RNA replication, influenza viruses can escape pre-existing humoral immunity and reinfect humans, resulting in recurrent epidemics within populations. From year to year, individual epidemics differ substantially in timing, duration and size. Despite intensive study, characterising the spatiotemporal patterns of virus circulation and identifying the underlying sources of this variability at global, regional and local scales remain as ongoing challenges. There is a need to reconcile environmental, virological and host drivers of virus epidemiological dynamics across diverse contexts. Such insights can only be generated through a holistic approach that integrates observational, ecological, experimental and modelling studies: this would enable more accurate and timely epidemiological forecasts and more efficient allocation of public health resources. In this thesis, I investigate the phylodynamical interactions between the seasonal influenza virus, environment and human host population, integrating analyses from observational study and theoretical modelling approaches. The current knowledge gap on the drivers of local city-level epidemics is identified in Chapter 2 and subsequently addressed over 4 research chapters. In Chapter 3, I review existing epidemic detection algorithms and present a novel statistical model that I developed for use with noisy disease surveillance data and is optimised for the context of seasonal influenza. In Chapter 4, I apply this novel algorithm and analyse a 15-year dataset of 18,250 typed, subtyped, and antigenically characterised seasonal influenza viruses from the five most populous cities in Australia. With the necessary geographical and virus resolution, I quantify the effects of previously hypothesised environmental and virological factors. Most surprisingly, despite an apparent lack of marked change in virus antigenicity, individual antigenic variants are capable of reinvading the same population over consecutive seasons, which runs contrary to predictions made by existing mathematical models. In Chapters 5 and 6, I investigate how antigenic variants are capable of causing recurrent epidemics at local scales by building upon previous theoretical modelling studies and developing a modelling framework to investigate the interactions between and joint effects exerted by the topology of cross-immunity and host contact structure within a population. In Chapter 5, I investigate the effects of correlations between network structure and individual susceptibility. In Chapter 6, I examine the population-level significance of age-specific changes to an individual's immune response. In Chapter 7, I review my findings and discuss how these new insights into virus ecology can open new avenues for better influenza control and future research

On the Dynamics and Structure of Multiple Strain Epidemic Models and Genotype Networks

Mathematical disease modeling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen. This paradigm has been useful in simplifying the biological reality of epidemics and has allowed the modeling community to focus on the complexity of other factors such as contact structure and interventions. However, there is an increasing amount of evidence that the strain diversity of pathogens, and their interplay with the host immune system, can play a large role in shaping the dynamics of epidemics. This body of work first explores the role of strain-transcending immunity in mathematical disease models, and how genotype networks may be used to explore the evolution of multistrain pathogens. A model is introduced to follow multistrain epidemics with an underlying genotype network. Consequently, the genotype network structure of the antigenic hemagglutinin protein of influenza A (H3N2) is analyzed, suggesting the important role of strain-transcending immunity in the evolution of the virus. The unique structure of the influenza genotype network is then explored with age-weighted preferential attachment models, utilizing approximate Bayesian computation of the network growth mechanisms. Finally, multistrain vaccination strategies are identified through the application of a genetic algorithm towards minimization of super-critical strains. Altogether, we show the impact of genotype networks on multistrain disease modeling, explore the role of empirical genotype network structure, and identify applications that include network generative models and vaccine strain selection

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

Correlation of influenza infection with glycan array

Poster Presentation: SPB1 / SPB2 - Virus Host Interaction/Pathogensis/Transmission: abstract no. B109PINTRODUCTION: The past 6 years has seen the introduction of glycan arrays containing large numbers of sialic acid (Sia) containing compounds and these arrays have been used to demonstrate the relative binding affinity of influenza viruses to different glycans. Though infor...postprin

The antigenic evolution of human influenza A haemagglutinin

A detailed understanding of the B-cell response to influenza A haemagglutinin is key to the accurate matching of vaccines to seasonal strains, and may inform the development of broader spectrum vaccines. In this study, I develop techniques for predicting the location of the epitopes of protective antibodies by observing the physical locations of amino acid substitutions in human wild-type strains. By linking the understanding gained from this analysis with a large body of assay data, I present a model which can predict antigenic distance from HA1 amino acid sequences and which meets or exceeds the predictive power of previously developed models while retaining generality. An interesting conclusion from the epitope analysis discussed above is that antibodies to the HA head bind in two regions. The antigenic evolution of influenza H3N2 is more punctuated than its genetic evolution. I propose that the dual regions might contribute to the punctuated nature of antigenic evolution, and explore this through the use of a simple simulation. Stalk-binding antibodies to HA have attracted much interest in recent years: a number of broad-binding examples have been isolated, and the slower evolution of the stalk gives hope that these may provide broad protection against future strains. Stalk-binding neutralising antibodies to H3 are known to bind in two regions, and I use data from crystal studies to identify the constituent residues of these regions, which I term antigenic sites F and G, in a manner that is consistent with previous analyses of the constituent residues of HA1 antigenic sites A-E. I analyse the degree of conservation of residues in sites F and G, and conclude that there have been episodes of change in the H3 stalk which are consistent with antigenic evolution