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.

Investigating the role of target cell availability in the pathogenesis of feline immunodeficiency virus infection

Feline immunodeficiency virus (FIV) is a naturally occurring lentivirus of domestic cats, which shares many similarities with its human counterpart, human immunodeficiency virus (HIV). FIV infects its main target cell, the CD4+ T lymphocyte, via interactions with its primary receptor CD134 (an activation marker expressed on activated CD4+ T lymphocytes), and, the chemokine receptor CXCR4. According to the different ways in which FIV isolates interact with CD134, FIV may be categorised into two groups. The first group contains strains that tend to dominate during the earlier phase of infection, such as GL8 and CPG41. These strains are characterized by their requirement for an additional interaction with the second cysteine rich domain (CRD2) of the CD134 molecule and are classified as “CRD2-dependent” strains. The second group, on the other hand, contains either laboratory-adapted isolates or isolates that emerge after several years of infection, such as PPR or the GL8 variants that emerged in cats 6 years post experimental infection and were studied in this thesis. These isolates are designated “CRD2-independent” as they can infect target cells without interacting with CRD2 of the CD134 molecule. This study provides the first evidence that FIV compartmentalisation is related to FIV-CD134 usage and the tissue availability of CD134+ target cells. In tissue compartments containing high levels of CD134+ cells such as peripheral blood and lymph nodes, CRD2-dependent viruses predominated, whereas CRD2-independent viruses predominated in compartments with fewer CD134+ cells, such as the thymus. The dynamics of CD4+CD134+ T lymphocytes at different stages of FIV infection were also described. The levels of CD4+CD134+ T lymphocytes, which were very high in the early phase, gradually decreased in the later phase of infection. The dynamics of CD4+CD134+ T lymphocyte numbers appeared to correlate with FIV tropism switching, as more CRD2-independent viruses were isolated from cats in the late phase of infection. Moreover, it was observed that pseudotypes bearing Envs of CRD2-dependent variants infected CD134+ target cells more efficiently than pseudotypes bearing Envs of CRD2-independent variants, confirming the selective advantage of CRD2-dependent variants in environments with high levels of CD134+ target cells. In conclusion, this study demonstrated that target cell types and numbers, as well as their dynamics, play important roles in the selection and expansion of FIV variants within the viral quasispecies. Improved understanding of the roles of target cells in FIV transmission and pathogenesis will provide important information required for the development of an improved, more successful protective FIV vaccine and will provide insight into the development of effective vaccines against other lentiviral infections such as HIV.