A Computational study of Ebolavirus Pathogenicity and a Modeling approach for human non-synonymous variants

Recent advances in genome sequencing are improving our better understanding of genetic variation. However, the investigation of the genotype-phenotype relationship is still challenging, especially for the interpretation of the myriad of discovered genetic variants that weakly relate to disease. Recently, researchers have confirmed that disease causing genetic variants typically occur at functional sites, such as protein-protein or protein-ligand interaction sites. Giving this observation, several bioinformatics tools have been developed. This thesis first details VarMod (Variant Modeller), an algorithm that predicts whether nonsynonymous single nucleotide variants (nsSNVs) affect protein function. The recent Ebola virus outbreak in West Africa demonstrated the potential for the virus to cause edipdemics and highlighted our limited understanding of Ebola virus biology. The second part of this thesis focuses on the investigation of the molecular determinants of Ebolavirus pathogenicity. In two related analyses knowledge of differing pathogenicity of Ebolavirus species is used. Firstly, comparison of the sequences of Reston viruses (the only Ebolavirus species that is not pathogenic in humans) with the four pathogenic Ebolavirus species, enabled the identification of Specificity Determining Positions (SDPs) that are differentially conserved between these two groups. These SDPs were further investigated using analysis of protein structure and identified variation in the Ebola virus VP24 as likely to have a role in determining species-specific pathogenicity. The second approach investigated rodent-adapted Ebola virus. Ebola virus is not pathogenic in rodents but it can be passaged to induce pathogenicity. Analysis of the mutations identified in four adaption studies identified that very few mutations are required for adaptation to a new species and once again the VP24 is likely to have a central role. Subsequent molecular dynamics simulations compared the interaction of Ebola and Reston virus VP24 with human karyopherin alpha5. The analysis suggests that Reston virus VP24 has weaker binding with karyopherins and we propose that this change in binding may reduce the ability of Reston VP24 to inhibit human interferon signaling

GLUE: a flexible software system for virus sequence data

Background: Virus genome sequences, generated in ever-higher volumes, can provide new scientific insights and inform our responses to epidemics and outbreaks. To facilitate interpretation, such data must be organised and processed within scalable computing resources that encapsulate virology expertise. GLUE (Genes Linked by Underlying Evolution) is a data-centric bioinformatics environment for building such resources. The GLUE core data schema organises sequence data along evolutionary lines, capturing not only nucleotide data but associated items such as alignments, genotype definitions, genome annotations and motifs. Its flexible design emphasises applicability to different viruses and to diverse needs within research, clinical or public health contexts. Results: HCV-GLUE is a case study GLUE resource for hepatitis C virus (HCV). It includes an interactive public web application providing sequence analysis in the form of a maximum-likelihood-based genotyping method, antiviral resistance detection and graphical sequence visualisation. HCV sequence data from GenBank is categorised and stored in a large-scale sequence alignment which is accessible via web-based queries. Whereas this web resource provides a range of basic functionality, the underlying GLUE project can also be downloaded and extended by bioinformaticians addressing more advanced questions. Conclusion: GLUE can be used to rapidly develop virus sequence data resources with public health, research and clinical applications. This streamlined approach, with its focus on reuse, will help realise the full value of virus sequence data

Applications of Evolutionary Bioinformatics in Basic and Biomedical Research

With the revolutionary progress in sequencing technologies, computational biology emerged as a game-changing field which is applied in understanding molecular events of life for not only complementary but also exploratory purposes. Bioinformatics resources and tools significantly help in data generation, organization and analysis. However, there is still a need for developing new approaches built based on a biologist’s point of view. In protein bioinformatics, there are several fundamental problems such as (i) determining protein function; (ii) identifying protein-protein interactions; (iii) predicting the effect of amino acid variants. Here, I present three chapters addressing these problems from an evolutionary perspective. Firstly, I describe a novel search pipeline for protein domain identification. The algorithm chain provides sensitive domain assignments with the highest possible specificity. Secondly, I present a tool enabling large-scale visualization of presences and absences of proteins in hierarchically clustered genomes. This tool visualizes multi-layer information of any kind of genome-linked data with a special focus on domain architectures, enabling identification of coevolving domains/proteins, which can eventually help in identifying functionally interacting proteins. And finally, I propose an approach for distinguishing between benign and damaging missense mutations in a human disease by establishing the precise evolutionary history of the associated gene. This part introduces new criteria on how to determine functional orthologs via phylogenetic analysis. All three parts use comparative genomics and/or sequence analyses. Taken together, this study addresses important problems in protein bioinformatics and as a whole it can be utilized to describe proteins by their domains, coevolving partners and functionally important residues

Pneumovirus Infections: Understanding RSV and HMPV Entry, Replication, and Spread

Pneumoviruses including human metapneumovirus (HMPV) and respiratory syncytial virus (RSV) are significant causes of respiratory tract infections globally. Children, elderly, and immunocompromised patients are at the greatest risk for developing severe infections, which can have devastating outcomes. Although these viruses are ubiquitous with significant impacts on human health, there are no antivirals or vaccines available. The only FDA approved therapy is a monoclonal antibody for RSV, given prophylactically during the infectious season, and this treatment is only available for high risk infants. The work presented in this thesis aims to increase our understanding of how these viruses enter, replicate, and spread to better characterize the basic molecular mechanisms used, opening avenues for potential antiviral therapies. We first analyzed the fusion protein of HMPV and how low pH is important for entry of some viral strains. We analyzed previously uncharacterized strains and found that residues initially hypothesized to be critical for low pH fusion are not always required, suggesting a more complex regulation of fusion. We then explored the role of the proteolytic cleavage event which is required for HMPV F as well as many other important respiratory pathogens, including influenza. We found that many proteases involved in activating influenza HA are also important for activating HMPV F, which has not previously been reported. We then used our understanding of cleavage to employ a treatment strategy targeting host proteases involved in this activation to prevent entry and spread. We next conducted a side-by-side comparison of infection, spread, and inhibition using a physiologically relevant 3-D human airway epithelial model system. We found that RSV and HMPV demonstrate significantly different infection and spread kinetics as well as phenotypes during infection, highlighting an interesting dichotomy between two closely related viruses. We further analyzed therapeutic potential for several monoclonal antibodies, finding that prophylactic interventions prevent entry and spread, but treatment after entry suggests that both HMPV and RSV can be inhibited during entry. However, RSV likely spreads through cellular release and re-entry whereas HMPV utilizes a mechanism that is antibody independent after establishing the initial infection. Lastly, we examined the concept of viral co-infections, as co-infections with RSV and HMPV have been reported to cause more severe disease in patients. We provide evidence that RSV and HMPV co-infected cells can occupy the same inclusion bodies, but further investigation suggests that HMPV and RSV replication synergy may be limited. Collectively, the data presented in this dissertation provide new understanding of pneumovirus infections and reveals important information about the molecular mechanisms of pneumovirus entry and spread