Pathogenic Viruses and their Interaction with Human Host Cells

Abstract

Virology, like many other biological research topics, has benefited from the application of large-scale data generation and analysis. Particular effort has been applied to developing a greater understanding of prevalent human-pathogenic viruses including type-1 Human immuno-deficiecy virus (HIV-1) and Hepatitis C virus (HCV). For example, host-virus interaction networks have been researched and important factors required for virus replication or innate cellular defence have been elucidated. Thus, large-scale data sources have provided a wealth of information regarding virus replication and virus-host interaction that may directly influence research and development of new antiviral treatments. In this thesis, we present research into the interaction between pathogenic viruses -- HCV, HIV-1 and SIV (the simian equivalent of HIV) -- and their host cells. Our research is largely integrative, computational research of large-scale data sources. In particular, we employ network models and related modes of analysis, with emphasis on identifying novel drug targets among cellular factors. Initially, we provide relevant background on virology, large-scale data sources and associated computational methods. Following this, we present four research projects that investigate either HIV-1, HCV or SIV interaction with host cells. Finally, we present a detailed analysis of the relationships between large-scale network data and biological function using the Saccharomyces cerevisiae model and demonstrate the importance of composite interaction networks. In our research we show that integration of large-scale data, combined with bespoke computational analyses, can provide a means for investigating specific aspects of viral infection. In particular, using this approach we provide insight into host-virus interactions that influence HIV-1, SIV and HCV infection and we infer cellular functions and specific host factors that may be useful in the search for novel antiviral drug targets. Thus, we recommend that computational methods for analysing large-scale data sources continue to be developed, with particular emphasis on methods that integrate data sources, so that results can be derived from multiple types of information and more complete biological models.

Date of Award	1 Aug 2012
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Robertson (Supervisor)