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dc.contributor.authorGaramszegi, Saraen_US
dc.date.accessioned2016-04-05T14:51:05Z
dc.date.available2016-04-05T14:51:05Z
dc.date.issued2014
dc.identifier.urihttps://hdl.handle.net/2144/15414
dc.description.abstractHost-virus systems biology seeks to elucidate the complex interactions between a virus and its host, and to determine the downstream consequences of these interactions for the host. Traditional studies of host-virus interactions, conducted one-at-a-time, yield high-quality results, but these have limited scope. By contrast, systems biology uses a holistic approach to examine many interactions simultaneously, thereby increasing the breadth of interactions revealed. However, these studies have largely focused on common human pathogens (e.g., influenza or HIV), and their results may not apply to unrelated viruses, such as those that cause hemorrhagic fevers. Combining experimental and computational techniques can yield novel information about host-virus interactions that traditional virological or purely computational systems-biology methods cannot uncover. In this thesis, I demonstrate the utility of combined experimental and computational approaches by: (1) revealing general principles of host-virus interactions, broadly applicable to a wide range of viruses; and (2) probing a specific host-virus interaction system to identify transcriptional signatures which elucidate host response to Ebola virus. I identify general mechanisms governing host-virus protein-protein interactions (PPIs) using domain-resolved PPI networks. This method identifies mechanistic differences between virus-human and within-human interactions, such as the preference viral proteins exhibit for binding human proteins containing linear motif-binding domains. Using domain-resolved PPIs reveals novel signatures of pleiotropy, economy, and convergent evolution in the viral-host interactome not previously identified in other PPI networks. I further identify transcriptional signatures of host response to Ebola virus (EBOV) infection by pairing high-throughput microarray data with advanced pathway analyses. I compare EBOV-infected, non-human primates with and without anticoagulant treatment, to identify transcriptional signatures associated with survival following infection. Having found that CCAAT-enhancer binding proteins (CEBPs) are associated with survival, I determine the role CEBPs have in EBOV infection by using comparative microarray analysis of multiple viral infections of hemorrhagic and non-hemorrhagic origin. I also identify unique transcriptional changes in the host that distinguish EBOV infection from other viral infections, such as Influenza. Integrating these two areas of research provides information about universally applicable patterns of viral infection, while simultaneously examining the consequences of specific host-pathogen interactions.en_US
dc.language.isoen_US
dc.subjectBioinformaticsen_US
dc.titleIntegrated computational and experimental analysis of host-virus interaction systemsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-03-12T07:12:56Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBioinformaticsen_US
etd.degree.grantorBoston Universityen_US


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