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Statistical modeling of Epstein-Barr virus infection using scRNA-Seq host expression

Patel, Japan (2024) Statistical modeling of Epstein-Barr virus infection using scRNA-Seq host expression. Master's Thesis, University of Pittsburgh. (Unpublished)

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Epstein-Barr virus (EBV) is a ubiquitous virus that infects the majority of people
worldwide. EBV replication is characterized by latent and lytic cycles where viral gene
expression during latency is associated with certain cancers, such as Burkitt's Lymphoma and
Nasopharyngeal Carcinoma (NPC). The field’s understanding of viral-host mechanisms of EBV
in epithelial cells is incomplete. To determine host gene expression profiles which influence the
lifecycle of EBV we modeled Single Cell RNA Sequencing (scRNA-seq) data of EBV-infected
cell lines by a Random Forest algorithm and multinomial logistic regression approaches. This
methodology allowed us to refine EBV infection status into established and newly identified
classifications, defining subcategories of lytic and latent cycles as well as unveiling specific host
genes and biological pathways influential to EBV pathogenesis. Our analysis revealed that
certain host genes, implicated in pathways related to viral mRNA translation, keratinization,
neutrophil degranulation, and cytokine signaling, play a significant role in shaping the viral-host
interaction landscape. Traditionally, scRNA-data is limited by the prevalence of false negatives
which arise due to low abundance of EBV transcripts in most cells. These host genes served as
surrogate markers of infection which enabled us to predict infection status in cells that otherwise
appear as void of EBV infection. Future in-vivo and in-vitro analysis should be conducted on a
variety of epithelial and B-cell lines to detect conserved host markers of EBV infection to further
the field’s understanding of EBV viral-host interactions possibly contributing to the development
diagnostic markers or therapeutic targets for EBV associated diseases.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Patel, Japanjap282@pitt.edujap282
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorBuchanich, Jeaninejeanine@pitt.edujeanine
Committee MemberWang, Jiebiaojbwang@pitt.edujbwang
Committee MemberShair, Kathy H.Y.kas361@pitt.edukas361
Date: 16 May 2024
Date Type: Publication
Defense Date: 16 April 2024
Approval Date: 16 May 2024
Submission Date: 26 April 2024
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 93
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: NA
Date Deposited: 16 May 2024 19:19
Last Modified: 16 May 2024 19:19


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