Pathogen-host omics analyses of human papillomavirus type 16 sub-lineages in a human epithelial organoid model
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Pathogens such as human papillomaviruses (HPVs) have co-evolved with their hosts and form a molecular basis for common diseases. Persistent infection with the “high-risk” HPV type 16 (HPV16) is a potent cause of anogenital and oropharyngeal cancers. Taxonomic HPV16 sub-lineages, based on geographic origin of discovery, are noteworthy due to their variable tumourigenicity. In this dissertation, I present basic research and the resulting biotechnologies we developed, improved, and utilized to study their fascinating pathogen-host relationship with human stratified epithelia. A small number of variations in the E6 gene of HPV16, found in the D2 and D3 sub-lineages, lead to increased tumourigenic risk compared to the prototype A1 sub-lineage. Using an organotypic human epithelial model (or in vitro organoid) we recapitulated the viral life cycle and used “-omics” analyses to assess viral and host molecular differences due to sub-lineage variation. Sub-lineage variants of E6 were associated with host genome instability and viral integration into host DNA. Following these initial findings, I provide perspective on epithelial organoids, namely that the trade-off between model complexity and feasibility should be sensibly considered based on its utility for answering the biological research question at hand. Model applications and improvements are presented, including time-series epithelial stratification measurements, strategies for introducing full-length sub-lineage HPV16 genomes into host keratinocytes, and experiments to study innate immune evasion. These wet-lab works are accompanied by software to aid biologists in analyzing sequencing data. As well, we present current work using The Cancer Genome Atlas to test the association between HPV16 sub-lineage and integration. Overall, this interdisciplinary and interconnected collection has significance for basic researchers, providing insight on how a small number of natural viral variations can lead to increased tumourigenic risk, as well as for experimentalists to gain insight on organoid modelling and novel bioinformatics tools. More broadly, characterizing these molecular interactions between pathogen and host enables us to form a basis for diagnosis, treatment, and ultimately prevention of disease. Future research should aim to closely integrate biological and computational sciences for improving experimental approaches and our ability to make meaningful biological interpretations given the complexity and variability of biological systems.