Application of Next Generation Sequencing and Bioinformatic Approaches to the Study of Influenza Virus Strains and MiRNA Profiling in Liver Diseases

• Author / Creator

  Lin, Zhen

• Analysis and interpretation of next generation sequencing data has posed a significant challenge to researchers, especially to those who are not equipped with expertise in bioinformatics. In this thesis, I investigated three different biomedical questions using NGS and bioinformatic approaches, and explored the application of these methods to meet different needs. Influenza epidemics result in up to 5 million severe cases worldwide, causing approximately 500,000 deaths annually. The characterization of emerging new strains can be challenging due to the diversity and high dissimilarity of influenza subtypes. Herein, we used NGS to analyze the RNA from the lungs of ferrets infected with influenza A/California/07/2009. Several bioinformatic approaches were used to catalogue the viral genes and investigate the quasi-species. Using the optimal bioinformatic approach, we were able fully characterize viral isolates following NGS. For the next project, we used NGS to study microRNA expression in the liver of patients with end stage liver disease. These non-coding RNAs regulate levels of cellular mRNA and we sought to investigate whether miRNA profiles were associated with different disease processes. However, we found heterogeneous miRNA expression within samples derived from patients with the same disorder, which has limited our ability to link specific profiles with one disease process. Further multidimensional analysis and hierarchical clustering revealed the existence of two sub-groups in several diseases. A sizable number of miRNAs were found differentially expressed between those two groups, including miR-29 and miRNAs from miR-17-92 cluster. These miRNAs regulate the PI3K-AKT signaling and focal adhesion pathways that play a critical role in modulating diverse cellular functions including metabolism, growth, proliferation, survival and oncogenesis. However, the exact role that miR-29 and miRNAs from miR-17-92 cluster exert in end stage liver disease requires further clarification. An analysis comparing miRNA expression in hepatocellular carcinoma and in the surrounding liver tissues was more productive. We therefore expanded the investigation to include more paired samples; this form of analyses allows statistical modeling to incorporate both the variance between patients and the variance between diseased and non-diseased tissues. Moreover, by profiling miRNA expression and gene expression in paired human HCC tumor and non-tumor tissues, we were able to characterize the interaction between miRNAs and genes in tumor tissues. We found known oncomiRs (e.g. miR-21) and tumor suppressive miRNAs (e.g. miR-200 family) and observed reciprocal changes in gene expression. We also found a set of rarer oncomiRs such as miR-1269a, miR-518b, and miR-512-3p; the latter two miRNAs come from the largest miRNA cluster, C19MC. These studies provide new avenues for investigation for deregulated gene expression in HCC. In summary, I have used a series of bioinformatic approaches to investigate complex NGS datasets to better understand the biology of diverse processes, including infectious diseases, non-infectious diseases and cancer.

• Subjects / Keywords

  • Next-generation sequencing
  • Influenza
  • MiRNA
  • Hepatocellular carcinoma
  • End-stage liver diseases

• Graduation date

  Spring 2016

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3901ZW84

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.