Kremyda-Vlachou, Myrto;
(2022)
Bioinformatics analyses of next generation sequencing Hepatitis C virus samples.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Background: There are 118,000 patients with chronic Hepatitis C virus (HCV) in the UK and the virus is considered a major public health threat by the WHO. Pangenotypic treatment has increased cure rates (>95%), which could reduce the need for subtyping, but currently in the UK genotype-dependent drug combinations are implemented. Injecting drug users are particularly affected and driving transmission. Sequencing could be used at scale to inform the clinical and public treatment of HCV, but the added value of sequencing over current methods for subtyping remains unclear. The aim of this PhD was to explore the utility of HCV WGS to support clinical and public health responses to HCV. Methods: I used the Infection response through virus genomics (ICONIC) pipeline to carry out de novo assembly of 1682 HCV samples and conducted quality control analyses of these genomes. I compared different genotyping tools and investigated whether whole genomes provided more accurate genotyping results compared to specific regions of the HCV genome. I also compared WGS genotyping with genotyping information from a clinical dataset. Lastly, I used clinical and genotype data for phylogenetic and statistical analyses to uncover associations between patient characteristics and transmission clustering. Results: Genotype-specific primers produced lower quality of genomes compared to pan-genotypic primers. WGS is as accurate as NS5B Sanger sequencing in assigning genotypes and subtypes, but was more accurate than 5’UTR and core regions. I found that PWIDs, White ethnicity and being in Glasgow were associated with HCV transmission chains. Conclusion: WGS offers a lot of information in the study of HCV. Its utility in the clinic is questioned due to the high efficacy of the treatments. However, research and surveillance using WGS can help support public health policies to reduce transmission of HCV and can be used to monitor the emergence of drug resistance.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Bioinformatics analyses of next generation sequencing Hepatitis C virus samples |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10148502 |
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