The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset
type: CreativeWork
description: Human Immunodeficiency Virus (HIV) rapidly escapes cytotoxic T-cell lymphocyte (CTL) immune responses exerted by the host. Mutation patterns and HLA associated footprints linked to viral escape have been identified, making it possible to use viral sequence data, combined with the host HLA allele information, to predict escape. Next-Generation Sequencing (NGS) approaches enable the generation of large sequence datasets, and the detection of viral populations present at very low frequencies in an infected individual at any given time. These datasets allow for the study of changes in viral populations within a host over time and provide a means to understand the kinetics and pathway(s) of escape. While tools exist that allow the prediction of escape in sequence data with small sequence numbers per sampling timepoint, these tools often have limitations in analysing large NGS data sets. In this project, we developed a workflow for identifying the kinetics of CTL escape in longitudinal HIV-1 next-generation datasets of gag sequences generated using an Illumina Miseq platform over the duration of drug-naïve infection. This acquired data set was generated from 15 women over a period of one to seven years and comprised of 4583 short read gag sequences (544 bp). We identified tools for identifying CTL escape in deep sequencing datasets and used pre-defined criteria to screen these tools. The outputs were validated using a test dataset from a previous study that identified escape. We selected the Epitope Matcher tool as having the most potential to identify CTL epitopes and escape mutations. To further support evidence of escape and identify additional putative escape mutations, we identified sites with high Shannon entropy (>=0.25) and sites evolving under positive selection using HyphyFUBAR. The sites were verified using the HLA association and CTL epitope variants and escape mutations lists, or data generated by Epitope Matcher. Using the Epitope Matcher tool, we identified…
abstract: Human Immunodeficiency Virus (HIV) rapidly escapes cytotoxic T-cell lymphocyte (CTL) immune responses exerted by the host. Mutation patterns and HLA associated footprints linked to viral escape have been identified, making it possible to use viral sequence data, combined with the host HLA allele information, to predict escape. Next-Generation Sequencing (NGS) approaches enable the generation of large sequence datasets, and the detection of viral populations present at very low frequencies in an infected individual at any given time. These datasets allow for the study of changes in viral populations within a host over time and provide a means to understand the kinetics and pathway(s) of escape. While tools exist that allow the prediction of escape in sequence data with small sequence numbers per sampling timepoint, these tools often have limitations in analysing large NGS data sets. In this project, we developed a workflow for identifying the kinetics of CTL escape in longitudinal HIV-1 next-generation datasets of gag sequences generated using an Illumina Miseq platform over the duration of drug-naïve infection. This acquired data set was generated from 15 women over a period of one to seven years and comprised of 4583 short read gag sequences (544 bp). We identified tools for identifying CTL escape in deep sequencing datasets and used pre-defined criteria to screen these tools. The outputs were validated using a test dataset from a previous study that identified escape. We selected the Epitope Matcher tool as having the most potential to identify CTL epitopes and escape mutations. To further support evidence of escape and identify additional putative escape mutations, we identified sites with high Shannon entropy (>=0.25) and sites evolving under positive selection using HyphyFUBAR. The sites were verified using the HLA association and CTL epitope variants and escape mutations lists, or data generated by Epitope Matcher. Using the Epitope Matcher tool, we identified…
author: Mphahlele, Ruth
keywords: Bioinformatics
date published: 2023
date modified: 2026-07-22T22:23:21Z
provider: University of Cape Town
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Computational Biology Division

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property ID: Handle
value: http://hdl.handle.net/11427/39727