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The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset
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 inf...
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| Format: | Thesis |
| Language: | English |
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Computational Biology Division
2024
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| _version_ | 1867613305694060544 |
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| access_status_str | Open Access |
| author | Mphahlele, Ruth |
| author2 | Williamson, Carolyn |
| author_browse | Mphahlele, Ruth Williamson, Carolyn |
| author_facet | Williamson, Carolyn Mphahlele, Ruth |
| author_sort | Mphahlele, Ruth |
| collection | Thesis |
| 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 seven HLA-B restricted gag epitopes in six individuals of which putative escape was identified in seven epitopes, commonly occurring in the late chronic phase of infection. The most common epitope in the population was YL9 (found in 60% of the participants) (Gag HXB2 coordinates 296 to 304) restricted by HLA B*15:03, B*15:10 and B*42:01. Toggling of amino acids within epitopes as a result of potential fitness cost associated with a specific change, was observed in five of seven epitopes. We further identified 35 high Shannon entropy sites, where nine of these sites were found within epitopes identified by Epitope Matcher. Additionally, nine of the high Shannon entropy sites were evolving under positive selection. With supporting evidence, we can predict that the mutation T310S (found in the AW11 epitope, restricted by allele B*58:01), is likely to be associated with escape. This study is important in that it provides a pipeline that will enable semiautomated analysis of NGS data. Using this approach, we have provided a better understanding of the kinetics and frequency of CTL escape over the course of HIV infection. Additionally, we have identified frequently targeted sites across the Gag p24 region and across individuals. This study is relevant to inform CTL-based vaccine prevention and treatment strategies. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39727 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:00.978Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Computational Biology Division |
| publisherStr | Computational Biology Division |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39727 The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset Mphahlele, Ruth Williamson, Carolyn Martin Darren Bioinformatics 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 seven HLA-B restricted gag epitopes in six individuals of which putative escape was identified in seven epitopes, commonly occurring in the late chronic phase of infection. The most common epitope in the population was YL9 (found in 60% of the participants) (Gag HXB2 coordinates 296 to 304) restricted by HLA B*15:03, B*15:10 and B*42:01. Toggling of amino acids within epitopes as a result of potential fitness cost associated with a specific change, was observed in five of seven epitopes. We further identified 35 high Shannon entropy sites, where nine of these sites were found within epitopes identified by Epitope Matcher. Additionally, nine of the high Shannon entropy sites were evolving under positive selection. With supporting evidence, we can predict that the mutation T310S (found in the AW11 epitope, restricted by allele B*58:01), is likely to be associated with escape. This study is important in that it provides a pipeline that will enable semiautomated analysis of NGS data. Using this approach, we have provided a better understanding of the kinetics and frequency of CTL escape over the course of HIV infection. Additionally, we have identified frequently targeted sites across the Gag p24 region and across individuals. This study is relevant to inform CTL-based vaccine prevention and treatment strategies. 2024-05-27T08:47:40Z 2024-05-27T08:47:40Z 2023 2024-05-22T08:34:57Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/39727 eng application/pdf Computational Biology Division Faculty of Health Sciences |
| spellingShingle | Bioinformatics Mphahlele, Ruth The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset |
| thesis_degree_str | Master's |
| title | The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset |
| title_full | The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset |
| title_fullStr | The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset |
| title_full_unstemmed | The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset |
| title_short | The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset |
| title_sort | identification of cytotoxic t lymphocyte ctl escape in a large longitudinal subtype c hiv 1 sequence dataset |
| topic | Bioinformatics |
| url | http://hdl.handle.net/11427/39727 |
| work_keys_str_mv | AT mphahleleruth theidentificationofcytotoxictlymphocytectlescapeinalargelongitudinalsubtypechiv1sequencedataset AT mphahleleruth identificationofcytotoxictlymphocytectlescapeinalargelongitudinalsubtypechiv1sequencedataset |