eprintid: 10079890
rev_number: 40
eprint_status: archive
userid: 608
dir: disk0/10/07/98/90
datestamp: 2019-10-03 09:12:40
lastmod: 2021-12-06 23:29:59
status_changed: 2019-10-03 09:12:40
type: thesis
metadata_visibility: show
creators_name: Margetts, Ben Keith
title: Monitoring Viral Infections and Immune Repertoires in Transplanted Children: A Statistical Approach
ispublished: unpub
divisions: UCL
divisions: B02
divisions: C08
divisions: D09
divisions: D13
divisions: G24
keywords: CMV, TCR, HSCT
note: Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/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.
abstract: Transplantation in children is a relatively high-risk procedure, where pharmacological treatment and disease often render the recipient immunocompromised. This state allows typically non-serious infectious organisms such as cytomegalovirus (CMV) to grow uninhibited, leading to morbidity and mortality in the host. Underpinning the dynamics of these infections lies the immune response, driven in part by T cells and their innate ability to recognise pathogens via the T cell receptor (TCR). Applying deep sequencing techniques to the TCR repertoire allows researchers to explore this cellular response in unprecedented resolution. // This thesis explores the application of statistical modelling and data analysis techniques to two key areas of interest: the reconstituting immune response and the consequences of immunodeficiency within transplanted children. Specifically, the first part of this thesis focuses on the TCR repertoire, opening with an automated TCR next generation sequencing (NGS) data processing, subsampling, and analysis pipeline. This pipeline is then applied to NGS data from two distinct patient cohorts, facilitating a detailed statistical analysis on the clinical applicability of TCR deep sequencing, alongside an analysis of repertoire reconstitution. Lastly, open access complementarity determining region 3 (CDR3) epitope specificity data are integrated with the processed sequencing data to model antigen-specific CDR3 sequence profiles using profile hidden Markov models (PHMMs) and unsupervised CDR3 sequence clustering. This section of the thesis explores the utility of CDR3 sequencing for clinical TCR repertoire evaluation and demonstrates the wide-ranging potential applications of the sequencing data. // The second part of the thesis focuses on CMV infections, opening with a quantitative clinical audit on the treatment of CMV infections post-haematopoietic stem cell transplant (HSCT). The audit includes an analysis of the efficacy of anti-CMV drugs in children and a viral load-based time-to-event (survival) model. This work is then expanded with a Bayesian nonlinear mixed effects model for predicting CMV loads in infected transplant recipients, using a workflow that prioritises identifiability and biological plausibility of model parameters.
date: 2019-08-28
date_type: published
oa_status: green
full_text_type: other
thesis_class: doctoral_open
thesis_award: Ph.D
language: eng
thesis_view: UCL_Thesis
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1682522
lyricists_name: Klein, Nigel
lyricists_name: Margetts, Ben
lyricists_id: NJKLE20
lyricists_id: MARGE80
actors_name: Nonhebel, Lucinda
actors_id: LNONH33
actors_role: owner
full_text_status: public
pagerange: 1-357
pages: 357
institution: UCL (University College London)
department: CoMPLEX
thesis_type: Doctoral
editors_name: Standing, J
editors_name: Breuer, J
editors_name: Klein, N
citation:        Margetts, Ben Keith;      (2019)    Monitoring Viral Infections and Immune Repertoires in Transplanted Children: A Statistical Approach.                   Doctoral thesis  (Ph.D), UCL (University College London).     Green open access   
 
document_url: https://discovery-pp.ucl.ac.uk/id/eprint/10079890/1/ThesisBenMargetts.pdf