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Title: Phenotypic modelling of the polyfunctional T cell response
Author: Trendel, Nicola Charlotte
ISNI:       0000 0004 7653 3145
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2018
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T cells are important mediators of adaptive immunity and promising agents in immunotherapies. When T cell receptors (TCRs) on the surface of CD8+ T cells detect pathogenic peptides presented by major histocompatibility complexes class I (pMHC) on antigen-presenting cells (APCs), a complicated intracellular signalling process is initiated that results, among other functional responses, in the secretion of cytokines to fight the pathogenic threat. An overwhelming number of molecular components that are involved in the translation of the pMHC stimulus into an appropriate cytokine response have been identified, but a mechanistic understanding of how the quality of the pMHC stimulus, i.e. pMHC dose and affinity, determines the response phenotype is lacking. Here, reductionist T cell stimulation experiments were combined with a coarse-grained phenotypic modelling approach to identify prevailing modules that shape the phenotype of cytokine secretion in response to stimulation with pMHC. CD8+ primary human T cells transiently produced the four cytokines TNFα, MIP-1β, IFNγ and IL-2 and adaptation was paralleled by rapid and sustained TCR downregulation. Direct fitting of a phenotypic model constructed from known features of T cell signalling to the experimental data showed that TCR downregulation was sufficient to explain adaptation as well as other striking features of the cytokine response, including high-dose suppression (bell-shaped dose-response curve). Interestingly, secretion of TNFα, MIP-1β, IFNγ and IL-2 ceased at similar time scales and appeared equally sensitive to pMHC dose and affinity, contrary to previous reports. These findings thus suggest that T cells possess an intrinsic adaptation mechanism and reveal a surprisingly uniform early activation phenotype for multiple cytokines.
Supervisor: Dushek, Omer ; van der Merwe, P. Anton Sponsor: Konrad-Adenauer-Stiftung ; Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computational Immunology ; Systems Biology ; Immunology