Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770269
Title: An in silico approach to predict alterations in the Golgi N-glycosylation machinery
Author: Fisher, Peter
ISNI:       0000 0004 7651 8463
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2018
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Abstract:
Protein glycosylation is responsible for modulating the numerous properties of secreted and membrane proteins. The glycan structures that are produced by the cell in the Golgi apparatus are only partially a result of the transcriptome of the enzymes responsible. The organisation of glycosylation enzymes, including levels and localizations within the Golgi apparatus has only been accessible with advanced microscopy techniques and is limited to a small number of enzymes. In this work a computational model capable of predicting changes in Golgi enzyme localizations and activities informed by glycan profiles has been developed. This model is used to predict the localization of N-linked glycan-modifying enzymes in three human cell lines and then to predict changes in enzyme homeostasis in Cog4-deficient cell lines and upon osteogenic differentiation.
Supervisor: Ungar, Daniel ; Wood, Jamie ; Thomas-Oates, Jane Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.770269  DOI: Not available
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