Hällqvist, Jenny Cecilia;
(2022)
Biomarker discovery in Parkinson's disease and centenarians - Proteomic studies of neurodegeneration and healthy ageing by mass spectrometry and machine learning.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by motor and cognitive symptoms. The pathology hallmarks include alpha-synuclein aggregation and predominant dopaminergic cell loss in the midbrain. Advancing age is the main risk-factor and the reasons behind development of non-hereditary Parkinson’s disease remain largely unknown. Although much effort has gone into finding biomarkers, there are currently no specific biomarkers allowing for screening of PD. Aiming to discover new biomarkers and affected pathways for PD, and to probe the divergence between healthy and non-healthy ageing, discovery proteomics was performed and followed by a targeted validation. The protein expression associated with Parkinson’s disease and healthy ageing was explored using a label-free, bottom-up mass spectrometry-based discovery methodology applied to serum, plasma and urine from Parkinson’s disease patients, and plasma from cognitively healthy centenarians, all groups matched with controls. The discovery phase identified several proteins putatively related to Parkinson’s disease and to longevity in the centenarians. Pathway analysis suggested an altered inflammatory response in both groups. The biomarker targets which emerged from the discovery phase were developed into a mass spectrometric, multiple reaction monitoring-based assay, augmented with inflammatory proteins from the literature, and applied to new and larger sample cohorts. Several proteins from the pathways were successfully confirmed in the targeted validation phase, and the results indicated activation of the unfolded protein response, reduced Wnt signalling and increased complement-mediated inflammation in the Parkinson’s patients. In the centenarians, a longevity-promoting protein expression consisting of downregulated C3 and upregulated A2M and ADIPOQ was identified. Supervised machine learning models were trained to classify individuals as PD or healthy controls, and when predicting new samples, Parkinson’s disease patients and controls could be discriminated perfectly in plasma and with 85.1% accuracy in urine.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Biomarker discovery in Parkinson's disease and centenarians - Proteomic studies of neurodegeneration and healthy ageing by mass spectrometry and machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Genetics and Genomic Medicine Dept UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151005 |
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