The outbreaks of AIDS and COVID-19 showed clearly how infectious viruses can influence people’s lives. Investigating the changes in the host metabolism may provide a paradigm shift to consider... Show moreThe outbreaks of AIDS and COVID-19 showed clearly how infectious viruses can influence people’s lives. Investigating the changes in the host metabolism may provide a paradigm shift to consider immune-metabolic interactions as therapeutic targets. The aim of this thesis is to examine the interplay between the immune system and metabolism during viral infections, such as HIV and coronavirus. These investigations will utilize metabolomic and lipidomic mass spectrometry techniques to gain a comprehensive understanding of the metabolic changes that occur during viral infections. To enhance the coverage of the lipidome, a new method will be developed. Show less
Neurodegenerative diseases, including Parkinson’s disease (PD), are increasing in prevalence due to the aging population. Despite extensive study, these diseases are still not fully understood and... Show moreNeurodegenerative diseases, including Parkinson’s disease (PD), are increasing in prevalence due to the aging population. Despite extensive study, these diseases are still not fully understood and the lack of personalised treatment options that can target the cause of the diseases, rather than the symptoms, has led to a greater demand for improved disease understanding, therapies and diagnostic procedures. In this thesis, we use systems biology approaches to construct disease-specific models intended for biomarker discovery, therapeutic treatment strategy identification and drug repurposing in PD. Systems biology is a mathematical field of research that analyses biological systems via construction of a computational model using experimental data. This is achieved by integration of omics data, including genomics, proteomics, transcriptomics and metabolomics. A specific approach used to identify the physico- and biochemical bounds within a biological system is constraint-based modelling, which requires the input of absolute quantitative metabolomics data. To improve our absolute quantitative coverage of the metabolome, we developed and improved new quantitative metabolomics methods using a targeted mass spectrometry workflow to obtain data intended to be integrated into constraint-based metabolic models for the study of PD. Show less
A new method, based on shotgun spectral matching of peptide tandem mass spectra, was successfully applied to the identification of different food species. The method was demonstrated to work on raw... Show moreA new method, based on shotgun spectral matching of peptide tandem mass spectra, was successfully applied to the identification of different food species. The method was demonstrated to work on raw as well as processed samples from 16 mammalian and 10 bird species by counting spectral matches to spectral libraries in a reference database with one spectral library per species. A phylogenetic tree could also be constructed directly from the spectra. Nearly all samples could be correctly identified at the species level, and 100% at the genus level. The method does not use any genomic information and unlike targeted methods, no prior knowledge of genetic variation within a genus or species is necessary. (c) 2016 Elsevier Ltd. All rights reserved. Show less
The aim of this thesis was to develop concepts and methods to extract qualitative and quantitative information about metabolites from untargeted mass spectrometric data of biological samples.... Show moreThe aim of this thesis was to develop concepts and methods to extract qualitative and quantitative information about metabolites from untargeted mass spectrometric data of biological samples. Several typical challenges in data handling were addressed that prevent a straightforward interpretation (data analysis) of the data acquired with different types of mass spectrometric-based metabolomics methods (GC-MS, LC-MS, CE-MS or DI-MS) methods. The critical parameters causing variation in quantitative results were identified and studied at different stages in the metabolomics workflow such as data acquisition, data pre-processing and data analysis. Different methods and concepts were developed to address these and to improve the quantitation of metabolites and the comparison between metabolite data in different samples of the same study measured at different moments or between studies. The methods developed focused on improved normalization, data pre-processing of untargeted analysis and data pre-processing of high resolution direct infusion mass spectrometry data. Furthermore it was demonstrated that even for metabolomic studies with few samples cross-validation of multivariate models can be very time consuming and parallel implementation on a (large) cluster of computers is the way to make such computations feasible Show less
