Developing metabolomics for a systems biology approach to understand Parkinson's disease

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 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. The research was financially supported by the SysMedPD project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 668738.Analytical BioScience

Profiling acidic metabolites by capillary electrophoresis‐mass spectrometry in low numbers of mammalian cells using a novel chemical derivatization approach

Medicinal ChemistryAnalytical BioScience

Metabolic profiling of material-limited cell samples by dimethylaminophenacyl bromide derivatization with UPLC-MS/MS analysis

The ability to dissect the intracellular metabolome is vital in the study of diverse biological systems and models. However, limited cell availability is a challenge in metabolic profiling due to the low concentrations affecting the sensitivity. This is further exacerbated by modern technologies such as 3D microfluidic cell culture devices that provide a physiologically realistic environment, compared to traditional techniques such as cell culture in 2D well-plates. Attempts to address sensitivity issues have been made via advances in microscale separation such as CE and micro/nano-LC coupled to mass spectrometers with low-diameter ionization emitter sources. An alternative approach is sample derivatization, which improves the chromatographic separation, enhances the MS ionization, and promotes favourable fragmentation in terms of sensitivity and specificity. Although chemical derivatization is widely used for various applications, few derivatization methods allow sensitive analysis below 1 x 10(4) cells. Here, we conduct RPLC-MS/MS analysis of HepG2 cells ranging from 250 cells to 1 x 10(5) cells, after fast and accessible derivatization by dimethylaminophenacyl bromide (DmPABr), which labels the primary amine, secondary amine, thiol and carboxyl submetabolome, and also utilizes the isotope-coded derivatization (ICD). The analysis of 1 x 10(4) HepG2 cells accomplished quantification of 37 metabolites within 7-minute elution, and included amino acids, N-acetylated amino acids, acylcarntines, fatty acids and TCA cycle metabolites. The metabolic coverage includes commonly studied metabolites involved in the central carbon and energy-related metabolism, showing applicability in various applications and fields. The limit of detection of the method was below 20 nM for most amino acids, and sub 5 nM for the majority of N-acetylated amino acids and acylcarnitines. Good linearity was recorded for derivatized standards in a wide biological range representing expected metabolite levels in 2-10,000 cells. Intraday variability in 5 x 10(3) HepG2 cells was below 20% RSD for concentrations measured of all but two metabolites. The method sensitivity at the highest dilution of cell extract, 250 HepG2 cells, enabled the quantification of twelve metabolites and the detection of three additional metabolites below LLOQ. Where possible, performance parameters were compared to published methodologies that measure cell extract samples. The presented work shows a proof of concept for harnessing a derivatization method for sensitive analysis of material-limited biological samples. It offers an attractive tool with further potential for enhanced performance when coupled to low-material suitable technologies such as CE-MS and micro/nano LC-MS.Analytical BioScience

LC-MS/MS analysis of the central energy and carbon metabolites in biological samples following derivatization by dimethylaminophenacyl bromide

Recent advances in metabolomics have enabled larger proportions of the human metabolome to be analyzed quantitatively. However, this usually requires the use of several chromatographic methods coupled to mass spectrometry to cover the wide range of polarity, acidity/basicity and concentration of metabolites. Chemical derivatization allows in principle a wide coverage in a single method, as it affects both the separation and the detection of metabolites: it increases retention, stabilizes the analytes and improves the sensitivity of the analytes. The majority of quantitative derivatization techniques for LC-MS in metabolomics react with amines, phenols and thiols; however, there are unfortunately very few methods that can target carboxylic acids at the same time, which contribute to a large proportion of the human metabolome. Here, we describe a derivatization technique which simultaneously labels carboxylic acids, thiols and amines using the reagent dimethylaminophenacyl bromide (DmPABr). We further improve the quantitation by employing isotope-coded derivatization (lCD), which uses internal standards derivatized with an isotopically-labelled reagent (DmPABr-D-6). We demonstrate the ability to measure and quantify 64 central carbon and energy-related metabolites including amino acids, N-acetylated amino acids, metabolites from the TCA cycle and pyruvate metabolism, acylcarnitines and medium-/long-chain fatty acids. To demonstrate the applicability of the analytical approach, we analyzed urine and SUIT-2 cells utilizing a 15-minute single UPLC-MS/MS method in positive ionization mode. SUIT-2 cells exposed to rotenone showed definitive changes in 28 out of the 64 metabolites, including metabolites from all 7 classes mentioned. By realizing the full potential of DmPABr to derivatize and quantify amines and thiols in addition to carboxylic acids, we extended the coverage of the metabolome, producing a strong platform that can be further applied to a variety of biological studies. (C) 2019 The Authors. Published by Elsevier B.V.Analytical BioScience