No abstract

Patient-derived cellular models are a powerful approach to study human disease, especially neurodegenerative diseases, such as Parkinson's disease, where affected primary neurons, e.g., substantia nigra dopaminergic neurons, are almost inaccessible. Starting with a comprehensive generic reconstruction of human metabolism, Recon3D, we generated a high-quality, constraint-based, genome-scale, in silico model of human dopaminergic neuronal metabolism (iDopaNeuro1). It is a synthesis of extensive manual curation of the biochemical literature on neuronal metabolism, together with novel, quantitative, transcriptomic and targeted exometabolomic data from human stem cell-derived, midbrain-specific, dopaminergic neurons in vitro. Thermodynamic constraint-based modelling with iDopaNeuro1 is qualitatively accurate (92% correct) and quantitatively accurate (Spearman rank 0.7) at predicting metabolite secretion or uptake, given quantitative exometabolomic constraints on uptakes, or secretions, respectively. iDopaNeuro1 is also qualitatively accurate at predicting the consequences of metabolic perturbations, e.g., complex I inhibition (Spearman rank 0.69) in a manner consistent with literature on monogenic mitochondrial Parkinson's disease. The iDopaNeuro1 model provides a foundation for a quantitative systems biochemistry approach to metabolic dysfunction in Parkinson's disease. Moreover, the plethora of novel mathematical and computational approaches required to develop it are generalisable to study any other disease associated with metabolic dysfunction.

No abstract

The simultaneous analysis of a broad range of polar ionogenic metabolites using capillary electrophoresis-mass spectrometry (CE-MS) can be challenging, as two different analytical methods are often required, that is, one for cations and one for anions.Even though CE-MS has shown to be an effective method for cationic metabolite profiling, the analysis of small anionic metabolites often results in relatively low sensitivity and poor repeatability. In this work, a novel derivatization strategy based on trimethylmethaneaminophenacetyl bromide was developed to enable CE-MS analysis of carboxylic acid metabolites using normal CE polarity (i.e., cathode in the outlet) and detection by mass spectrometry in positive ionization mode. Optimization of derivatization conditions was performed using a response surface methodology after which the optimized method (incubation time 50 min, temperature 90 • C, and pH 10) was used for the analysis of carboxylic acid metabolites in extracts from HepG2 cells. For selected metabolites, detection limits were down to 8.2 nM, and intraday relative standard deviation values for replicates (n = 3) for peak areas were below 21.5%. Metabolites related to glycolysis, tricarboxylic acid cycle, and anaerobic respiration pathways were quantified in 250,000 cell lysates, and could still be detected in extracts from only 25,000 HepG2 cell lysates (∼70 cell lysates injected).