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Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism

Preciat, G.; Moreno, E. L.; Wegrzyn, A.; Willacey, C. C. W.; Modamio, J.; Monteiro, F. L.; El Assal, D.; Schurink, A.; Oliveira, M. A. P.; Zhang, Z.; Cousins, B.; Zach, S.; Vempala, S.; Hengerer, B.; Harms, A.; Schwamborn, J. C.; Thiele, I.; Hankemeier, T.; Fleming, R. M.

2021-07-01 systems biology
10.1101/2021.06.30.450562 bioRxiv
Show abstract

Starting with a comprehensive generic reconstruction of human metabolism, we generated high-quality, constraint-based, genome-scale, cell-type and condition specific models of metabolism in human dopaminergic neurons, the cell type most vulnerable to degeneration in Parkinsons disease. They are 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, midbrainspecific, dopaminergic neurons in vitro. Thermodynamic constraint-based modelling enabled qualitatively accurate and moderately quantitatively accurate prediction of dopaminergic neuronal metabolite exchange fluxes, including predicting the consequences of metabolic perturbations in a manner also consistent with literature on monogenic mitochondrial Parkinsons disease. These dopaminergic neurons models provide a foundation for a quantitative systems biochemistry approach to metabolic dysfunction in Parkinsons disease. Moreover, the plethora of novel mathematical and computational approaches required to develop them are generalisable to study any other disease associated with metabolic dysfunction.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.