Integrating simulated and experimental data to identify mitochondrial bioenergetic defects in Parkinson’s Disease models

Abstract Mitochondrial bioenergetics are vital for ATP production and are associated with several diseases, including Parkinson’s Disease. Here, we simulated a computational model of mitochondrial ATP production to interrogate mitochondrial bioenergetics under physiological and pathophysiological conditions, and provide a data resource that can be used to interpret mitochondrial bioenergetics experiments. We first characterised the impact of several common respiratory chain impairments on experimentally-observable bioenergetic parameters. We then established an analysis pipeline to integrate simulations with experimental data and predict the molecular defects underlying experimental bioenergetic phenotypes. We applied the pipeline to data from Parkinson’s Disease models. We verified that the impaired bioenergetic profile previously measured inParkinknockout neurons can be explained by increased mitochondrial uncoupling. We then generated primary cortical neurons from aPink1KO mouse model of Parkinson’s, and measured reduced OCR capacity and increased resistance to Complex III inhibition. Here, our pipeline predicted that multiple respiratory chain impairments are required to explain this bioenergetic phenotype. Finally, we provide all simulated data as a user-friendly resource that can be used to interpret mitochondrial bioenergetics experiments, predict underlying molecular defects, and inform experimental design. Highlights The complexity of mitochondrial bioenergetics can make experimental data difficult to interpret. We simulated a computational model of mitochondrial bioenergetics in healthy and pathological conditions, and established an analysis pipeline to integrate model simulations with experimental data. We applied the pipeline to data from Parkinson’s Disease models to predict the molecular defects underlying Parkinson’s-related pathology. We provide all outputs in a user-friendly Excel file, which serves as a valuable resource to the community for insight into the effects of pathology on mitochondrial bioenergetics and for interpretation of experimental results.