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

## TL;DR

This paper combines simulations and experiments to study mitochondrial energy issues in Parkinson’s Disease models and provides a tool to interpret such experiments.

## Contribution

A novel pipeline integrating simulated and experimental data to identify mitochondrial defects in Parkinson’s Disease models.

## Key findings

- Impaired bioenergetic profile in Parkin KO neurons is explained by increased mitochondrial uncoupling.
- Primary cortical neurons from Pink1 KO mice show reduced OCR and resistance to Complex III inhibition.
- Multiple impairments are likely needed to explain the observed bioenergetic phenotype in Pink1 KO neurons.

## Abstract

Mitochondrial bioenergetics are vital for ATP production and are associated with several diseases, including Parkinson’s Disease (PD). 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 electron transport chain (ETC) 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 PD models. We verified that the impaired bioenergetic profile previously measured in Parkin knockout (KO) neurons can be explained by increased mitochondrial uncoupling. We then generated primary cortical neurons from a Pink1 KO mouse model of PD, and measured reduced oxygen consumption rate (OCR) capacity and increased resistance to Complex III inhibition. Here, our pipeline predicted that multiple 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.