Machine learning discriminates a movement disorder in a zebrafish model of Parkinson's disease

Gideon L. Hughes, Michael Adam Lones, Matthew Bedder, Peter D. Currie, Stephen L. Smith, Mary E. Pownall

Research output: Contribution to journalArticle


Animal models of human disease provide an in vivo system that can reveal molecular mechanisms by which mutations cause pathology, and, moreover, have the potential to provide a valuable tool for drug development. Here we have developed a zebrafish model of Parkinson’s disease (PD) together with a novel method to screen for movement disorders in adult fish, pioneering a more efficient drug testing route. Mutation of the PARK7 gene (DJ-1) is known to cause monogenic autosomal recessive PD in humans and, using CRISPR/Cas9 gene editing we have generated a DJ-1 loss of function zebrafish with molecular hallmarks of PD. To establish whether there is a human-relevant parkinsonian phenotype in our model, we have adapted proven tools used to diagnose PD in clinics and have developed a novel and unbiased computational method to classify movement disorders in adult zebrafish. Using high-resolution video capture and machine learning, we have extracted novel features of movement from continuous data streams and used an evolutionary algorithm (EA) to classify parkinsonian fish. This method will be widely applicable for assessing zebrafish models of human motor diseases and provide a valuable asset for the therapeutics pipeline. In addition, interrogation of RNA-seq data indicate metabolic reprogramming of brains in the absence of DJ-1, adding to growing evidence that disruption of bioenergetics is a key feature of neurodegeneration.
Original languageEnglish
JournalDisease Models and Mechanisms
Early online date28 Aug 2020
Publication statusE-pub ahead of print - 28 Aug 2020

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