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Title: Machine learning discriminates parkinsonian movement disorders in zebrafish
Author: Hughes, Gideon
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2019
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Parkinson’s disease is caused by a progressive loss of dopamine neurons in the substantia nigra. A loss of motor control occurs in patients, with symptoms including bradykinesia, resting tremor and muscle rigidity. There is currently no cure for Parkinson’s disease and the commonly used treatment, L-DOPA, can cause side effects including dyskinesia. To identify potential treatments, drug screening using a suitable animal model is required before reaching clinical trials. Zebrafish are a vertebrate model organism well suited for high throughput drug screens, and genome editing can be used to create heritable mutations in causative genes to model human disease. This thesis presents five genetic models of Parkinson’s disease in the zebrafish created by CRISPR/Cas9 targeting (pink1, parkin, dj-1, fbxo7, and gba). Molecular analyses show a loss of dopamine neurons in the brain of the DJ-1 deficient zebrafish. This thesis also presents findings from a transcriptomic analysis of the dj-1 mutant brain revealing dysregulated genes consistent with known parkinsonian defects. An important focus of this work is the development of a novel computational method to analyse the movement phenotype of a zebrafish. The method developed uses high-speed recordings of zebrafish swimming, processed with a new fish tracking software. By measuring spatial coordinates and angles along the spine, swimming movement was converted into data suitable for computational analysis. The movement data was subjected to unbiased analysis employing a white-box supervised machine learning method (an Evolutionary Algorithm) which successfully discriminated the dj-1 mutant from wild type. This thesis concludes that the DJ-1 deficient zebrafish is a representative animal model of Parkinson’s disease, and that machine learning can be used to classify the model based on movement data alone. It is proposed that the techniques developed here, have the potential for drug screens on the dj-1 mutant using evolved classifiers to assess treatment effectiveness.
Supervisor: Pownall, Betsy ; Smith, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available