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.

中文翻译：

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机器学习可以区分帕金森病斑马鱼模型中的运动障碍。

人类疾病的动物模型提供了一个体内系统，可以揭示突变引起病理的分子机制，而且有可能为药物开发提供有价值的工具。在这里，我们开发了帕金森病 (PD) 的斑马鱼模型以及一种筛选成年鱼运动障碍的新方法，开创了更有效的药物测试路线。已知PARK7基因 ( DJ-1 )突变会导致人类单基因常染色体隐性遗传性 PD，利用 CRISPR/Cas9 基因编辑，我们培育出了具有 PD 分子特征的 DJ-1 功能丧失斑马鱼。为了确定我们的模型中是否存在与人类相关的帕金森表型，我们采用了用于临床诊断帕金森病的行之有效的工具，并开发了一种新颖且公正的计算方法来对成年斑马鱼的运动障碍进行分类。利用高分辨率视频捕捉和机器学习，我们从连续数据流中提取了新颖的运动特征，并使用进化算法 (EA) 对帕金森鱼进行分类。该方法将广泛适用于评估人类运动疾病的斑马鱼模型，并为治疗管道提供宝贵的资产。此外，对 RNA-seq 数据的询问表明，在缺乏 DJ-1 的情况下大脑会发生代谢重编程，这进一步证明生物能学的破坏是神经退行性疾病的一个关键特征。