RESUMEN
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 (which encodes DJ-1) is known to cause monogenic autosomal recessive PD in humans, and, using CRISPR/Cas9 gene editing, we 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 adapted proven tools used to diagnose PD in clinics and developed a novel and unbiased computational method to classify movement disorders in adult zebrafish. Using high-resolution video capture and machine learning, we extracted novel features of movement from continuous data streams and used an evolutionary algorithm 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.This article has an associated First Person interview with the first author of the paper.
Asunto(s)
Aprendizaje Automático , Trastornos del Movimiento/fisiopatología , Enfermedad de Parkinson/fisiopatología , Pez Cebra/fisiología , Algoritmos , Alelos , Animales , Secuencia de Bases , Encéfalo/patología , Modelos Animales de Enfermedad , Neuronas Dopaminérgicas/patología , Perfilación de la Expresión Génica , Marcación de Gen , Movimiento , Mutación/genética , Proteína Desglicasa DJ-1/genéticaRESUMEN
This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24h period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviours, but can be extracted as mathematical formulae for the parameterization of computational models.
Asunto(s)
Algoritmos , Rastreo Celular/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Imagen de Lapso de Tiempo/métodos , Adenosina Trifosfato/farmacología , Adhesión Celular , Recuento de Células , Técnicas de Cultivo de Célula , Línea Celular , Movimiento Celular/efectos de los fármacos , Células Epiteliales/clasificación , Células Epiteliales/efectos de los fármacos , Células Epiteliales/metabolismo , Redes Reguladoras de Genes/efectos de los fármacos , Humanos , Microscopía por Video/métodos , Fosfato de Piridoxal/análogos & derivados , Fosfato de Piridoxal/farmacología , Transducción de Señal/efectos de los fármacos , Transducción de Señal/genética , Urotelio/citologíaRESUMEN
This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way.