Forward Genetics-Based Approaches to Understanding the Systems Biology and Molecular Mechanisms of Epilepsy.

Epilepsy is a highly prevalent, severely debilitating neurological disorder characterized by seizures and neuronal hyperactivity due to an imbalanced neurotransmission. As genetic factors play a key role in epilepsy and its treatment, various genetic and genomic technologies continue to dissect the genetic causes of this disorder. However, the exact pathogenesis of epilepsy is not fully understood, necessitating further translational studies of this condition. Here, we applied a computational in silico approach to generate a comprehensive network of molecular pathways involved in epilepsy, based on known human candidate epilepsy genes and their established molecular interactors. Clustering the resulting network identified potential key interactors that may contribute to the development of epilepsy, and revealed functional molecular pathways associated with this disorder, including those related to neuronal hyperactivity, cytoskeletal and mitochondrial function, and metabolism. While traditional antiepileptic drugs often target single mechanisms associated with epilepsy, recent studies suggest targeting downstream pathways as an alternative efficient strategy. However, many potential downstream pathways have not yet been considered as promising targets for antiepileptic treatment. Our study calls for further research into the complexity of molecular mechanisms underlying epilepsy, aiming to develop more effective treatments targeting novel putative downstream pathways of this disorder.

A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish.

BACKGROUND: Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications have empowered automated image recognition and video-tracking to ensure more efficient behavioral testing. NEW METHOD: Capitalizing on several AI tools that most recently became available, here we present a novel open-access AI-driven platform to analyze tracks of adult zebrafish collected from in vivo neuropharmacological experiments. For this, we trained the AI system to distinguish zebrafish behavioral patterns following systemic treatment with several well-studied psychoactive drugs - nicotine, caffeine and ethanol. RESULTS: Experiment 1 showed the ability of the AI system to distinguish nicotine and caffeine with 75 % and ethanol with 88 % probability and high (81 %) accuracy following a post-training exposure to these drugs. Experiment 2 further validated our system with additional, previously unexposed compounds (cholinergic arecoline and varenicline, and serotonergic fluoxetine), used as positive and negative controls, respectively. COMPARISON WITH EXISTING METHODS: The present study introduces a novel open-access AI-driven approach to analyze locomotor activity of adult zebrafish. CONCLUSIONS: Taken together, these findings support the value of custom-made AI tools for unlocking full potential of zebrafish CNS drug research by monitoring, processing and interpreting the results of in vivo experiments.