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.

Decoding Molecular Bases of Rodent Social Hetero-Grooming Behavior Using in Silico Analyses and Bioinformatics Tools.

Highly prevalent in laboratory rodents, 'social' hetero-grooming behavior is translationally relevant to modeling a wide range of neuropsychiatric disorders. Here, we comprehensively evaluated all known to date mouse genes linked to aberrant hetero-grooming phenotype, and applied bioinformatics tools to construct a network of their established protein-protein interactions (PPI). We next identified several distinct molecular clusters within this complex network, including neuronal differentiation, cytoskeletal, WNT-signaling and synapsins-associated pathways. Using additional bioinformatics analyses, we further identified 'central' (hub) proteins within these molecular clusters, likely key for mouse hetero-grooming behavior. Overall, a more comprehensive characterization of intricate molecular pathways linked to aberrant rodent grooming may markedly advance our understanding of underlying cellular mechanisms and related neurological disorders, eventually helping discover novel targets for their pharmacological or gene therapy interventions.

The emerging complexity of molecular pathways implicated in mouse self-grooming behavior.

Rodent self-grooming is an important complex behavior, and its deficits are translationally relevant to a wide range of neuropsychiatric disorders. Here, we analyzed a comprehensive dataset of 227 genes whose mutations are known to evoke aberrant self-grooming in mice. Using these genes, we constructed the network of their established protein-protein interactions (PPI), yielding several distinct molecular clusters related to postsynaptic density, the Wnt signaling, transcription factors, neuronal cell cycle, NOS neurotransmission, microtubule regulation, neuronal differentiation/trafficking, neurodevelopment and mitochondrial function. Utilizing further bioinformatics analyses, we also identified novel central ('hub') proteins within these clusters, whose genes may also be implicated in aberrant self-grooming and other repetitive behaviors in general. Untangling complex molecular pathways of this important behavior using in silico approaches contributes to our understanding of related neurological disorders, and may suggest novel potential targets for their pharmacological or gene therapy.