Abstract
Zebrafish have been used a prominent model for high-throughput phenotypic screens of candidate risk gene mutations for several disorders. This also includes models for attention deficit/hyperactivity disorder (ADHD). Traditional behavioural tests, such as the forced light/dark assay, concentrate on basic locomotion measures. However, recently developed visually-driven locomotion assays, for example closed-loop systems using virtual reality, have allowed extraction of richer data on animal locomotion and decision-making under different sensory stimuli. Here, we have used such a system to assess the behaviour in adgrl3.1 mutant fish, an established model for ADHD. Our results show that mutants exhibit a higher baseline excitability and a lower threshold for initiating motor events, demonstrating that collecting behavioural responses in an interactive environment enables a more precise characterisation of ADHD-relevant phenotypes associated with adgrl3.1 disruption. More generally, we establish a scalable translational platform to screen gene-function relationships and possible therapeutic interventions, not only for ADHD but multiple neurodevelopmental disorders.
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Abstract
Zebrafish have been used a prominent model for high-throughput phenotypic screens of candidate risk gene mutations for several disorders. This also includes models for attention deficit/hyperactivity disorder (ADHD). Traditional behavioural tests, such as the forced light/dark assay, concentrate on basic locomotion measures. However, recently developed visually-driven locomotion assays, for example closed-loop systems using virtual reality, have allowed extraction of richer data on animal locomotion and decision-making under different sensory stimuli. Here, we have used such a system to assess the behaviour in adgrl3.1 mutant fish, an established model for ADHD. Our results show that mutants exhibit a higher baseline excitability and a lower threshold for initiating motor events, demonstrating that collecting behavioural responses in an interactive environment enables a more precise characterisation of ADHD-relevant phenotypes associated with adgrl3.1 disruption. More generally, we establish a scalable translational platform to screen gene-function relationships and possible therapeutic interventions, not only for ADHD but multiple neurodevelopmental disorders.
Competing Interest Statement
The authors have declared no competing interest.
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