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Title: Autism-associated genes, circuits & behaviour in zebrafish
Author: Ghosh, Marcus
ISNI:       0000 0004 7965 0929
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Autism spectrum disorder (autism) is a debilitating and highly prevalent disorder, thought to affect approximately one percent of the population. Clinically autism is defined by deficits in social interaction and abnormal repetitive behaviours, though the rate of comorbidities, including epilepsy and sleep disorders, is high. Recent genetic studies have linked hundreds of genes with diverse functions to autism. However, the mechanisms by which these genetic lesions lead to autism remain unclear and consequently there are currently no effective treatments available. To uncover these mechanisms, I developed a generalisable framework for mapping the behavioural and neuronal effects of autism-associated mutations in zebrafish, an ideal model system for this approach given their genetic tractability, vertebrate brain plan and amenability to pharmacological screening. At a behavioural level I used a highthroughput set-up to monitor the activity of hundreds of zebrafish larvae across multiple days and nights and developed machine learning tools to describe behaviour in this assay. This approach provided insight into the organisation of zebrafish behaviour across the day/ night cycle as well as the effects of drug exposure. Extending this methodology to mutants in two autism-associated genes, chd8 and bckdk, identified behavioural abnormalities in both, demonstrating the power of this approach. At a circuit level I used a registration algorithm to align whole-brain 'snapshots' of neuronal activity in three dimensions to identify areas with differential activity in mutant animals. Mapping the chd8 behavioural phenotype to circuits revealed increased neuronal activity in the right, ventral tectum stratum periventriculare in chd8 mutants, providing a starting point for dissecting how this mutation impacts the brain and behaviour. Ultimately, the work described herein establishes a generalisable framework for uncovering the mechanisms by which genetic mutations lead to disease as well as for identifying potential pharmacological therapeutics for such disorders.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available