Primary Supervisor: Dr Jenny Tullet (University of Kent)
Secondary Supervisor: Dr Kieron Edwards (Sibelius Natural Products)
Over-eating can lead to serious health complications, but how do we know when to stop eating? This project focuses on how the systems controlling these decisions are made, so that they can be harnessed to improve the health and fitness of our population.
Information on food type and its availability is obtained via the sensory nervous system and integrates with internal cues from the body relating to hunger or satiety. These signals control food intake, maintain homeostasis and prevent disease. The nematode worm C. elegans is an excellent model to study this because they have a fully mapped nervous system, and a wide selection of strong genetic, molecular and behavioural tools. In worms, chemosensory neurons sense food in the environment and relay information to the rest of the animal via hormones (e.g. insulins) to control behaviour and physiology.
Our work in worms has recently identified that neuronal SKN-1 controls these processes. This multi-disciplinary project will map the neuronal circuits involved, identify novel genetic interactors of SKN-1 and fully explore the physiological effects of disrupted satiety on whole body physiology. SKN-1 is also a well-characterised longevity gene (Tullet et al, 2008, Tullet et al., 2017), and as food intake has massive implications for disease (e.g. diabetes, cardiovascular disease), by studying SKN-1’s neurological role we will be able to link food-related behaviour, longevity, and age-related health. In parallel, through our partnership with Sibelius Natural products (see letter of support) we will explore ways to increase our productivity using their high throughput platform (Chronoscreen™).
The mammalian equivalent of SKN-1, Nrf, is expressed in regions of the brain important for regulating food-related behaviour, but has so far not been implicated in these processes. This creative, curiosity-driven project will provide a step change in the way we view Nrf function.