Eating and Sleeping: How neuronal SKN-1/Nrf regulates satiety using the worm C. elegans.

Over-eating can lead to serious health complications. So understanding satiety regulation (when to stop eating) is important. This project focuses on how these biological decisions work so that they can be harnessed to improve human health and fitness. Information on food is obtained via the sensory nervous system and integrates with internal cues from the body relating to hunger or satiety. Integration of these signals controls food intake, maintains homeostasis and prevents disease. We study this in the nematode worm C. elegans which have a fully mapped nervous system, and come complete with a selection of strong genetic, molecular and behavioural tools. In C. elegans, chemosensory neurons sense food and relay information to the rest of the animal via hormones to control behaviour and physiology. Our lab has found that SKN-1, the worm equivalent of mammalian Nrf, acts in two ASI neurons to sense food and control satiety. To achieve this SKN-1 integrates insulin and TGF-B signalling and promotes a robust mitochondrial network required for energy homeostasis. SKN-1 is also a well-characterised longevity gene (Tullet et al, 2008), and as food intake has massive implications for disease (e.g. diabetes, cardiovascular disease), via studying SKN-1's neurological role we will not only define its role in food-related behaviour but also important links between food-related behaviour, longevity, and agerelated health. This project uses a variety of techniques: genetics, behavioural assays, molecular biology, and a variety of microscopy (including confocal and TEM) to delve into the underlying genetic and biological processes involved. The student will map the neuronal circuits, identify novel genetic interactors of SKN-1, and fully explore the physiological effects of disrupted satiety. As Nrf is expressed in regions of the mammalian brain important for regulating food-related behaviour, this project will provide a step change in the way we view Nrf function.