Award details

Metabolic and behavioural phenotyping platform for obesity, diabetes, aging and exercise studies in mouse

Reference	BB/W020009/1
Principal Investigator / Supervisor	Professor Stefan Trapp
Co-Investigators / Co-Supervisors	Dr Daniel Brierley, Professor Frances Martha Brodsky, Mr Michael Brown, Dr Alexander Gourine, Dr Huiliang Li, Dr Andrew MacAskill, Professor Ahad Rahim, Professor Bart Vanhaesebroeck
Institution	University College London
Department	Neuroscience Physiology and Pharmacology
Funding type	Research
Value (£)	310,569
Status	Current
Type	Research Grant
Start date	01/08/2022
End date	31/07/2023
Duration	12 months

Abstract

The requested equipment is a 16-Cage Promethion CORE metabolic and behavioural phenotyping platform for mouse studies, from Sable Systems. This equipment, which will be unique within London, enables precise and reproducible, simultaneous high-resolution recording of mouse energy intake (food and liquid), energy expenditure (respiratory gases) and activity in a homecage-like environment over extended periods of time without the need for any interference from the experimenter. For a wide range of metabolic studies, it will enable a significant advance in terms of data quality, the temporal resolution of key parameters and the ability to monitor multiple outputs, compared to what we are currently able to achieve. Recording a broad spectrum of parameters will be invaluable for comprehensive phenotyping of new transgenic mouse strains, for characterising the consequences of molecular manipulation of specific cell populations, and for studies aimed at understanding the effects of aging or exercise on metabolic, feeding and activity parameters. Of significance, the quality of the data obtained with this system allows detection of subtle behavioural phenotypes that are easily missed in acute studies using manually operated equipment.

Summary

This proposal requests funding for a Promethion CORE high-resolution behavioural and metabolic phenotyping system for mouse. Mice have become a widely used animal model for research in Diabetes, Obesity, Cancer as well as in basic research to increase our understanding of mammalian physiology, particularly in Neuroscience. Being able to gather high quality physiological data from these animals under experimental conditions is vital to further our understanding of how their bodies, as well as our own, work. In order to generate these high-quality data, it is desirable to a) keep the mice in an as 'natural' environment as possible, b) keep them in this familiar environment for the entirety of the tests to be conducted, and c) gather as many different, but connected, data sets from each animal as possible. This means to record food intake, fluid intake, body weight, energy expenditure (measured via gas analysis of breathing), activity (movement within the cage system) and exercise (wheel running) in parallel to get a complete picture of the metabolic phenotype and the behaviour of the mouse under those experimental conditions. This is achieved by the requested piece of equipment which thus also contributes to efforts to reduce and refine the use of animals in research, as it provides higher quality and quantity of data from a smaller group of animals reducing the number of mice used in research. The results from the experiments conducted using this equipment will substantially enhance ongoing and future research programmes by the applicants and others for studies to understand the physiological mechanisms underlying obesity, diabetes, cancer, exercise and aging. This technology is currently not available to UCL researchers (or elsewhere in London), hence establishing the phenotyping facility will bridge a critical technology gap at UCL.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	Advanced Life Sciences Research Technology Initiative (ALERT) [2013-2014]
Funding Scheme	X – not Funded via a specific Funding Scheme

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