A live model to study mucociliary clearance in health and disease

A critical defence mechanism in the respiratory tract against inhaled particulates and pathogens is mucociliary clearance (MCC) in which mucus secreted by specialist cells is moved by ciliated epithelial cells. Defective MCC occurs in asthma, cystic fibrosis, chronic obstructive pulmonary disease and in primary ciliary dyskinesia leading to infection.

It is important to study the respiratory epithelium to understand its function in both health and in disease with the aim of developing new therapeutic strategies for treatment. Mammalian models are widely used in respiratory research. However, studying the respiratory tract in these systems has inherent difficulties, particularly observing events in real time. This is because the lungs are located internally and studying live events requires harvesting and re-constituting the tissue in vitro, taking the tissue out of its normal context and resulting in the death of many animals.

In this project, we propose the skin of the Xenopus tadpole as a model for the replacement of mammals to study mucociliary epithelia. The tadpole skin has a mucociliary surface, with both mucin-secreting cells and ciliated cells, making it ideal for live imaging of its surface and for challenge through changes in the local environment. We will use transgenic tadpoles that secrete fluorescently tagged mucins onto the skin and cilia that are also fluorescently tagged allowing study of the dynamics of MCC. The model will be challenged with agonists/ antagonists of mucin secretion and cilia beating, and pathogen products. Such a live fluorescent model for MCC has the potential to replace mammalian models, particularly mice. We anticipate a diverse use of the model in fundamental research, in the testing of novel therapeutics (as well as measuring secondary impacts of other drugs on MCC) and in mimicking human disease, including those due to genetic mutations.

Back to top
Project grant

Grant reference number

NC/S001034/1

Award date:

Aug 2018 - Jul 2021

Grant amount

£494,136