JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
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Optimisation of growth conditions for ovine airway epithelial cell differentiation at an air-liquid interface.

Respiratory tract infections are of significant concern in the agriculture industry. There is a requirement for the development of well-characterised in vitro epithelial cell culture models in order to dissect the diverse molecular interactions occurring at the host-pathogen interface in airway epithelia. We have analysed key factors that influence growth and differentiation of ovine tracheal epithelial cells in an air-liquid interface (ALI) culture system. Cellular differentiation was assessed at 21 days post-ALI, a time-point which we have previously shown to be sufficient for differentiation in standard growth conditions. We identified a dose-dependent response to epidermal growth factor (EGF) in terms of both epithelial thickening and ciliation levels. Maximal ciliation levels were observed with 25 ng ml-1 EGF. We identified a strict requirement for retinoic acid (RA) in epithelial differentiation as RA exclusion resulted in the formation of a stratified squamous epithelium, devoid of cilia. The pore-density of the growth substrate also had an influence on differentiation as high pore-density inserts yielded higher levels of ciliation and more uniform cell layers than low pore-density inserts. Differentiation was also improved by culturing the cells in an atmosphere of sub-ambient oxygen concentration. We compared two submerged growth media and observed differences in the rate of proliferation/expansion, barrier formation and also in terminal differentiation. Taken together, these results indicate important differences between the response of ovine tracheal epithelial cells and other previously described airway epithelial models, to a variety of environmental conditions. These data also indicate that the phenotype of ovine tracheal epithelial cells can be tailored in vitro by precise modulation of growth conditions, thereby yielding a customisable, potential infection model.

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