Proteomic profiling of the lung epithelium to identify targets and treatments for the prevention of diabetes associated lung infection

Diabetes Mellitus (DM) is a chronic, non-communicable disease where the homeostasis of circulatory glucose fails. Diabetes is burden on health economies due the long term care of chronic symptoms and risk of acute infections. This burden is on the rise, as prevalence of diabetes increases globally.
DM is known to effect the host's immune response and can contribute to the probability of infection in chronic non-communicable diseases. Chronic conditions such as chronic obstructive pulmonary disease and cystic fibrosis, both commonly comorbid with DM, show an increased frequency of respiratory infection when DM's present. DM causes glucose to diffuse from the blood, across the lung epithelial cells to the protective airway surface liquid (ASL). Inflammation from infection loosens the tight junctions between epithelial cells exacerbating this effect. Raised glucose levels provides a carbon source, allowing infectious bacterial agents to become established.
DM during a lung infections induces changes to the ASL chemistry, such as changes to the anti-microbial peptides and proteins responsible for pH and volume regulation by the lung epithelium. This can specifically favour bacterial infections such as Pseudomonas aeruginosa.
This project will focus on the effect DM's hyperglycaemia has on the proteome of the lung epithelium. This will be done by examining changes to both abundance and modifications of the proteins and peptides of the ASL. The potential effect these changes to the proteins and peptides of the ASL have on infection can be measured through the respiratory pathogens fitness. Proteins identified by this project could provide new targets and prevention strategies for DM associated lung infections.
Each cell line will be grown in various glucose concentration conditions normoglycaemic (base of 5mM), acute hyperglycaemic and chronic hyperglycaemic (20mM). Media will be osmotically balanced with mannitol.
Acute hyperglycaemic cells will be exposed for 24 hours and the chronic will be exposed for 120 hours, times chosen from their effect on membranous receptor for advanced glycation end products and cytosolic aldose reductase. The ASL will then be collected by washing the apical surface with 100mcL of Dulbecco's phosphate buffered saline (++). These washes will then be separated for de novo sequencing of peptides and digestion of the proteome using trypsin.
Liquid chromatography (LC) tandem mass spectrometry (MS2) has been chosen to analyse the proteome and peptidome. Native and digested peptides will be separated through LC and ionised in the 'soft' ionisation phase, size sorted fragments will be ionised and identified through their mass:charge ratio (m/z). Detected peptides will go on to the 'hard' ionisation phase, which will further fragment the peptides detailing the composition of each fragment through their m/z. The sequence of native peptides will be identified from the spectra produced, while digested peptides will be analysed against a database of spectra to identify the protein of origin and their relative abundances examined between the glycaemic conditions.
LC-MS2 will provide the quantitative data to show the abundance of proteins and structural data to show any chemical changes in each growth condition. While all the proteome will be examined, the focus will be proteins related to innate immunity and ASL composition. Peptides suspected to be bioactive (i.e. those related to histones and fibrinogen) will be synthetically synthesised to examine their effect on cellular response and bacterial fitness.
The complex nature of the experimental component, and the large volumes of data will require collaboration, education and training with experts from a variety of backgrounds. These will range from statisticians to clinical and non-clinical scientists. To support these requirements additional training is being taken through the graduate skills program and a range of Statistical-computing module.