Cancer is a major cause of illness and death in Australia. Key risk factors for colorectal cancer and many other cancer types are poor diet, obesity and age. In animals, short periods of nutrient deprivation, such as intermittent fasting, have been shown to provide benefits with regard to cancer risk and ageing. We aim to identify how this is occurring at the protein-level, determine how intermittent fasting can improve metabolic health and improve the nutrient deprivation regimes for implementation in humans, for the prevention and treatment of cancer and metaboilc disease.
Intermittent Fasting Biology
Regimes of nutrient deprivation such as intermittent fasting, has been shown to reduce metabolic disease risk and improve longevity with healthier ageing. The beneficial effects of intermittent fasting have been observed in many model organisms and humans. We are applying state-of-the-art quantitative proteomics to give an unprecedented insight into proteins and their interactions during intermittent fasting in both mice as model mammals and humans.
Method Development for
Mass Spectrometry-based Proteomics
Mass spectrometry when coupled with high resolution liquid chromatography is a key technology for protein analysis. We have established several workflows for the sensitive analysis of protein-protein interactions in mammalian tissues such as liver and protein abundance analysis in human blood plasma. These methods allow us to monitor the effects of intermittent fasting and other dietary interventions in great detail.
Protein/Peptide Fractionation and Purification
Prior to mass spectrometry-based proteome analysis we often need to fractionate either protein complexes, proteins, or peptides. This allows us to improve sensitivity and/or derive more biological information from each sample. One of the most common techniques we employ is size-exclusion chromatography (SEC). For the analysis of specific proteins, we often using GFP-tagged proteins and immunoprecipitation.
Data Analysis and Visualisation
Experiments involving mass spectrometry-based proteomics generate enormous amounts of peptide/protein data, that need to be processed, filtered, normalised and tested for statistical significance. These data also need to be visualised in a meaningful way, with the possibility of interactive visualisations.