Poster Presentation 2nd Australian Cancer and Metabolism Meeting 2017

Elucidating and modelling the amino acid transportomes of A549 lung cancer and HCC1806 breast cancer cells. (#61)

Gregory Gauthier-Coles 1 , Ong JS 1 , Bröer S 1
  1. Research School of Biology, The Australian National University, Acton, ACT, Australia

Cancer cells’ proliferative demands are met, in part, through their high consumption of amino acids, the supply of which is primarily maintained through extracellular uptake via amino acid transporters (AATs). Understanding how transporters contribute to cellular amino acid homeostasis could identify novel targets for chemotherapy. Mathematical modelling provides feedback as to the level of our understanding of complex systems and it has therefore been the aim of our lab to devise and validate a computational model of amino acid homeostasis. The expression and activities of plasma membrane AATs serve as main inputs for the model and were studied in two cancer cell lines (A549 and HCC1806) using methods such as RT-PCR, surface biotinylation and western blotting, radiolabelled amino acid uptake experiments, and quantification of intracellular amino acids using HPLC. Measured cytosolic amino acid concentrations were then compared to the simulated concentrations. These experiments confirmed that most neutral amino acid transport occurs via ASCT2, LAT1, and SNAT1: AATs that were highly expressed in both cell lines. Increasing specific amino acid concentrations in the media, resulted in corresponding increases of the same amino acids in the cytosol without lowering concentrations of other amino acids through competitive inhibition and these results were mirrored in computer simulations. This finding supports the notion that amino acid antiporters, AATs which exchange intracellular for extracellular substrates, function by equilibrating cytosolic and extracellular amino acid pools. Although rates of intracellular amino acid depletion through protein synthesis have been measured and included in our model, the elucidation of depletion rates through metabolism remain a priority in improving upon the predictive power of the model.