GEM-based metabolic profiling for Human Bone Osteosarcoma under different glucose and glutamine availability

Cancer cell metabolism is dependent on cell-intrinsic factors like genetics, and cell-extrinsic factors like nutrient availability. In this context, understanding how these two aspects interact and how diet influences cellular metabolism is important for developing personalized treatment. In order to achieve this goal, genome-scale metabolic models (GEMs) are used, however, genetics and nutrient availability are rarely considered together. Here, we propose an integrated metabolic profiling, a framework that allows to enrich GEMs with metabolic gene expression data and information about nutrients. First, the RNA-seq is converted into Reaction Activity Score (RAS) to further scale reaction bounds. Second, nutrient availability is converted to Maximal Uptake Rate (MUR) to modify exchange reactions in a GEM. We applied our framework to the human osteosarcoma cell line (U2OS). Osteosarcoma is a common and primary malignant form of bone cancer with poor prognosis, and, as indicated in our study, a glutamine-dependent type of cancer.