Líffræðifélag Íslands
Líffræðiráðstefnan 2015
Erindi/veggspjald / Talk/poster V71
Sonal Kumari(1), Neha Rohatgi(1), Skarphéðinn Halldórsson(1), Steinn Gudmundsson(1) and Óttar Rolfsson(1)
1. Center for Systems Biology, University of Iceland, Sturlugata 8, Reykjavik, Iceland
Kynnir / Presenter: Sonal Kumari
Tengiliður / Corresponding author: Óttar Rolfsson (ottarr@hi.is)
EMT is a cellular developmental event that allows polarized epithelial cells to assume a mesenchymal like phenotype with enhanced migratory and invasive properties. EMT is important for tumor progression and metastasis yet knowledge of metabolism during EMT is limited. Here, we report the construction of signaling networks and their integration with cell specific metabolic networks to afford a systems view of EMT signalling and metabolic cross talk in breast epithelium. A network of major signaling events in EMT was built using a stoichiometric approach describing molecular transformation and a Boolean description of activations and inhibitions. The network was constrained with transcriptomic (RNA sequencing) data to incorporate changes in altered gene expression of signalling proteins in an in-house immortalised breast epithelial cell prior to, and following EMT1. This was followed by building a literature based matrix describing regulatory rules connecting signaling pathways and their target metabolic genes. This enabled predictions of how signaling and transcriptional perturbations are translated into flux responses at the metabolic level. Network of EGFR built using the above constraints predicted EGFR signaling is more pronounced in epithelial than in mesenchymal cells. This suggests that EGFR signaling pathway increases during EMT and once the mesenchymal state is reached, the pathway gets attenuated. Upregulated signaling increases glycolysis suggesting EMT shifts glycolytic capacity of the cells. In order to address whether the identified hallmarks of the EMT are general, we intend further to map relevant publically available transcriptomic datasets onto the regulatory EMT reconstruction and compare resulting computed metabolic phenotypes through a similar pipeline process. This will result in a detailed description of metabolic alterations due to molecular signalling associated with EMT.