Thursday, June 7, 2018 -
12:00 to 12:45
Specific location: 
Room 343

BMC Seminar Thursday, June 7th at 12:00 in room 343 Læknagarður

Speaker: Sigurður Trausti Karvelsson, PhD student of Óttar Rolfsson at the Center for systems biology, Faculty of Medicine, University of Iceland.

Title: Model-driven analysis of metabolism reveals possible drug targets for EMT in breast tissue

Breast cancer is a prevalent disease in women worldwide. Metastatic breast cancer is a more severe form of the disease, which is characterized by tumor cells that have detached from the primary tumor and spread around the body to form secondary tumors. The mechanism by which tumor cells leave the primary tumor is believed to be epithelial-to-mesenchymal transition (EMT). EMT is a known developmental process important for embryogenesis and wound healing. During EMT, epithelial cells gain a more mesenchymal phenotype: they lose polarity, adhesion to surrounding cells and/or basement membrane and become more mobile.

The metabolic changes following EMT in breast are not well defined. Metabolism is a large, complex network of interacting enzymes and metabolites. To study it as a whole system, systems biological methods are an excellent choice. This includes constraint-based modelling, linear optimization and random sampling of a metabolic network reconstruction descriptive of the cell/organism of interest.

We have built a genome-scale metabolic model (GEM) descriptive of EMT in human breast epithelium. This model is built from transcriptomic, proteomic and metabolomic data. It has been used to predict the metabolic phenotypes of breast epithelium before and after EMT. Analysis also reveals possible biomarkers and predicts metabolic genes exclusively essential for the cells pre- and post-EMT.

The model’s predictions have been validated in vitro. Isotopic labelling experiments using labelled glucose and glutamine were used to confirm changes in metabolic flux pattern. Knockdown studies using siRNA have been performed to verify the genes predicted to be essential for cells before and after EMT.

The model predicted EMT-dependent reversed flux through mitochondrial NADP-dependent isocitrate dehydrogenase, indicative of reductive carboxylation. Labelling experiments confirmed this. Two genes predicted to be essential for the mesenchymal cells were confirmed in vitro, providing potential drug targets for targeting cancer cells undergoing malignant transformation.

Taken together, we have identified EMT-related metabolic changes in breast epithelium by integrating several types of omics-data into a genome-scale metabolic network. It has allowed us to predict differential metabolic pathway usage and potential biomarkers and/or drug targets of EMT in breast epithelium.

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