Líffræðifélag Íslands
Líffræðiráðstefnan 2015
Erindi/veggspjald / Talk/poster V70
Sarah McGarrity (1), Haraldur Halldórsson (2), Sirus Palsson (4), Pär Johansson (3), and Ottar Rolfsson (1)
1. Center for Systems Biology, University of Iceland, Reykjavik, Iceland, 2. Department of Pharmacology and Toxicology, School of Health Sciences, University of Iceland, Reykjavik, Iceland, 3. Section for Transfusion Medicine, Capital Region Blood Bank, Copenhagen University Hospital, Rigshospitalet, Denmark, 4. Sinopia Biosciences Inc, San Diego, California, USA,
Kynnir / Presenter: Sarah McGarrity
Tengiliður / Corresponding author: Ottar Rolfsson (ottarr@hi.is)
Endothelial cells line blood vessels and perform many important functions. Endothelial dysfunction contributes to chronic and acute conditions such as atherosclerosis and sepsis. Links between endothelial permeability and metabolism have been shown [1]. Links between endothelial metabolism and function, however, remain incompletely defined. To address this we have constructed a genome scale metabolic model (GEM) of human umbilical vein endothelial cells (HUVEC), a cell culture model for endothelium. Recon 1 [2] was constrained to include reactions specific to HUVECs using publically available transcriptomic data. Model curation is currently ongoing. Gaps are addressed by adding reactions from Recon 2 [3] supported by bibliomic data. To further constrain the model new metablomic data from HUVECs was used to further constrain baseline metabolite fluxes, capturing key metabolic phenotypes. Investigating endothelial cell metabolism will increase understanding of endothelial cell functions. The context-specific model of HUVEC cell metabolism described here will ultimately allow correlation of metabolic phenotypes with biomarkers of endothelial dysfunction. 1. Patella, F., et al., Proteomics-Based Metabolic Modeling Reveals That Fatty Acid Oxidation (FAO) Controls Endothelial Cell (EC) Permeability. Molecular & Cellular Proteomics, 2015. 14(3): p. 621-634. 2. Duarte, N., et al., Global reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS, 2007. 104(6): p. 1777-1782. 3. Thiele, I., et al., A community-driven global reconstruction of human metabolism. Nature Biotechnology, 2013. 31(5): p. 419-425.