Líffræðifélag Íslands - biologia.is
Líffræðiráðstefnan 2019

Erindi/veggspjald / Talk/poster E54

Using Genome Scale Metabolic Models to Optimise Differentiation in Mesenchymal Stem Cells

Höfundar / Authors: Sarah McGarrity(1), Þóra B. Sigmarsdóttir(1), David Snorrasson(1), Óttar Rolfsson(2,3) and Ólafur Sigurjónsson(1,4)

Starfsvettvangur / Affiliations: 1. School of Science and Engineering, University of Reykjavík, Reykjavík, Iceland 2. Biomedical Center, University of Iceland, Reykjavík, Iceland 3. Center For Systems Biology, University of Iceland, Reykjavík, Iceland 4. The Blood bank, Landspitali The University Hosptial of Iceland, Reykjavík, Iceland

Kynnir / Presenter: Sarah McGarrity

Mesenchymal stem cells are a promising but not perfect source of cells in regenerative. One area that may be optimised to improve cell growth and differentiation is metabolism. It is known that the metabolism of MSCs alters during differentiation. In particular how the availability of metabolites may affect the success of any implantation. Genome scale constraint based metabolic integrates various data types; genomic, transcriptomic and metabolomics. Initial steps in applying this methodology to chondrocyte differentiation in MSCs have been made by another group. We are now using this methodology to apply to osteogenesis and adipogenesis.
We have combined transcriptomic data and new metabolic measurements in the CobraToolbox to create models of the early stages of expansion, adipogenic and osteogenic differentiation. By applying COBRA techniques to this data, such as the reporter metabolites and metabolic transition algorithms, we are able to explore the metabolic changes that favour osteogenesis or expansion.
The models were able to recreate known metabolic features of mesenchymal stem cells during expansion and differentiation. These include relative levels of oxidative phosphorylation and glycolysis, in the expansion versus the differentiation models. By better understanding these metabolic changes during osteogenesis it will be possible to optimise future growth and differentiation experiments.