Describing the Glucose-Lactate Consumption Rate during Expansion and Osteogenic Differentiation of Human Bone Marrow Derived Mesenchymal Stem Cells: A Premise for Building a Systems Biology Model of Osteogenesis Using Metabolomics Analysis

Transplant of cells derived from mesenchymal stem cells to treat diseases such as osteoporosis is tantalisingly close. The production of cells for transplant requires further study and currently there is lack of knowledge on metabolomic changes that occur during expansion and osteogenic differentiation (OD) of MSCs. It has been shown that mesenchymal stem cell osteogenic differentiation is accompanied by metabolic shifts, especially the utilisation of glucose. In order to better understand this process and expand the knowledge of other metabolic pathways that interact with stem cell fate this project is building constraint based metabolic models to integrate transcriptomic and metabolomics data.

Publically available transcriptomic data and new glucose and lactate measurements were combined in the CobraToolbox. This created initial models of the first stages of expansion and osteogenic differentiation based. Functional assays confirmed the process of osteogenesis in vitro.

Glucose and lactate measurements from six donors during expansion and osteogenic differentiation indicate multiple stages to these processes. . The final stage of expansion shows an increase in the use of glucose, without an increase in lactate production. In osteogenesis the average glucose consumption and lactate production measures higher over the later 14 days, giving an indication of the possibility of two phenotypes.

The models were able to recreate known metabolic features of mesenchymal stem cells during expansion and osteogenesis. These include relative levels of oxidative phosphorylation to glycolysis and production of kynurenine from tryptophan. These models also suggest glycan production as well as more central metabolism as areas key to the shift from expansion to osteogenic differentiation.

Genome scale metabolic modelling offers a way of combining multiple types of omics data in order to better understand and manipulate the features of a biological system.