| Summary: | Cellular energy metabolism is a key player in both physiological and pathological scenarios. In mouse mesenchymal stem cell populations (MSC), changes in bioenergetics are associated with differentiation into adipocyte and osteoblast lineages. As new phenotypes are adopted by differentiated cells, unique metabolic profiles are observed. On a tissue scale, metabolites associated with cellular bioenergetics (e.g. pyruvate, lactate) show potential as biomarkers for disease, such as Alzheimer’s. Changes in tissue metabolic profiles are the result of the pathological mechanisms associated with disease. Additionally, metabolites are known to reflect upstream perturbations in the proteome and the genome. Thus, detection of specific metabolite signatures is a promising approach for the interpretation of physiological and pathological processes.
In this project, we explored the possible application of 1H nuclear magnetic resonance spectroscopy to observe pyruvate metabolism in real-time in mMSCs and mouse tissues (wild-type and Alzheimer’s model). Following administration of 13C-labelled pyruvate, we observed a significant increase in the production of 13C-labelled lactate and alanine. Comparison of newly generated metabolite ratios allowed us to determine the metabolic phenotype of cells and tissues. Following from there, we investigated changes in upstream gene and protein expression in relation to observed metabolite signatures. Our target enzymes were lactate dehydrogenase, alanine aminotransferase, and pyruvate dehydrogenase, as well as the genes associated with the expression of these enzymes.
Our results suggest that 1H NMR spectroscopy is a viable technique for real-time metabolic studies, as we were able to discern cell and tissue phenotypes based on the observed [1-13C]lactate/[1-13C]alanine ratios. In Alzheimer’s tissue, metabolites levels also reflected sexual dimorphism and treatment-associated effects. Indecisive results were observed when correlating metabolite signatures with changes in protein and gene expression. Lastly, using metabolomics, we identified some potential target metabolites for future investigation.
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