RNA sequencing profiles reveal dynamic signaling and glucose metabolic features during bone marrow mesenchymal stem...

Stem cell senescence is considered as a significant driver of organismal aging. As individuals age, the number of stem cells is declined, and the ability to proliferate and survive is also weakened. It has been reported that metabolism plays an important role in stem cell self-renewal, multilineage differentiation, senescence and fate determination, which has aroused widespread concerns. However, whether metabolism-related genes or signalling pathways are involved in physiological aging remain largely undetermined.

In the current study, we showed 868 up-regulated and 2006 down-regulated differentially expressed genes (DEGs) in bone marrow mesenchymal stem cells (MSCs) from old rats in comparison with that from young rats by performing RNA sequence. And DEGs functions and pathways were further selected by function enrichment analysis. The results indicated that the high expression of DEGs might participate in cell differentiation, growth factor binding and etc., while the down-regulated DEGs were majorly enriched in metabolism process, such as the cellular metabolic process and mitochondria. Then, we screened and verified DEGs related to glucose metabolism and investigated the glycolysis levels. We identified that glucose uptake, lactate secretion, ATP production and relative extracellular acidification rates (ECAR) were all diminished in MSCs from old rats. More importantly, we conducted microRNA prediction on the key DEGs of glycolysis to elucidate the potential molecular mechanisms of glucose metabolism affecting MSC senescence.

Our study unravelled the profiles of DEGs in age-associated MSC senescence and their functions and pathways. We also clarified DEGs related to glucose metabolism and down-regulated glycolysis level in age-associated MSC senescence. This study will uncover the metabolic effects on regulating stem cell senescence, and provide novel therapeutic targets for ameliorating age-associated phenotypes.