Intracellular Calcium Determines the Adipogenic Differentiation Potential of Human Umbilical Cord Blood-Derived Mesenchymal Stem Cells via the Wnt5a/β-Catenin Signaling Pathway.
Yun Kyung BaeJi Hye KwonMiyeon KimGee-Hye KimSoo Jin ChoiWonil OhYoon Sun YangHye Jin JinHong Bae JeonPublished in: Stem cells international (2018)
Mesenchymal stem cells- (MSCs-) based therapies show different degrees of efficacies for the treatment of various diseases, including lipogenesis. We evaluated the adipogenic differentiation ability of human umbilical cord blood-derived MSCs (hUCB-MSCs) from different donors and examined the contribution of the intracellular calcium (Ca2+) level to this diversity. hUCB-MSCs treated with Ca2+ or the Ca2+ chelator BAPTA-AM increased and decreased adipogenic differentiation, respectively. Canonical Wnt5a/β-catenin expression decreased during adipogenic differentiation of hUCB-MSCs. Treatment with Wnt5a blocked the adipogenic differentiation of hUCB-MSCs and activated the Wnt pathway, with a decrease in the adipogenesis markers PPARγ and leptin, and reduced lipid vacuole-associated Oil red O activity. In contrast, inhibition of the Wnt pathway with dickkopf-1 and β-catenin small interfering RNA transfection promoted the adipogenic potential of hUCB-MSCs. Interestingly, the Ca2+-based system exhibited a synergic effect on adipogenic potential through the Wnt5a/β-catenin pathway. Our data suggest that the variable adipogenic differentiation potential of hUCB-MSCs from different lots is due to variation in the intracellular Ca2+ level, which can be used as a marker to predict hUCB-MSCs selection for lipogenesis therapy. Overall, these results demonstrate that exogenous calcium treatment enhanced the adipogenic differentiation of hUCB-MSCs via negatively regulating the Wnt5a/β-catenin signaling pathway.
Keyphrases
- mesenchymal stem cells
- umbilical cord
- cell proliferation
- cord blood
- stem cells
- bone marrow
- cell therapy
- computed tomography
- epithelial mesenchymal transition
- type diabetes
- machine learning
- magnetic resonance
- skeletal muscle
- fatty acid
- metabolic syndrome
- protein kinase
- risk assessment
- combination therapy
- signaling pathway
- binding protein
- newly diagnosed
- kidney transplantation
- artificial intelligence