Human Placental Mesenchymal Stem/Stromal cells (pMSCs) inhibit agonist-induced platelet functions reducing atherosclerosis and thrombosis phenotypes.
Abdullah Al SubayyilYasser S BasmaeilReem AlenziTanvir KhatlaniPublished in: Journal of cellular and molecular medicine (2021)
Mesenchymal stem/stromal cells isolated from human term placenta (pMSCs) have potential to treat clinically manifested inflammatory diseases. Atherosclerosis is a chronic inflammatory disease, and platelets play a contributory role towards its pathogenesis. During transplantation, MSCs interact with platelets and exert influence on their functional outcome. In this study, we investigated the consequences of interaction between pMSCs and platelets, and its impact on platelet-mediated atherosclerosis in vitro. Human platelets were treated with various types of pMSCs either directly or with their secretome, and their effect on agonist-mediated platelet activation and functional characteristics were evaluated. Human umbilical vein endothelial cells (HUVECs) were used as control. The impact of pMSCs treatment on platelets was evaluated by the expression of activation markers and by platelet functional analysis. A subset of pMSCs reduced agonist-induced activation of platelets, both via direct contact and with secretome treatments. Decrease in platelet activation translated into diminished spreading, limited adhesion and minimized aggregation. In addition, pMSCs decreased oxidized LDL (ox-LDL)-inducedCD36-mediated platelet activation, establishing their protective role in atherosclerosis. Gene expression and protein analysis show that pMSCs express pro- and anti-thrombotic proteins, which might be responsible for the modulation of agonist-induced platelet functions. These data suggest the therapeutic benefits of pMSCs in atherosclerosis.
Keyphrases
- endothelial cells
- high glucose
- gene expression
- cardiovascular disease
- diabetic rats
- bone marrow
- drug induced
- poor prognosis
- pluripotent stem cells
- binding protein
- vascular endothelial growth factor
- risk assessment
- low density lipoprotein
- machine learning
- pulmonary embolism
- electronic health record
- dna methylation
- big data
- cystic fibrosis
- escherichia coli
- pseudomonas aeruginosa
- high resolution
- artificial intelligence
- deep learning
- biofilm formation
- atomic force microscopy
- anti inflammatory
- human health
- gestational age
- amino acid