HGF Mediates Clinical-Grade Human Umbilical Cord-Derived Mesenchymal Stem Cells Improved Functional Recovery in a Senescence-Accelerated Mouse Model of Alzheimer's Disease.
Yali JiaNing CaoJinglei ZhaiQuan ZengPei ZhengRuyu SuTuling LiaoJiajing LiuHaiyun PeiZeng FanJunnian ZhouJiafei XiLijuan HeLin ChenXue NanWen YueXuetao PeiPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2020)
Stem cells have emerged as a potential therapy for a range of neural insults, but their application in Alzheimer's disease (AD) is still limited and the mechanisms underlying the cognitive benefits of stem cells remain to be elucidated. Here, the effects of clinical-grade human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) on the recovery of cognitive ability in SAMP8 mice, a senescence-accelerated mouse model of AD is explored. A functional assay identifies that the core functional factor hepatocyte growth factor (HGF) secreted from hUC-MSCs plays critical roles in hUC-MSC-modulated recovery of damaged neural cells by down-regulating hyperphosphorylated tau, reversing spine loss, and promoting synaptic plasticity in an AD cell model. Mechanistically, structural and functional recovery, as well as cognitive enhancements elicited by exposure to hUC-MSCs, are at least partially mediated by HGF in the AD hippocampus through the activation of the cMet-AKT-GSK3β signaling pathway. Taken together, these data strongly implicate HGF in mediating hUC-MSC-induced improvements in functional recovery in AD models.
Keyphrases
- umbilical cord
- mesenchymal stem cells
- stem cells
- endothelial cells
- signaling pathway
- mouse model
- growth factor
- cell therapy
- induced apoptosis
- high glucose
- pi k akt
- bone marrow
- cognitive decline
- dna damage
- induced pluripotent stem cells
- pluripotent stem cells
- gene expression
- stress induced
- drug induced
- epithelial mesenchymal transition
- metabolic syndrome
- genome wide
- cell proliferation
- liver injury
- oxidative stress
- mild cognitive impairment
- type diabetes
- big data
- endoplasmic reticulum stress
- machine learning
- risk assessment
- adipose tissue
- deep learning
- insulin resistance
- cerebrospinal fluid
- artificial intelligence