Essential Role of Adhesion GPCR, GPR123, for Human Pluripotent Stem Cells and Reprogramming towards Pluripotency.
Olga A KrasnovaKarina A KulakovaJulia V SopovaEvgenyi Y SmirnovSergey A SilonovEkaterina V LomertOlga A BystrovaMarina G MartynovaIrina E NeganovaPublished in: Cells (2023)
G-protein-coupled receptors (GPCRs) are the largest family of cell surface receptors. They modulate key physiological functions and are required in diverse developmental processes including embryogenesis, but their role in pluripotency maintenance and acquisition during the reprogramming towards hiPSCs draws little attention. Meanwhile, it is known that more than 106 GPCRs are overexpressed in human pluripotent stem cells (hPSCs). Previously, to identify novel effectors of reprogramming, we performed a high-throughput RNA interference (RNAi) screening assay and identified adhesion GPCR, GPR123, as a potential reprogramming effector. Its role has not been explored before. Herein, by employing GPR123 RNAi we addressed the role of GPR123 for hPSCs. The suppression of GPR123 in hPSCs leads to the loss of pluripotency and differentiation, impacted colony morphology, accumulation of cells at the G2 phase of the cell cycle, and absence of the scratch closure. Application of the GPR123 RNAi at the initiation stage of reprogramming leads to a decrease in the percentage of the "true" hiPSC colonies, a drop in E-cadherin expression, a decrease in the percentage of NANOG+ nuclei, and the absence of actin cytoskeleton remodeling. Together this leads to the absence of the alkaline-phosphatase-positive hiPSCs colonies on the 18th day of the reprogramming process. Overall, these data indicate for the first time the essential role of GPR123 in the maintenance and acquisition of pluripotency.
Keyphrases
- pluripotent stem cells
- fatty acid
- cell cycle
- high throughput
- endothelial cells
- embryonic stem cells
- cell surface
- poor prognosis
- escherichia coli
- induced pluripotent stem cells
- long non coding rna
- biofilm formation
- cell migration
- electronic health record
- single cell
- machine learning
- big data
- regulatory t cells
- artificial intelligence