Genome-scale screens identify JNK-JUN signaling as a barrier for pluripotency exit and endoderm differentiation.
Qing V LiGary DixonNipun VermaBess P RosenMiriam GordilloRenhe LuoChunlong XuQiong WangChew-Li SohDapeng YangMiguel CrespoAbhijit ShuklaQing XiangFriederike DündarPaul ZumboMatthew WitkinRichard KocheDoron BetelShuibing ChenJoan MassaguéRalph GarippaTodd EvansMichael A BeerDanwei HuangfuPublished in: Nature genetics (2019)
Human embryonic stem cells (ESCs) and human induced pluripotent stem cells hold great promise for cell-based therapies and drug discovery. However, homogeneous differentiation remains a major challenge, highlighting the need for understanding developmental mechanisms. We performed genome-scale CRISPR screens to uncover regulators of definitive endoderm (DE) differentiation, which unexpectedly uncovered five Jun N-terminal kinase (JNK)-JUN family genes as key barriers of DE differentiation. The JNK-JUN pathway does not act through directly inhibiting the DE enhancers. Instead, JUN co-occupies ESC enhancers with OCT4, NANOG, SMAD2 and SMAD3, and specifically inhibits the exit from the pluripotent state by impeding the decommissioning of ESC enhancers and inhibiting the reconfiguration of SMAD2 and SMAD3 chromatin binding from ESC to DE enhancers. Therefore, the JNK-JUN pathway safeguards pluripotency from precocious DE differentiation. Direct pharmacological inhibition of JNK significantly improves the efficiencies of generating DE and DE-derived pancreatic and lung progenitor cells, highlighting the potential of harnessing the knowledge from developmental studies for regenerative medicine.
Keyphrases
- embryonic stem cells
- signaling pathway
- genome wide
- induced pluripotent stem cells
- epithelial mesenchymal transition
- cell death
- transforming growth factor
- induced apoptosis
- endothelial cells
- drug discovery
- healthcare
- transcription factor
- dna methylation
- gene expression
- crispr cas
- dna damage
- big data
- mesenchymal stem cells
- single cell
- squamous cell carcinoma
- oxidative stress
- bone marrow
- genome editing
- artificial intelligence
- machine learning