INO80 Is Required for the Cell Cycle Control, Survival, and Differentiation of Mouse ESCs by Transcriptional Regulation.
Seonho YooEun Joo LeeNguyen Xuan ThangHyeonwoo LaHyeonji LeeChanhyeok ParkDong Wook HanSang Jun UhmHyuk SongJeong-Tae DoYoungsok ChoiKwonho HongPublished in: International journal of molecular sciences (2022)
Precise regulation of the cell cycle of embryonic stem cells (ESCs) is critical for their self-maintenance and differentiation. The cell cycle of ESCs differs from that of somatic cells and is different depending on the cell culture conditions. However, the cell cycle regulation in ESCs via epigenetic mechanisms remains unclear. Here, we showed that the ATP-dependent chromatin remodeler Ino80 regulates the cell cycle genes in ESCs under primed conditions. Ino80 loss led to a significantly extended length of the G1-phase in ESCs grown under primed culture conditions. Ino80 directly bound to the transcription start site and regulated the expression of cell cycle-related genes. Furthermore, Ino80 loss induced cell apoptosis. However, the regulatory mechanism of Ino80 in differentiating ESC cycle slightly differed; an extended S-phase was detected in differentiating inducible Ino80 knockout ESCs. RNA-seq analysis of differentiating ESCs revealed that the expression of genes associated with organ development cell cycle is persistently altered in Ino80 knockout cells, suggesting that cell cycle regulation by Ino80 is not limited to undifferentiated ESCs. Therefore, our study establishes the function of Ino80 in ESC cycle via transcriptional regulation, at least partly. Moreover, this Ino80 function may be universal to other cell types.
Keyphrases
- cell cycle
- cell proliferation
- single cell
- rna seq
- induced apoptosis
- transcription factor
- poor prognosis
- gene expression
- dna methylation
- embryonic stem cells
- cell cycle arrest
- oxidative stress
- binding protein
- stem cells
- endoplasmic reticulum stress
- cell therapy
- signaling pathway
- cell death
- pi k akt
- diabetic rats
- bioinformatics analysis