HIF‐2α Suppresses p53 to Enhance the Stemness and Regenerative Potential of Human Embryonic Stem Cells
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) have been reported to exert cytoprotective activity in the area of tissue injury. However, hypoxia/oxidative stress prevailing in the area of injury could activate p53, leading to death and differentiation of hESCs. Here we report that when exposed to hypoxia/oxidative stress, a small fraction of hESCs, namely the SSEA3+/ABCG2+ fraction undergoes a transient state of reprogramming to a low p53 and high hypoxia inducible factor (HIF)-2α state of transcriptional activity. This state can be sustained for a period of 2 weeks and is associated with enhanced transcriptional activity of Oct-4 and Nanog, concomitant with high teratomagenic potential. Conditioned medium obtained from the post-hypoxia SSEA3+/ABCG2+ hESCs showed cytoprotection both in vitro and in vivo. We termed this phenotype as the "enhanced stemness" state. We then demonstrated that the underlying molecular mechanism of this transient phenotype of enhanced stemness involved high Bcl-2, fibroblast growth factor (FGF)-2, and MDM2 expression and an altered state of the p53/MDM2 oscillation system. Specific silencing of HIF-2α and p53 resisted the reprogramming of SSEA3+/ABCG2+ to the enhanced stemness phenotype. Thus, our studies have uncovered a unique transient reprogramming activity in hESCs, the enhanced stemness reprogramming where a highly cytoprotective and undifferentiated state is achieved by transiently suppressing p53 activity. We suggest that this transient reprogramming is a form of stem cell altruism that benefits the surrounding tissues during the process of tissue regeneration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".