Sirtuin 1 Facilitates Generation of Induced Pluripotent Stem Cells from Mouse Embryonic Fibroblasts through the miR-34a and p53 Pathways
Bibliographic record
Abstract
Forced-expression of transcription factors can reprogram somatic cells into induced pluripotent stem cells (iPSC). Recent studies show that the reprogramming efficiency can be improved by inclusion of small molecules that regulate chromatin modifying enzymes. We report here that sirtuin 1 (SIRT1), a member of the sirtuin family of NAD(+)-dependent protein deacetylases, is involved in iPSC formation. By using an efficient mouse secondary fibroblast reprogramming system with doxycycline (DOX) inducible Yamanaka's transcription factors delivered by piggyBac (PB) transposition (2°F/1B MEF), we show that SIRT1 knockdown decreased while resveratrol (RSV) increased the efficiency of iPSC formation. The treatments were associated with altered acetylated p53 and its downstream Nanog but not p21 expression. The stimulatory effect was also confirmed by SIRT1 over-expression, which stimulated the formation of colonies with induced Nanog and reduced p21 expression. Furthermore, the effects of RSV and SIRT1 knockdown on reprogramming were most pronounced during the initiation phase of reprogramming. MicroRNA-34a is a known regulator of SIRT1. Its inhibitor increased, while its mimics reduced iPSC formation. The stimulatory effect of SIRT1 during reprogramming was also confirmed in the primary MEF. RSV increased while tenovin-6, a small molecule that activates p53 through SIRT1 inhibition, suppressed reprogramming. In conclusion, SIRT1 enhances iPSC generation, in part, through deacetylation of p53, inhibition of p21 and enhancement of Nanog expression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".