Embryonic Stem Cell–Derived mmu-miR-291a-3p Inhibits Cellular Senescence in Human Dermal Fibroblasts Through the TGF-β Receptor 2 Pathway
Bibliographic record
Abstract
Senescent cells accumulate in various tissues over time and contribute to tissue dysfunction and aging-associated phenotypes. Accumulating evidence suggests that cellular senescence can be inhibited through pharmacological intervention, as well as through treatment with soluble factors derived from embryonic stem cells (ESCs). In an attempt to investigate the anti-senescence factors secreted by ESCs, we analyzed mouse ESC-derived extracellular microRNAs in conditioned medium via microRNA array analysis. We selected mmu-miR-291a-3p as a putative anti-senescence factor via bioinformatics analysis. We validated its inhibitory effects on replicative, Adriamycin-induced, and ionizing radiation-induced senescence in human dermal fibroblasts. Treatment of senescent cells with mmu-miR-291a-3p decreased senescence-associated β-galactosidase activity, enhanced proliferative potential, and reduced mRNA and protein expression of TGF-β receptor 2, p53, and p21. mmu-miR-291a-3p in conditioned medium was enclosed in ESC-derived exosomes and exosomes purified from ESC conditioned medium inhibited cellular senescence. The inhibitory effects of mmu-miR-291a-3p were mediated through the TGF-β receptor 2 signaling pathway. Hsa-miR-371a-3p and hsa-miR-520e, the human homologs of mmu-miR-291a-3p, showed similar anti-senescence activity. Furthermore, mmu-miR-291a-3p accelerated the excisional skin wound healing process in aged mice. Our results indicate that the ESC-derived mmu-miR-291a-3p is a novel candidate agent that can be utilized for cell-free therapeutic intervention against aging and aging-related diseases.
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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 | 0.000 |
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".