miR-127-3p Is an Epigenetic Activator of Myofibroblast Senescence Situated within the MicroRNA-Enriched Dlk1-Dio3‒Imprinted Domain on Mouse Chromosome 12
Bibliographic record
Abstract
During wound healing, fibroblasts differentiate into nonproliferative contractile myofibroblasts, contribute to skin repair, and eventually undergo apoptosis or become senescent. MicroRNAs are post-transcriptional regulators of gene expression networks that control cell fate and survival and may also regulate senescence. In this study, we determined the regulated microRNAs in myofibroblasts isolated from wounds and analyzed their role in senescent myofibroblast formation. Transcriptome profiling showed that a 200 kilobase pair region of the Dlk1-Dio3‒imprinted domain on mouse chromosome 12 encodes for most of the upregulated microRNAs in the entire genome of mouse myofibroblasts. Among those, miR-127-3p induced a myofibroblast-like phenotype associated with a block in proliferation. Molecular analysis revealed that miR-127-3p induced a prolonged cell cycle arrest with unique molecular features of senescence, including the activation of the senescence-associated β-galactosidase, increase in p53 and p21 levels, inhibition of lamin B1, proliferation factors, and the production of senescence-associated inflammatory and extracellular matrix‒remodeling components. Hence, miR-127-3p emerges as an epigenetic activator regulating the transition from repair to remodeling during skin wound healing but may also induce age-related defects, pathological scarring, and fibrosis, all linked to myofibroblast senescence.
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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".