<i>MicroRNA-139-5p</i> regulates C2C12 cell myogenesis through blocking Wnt/β-catenin signaling pathway
Bibliographic record
Abstract
MicroRNAs (miRNAs) are novel and potent regulators in myogenesis. However, the molecular mechanisms that many miRNAs regulate myoblast proliferation and differentiation which are largely unknown. Here, we found that miR-139-5p increased during C2C12 myoblast proliferation, while presenting an inverse trend during C2C12 myoblast differentiation. Flow cytometry and EdU incorporation assay showed that miR-139-5p slowed down the growth of C2C12 cells. Additional study demonstrated that ectopic introduction of miR-139-5p into C2C12 cells blocked myoblast differentiation. Importantly, we demonstrated for the first time that Wnt1, which is associated with the Wnt/β-catenin signaling pathway, was a direct target of miR-139-5p. Moreover, we found that the expression level of Wnt1 was suppressed significantly (p < 0.01) by miR-139-5p, which triggered inhibition of Wnt/β-catenin signaling through upregulation of glycogen synthase kinase 3 beta (GSK-3β; p < 0.05) and downregulation of p-GSK-3β (p < 0.01), β-catenin (p < 0.05), and nuclear β-catenin (p < 0.01). Taken together, these results suggest that miR-139-5p is an important negative regulator in myogenesis through blocking the Wnt1-mediated Wnt/β-catenin signaling pathway.
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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) | 0.001 | 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) | 0.002 | 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".