The Circular RNA Interacts with STAT3, Increasing Its Nuclear Translocation and Wound Repair by Modulating Dnmt3a and miR-17 Function
Bibliographic record
Abstract
Delayed or impaired wound healing is a major health issue worldwide, especially in patients with diabetes and atherosclerosis. Here we show that expression of the circular RNA circ-Amotl1 accelerated healing process in a mouse excisional wound model. Further studies showed that ectopic circ-Amotl1 increased protein levels of Stat3 and Dnmt3a. The increased Dnmt3a then methylated the promoter of microRNA miR-17, decreasing miR-17-5p levels but increasing fibronectin expression. We found that Stat3, similar to Dnmt3a and fibronectin, was a target of miR-17-5p. Decreased miR-17-5p levels would increase expression of fibronectin, Dnmt3a, and Stat3. All of these led to increased cell adhesion, migration, proliferation, survival, and wound repair. Furthermore, we found that circ-Amotl1 not only increased Stat3 expression but also facilitated Stat3 nuclear translocation. Thus, the ectopic expressed circ-Amotl1 and Stat3 were mainly translocated to nucleus. In the presence of circ-Amotl1, Stat3 interacted with Dnmt3a promoter with increased affinity, facilitating Dnmt3a transcription. Ectopic application of circ-Amotl1 accelerating wound repair may shed light on skin wound healing clinically.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".