Inhibition of DNA methyltransferase-1 instigates the expression of DNA methyltransferase-3a in angioplasty-induced restenosis
Bibliographic record
Abstract
Increased expression of DNA methyltransferase-1 (DNMT1) associates with the progression of many human diseases. Because DNMT1 induces cell proliferation, drugs that inhibit DNMT1 have been used to treat proliferative diseases. Because these drugs are nonspecific inhibitors of DNMT1, subsidiary events or the compensatory mechanisms that are activated in the absence of DNMT1 limit their therapeutic application. Here, we studied the molecular mechanisms that occur during angioplasty-induced restenosis and found that DNMT1 inhibition in both in vitro and in vivo approaches resulted in the induction of DNA methyltransferase-3a (DNMT3a) expression. In vascular smooth muscle cells (VSMCs), the microRNA hsa-miR-1264 mimic, specifically inhibiting DNMT1, induced nuclear expression of DNMT3a. On the contrary, there was no induced expression of DNMT3a in VSMCs that were transfected with hsa-miR-1264 inhibitor. Further, ectopic expression of suppressor of cytokine signaling 3 (SOCS3) through adeno-associated virus (AAV)-mediated gene delivery in the coronary arteries of Yucatan microswine showed inhibition of both DNMT1 and DNMT3a in vivo. These findings show the existence of an inter-regulatory mechanism between DNMT1 and DNMT3a where, in the absence of DNMT1, induction of DNMT3a compensates for the loss of DNMT1 functions, suggesting that the inhibition of both DNMT1 and DNMT3a are required to prevent restenosis.
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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) | 0.001 | 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 | 0.000 |
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".