Myocyte Enhancer Factor 2A Regulates Hydrogen Peroxide‐Induced Senescence of Vascular Smooth Muscle Cells Via microRNA‐143
Bibliographic record
Abstract
Myocyte enhancer factor 2A (MEF2A) is involved in vascular smooth muscle cell (VSMC) proliferation, migration, and senescence. MicroRNA-143/145 (miR-143/145), which may be regulated by MEF2A, is known to promote cellular senescence. We hypothesized that MEF2A may promote VSMC senescence via miR-143/145. VSMC senescence was induced by hydrogen peroxide (H(2)O(2)), followed by detection using a senescence-associated β-galactosidase staining kit. The MEF2A protein, mRNA, and miR-143/145 levels in VSMCs were detected using Western blot analysis and SYBR green real-time quantitative PCR, respectively. We further manipulated the expression levels of MEF2A and miR-143 through viral or transient transfection. VSMC proliferation and migration were determined by methylthiazolyldiphenyl-tetrazolium bromide and Millicell chamber, respectively. Both MEF2A and miR-143, but not miRNA-145, were up-regulated in senescent VSMCs. Overexpression of either MEF2A or miR-143 significantly enhanced VSMC senescence, but reduced proliferation and migration. MEF2A knockdown or miR-143 inhibitor suppressed cellular senescence and increased proliferation and migration. We further revealed AKT signaling as a potential miR-143 target, and an induction of miR-143 expression by MEF2A via KLF2. Additionally, overexpression of MEF2A and miR-143 resulted in synergistic effects on promotion of senescence, and MEF2A knockdown and miR-143 reduction by inhibitor had synergistic inhibitory effects. Finally, MEF2A barely promoted VSMC senescence when miR-143 was inhibited, and miR-143 overexpression antagonized the inhibitory effect of MEF2A knockdown on VSMC senescence. Our results revealed a link and interaction between MEF2A and miR-143 and suggested a potential mechanism for MEF2A to regulate H(2)O(2) -induced VSMC 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 | 0.001 |
| 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".