Alteration of E2F2 Expression in Governing Endothelial Cell Senescence
Bibliographic record
Abstract
Endothelial cell senescence has a vital implication for vascular dysfunction, leading to age-related cardiovascular disease, especially hypertension and atherosclerosis. E2F transcription factor 2 (E2F2) plays a critical role in cell proliferation, differentiation, and DNA damage response. Up to date, no study has ever connected E2F2 to vascular endothelial cell senescence. Here, we demonstrate that E2F2 is involved in endothelial cellular senescence. We found that E2F2 expression is decreased during the replicative senescence of human umbilical vein endothelial cells (HUVECs) and the aortas of aged mice. The knockdown of E2F2 in young HUVECs induces premature senescence characterized by an increase in senescence-associated β-galactosidase (SA-β-gal) activity, a reduction in phosphorylated endothelial nitric oxide synthase (p-eNOS) and sirtuin 1 (SIRT1), and the upregulation of senescence-associated secretory phenotype (SASP) IL-6 and IL-8. The lack of E2F2 promoted cell cycle arrest, DNA damage, and cell proliferation inhibition. Conversely, E2F2 overexpression reversed the senescence phenotype and enhanced the cellular function in the senescent cells. Furthermore, E2F2 deficiency downregulated downstream target genes including CNNA2, CDK1, and FOXM1, and overexpression restored the expression of these genes. Our findings demonstrate that E2F2 plays an indispensable role in endothelial cell 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".