Activation function 2 (AF2) of estrogen receptor-α is required for the atheroprotective action of estradiol but not to accelerate endothelial healing
Bibliographic record
Abstract
17β-Estradiol (E2) regulates estrogen receptor-α (ERα) target gene transcription through the two independent activation functions (AFs), AF1 and AF2, located in the N-terminal and ligand binding domain of ERα, respectively. We previously reported that ERα is required for the E2 atheroprotective action as well as for its accelerative action on endothelial healing, but its AF1 function is dispensable. Here, we investigated the role of ERαAF2 in these two major beneficial actions of E2 by electively targeting ERαAF2 (named ERαAF2(0)). Our results prove four points. (i) Compared with WT ERα, the ability of ERαAF2(0) to stimulate the C3 complement or the estrogen response element-thymidine kinase promoter in two cell lines was dramatically decreased, confirming the importance of AF2 in the E2-induced transcriptional activity of ERα. (ii) The uterotrophic action of E2 was totally absent in ERαAF2(0) mice, showing the crucial role of ERαAF2 in E2-induced uterus hyperplasia. (iii) ERαAF2 was dispensable for the accelerative action of E2 on endothelial healing, underlining the functionality of ERαAF2(0) in vivo. (iv) Finally, the atheroprotective effect of E2 was abrogated in ERαAF2(0) LDL-r(-/-) mice. Thus, whereas ERαAF1 and ERαAF2 are both required for the uterotrophic action of E2, we show that only ERαAF2 is necessary for its atheroprotective effect.
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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) | 0.003 | 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".