Abstract 1041: Loss Of The ETS Transcription Factor ERG Disrupts Fate-defining Programs In The Aortic Endothelium And Promotes Expansion Of Endothelial Lineage Cells In Atherosclerotic Plaque
Bibliographic record
Abstract
Aim: The ETS transcription factor ERG has been identified as a principal regulator of endothelial function through its ability to repress inflammation in endothelial cells (ECs), and loss of ERG induces endothelial to mesenchymal transition (EndMT) in fibrotic disease. To investigate ERG function in atherosclerosis, we employed an EC-specific Erg knockout ( Erg EC - KO ) in PCSK9-overexpression and apoE-deficient murine atherosclerosis models. Methods: Atherosclerosis was induced in 10-week-old Erg EC - KO mice or wild-type counterparts that had a Cre-inducible EC lineage tag ( Cdh5 CreER ; R26-CAG-LSL-Sun1-sfGFP ). Atherosclerosis was modeled by Apoe deletion or AAV8-PCSK9 with 12 weeks of high-cholesterol diet (1.25%). Aortic intimal cells were assessed with flow cytometry. Plaque burden, morphology, and cellularity were characterized with brightfield, Oil Red O, H&E, Movat, and immunofluorescence imaging. Results: Erg EC-KO in PCSK9-overexpression mice led to a 1.6-fold increase in aortic plaque burden and plaque formation in regions that are usually protected (e.g., greater curvature; p≤0.03; n=9-10). Loss of ERG increased elastin breaks (1.7-fold; p=0.02; n=9) and decreased fibrous cap thickness (0.76-fold; p=0.02; n=9-10) in aortic arch cross sections. Aortic intimal digests from Erg EC- KO mice contained increased Sun1-sfGFP + EC lineage cells (1.9-fold; p=0.01; n=4), which had reduced CD31 positivity (0.79-fold; p=0.03; n=4). Notably, a 5.3-fold increase in EC lineage cells was observed in plaques from brachiocephalic artery cross sections (p=0.0001; n=6-15), which had hallmarks of EndMT (e.g., 31.5-fold increase in GFP + ACTA2 + cells, p=0.02, n=3-5). Increased aortic plaque burden was recapitulated in apoE-deficient E rg EC- KO mice (2.1-fold; p=0.02; n=4-8). Conclusion: Endothelial Erg KO results in increased plaque burden and altered plaque topography in murine atherosclerosis. Loss of ERG induces EndMT and marked ingression of ECs from the luminal endothelium to the plaque interior. These findings highlight loss of ERG as a novel mechanism by which intimal ECs acquire mesenchymal identity in atherosclerosis and implicate EC identity loss as a driver of EC migration into plaques, which is associated with a vulnerable morphology.
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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) | 0.006 | 0.002 |
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".