Gamma protocadherins in vascular endothelial cells inhibit Klf2/4 to promote atherosclerosis
Bibliographic record
Abstract
Abstract Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of mortality worldwide 1 . Laminar shear stress (LSS) from blood flow in straight regions of arteries protects against ASCVD by upregulating the Klf2/4 anti-inflammatory program in endothelial cells (ECs) 2–8 . Conversely, disturbed shear stress (DSS) at curves or branches predisposes these regions to plaque formation 9,10 . We previously reported a whole genome CRISPR knockout screen 11 that identified novel inducers of Klf2/4. Here we report suppressors of Klf2/4 and characterize one candidate, protocadherin gamma A9 (Pcdhga9), a member of the clustered protocadherin gene family 12 . Pcdhg deletion increases Klf2/4 levels in vitro and in vivo and suppresses inflammatory activation of ECs. Pcdhg suppresses Klf2/4 by inhibiting the Notch pathway via physical interaction of cleaved Notch1 intracellular domain (NICD Val1744) with nuclear Pcdhg C-terminal constant domain (CCD). Pcdhg inhibition by EC knockout (KO) or blocking antibody protects from atherosclerosis. Pcdhg is elevated in the arteries of human atherosclerosis. This study identifies a novel fundamental mechanism of EC resilience and therapeutic target for treating inflammatory vascular disease.
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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) | 0.002 | 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".