Possible Role of Efnb1 Protein, a Ligand of Eph Receptor Tyrosine Kinases, in Modulating Blood Pressure
Bibliographic record
Abstract
Eph kinases constitute the largest receptor tyrosine kinase family, and their ligands, ephrins (Efns), are also cell surface molecules. Although they are ligands, Efns can transduce signals reversely into cells. We have no prior knowledge of the role played by any members of this family of kinases or their ligands in blood pressure (BP) regulation. In the present studies, we investigated the role of Efnb1 in vascular smooth muscle cell (VSMC) contractility and BP regulation. We revealed that reverse signaling through Efnb1 led to a reduction of RhoA activation and VSMC contractility in vitro. Consistent with this finding, ex vivo, there was an increase of RhoA activity accompanied by augmented myosin light chain phosphorylation in mesenteric arteries from mice with smooth muscle-specific conditional Efnb1 gene knock-out (KO). Small interfering RNA knockdown of Grip1, a molecule associated with the Efnb1 intracellular tail, partially eliminated the effect of Efnb1 on VSMC contractility and myosin light chain phosphorylation. In support of these in vitro and ex vivo results, Efnb1 KO mice on a high salt diet showed a statistically significant heightened increment of BP at multiple time points during stress compared with wild type littermates. Our results demonstrate that Efnb1 is a previously unknown negative regulator of VSMC contractility and BP and that it exerts such effects via reverse signaling through Grip1.
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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) | 0.000 | 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".