Endothelin-1, but not Ang II, Activates MAP Kinases Through c-Src–Independent Ras-Raf–Dependent Pathways in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
OBJECTIVE: Endothelin-1 (ET-1) and angiotensin II (Ang II) activate common signaling pathways to promote changes in vascular reactivity, remodeling, inflammation, and oxidative stress. Here we sought to determine whether upstream regulators of mitogen-activated protein kinases (MAPKs) are differentially regulated by ET-1 and Ang II focusing on the role of c-Src and the small GTPase Ras. METHODS AND RESULTS: Mesenteric vascular smooth muscle cells (VSMCs) from mice with different disruption levels in the c-Src gene (c-Src(+/-) and c-Src(-/-)) and wild-type (c-Src(+/+)) were used. ET-1 and Ang II induced extracellular signal-regulated kinase (ERK) 1/2, SAPK/JNK, and p38MAPK phosphorylation in c-Src(+/+) VSMCs. In VSMCs from c-Src(+/-) and c-Src(-/-), Ang II effects were blunted, whereas c-Src deficiency had no effect in ET-1-induced MAPK activation. Ang II but not ET-1 induced c-Src phosphorylation in c-Src(+/+) VSMCs. Activation of c-Raf, an effector of Ras, was significantly increased by ET-1 and Ang II in c-Src(+/+) VSMCs. Ang II but not ET-1-mediated c-Raf phosphorylation was inhibited by c-Src deficiency. Knockdown of Ras by siRNA inhibited both ET-1 and Ang II-induced MAPK phosphorylation. CONCLUSIONS: Our data indicate differential regulation of MAPKs by distinct G protein-coupled receptors. Whereas Ang II has an obligatory need for c-Src, ET-1 mediates its actions through a c-Src-independent Ras-Raf-dependent pathway for MAPK activation. These findings suggest that Ang II and ET-1 can activate similar signaling pathways through unrelated mechanisms. MAP kinases are an important point of convergence for Ang II and ET-1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".