Implication of multiple signaling pathways in the regulation of angiotensin II induced enhanced expression of Giα proteins in vascular smooth muscle cells
Bibliographic record
Abstract
We have previously shown that A10 vascular smooth muscle cells (VSMC) exposed to angiotensin II (Ang II) exhibited overexpression of Giα proteins. In the present study, we examined the involvement of different signaling pathways in regulating Ang II induced enhanced expression of Giα proteins in VSMC by using pharmacological inhibitors. Ang II induced increased expression of Giα proteins in A10 VSMC was markedly attenuated by actinomycin D, losartan (an AT(1) receptor antagonist), dibutyryl cAMP, phospholipase C (PLC) inhibitor U73122, protein kinase C (PKC) inhibitors staurosporine and GP109203X, but not by PD123319 (an AT(2) receptor antagonist). In addition, BAPTA-AM and TMB-8 (chelators of intracellular Ca(2+)); and nifedipine (a blocker of L-type Ca(2+) channels) significantly inhibited Ang II induced enhanced expression of Giα proteins. On the other hand, extracellular Ca(2+) chelation using EGTA did not affect the Ang II evoked enhanced levels of Giα proteins. Furthermore, pretreatment of A10 VSMC with calmidazolium (an inhibitor of calmodulin), or KN93 (an inhibitor of CaM kinase), or genistein (an inhibitor of protein tyrosine kinase, PTK), also attenuated the increased levels of Giα proteins induced by Ang II. These results suggest that Ang II induced enhanced expression of Giα proteins may be regulated by different signaling pathways through AT(1) receptors in A10 VSMC.
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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) | 0.001 | 0.001 |
| 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 | 0.000 |
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".