Knockdown of Natriuretic Peptide Receptor‐ A Enhances Receptor‐C Expression and Signaling in Vascular Smooth Muscle Cells (VSMC)
Bibliographic record
Abstract
Natriuretic peptide receptor‐A (NPR‐A) knockout mice were reported to exhibit an increased blood pressure which may also be attributed to the upregulation of NPR‐C and associated signaling, however, the interaction between the two receptors was not investigated. In the present study, we investigated the effect of knockdown of NPR‐A using NPR‐A antisense (AS) on the expression of NPR‐C and adenylyl cyclase (AC) signaling in VSMC. Treatment of VSMC with NPR‐A AS decreased the expression of NPR‐A and enhanced the expression of NPR‐C but not of AT1 and M2 receptor. In addition, siRNA‐NPR‐A also upregulated NPR‐C. The re‐expression of NPR‐A in AS‐treated cells reversed the enhanced expression of NPR‐C to control levels. In addition, the receptor‐mediated inhibitions of AC activity and Gi α expression were enhanced in AS‐treated cells, whereas NPR‐A‐mediated cGMP formation was significantly reduced. Pertussis toxin treatment attenuated AS‐induced enhanced inhibitions of AC to control levels. Furthermore, the enhanced levels of NPR‐C and Gi α proteins were reversed to control levels by 8Br‐cGMP and PD98059. In addition, 8Br‐cGMP also attenuated AS‐induced enhanced ERK1/2 phosphorylation to control levels. These results demonstrate that knockdown of NPR‐A upregulates the expression of NPR‐C, Gi α proteins and NPR‐C‐linked AC signaling and suggest a cross‐talk between NPR‐A and NPR‐C(Supported by CIHR).
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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) | 0.001 | 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".