C-type Natriuretic Peptide Inhibits L-type Ca<sup>2+</sup>Current in Rat Magnocellular Neurosecretory Cells by Activating the NPR-C Receptor
Bibliographic record
Abstract
Magnocellular neurosecretory cells (MNCs), of the paraventricular and supraoptic nuclei of the hypothalamus, secrete the hormones vasopressin and oxytocin. As a result, they have an essential role in fundamental physiological responses including regulation of blood volume and fluid homeostasis. C-type natriuretic peptide (CNP) is present at high levels in the hypothalamus. Although CNP is known to decrease hormone secretion from MNCs, no studies have examined the role of the natriuretic peptide C receptor (NPR-C) in these neurons. In this study, whole cell recordings from acutely isolated MNCs, and MNCs in a coronal slice preparation, show that CNP (2 x 10(-8) M) and the selective NPR-C agonist, cANF (2 x 10(-8) M), significantly inhibit L-type Ca2+ current (I(Ca(L))) by approximately 50%. This effect on I(Ca(L)) is mimicked by dialyzing a G(i)-activator peptide (10(-7) M) into these cells, implicating a role for the inhibitory G protein, G(i). These NPR-C-mediated effects were specific to I(Ca(L)). T-type Ca2+ channels were unaffected by CNP. Current-clamp experiments revealed the ability of CNP, acting via the NPR-C receptor, to decrease (approximately 25%) the number of action potentials elicited during a 500 ms depolarizing stimulus. Analysis of action potential duration revealed that CNP and cANF significantly decreased 50% repolarization time (APD50) in MNCs. In summary, our findings show that CNP has a potent and selective inhibitory effect on I(Ca(L)) and on excitability in MNCs that is mediated by the NPR-C receptor. These data represent the first electrophysiological evidence of a functional role for the NPR-C receptor in the mammalian hypothalamus.
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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".