Prolactin‐releasing peptide effects in the rat brain are mediated through the Neuropeptide FF receptor
Bibliographic record
Abstract
Prolactin-releasing peptide (PrRP), an RF amide peptide present in the brain, generates a wide variety of centrally generated autonomic responses, including increases in arterial blood pressure and heart rate. The identity of the receptor mediating the effects of PrRP is unknown. In addition to GPR10, which is its putative endogenous receptor, PrRP demonstrates a high binding affinity for Neuropeptide FF (NPFF) receptors, specifically the NPFF2 receptor. In the present study, we examined whether the central cardiovascular effects of PrRP in the intact animal and its cellular effects on parvocellular paraventricular nucleus (PVN) neurons are mediated via NPFF receptors. In conscious rats, intracerebroventricular (i.c.v.) PrRP caused an increase in arterial blood pressure and heart rate, which was blocked with RF9, a specific NPFF receptor antagonist. These PrRP-evoked cardiovascular effects were preserved in the Otsuka Long-Evans Tokushima Fatty (OLETF) rat strain, in which the GRP10 receptor gene was mutated. In rat brain slices, whole-cell patch clamp recordings of parvocellular paraventricular nucleus neurons show PrRP caused a decrease in evoked and miniature GABAergic inhibitory postsynaptic currents (IPSCs), effects that were antagonized by RF9, but not neuropeptide Y, a putative GPR10 receptor antagonist. The effects of PrRP on IPSCs in OLETF rats were similar to those in wild-type rats. Both in vivo and in vitro data strongly suggest that certain PrRP effects in the brain are expressed via NPFF receptors, probably NPFF2, rather than the GPR10 receptor. These observations may assume clinical relevance as RF amide peptides such NPFF and PrRP become therapeutic targets for a variety of autonomically related disorders.
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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) | 0.000 | 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".