Kisspeptin Directly Regulates Neuropeptide Y Synthesis and Secretion via the ERK1/2 and p38 Mitogen-Activated Protein Kinase Signaling Pathways in NPY-Secreting Hypothalamic Neurons
Bibliographic record
Abstract
Kisspeptin is a key component of reproduction that directly stimulates GnRH neurons. However, recent studies indicate that kisspeptin can indirectly stimulate GnRH neurons through unidentified afferent networks. Neuropeptide Y (NPY) is another key reproductive hormone that is an afferent stimulator of GnRH neurons. Herein, we report kisspeptin receptor Kiss1r mRNA expression in native NPY neurons FAC-sorted from NPY-GFP transgenic mice. Thus, we hypothesized that kisspeptin indirectly stimulates GnRH neurons through direct regulation of NPY neurons. Using hypothalamic NPY-secreting cell lines, we determined that kisspeptin stimulates NPY mRNA expression and secretion in the mHypoE-38 cells, but not the mHypoE-42 cells, using quantitative RT-PCR and enzyme immunoassays. Furthermore, agouti-related peptide, ghrelin, neurotensin, or Kiss1r mRNA expression was not changed upon exposure to kisspeptin in either cell line. These results concur with our previous work identifying the mHypoE-38 cell line as a putative reproductive NPY neuron and the mHypoE-42 cell line as a potential feeding-related NPY neuron. In the mHypoE-38 cells, kisspeptin activated the ERK1/2 and p38 MAPK kinases as shown by Western blot analysis. Moreover, inhibiting the ERK1/2 and p38 pathways with U0126 and SB239063, respectively, prevented kisspeptin induction of NPY mRNA expression and secretion. Altogether, we find that kisspeptin directly regulates NPY synthesis and secretion via the ERK1/2 and p38 MAPK pathways in a NPY-secreting cell line, and we propose NPY neurons as an afferent network by which kisspeptin indirectly stimulates GnRH secretion.
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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 | 0.000 |
| 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".