Nesfatin-1 and nesfatin-1-like peptide suppress growth hormone synthesis via the AC/PKA/CREB pathway in mammalian somatotrophs
Bibliographic record
Abstract
Nesfatin-1 (NESF) and NESF-like peptide (NLP), encoded in nucleobindin 2 and 1 (NUCB2 and NUCB1), respectively, are orphan ligands and metabolic factors. We hypothesized that NESF and NLP suppress growth hormone (GH) synthesis, and aimed to determine whether mammalian somatotrophs are a source and site of action of these peptides. Using immortalized rat somatotrophs (GH3 cells), NUCB expression was determined by qPCR, immunofluorescence and Western blot. NESF and NLP binding to GH3 cells was tested using fluorescence imaging. Both time- and concentration-dependent studies were performed to test whether NESF and NLP affect GH. Moreover, the ability of these peptides to modulate the effects of ghrelin, and cell-signaling pathways were studied. GH3 cells express NUCB mRNAs and protein. Labeled NESF and NLP bind to the surface of GH3 cells, and incubation with either NESF or NLP decreased GH mRNA and protein expression, downregulated pit-1 mRNA, and blocked the GH stimulatory effects of ghrelin. Pre-incubation with either of these peptides reduced CREB phosphorylation by an AC-activator, but not when PKA was directly activated by a cAMP analog. Our results indicate that rat somatotrophs are a source of NUCBs, and that NESF and NLP downregulate GH synthesis through the AC/PKA/CREB signaling pathway.
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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".