Suppressive action of nesfatin-1 and nesfatin-1-like peptide on cortisol synthesis in human adrenal cortex cells
Bibliographic record
Abstract
Nucleobindin-derived peptides, nesfatin-1 [NESF-1] and nesfatin-1-like-peptide [NLP] have diverse roles in endocrine and metabolic regulation. While both peptides showed a stimulatory effect on the synthesis of proopiomelanocortin (POMC), the adrenocorticotropic hormone (ACTH) precursor in mouse corticotrophs, whether NESF-1 and NLP have any direct effect on glucocorticoid [GC] synthesis in the adrenal cortex remains unknown. The main aim of this study was to determine if NESF-1 and/or NLP act directly on adrenal cortex cells to regulate cortisol synthesis in vitro. Whether NLP injection affects stress-hormone gene expression in the adrenal gland and pituitary in vivo in mice was also assessed. In addition, cortisol synthetic pathway in Nucb1 knockout mice was studied. Human adrenal cortical [H295R] cells showed immunoreactivity for both NUCB1/NLP and NUCB2/NESF-1. NLP and NESF-1 decreased the abundance of steroidogenic enzyme mRNAs, and cortisol synthesis and release through the AC/PKA/CREB pathway in H295R cells. Similarly, intraperitoneal injection of NLP in mice decreased the expression of enzymes involved in glucocorticoid (GC) synthesis in the adrenal gland while increasing the expression of Pomc, Pcsk1 and Crhr1 in the pituitary. Moreover, the melanocortin 2 receptor (Mc2r) mRNA level was enhanced in the adrenal gland samples of NLP injected mice. However, the global genetic disruption in Nucb1 did not affect most steroidogenic enzyme mRNAs, and Pomc, Pcsk2 and Crhr1 mRNAs in mice adrenal gland and pituitary gland, respectively. Collectively, these data provide the first evidence for a direct inhibition of cortisol synthesis and secretion by NLP and NESF-1. NUCB peptides might still elicit a net stimulatory effect on GC synthesis and secretion through their positive effects on ACTH-MC2R pathway in the pituitary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".