Negative Regulation of Human Growth Hormone Gene Expression by Insulin Is Dependent on Hypoxia-inducible Factor Binding in Primary Non-tumor Pituitary Cells
Bibliographic record
Abstract
Insulin controls growth hormone (GH) production at multiple levels, including via a direct effect on pituitary somatotrophs. There are no data, however, on the regulation of the intact human (h) GH gene (hGH1) by insulin in non-tumor pituitary cells, but the proximal promoter region (nucleotides -496/+1) responds negatively to insulin in transfected pituitary tumor cells. A DNA-protein interaction was also induced by insulin at nucleotides -308/-235. Here, we confirmed the presence of a hypoxia-inducible factor 1 (HIF-1) binding site within these sequences (-264/-259) and investigated whether HIF-1 is associated with insulin regulation of "endogenous" hGH1. In the absence of primary human pituitary cells, transgenic mice expressing the intact hGH locus in a somatotroph-specific manner were generated. A significant and dose-dependent decrease in hGH and mouse GH RNA levels was detected in primary pituitary cell cultures from these mice with insulin treatment. Increasing HIF-1α availability with a hypoxia mimetic significantly decreased hGH RNA levels and was accompanied by recruitment of HIF-1α to the hGH1 promoter in situ as seen with insulin. Both inhibition of HIF-1 DNA binding by echinomycin and RNA interference of HIF-1α synthesis blunted the negative effect of insulin on hGH1 but not mGH. The insulin response is also sensitive to histone deacetylase inhibition/trichostatin A and associated with a decrease in H3/H4 hyperacetylation in the proximal hGH1 promoter region. These data are consistent with HIF-1-dependent down-regulation of hGH1 by insulin via chromatin remodeling specifically in the proximal promoter region.
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) | 0.002 | 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".