Negative Regulation of the Human Growth Hormone Gene by Insulin in Primary Pituitary Cell Cultures and a Possible Role for Hypoxia Inducible Factor (HIF)
Bibliographic record
Abstract
Hyperinsulinemia is a key component of metabolic syndrome, and is associated with decreased growth hormone (GH) levels. There is evidence that the human (h) GH gene (hGH1) is a direct target for insulin. A hybrid reporter gene driven by upstream −496/+1 hGH1 sequences, including a putative insulin‐responsive HIF site, was negatively regulated by insulin in transfected tumor cells. Here, we have started to assess whether HIF is associated with insulin regulation of “endogenous” hGH1. In the absence of primary human pituitary cell cultures, transgenic (TG) mice expressing the intact hGH1 locus in a somatotroph‐specific manner were generated. Primary pituitary cell cultures from these TG mice were treated with or without 1–15 nM insulin. A significant decrease in hGH1 RNA levels was detected with a physiological dose of insulin. Stabilization of HIF protein by exposure to the hypoxia mimetic CoCl2 decreased hGH1 RNA levels significantly, and to the same extent as insulin treatment. The effects of inhibition of HIF synthesis by RNA interference, and HIF DNA binding by echinomycin treatment were also pursued. In both cases, the decrease in hGH1 RNA levels seen with insulin treatment was blocked significantly. These data indicate hGH1 RNA levels are regulated negatively by insulin, and that this effect is dependent on HIF protein/DNA binding.
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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".