Peroxisome-Proliferator Receptor γ Represses Hepatic Sex Hormone-Binding Globulin Expression
Bibliographic record
Abstract
Plasma SHBG production by the liver is influenced by its metabolic state, and hepatocyte nuclear factor-4alpha regulates SHBG expression in response to changes in lipogenesis. Peroxisome-proliferator receptors (PPARs) also regulate glucose homeostasis and fatty acid metabolism. The human SHBG promoter contains a PPAR-response element (PPAR-RE), and plasma SHBG levels increase in polycystic ovarian syndrome patients treated with the PPARgamma agonist, rosiglitazone. In addition, plasma SHBG levels are associated with a genetic polymorphism in the PPARgamma-2 coding sequence that alters its transcriptional activity. Therefore, we set out to determine whether PPARgamma influences hepatic production of SHBG by using human HepG2 hepatoblastoma cells as an in vitro model. Surprisingly, treatment of HepG2 cells with rosiglitazone reduced SHBG production and SHBG promoter activity (as assessed in a luciferase reporter gene assay) by 20-25%, whereas the PPARgamma antagonist, GW9662, increased both by 2- to 3-fold. The effects of PPARgamma agonists and antagonists on SHBG promoter activity were substantially diminished when the PPAR-RE in the SHBG promoter was mutated. A PPARgamma small interfering RNA also increased SHBG production by HepG2 cells as well as SHBG promoter activity, and the latter was accentuated by cotreatment with GW9662. Importantly, overexpression of a PPARgamma-2 Pro12 variant in HepG2 cells was more effective at reducing SHBG promoter activity, when compared with PPARgamma-2 Ala12, consistent with its superior PPAR-RE binding activity. We conclude that PPARgamma represses human SHBG expression in liver cells, and that differences in PPARgamma levels and activity contribute directly to variations in plasma SHBG levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".