Growth Hormone-induced Diacylglycerol and Ceramide Formation via Gαi3 and Gβγ in GH4 Pituitary Cells
Bibliographic record
Abstract
Growth hormone (GH) secretion is regulated by indirect negative feedback mechanisms. To address whether GH has direct actions on pituitary cells, lipid signaling in GH(4)ZR(7) somatomammotroph cells was examined. GH (EC(50) = 5 nm) stimulated diacylglycerol (DAG) and ceramide formation in parallel by over 10-fold within 15 min and persisting for >3 h. GH-induced DAG/ceramide formation was blocked by pertussis toxin (PTX) implicating G(i)/G(o) proteins and was potentiated 1.5-fold by activation of G(i)/G(o)-coupled dopamine-D2S receptors, which had no effect alone. Following PTX pretreatment, only PTX-resistant Galpha(i)3, not Galpha(o) or Galpha(i)2, rescued GH-induced DAG/ceramide signaling. GH-induced DAG/ceramide formation was also blocked in cells expressing Gbetagamma blocker GRK-ct. In GH(4)ZR(7) cells, GH induced phosphorylation of JAK2 and STAT5, which was blocked by PTX and mimicked by ceramide analogue C2-ceramide or sphingomyelinase treatment to increase endogenous ceramide. We conclude that in GH(4) pituitary cells, GH induces formation of DAG/ceramide via a novel Galpha(i)3/Gbetagamma-dependent pathway. This novel pathway suggests a mechanism for autocrine feedback regulation by GH of pituitary function.
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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 | 0.001 |
| 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".