Role of Calcium-Calmodulin-Dependent Protein Kinase Cascade in Thyrotropin (TSH)-Releasing Hormone Induction of TSH and Prolactin Gene Expression
Bibliographic record
Abstract
TRH binds to a membrane receptor that activates several intracellular signaling pathways and increases transcription of the TSH and prolactin (PRL) genes. Although TRH induces TSH and PRL gene expression, the underlying mechanism is not clear. In this report we examined the role of the Ca(2+)/calmodulin-dependent protein (CaM) kinase cascade in mediating TRH-stimulated transcription of TSH and PRL. RT-PCR and Western blot analysis were used to show that CaM kinase kinase (CaM-KK) and CaM IV (CaM-KIV) were present in rat anterior pituitary and its cell line GH(3). Next, the effects of constitutively active CaM-KIV (CaM-KIVc) or its dominant negative mutant (CaM-KIVdn) on TSH and PRL promoter activity were tested in GH(3) cells. The results showed that either CaM-KIVc alone or an upstream kinase, CaM-KK, induced the activity of both TSH and PRL promoters. Exposure of GH(3) cells to 100 microm TRH induced CaM-KIV activity within 5 min and, as expected, also increased both TSH and PRL promoter activity. In contrast, cells carrying the CaM-KIVdn isoform had suppressed TRH induction of both TSH and PRL promoter activity. These results indicate that the CaM-KK-CaM-KIV cascade probably plays an important role in TRH induction of TSH and PRL transcriptional activity in pituitary cells.
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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".