Cell‐type specific induction of tryptophan hydroxylase‐2 transcription by calcium mobilization
Bibliographic record
Abstract
Alterations in brain serotonin levels are implicated in major depression and are regulated by tryptophan hydroxylase-2 (TPH2). To study its regulation, we measured TPH2 RNA by quantitative RT-PCR in differentiated serotonergic rat raphe RN46A and GH4C1 pituitary cells, which express TPH2. Upon calcium mobilization using KCl (40 mmol/L), TPH2 RNA was rapidly (1 h) and strongly (> 10-fold) induced in differentiated RN46A cells, but not in GH4C1 cells. This effect was blocked by actinomycin D, implicating transcriptional activation. Similarly, calcium ionophore ionomycin induced TPH2 RNA by threefold in RN46A cells. To address the promoter sites involved, the transcription start site was identified and a series of TATA-containing TPH2 promoter-luciferase constructs were analyzed. In differentiated RN46A cells, the TPH2 promoter was induced 2.5-fold by ionomycin, similar to its action on TPH2 RNA. By contrast, ionomycin had no effect on TPH2 promoter activity in GH4C1 cells or TPH2-negative L6 myoblasts. Ionomycin sensitivity was localized to within 88 bp of the start site, containing putative CCATT-enhancer binding protein element, activator protein-1 and -2 (AP-1, AP-2) elements. These results are the first to identify calcium-mediated regulation of the proximal TPH2 promoter as critical for cell-specific TPH2 expression.
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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".