Regulation of the S100B gene by α<sub>1</sub>-adrenergic stimulation in cardiac myocytes
Bibliographic record
Abstract
We previously reported that S100B, a 20-kDa Ca(2+)-binding homodimer, inhibited the postinfarct myocardial hypertrophic response mediated by alpha(1)-adrenergic stimulation through the protein kinase C (PKC) signaling pathway. In the present study, we examined whether the same pathway induced the S100B gene, supporting the hypothesis that S100B is a feedback negative regulator of this pathway. We transfected cultured neonatal rat cardiac myocytes with a luciferase reporter gene driven by the maximal human S100B promoter and progressively shorter segments of this promoter sequentially deleted from the 5' end. We identified a basic promoter essential for transcription spanning 162 bp upstream of the transcription initiation site and positive (at -782/-162 and -6,689/-4,463) and negative (at -4,463/-782) myocyte-selective regulatory elements. We showed that the basic and maximal S100B promoters were activated specifically by alpha(1)-adrenergic agonists through the alpha(1A)-adrenergic receptor, but not by any other trophic hormonal stimuli. The activation of the S100B promoter was mediated through the PKC signaling pathway. Transcription enhancer factor-1 (TEF-1) and related to TEF-1 (RTEF-1) influenced transcription from the maximal, but not the basic, promoter implicating active MCAT elements upstream from the basic promoter. Acting in opposing fashions, TEF-1 transrepressed the S100B promoter and RTEF-1 transactivated the promoter. Our results suggest that alpha(1)-adrenergic stimulation induces the S100B gene after myocardial infarction through the PKC signaling pathway and that this induction is modulated by TEF-1 and RTEF-1.
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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".