Reciprocal regulation of transcription factors and PLC isozyme gene expression in adult cardiomyocytes
Bibliographic record
Abstract
By employing a pharmacological approach, we have shown that phospholipase C (PLC) activity is involved in the regulation of gene expression of transcription factors such as c-Fos and c-Jun in cardiomyocytes in response to norepinephrine (NE). However, there is no information available regarding the identity of specific PLC isozymes involved in the regulation of c-Fos and c-Jun or on the involvement of these transcription factors in PLC isozyme gene expression in adult cardiomyocytes. In this study, transfection of cardiomyocytes with PLC isozyme specific siRNA was found to prevent the NE-mediated increases in the corresponding PLC isozyme gene expression, protein content and activity. Unlike PLC gamma(1) gene, silencing of PLC beta(1), beta(3) and delta(1) genes with si RNA prevented the increases in c-Fos and c-Jun gene expression in response to NE. On the other hand, transfection with c-Jun si RNA suppressed the NE-induced increase in c-Jun as well as PLC beta(1), beta(3) and delta(1) gene expression, but had no effect on PLC gamma(1) gene expression. Although transfection of cardiomyocytes with c-Fos si RNA prevented NE-induced expression of c-Fos, PLC beta(1) and PLC beta(3) genes, it did not affect the increases in PLC delta(1) and PLC gamma(1) gene expression. Silencing of either c-Fos or c-Jun also depressed the NE-mediated increases in PLC beta(1), beta(3) and gamma(1) protein content and activity in an isozyme specific manner. Furthermore, silencing of all PLC isozymes as well as of c-Fos and c-Jun resulted in prevention of the NE-mediated increase in atrial natriuretic factor gene expression. These findings, by employing gene silencing techniques, demonstrate that there occurs a reciprocal regulation of transcription factors and specific PLC isozyme gene expression in cardiomyocytes.
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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".