Regulation of the ANF and BNP promoters by GATA factors: Lessons learned for cardiac transcription
Bibliographic record
Abstract
The identification and molecular cloning of the cardiac transcription factors GATA-4, -5, and -6 has greatly contributed to our understanding of how tissue-specific transcription is achieved during cardiac growth and development. Through analysis of their interacting partners, it has also become apparent that a major mechanism underlying spatial and temporal specificity within the heart as well as in the response to cardiogenic regulators is the combinatorial interaction between cardiac-restricted and inducible transcription factors. The cardiac GATA factors appear to be fundamental contributors to these regulatory networks. Two of the first targets identified for the cardiac GATA factors were the natriuretic peptide genes encoding atrial natriuretic factor (ANF) and B-type natriuretic peptide (BNP), the major heart secretory products that are also accepted clinical markers of the diseased heart. Studies using the ANF and BNP promoters as models of cardiac-specific transcription have unraveled the pivotal role that GATA proteins play in cardiac gene expression. We review the current knowledge on the modulation of the natriuretic peptide promoters by GATA factors, including examples of combinatorial interactions between GATA proteins and diverse transcription factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".