Role of the Epidermal Growth Factor Receptor in Signaling Strain-dependent Activation of the Brain Natriuretic Peptide Gene
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) and ectoshedding of heparin-binding epidermal growth factor (HBEGF), an EGFR ligand, have been linked to the development of cardiac myocyte hypertrophy. However, the precise role that the liganded EGFR plays in the transcriptional activation of the gene program that accompanies hypertrophy remains undefined. Utilizing the human (h) BNP gene as a model of hypertrophy-dependent gene activation, we show that activation of the EGFR plays an important role in mediating mechanical strain-dependent stimulation of the hBNP promoter. Strain promotes endothelin (ET) generation through NAD(P)H oxidase-dependent production of reactive oxygen species. ET in turn induces metalloproteinase-mediated cleavage of pro-HBEGF and ectoshedding of HBEGF, which activates the EGFR and stimulates hBNP promoter activity. HBEGF also stimulates other phenotypic markers of hypertrophy including protein synthesis and sarcomeric assembly. The antioxidant N-acetylcysteine or the NAD(P)H oxidase inhibitor, apocynin, inhibited strain-dependent activation of the ET-1 promoter, HBEGF shedding, and hBNP promoter activation. The metalloproteinase inhibitor, GM-6001, prevented the induction of HBEGF ectoshedding and the hBNP promoter response to strain, suggesting a critical role for the metalloproteinase-dependent cleavage event in signaling the strain response. These findings suggest that metalloproteinase activity as an essential step in this pathway may prove to be a relevant therapeutic target in the management of cardiac hypertrophy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".