Tissue-specific GATA factors are transcriptional effectors of the small GTPase RhoA
Bibliographic record
Abstract
Rho-like GTPases play a pivotal role in the orchestration of changes in the actin cytoskeleton in response to receptor stimulation, and have been implicated in transcriptional activation, cell growth regulation, and oncogenic transformation. Recently, a role for RhoA in the regulation of cardiac contractility and hypertrophic cardiomyocyte growth has been suggested but the mechanisms underlying RhoA function in the heart remain undefined. We now report that transcription factor GATA-4, a key regulator of cardiac genes, is a nuclear mediator of RhoA signaling and is involved in the control of sarcomere assembly in cardiomyocytes. Both RhoA and GATA-4 are essential for sarcomeric reorganization in response to hypertrophic growth stimuli and overexpression of either protein is sufficient to induce sarcomeric reorganization. Consistent with convergence of RhoA and GATA signaling, RhoA potentiates the transcriptional activity of GATA-4 via a p38 MAPK-dependent pathway that phosphorylates GATA-4 activation domains and GATA binding sites mediate RhoA activation of target cardiac promoters. Moreover, a dominant-negative GATA-4 protein abolishes RhoA-induced sarcomere reorganization. The identification of transcription factor GATA-4 as a RhoA mediator in sarcomere reorganization and cardiac gene regulation provides a link between RhoA effects on transcription and cell remodeling.
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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) | 0.002 | 0.002 |
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".