Abstract 286: Myocardin-related Transcription Factors Control Epicardial EMT and Coronary Vessel Homeostasis
Bibliographic record
Abstract
The epicardium is a source of multi-potent progenitor cells that envelop the heart and contribute to various cardiac lineages through the process of epithelial-to-mesenchymal transition (EMT). Although known upstream cues promote epicardial EMT via changes in gene expression and actin cytoskeletal dynamics, the link between these signals and progenitor cell motility remain unclear. Myocardin-related transcription factor (MRTF) cofactors are primarily retained in the cytoplasm through interactions with G-actin. However, upon cytoskeletal reorganization and G-actin depletion, nuclear MRTFs associate with serum response factor (SRF) to drive cellular motility and contractility gene programs reminiscent of EMT. Here, we identify the SRF/MRTF gene regulatory axis as a key mediator of epicardial EMT. We found significant enrichment of MRTF-A and -B in the epicardium prior to EMT. MRTFs were later expressed in a spatial and temporal manner concurrent with EMT and epicardial-derived cell (EPDC) differentiation. Furthermore, MRTF deletion attenuates contractile gene expression in epicardial explants and impairs migration of EPDCs into subjacent cell layers using ex vivo assays. Epicardial EMT and EPDCs differentiation are necessary for proper coronary vessel formation. We found that genetic ablation of MRTFs results in disrupted coronary plexus formation, endothelial cell dysfunction, and sub-epicardial hemorrhage. The vascular phenotype observed in MRTF-A/B epiDKO mice results in part from the depletion (~50% reduction) of epicardial-derived coronary pericytes. These data suggest a critical role for MRTFs in coronary vessel formation by regulating epicardial EMT and mobilizing EPDCs.
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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) | 0.001 | 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) | 0.004 | 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".