Cardiomyocytes stimulate angiogenesis after ischemic injury in a ZEB2-dependent manner
Bibliographic record
Abstract
The disruption in blood supply due to myocardial infarction is a critical determinant for infarct size and subsequent deterioration in function. The identification of factors that enhance cardiac repair by the restoration of the vascular network is, therefore, of great significance. Here, we show that the transcription factor Zinc finger E-box-binding homeobox 2 (ZEB2) is increased in stressed cardiomyocytes and induces a cardioprotective cross-talk between cardiomyocytes and endothelial cells to enhance angiogenesis after ischemia. Single-cell sequencing indicates ZEB2 to be enriched in injured cardiomyocytes. Cardiomyocyte-specific deletion of ZEB2 results in impaired cardiac contractility and infarct healing post-myocardial infarction (post-MI), while cardiomyocyte-specific ZEB2 overexpression improves cardiomyocyte survival and cardiac function. We identified Thymosin β4 (TMSB4) and Prothymosin α (PTMA) as main paracrine factors released from cardiomyocytes to stimulate angiogenesis by enhancing endothelial cell migration, and whose regulation is validated in our in vivo models. Therapeutic delivery of ZEB2 to cardiomyocytes in the infarcted heart induces the expression of TMSB4 and PTMA, which enhances angiogenesis and prevents cardiac dysfunction. These findings reveal ZEB2 as a beneficial factor during ischemic injury, which may hold promise for the identification of new therapies.
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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 | 0.000 |
| 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".