mir-17–92 Cluster Is Required for and Sufficient to Induce Cardiomyocyte Proliferation in Postnatal and Adult Hearts
Bibliographic record
Abstract
RATIONALE: Cardiomyocytes in adult mammalian hearts are terminally differentiated cells that have exited from the cell cycle and lost most of their proliferative capacity. Death of mature cardiomyocytes in pathological cardiac conditions and the lack of regeneration capacity of adult hearts are primary causes of heart failure and mortality. However, how cardiomyocyte proliferation in postnatal and adult hearts becomes suppressed remains largely unknown. The miR-17-92 cluster was initially identified as a human oncogene that promotes cell proliferation. However, its role in the heart remains unknown. OBJECTIVE: To test the hypothesis that miR-17-92 participates in the regulation of cardiomyocyte proliferation in postnatal and adult hearts. METHODS AND RESULTS: We deleted miR-17-92 cluster from embryonic and postnatal mouse hearts and demonstrated that miR-17-92 is required for cardiomyocyte proliferation in the heart. Transgenic overexpression of miR-17-92 in cardiomyocytes is sufficient to induce cardiomyocyte proliferation in embryonic, postnatal, and adult hearts. Moreover, overexpression of miR-17-92 in adult cardiomyocytes protects the heart from myocardial infarction-induced injury. Similarly, we found that members of miR-17-92 cluster, miR-19 in particular, are required for and sufficient to induce cardiomyocyte proliferation in vitro. We identified phosphatase and tensin homolog, a tumor suppressor, as an miR-17-92 target to mediate the function of miR-17-92 in cardiomyocyte proliferation. CONCLUSIONS: Our studies therefore identify miR-17-92 as a critical regulator of cardiomyocyte proliferation, and suggest this cluster of microRNAs could become therapeutic targets for cardiac repair and heart regeneration.
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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 | 0.001 |
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".