A balanced circadian glycolytic rhythm drives cardiomyocyte cell cycle progression during fish heart regeneration
Bibliographic record
Abstract
Abstract The ability of heart tissue to repair itself after injury has fascinated scientists for decades 1,2 . Researchers have long studied the internal body clock, or circadian rhythm, for its role in coordinating daily cycles of metabolism and cell activity 3,4 , but its relevance to heart repair has remained unknown. This study explores, for the first time, whether natural daily rhythms influence heart regeneration—a process driven by cardiomyocyte proliferation. We discovered that DNA replication, mitosis, oxidative phosphorylation, and glycolysis follow a precise daily order in regenerating zebrafish hearts. Disrupting core clock gene expression abolishes the rhythms of glycolysis and mitosis, preventing cardiomyocyte cell cycle progression and regeneration. Insulin-resistant Astyanax mexicanus cavefish, which have adapted to dark caves, similarly show a loss of mitosis rhythm and cardiomyocyte cell cycle progression, which we find is caused by reduced glycolysis. Despite this reduction, glycolysis rhythm displays a larger amplitude in cavefish—a pattern recapitulated in insulin-resistant zebrafish. Insulin resistance resets metabolic rhythms to the morning, which is equally detrimental to regeneration. Here, we show that successful cardiac regeneration depends on synchronised clock and glucose rhythms, which together orchestrate the cell cycle events essential for cardiomyocyte proliferation and tissue repair.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".