Intergenerational effects of dietary iron on swimming and metabolic performance in zebrafish
Bibliographic record
Abstract
Iron is an essential trace metal required for various physiological processes, yet both deficiency and excess can disrupt metal homeostasis and compromise fitness. In this study, we investigated how dietary iron availability influences physiological performance across generations in zebrafish (Danio rerio). Fish were fed diets spanning a gradient from deficiency to supplementation (Low Fe, 11 mg Fe/kg; Medium Fe, 420 mg Fe/kg, and High Fe, 2,300 mg Fe/kg), and effects on growth, metal homeostasis, swimming performance, energy metabolism, and reproduction were assessed. Following reproductive assays, offspring were raised under control conditions and subsequently challenged with the same dietary iron treatments (Low Fe, Medium Fe, and High Fe as parents) in adulthood. Sub-acute exposure (20 days) to elevated dietary iron enhanced aerobic scope, maximum metabolic rate, and critical swimming speed, alongside improved reproductive output as measured by embryo survival and early development. However, sub-chronic exposure (40 days) to High Fe diminished swimming performance benefits and was also associated with tissue iron loading. Notably, zebrafish tolerated sub-chronic exposure to Low Fe without significant impacts on condition factor or energetic performance. Interestingly, the difference in swimming and metabolic performance between high and low iron treatments was more pronounced in the offspring, suggesting an intergenerational effect of parental iron status. Together, these findings suggest that dietary iron availability can shape both immediate and inherited performance phenotypes, underscoring its dual role as a nutritional requirement and a regulator of ecological fitness.
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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 | 0.001 |
| 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".