Fasted Zebrafish Mimic Genetic and Physiological Responses in Mammals: A Model for Obesity and Diabetes?
Bibliographic record
Abstract
With worldwide rates of obesity and type-II diabetes increasing, it is essential to identify and understand the mechanisms involved during nutrient absorption and fuel allocation. Recent studies demonstrate that nutrients (e.g., lipids and carbohydrates) play a major regulatory role in gene transcription of glycolytic and lipogenic enzymes in addition to hormones, including insulin and glucagon. These nutrients generally exert their effects through key cellular nutrient/energy receptors. Fasting was used to identify these nutrient/energy receptors known from mammalian studies to ascertain if zebrafish (Danio rerio) are a suitable model for the study of metabolic disorders. Zebrafish were subjected to a fasting/re-feeding regime for 3 weeks, and gene expression of sterol responsive binding protein 1 and 2 (SREBP), the mammalian target of rapamycin (mTOR), cAMP response element binding protein 3-like 3 (CREB3l3), and AMP-activated protein kinase alpha (AMPKα) was assessed. Fasted zebrafish lost ∼10% of their body mass over the 3-week experiment, with an associated depression in oxygen consumption. Increases in liver AMPKα and CREB3l3 mRNA transcript level were noted, concurrent with increases in the activities of the β-oxidation and gluconeogenic markers β-hydroxyacyl CoA dehydrogenase and phosphoenolpyruvate carboxykinase, respectively. Conversely, a depression in liver mTOR and SREBP1 and 2 expression was noted, with a decrease in pyruvate kinase and alanine aminotransferase activities and decreases in liver lipid and glycogen contents. Twenty-four hours after re-feeding, zebrafish rapidly recover, and the majority of parameters return to control values. Taken together, these data suggest adult zebrafish are an appropriate model for the further study of human metabolic disorders.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".