Role of the PGC-1 family in the metabolic adaptation of goldfish to diet and temperature
Bibliographic record
Abstract
In mammals, the peroxisome proliferator-activated receptor (PPAR) gamma coactivator-1 (PGC-1) family members and their binding partners orchestrate remodelling in response to diverse challenges such as diet, temperature and exercise. In this study, we exposed goldfish to three temperatures (4, 20 and 35 degrees C) and to three dietary regimes (food deprivation, low fat and high fat) and examined the changes in mitochondrial enzyme activities and transcript levels for metabolic enzymes and their genetic regulators in red muscle, white muscle, heart and liver. When all tissues and conditions were pooled, there were significant correlations between the mRNA for the PGC-1 coactivators (both alpha and beta) and mitochondrial transcripts (citrate synthase), metabolic gene regulators including PPARalpha, PPARbeta and nuclear respiratory factor-1 (NRF-1). PGC-1beta was the better predictor of the NRF-1 axis, whereas PGC-1alpha was the better predictor of the PPAR axis (PPARalpha, PPARbeta, medium chain acyl CoA dehydrogenase). In contrast to these intertissue/developmental patterns, the response of individual tissues to physiological stressors displayed no correlations between mRNA for PGC-1 family members and either the NRF-1 or PPAR axes. For example, in skeletal muscles, low temperature decreased PGC-1alpha transcript levels but increased mitochondrial enzyme activities (citrate synthase and cytochrome oxidase) and transcripts for COX IV and NRF-1. These results suggest that in goldfish, as in mammals, there is a regulatory relationship between (i) NRF-1 and mitochondrial gene expression and (ii) PPARs and fatty acid oxidation gene expression. In contrast to mammals, there is a divergence in the roles of the coactivators, with PGC-1alpha linked to fatty acid oxidation through PPARalpha, and PGC-1beta with a more prominent role in mediating NRF-1-dependent control of mitochondrial gene expression, as well as distinctions between their respective roles in development and physiological responsiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".