Regulation of Peroxisome Proliferator-Activated Receptor Pathway During Torpor in the Garden Dormouse, Eliomys quercinus
Bibliographic record
Abstract
Differential levels of n-6 and n-3 essential polyunsaturated fatty acids (PUFAs) are incorporated into the hibernator’s diet in the fall season preceding prolonged, multi-days bouts of torpor, known as hibernation. Peroxisome proliferator-activated receptor (PPAR) transcriptional activators bind lipids and regulate genes involved in fatty acid transport, beta-oxidation, ketogenesis, and insulin sensitivity; essential processes for survival during torpor. Thus, the DNA-binding activity of PPARα, PPARδ, PPARγ, as well as the levels of PPARγ coactivator 1α (PGC-1α) and L-fatty acid binding protein (L-FABP) were investigated in the hibernating garden dormouse (Eliomys quercinus). We found that dormice were hibernating in a similar way regardless of the n-6/n-3 PUFA diets fed to the animals during the fattening phase prior to hibernation. Further, metabolic rates and body mass loss during hibernation did not differ between dietary groups, despite marked differences in fatty acid profiles observed in white adipose tissue prior and at mid-hibernation. Overall, maintenance of PPAR DNA-binding activity was observed during torpor, and across three n-6/n-3 ratios, suggesting alternate mechanisms for the prioritization of lipid catabolism during torpor. Additionally, while no change was seen in L-FABP, significantly altered levels of PGC-1α were observed within the white adipose tissue and likely contributes to enhanced lipid metabolism when the diet favors n-6 PUFAs, i.e., high n-6/n-3 ratio, in both the torpid and euthermic state. Altogether, the maintenance of lipid metabolism during torpor makes it likely that consistent activity or levels of the investigated proteins are in aid of this metabolic profile.
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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 | 0.000 |
| 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".