Peroxisome Proliferator-Activated Receptor -<i>β</i>/<i>δ</i>, -<i>γ</i>Agonists and Resveratrol Modulate Hypoxia Induced Changes in Nuclear Receptor Activators of Muscle Oxidative Metabolism
Bibliographic record
Abstract
PPAR-α, PPAR-β, and PPAR-γ, and RXR in conjunction with PGC-1α and SIRT1, activate oxidative metabolism genes determining insulin sensitivity. In utero, hypoxia is commonly observed in Intrauterine Growth Restriction (IUGR), and reduced insulin sensitivity is often observed in these infants as adults. We sought to investigate how changes in oxygen tension might directly impact muscle PPAR regulation of oxidative genes. Following eight days in culture at 1% oxygen, C(2)C(12) muscle myoblasts displayed a reduction of PGC-1α, PPAR-α, and RXR-α mRNA, as well as CPT-1b and UCP-2 mRNA. SIRT1 and PGC-1α protein was reduced, and PPAR-γ protein increased. The addition of a PPAR-β agonist (L165,041) for the final 24 hours of 1% treatment resulted in increased levels of UCP-2 mRNA and protein whereas Rosiglitazone induced SIRT1, PGC-1α, RXR-α, PPAR-α, CPT-1b, and UCP-2 mRNA and SIRT1 protein. Under hypoxia, Resveratrol induced SIRT1, RXR-α, PPAR-α mRNA, and PPAR-γ and UCP-2 protein. These findings demonstrate that hypoxia alters the components of the PPAR pathway involved in muscle fatty acid oxidative gene transcription and translation. These results have implications for understanding selective hypoxia adaptation and how it might impact long-term muscle oxidative metabolism and insulin sensitivity.
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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 | 0.001 |
| 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".