Differential effects of contraction and PPAR agonists on the expression of fatty acid transporters in rat skeletal muscle
Bibliographic record
Abstract
We have examined over the course of a 1-week period the independent and combined effects of chronically increased muscle contraction and the peroxisome proliferator-activated receptor (PPAR)alpha and PPARgamma activators, Wy 14,643 and rosiglitazone, on the expression and plasmalemmal content of the fatty acid transporters, FAT/CD36 and FABPpm, as well as on the rate of fatty acid transport. In resting muscle, the activation of either PPARalpha or PPARgamma failed to induce the protein expression of FAT/CD36. PPARalpha activation also failed to induce the protein expression of FABPpm. In contrast, PPARgamma activation induced the expression of FABPpm protein (40%; P < 0.05). Chronic muscle contraction increased the protein expression of FAT/CD36 (approximately 50%; P < 0.05), whereas FABPpm was slightly increased (12%; P < 0.05). Neither PPARalpha nor PPARgamma activation altered the contraction-induced expression of FAT/CD36 or FABPpm. Changes in protein expression of FAT/CD36 or FABPpm, induced by either contractions or by administration of rosiglitazone, were largely attributable to increased transcription. The contraction-induced increments in FAT/CD36 were accompanied by parallel increments in plasmalemmal FAT/CD36 and in rates of fatty acid transport (P < 0.05). Up-regulation of FABPpm expression was, however, accompanied by a reduction in plasmalemmal FABPpm, which did not affect the rates of long chain fatty acid (LCFA) transport. These studies have shown that in skeletal muscle (i) neither PPARalpha nor PPARgamma activation alters FAT/CD36 expression, (ii) PPARgamma activation selectively up-regulates FABPpm expression and (iii) contraction-induced up-regulation of LCFA transport does not appear to occur via activation of either PPARalpha or PPARgamma.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".