IL-6 Indirectly Modulates the Induction of Glyceroneogenic Enzymes in Adipose Tissue during Exercise
Bibliographic record
Abstract
BACKGROUND: Glyceroneogenesis is an important step in the control of fatty acid re-esterification with PEPCK and PDK4 being identified as key enzymes in this process. We have previously shown that glyceroneogenic enzymes such as PDK4 are rapidly induced in white adipose tissue during exercise. Recent studies have suggested that IL-6 regulates adipose tissue metabolism and gene expression during exercise. Interestingly, IL-6 has been reported to directly decrease PEPCK expression. The purpose of this investigation was to determine the role of IL-6 in modulating the effects of exercise on the expression of glyceroneogenic enzymes in mouse adipose tissue. We hypothesized that the exercise-mediated induction of PDK4 and PEPCK would be greater in adipose tissue from IL-6 deficient mice compared to wild type controls. METHODOLOGY AND PRINCIPLE FINDINGS: Treatment of cultured epididymal adipose tissue (eWAT) with IL-6 (150 ng/ml) increased the phosphorylation of AMPK, ACC and STAT3 and induced SOCS3 mRNA levels while decreasing PEPCK and PDK4 mRNA. AICAR decreased the expression of PDK4 and PEPCK. The activation of AMPK by IL-6 was independent of increases in lipolysis. An acute bout of treadmill running (15 meters/minute, 5% incline, 90 minutes) did not induce SOCS3 or increase phosphorylation of STAT3 in eWAT, indicating that IL-6 signalling was not activated. Exercise-induced increases in PEPCK and PDK4 mRNA expression were attenuated in eWAT from IL-6(-/-) mice in parallel with a greater relative increase in AMPK phosphorylation compared to exercised WT mice. These changes occurred independent of alterations in beta-adrenergic signalling in adipose tissue from IL-6(-/-) mice. CONCLUSIONS AND SIGNIFICANCE: Our findings question the role of IL-6 signalling in adipose tissue during exercise and suggest an indirect effect of this cytokine in the regulation of adipose tissue gene expression during exercise.
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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) | 0.002 | 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".