IL-6 and epinephrine have divergent fiber type effects on intramuscular lipolysis
Bibliographic record
Abstract
IL-6 is an exercise-regulated myokine that has been suggested to increase lipolysis in fast-twitch skeletal muscle. However, it is not known if a similar effect is present in slow-twitch muscle. Furthermore, epinephrine increases IL-6 secretion from skeletal muscle, suggesting that IL-6 could play a role in mediating the lipolytic effects of catecholamines. The purpose of this study was to determine whether IL-6 stimulates skeletal muscle lipolysis in a fiber type dependent manner and is required for epinephrine-stimulated lipolysis in murine skeletal muscle. Soleus and extensor digitorum longus (EDL) muscles from male C57BL/6J wild-type and IL-6(-/-) mice were incubated with 1 μM (183 ng/ml) epinephrine or 75 ng/ml recombinant IL-6 (rIL-6) for 60 min. IL-6 treatment increased 5'-AMP-activated protein kinase and signal transducer and activator of transcription 3 phosphorylation and glycerol release in isolated EDL but not soleus muscles from C57BL/6J mice. Conversely, epinephrine increased glycerol release in soleus but not EDL muscles from C57BL/6J mice. Basal lipolysis was elevated in soleus muscle from IL-6(-/-) mice, and this was associated with increases in adipose triglyceride lipase (ATGL) and its coactivator comparative gene identification-58 (CGI-58). The increase in ATGL content does not appear to be due to a loss of IL-6's direct effects, because ex vivo treatment with IL-6 failed to alter the expression of ATGL mRNA in soleus muscle. In summary, IL-6 stimulates lipolysis in glycolytic but not oxidative muscle, whereas the opposite fiber type effect is seen with epinephrine. The absence of IL-6 indirectly upregulates lipolysis, and this is associated with increases in ATGL and its coactivator CGI-58.
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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".