Sarcolipin trumps β‐adrenergic receptor signaling as the favored mechanism for muscle‐based diet‐induced thermogenesis
Bibliographic record
Abstract
Sarcolipin (SLN) regulates muscle‐based nonshivering thermogenesis and is up‐regulated with high‐fat feeding (HFF). To investigate whether other muscle‐based thermogenic systems compensate for a lack of Sln and to firmly establish SLN as a mediator of diet‐induced thermogenesis (DIT), we measured muscle and whole‐body energy expenditure in chow‐ and high‐fat‐fed Sln –/– and wild‐type (WT) mice. Following HFF, resting muscle metabolic rate ( VO 2 , μl/g/s) was increased similarly in WT (0.28±0.02 vs. 0.31 ±0.03) and Sln –/– (0.23±0.03 vs. 0.35±0.02) mice due to increased sympathetic nervous system activation in Sln –/– mice; however, whole‐body metabolic rate ( VO 2 , ml/kg/h) was lower in Sln –/– compared with WT mice following HFF but only during periods when the mice were active in their cages (WT, 2894±87 vs. Sln –/– , 2708±61). Treatment with the β‐adrenergic receptor (β‐AR) antagonist propranolol during HFF completely prevented muscle‐based DIT in Sln –/– mice; however, it had no effect in WT mice, resulting in greater differences in whole‐body metabolic rate and diet‐induced weight gain. Our results suggest that β‐AR signaling partially compensates for a lack of SLN to activate muscle‐based DIT, but SLN is the primary and more effective mediator.—Bombardier, E., Smith, I. C., Gamu, D., Fajardo, V. A., Vigna, C., Sayer, R. A., Gupta, S. C., Bal, N. C., Periasamy, M., Tupling, A. R., Sarcolipin trumps β‐adrenergic receptor signaling as the favored mechanism for muscle‐based diet‐induced thermogenesis. FASEB J. 27, 3871–3878 (2013). www.fasebj.org
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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".