The sarcoplasmic reticulum and SERCA: a nexus for muscular adaptive thermogenesis
Bibliographic record
Abstract
We are currently facing an “obesity epidemic” worldwide. Promoting inefficient metabolism in muscle represents a potential treatment for obesity and its complications. Sarco(endo)plasmic reticulum (SR) Ca2+-ATPase (SERCA) pumps in muscle are responsible for maintaining low cytosolic Ca2+ concentration through the ATP-dependent pumping of Ca2+ from the cytosol into the SR lumen. SERCA activity has the potential to be a critical regulator of body mass and adiposity given that it is estimated to contribute upwards of 20% of daily energy expenditure. More interestingly, this fraction can be modified physiologically in the face of stressors, such as ambient temperature and diet, through its physical interaction with several regulators known to inhibit Ca2+ uptake and muscle function. In this review, we discuss advances in our understanding of Ca2+-cycling thermogenesis within skeletal muscle, focusing on SERCA and its protein regulators, which were thought previously to only modulate muscular contractility. Novelty ATP consumption by SERCA pumps comprises a large proportion of resting energy expenditure in muscle and is dynamically regulated through interactions with small SERCA regulatory proteins. SERCA efficiency correlates significantly with resting metabolism, such that individuals with a higher resting metabolic rate have less energetically efficient SERCA Ca2+ pumping in muscle (i.e., lower coupling ratio). Futile Ca2+ cycling is a versatile heat generating mechanism utilized by both skeletal muscle and beige fat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".