Low-dose lithium supplementation promotes adipose tissue browning and sarco(endo)plasmic reticulum Ca2+ ATPase uncoupling in muscle
Bibliographic record
Abstract
-ATPase (SERCA) uncoupling in skeletal muscle and mitochondrial uncoupling via uncoupling protein 1 (UCP1) in brown/beige adipose tissue are two mechanisms implicated in energy expenditure. Here, we investigated the effects of glycogen synthase kinase 3 (GSK3) inhibition via lithium chloride (LiCl) treatment on SERCA uncoupling in skeletal muscle and UCP1 expression in adipose. C2C12 and 3T3-L1 cells treated with LiCl had increased SERCA uncoupling and UCP1 protein levels, respectively, ultimately raising cellular respiration; however, this was only observed when LiCl treatment occurred throughout differentiation. In vivo, LiCl treatment (10 mg/kg/day) increased food intake in chow-fed diet and high-fat diet (HFD; 60% kcal)-fed male mice without increasing body mass-a result attributed to elevated daily energy expenditure. In soleus muscle, we determined that LiCl treatment promoted SERCA uncoupling via increased expression of SERCA uncouplers, sarcolipin and/or neuronatin, under chow-fed and HFD-fed conditions. We attribute these effects to the GSK3 inhibition observed with LiCl treatment as partial muscle-specific GSK3 knockdown produced similar effects. In adipose, LiCl treatment inhibited GSK3 in inguinal white adipose tissue (iWAT) but not in brown adipose tissue under chow-fed conditions, which led to an increase in UCP1 in iWAT and a beiging-like effect with a multilocular phenotype. We did not observe this beiging-like effect and increase in UCP1 in mice fed a HFD, as LiCl could not overcome the ensuing overactivation of GSK3. Nonetheless, our study establishes novel regulatory links between GSK3 and SERCA uncoupling in muscle and GSK3 and UCP1 and beiging in iWAT.
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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 | 0.001 |
| 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".