The effects of neurogranin knockdown on SERCA pump efficiency in soleus muscles of female mice fed a high fat diet
Bibliographic record
Abstract
The sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) pump is responsible for the transport of Ca2+ from the cytosol into the sarcoplasmic reticulum at the expense of ATP, making it a regulator of both muscle relaxation and muscle-based energy expenditure. Neurogranin (Ng) is a small protein that negatively regulates calcineurin signaling. Calcineurin is Ca2+/calmodulin dependent phosphatase that promotes the oxidative fibre type in skeletal muscle and regulates muscle-based energy expenditure. A recent study has shown that calcineurin activation reduces SERCA Ca2+ transport efficiency, ultimately raising energy expenditure. Since the biomedical view of obesity states that it arises as an imbalance between energy intake and expenditure which favors the former, we questioned whether heterozygous Ng deletion (Ng+/-) would reduce SERCA efficiency and increase energy expenditure in female mice fed a high-fat diet (HFD). Young (3–4-month-old) female wild type (WT) and Ng+/- mice were fed a HFD for 12 weeks with their metabolic profile being analyzed using metabolic cages and DXA scanning, while soleus SERCA efficiency was measured using SERCA specific Ca2+ uptake and ATPase activity assays. Ng+/- mice showed significantly less cage ambulation compared to WT mice but this did not lead to any added weight gain nor changes in daily energy expenditure, glucose or insulin tolerance despite a similar level of food intake. Furthermore, we observed significant reductions in SERCA’s apparent coupling ratio which were associated with significant reductions in SERCA1 and phospholamban content. Thus, our results show that Ng regulates SERCA pump efficiency, and future studies should further investigate the potential cellular mechanisms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".