Muscle‐specific sirtuin 3 overexpression does not attenuate the pathological effects of high‐fat/high‐sucrose feeding but does enhance cardiac SERCA2a activity
Bibliographic record
Abstract
Obesity, type 2 diabetes, and heart disease are linked to an unhealthy diet. Sarco(endo)plasmic reticulum calcium (Ca2+) ATPase 2a (SERCA2a) controls cardiac function by transporting Ca2+ in cardiomyocytes. SERCA2a is altered by diet and acetylation, independently; however, it is unknown if diet alters cardiac SERCA2a acetylation. Sirtuin (SIRT) 3 is an enzyme that might preserve health under conditions of macronutrient excess by modulating metabolism via regulating deacetylation of target proteins. Our objectives were to determine if muscle-specific SIRT3 overexpression attenuates the pathological effects of high fat-high sucrose (HFHS) feeding and if HFHS feeding alters cardiac SERCA2a acetylation. We also determined if SIRT3 alters cardiac SERCA2a acetylation and regulates cardiac SERCA2a activity. C57BL/6J wild-type (WT) mice and MCK-mSIRT3-M1-Flag transgenic (SIRT3TG) mice, overexpressing SIRT3 in cardiac and skeletal muscle, were fed a standard-diet or a HFHS-diet for 4 months. SIRT3TG and WT mice developed obesity, glucose intolerance, cardiac dysfunction, and pathological cardiac remodeling after 4 months of HFHS feeding, indicating muscle-specific SIRT3 overexpression does not attenuate the pathological effects of HFHS-feeding. Overall cardiac lysine acetylation was increased by 63% in HFHS-fed mice (p = 0.022), though HFHS feeding did not alter cardiac SERCA2a acetylation. Cardiac SERCA2a acetylation was not altered by SIRT3 overexpression, whereas SERCA2a Vmax was 21% higher in SIRT3TG (p = 0.039) than WT mice. This suggests that SIRT3 overexpression enhanced cardiac SERCA2a activity without direct SERCA2a deacetylation. Muscle-specific SIRT3 overexpression may not prevent the complications associated with an unhealthy diet in mice, but it appears to enhance SERCA2a activity in the mouse heart.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".