Exercise improves lipid metabolism disorders induced by high-fat diet in a SESN2/JNK-independent manner
Bibliographic record
Abstract
SESN2 and JNK are emerging powerful stress-inducible proteins in regulating lipid metabolism. The aim of this study was to determine the underlying mechanism of SESN2/JNK signaling in exercise to improve lipid disorder induced by high-fat diet (HFD). Our data showed that HFD and SESN2 knockout resulted in abnormalities including elevated body weight, increased fat mass, serum total cholesterol, lipid biosynthesis related proteins, and a concomitant increase of pJNK-Thr183/Tyr185. The above changes were reversed by exercise training. SESN2 silencing or JNK inhibition in palmitate-treated C2C12 further confirmed that SESN2 and JNK play a vital role in lipid biosynthesis. Rescue experiment further demonstrated that SESN2 reduced lipid biosynthesis through inhibition of JNK. SESN2/JNK signaling axis regulates lipid biosynthesis in both animal and cell models with abnormalities of lipid metabolism induced by HFD or palmitate treatment. This study provided evidence that exercise ameliorated lipid metabolic disorder induced by HFD feeding or by SESN2 knockout. SESN2 may improve lipid metabolism through inhibition JNK expression in skeletal muscle cells, providing a molecular mechanism that may represent an attractive target for the treatment of lipid disorder. Novelty: Exercise improved lipid disorder induced by HFD feeding and SESN2 knockout. SESN2 and JNK play a vital role in lipid biosynthesis in vivo and in vitro. SESN2 suppressed JNK to improve lipid metabolism in skeletal muscle cells.
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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) | 0.002 | 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".