G0/G1 Switch Gene 2 controls adipose triglyceride lipase activity and lipid metabolism in skeletal muscle
Bibliographic record
Abstract
OBJECTIVE: Recent data suggest that adipose triglyceride lipase (ATGL) plays a key role in providing energy substrate from triglyceride pools and that alterations of its expression/activity relate to metabolic disturbances in skeletal muscle. Yet little is known about its regulation. We here investigated the role of the protein G0/G1 Switch Gene 2 (G0S2), recently described as an inhibitor of ATGL in white adipose tissue, in the regulation of lipolysis and oxidative metabolism in skeletal muscle. METHODS: We first examined G0S2 protein expression in relation to metabolic status and muscle characteristics in humans. We next overexpressed and knocked down G0S2 in human primary myotubes to assess its impact on ATGL activity, lipid turnover and oxidative metabolism, and further knocked down G0S2 in vivo in mouse skeletal muscle. RESULTS: G0S2 protein is increased in skeletal muscle of endurance-trained individuals and correlates with markers of oxidative capacity and lipid content. Recombinant G0S2 protein inhibits ATGL activity by about 40% in lysates of mouse and human skeletal muscle. G0S2 overexpression augments (+49%, p < 0.05) while G0S2 knockdown strongly reduces (-68%, p < 0.001) triglyceride content in human primary myotubes and mouse skeletal muscle. We further show that G0S2 controls lipolysis and fatty acid oxidation in a strictly ATGL-dependent manner. These metabolic adaptations mediated by G0S2 are paralleled by concomitant changes in glucose metabolism through the modulation of Pyruvate Dehydrogenase Kinase 4 (PDK4) expression (5.4 fold, p < 0.001). Importantly, downregulation of G0S2 in vivo in mouse skeletal muscle recapitulates changes in lipid metabolism observed in vitro. CONCLUSION: Collectively, these data indicate that G0S2 plays a key role in the regulation of skeletal muscle ATGL activity, lipid content and oxidative metabolism.
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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) | 0.000 | 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 | 0.000 |
| 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".