Hepatic glycerol shunt and glycerol-3-phosphate phosphatase control liver metabolism and glucodetoxification under hyperglycemia
Bibliographic record
Abstract
OBJECTIVE: Glycerol-3-phosphate (Gro3P) phosphatase (G3PP) hydrolyzes Gro3P to glycerol that exits the cell, thereby operating a "glycerol shunt", a metabolic pathway that we identified recently in mammalian cells. We have investigated the role of G3PP and the glycerol shunt in the regulation of glucose metabolism and lipogenesis in mouse liver. METHODS: mice. Controls received AAV8-TBG-eGFP. Both groups were fed chow diet for 10 weeks. Hyperglycemia (16-20 mM) was induced by glucose infusion for 55 h. Hepatocytes were isolated from normoglycemic mice for ex vivo studies and targeted metabolomics were measured in mice liver after glucose infusion. RESULTS: LKO mice showed no change in body weight, food intake, fed and fasted glycemia but had increased fed plasma triglycerides. Hepatic glucose production from glycerol was increased in fasted LKO mice. LKO mouse hepatocytes displayed reduced glycerol production, elevated triglyceride and lactate production at high glucose concentration. Hyperglycemia in LKO mice led to increased liver weight and accumulation of triglycerides, glycogen and cholesterol together with elevated levels of Gro3P, dihydroxyacetone phosphate, acetyl-CoA and some Krebs cycle intermediates in liver. Hyperglycemic LKO mouse liver showed elevated expression of proinflammatory cytokines and M1-macrophage markers accompanied by increased plasma triglycerides, LDL/VLDL, urea and uric acid and myocardial triglycerides. CONCLUSIONS: The glycerol shunt orchestrated by G3PP acts as a glucose excess detoxification pathway in hepatocytes by preventing metabolic disturbances that contribute to enhanced liver fat, glycogen storage, inflammation and lipid build-up in the heart. We propose G3PP as a novel therapeutic target for hepatic disorders linked to nutrient excess.
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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) | 0.001 | 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".