New Mammalian Glycerol-3-Phosphate Phosphatase: Role in β-Cell, Liver and Adipocyte Metabolism
Bibliographic record
Abstract
Cardiometabolic diseases, including type 2 diabetes, obesity and non-alcoholic fatty liver disease, have enormous impact on modern societies worldwide. Excess nutritional burden and nutri-stress together with sedentary lifestyles lead to these diseases. Deranged glucose, fat, and energy metabolism is at the center of nutri-stress, and glycolysis-derived glycerol-3-phosphate (Gro3P) is at the crossroads of these metabolic pathways. Cellular levels of Gro3P can be controlled by its synthesis, utilization or hydrolysis. The belief that mammalian cells do not possess an enzyme that hydrolyzes Gro3P, as in lower organisms and plants, is challenged by our recent work showing the presence of a Gro3P phosphatase (G3PP) in mammalian cells. A previously described phosphoglycolate phosphatase (PGP) in mammalian cells, with no established physiological function, has been shown to actually function as G3PP, under physiological conditions, particularly at elevated glucose levels. In the present review, we summarize evidence that supports the view that G3PP plays an important role in the regulation of gluconeogenesis and fat storage in hepatocytes, glucose stimulated insulin secretion and nutri-stress in β-cells, and lipogenesis in adipocytes. We provide a balanced perspective on the pathophysiological significance of G3PP in mammals with specific reference to cardiometabolic diseases.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".