Glycerol‐3‐phosphate phosphatase/ Pgp in Pancreatic ß‐cells Functions as a Glucose Excess Security Valve Preventing Oversecretion of Insulin Secretion and Glucotoxicity
Bibliographic record
Abstract
Objective The recently identified enzyme in mammalian cells, glycerol‐3‐phosphate (Gro3P) phosphatase (G3PP), gene name Pgp , was proposed to regulate intermediary metabolism. G3PP was shown in in vitro studies to regulate metabolism and glucose stimulated insulin secretion (GSIS) in ß‐cells. We now examined the in vivo role of G3PP in the control of insulin secretion and ß‐cell glucotoxicity. Methods Glucose and insulin tolerance were studied in ß‐cell specific G3PP‐KO (BKO) mice. Body weight gain, fed glycemia and insulinemia were measured. Pancreatic islets were isolated for ex vivo GSIS and biochemical measurements. Results BKO mice show increased body weight gain, unaltered fed glycemia and insulinemia, and enhanced insulin secretion in response to glucose load in an intraperitoneal but not oral glucose tolerance test, reaching 21 and 14 mM glucose, respectively. Insulin sensitivity in vivo remains unchanged in the BKO mice . GSIS response ex vivo at 16 mM but not 8 mM glucose is higher in BKO mouse islets. BKO islets show reduced glucose‐induced glycerol release and elevated O 2 consumption and ATP production at high (16 mM) but not low (4 mM) glucose levels. Glucotoxicity at 30 mM glucose for 7 days led to increased apoptosis, reduced insulin content and expression of Pdx‐1 and Ins‐2 genes in BKO islets. Conclusion G3PP impacts insulin secretion in vivo and ex vivo only under conditions of high but not intermediate and low glucose levels. G3PP plays a role in preventing ß‐cell glucotoxicity. These effect likely result from the capacity of G3PP to redirect the excess glucose carbons from intermediate metabolism to glycerol that exits ß‐cells. We propose that G3PP acts as a glucose excess security valve to prevent excessive insulin secretion and ß‐cell dysfunction when blood glucose reaches very high levels.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".