Influence of glucosamine on glomerular mesangial cell turnover: implications for hyperglycemia and hexosamine pathway flux
Bibliographic record
Abstract
Cells exposed to high glucose may undergo hypertrophy, proliferation, and apoptosis, but the role of hexosamine flux in mediating these effects has not been fully elucidated. Accordingly, we studied the effects of glucose and glucosamine on rat glomerular mesangial cells (MC) turnover. Compared with physiological glucose (5.6 mM), treatment with high glucose (25 mM) for 24 h stimulated MC proliferation, an effect that was mimicked by exposure to low concentrations of glucosamine (0.05 mM). The percentage of cells in G(0)/G(1) phase of the cell cycle was reduced with a concomitant increase of the number of cells in G(2)/M phase. Proliferating cell nuclear antigen, phosphorylated mammalian target of rapamycin [phospho-mTOR (Ser(2448))], and total regulatory-associated protein of mTOR were increased by high glucose and glucosamine treatment. Inhibition of glutamine:fructose-6-phosphate amidotransferase (GFAT), the rate-limiting enzyme for hexosamine flux, with 6-diazo-5-oxonorleucine (10 muM) and of mTOR with rapamycin both attenuated glucose-mediated MC proliferation. Higher glucosamine concentrations (0.25-10 mM) caused MC apoptosis after 48 h, and, in addition, GFAT overexpression also increased MC apoptosis (TdT-dUTP nick end-labeling-positive cells: 3.8 +/- 0.3 vs. 1.1 +/- 0.2% for empty vector; P < 0.05). Hence, hexosamine flux is an important determinant of MC proliferation and apoptosis. The proliferative response to high glucose and hexosamine flux is rapamycin-sensitive, suggesting that this effect is associated with signaling through rapamycin-sensitive mTOR complex 1 (mTORC1).
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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 | 0.000 |
| 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".