Flux Through the Hexosamine Pathway Is a Determinant of Nuclear Factor κB– Dependent Promoter Activation
Bibliographic record
Abstract
The hexosamine pathway may mediate some of the toxic effects of glucose. We hypothesized that flux through this pathway might regulate the activity of nuclear factor kappaB (NF-kappaB)-dependent genes in mesangial cells (MCs). In MCs, RT-PCR revealed that high glucose (30 mmol/l) and glucosamine (1 mmol/l) increased mRNA levels for vascular cell adhesion molecule 1 (VCAM-1) and increased the activity of an NF-kappaB enhancer by 1.5- and 2-fold, respectively. Overexpression of glutamine:fructose-6-phosphate amidotransferase (GFAT), the rate-limiting enzyme for flux through the hexosamine pathway, led to a 2.2-fold increase in NF-kappaB enhancer activity; the combination of GFAT overexpression and high glucose increased activity 2.8-fold, and these increases were prevented by 40 micromol/l O-diazoacetyl-L-serine (azaserine) or 6-diazo-5-oxonorleucine. High glucose, glucosamine, and GFAT overexpression increased binding of MC nuclear proteins to NF-kappaB consensus sequences. Immunoblotting revealed that the p65 subunit of NF-kappaB was O-glycosylated in MC cultured in physiologic glucose and that significant enhancement occurred with high glucose and glucosamine. Both glucose and glucosamine dose-dependently increased human VCAM-1 promoter activity. In addition, GFAT overexpression activated the VCAM-1 promoter (2.25-fold), with further augmentation by high glucose and abrogation by inhibitors of GFAT, NF-kappaB, and O-glycosylation. Inactivation of the two NF-kappaB sites in the VCAM-1 promoter abolished its response to high glucose, glucosamine, and GFAT overexpression. These results suggest that increased flux through the hexosamine pathway leads to NF-kappaB-dependent promoter activation in MCs.
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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) | 0.002 | 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".