Abstract 670: A Metabolic Approach to Examining the Hexosamine Biosynthetic Pathway and Its Role in the Development of Diabetes-Associated Atherosclerosis
Bibliographic record
Abstract
Introduction: Diabetes is a disease affecting millions of people worldwide, and is a major independent risk factor for cardiovascular disease (CVD). Despite a vast amount of research, the molecular mechanisms that link diabetes to CVD are not well understood. Current evidence suggests that increased flux through the hexosamine biosynthetic pathway (HBP) contributes to the development of hyperglycemia-associated diabetic complications. Our data suggest that increased HBP flux can induce vascular ER stress and accelerate atherogenesis in a mouse model. We hypothesized that this process can be attenuated by inhibiting the first and rate-limiting enzyme in the HBP - glutamine fructose-6-phosphate amidotransferase (GFAT) - using small molecules. Methods and Results: Using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) we have developed a methodology to monitor and quantify the levels of the end product of the HBP, uridine diphosphate N -acetylglucosamine (UDP-GlcNAc). Treatment of HepG2 cells with glucosamine (0.2-5 mM), or adenovirus-directed overexpression of GFAT caused a 3-7 fold increase in UDP-GlcNAc ( P< 0.001 & P< 0.05, respectively). Inhibition of GFAT with three novel compounds - amrinone, lapachol or alloxan - decreased levels of UDP-GlcNAc by 1.5 ( P< 0.05), 3 ( P< 0.05) and 3.5-fold ( P< 0.001), respectively. Furthermore, we show that by modulating HBP flux, we can regulate ER stress levels in cultured HepG2 cells. The physiological relevance of this mechanism is supported by evidence of HBP augmentation in a hyperglycemic mouse model. Conclusions: These results support a role for the HBP in the development of atherosclerosis. Currently, MALDI imaging mass spectrometry is being performed on tissue sections to compare the levels of UDP-GlcNAc directly in hyperglycemic vs. normoglycemic mice. These studies may lead to the identification and validation of novel targets for the development of new pharmaceuticals to prevent diabetic atherosclerosis.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".