Probing the Hexosamine Biosynthetic Pathway in Human Tumor Cells by Multitargeted Tandem Mass Spectrometry
Bibliographic record
Abstract
Cancer progression is accompanied by increases in glucose and glutamine metabolism, providing the carbon and nitrogen required in downstream anabolic pathways. Fructose-6P, glutamine, and acetyl-CoA are central metabolites and substrates of the hexosamine biosynthesis pathway (HBP) to UDP-N-acetylglucosamine (UDP-GlcNAc), an essential high-energy donor for protein glycosylation. Golgi and cytosolic glycosylation pathways are sensitive to UDP-GlcNAc levels, which in turn regulates metabolic homeostasis in a poorly understood manner. To study the hexosamine biosynthesis pathway in cancer cells, we developed a targeted approach for cellular metabolomics profiling by liquid chromatography-tandem mass spectrometry. Human cervical (HeLa) and prostate cancer (PC-3) cell lines were cultured in medium with increasing concentrations of glucose, glutamine, or GlcNAc to perturb the metabolic network. Principal component analysis indicated trends that were further analyzed as individual metabolites and pathways. HeLa cell metabolism was predominantly glycolytic, while PC-3 cells showed a greater dependency on extracellular glutamine. In both cell lines, UDP-GlcNAc levels declined with glucose but not glutamine starvation, whereas glutamine abundance increased UDP-GlcNAc levels 2-3-fold. GlcNAc supplementation increased UDP-GlcNAc 4-8-fold in both HeLa and PC-3 cells. GlcNAc supplementation in HeLa cells induced nonmonotonic changes in NADH/NAD+, NADPH/NADP+, reactive oxygen species, and reduced/oxidized glutathione. In PC-3 cells, GlcNAc supplementation also increased glucose and glutamine uptake and catabolism. Our results suggest that stimulation of the HBP in cancer cells regulates metabolism and redox potential, which might be exploited to target cancer cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".