Differences in intrinsic cellular O-GlcNAcylation impact response to metabolic stress
Bibliographic record
Abstract
Abstract The cellular response to metabolic stress is complex and involves various signals and pathways leading to cellular adaptation. AMP-activated Protein Kinase (AMPK) senses nutrient insufficiency reflected in the ratio of ATP to AMP/ADP and/or glucose availability. In addition, the dynamic modification of proteins with O-linked β-N-Acetylglucosamine (O-GlcNAc) by O-GlcNAc transferase (OGT) is also nutrient sensitive, given that the substrate for this modification is the product of the hexosamine biosynthetic pathway. AMPK can also regulate the hexosamine biosynthetic pathway, and O-GlcNAc may reciprocally regulate AMPK activity under some contexts. However, it remains unclear which parameters establish the extent of reciprocal regulation of AMPK and protein O-GlcNAc modification and whether these signals can be orthogonal under some circumstances. Here, we used ARPE-19 and MDA-MB-231 cells to examine how nutrient limitation or pharmacological activation combined with inhibition of AMPK and O-GlcNAcylation impacts the activation of the reciprocal pathway. We found that the intrinsic level of global protein O-GlcNAc modification may impact how AMPK and metabolic stress regulate global O-GlcNAcylation, and that both signaling systems could be orthogonal in some contexts. These results contribute to understanding the complexity of cellular signaling in response to nutrient availability.
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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 | 0.001 | 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".