Transcriptional regulation of O-GlcNAc homeostasis is disrupted in pancreatic cancer
Bibliographic record
Abstract
Many intracellular proteins are reversibly modified by O -linked GlcNAc ( O -GlcNAc), a post-translational modification that dynamically regulates fundamental cellular processes in response to diverse environmental cues. Accumulating evidence indicates that both excess and deficiency of protein O -GlcNAcylation can have deleterious effects on the cell, suggesting that maintenance of O -GlcNAc homeostasis is essential for proper cellular function. However, the mechanisms through which O -GlcNAc homeostasis is maintained in the physiologic state and altered in the disease state have not yet been investigated. Here, we demonstrate the existence of a homeostatic mechanism involving mutual regulation of the O -GlcNAc–cycling enzymes O -GlcNAc transferase (OGT) and O -GlcNAcase (OGA) at the transcriptional level. Specifically, we found that OGA promotes Ogt transcription through cooperation with the histone acetyltransferase p300 and transcription factor CCAAT/enhancer-binding protein β (C/EBPβ). To examine the role of mutual regulation of OGT and OGA in the disease state, we analyzed gene expression data from human cancer data sets, which revealed that OGT and OGA expression levels are highly correlated in numerous human cancers, particularly in pancreatic adenocarcinoma. Using a Kras G12D -driven primary mouse pancreatic ductal adenocarcinoma (PDAC) cell line, we found that inhibition of extracellular signal–regulated kinase (ERK) signaling decreases OGA glycosidase activity and reduces OGT mRNA and protein levels, suggesting that ERK signaling may alter O -GlcNAc homeostasis in PDAC by modulating OGA-mediated Ogt transcription. Our study elucidates a transcriptional mechanism that regulates cellular O -GlcNAc homeostasis, which may lay a foundation for exploring O -GlcNAc signaling as a therapeutic target for human disease.
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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) | 0.002 | 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".