Effects of Global <i>O</i>-GlcNAcylation on Galectin Gene-expression Profiles in Human Cancer Cell Lines
Bibliographic record
Abstract
BACKGROUND/AIM: The effects of O-linked β-N-acetyl-D-glucosamine (O-GlcNAc) transferase (OGT) and O-GlcNAcase (OGA) inhibitors on galectin gene expression profiles were examined in MCF7, HT-29, and HL-60 cancer cell lines. MATERIALS AND METHODS: Cell cultures were treated for 24 h with OGA inhibitor thiamet G or OGT inhibitor 2-acetamido-1,3,4,6-tetra-O-acetyl-2-deoxy-5-thio-α-D-glucopyranose, and global O-GlcNAc levels and expression of galectin genes were determined using an immunodot blot assay and real-time quantitative polymerase chain reaction. RESULTS: Two galectin genes, LGALS3 in MCF7 cells and LGALS12 in HL-60 cells, were up-regulated by O-GlcNAc, whereas other cell-specific galectins were unresponsive to changes in O-GlcNAc level. Of interest, basal levels of O-GlcNAc in resting HL-60 and HT-29 cells were significantly higher than those in cells differentiated into neutrophilic or enterocytic lineages, respectively. CONCLUSION: O-GlcNAc-mediated signaling pathways may be involved in regulating the expression of only a limited number of galectin genes. Additional O-GlcNAc-dependent mechanisms may work at the protein level (galectin secretion and intracellular localization) and warrant further investigation.
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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) | 0.001 | 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".