Inhibition of the Sodium/Potassium ATPase Impairs <i>N</i>-Glycan Expression and Function
Bibliographic record
Abstract
Aberrant N-linked glycans promote the malignant potential of cells by enhancing the epithelial-to-mesenchymal transition and the invasive phenotype. To identify small molecule inhibitors of N-glycan biosynthesis, we developed a chemical screen based on the ability of the tetravalent plant lectin L-phytohemagglutinin (L-PHA) to bind and crosslink surface glycoproteins with beta1,6GlcNAc-branched complex type N-glycans and thereby induce agglutination and cell death. In this screen, Jurkat cells were treated with a library of off-patent chemicals (n = 1,280) to identify molecules that blocked L-PHA-induced death. The most potent hit from this screen was the cardiac glycoside (CG) dihydroouabain. In secondary assays, a panel of CGs was tested for their effects on L-PHA-induced agglutination and cell death. All of the CGs tested inhibited L-PHA-induced death in Jurkat cells, and the most potent CG tested was digoxin with an EC(50) of 60 +/- 20 nmol/L. Digoxin also increased the fraction of some concanavalin A-binding N-glycans. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, digoxin specifically increased GlcNAc(1)Man(3)GlcNAc(2)Fuc(1) and GlcNAc(2)Man(3)GlcNAc(2)Fuc(1) oligosaccharides demonstrating an impairment of the N-glycan pathway. Consistent with this effect on the N-glycan pathway, digoxin inhibited N-glycosylation-mediated processes of tumor cell migration and invasion. Furthermore, digoxin prevented distant tumor formation in two mouse models of metastatic prostate cancer. Thus, taken together, our high throughput screen identified CGs as modifiers of the N-glycan pathway. These molecules can be used as tools to better understand the role of N-glycans in normal and malignant cells. Moreover, these results may partly explain the anticancer effect of CGs in cardiovascular patients.
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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".