Metabolic Inhibition of Sialyl-Lewis X Biosynthesis by 5-Thiofucose Remodels the Cell Surface and Impairs Selectin-Mediated Cell Adhesion
Bibliographic record
Abstract
Sialyl-Lewis X (sLe(X)) is a tetrasaccharide that serves as a ligand for the set of cell adhesion proteins known as selectins. This interaction enables adhesion of leukocytes and cancer cells to endothelial cells within capillaries, resulting in their extravasation into tissues. The last step in sLe(X) biosynthesis is the α1,3-fucosyltrasferase (FUT)-catalyzed transfer of an L-fucose residue to carbohydrate acceptors. Impairing FUT activity compromises leukocyte homing to sites of inflammation and renders cancer cells less malignant. Inhibition of FUTs is, consequently, of great interest, but efforts to generate glycosyltransferase inhibitors, including FUT inhibitors, has proven challenging. Here we describe a metabolic engineering strategy to inhibit the biosynthesis of sLe(X) in cancer cells using peracetylated 5-thio-L-fucose (5T-Fuc). We show that 5T-Fuc is taken up by cancer cells and then converted into a sugar nucleotide analog, GDP-5T-Fuc, that blocks FUT activity and limits sLe(X) presentation on HepG2 cells with an EC(50) in the low micromolar range. GDP-5T-Fuc itself does not get transferred by either FUT3 or FUT7 at a measurable rate. We further demonstrate that treatment of cells with 5T-Fuc impaired their adhesive properties to immobilized adhesion molecules and human endothelial cells. 5T-Fuc, therefore, is a useful probe that can be used to modulate sLe(X) levels in cells to evaluate the consequences of inhibiting FUT-mediated sLe(X) formation. These data also reveal the utility of using sugar analogues that lead to formation of donor substrate analogues within cells as a general approach to blocking glycosyltransferases in cells.
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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".