Adaptive Regulation at the Cell Surface by <i>N</i>‐Glycosylation
Bibliographic record
Abstract
The association of receptors and solute transporters with components of the endocytic machinery regulates their surface levels, and thereby cellular sensitivity to cytokines, ligands and nutrients in the extracellular environment. Most transmembrane receptors and solute transporters are glycoproteins, and the Asn (N)-linked oligosaccharides (N-glycans) can bind animal lectins, forming multivalent lattices or microdomains that regulate glycoprotein mobility in the plane of membrane. The N-glycan number (sequence-encoded NXS/T) and context-dependent Golgi N-glycan branching cooperate to regulate glycoprotein affinities for the galectin family of lectins. Galectin-3 binding reduces EGF receptor trafficking into clathrin-coated pits and caveolae lipid rafts, decreases ligand-independent receptor activation and promotes alpha5beta1 integrin remodelling in focal adhesions. N-glycan branching in the medial Golgi increases glycan affinity for galectins, and the Golgi pathway is sensitive to uridine diphosphate-N-acetylglucosamine (UDP-GlcNAc) supply, in turn hexosamine pathway metabolites (fructose-6-P, glutamine and acetyl-CoA). Thus, lattice avidity and cellular responsiveness to extracellular cues are regulated in an adaptive manner by metabolism and Golgi modification to glycoproteins. Computational modelling of the hexosamine/Golgi/lattice has provided new insight on cell surface adaptation in cancer and autoimmune 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".