Galectins as Adaptors: Linking Glycosylation and Metabolism with Extracellular Cues
Bibliographic record
Abstract
Galectins interact with N-acetyllactosamine (LacNAc) epitopes in transmembrane glycoproteins at the cell surface in a multivalent manner forming a “lattice.” The term “galectin lattice” was first used to describe the impact of galectin-3 on immune synapse formation, T cell activation and autoimmunity (Demetriou et al. (2001) Nature 409, 733). The galectin lattice displays rapid exchange of binding partners or stochastic-binding, thereby acting as an intermediary between free diffusion of glycoproteins and stable complexes in the membrane. This includes (i) slowing diffusion and loss of receptor and transporters to coated-pit endocytosis and/or caveolin domains, (ii) slowing the integration of transmembrane phosphatases with signaling microdomains and (iii) promoting turnover (i.e., opposing stability) of cell-cell and focal adhesion complexes. The lattice model classifies galectins as adaptors of glycoprotein functions; regulating their localization, trafficking and thereby activity thresholds. The lattice model has been validated in immune regulation, cell adhesion and motility, and glucose homeostasis in mice. Here we review physical attributes of galectins and their N-glycan ligands and apply logical inference, coupled with convergence of biochemical, cell biology and genetic evidence that provide a strong Bayesian probability for greater utility of the lattice model.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".