Plasma membrane domain organization regulates EGFR signaling in tumor cells
Bibliographic record
Abstract
Macromolecular complexes exhibit reduced diffusion in biological membranes; however, the physiological consequences of this characteristic of plasma membrane domain organization remain elusive. We report that competition between the galectin lattice and oligomerized caveolin-1 microdomains for epidermal growth factor (EGF) receptor (EGFR) recruitment regulates EGFR signaling in tumor cells. In mammary tumor cells deficient for Golgi beta1,6N-acetylglucosaminyltransferase V (Mgat5), a reduction in EGFR binding to the galectin lattice allows an increased association with stable caveolin-1 cell surface microdomains that suppresses EGFR signaling. Depletion of caveolin-1 enhances EGFR diffusion, responsiveness to EGF, and relieves Mgat5 deficiency-imposed restrictions on tumor cell growth. In Mgat5(+/+) tumor cells, EGFR association with the galectin lattice reduces first-order EGFR diffusion rates and promotes receptor interaction with the actin cytoskeleton. Importantly, EGFR association with the lattice opposes sequestration by caveolin-1, overriding its negative regulation of EGFR diffusion and signaling. Therefore, caveolin-1 is a conditional tumor suppressor whose loss is advantageous when beta1,6GlcNAc-branched N-glycans are below a threshold for optimal galectin lattice formation.
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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".