Compartmentalization of Signaling-Competent Epidermal Growth Factor Receptors in Endosomes
Bibliographic record
Abstract
In this study, the preparation of detergent-resistant membranes (DRMs) and the immunoisolation of intracellular vesicles enriched in raft markers were used to investigate the effect of physiological doses of epidermal growth factor (EGF) in vivo on the compartmentalization and activation of EGF receptor (EGFR) in rat liver endosomes. Both of these techniques show that after EGF administration, a distinctive population of intracellular EGFR, which was characterized by a high level of tyrosine phosphorylation, accumulated in endosomes. EGFR recruited to early endosomes were more tyrosine phosphorylated than those from late endosomes. However, the level of tyrosine phosphorylation of EGFR in DRMs isolated from early and late endosomes was comparable, suggesting that EGFR in endosomal DRMs are more resistant to tyrosine dephosphorylation. In accordance with the higher level of Tyr phosphorylation, EGF induced an augmented recruitment of Grb2 and Shc to endosomal DRMs compared with whole endosomes. Furthermore, a proteomic analysis identified a selective increase of many alpha-subunits of heterotrimeric G proteins in endosomal DRMs in response to EGF. These observations suggest that a distinctive pool of endocytic EGFR, potentially competent for signaling, is actively trafficking through intracellular compartments with the characteristic of lipid rafts.
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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) | 0.000 | 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 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".