Endosomal Signaling of Epidermal Growth Factor Receptor Stimulates Signal Transduction Pathways Leading to Cell Survival
Bibliographic record
Abstract
In spite of intensified efforts to understand cell signaling from endosomes, there is no direct evidence demonstrating that endosomal signaling is sufficient to activate signal transduction pathways and no evidence to demonstrate that endosomal signaling is able to produce a biological outcome. The lack of breakthrough is due in part to the lack of means to generate endosomal signals without plasma membrane signaling. In this paper, we report the establishment of a system to specifically activate epidermal growth factor (EGF) receptor (EGFR) when it endocytoses into endosomes. We treated cells with EGF in the presence of AG-1478, a specific EGFR tyrosine kinase inhibitor, and monensin, which blocks the recycling of EGFR. This treatment led to the internalization of nonactivated EGF-EGFR complexes into endosomes. The endosome-associated EGFR was then activated by removing AG-1478 and monensin. During this procedure we did not observe any surface EGFR phosphorylation. We also achieved specific activation of endosome-associated EGFR without using monensin. By using this system, we provided original evidence demonstrating that (i) the endosome can serve as a nucleation site for the formation of signaling complexes, (ii) endosomal EGFR signaling is sufficient to activate the major signaling pathways leading to cell proliferation and survival, and (iii) endosomal EGFR signaling is sufficient to suppress apoptosis induced by serum withdrawal.
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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".