The ESCRT-III Subunit hVps24 Is Required for Degradation but Not Silencing of the Epidermal Growth Factor Receptor
Bibliographic record
Abstract
The endosomal sorting complexes required for transport, ESCRT-I, -II, and -III, are thought to mediate the biogenesis of multivesicular endosomes (MVEs) and endosomal sorting of ubiquitinated membrane proteins. Here, we have compared the importance of the ESCRT-I subunit tumor susceptibility gene 101 (Tsg101) and the ESCRT-III subunit hVps24/CHMP3 for endosomal functions and receptor signaling. Like Tsg101, endogenous hVps24 localized mainly to late endosomes. Depletion of hVps24 by siRNA showed that this ESCRT subunit, like Tsg101, is important for degradation of the epidermal growth factor (EGF) receptor (EGFR) and for transport of the receptor from early endosomes to lysosomes. Surprisingly, however, whereas depletion of Tsg101 caused sustained EGF activation of the mitogen-activated protein kinase pathway, depletion of hVps24 had no such effect. Moreover, depletion of Tsg101 but not of hVps24 caused a major fraction of internalized EGF to accumulate in nonacidified endosomes. Electron microscopy of hVps24-depleted cells showed an accumulation of EGFRs in MVEs that were significantly smaller than those in control cells, probably because of an impaired fusion with lyso-bisphosphatidic acid-positive late endosomes/lysosomes. Together, our results reveal functional differences between ESCRT-I and ESCRT-III in degradative protein trafficking and indicate that degradation of the EGFR is not required for termination of its signaling.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".