Differential recruitment of E3 ubiquitin ligase complexes regulates RET isoform internalization
Bibliographic record
Abstract
The RET receptor tyrosine kinase is implicated in normal development and cancer. RET is expressed as two isoforms, RET9 and RET51, with unique C-terminal tail sequences that recruit distinct protein complexes to mediate signals. Upon activation, RET isoforms are internalized with distinct kinetics, suggesting differences in regulation. Here, we demonstrate that RET9 and RET51 differ in their abilities to recruit E3 ubiquitin ligases to their unique C-termini. RET51, but not RET9, interacts with, and is ubiquitylated by CBL, which is recruited through interactions with the GRB2 adaptor protein. RET51 internalization was not affected by CBL knockout but was delayed in GRB2-depleted cells. In contrast, RET9 ubiquitylation requires phosphorylation-dependent changes in accessibility of key RET9 C-terminal binding motifs that facilitate interactions with multiple adaptor proteins, including GRB10 and SHANK2, to recruit the NEDD4 ubiquitin ligase. We showed that NEDD4-mediated ubiquitylation is required for RET9 localization to clathrin-coated pits and subsequent internalization. Our data establish differences in the mechanisms of RET9 and RET51 ubiquitylation and internalization that may influence the strength and duration of RET isoform signals and cellular outputs.This article has an associated First Person interview with the first authors of the paper.
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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 | 0.001 | 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".