Alternative splicing results in RET isoforms with distinct trafficking properties
Bibliographic record
Abstract
RET encodes a receptor tyrosine kinase that is essential for spermatogenesis, development of the sensory, sympathetic, parasympathetic, and enteric nervous systems and the kidneys, as well as for maintenance of adult midbrain dopaminergic neurons. RET is alternatively spliced to encode multiple isoforms that differ in their C-terminal amino acids. The RET9 and RET51 isoforms display unique levels of autophosphorylation and have differential interactions with adaptor proteins. They induce distinct gene expression patterns, promote different levels of cell differentiation and transformation, and play unique roles in development. Here we present a comprehensive study of the subcellular localization and trafficking of RET isoforms. We show that immature RET9 accumulates intracellularly in the Golgi, whereas RET51 is efficiently matured and present in relatively higher amounts on the plasma membrane. RET51 is internalized faster after ligand binding and undergoes recycling back to the plasma membrane. This differential trafficking of RET isoforms produces a more rapid and longer duration of signaling through the extracellular-signal regulated kinase/mitogen-activated protein kinase pathway downstream of RET51 relative to RET9. Together these differences in trafficking properties contribute to some of the functional differences previously observed between RET9 and RET51 and establish the important role of intracellular trafficking in modulating and maintaining RET 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) | 0.000 | 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 | 0.001 |
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".