Multiple Functional Effects of RET Kinase Domain Sequence Variants in Hirschsprung Disease
Bibliographic record
Abstract
The REarranged during Transfection (RET) gene encodes a receptor tyrosine kinase required for maturation of the enteric nervous system. RET sequence variants occur in the congenital abnormality Hirschsprung disease (HSCR), characterized by absence of ganglia in the intestinal tract. Although HSCR-RET variants are predicted to inactivate RET, the molecular mechanisms of these events are not well characterized. Using structure-based models of RET, we predicted the molecular consequences of 23 HSCR-associated missense variants and how they lead to receptor dysfunction. We validated our predictions in biochemical and cell-based assays to explore mutational effects on RET protein functions. We found a minority of HSCR-RET variants abrogated RET kinase function, while the remaining mutants were phosphorylated and transduced intracellular signals. HSCR-RET sequence variants also impacted on maturation, stability, and degradation of RET proteins. We showed that each variant conferred a unique combination of effects that together impaired RET protein activity. However, all tested variants impaired RET-mediated cellular functions, including cell transformation and migration. Our data indicate that the molecular mechanisms of impaired RET function in HSCR are highly variable. Although a subset of variants cause loss of RET kinase activity and downstream signaling, enzymatic inactivation is not the sole mechanism at play in HSCR.
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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 | 0.000 |
| 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".