Subnuclear localization and mobility are key indicators of PAX3 dysfunction in Waardenburg syndrome
Bibliographic record
Abstract
Mutations in the transcription factor PAX3 cause Waardenburg syndrome (WS) in humans and the mouse Splotch mutant, which display similar neural crest-derived defects. Previous characterization of disease-causing mutations revealed pleiotropic effects on PAX3 DNA binding and transcriptional activity. In this study, we evaluated the impact of disease alleles on PAX3 localization and mobility. Immunofluorescence analyses indicated that the majority of PAX3 occupies the interchromatin space, with only sporadic colocalization with sites of transcription. Interestingly, PAX3 disease alleles fell into two distinct categories when localization and dynamics in fluorescence recovery after photobleaching (FRAP) were assessed. The first group (class I), comprising N47H, G81A and V265F exhibit a diffuse distribution and markedly increased mobility when compared with wild-type PAX3. In contrast, the G42R, F45L, S84F, Y90H and R271G mutants (class II) display evidence of subnuclear compartmentalization and mobility intermediate between wild-type PAX3 and class I proteins. However, unlike class I mutants, which retain DNA binding, class II proteins are deficient for this activity, indicating that DNA binding is not a primary determinant of PAX3 distribution and movement. Importantly, class I properties prevail when combined with a class II mutation, which taken with the proximity of the two mutant classes within the PAX3 protein, suggests class I mutants act by perturbing PAX3 conformation. Together, these results establish that altered localization and dynamics play a key role in PAX3 dysfunction and that loss of the underlying determinants represents the principal defect for a subset of Waardenburg mutations.
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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 | 0.001 | 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".