Spliceosomopathies and neurocristopathies: Two sides of the same coin?
Bibliographic record
Abstract
Mutations in core components of the spliceosome are responsible for a group of syndromes collectively known as spliceosomopathies. Patients exhibit microcephaly, micrognathia, malar hypoplasia, external ear anomalies, eye anomalies, psychomotor delay, intellectual disability, limb, and heart defects. Craniofacial malformations in these patients are predominantly found in neural crest cells-derived structures of the face and head. Mutations in eight genes SNRPB, RNU4ATAC, SF3B4, PUF60, EFTUD2, TXNL4, EIF4A3, and CWC27 are associated with craniofacial spliceosomopathies. In this review, we provide a brief description of the normal development of the head and the face and an overview of mutations identified in genes associated with craniofacial spliceosomopathies. We also describe a model to explain how and when these mutations are most likely to impact neural crest cells. We speculate that mutations in a subset of core splicing factors lead to disrupted splicing in neural crest cells because these cells have increased sensitivity to inefficient splicing. Hence, disruption in splicing likely activates a cellular stress response that includes increased skipping of regulatory exons in genes such as MDM2 and MDM4, key regulators of P53. This would result in P53-associated death of neural crest cells and consequently craniofacial malformations associated with spliceosomopathies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".