A new mouse model of ATR-X syndrome carrying a common patient mutation exhibits neurological and morphological defects
Bibliographic record
Abstract
ATRX is a chromatin remodelling ATPase that is involved in transcriptional regulation, DNA damage repair and heterochromatin maintenance. It has been widely studied for its role in ALT-positive cancers, but its role in neurological function remains elusive. Hypomorphic mutations in the X-linked ATRX gene cause a rare form of intellectual disability combined with alpha-thalassemia called ATR-X syndrome in hemizygous males. Clinical features also include facial dysmorphism, microcephaly, short stature, musculoskeletal defects and genital abnormalities. As complete deletion of ATRX in mice results in early embryonic lethality, the field has largely relied on conditional knockout models to assess the role of ATRX in multiple tissues. Given that null alleles are not found in patients, a more patient-relevant model was needed. Here, we have produced and characterized the first patient mutation knock-in model of ATR-X syndrome, carrying the most common causative mutation, R246C. This is one of a cluster of missense mutations located in the chromatin-binding domain and disrupts its function. The knock-in mice recapitulate several aspects of the patient disorder, including craniofacial defects, microcephaly, reduced body size and impaired neurological function. They provide a powerful model for understanding the molecular mechanisms underlying ATR-X syndrome and testing potential therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".