How do genes that escape from X‐chromosome inactivation contribute to Turner syndrome?
Bibliographic record
Abstract
X-chromosome inactivation generally results in dosage equivalence for expression of X-linked genes between 46,XY males and 46,XX females. The 20-30% of genes that escape silencing are thus candidates for having a role in the phenotype of Turner syndrome. Understanding which genes escape from silencing, and how they avoid this chromosome-wide inactivation is therefore an important step toward understanding Turner Syndrome. We have examined the mechanism of escape using a previously reported knock-in of a BAC containing the human escape gene RPS4X in mouse. We now demonstrate that escape from inactivation for RPS4X is already established by embryonic Day 9.5, and that both silencing and escape are faithfully maintained across the lifespan. No overt abnormalities were observed for transgenic mice up to 1 year of age despite robust transcription of the human RPS4X gene with no detectable downregulation of the mouse homolog. However, there was no significant increase in protein levels, suggesting translational compensation in the mouse. Finally, while many of the protein-coding genes have been assessed for their inactivation status, less is known about the X-linked RNA genes, and we propose that for many microRNA genes their inactivation status can be predicted as they are intronic to genes for which the inactivation status is known.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".