Detection of rarely identified multiple mutations in <i>MECP2</i> gene do not contribute to enhanced severity in rett syndrome
Bibliographic record
Abstract
The objective of our study was to characterize the influence of multiple mutations in the MECP2 gene in a cohort of individuals with Rett syndrome. Further analysis demonstrated that nearly all resulted from de novo in cis mutations, where the disease severity was indistinguishable from single mutations. Our methods involved enrolling participants in the RTT Natural History Study (NHS). After providing informed consent through their parents or principal caretakers, additional molecular assessments were performed in the participants and their parents to assess the presence and location of more than one mutation in each. Clinical severity was assessed at each visit in those participants in the NHS. Non-contiguous MECP2 gene variations were detected in 12 participants and contiguous mutations involving a deletion and insertion in three participants. Thirteen of 15 participants had mutations that were in cis; four (of 13) had three MECP2 mutations; two (of 15) had mutations that were both in cis and in trans (i.e., on different alleles). Clinical severity did not appear different from NHS participants with a single similar mutation. Mutations in cis were identified in most participants; two individuals had mutations both in cis and in trans. The presence of multiple mutations was not associated with greater severity. Nevertheless, multiple mutations will require greater thought in the future, if genetic assignment to drug treatment protocols is considered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".