<i>De Novo</i> , Post-Zygotic, Inter-Tissue Mosaicism of Cell Autonomous <i>ADNP</i> Mutations in Autistic Individuals: Restricted Environmental Contribution
Bibliographic record
Abstract
ABSTRACT Many neurodevelopmental disorders, including autism, are caused by de novo mutations, that might arise as early as in the parental germline, during embryonic, fetal development, or as late as post-natal aging. Intra-tissue mutation-load variations could impact clinical presentation. One of the most common causes of autism is de novo mutations in ADNP . We developed an ultra-sensitive, highly-quantitative droplet digital PCR assay to determine ADNP mutation levels in patient tissues, including blood, teeth, hair, and 24 different tissues from a post-mortem de novo ADNP -mutated child (∼6-years old), including a transplanted liver from a non-mutant donor (retained for 22 months). Striking variations of ADNP mosaicism arose between tissues of the same individual. Mutation load differences were evident between post-mortem tissues, but not in the transplanted liver — supporting a cell autonomous genetic vulnerability to de novo mutations, arguing against a transferable environmentally-sensitive DNA damage/mutation predisposition. Variations between tissues suggest a developmental timing of the mutations. Most individuals showed at least one tissue with less than heterozygous mutations, where the presence of the homozygous non-mutant cells indicates that de novo ADNP mutations arose post-zygotically. Highly variable ADNP mosaicism between tissues, that within an individual can be less than heterozygous or approach homozygosity, indicate rapid ongoing post-zygotic, and possibly post-natal, somatic mutations, contributing to clinical variability.
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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) | 0.002 | 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".