A nonsense variant in the N‐terminal acetyltransferase <i>NAA30</i> may be associated with global developmental delay and tracheal cleft
Bibliographic record
Abstract
Most human proteins are N-terminally acetylated by N-terminal acetyltransferases (NATs), which play crucial roles in many cellular functions. The NatC complex, comprising the catalytic subunit NAA30 and the auxiliary subunits NAA35 and NAA38, is estimated to acetylate up to 20% of the human proteome in a co-translational manner. Several NAT enzymes have been linked to rare genetic diseases, causing developmental delay, intellectual disability, and heart disease. Here, we report a de novo heterozygous NAA30 nonsense variant c.244C>T (p.Q82*) (NM_001011713.2), which was identified by whole exome sequencing in a 5-year-old boy presenting with global development delay, autism spectrum disorder, hypotonia, tracheal cleft, and recurrent respiratory infections. Biochemical studies were performed to assess the functional impact of the premature stop codon on NAA30's catalytic activity. We find that NAA30-Q82* completely disrupts the N-terminal acetyltransferase activity toward a classical NatC substrate using an in vitro acetylation assay. This finding corresponds with structural modeling showing that the truncated NAA30 variant lacks the entire GNAT domain, which is required for catalytic activity. This study suggests that defective NatC-mediated N-terminal acetylation can cause disease, thus expanding the spectrum of NAT variants linked to genetic disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".