Biallelic pathogenic variants in TRMT1 disrupt tRNA modification and induce a syndromic neurodevelopmental disorder
Bibliographic record
Abstract
Abstract The post-transcriptional modification of tRNAs plays a key role in tRNA folding and function to ensure proper levels of protein synthesis during growth and development. Pathogenic variants in tRNA modification enzymes have been implicated in diverse human neurodevelopmental and neurological disorders. However, the molecular basis for many of these disorders remains unknown, thereby limiting our understanding and potential treatment of pathologies linked to tRNA modification. Here, we describe an extensive cohort of 31 individuals from 24 unrelated families with bi-allelic variants in the tRNA methyltransferase 1 ( TRMT1 ) gene who present with a syndromic neurodevelopmental disorder universally characterized by intellectual disability in affected patients. Developmental delay, behavioral abnormalities and facial dysmorphisms represent additional core phenotypes of this syndrome. The variants include novel and ultra-rare TRMT1 variants that segregate with clinical pathology. We found that a subset of variants causes mis-splicing and loss of TRMT1 protein expression. Notably, patient cells with TRMT1 variants exhibit a deficiency in tRNA modifications catalyzed by TRMT1. Molecular analysis of TRMT1 variants reveal distinct regions of the TRMT1 protein required for tRNA modification activity and binding, including a TRMT1 subdomain critical for tRNA interaction. Importantly, depletion of TRMT1 in zebrafish is sufficient to induce developmental and behavioral phenotypes that recapitulate those observed in human patients with pathogenic TRMT1 variants. Altogether, these findings demonstrate that loss of TRMT1-catalyzed tRNA modifications leads to a syndromic form of intellectual disability and elucidate the molecular underpinnings of tRNA modification deficiency caused by pathogenic TRMT1 variants.
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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 | 0.000 |
| 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 | 0.001 |
| 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".