Bi-allelic pathogenic variants in TRMT1 disrupt tRNA modification and induce a neurodevelopmental disorder
Bibliographic record
Abstract
The post-transcriptional modification of tRNAs plays a crucial role in tRNA structure and function. Pathogenic variants in tRNA-modification enzymes have been implicated in a wide range of human neurodevelopmental and neurological disorders. However, the molecular basis for many of these disorders remains unknown. Here, we describe a comprehensive cohort of 43 individuals from 31 unrelated families with bi-allelic variants in tRNA methyltransferase 1 (TRMT1). These individuals present with a neurodevelopmental disorder universally characterized by developmental delay and intellectual disability, accompanied by variable behavioral abnormalities, epilepsy, and facial dysmorphism. The identified variants include ultra-rare TRMT1 variants, comprising missense and predicted loss-of-function variants, which segregate with the observed clinical pathology. Our findings reveal that several variants lead to mis-splicing and a consequent loss of TRMT1 protein accumulation. Moreover, cells derived from individuals harboring TRMT1 variants exhibit a deficiency in tRNA modifications catalyzed by TRMT1. Molecular analysis reveals distinct regions of TRMT1 required for tRNA-modification activity and binding. Notably, depletion of Trmt1 protein in zebrafish is sufficient to induce developmental and behavioral phenotypes along with gene-expression changes associated with disrupted cell cycle, immune response, and neurodegenerative disorders. Altogether, these findings demonstrate that loss of TRMT1-catalyzed tRNA modifications leads to intellectual disability and provides insight into 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) | 0.003 | 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".