TRMT10A dysfunction perturbs codon translation of initiator methionine and glutamine and impairs brain functions in mice
Bibliographic record
Abstract
In higher eukaryotes, tRNA methyltransferase 10A (TRMT10A) is responsible for N1-methylguanosine modification at position nine of various cytoplasmic tRNAs. Pathogenic mutations in TRMT10A cause intellectual disability, microcephaly, diabetes, and short stature in humans, and generate cytotoxic tRNA fragments in cultured cells; however, it is not clear how TRMT10A supports codon translation or brain functions. Here, we generated Trmt10a null mice and showed that tRNAGln(CUG) and initiator methionine tRNA levels were universally decreased in various tissues; the same was true in a human cell line lacking TRMT10A. Ribosome profiling of mouse brain revealed that dysfunction of TRMT10A causes ribosome slowdown at the Gln(CAG) codon and increases translation of Atf4 due to higher frequency of leaky scanning of its upstream open reading frames. Broadly speaking, translation of a subset of mRNAs, especially those for neuronal structures, is perturbed in the mutant brain. Despite not showing discernable defects in the pancreas, liver, or kidney, Trmt10a null mice showed lower body weight and smaller hippocampal postsynaptic densities, which is associated with defective synaptic plasticity and memory. Taken together, our study provides mechanistic insight into the roles of TRMT10A in the brain, and exemplifies the importance of universal tRNA modification during translation of specific codons.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".