TRNT-1 Deficiency Is Associated with Loss of tRNA Integrity and Imbalance of Distinct Proteins
Bibliographic record
Abstract
Mitochondrial diseases are a group of heterogeneous disorders caused by dysfunctional mitochondria. Interestingly, a large proportion of mitochondrial diseases are caused by defects in genes associated with tRNA metabolism. We recently discovered that partial loss-of-function mutations in tRNA Nucleotidyl Transferase 1 (TRNT1), the nuclear gene encoding the CCA-adding enzyme essential for modifying both nuclear and mitochondrial tRNAs, causes a multisystemic and clinically heterogenous disease termed SIFD (sideroblastic anemia with B-cell immunodeficiency, periodic fevers, and developmental delay; SIFD). However, it is not clear how mutations in a general and essential protein like TRNT1 cause disease with such clinically broad but unique symptomatology and tissue involvement. Using biochemical, cell, and mass spectrometry approaches, we demonstrate that TRNT1 deficiency is associated with sensitivity to oxidative stress, which is due to exacerbated, angiogenin-dependent cleavage of tRNAs. Furthermore, reduced levels of TRNT1 lead to phosphorylation of Eukaryotic Translation Initiation Factor 2 Subunit Alpha (eIF2α), increased reactive oxygen species (ROS) production, and changes in the abundance of distinct proteins. Our data suggest that the observed variable SIFD phenotypes are likely due to dysregulation of tRNA maturation and abundance, which in turn negatively affects the translation of distinct proteins.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".