P.133 Expanding the phenotype of TRNT1 mutations to include Leigh syndrome
Bibliographic record
Abstract
Background: Children with biallelic mutations in TRNT1 have multi-organ involvement with congenital sideroblastic anemia, -B-cell immunodeficiency, periodic fevers, and developmental delay (SIFD) as well as seizures, ataxia and sensorineural hearing loss. The TRNT1 gene encodes the CCA-adding enzyme essential for maturation of both nuclear and mitochondrial transfer RNAs accounting for phenotypic pleitropy. Neurodegenerative Leigh syndrome has not been previously reported. Methods:Case summary: A Portuguese boy presented with global developmental delay, 2 episodes of infantile Leigh encephalopathy at 8 mo and 4 yr responsive to high-dose steroids, slow neurodegeneration of cognitive, language and motor functions with optic atrophy, pigmentary retinopathy, spasticity, dystonia, and focal dyscognitive seizures, pancytopenia, transfusion dependent sideroblastic anemia, recurrent febrile infections (pulmonary, gastrointestinal), hypernatremia, with tracheostomy dependence at age 5 yr, malabsorption and TPN dependence at 9 yr, and survival to early adulthood. Neuroimaging showed symmetric hemorrhagic lesions in the thalamus, brain stem (periaqueductal grey) and cerebellum consistent with Leigh syndrome but no lactate peak on MRS. Results: Whole exome sequencing identified a homozygous missense pathogenic variant in TRNT1, c.668T>C (p.I223T) in the affected individual. Conclusions: This report expands the neurological phenotype of TRNT1 mutations and highlights the importance of considering this gene in the evaluation of Leigh syndrome.
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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) | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".