Recessive <scp><i>ITPA</i></scp> mutations cause an early infantile encephalopathy
Bibliographic record
Abstract
OBJECTIVE: To identify the etiology of a novel, heritable encephalopathy in a small group of patients. METHODS: Magnetic resonance imaging (MRI) pattern analysis was used to select patients with the same pattern. Homozygosity mapping and whole exome sequencing (WES) were performed to find the causal gene mutations. RESULTS: Seven patients from 4 families (2 consanguineous) were identified with a similar MRI pattern characterized by T2 signal abnormalities and diffusion restriction in the posterior limb of the internal capsule, often also optic radiation, brainstem tracts, and cerebellar white matter, in combination with delayed myelination and progressive brain atrophy. Patients presented with early infantile onset encephalopathy characterized by progressive microcephaly, seizures, variable cardiac defects, and early death. Metabolic testing was unrevealing. Single nucleotide polymorphism array revealed 1 overlapping homozygous region on chromosome 20 in the consanguineous families. In all patients, WES subsequently revealed recessive predicted loss of function mutations in ITPA, encoding inosine triphosphate pyrophosphatase (ITPase). ITPase activity in patients' erythrocytes and fibroblasts was severely reduced. INTERPRETATION: Until now ITPA variants have only been associated with adverse reactions to specific drugs. This is the first report associating ITPA mutations with a human disorder. ITPase is important in purine metabolism because it removes noncanonical nucleotides from the cellular nucleotide pool. Toxicity of accumulated noncanonical nucleotides, leading to neuronal apoptosis and interference with proteins normally using adenosine triphosphate/guanosine triphosphate, probably explains the disease. This study confirms that combining MRI pattern recognition to define small, homogeneous patient groups with WES is a powerful approach for providing a fast diagnosis in patients with an unclassified genetic encephalopathy.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".