Arginine-Glycine Amidinotransferase Deficiency and Functional Characterization of Missense Variants in<i>GATM</i>
Bibliographic record
Abstract
Arginine-glycine amidinotransferase (GATM) deficiency is an autosomal-recessive disorder caused by pathogenic variants in GATM. Clinical features include intellectual disability, hypotonia, and myopathy. Due to normal neurodevelopment in asymptomatic individuals on creatine monotherapy, GATM deficiency is a good candidate for newborn screening. To determine the carrier frequency of GATM deficiency, we performed functional characterization of rare missense variants in GATM reported as heterozygous in the Exome Variant Server database. To assess phenotype and genotype correlation, we developed a clinical severity scoring system. Two patients with mild phenotype had a nonsense missense variant. Severe phenotype was present in patients with missense as well as truncating variants. There seems to be no phenotype and genotype correlation. We cloned a novel GATM transcript. We found seven missense variants retaining 0% of wild-type GATM activity indicating putative pathogenicity. Based on our study results, high Genomic Evolutionary Rate Profiling conservation score, conserved amino acid substitution in species, and low allele frequency in exome databases would be the most sensitive in silico analysis tools to predict pathogenicity of missense variants. We present first study of the functional characterization of missense variants in GATM as well as clinical severity score of patients with GATM deficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".