A study in a Polish ataxia cohort indicates genetic heterogeneity and points to <i>MTCL1</i> as a novel candidate gene
Bibliographic record
Abstract
Inherited ataxias are a group of highly heterogeneous, complex neurological disorders representing a significant diagnostic challenge in clinical practice. We performed a next-generation sequencing (NGS) analysis in 10 index cases with unexplained progressive cerebellar ataxia of suspected autosomal recessive inheritance. A definite molecular diagnosis was obtained in 5/10 families and included the following diseases: autosomal recessive spastic ataxia of Charlevoix-Saguenay, POLR3B-related hypomyelinating leukodystrophy, primary coenzyme Q10 deficiency type 4, Niemann-Pick disease type C1 and SYNE1-related ataxia. In addition, we found a novel homozygous MTCL1 loss of function variant p.(Lys407fs) in a 23-year-old patient with slowly progressive cerebellar ataxia, mild intellectual disability, seizures in childhood and episodic pain in the lower limbs. The identified variant is predicted to truncate the protein after first 444 of 1586 amino acids. MTCL1 encodes a microtubule-associated protein highly expressed in cerebellar Purkinje cells; its knockout in a mouse model causes ataxia. We propose MTCL1 as a candidate gene for autosomal recessive cerebellar ataxia in humans. In addition, our study confirms the high diagnostic yield of NGS in early-onset cerebellar ataxias, with at least 50% detection rate in our ataxia cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".