De novo pathogenic variant in SETX causes a rapidly progressive neurodegenerative disorder of early childhood-onset with severe axonal polyneuropathy
Bibliographic record
Abstract
Pathogenic variants in SETX cause two distinct neurological diseases, a loss-of-function recessive disorder, ataxia with oculomotor apraxia type 2 (AOA2), and a dominant gain-of-function motor neuron disorder, amyotrophic lateral sclerosis type 4 (ALS4). We identified two unrelated patients with the same de novo c.23C > T (p.Thr8Met) variant in SETX presenting with an early-onset, severe polyneuropathy. As rare private gene variation is often difficult to link to genetic neurological disease by DNA sequence alone, we used transcriptional network analysis to functionally validate these patients with severe de novo SETX-related neurodegenerative disorder. Weighted gene co-expression network analysis (WGCNA) was used to identify disease-associated modules from two different ALS4 mouse models and compared to confirmed ALS4 patient data to derive an ALS4-specific transcriptional signature. WGCNA of whole blood RNA-sequencing data from a patient with the p.Thr8Met SETX variant was compared to ALS4 and control patients to determine if this signature could be used to identify affected patients. WGCNA identified overlapping disease-associated modules in ALS4 mouse model data and ALS4 patient data. Mouse ALS4 disease-associated modules were not associated with AOA2 disease modules, confirming distinct disease-specific signatures. The expression profile of a patient carrying the c.23C > T (p.Thr8Met) variant was significantly associated with the human and mouse ALS4 signature, confirming the relationship between this SETX variant and disease. The similar clinical presentations of the two unrelated patients with the same de novo p.Thr8Met variant and the functional data provide strong evidence that the p.Thr8Met variant is pathogenic. The distinct phenotype expands the clinical spectrum of SETX-related disorders.
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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 | 0.000 | 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".