<i>SEPT9</i> gene sequencing analysis reveals recurrent mutations in hereditary neuralgic amyotrophy
Bibliographic record
Abstract
BACKGROUND: Hereditary neuralgic amyotrophy (HNA) is an autosomal dominant disorder that manifests as recurrent, episodic, painful brachial neuropathies. A gene for HNA maps to chromosome 17q25.3 where mutations in SEPT9, encoding the septin-9 protein, have been identified. OBJECTIVE: To determine the frequency and type of mutations in the SEPT9 gene in a new cohort of 42 unrelated HNA pedigrees. METHODS: DNA sequencing of all exons and intron-exon boundaries for SEPT9 was carried out in an affected individual in each pedigree from our HNA cohort. Genotyping using microsatellite markers spanning the SEPT9 gene was also used to identify pedigrees with a previously reported founder haplotype. RESULTS: Two missense mutations were found: c.262C>T (p.Arg88Trp) in seven HNA pedigrees and c.278C>T (p.Ser93Phe) in one HNA pedigree. Sequencing of other known exons in SEPT9 detected no additional disease-associated mutations. A founder haplotype, without defined mutations in SEPT9, was present in seven pedigrees. CONCLUSIONS: We provide further evidence that mutation of the SEPT9 gene is the molecular basis of some cases of hereditary neuralgic amyotrophy (HNA). DNA sequencing of SEPT9 demonstrates a restricted set of mutations in this cohort of HNA pedigrees. Nonetheless, sequence analysis will have an important role in mutation detection in HNA. Additional techniques will be required to find SEPT9 mutations in an HNA founder haplotype and other pedigrees.
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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) | 0.000 | 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 | 0.000 | 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".