Identification of novel mutations and sequence variation in the Zellweger syndrome spectrum of peroxisome biogenesis disorders
Bibliographic record
Abstract
Peroxisome biogenesis disorders (PBD) are a heterogeneous group of autosomal recessive neurodegenerative disorders that affect multiple organ systems. Approximately 80% of PBD patients are classified in the Zellweger syndrome spectrum (PBD-ZSS). Mutations in the PEX1, PEX6, PEX10, PEX12, or PEX26 genes are found in approximately 90% of PBD-ZSS patients. Here, we sequenced the coding regions and splice junctions of these five genes in 58 PBD-ZSS cases previously subjected to targeted sequencing of a limited number of PEX gene exons. In our cohort, 71 unique sequence variants were identified, including 18 novel mutations predicted to disrupt protein function and 2 novel silent variants. We identified 4 patients who had two deleterious mutations in one PEX gene and a third deleterious mutation in a second PEX gene. For two such patients, we conducted cell fusion complementation analyses to identify the defective gene responsible for aberrant peroxisome assembly. Overall, we provide empirical data to estimate the relative fraction of disease-causing alleles that occur in the coding and splice junction sequences of these five PEX genes and the frequency of cases where mutations occur in multiple PEX genes. This information is beneficial for efforts aimed at establishing rapid and sensitive clinical diagnostics for PBD-ZSS patients and interpreting the results from these genetic tests.
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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".