Using multiple modalities to confirm diagnosis in patients with suspected peroxisome biogenesis disorders
Bibliographic record
Abstract
Zellweger spectrum disorder (ZSD) results from biallelic variants in any one of 13 PEX genes involved in peroxisome biogenesis and function. The majority of ZSD cases result from pathogenic variants in PEX1. Here, we present 3 patients with suspected PEX1-related ZSD and non-diagnostic whole exome sequencing and describe the use of multiple modalities to ascertain their diagnosis. We confirmed peroxisomal dysfunction in the patients by demonstrating abnormal peroxisome metabolite levels in blood and peroxisome import dysfunction in patient fibroblasts. RNA studies including RNA-seq and RT-PCR, followed by Sanger sequencing showed leaky splice variants including an intron 13 variant causing exon 14 skipping (Patient 1), an intron 22 variant causing intron 22 retention (Patient 2), and a synonymous splice-site variant causing exon 16 skipping (Patient 3). All three patients had very low amounts of canonical PEX1 transcripts on RNA-seq, as well as residual but reduced PEX1 protein levels on immunoblotting, which likely explains their non-severe ZSD phenotype. This study suggests that a multi-modality approach combining biochemical testing, functional assays in fibroblasts and molecular investigations including sequencing of non-coding regions and RNA analysis may aid in diagnosis of patients with suspected PBD-ZSD and inconclusive WES.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".