The Nitric Oxide Donor, S-Nitrosoglutathione, Rescues Peroxisome Number and Activity Defects in PEX1G843D Mild Zellweger Syndrome Fibroblasts
Bibliographic record
Abstract
Peroxisome biogenesis disorders (PBDs) are a group of metabolic developmental diseases caused by mutations in one or more genes encoding peroxisomal proteins. Zellweger syndrome spectrum (PBD-ZSS) results from metabolic dysfunction caused by damaged or non-functional peroxisomes and manifests as a multi-organ syndrome with significant morbidity and mortality for which there is no current drug therapy. Mild PBD-ZSS patients can exhibit a more progressive disease course and could benefit from the identification of drugs to improve the quality of life and extend the lifespan of affected individuals. Our study used a high-throughput screen of FDA-approved compounds to identify compounds that improve peroxisome function and biogenesis in human fibroblast cells carrying the mild PBD-ZSS variant,PEX1G843D. Our screen identified the nitrogen oxide donor,S-nitrosoglutathione (GSNO), as a potential therapeutic for this mild form of PBD-ZSS. Further biochemical characterization showed that GSNO enhances both peroxisome number and function inPEX1G843Dmutant fibroblasts and leads to increased survival and longer lifespan in anin vivohumanizedDrosophilamodel carrying thePEX1G843Dmutation. GSNO is therefore a strong candidate to be translated to clinical trials as a potential therapeutic for mild PBD-ZSS.
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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) | 0.000 | 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".