Promoting translational readthrough to augment fibrillin-1 (FBN1) deposition in Marfan syndrome fibroblasts: A proof-of-concept study
Bibliographic record
Abstract
Abstract Marfan syndrome (MFS) is a connective tissue disorder characterized by long bone overgrowth, enlargement of the aorta, ocular anomalies and other symptoms. Current treatment focuses on managing aortic aneurysms to avoid dissection or rupture. However, no cures are available. MFS is caused by one of >1,800 dominant pathogenic variants in FBN1 , which encodes the extracellular matrix (ECM) protein fibrillin-1. A significant number of FBN1 variants result in premature termination codons (PTCs). Recently, small molecules were identified that can promote translational readthrough of PTCs and were evaluated in preclinical and clinical trials for several genetic disorders. Here, we show that the translational readthrough drugs ataluren and gentamicin ameliorated FBN1 deposition in some MFS patient-derived skin fibroblast lines harboring PTC variants in FBN1 . In contrast, inhibitors of NMD were cytotoxic to the skin fibroblast lines that we analyzed. We conclude that promoting translational readthrough of PTC variants in FBN1 could result in a therapeutic benefit for MFS patients with specific PTCs in FBN1 and that its efficacy will likely depend on the PTC sequence context, the amino acids that are incorporated in FBN1 after PTC suppression and the overall increase of FBN1 deposition in the ECM.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".