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Record W4309925719 · doi:10.1101/2022.11.23.517642

Promoting translational readthrough to augment fibrillin-1 (FBN1) deposition in Marfan syndrome fibroblasts: A proof-of-concept study

2022· preprint· en· W4309925719 on OpenAlexfundno aff
Zerina Balic, Dirk Hubmacher

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsnot available
FundersMcGill University
KeywordsFibrillinMarfan syndromeFibroblastExtracellular matrixConnective tissueTranslational medicineEctopia lentisBiologyMedicinePathologyGeneticsCell cultureInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.271
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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