Factors influencing readthrough therapy for frequent cystic fibrosis premature termination codons
Bibliographic record
Abstract
Premature termination codons (PTCs) are generally associated with severe forms of genetic diseases. Readthrough of in-frame PTCs using small molecules is a promising therapeutic approach. Nonetheless, the outcome of preclinical studies has been low and variable. Treatment efficacy depends on: 1) the level of drug-induced readthrough, 2) the amount of target transcripts, and 3) the activity of the recoded protein. The aim of the present study was to identify, in the cystic fibrosis transmembrane conductance regulator (CFTR) model, recoded channels from readthrough therapy that may be enhanced using CFTR modulators. First, drug-induced readthrough of 15 PTCs was measured using a dual reporter system under basal conditions and in response to gentamicin and negamycin. Secondly, exon skipping associated with these PTCs was evaluated with a minigene system. Finally, incorporated amino acids were identified by mass spectrometry and the function of the predicted recoded CFTR channels corresponding to these 15 PTCs was measured. Nonfunctional channels were subjected to CFTR-directed ivacaftor-lumacaftor treatments. The results demonstrated that CFTR modulators increased activity of recoded channels, which could also be confirmed in cells derived from a patient. In conclusion, this work will provide a framework to adapt treatments to the patient's genotype by identifying the most efficient molecule for each PTC and the recoded channels needing co-therapies to rescue channel function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".