Readthrough-induced misincorporated amino acid ratios guide mutant-specific therapeutic approaches for two CFTR nonsense mutations
Bibliographic record
Abstract
) gene. Premature termination codons (PTCs) represent ∼9% of CF mutations that typically cause severe expression defects of the CFTR anion channel. Despite the prevalence of PTCs as the underlying cause of genetic diseases, understanding the therapeutic susceptibilities of their molecular defects, both at the transcript and protein levels remains partially elucidated. Given that the molecular pathologies depend on the PTC positions in CF, multiple pharmacological interventions are required to suppress the accelerated nonsense-mediated mRNA decay (NMD), to correct the CFTR conformational defect caused by misincorporated amino acids, and to enhance the inefficient stop codon readthrough. The G418-induced readthrough outcome was previously investigated only in reporter models that mimic the impact of the local sequence context on PTC mutations in CFTR. To identify the misincorporated amino acids and their ratios for PTCs in the context of full-length CFTR readthrough, we developed an affinity purification (AP)-tandem mass spectrometry (AP-MS/MS) pipeline. We confirmed the incorporation of Cys, Arg, and Trp residues at the UGA stop codons of G542X, R1162X, and S1196X in CFTR. Notably, we observed that the Cys and Arg incorporation was favored over that of Trp into these CFTR PTCs, suggesting that the transcript sequence beyond the proximity of PTCs and/or other factors can impact the amino acid incorporation and full-length CFTR functional expression. Additionally, establishing the misincorporated amino acid ratios in the readthrough CFTR PTCs aided in maximizing the functional rescue efficiency of PTCs by optimizing CFTR modulator combinations. Collectively, our findings contribute to the understanding of molecular defects underlying various CFTR nonsense mutations and provide a foundation to refine mutation-dependent therapeutic strategies for various CF-causing nonsense mutations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".