Phenotyping Rare CFTR Mutations Reveal Functional Expression Defects Restored by TRIKAFTATM
Bibliographic record
Abstract
The rare Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) mutations, c.1826A > G (H609R) and c.3067_3072delATAGTG (I1023_V1024del), are associated with severe lung disease. Despite the existence of four CFTR targeted therapies, none have been approved for individuals with these mutations because the associated molecular defects were not known. In this study we examined the consequences of these mutations on protein processing and channel function in HEK293 cells. We found that, similar to F508del, H609R and I1023_V1024del-CFTR exhibited reduced protein processing and altered channel function. Because the I1023_V1024del mutation can be linked with the mutation, I148T, we also examined the protein conferred by transfection of a plasmid bearing both mutations. Interestingly, together with I148T, there was no further reduction in channel function exhibited by I1023-V1024del. Both H609R and I1023_V1024del failed to exhibit significant correction of their functional expression with lumacaftor and ivacaftor. In contrast, the triple modulator combination found in TRIKAFTATM, i.e., tezacaftor, elexacaftor and ivacaftor rescued trafficking and function of both of these mutants. These in-vitro findings suggest that patients harbouring H609R or I1023_V1024del, alone or with I148T, may benefit clinically from treatment with TRIKAFTATM.
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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".