Deleterious effect of <i>Pseudomonas aeruginosa</i> on F508del-CFTR rescued by elexacaftor/tezacaftor/ivacaftor is clinical strain-dependent in patient-derived nasal cells
Bibliographic record
Abstract
Background The triple cystic fibrosis transmembrane conductance regulator (CFTR) modulators combination elexacaftor/tezacaftor/ivacaftor (ETI) has been approved for people with cystic fibrosis (pwCF) bearing at least one F508del allele. Despite the development of CFTR modulators having dramatically improved respiratory outcomes in pwCF, clinical studies have showed variable responses to this drug formulation. Of note, airway inflammation and bacterial colonisation persist in the upper and lower respiratory tract even in ETI-treated patients. Methods We first tested the clinical exoproducts (EXO) of Pseudomonas aeruginosa isolated from 15 CF patients in wild-type (WT) and F508del-CFTR CF bronchial epithelial (CFBE) cells. We were then prompted to evaluate the effects of EXO in ex-vivo patient-derived tissues. Therefore, we cultured primary nasal epithelial cells (HNECs) with EXO isolated from the corresponding pwCF to mimic the native status of CF airway. Results We found that EXO variably decreased WT-, F508del- and ETI-dependent F508del-CFTR function and increased proinflammatory cytokines and reactive oxygen species (ROS) levels in a clinical strain-specific manner. Similarly, we observed a variable reduction of F508del-CFTR function in presence or absence of ETI and upregulation of proinflammatory cytokines and ROS levels. Interestingly, HNECs treated with EXO isolated from the corresponding donor and three different pwCF showed a variable reduction of ETI-dependent F508del-CFTR function mainly due to clinical strains with limited effect of patient background. Furthermore, we demonstrated that ETI pretreatment decreased the cytokines and ROS levels down to the levels of uninfected cells. Conclusion These preclinical studies suggest that in vitro screening of patient-specific response to CFTR modulators under infection/inflammation conditions could prove to be a valuable tool to enhance the prediction of clinical response.
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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 | 0.001 |
| 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".