Anti-Infectives Restore ORKAMBI® Rescue of F508del-CFTR Function in Human Bronchial Epithelial Cells Infected with Clinical Strains of P. aeruginosa
Bibliographic record
Abstract
Chronic infection and inflammation are the primary causes of declining lung function in Cystic Fibrosis (CF) patients. ORKAMBI® (Lumacaftor-Ivacaftor) is an approved combination therapy for Cystic Fibrosis (CF) patients bearing the most common mutation, F508del, in the cystic fibrosis conductance regulator (CFTR) protein. It has been previously shown that ORKAMBI®-mediated rescue of CFTR is reduced by a pre-existing Pseudomonas aeruginosa infection. Here, we show that the infection of F508del-CFTR human bronchial epithelial (HBE) cells with lab strain and four different clinical strains of P. aeruginosa, isolated from the lung sputum of CF patients, decreases CFTR function in a strain-specific manner by 48 to 88%. The treatment of infected cells with antibiotic tobramycin or cationic antimicrobial peptide 6K-F17 was found to decrease clinical strain bacterial growth on HBE cells and restore ORKAMBI®-mediated rescue of F508del-CFTR function. Further, 6K-F17 was found to downregulate the expression of pro-inflammatory cytokines, interleukin (IL)-8, IL-6, and tumor necrosis factor-α in infected HBE cells. The results provide strong evidence for a combination therapy approach involving CFTR modulators and anti-infectives (i.e., tobramycin and/or 6K-F17) to improve their overall efficacy in CF patients.
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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".