Bacteriophages ϕMR299-2 and ϕNH-4 Can Eliminate Pseudomonas aeruginosa in the Murine Lung and on Cystic Fibrosis Lung Airway Cells
Bibliographic record
Abstract
UNLABELLED: Pseudomonas aeruginosa is a common cause of infection in the lungs of patients with cystic fibrosis (CF). In addition, biofilm formation and antibiotic resistance of Pseudomonas are major problems that can complicate antibiotic therapy. We evaluated the efficacy of using bacteriophages to kill the pathogen in both biofilms and in the murine lung. We isolated and characterized two phages from a local wastewater treatment plant, a myovirus (φNH-4) and a podovirus (φMR299-2). Both phages were active against clinical isolates of P. aeruginosa. Together, the two phages killed all 9 clinical isolate strains tested, including both mucoid and nonmucoid strains. An equal mixture of the two phages was effective in killing P. aeruginosa NH57388A (mucoid) and P. aeruginosa MR299 (nonmucoid) strains when growing as a biofilm on a cystic fibrosis bronchial epithelial CFBE41o- cell line. Phage titers increased almost 100-fold over a 24-h period, confirming replication of the phage. Furthermore, the phage mix was also effective in killing the pathogen in murine lungs containing 1 × 10(7) to 2 × 10(7) P. aeruginosa. Pseudomonas was effectively cleared (reduced by a magnitude of at least 3 to 4 log units) from murine lungs in 6 h. Our study demonstrates the efficacy of these two phages in killing clinical Pseudomonas isolates in the murine lung or as a biofilm on a pulmonary cell line and supports the growing interest in using phage therapy for the control and treatment of multidrug-resistant Pseudomonas lung infections in CF patients. IMPORTANCE: Given the rise in antibiotic resistance, nonantibiotic therapies are required for the treatment of infection. This is particularly true for the treatment of Pseudomonas infection in patients with cystic fibrosis. We have identified two bacterial viruses (bacteriophages) that can kill Pseudomonas growing on human lung cells and in an animal model of lung infection. The use of bacteriophages is particularly appropriate because the killing agent can replicate on the target cell, generating fresh copies of the bacteriophage. Thus, in the presence of a target, the killing agent multiplies. By using two bacteriophages we can reduce the risk of resistant colonies developing at the site of infection. Bacteriophage therapy is an exciting field, and this study represents an important demonstration of efficacy in validated infection models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".