The Susceptibility of Pseudomonas aeruginosa Strains from Cystic Fibrosis Patients to Bacteriophages
Bibliographic record
Abstract
Phage therapy may become a complement to antibiotics in the treatment of chronic Pseudomonas aeruginosa infection. To design efficient therapeutic cocktails, the genetic diversity of the species and the spectrum of susceptibility to bacteriophages must be investigated. Bacterial strains showing high levels of phage resistance need to be identified in order to decipher the underlying mechanisms. Here we have selected genetically diverse P. aeruginosa strains from cystic fibrosis patients and tested their susceptibility to a large collection of phages. Based on plaque morphology and restriction profiles, six different phages were purified from "pyophage", a commercial cocktail directed against five different bacterial species, including P. aeruginosa. Characterization of these phages by electron microscopy and sequencing of genome fragments showed that they belong to 4 different genera. Among 47 P. aeruginosa strains, 13 were not lysed by any of the isolated phages individually or by pyophage. We isolated two new phages that could lyse some of these strains, and their genomes were sequenced. The presence/absence of a CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats and Crisper associated genes) was investigated to evaluate the role of the system in phage resistance. Altogether, the results show that some P. aeruginosa strains cannot support the growth of any of the tested phages belonging to 5 different genera, and suggest that the CRISPR-Cas system is not a major defence mechanism against these lytic phages.
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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 | 0.001 |
| 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".