Evolution of Pseudomonas aeruginosa Virulence as a Result of Phage Predation
Bibliographic record
Abstract
The rapid increase in the emergence of antibiotic-resistant bacteria has attracted attention to bacteriophages for treating and preventing bacterial infections. Bacteriophages can drive the diversification of Pseudomonas aeruginosa, giving rise to phage-resistant variants with different phenotypes from their ancestral hosts. In this study, we sought to investigate the effect of phage resistance on cytotoxicity of host populations toward cultured mammalian cells. The library of phage-resistant P. aeruginosa PAO1 variants used was developed previously via experimental evolution of an isogenic host population using phages PP7 and E79. Our results presented herein indicate that the phage-resistant variants developed in a heterogeneous phage environment exhibit a greater ability to impede metabolic action of cultured human keratinocytes and have a greater tendency to cause membrane damage even though they cannot invade the cells in large numbers. They also show a heightened resistance to phagocytosis by model murine macrophages. Furthermore, all isolates produced higher levels of at least one of the secreted virulence factors, namely, total proteases, elastase, phospholipase C, and hemolysins. Reverse transcription-quantitative PCR (RT-qPCR) revealed upregulation in the transcription of a number of genes associated with virulence of P. aeruginosa for the phage-resistant variants. The results of this study indicate a significant change in the in vitro virulence of P. aeruginosa following phage predation and highlight the need for caution in the selection and design of phages and phage cocktails for therapeutic use.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".