Effect of multidrug resistance-conferring mutations on the fitness and virulence of Pseudomonas aeruginosa
Bibliographic record
Abstract
BACKGROUND: Multidrug resistance has become a quandary in the treatment of bacterial infections. The effect of resistance mutations and the fitness cost on the pathogenicity of Pseudomonas aeruginosa is not well established. The objective of this study was to examine the impact of multidrug resistance on the fitness and virulence of P. aeruginosa. METHODS: Fourteen P. aeruginosa strains with various resistance mechanisms were used. In vitro growth of these isolates was investigated in full-strength and 0.25-strength Mueller-Hinton broth (MHB). Exponential growth rates were estimated from serial bacterial burden over 24 h. In vitro growth of two multidrug-resistant strains (PAO1ΔmexRΔoprD and PA9019) was studied when each was grown in co-culture with wild-type strain PAO1. In vivo growth was compared between PAO1 and PAO1ΔmexRΔopD using a murine pneumonia model; virulence over 10 days was studied in six isolates. RESULTS: Significant reduction in growth rate was observed in selected mutants (P < 0.01). PAO1 out-competed PAO1ΔmexRΔoprD and PA9019 in vitro, and in vivo growth of PAO1 was faster than PAO1ΔmexRΔoprD. Compared with PAO1, PAO1ΔmexR and PAO1ΔoprD showed a slight reduction in mortality rate; significantly lower mortality was seen in PAO1ΔmexRΔoprD (P < 0.01). However, virulence of PA9019 was not significantly different from that of PAO1. CONCLUSIONS: Specific resistance mutations were associated with fitness cost in P. aeruginosa, and accumulation of such mutations was associated with a reduction in virulence. However, it was difficult to predict the impact in clinical isolates. Knowledge of multidrug resistance mechanisms and compensatory mutations would likely be helpful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".