Synergistic activity of fosfomycin, β-lactams and peptidoglycan recycling inhibition against<i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
OBJECTIVES: To evaluate the interconnection between peptidoglycan (PG) recycling, fosfomycin susceptibility and synergy between fosfomycin and β-lactams in Pseudomonas aeruginosa METHODS: Fosfomycin MICs were determined by broth microdilution and Etest for a panel of 47 PAO1 mutants defective in several components of PG recycling and/or AmpC induction pathways. PAO1 fosfomycin MICs were also determined in the presence of a 5 mM concentration of the NagZ inhibitor PUGNAc. Population analysis of fosfomycin susceptibility and characterization of the resistant mutants that emerged was also performed for selected strains. Finally, fosfomycin, imipenem and fosfomycin + imipenem killing curves were assessed. RESULTS: Mutants defective in AmpG, NagZ or all three AmpD amidases showed a marked increase in fosfomycin susceptibility (at least two 2-fold dilutions with respect to WT PAO1). Moreover, PAO1 fosfomycin MICs were consistently reduced from 48 to 24 mg/L in the presence of a 5 mM concentration of PUGNAc. Fosfomycin hypersusceptibility of the ampG, nagZ and triple ampD mutants was also clearly confirmed in the performed population analysis, although the emergence of resistant mutants, through GlpT mutations, was not avoided. Synergy between fosfomycin and imipenem was evidenced for the WT strain, the AmpC-hyperproducing strain (triple AmpD mutant) and the NagZ and AmpG mutants in killing curves. Moreover, regrowth of resistant mutants was not evidenced for the combination. CONCLUSIONS: PG recycling inhibitors are envisaged as useful adjuvants in the treatment of P. aeruginosa infections with β-lactams and fosfomycin and therefore further development of these molecules is encouraged.
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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 | 0.000 |
| 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".