<i>Pseudomonas aeruginosa</i> Reveals High Intrinsic Resistance to Penem Antibiotics: Penem Resistance Mechanisms and Their Interplay
Bibliographic record
Abstract
Pseudomonas aeruginosa exhibits high intrinsic resistance to penem antibiotics such as faropenem, ritipenem, AMA3176, sulopenem, Sch29482, and Sch34343. To investigate the mechanisms contributing to penem resistance, we used the laboratory strain PAO1 to construct a series of isogenic mutants with an impaired multidrug efflux system MexAB-OprM and/or impaired chromosomal AmpC beta-lactamase. The outer membrane barrier of PAO1 was partially eliminated by inducing the expression of the plasmid-encoded Escherichia coli major porin OmpF. Susceptibility tests using the mutants and the OmpF expression plasmid showed that MexAB-OprM and the outer membrane barrier, but not AmpC beta-lactamase, are the main mechanisms involved in the high intrinsic penem resistance of PAO1. However, reducing the high intrinsic penem resistance of PAO1 to the same level as that of penem-susceptible gram-negative bacteria such as E. coli required the loss of either both MexAB-OprM and AmpC beta-lactamase or both MexAB-OprM and the outer membrane barrier. Competition experiments for penicillin-binding proteins (PBPs) revealed that the affinity of PBP 1b and PBP 2 for faropenem were about 1.8- and 1.5-fold lower, than the respective affinity for imipenem. Loss of the outer membrane barrier, MexAB, and AmpC beta-lactamase increased the susceptibility of PAO1 to almost all penems tested compared to the susceptibility of the AmpC-deficient PAO1 mutants to imipenem. Thus, it is suggested that the high intrinsic penem resistance of P. aeruginosa is generated from the interplay among the outer membrane barrier, the active efflux system, and AmpC beta-lactamase but not from the lower affinity of PBPs for penems.
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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 | 0.001 | 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".