Changes to Its Peptidoglycan-Remodeling Enzyme Repertoire Modulate β-Lactam Resistance in Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa is a leading cause of hospital-acquired infections and is resistant to many antibiotics. Among its primary mechanisms of resistance is expression of a chromosomally encoded AmpC β-lactamase that inactivates β-lactams. The mechanisms leading to AmpC expression in P. aeruginosa remain incompletely understood but are intricately linked to cell wall metabolism. To better understand the roles of peptidoglycan-active enzymes in AmpC expression-and consequent β-lactam resistance-a phenotypic screen of P. aeruginosa mutants lacking such enzymes was performed. Mutants lacking one of four lytic transglycosylases (LTs) or the nonessential penicillin-binding protein PBP4 (dacB) had altered β-lactam resistance. mltF and slt mutants with reduced β-lactam resistance were designated WIMPs (wall-impaired mutant phenotypes), while highly resistant dacB, sltB1, and mltB mutants were designated HARMs (high-level AmpC resistant mutants). Double mutants lacking dacB and sltB1 had extreme piperacillin resistance (>256 μg/ml) compared to either of the single knockouts (64 μg/ml for a dacB mutant and 12 μg/ml for an sltB1 mutant). Inactivation of ampC reverted these mutants to wild-type susceptibility, confirming that AmpC expression underlies resistance. dacB mutants had constitutively elevated AmpC expression, but the LT mutants had wild-type levels of AmpC in the absence of antibiotic exposure. These data suggest that there are at least two different pathways leading to AmpC expression in P. aeruginosa and that their simultaneous activation leads to extreme β-lactam resistance.
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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".