Inactivation of the Glycoside Hydrolase NagZ Attenuates Antipseudomonal β-Lactam Resistance in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
The overproduction of chromosomal AmpC beta-lactamase poses a serious challenge to the successful treatment of Pseudomonas aeruginosa infections with beta-lactam antibiotics. The induction of ampC expression by beta-lactams is mediated by the disruption of peptidoglycan (PG) recycling and the accumulation of cytosolic 1,6-anhydro-N-acetylmuramyl peptides, catabolites of PG recycling that are generated by an N-acetyl-beta-D-glucosaminidase encoded by nagZ (PA3005). In the absence of beta-lactams, ampC expression is repressed by three AmpD amidases encoded by ampD, ampDh2, and ampDh3, which act to degrade these 1,6-anhydro-N-acetylmuramyl peptide inducer molecules. The inactivation of ampD genes results in the stepwise upregulation of ampC expression and clinical resistance to antipseudomonal beta-lactams due to the accumulation of the ampC inducer anhydromuropeptides. To examine the role of NagZ on AmpC-mediated beta-lactam resistance in P. aeruginosa, we inactivated nagZ in P. aeruginosa PAO1 and in an isogenic triple ampD null mutant. We show that the inactivation of nagZ represses both the intrinsic beta-lactam resistance (up to 4-fold) and the high antipseudomonal beta-lactam resistance (up to 16-fold) that is associated with the loss of AmpD activity. We also demonstrate that AmpC-mediated resistance to antipseudomonal beta-lactams can be attenuated in PAO1 and in a series of ampD null mutants using a selective small-molecule inhibitor of NagZ. Our results suggest that the blockage of NagZ activity could provide a strategy to enhance the efficacies of beta-lactams against P. aeruginosa and other gram-negative organisms that encode inducible chromosomal ampC and to counteract the hyperinduction of ampC that occurs from the selection of ampD null mutations during beta-lactam therapy.
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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 | 0.001 |
| 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".