Loss of membrane‐bound lytic transglycosylases increases outer membrane permeability and <i>β</i>‐lactam sensitivity in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
The opportunistic pathogen Pseudomonas aeruginosa is a leading cause of nosocomial infections. Its relatively impermeable outer membrane (OM) limits antibiotic entry, and a chromosomally encoded AmpC β-lactamase inactivates β-lactam antibiotics. AmpC expression is linked to peptidoglycan (PG) recycling, and soluble (sLT) or membrane-bound (mLT) lytic transglycosylases are responsible for generating the anhydromuropeptides that induce AmpC expression. Thus, inhibition of LT activity could reduce AmpC-mediated β-lactam resistance in P. aeruginosa. Here, we characterized single and combination LT mutants. Strains lacking SltB1 or MltB had increased β-lactam minimum inhibitory concentrations (MICs) compared to wild type, while only loss of Slt decreased MICs. An sltB1 mltB double mutant had elevated β-lactam MICs compared to either the sltB1 or mltB single mutants (96 vs. 32 μg/mL cefotaxime), without changes to AmpC levels. Time-kill assays with β-lactams suggested that increased MIC correlated with a slower rate of autolysis in the sltB1 mltB mutant - an antisuicide phenotype. Strains lacking multiple mLTs were more sensitive to β-lactams and up to 16-fold more sensitive to vancomycin, normally incapable of crossing the OM. Multi-mLT mutants were also sensitive to bile salts and osmotic stress, and were hyperbiofilm formers, all phenotypes consistent with cell envelope compromise. Complementation with genes encoding inactive forms of the enzymes - or alternatively, overexpression of Braun's lipoprotein - reversed the mutants' cell envelope damage phenotypes, suggesting that mLTs help to stabilize the OM. We conclude that P. aeruginosa mLTs contribute physically to cell envelope stability, and that Slt is the preferred target for future development of LT inhibitors that could synergize with β-lactams.
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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".