Multidrug Efflux Systems Play an Important Role in the Invasiveness of <i>Pseudomonas aeruginosa </i>
Bibliographic record
Abstract
Pseudomonas aeruginosa is an important opportunistic human pathogen. Certain strains can transmigrate across epithelial cells, and their invasive phenotype is correlated with capacity to cause invasive human disease and fatal septicemia in mice. Four multidrug efflux systems have been described in P. aeruginosa, however, their contribution to virulence is unclear. To clarify the role of efflux systems in invasiveness, P. aeruginosa PAO1 wild-type (WT) and its efflux mutants were evaluated in a Madin-Darby canine kidney (MDCK) epithelial cell monolayer system and in a murine model of endogenous septicemia. All efflux mutants except a deltamexCD-oprJ deletion demonstrated significantly reduced invasiveness compared with WT. In particular, a deltamexAB-oprM deletion strain was compromised in its capacity to invade or transmigrate across MDCK cells, and could not kill mice, in contrast to WT which was highly invasive (P < 0.0006) and caused fatal infection (P < 0.0001). The other mutants, including deltamexB and deltamexXY mutants, were intermediate between WT and the deltamexAB-oprM mutant in invasiveness and murine virulence. Invasiveness was restored to the deltamexAB-oprM mutant by complementation with mexAB-oprM or by addition of culture supernatant from MDCK cells infected with WT. We conclude that the P. aeruginosa MexAB-OprM efflux system exports virulence determinants that contribute to bacterial virulence.
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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".