Involvement of a Novel Efflux System in Biofilm-Specific Resistance to Antibiotics
Bibliographic record
Abstract
Bacteria growing in biofilms are more resistant to antibiotics than their planktonic counterparts. How this transition occurs is unclear, but it is likely there are multiple mechanisms of resistance that act together in order to provide an increased overall level of resistance to the biofilm. We have identified a novel efflux pump in Pseudomonas aeruginosa that is important for biofilm-specific resistance to a subset of antibiotics. Complete deletion of the genes encoding this pump, PA1874 to PA1877 (PA1874-1877) genes, in an P. aeruginosa PA14 background results in an increase in sensitivity to tobramycin, gentamicin, and ciprofloxacin, specifically when this mutant strain is growing in a biofilm. This efflux pump is more highly expressed in biofilm cells than in planktonic cells, providing an explanation for why these genes are important for biofilm but not planktonic resistance to antibiotics. Furthermore, expression of these genes in planktonic cells increases their resistance to antibiotics. We have previously shown that ndvB is important for biofilm-specific resistance (T. F. Mah, B. Pitts, B. Pellock, G. C. Walker, P. S. Stewart, and G. A. O'Toole, Nature 426:306-310, 2003). Our discovery that combining the ndvB mutation with the PA1874-1877 gene deletion results in a mutant strain that is more sensitive to antibiotics than either single mutant strain suggests that ndvB and PA1874-1877 contribute to two different mechanisms of biofilm-specific resistance to antibiotics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".