Extracellular DNA enhances <i>Pseudomonas aeruginosa</i> biofilm resistance to sodium hypochlorite stress
Bibliographic record
Abstract
The opportunistic human pathogen Pseudomonas aeruginosa exhibits high pathogenicity and antimicrobial resistance, largely due to its ability to form robust biofilms. In addition to the exopolysaccharides Psl and Pel, extracellular DNA (eDNA) is an important matrix component in P. aeruginosa PAO1 biofilms. It has been shown previously that eDNA is involved in biofilm initiation and integrity as well as antibiotic resistance; however, its involvement in resistance to oxidative stressors such as the widely used disinfectant sodium hypochlorite (NaOCl) is less explored. Here, we examined the function of eDNA in NaOCl resistance of P. aeruginosa PAO1 biofilms. Using different biofilm assays in combination with a PAO1 ∆ pslA pelF double mutant, which lacks the exopolysaccharides Psl and Pel, and a Tn- bfmR mutant, which exhibits increased eDNA amounts in biofilms, we were able to show that eDNA contributes to NaOCl resistance in P. aeruginosa PAO1 biofilms, in particular when exopolysaccharides are absent. Interestingly, NaOCl was more effective after DNase treatment against ∆ pslA pelF biofilms . These findings indicate that the protective function of eDNA in biofilm resistance is matrix composition-dependent and becomes more pronounced in the absence of Psl and Pel.
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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) | 0.002 | 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".