Genetic determinants of increased sodium hypochlorite and ciprofloxacin susceptibility in <i>Pseudomonas aeruginosa</i> PA14 biofilms
Bibliographic record
Abstract
Reactive chlorine species (RCS) like sodium hypochlorite (NaOCl) are potent oxidizing agents and widely used biocides in surface disinfection, water treatment, and biofilm elimination. Moreover, RCS are also produced by the human immune system to kill invading pathogens. However, bacteria have developed mechanisms to survive the damage caused by RCS. Using the comprehensive Pseudomonas aeruginosa PA14 transposon mutant library in a genetic screen, we identified a total of 28 P. aeruginosa PA14 mutants whose biofilms showed increased susceptibility to NaOCl in comparison to PA14 WT biofilms. Of these, ten PA14 mutants with a disrupted apaH, PA0793, acsA, PA1506, PA1547, PA3728, yajC, queA, PA3869, or PA14_32840 gene presented a 4-fold increase in NaOCl susceptibility compared to wild-type biofilms. While none of these mutants showed a defect in biofilm formation or attenuated susceptibility of biofilms toward the oxidant hydrogen peroxide (H2O2), all but PA14_32840 also exhibited a 2–4-fold increase in susceptibility toward the antibiotic ciprofloxacin. Further analyses revealed attenuated levels of intracellular ROS and catalase activity only for the apaH and PA1547 mutant, providing insights into the oxidative stress response in P. aeruginosa biofilms. The findings of this paper highlight the complexity of biofilm resistance and the intricate interplay between different mechanisms to survive oxidative stress. Understanding resistance strategies adopted by biofilms is crucial for developing more effective ways to fight resistant bacteria, ultimately contributing to better management of bacterial growth and resistance in clinical and environmental settings.
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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 | 0.001 |
| 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".