The secondary metabolite hydrogen cyanide protects Pseudomonas aeruginosa against sodium hypochlorite-induced oxidative stress
Bibliographic record
Abstract
The high pathogenicity of Pseudomonas aeruginosa is attributed to the production of many virulence factors and its resistance to several antimicrobials. Among them, sodium hypochlorite (NaOCl) is a widely used disinfectant due to its strong antimicrobial effect. However, bacteria develop many mechanisms to survive the damage caused by this agent. Therefore, this study aimed to identify novel mechanisms employed by P. aeruginosa to resist oxidative stress induced by the strong oxidizing agent NaOCl. We analyzed the growth of the P. aeruginosa mutants ΔkatA, ΔkatE, ΔahpC, ΔahpF, ΔmsrA at 1 μg/mL NaOCl, and showed that these known H2O2 resistance mechanisms are also important for the survival of P. aeruginosa under NaOCl stress. We then conducted a screening of the P. aeruginosa PA14 transposon insertion mutant library and identified 48 mutants with increased susceptibility toward NaOCl. Among them were 10 mutants with a disrupted nrdJa, bvlR, hcnA, orn, sucC, cysZ, nuoJ, PA4166, opmQ, or thiC gene, which also exhibited a significant growth defect in the presence of NaOCl. We focussed our follow-up experiments (i.e., growth analyzes and kill-kinetics) on mutants with defect in the synthesis of the secondary metabolite hydrogen cyanide (HCN). We showed that HCN produced by P. aeruginosa contributes to its resistance toward NaOCl as it acts as a scavenger molecule, quenching the toxic effects of NaOCl.
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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".