Surviving Reactive Chlorine Stress: Responses of Gram-Negative Bacteria to Hypochlorous Acid
Why this work is in the frame
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Bibliographic record
Abstract
Sodium hypochlorite (NaOCl) and its active ingredient, hypochlorous acid (HOCl), are the most commonly used chlorine-based disinfectants. HOCl is a fast-acting and potent antimicrobial agent that interacts with several biomolecules, such as sulfur-containing amino acids, lipids, nucleic acids, and membrane components, causing severe cellular damage. It is also produced by the immune system as a first-line of defense against invading pathogens. In this review, we summarize the adaptive responses of Gram-negative bacteria to HOCl-induced stress and highlight the role of chaperone holdases (Hsp33, RidA, Cnox, and polyP) as an immediate response to HOCl stress. We also describe the three identified transcriptional regulators (HypT, RclR, and NemR) that specifically respond to HOCl. Besides the activation of chaperones and transcriptional regulators, the formation of biofilms has been described as an important adaptive response to several stressors, including HOCl. Although the knowledge on the molecular mechanisms involved in HOCl biofilm stimulation is limited, studies have shown that HOCl induces the formation of biofilms by causing conformational changes in membrane properties, overproducing the extracellular polymeric substance (EPS) matrix, and increasing the intracellular concentration of cyclic-di-GMP. In addition, acquisition and expression of antibiotic resistance genes, secretion of virulence factors and induction of the viable but nonculturable (VBNC) state has also been described as an adaptive response to HOCl. In general, the knowledge of how bacteria respond to HOCl stress has increased over time; however, the molecular mechanisms involved in this stress response is still in its infancy. A better understanding of these mechanisms could help understand host-pathogen interactions and target specific genes and molecules to control bacterial spread and colonization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it