Nitric oxide (NO) elicits aminoglycoside tolerance in Escherichia coli but antibiotic resistance gene carriage and NO sensitivity have not co-evolved
Bibliographic record
Abstract
Abstract The spread of multidrug-resistance in Gram-negative bacterial pathogens presents a major clinical challenge, and new approaches are required to combat these organisms. Nitric oxide (NO) is a well-known antimicrobial that is produced by the immune system in response to infection, and numerous studies have demonstrated that NO is a respiratory inhibitor with both bacteriostatic and bactericidal properties. However, given that loss of aerobic respiratory complexes is known to diminish antibiotic efficacy, it was hypothesised that the potent respiratory inhibitor NO would elicit similar effects. Indeed, the current work demonstrates that pre-exposure to NO-releasers elicits a > tenfold increase in IC50 for gentamicin against pathogenic E. coli (i.e. a huge decrease in lethality). It was therefore hypothesised that hyper-sensitivity to NO may have arisen in bacterial pathogens and that this trait could promote the acquisition of antibiotic-resistance mechanisms through enabling cells to persist in the presence of toxic levels of antibiotic. To test this hypothesis, genomics and microbiological approaches were used to screen a collection of E. coli clinical isolates for antibiotic susceptibility and NO tolerance, although the data did not support a correlation between increased carriage of antibiotic resistance genes and NO tolerance. However, the current work has important implications for how antibiotic susceptibility might be measured in future (i.e. ± NO) and underlines the evolutionary advantage for bacterial pathogens to maintain tolerance to toxic levels of NO.
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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".