Synergy Screening Identifies a Compound That Selectively Enhances the Antibacterial Activity of Nitric Oxide
Bibliographic record
Abstract
Antibiotic resistance poses a serious threat to global health. To reinforce the anti-infective arsenal, many novel therapeutic strategies to fight bacterial infections are being explored. Among them, anti-virulence therapies, which target pathways important for virulence, have attracted much attention. Nitric oxide (NO) defense systems have been identified as critical for the pathogenesis of various bacteria, making them an appealing therapeutic target. In this study, we performed chemical screens to identify inhibitors of NO detoxification in Escherichia coli. We found that 2-mercaptobenzothiazole (2-MBT) can potently inhibit cellular detoxification of NO, achieving a level of inhibition that resembled the effect of genetically removing Hmp, the dominant detoxification enzyme under oxygenated conditions. Further analysis revealed that in the presence of NO, 2-MBT impaired the catalysis of Hmp and synthesis of Hmp and other proteins, whereas in its absence there were minimal perturbations to growth and protein synthesis. In addition, by studying the structure-activity relationship of 2-MBT, we found that both sulfur atoms in 2-MBT were vital for its inhibition of NO detoxification. Interestingly, when 2-mercaptothiazole (2-MT), which lacked the benzene ring, was used, differing biological activities were observed, although they too were NO dependent. Specifically, 2-MT could still prohibit NO detoxification, though it did not interfere with Hmp catalysis; rather, it was a stronger inhibitor of protein synthesis and it reduced the transcript levels of hmp, which was not observed with 2-MBT. Overall, these results provide a strong foundation for further exploration of 2-MBT and 2-MT for therapeutic applications.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".