Identification and Characterization of Novel Compounds Blocking Shiga Toxin Expression in Escherichia coli O157:H7
Bibliographic record
Abstract
Infections caused by Shiga toxin-producing E. coli strains constitute a health problem, as they are problematic to treat. Shiga toxin (Stx) production is a key virulence factor associated with the pathogenicity of enterohaemorrhagic E. coli (EHEC) and can result in the development of haemolytic uremic syndrome in infected patients. The genes encoding Stx are located on temperate lysogenic phages integrated into the bacterial chromosome and expression of the toxin is generally coupled to phage induction through the SOS response. We aimed to find new compounds capable of blocking expression of Stx type 2 (Stx2) as this subtype of Stx is more strongly associated with human disease. High-throughput screening of a small-molecule library identified a lead compound that reduced Stx2 expression in a dose-dependent manner. We show that the optimised compound interferes with the SOS response by directly affecting the activity and oligomerisation of RecA, thus limiting phage activation and Stx2 expression. Our work suggests that RecA is highly susceptible to inhibition and that targeting this protein is a viable approach to limiting production of Stx2 by EHEC. This type of approach has the potential to limit production and transfer of other phage induced and transduced determinants.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".