Transcriptional Inhibitor of Virulence Factors in Enteropathogenic<i>Escherichia coli</i>
Bibliographic record
Abstract
The type III secretion system (TTSS) is a key virulence mechanism of many important gram-negative bacterial pathogens. The TTSS is conserved among different bacterial pathogens, and mutations and deletions to the system significantly decrease virulence, making the TTSS an important potential therapeutic target. We have developed a high-throughput assay to search for inhibitors of the TTSS. We screened a commercial library of 20,000 small molecules for their ability to inhibit type III secretion by enteropathogenic Escherichia coli (EPEC). After discarding compounds that had no effect on secretion, inhibited bacterial growth, and/or caused degradation of EPEC-secreted proteins, the search was focused on a class of compounds that, while not direct inhibitors of type III secretion, inhibit expression of TTSS-related genes and other genes involved in virulence. This class of compounds does not affect bacterial viability or motility, indicating that it is not significantly affecting the expression of essential genes and is specific to virulence-associated genes. Transcriptional fusion assays confirmed that virulence-associated promoters were more sensitive to inhibition by this class of compounds. Overall, we have identified a class of compounds that can be used as a tool to probe the mechanism(s) that regulates virulence gene expression in EPEC.
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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".