Disarming Bacterial Virulence through Chemical Inhibition of the DNA Binding Domain of an AraC-like Transcriptional Activator Protein
Bibliographic record
Abstract
The misuse of antibiotics during past decades has led to pervasive antibiotic resistance in bacteria. Hence, there is an urgent need for the development of new and alternative approaches to combat bacterial infections. In most bacterial pathogens the expression of virulence is tightly regulated at the transcriptional level. Therefore, targeting pathogens with drugs that interfere with virulence gene expression offers an effective alternative to conventional antimicrobial chemotherapy. Many Gram-negative intestinal pathogens produce AraC-like proteins that control the expression of genes required for infection. In this study we investigated the prototypical AraC-like virulence regulator, RegA, from the mouse attaching and effacing pathogen, Citrobacter rodentium, as a potential drug target. By screening a small molecule chemical library and chemical optimization, we identified two compounds that specifically inhibited the ability of RegA to activate its target promoters and thus reduced expression of a number of proteins required for virulence. Biophysical, biochemical, genetic, and computational analyses indicated that the more potent of these two compounds, which we named regacin, disrupts the DNA binding capacity of RegA by interacting with amino acid residues within a conserved region of the DNA binding domain. Oral administration of regacin to mice, commencing 15 min before or 12 h after oral inoculation with C. rodentium, caused highly significant attenuation of intestinal colonization by the mouse pathogen comparable to that of an isogenic regA-deletion mutant. These findings demonstrate that chemical inhibition of the DNA binding domains of transcriptional regulators is a viable strategy for the development of antimicrobial agents that target bacterial pathogens. Background: New strategies are needed to combat antibiotic-resistant bacteria. Results: This work identified a small molecule inhibitor, regacin, that specifically disrupts the action of a master virulence regulator from an intestinal pathogen. Conclusion: Inhibition of virulence gene expression by small molecules is a valid therapeutic strategy. Significance: Inhibitors of this kind can be developed into drugs to prevent or treat bacterial infections.
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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 | 0.001 |
| 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".