Targeting the type III secretion system to treat bacterial infections
Bibliographic record
Abstract
INTRODUCTION: Causative agents of pneumonia, gastroenteritis, typhoid fever, and plague all utilize a type III secretion system (T3SS) to directly inject proteins into human cells and cause disease. These bacterial pathogens are frequently resistant to antibiotics and novel treatment options are needed. The T3SS is essential for virulence and can be inhibited to prevent disease. AREAS COVERED: T3SS structure and assembly are introduced in this review, highlighting targets for T3SS-specific therapeutics. Promising inhibitors of type III secretion (T3S), their modes of action, and successful techniques for their identification are reviewed. T3S inhibitor research has focused on small molecules identified in high-throughput screens, although recently inhibitors have also been identified or engineered by rational design. Promising compounds have emerged that inhibit T3S and attenuate virulence in several pathogens, including an engineered antibody in clinical trials. T3S inhibitor research may yield effective treatments and prophylactics that are effective against a wide range of human pathogens. EXPERT OPINION: More techniques are needed to identify the mode of action for compounds identified in high-throughput screens, a long-standing challenge. Although only a few groups have attempted rational design of inhibitors, the approach has seen initial success and mechanistic follow-up studies are greatly simplified.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".