Targeting Bacterial Secretion Systems: Benefits of Disarmament in the Microcosm
Bibliographic record
Abstract
Secretion systems are used by many bacterial pathogens for the delivery of virulence factors to the extracellular space or directly into host cells. They are attractive targets for the development of novel anti-virulence drugs as their inactivation would lead to pathogen attenuation or avirulence, followed by clearance of the bacteria by the immune system. This review will present the state of knowledge on the assembly and function of type II, type III and type IV secretion systems in Gram-negative bacteria focusing on insights provided by structural analyses of several key components. The suitability of transcription factors regulating the expression of secretion system components and of ATPases, lytic transglycosylases and protein assembly factors as drug targets will be discussed. Recent progress using innovative in vivo as well as in vitro screening strategies led to a first set of secretion system inhibitors with potential for further development as anti-infectives. The discovery of such inhibitors offers exciting and innovative opportunities to further develop these anti-virulence drugs into monotherapy or in combination with classical antibiotics. Bacterial growth per se would not be inhibited by such drugs so that the selection for mutations causing resistance could be reduced. Secretion system inhibitors may therefore avoid many of the problems associated with classical antibiotics and may constitute a valuable addition to our arsenal for the treatment of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".