Chemical-mediated virulence: the effects of host chemicals on microbial virulence and potential new antivirulence strategies
Bibliographic record
Abstract
The rising antimicrobial resistance rates and declining antimicrobial discovery necessitate alternative strategies to combat multidrug-resistant pathogens. Targeting microbial virulence is an emerging area of interest. Traditionally, virulence factors were largely restricted to bacteria-derived toxins, adhesins, capsules, quorum sensing systems, secretion systems, factors required to sense, respond to, acquire, or synthesize, and utilize trace elements (such as iron and other metals) and micronutrients (such as vitamins), and other factors bacteria use to establish infection, form biofilms, or damage the host tissues and regulatory elements thereof. However, this traditional definition overlooks bacterial virulence that may be induced or influenced by host-produced metabolites or other chemicals that bacteria may encounter at the infection site. This review will discuss virulence from a non-traditional perspective, shedding light on chemical-mediated host-pathogen interactions and outlining currently available mechanistic insight into increased bacterial virulence in response to host factors. This review aims to define a possibly underestimated theme of chemically mediated host-pathogen interactions and encourage future validation and characterization of the contribution of host chemicals to microbial virulence in vivo. From this perspective, we discuss proposed antivirulence compounds and suggest new potential targets for antimicrobials that prevent chemical-mediated virulence. We also explore proposed host-targeting therapeutics reducing the level of host chemicals that induce microbial virulence, serving as virulence attenuators. Understanding the host chemical-mediated virulence may enable new antimicrobial solutions to fight multidrug-resistant pathogens.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".