A Quorum Sensing Regulated Small Volatile Molecule Reduces Acute Virulence and Promotes Chronic Infection Phenotypes
Bibliographic record
Abstract
A significant number of environmental microorganisms can cause serious, even fatal, acute and chronic infections in humans. The severity and outcome of each type of infection depends on the expression of specific bacterial phenotypes controlled by complex regulatory networks that sense and respond to the host environment. Although bacterial signals that contribute to a successful acute infection have been identified in a number of pathogens, the signals that mediate the onset and establishment of chronic infections have yet to be discovered. We identified a volatile, low molecular weight molecule, 2-amino acetophenone (2-AA), produced by the opportunistic human pathogen Pseudomonas aeruginosa that reduces bacterial virulence in vivo in flies and in an acute mouse infection model. 2-AA modulates the activity of the virulence regulator MvfR (multiple virulence factor regulator) via a negative feedback loop and it promotes the emergence of P. aeruginosa phenotypes that likely promote chronic lung infections, including accumulation of lasR mutants, long-term survival at stationary phase, and persistence in a Drosophila infection model. We report for the first time the existence of a quorum sensing (QS) regulated volatile molecule that induces bistability phenotype by stochastically silencing acute virulence functions in P. aeruginosa. We propose that 2-AA mediates changes in a subpopulation of cells that facilitate the exploitation of dynamic host environments and promote gene expression changes that favor chronic 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 | 0.000 |
| 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".