Quorum sensing modulates bacterial virulence and colonization dynamics of the gastrointestinal pathogen <i>Citrobacter rodentium</i>
Bibliographic record
Abstract
Quorum Sensing (QS) is a form of cell-to-cell communication that enables bacteria to modify behavior according to their population density. While QS has been proposed as a potential intervention against pathogen infection, QS-mediated communication within the mammalian digestive tract remains understudied. Using an LC-MS/MS approach, we discovered that Citrobacter rodentium, a natural murine pathogen used to model human infection by pathogenic Escherichia coli, utilizes the CroIR system to produce three QS-molecules. We then profiled their accumulation both in vitro and across different gastrointestinal sites over the course of infection. Importantly, we found that in the absence of QS capabilities the virulence of C. rodentium is enhanced. This highlights the role of QS as an effective mechanism to regulate virulence according to the pathogen’s spatio-temporal context to optimize colonization and transmission success. These results also demonstrate that inhibiting QS may not always be an effective strategy for the control of virulence.
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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".