Interbacterial AI-2 communication drives stage-specific genetic programs to support <i>Salmonella</i> colonisation in the murine gut
Bibliographic record
Abstract
Abstract The cooperative response through AI-2 allows Salmonella to coordinate and regulate its community behaviours crucial for its survival and infection. Understanding these mechanisms is essential for developing strategies to interfere with Salmonella’s ability to cause disease. In this study, we report that Salmonella Typhimurium uses AI-2 signaling to modulate the expression of chemotaxis and motility-associated genes and the formation of flagella on the bacterial cell surface. The AI-2 acts as a chemoattractant, and the cascade mediates the chemotactic response of Salmonella. Such regulations assist in the attachment and invasion of Salmonella into intestinal epithelial cells both in vitro and in a murine model. Furthermore, we determined that HilD, known to regulate motility by induction of a chemoreceptor and SPI-1 in Salmonella , along with key SPI-1 genes, is underexpressed in the absence of LuxS/AI-2 signaling, which unravels a complex regulatory network by AI-2 in Salmonella . Beyond the initial phase, this signaling facilitates survival in the presence of polymyxin B and supports intracellular survival and persistence within epithelial cells by regulating the pmrD/AB system, thereby evading the antimicrobial peptide immune response. Additionally, our findings show that Salmonella activates its LuxS/AI-2 signalling cascade within the Caco-2 epithelial cells. Complimentary observation through transcriptomic profiling revealed that LuxS/AI-2 signalling governs the coordinated regulation of genes implicated in distinct phases of Salmonella pathogenesis. Lastly using a mouse model, we show that AI-2 signaling is critical for organ colonization and virulence of Salmonella, and inhibition of LuxS/AI-2 signaling in combination with antibiotics can be an alternative therapeutic approach. Thus, our study dissects an integrated regulatory mechanism by which AI-2 signaling mediates critical processes during multiple stages of Salmonella Typhimurium pathogenesis.
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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 | 0.001 |
| 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".