Inflammasome signalling is dynamically modulated during the course of a chronic infection by <i>Salmonella Typhimurium</i>
Bibliographic record
Abstract
Abstract Salmonella is a bacterial pathogen that typically causes acute infections, however, typhoid strains may chronically persist in an asymptomatic state after the acute infection has been resolved. Inflammatory cell death plays a key role in controlling bacterial infections and inflammasomes are a key innate signalling complex that respond to Salmonella infection. Inflammasomes may be triggered by the recognition of pathogen associated molecular patterns such as virulence factors, including the flagella and type III secretion systems of Salmonella. Inflammasome activation results in caspase-1 mediated inflammation and inflammatory cell death. As a consequence, pathogens may regulate their expression of virulence factors to avoid immune recognition and encourage chronicity. In this study, we sought to assess whether Salmonella-induced inflammasome signalling is modulated throughout the stages of infection to promote its chronic persistence. Through the use of in vivo models of chronic infection, we show that Salmonella virulence is dynamically modulated over the course of a chronic infection to circumvent inflammasome activation. Bacteria isolated during acute phases of infection exhibited an increased ability to induce cell death and inflammation whereas bacteria isolated at later time intervals displayed a progressive decrease in their ability to induce inflammasome signalling. The inflammation and inflammatory cell death observed was primarily dependent on the activation of the NLRP3 and NLRC4 inflammasomes. Together, these results demonstrate that Salmonella dynamically modulates the expression of its virulence factors over the course of an infection to evade immune recognition and promote chronic carriage.
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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".