Recognition of Lipoproteins by Toll-like Receptor 2 and DNA by the AIM2 Inflammasome Is Responsible for Production of Interleukin-1β by Virulent Suilysin-Negative Streptococcus suis Serotype 2
Bibliographic record
Abstract
Streptococcus suis serotype 2 is an important porcine bacterial pathogen and zoonotic agent causing sudden death, septic shock and meningitis. These pathologies are the consequence of an exacerbated inflammatory response composed of various mediators including interleukin (IL)-1β. Elevated levels of the toxin suilysin (SLY) were demonstrated to play a key role in S. suis-induced IL-1β production. However, 95% of serotype 2 strains isolated from diseased pigs in North America, many of which are virulent, do not produce SLY. In this study, we demonstrated that SLY-negative S. suis induces elevated levels of IL-1β in systemic organs, with dendritic cells contributing to this production. SLY-negative S. suis-induced IL-1β production requires MyD88 and TLR2 following recognition of lipoproteins. However, the higher internalization rate of the SLY-negative strain results in intracellularly located DNA being recognized by the AIM2 inflammasome, which promotes IL-1β production. Finally, the role of IL-1 in host survival during the S. suis systemic infection is beneficial and conserved, regardless of SLY production, via modulation of the inflammation required to control bacterial burden. In conclusion, this study demonstrates that SLY is not required for S. suis-induced IL-1β production.
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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) | 0.000 | 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".