Interleukin-1 signaling induced by Streptococcus suis serotype 2 is strain-dependent and contributes to bacterial clearance and inflammation during systemic disease in a mouse model of infection
Bibliographic record
Abstract
Streptococcus suis serotype 2 is an important porcine pathogen and zoonotic agent causing sudden death, septic shock and meningitis, with exacerbated inflammation being a hallmark of the infection. A rapid, effective and balanced innate immune response against S. suis is critical to control bacterial growth without causing excessive inflammation. Even though interleukin (IL)-1 is one of the most potent and earliest pro-inflammatory mediators produced, its role in the S. suis pathogenesis has not been studied. We demonstrated that a classical virulent European sequence type (ST) 1 strain and the highly virulent ST7 strain induce important levels of IL-1 in systemic organs. Moreover, bone marrow-derived dendritic cells and macrophages contribute to its production, with the ST7 strain inducing higher levels. To better understand the underlying mechanisms involved, different cellular pathways were studied. Independently of the strain, IL-1β production required MyD88 and involved recognition via TLR2 and possibly TLR7 and TLR9. This suggests that the recognized bacterial components are similar and conserved between strains. However, very high levels of the pore-forming toxin suilysin, produced only by the ST7 strain, are required for efficient maturation of pro-IL-1β via activation of different inflammasomes resulting from pore formation and ion efflux. Using IL-1R −/− mice, we demonstrated that IL-1 signaling plays a beneficial role during S. suis systemic infection by modulating the inflammation required to control and clear bacterial burden, thus promoting host survival. Beyond a certain threshold, however, S. suis -induced inflammation cannot be counterbalanced by this signaling, making it difficult to discriminate its role.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".