Interactions of Streptococcus suis serotype 9 with host cells and role of the capsular polysaccharide: Comparison with serotypes 2 and 14
Bibliographic record
Abstract
Streptococcus suis is an important porcine bacterial pathogen and a zoonotic agent responsible for sudden death, septic shock and meningitis, of which serotype 2 is the most widespread, with serotype 14 also causing infections in humans in South-East Asia. Knowledge of its pathogenesis and virulence are almost exclusively based on these two serotypes. Though serotype 9 is responsible for the greatest number of porcine cases in Spain, the Netherlands and Germany, very little information is currently available regarding this serotype. Of the different virulence factors, the capsular polysaccharide (CPS) is required for S. suis virulence as it promotes resistance to phagocytosis and killing and masks surface components responsible for host cell activation. However, these roles have been described for serotypes 2 and 14, whose CPSs are structurally and compositionally similar, both containing sialic acid. Consequently, we evaluated herein the interactions of serotype 9 with host cells and the role of its CPS, which greatly differs from those of serotypes 2 and 14. Results demonstrated that serotype 9 adhesion to but not invasion of respiratory epithelial cells was greater than that of serotypes 2 and 14. Furthermore serotype 9 was more internalized by macrophages but equally resistant to whole blood killing. Though recognition of serotypes 2, 9 and 14 by DCs required MyD88-dependent signaling, in vitro pro-inflammatory mediator production induced by serotype 9 was much lower. In vivo, however, serotype 9 causes an exacerbated inflammatory response, which combined with persistent bacterial presence, is probably responsible for host death during the systemic infection. Though presence of the serotype 9 CPS masks surface components less efficiently than those of serotypes 2 and 14, the serotype 9 CPS remains critical for virulence as it is required for survival in blood and development of clinical disease, and this regardless of its unique composition and structure.
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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".