Study of the Role of Lipoprotein Maturation Enzymes in the Pathogenesis of the Infection Caused by the Streptococcus suis Serotype 2 Sequence Type 25 North American Prototype Strain
Bibliographic record
Abstract
Streptococcus suis serotype 2 is an important swine bacterial pathogen causing sudden death, septic shock, and meningitis. However, serotype 2 strains are phenotypically and genotypically heterogeneous and composed of a multitude of sequence types (STs) whose distributions greatly vary worldwide. It has been previously shown that the lipoprotein (LPP) maturation enzymes diacylglyceryl transferase (Lgt) and signal peptidase (Lsp) significantly modulate the inflammatory host response and play a differential role in virulence depending on the genetic background of the strain. Differently from Eurasian ST1/ST7 strains, the capsular polysaccharide of a North American S. suis serotype 2 ST25 representative strain only partially masks sub-capsular domains and bacterial wall components. Thus, our hypothesis is that since LPPs would be more surface exposed in ST25 strains than in their ST1 or ST7 counterparts, the maturation enzymes would play a more important role in the pathogenesis of the infection caused by the North American strain. Using isogenic Δlgt and Δlsp mutants derived from the wild-type ST25 strain, our studies suggest that these enzymes do not seem to play a role in the interaction between S. suis and epithelial and endothelial cells, regardless of the genetics background of the strain used. However, a role in the formation of biofilms (also independently of the STs) has been demonstrated. Moreover, the involvement of LPP dendritic cell activation in vitro seems to be somehow more pronounced with the ST25 strain. Finally, the Lgt enzyme seems to play a more important role in the virulence of the ST25 strain. Although some differences between STs could be observed, our original hypothesis that LPPs would be significantly more important in ST25 strains due to a better bacterial surface exposition could not be confirmed.
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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) | 0.000 | 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".