New putative virulence factors of Streptococcus suis involved in invasion of porcine brain microvascular endothelial cells
Bibliographic record
Abstract
Streptococcus suis serotype 2 is an important pathogen causing a wide range of infections in swine, the most important being meningitis. Few virulence factors have been identified and the pathogenesis of infection is not well understood. Recently, we demonstrated the ability of S. suis to adhere to and invade porcine brain microvascular endothelial cells (PBMEC) forming the blood-brain barrier. In this paper we describe the screening of a mutant library, produced by insertion of transposon Tn917 into the chromosome of S. suis strain P1/7, for mutants that are less able to interact with PBMEC. Both qualitative and quantitative screening assays were used to identify poorly invasive mutants. Tn917 insertion sites from nineteen poorly invasive mutants were sequenced and characterized. Five mutants were selected and their virulence was assessed in a mouse model of infection. Two out of these five mutants were attenuated as measured by decreased colonization of organs, as well as reduced mortality and morbidity. When tested in swine these two attenuated mutants led to decreased bacterial loads in blood, less severe and delayed clinical signs, and lower plasma IL-6 levels than did infection with the wild-type strain. Overall, our results suggest that these two genes may contribute to the virulence of S. suis.
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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".