Lipoteichoic acids influence cell shape and bacterial division of Streptococcus suis serotype 2, but play a limited role in the pathogenesis of the infection
Bibliographic record
Abstract
Streptococcus suis serotype 2 is a major swine pathogen and a zoonotic agent, causing meningitis in both swine and humans, responsible for substantial economic losses to the swine industry worldwide. The pathogenesis of infection and the role of bacterial cell wall components in virulence have not been fully elucidated. Lipoproteins, peptidoglycan, as well as lipoteichoic acids (LTA) have all been proposed to contribute to virulence. In the present study, the role of the LTA in the pathogenesis of the infection was evaluated through the characterisation of a mutant of the S. suis serotype 2 strain P1/7 lacking the LtaS enzyme, which mediates the polymerization of the LTA poly-glycerolphosphate chain. The ltaS mutant was confirmed to completely lack LTA and displayed significant morphological defects. Although the bacterial growth of this mutant was not affected, further results showed that LTA is involved in maintaining S. suis bacterial fitness. However, its role in the pathogenesis of the infection appears limited. Indeed, LTA presence reduces self-agglutination, biofilm formation and even dendritic cell activation, which are important aspects of the pathogenesis of the infection caused by S. suis. In addition, it does not seem to play a critical role in virulence using a systemic mouse model of infection.
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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".