<i>In Vitro</i>Characterization of the Microglial Inflammatory Response to<i>Streptococcus suis</i>, an Important Emerging Zoonotic Agent of Meningitis
Bibliographic record
Abstract
Streptococcus suis is an important swine and human pathogen responsible for septicemia and meningitis. In vivo research in mice suggested that in the brain, microglia might be involved in activating the inflammatory response against S. suis. The aim of this study was to better understand the interactions between S. suis and microglia. Murine microglial cells were infected with a virulent wild-type strain of S. suis. Two isogenic mutants deficient at either capsular polysaccharide (CPS) or hemolysin production were also included. CPS contributed to S. suis resistance to phagocytosis and regulated the inflammatory response by hiding proinflammatory components from the bacterial cell wall, while the absence of hemolysin, a potential cytotoxic factor, did not have a major impact on S. suis interactions with microglia. Wild-type S. suis induced enhanced expression of Toll-like receptor 2 by microglial cells, as well as phosphotyrosine, protein kinase C, and different mitogen-activated protein kinase signaling events. However, cells infected with the CPS-deficient mutant showed overall stronger and more sustained phosphorylation profiles. CPS also modulated inducible nitric oxide synthase expression and further nitric oxide production from S. suis-infected microglia. Finally, S. suis-induced NF-κB translocation was faster for cells stimulated with the CPS-deficient mutant, suggesting that bacterial cell wall components are potent inducers of NF-κB. These results contribute to increase the knowledge of mechanisms underlying S. suis inflammation in the brain and will be useful in designing more efficient anti-inflammatory strategies for meningitis.
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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) | 0.001 | 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 | 0.001 |
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".