Differential role of MyD88 signaling in <i>Streptococcus suis</i> serotype 2-induced systemic and central nervous system diseases
Bibliographic record
Abstract
Streptococcus suis serotype 2 is an important porcine bacterial pathogen and a zoonotic agent responsible for sudden death, septic shock and meningitis, with exacerbated inflammation being a hallmark of the systemic and central nervous system (CNS) infections. However, S. suis serotype 2 strains are genetically and phenotypically heterogeneous, being composed of a multitude of sequence types (STs) whose virulence greatly varies. Yet, most studies have used 'classical' virulent Eurasian ST1 or ST7 strains, even though ST25 and ST28 strains account for most isolates in North America. While recognition of S. suis by innate immune cells has been associated with the myeloid differentiation primary response 88 (MyD88)-dependent Toll-like receptor (TLR) pathway in vitro, particularly surface-associated TLR2, little information is available regarding its role in vivo. This study demonstrates for the first time a differential role of MyD88 signaling in S. suis-induced systemic and CNS diseases, regardless of strain background diversity. The MyD88-dependent pathway is critical for the development of systemic disease via its role in inflammation, which subsequently controls bacterial burden. However, and differently from what has been described in vitro, TLR2 and TLR4 individually do not contribute to systemic disease, suggesting possible compensation in their absence and/or a collaborative role with other MyD88-dependent TLRs. On the other hand, CNS disease does not necessarily require MyD88 signaling and, consequently, neither TLR2 nor TLR4, suggesting a partial implication of other pathways. Finally, regardless of its notable heterogeneity, recognition of S. suis serotype 2 appears to be similar, indicating that recognized components are conserved motifs.
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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".