Impact of Sub-Inhibitory Concentrations of Amoxicillin on Streptococcus suis Capsule Gene Expression and Inflammatory Potential
Bibliographic record
Abstract
Streptococcus suis is an important swine pathogen and emerging zoonotic agent worldwide causing meningitis, endocarditis, arthritis and septicemia. Among the 29 serotypes identified to date, serotype 2 is mostly isolated from diseased pigs. Although several virulence mechanisms have been characterized in S. suis, the pathogenesis of S. suis infections remains only partially understood. This study focuses on the response of S. suis P1/7 to sub-inhibitory concentrations of amoxicillin. First, capsule expression was monitored by qRT-PCR when S. suis was cultivated in the presence of amoxicillin. Then, the pro-inflammatory potential of S. suis P1/7 culture supernatants or whole cells conditioned with amoxicillin was evaluated by monitoring the activation of the NF-κB pathway in monocytes and quantifying pro-inflammatory cytokines secreted by macrophages. It was found that amoxicillin decreased capsule expression in S. suis. Moreover, conditioning the bacterium with sub-inhibitory concentrations of amoxicillin caused an increased activation of the NF-κB pathway in monocytes following exposure to bacterial culture supernatants and to a lesser extent to whole bacterial cells. This was associated with an increased secretion of pro-inflammatory cytokines (CXCL8, IL-6, IL-1β) by macrophages. This study identified a new mechanism by which S. suis may increase its inflammatory potential in the presence of sub-inhibitory concentrations of amoxicillin, a cell wall-active antibiotic, thus challenging its use for preventive treatments or as growth factor.
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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 | 0.001 |
| 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".