Characterization of DNase activity and gene in Streptococcus suis and evidence for a role as virulence factor
Bibliographic record
Abstract
BACKGROUND: The Gram-positive bacterium Streptococcus suis serotype 2 is an important swine pathogen and emerging zoonotic agent. Multilocus sequence typing allowed dividing S. suis serotype 2 into sequence types (STs). The three major STs of S. suis serotype 2 from North America are 1 (most virulent), 25 (intermediate virulence) and 28 (less virulent). Although the presence of DNase activity in S. suis has been previously reported, little data is available. The aim of this study was to investigate DNase activity in S. suis according to STs, to characterize the activity and gene, and to provide evidence for a potential role in virulence. RESULTS: We showed that ST1 and ST28 strains exhibited DNase activity that was absent in ST25 strains. The lack of activity in ST25 isolates was associated with a 14-bp deletion resulting in a shifted reading frame and a premature stop codon. The DNase of S. suis P1/7 (ST1) was cell-associated and active on linear DNA. A DNase-deficient mutant of S. suis P1/7 was found to be less virulent in an amoeba model. Stimulation of macrophages with the DNase mutant showed a decreased secretion of pro-inflammatory cytokines and matrix metalloproteinase-9 compared to the parental strain. CONCLUSIONS: This study further expands our knowledge of S. suis DNase and its potential role in virulence.
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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 | 0.001 | 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".