Identification and characterization of a Streptococcus suis immunogenic ornithine carbamoytransferase involved in bacterial adherence
Bibliographic record
Abstract
BACKGROUND: Streptococcus suis (SS) is a major swine pathogen and a serious zoonotic pathogen causing septicemia and meningitis in piglets and humans. Using an immunoproteomic approach, we previously brought evidence that ornithine carbamoytransferase (OCT) may represent a vaccine candidate to protect against S. suis biofilm-related and acute infections. METHOD: In this study, the gene encoding OCT was cloned into the expression vector pET-28a and the recombinant protein was expressed in Escherichia coli BL21. The immunogenicity and protective efficacy of the SS OCT was further investigated in a mouse model. RESULTS: The protein was found to be expressed in vivo and elicited high antibody titers following SS infections in mice. An animal challenge experiment with SS showed that 62.5% of mice immunized with the OCT protein were protected. Using an in vitro competitive adherence inhibition assay of adherence, evidence was obtained that OCT could significantly reduce the number of SS cells adhered to porcine kidney PK-15 cells. The bacterial levels recovered in mice of the OCT immunized group were significantly decreased in some organs, compared with the control group. CONCLUSION: In summary, our results suggest that the recombinant SS OCT protein, which is involved in bacterial adherence, may efficiently stimulate an immune response conferring protection against SS infections. It may therefore be considered as a potential vaccine candidate, although further studies are necessary to evaluate their use in swine.
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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) | 0.000 | 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".