Enolase and dipeptidyl peptidase IV protein sub-unit vaccines are not protective against a lethal Streptococcus suis serotype 2 challenge in a mouse model of infection
Bibliographic record
Abstract
BACKGROUND: Streptococcus suis is a major swine pathogen causing arthritis, meningitis and sudden death in post-weaning piglets and is also a zoonotic agent. S. suis comprises 35 different serotypes of which the serotype 2 is the most prevalent in both pigs and humans. In the absence of commercial vaccines, bacterins (mostly autogenous), are used in the field, with controversial results. In the past years, the focus has turned towards the development of sub-unit vaccine candidates. However, published results are sometimes contradictory regarding the protective effect of a same candidate. Moreover, the adjuvant used may significantly influence the protective capacity of a given antigen. This study focused on two protective candidates, the dipeptidyl peptidase IV (DPPIV) and the enolase (SsEno). Both proteins are involved in S. suis pathogenesis, and while contradictory protection results have been obtained with SsEno in the past, no data on the protective capacity of DPPIV was available. RESULTS: Results showed that among all the field strains tested, 86 and 88% were positive for the expression of the SsEno and DPPIV proteins, respectively, suggesting that they are widely expressed by strains of different serotypes. However, no protection was obtained after two vaccine doses in a CD-1 mouse model of infection, regardless of the use of four different adjuvants. Even though no protection was obtained, significant amounts of antibodies were produced against both antigens, and this regardless of the adjuvant used. CONCLUSIONS: Taken together, these results demonstrate that S. suis DPPIV and SsEno are probably not good vaccine candidates, at least not in the conditions evaluated in this study. Further studies in the natural host (pig) should still be carried out. Moreover, this work highlights the importance of confirming results obtained by different research groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".