Immunization with SsEno fails to protect mice against challenge with<i>Streptococcus suis</i>serotype 2
Bibliographic record
Abstract
In our ongoing efforts to develop a vaccine against Streptococcus suis infection, we tested the potential of S. suis enolase (SsEno), a recently described S. suis adhesin with fibronectin-binding activity, as a vaccine candidate in a mouse model of S. suis-induced septicemia and meningitis. Here, we show that SsEno is highly recognized by sera from convalescent pigs and is highly immunogenic in mice. Subcutaneous immunization of mice with SsEno elicited strong immunoglobulin G (IgG) antibody responses. All four IgG subclasses were induced, with IgG1, IgG2a and IgG2b representing the highest titers followed by IgG3. However, SsEno-vaccinated and nonvaccinated control groups showed similar mortality rates after challenge infection with the highly virulent S. suis strain 166'. Similar results were obtained upon passive immunization of mice with hyperimmunized rabbit IgG anti-SsEno. We also showed that anti-SsEno antibodies did not increase the ability of mouse phagocytes to kill S. suis in vitro. In conclusion, these data demonstrate that although recombinant SsEno formulated with Quil A triggers a strong antibody response, it does not confer effective protection against infection with S. suis serotype 2 in mice.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".