Engineering Antigens to Assemble into Polymer Particle Vaccines for Prevention of Streptococcus suis Infection
Bibliographic record
Abstract
Streptococcus suis is a zoonotic pathogen affecting pigs and humans. This bacterium causes severe economic losses in the swine industry and poses a serious threat to public health and food safety. There is no effective commercial vaccine available for pigs or humans. In this study, we applied the biopolymer particle (BP) vaccine technology to incorporate seven conserved S. suis antigens (38 kDa protein (38), enolase (Enol), SSU1915, SSU1355, SSU0185, SSU1215, and SSU1773 (SSU1 and SSU2)). Two combinations of these antigens (38 and Enol; all SSU antigens designated as SSU1 and SSU2) were engineered to mediate production of BPs coated with either antigens 38 and Enol or SSU1 and SSU2 inside recombinant Escherichia coli. The isolated and purified empty BPs, 38-BP-Enol and SSU1-BP-SSU2, showed size ranges of 312–428 nm and 292–344 nm with and without the QuilA® adjuvant, respectively, and all showed a negative surface charge. Further characterization of purified BPs confirmed the presence of the expected antigen-comprising fusion proteins as assessed by tryptic peptide fingerprinting analysis using quadrupole time-of-flight mass spectrometry and immunoblotting. Vaccination with 38-BP-Enol and SSU1-BP-SSU2 formulated with and without QuilA® adjuvant induced significant antigen-specific humoral immune responses in mice. Antigen-coated BPs induced significant and specific Ig (IgM + IgG) and IgG immune responses (1.0 × 106–1.0 × 107) when compared with mice vaccinated with empty BPs. Functionality of the immune response was confirmed in challenge experiments using an acute murine S. suis infection model, which showed 100% survival of the 38-BP-Enol and SSU1-BP-SSU2 vaccinated mice compared to 70% survival when vaccinated with empty BPs. Overall, our data suggest that S. suis antigen-coated BPs could be developed into particulate vaccines that induce protective immunity against S. suis infections.
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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".