TbpBY167A-Based Vaccine Can Protect Pigs against Glässer’s Disease Triggered by Glaesserella parasuis SV7 Expressing TbpB Cluster I
Bibliographic record
Abstract
Glaesserella parasuis is the etiological agent of Glässer’s disease (GD), one of the most important diseases afflicting pigs in the nursery phase. We analyzed the genetic and immunological properties of the TbpB protein naturally expressed by 27 different clinical isolates of G. parasuis that were typed as serovar 7 and isolated from pigs suffering from GD. All the strains were classified as virulent by LS-PCR. The phylogenetic analyses demonstrated high similarity within the amino acid sequence of TbpB from 24 clinical strains all belonging to cluster III of TbpB, as does the protective antigen TbpBY167A. Three G. parasuis isolates expressed cluster I TbpBs, indicating antigenic diversity within the SV7 group of G. parasuis. The antigenic analysis demonstrated the presence of common epitopes on all variants of the TbpB protein, which could be recognized by an in vitro analysis using pig IgG induced by a TbpBY167A-based vaccine. The proof of concept of the complete cross-protection between clusters I and III was performed in SPF pigs immunized with the TbpBY167A-based vaccine (cluster III) and challenged with G. parasuis SV7, strains LM 360.18 (cluster I). Additionally, pigs immunized with a whole-cell inactivated vaccine based on G. parasuis SV5 (Nagasaki strain) did not survive the challenge performed with SV7 (strain 360.18), demonstrating the absence of cross-protection between these two serovars. Based on these results, we propose that a properly formulated TbpBY167A-based vaccine may elicit a protective antibody response against all strains of G. parasuis SV7, despite TbpB antigenic diversity, and this might be extrapolated to other serovars. This result highlights the promising use of the TbpBY167A antigen in a future commercial vaccine for GD prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".