Molecular Characterization of Glaesserella parasuis Strains Circulating in North American Swine Production Systems
Bibliographic record
Abstract
Abstract Background Glaesserella parasuis is the causative agent of Glässer’s disease in pigs. Serotyping is the most common method used to type G. parasuis isolates. However, the high number of non-typables (NT) and low discriminatory power make serotyping problematic. In this study, 218 field clinical isolates and 15 G. parasuis reference strains were whole-genome sequenced (WGS). Multilocus sequence types (MLST), serotypes, core-genome phylogeny, antimicrobial resistance (AMR) genes, and putative virulence genes were determined. Results In silico WGS serotyping revealed 11 of 15 serotypes. The most frequently detected serotypes were 7, 13, 4, and 2. MLST identified 72 sequence types (STs) (66 novel). The most predominant ST was novel ST454. At least one group 1 vtaA virulence gene was observed in all isolates, except for serotype 8 (ST299 and ST406), 15 (ST408 and ST552) and NT (ST448). A few group 1 vtaA genes were significantly associated with certain serotypes or STs. Core-genome phylogeny depicted 3 primary lineages (LI, LII, and LIII), with LIIIA sublineage isolates lacking all vtaA genes. Conclusion This study showed the use of WGS to type G. parasuis isolates and can be considered an alternative to the more labor-intensive and traditional serotyping and standard MLST. Core-genome phylogeny provided the best strain discrimination. The presence of other putative virulence factors and AMR genes was also explored. These findings will lead to a better understanding of the molecular epidemiology and virulence in G. parasuis that can be applied to the future development of diagnostic tools, autogenous vaccines, evaluation of antibiotic use, prevention, and disease control.
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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 | 0.001 | 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 | 0.000 |
| 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".