Genomic and phenotypic analysis of invasive Streptococcus suis isolated in Spain reveals genetic diversification and associated virulence traits
Bibliographic record
Abstract
Streptococcus suis is a zoonotic pathogen that causes a major health problem in the pig production industry worldwide. Spain is one of the largest pig producers in the world. This work aimed to investigate the genetic and phenotypic features of invasive S. suis isolates recovered in Spain. A panel of 156 clinical isolates recovered from 13 Autonomous Communities, representing the major pig producers, were analysed. MLST and serotyping analysis revealed that most isolates (61.6%) were assigned to ST1 (26.3%), ST123 (18.6%), ST29 (9.6%), and ST3 (7.1%). Interestingly, 34 new STs were identified, indicating the emergence of novel genetic lineages. Serotypes 9 (27.6%) and 1 (21.8%) prevailed, followed by serotypes 7 (12.8%) and 2 (12.2%). Analysis of 13 virulence-associated genes showed significant associations between ST, serotype, virulence patterns, and clinical features, evidencing particular virulence traits associated with genetic clusters. The pangenome was generated, and the core genome was distributed in 7 Bayesian groups where each group included a variable set of over- and under-represented genes of different categories. The study provides comprehensive data and knowledge to improve the design of new vaccines, antimicrobial treatments, and bacterial typing approaches.
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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 | 0.001 |
| 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) | 0.000 | 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".