Comparative analysis of Streptococcus suis genomes identifies novel candidate virulence-associated genes in North American isolates
Bibliographic record
Abstract
Streptococcus suis is a significant economic and welfare concern in the swine industry. Pan-genome analysis provides an in-silico approach for the discovery of genes involved in pathogenesis in bacterial pathogens. In this study, we performed pan-genome analysis of 208 S. suis isolates classified into the pathogenic, possibly opportunistic, and commensal pathotypes to identify novel candidate virulence-associated genes (VAGs) of S. suis. Using chi-square tests and LASSO regression models, three accessory pan-genes corresponding to S. suis strain P1/7 markers SSU_RS09525, SSU_RS09155, and SSU_RS03100 (>95% identity) were identified as having a significant association with the pathogenic pathotype. The proposed novel SSU_RS09525 + /SSU_RS09155 + /SSU_RS03100 + genotype identified 96% of the pathogenic pathotype strains, suggesting a novel genotyping scheme for predicting the pathogenicity of S. suis isolates in North America. In addition, mobile genetic elements carrying antimicrobial resistance genes (ARGs) and VAGs were identified but did not appear to play a major role in the spread of ARGs and VAGs.
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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".