Development of Multiplex PCR Assays for the Identification of the 33 Serotypes of Streptococcus suis
Bibliographic record
Abstract
Streptococcussuis is an important zoonotic agent causing severe diseases in pigs and humans. To date, 33 serotypes of S. suis have been identified based on antigenic differences in the capsular polysaccharide. The capsular polysaccharide synthesis (cps) locus encodes proteins/enzymes that are responsible for capsular production and variation in the capsule structures are the basis of S. suis serotyping. Multiplex and/or simplex PCR assays have been developed for 15 serotypes based on serotype-specific genes in the cps gene cluster. In this study, we developed a set of multiplex PCR (mPCR) assays to identify the 33 currently known S. suis serotypes. To identify serotype-specific genes for mPCR, the entire genomes of reference strains for the 33 serotypes were sequenced using whole genome high-throughput sequencing, and the cps gene clusters from these strains were identified and compared. We developed a set of 4 mPCR assays based on the polysaccharide polymerase gene wzy, one of the serotype-specific genes. The assays can identify all serotypes except for two pairs of serotypes: 1 and 14, and 2 and 1/2, which have no serotype-specific genes between them. The first assay identifies 12 serotypes (serotypes 1 to 10, 1/2, and 14) that are the most frequently isolated from diseased pigs and patients; the second identifies 10 serotypes (serotypes 11 to 21 except 14); the third identifies the remaining 11 serotypes (serotypes 22 to 31, and 33); and the fourth identifies a new cps cluster of S. suis discovered in this study in 16 isolates that agglutinated with antisera for serotypes 29 and 21. The multiplex PCR assays developed in this study provide a rapid and specific method for molecular serotyping of S. suis.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".