Phylodynamics and genome-wide association studies reveal the expansion of modern <i>Streptococcus canis</i> in Germany
Bibliographic record
Abstract
Abstract Streptoccocus canis is a leading canine pathogen causing 22.4% of canine streptococcal infections. However, knowledge of factors contributing to S. canis expansion is limited. This study uses population genetics to structure a S. canis dataset of 585 isolates and identify genetic markers contributing to the success of each dominant lineage. The dataset is composed of canine, feline and bovine isolates primarily from Germany collected between the years 1996 and 2021. We performed Multi-Locus Sequence Typing (MLST) for an initial analysis and clustered the population with fastBAPS based on the whole genome. Dated phylogenies were inferred with BEAST and Genome-Wide Association Studies (GWAS) were conducted with Scoary . MLST showed that in Germany there are two dominant groups of canine S. canis , ST43 (n= 75) and ST9 (n= 51), which were grouped into BAPS-6 and BAPS-5, respectively. The BAPS-6 cluster emerged as early as 1988 with major expansion starting around 2010. We showed via GWAS that this cluster is associated with a putative Streptococcus anginosus derived integrative and conjugative element carrying a putative cysteine protease. Furthermore, the BAPS-5 cluster emerged around 1786 with a bovine/feline subcluster appearing around 1879. This subcluster is associated with a variant of the streptococcal lac operon, which appears to have resulted from an exchange with Streptococcus dysgalactiae. We present the largest population genetics study to date for S. canis where we show that its expansion is associated with genetic exchange with other streptococcal species leading to increased pathogenic potential (BAPS-6) or host adaptation (BAPS-5). Importance Streptococcus canis is a bacterium which poses considerable threat to the health of dogs through superficial infections. Current research could benefit from understanding the reasons for its expansion and spread into other hosts. We looked at the genomes of 585 S. canis isolates collected during a 25-year period and identified which parts of the population were the most successful. With powerful statistical methods, we found that two groups of S. canis were the most effective: a canine group which had become more pathogenic by acquiring DNA from Streptococcus anginosus , and another group which spread from dogs into cats and cows by acquiring genetic material for lactose consumption from Streptococcus dysgalactiae . For the first time, we link the expansion of major S. canis groups to genetic exchange events with closely related streptococcal species to understand S. canis evolution and spread.
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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 | 0.001 | 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".