Genome-wide molecular dissection of serotype M3 group A <i>Streptococcus</i> strains causing two epidemics of invasive infections
Bibliographic record
Abstract
Molecular factors that contribute to the emergence of new virulent bacterial subclones and epidemics are poorly understood. We hypothesized that analysis of a population-based strain sample of serotype M3 group A Streptococcus (GAS) recovered from patients with invasive infection by using genome-wide investigative methods would provide new insight into this fundamental infectious disease problem. Serotype M3 GAS strains (n = 255) cultured from patients in Ontario, Canada, over 11 years and representing two distinct infection peaks were studied. Genetic diversity was indexed by pulsed-field gel electrophoresis, DNA-DNA microarray, whole-genome PCR scanning, prophage genotyping, targeted gene sequencing, and single-nucleotide polymorphism genotyping. All variation in gene content was attributable to acquisition or loss of prophages, a molecular process that generated unique combinations of proven or putative virulence genes. Distinct serotype M3 genotypes experienced rapid population expansion and caused infections that differed significantly in character and severity. Molecular genetic analysis, combined with immunologic studies, implicated a 4-aa duplication in the extreme N terminus of M protein as a factor contributing to an epidemic wave of serotype M3 invasive infections. This finding has implications for GAS vaccine research. Genome-wide analysis of population-based strain samples cultured from clinically well defined patients is crucial for understanding the molecular events underlying bacterial epidemics.
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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 | 0.000 | 0.000 |
| 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".