Genomic investigation of the most common <i>Streptococcus pneumoniae</i> serotypes causing invasive infections in Canada: the SAVE study, 2011–2020
Bibliographic record
Abstract
OBJECTIVES: To investigate the lineages and genomic antimicrobial resistance (AMR) determinants of the 10 most common pneumococcal serotypes identified in Canada during the five most recent years of the SAVE study, in the context of the 10-year post-PCV13 period in Canada. METHODS: The 10 most common invasive Streptococcus pneumoniae serotypes collected by the SAVE study from 2016 to 2020 were 3, 22F, 9N, 8, 4, 12F, 19A, 33F, 23A and 15A. A random sample comprising ∼5% of each of these serotypes collected during each year of the full SAVE study (2011-2020) were selected for whole-genome sequencing (WGS) using the Illumina NextSeq platform. Phylogenomic analysis was performed using the SNVPhyl pipeline. WGS data were used to identify virulence genes of interest, sequence types, global pneumococcal sequence clusters (GPSC) and AMR determinants. RESULTS: Of the 10 serotypes analysed in this study, six increased significantly in prevalence from 2011 to 2020: 3, 4, 8, 9N, 23A and 33F (P ≤ 0.0201). Serotypes 12F and 15A remained stable in prevalence over time, while serotype 19A decreased in prevalence (P < 0.0001). The investigated serotypes represented four of the most prevalent international lineages causing non-vaccine serotype pneumococcal disease in the PCV13 era: GPSC3 (serotypes 8/33F), GPSC19 (22F), GPSC5 (23A) and GPSC26 (12F). Of these lineages, GPSC5 isolates were found to consistently possess the most AMR determinants. Commonly collected vaccine serotypes 3 and 4 were associated with GPSC12 and GPSC27, respectively. However, a more recently collected lineage of serotype 4 (GPSC192) was highly clonal and possessed AMR determinants. CONCLUSIONS: Continued genomic surveillance of S. pneumoniae in Canada is essential to monitor for the appearance of new and evolving lineages, including antimicrobial-resistant GPSC5 and GPSC162.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".