Whole-genome Sequencing for Surveillance of Invasive Pneumococcal Diseases in Ontario, Canada: Rapid Prediction of Genotype, Antibiotic Resistance and Characterization of Emerging Serotype 22F
Bibliographic record
Abstract
Background. Molecular typing is essential for inferring genetic relatedness between bacterial pathogens. In this study, we applied whole genome sequencing (WGS) for rapid prediction of sequence type and antibiotic resistance for invasive pneumococcal isolates. Methods. 240 isolates from adults (≥ 50 years old) in Ontario, Canada during 2009 to 2013 were subjected to WGS. Sequence type, antibiotic susceptibility and resistance were predicted directly from short reads. Emerging non-vaccine serotype 22F was further characterized by WGS. Results. Sequence type was successfully determined for 98.3% of isolates. The overall sensitivity and specificity for antibiotic resistance prediction were 95% and 100% respectively, compared to standard susceptibility testing methods. WGS-based phylogeny divided emerging 22F (ST433) strains into two distinct clades: clade A harboring a 23kb-prophage and anti-phage PhD/Doc system and clade B with virulence-related proteases. Five isolates in clade A developed macrolide resistance via 5.1kb mega element recombination (encoding mefE and msrD), while one isolate in clade B displayed quinolone resistance via a gyrA mutation. Conclusions. WGS is valuable for routine surveillance of pneumococcal clinical isolates and facilitates prediction of genotype and antibiotic resistance. The emergence of 22F in Ontario in the post-vaccine era and evidence of evolution and divergence of the 22F population warrants heightened pneumococcal molecular surveillance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".