High diversity within and low but significant genetic differentiation among geographic and temporal populations of the global <i>Streptococcus pneumoniae</i>
Bibliographic record
Abstract
Streptococcus pneumoniae is the major cause of invasive pneumococcal disease. However, the global population structure remains largely unexplored. In this study, we investigated the spatial and temporal patterns of genetic variation of S. pneumoniae based on archived multilocus sequence typing data from PubMLST.org. Our analyses demonstrated both shared and unique distributions of sequence types (STs) and allele types among regional populations. Among the 17 915 global STs, 36 representing 15 263 isolates were broadly shared among all six continents, consistent with recent clonal dispersal and expansion of this pathogen. The analysis of molecular variance revealed that >96% genetic variations were found within individual regional populations. However, though low (<4%), statistically significant genetic differentiation among regional populations was observed. Comparisons between non-clone-corrected and clone-corrected datasets showed that localized clonal expansion contributed significantly to the observed genetic differentiations among regions. Temporal analyses of the isolates showed that implementation of pneumococcal conjugate vaccine impacted the distributions of STs, but the effect on population structure was relatively limited. Linkage disequilibrium analyses identified evidence for recombination in all continental populations; however, the inferred recombination was not random. We discussed the limitations and implications of our analyses to the global epidemiology and future vaccine developments for S. pneumoniae.
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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) | 0.000 | 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".