Usefulness of High-Quality Core Genome Single-Nucleotide Variant Analysis for Subtyping the Highly Clonal and the Most Prevalent Salmonella enterica Serovar Heidelberg Clone in the Context of Outbreak Investigations
Bibliographic record
Abstract
Salmonella enterica serovar Heidelberg is the second most frequently occurring serovar in Quebec and the third-most prevalent in Canada. Given that conventional pulsed-field gel electrophoresis (PFGE) subtyping for common Salmonella serovars, such as S. Heidelberg, yields identical subtypes for the majority of isolates recovered, public health laboratories are desperate for new subtyping tools to resolve highly clonal S. Heidelberg strains involved in outbreak events. As PFGE was unable to discriminate isolates from three epidemiologically distinct outbreaks in Quebec, this study was conducted to evaluate whole-genome sequencing (WGS) and phylogenetic analysis as an alternative to conventional subtyping tools. Genomes of 46 isolates from 3 Quebec outbreaks (2012, 2013, and 2014) supported by strong epidemiological evidence were sequenced and analyzed using a high-quality core genome single-nucleotide variant (hqSNV) bioinformatics approach (SNV phylogenomics [SNVphyl] pipeline). Outbreaks were indistinguishable by conventional PFGE subtyping, exhibiting the same PFGE pattern (SHEXAI.0001/SHEBNI.0001). Phylogenetic analysis based on hqSNVs extracted from WGS separated the outbreak isolates into three distinct groups, 100% concordant with the epidemiological data. The minimum and maximum number of hqSNVs between isolates from the same outbreak was 0 and 4, respectively, while >59 hqSNVs were measured between 2 previously indistinguishable outbreaks having the same PFGE and phage type, thus corroborating their distinction as separate unrelated outbreaks. This study demonstrates that despite the previously reported high clonality of this serovar, the WGS-based hqSNV approach is a superior typing method, capable of resolving events that were previously indistinguishable using classic subtyping tools.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".