Salmonella enterica Prophage Sequence Profiles Reflect Genome Diversity and Can Be Used for High Discrimination Subtyping
Bibliographic record
Abstract
Non-typhoidal Salmonella is a leading cause of foodborne illness worldwide. Prompt and accurate identification of the sources of Salmonella responsible for disease outbreaks is crucial to minimise infections and eliminate ongoing sources of contamination. Current subtyping tools including single nucleotide polymorphism (SNP) typing may be inadequate, in some instances, to provide the required discrimination among epidemiologically unrelated Salmonella strains. Prophage genes represent the majority of the accessory genes in bacteria genomes and have potential to be used as high discrimination markers in Salmonella. In this study, the prophage sequence diversity in different Salmonella serovars and genetically related strains was investigated. Using whole genome sequences of 1760 isolates of S. enterica representing 151 Salmonella serovars and 66 closely related bacteria, prophage sequences were identified from assembled contigs using PHASTER. We detected 154 different prophages in S. enterica genomes. Prophage sequences were highly variable among S. enterica serovars with a median ± interquartile range (IQR) of 5 ± 3 prophage regions per genome. While some prophage sequences were highly conserved among the strains of specific serovars, few regions were lineage specific. Therefore, strains belonging to each serovar could be clustered separately based on their prophage content. Analysis of S. Enteritidis isolates from seven outbreaks generated distinct prophage profiles for each outbreak. Taken altogether, the diversity of the prophage sequences correlates with genome diversity. Prophage repertoires provide an additional marker for differentiating S. enterica subtypes during foodborne outbreaks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".