Evolutionary history and recurrent host adaptation in ancient <i>Salmonella enterica</i>
Bibliographic record
Abstract
Abstract Salmonella enterica subsp. enterica is an extremely diverse bacterial pathogen causing frequent infections and foodborne disease among human populations. More than 1500 different bacterial strains (serovars) have been described, many with a wide host range. A small number of serovars are adapted to infect specific hosts: of these, serovars Typhi and Paratyphi A, B, and C cause primate-specific systemic infections (typhoid and paratyphoid fever). Although Paratyphi C is one of the rarest human-specific serovars today, it was once widespread, and all ancient Salmonella genomes published to date belong to or are ancestral to this lineage. Here, we present 53 new ancient Salmonella genomes spanning Eurasia and dating between 3500 BCE and 1300 CE. This rich genomic dataset allows us to reconstruct the evolutionary history of this pathogen in unprecedented detail. We identify multiple extinct prehistoric lineages that caused infections throughout Eurasia. Multiple lineage replacement events are observed throughout prehistoric and historic times, and Bayesian phylogenetic analysis is used to date and identify host adaptation events within this lineage. We find that host-adapted sublineages Paratyphi C, Choleraesuis, and Typhisuis continued to evolve host specificity independently from each other. We reconstruct signals of convergent host adaptation in the studied lineages and other host-adapted strains by analysing shared pseudogenes and recurrent gene gain and loss events. This analysis demonstrates a role for host interactions as a particular target of selection, highlighting the gradual adaptation of this S. enterica lineage to humans that coincides with the intensification of animal husbandry in pastoralist and sedentary farming societies.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".