Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States

Emerg Infect Dis. 2019 Jan;25(1):82-91. doi: 10.3201/eid2501.180835.


Increasingly, routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case-control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. Relatively steady rates of sequence divergence in livestock lineages enabled the inference of their recent origins. Elevated accumulation of lineage-specific pseudogenes after divergence from generalist populations and possible metabolic acclimation in a representative swine isolate indicates possible emergence of host adaptation. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998-2013. We further identified 50 key genetic features that were sufficient for robust livestock source prediction.

Keywords: Salmonella; Salmonella enterica serotype Typhimurium; United States; bacteria; machine learning; population structure; source attribution; whole-genome sequencing; zoonoses.

MeSH terms

  • Animals
  • Case-Control Studies
  • Disease Outbreaks
  • Epidemiological Monitoring
  • Foodborne Diseases / epidemiology*
  • Foodborne Diseases / microbiology
  • Genomics
  • Humans
  • Livestock / microbiology
  • Phylogeny
  • Retrospective Studies
  • Salmonella Infections / epidemiology*
  • Salmonella Infections / microbiology
  • Salmonella typhimurium / genetics*
  • Salmonella typhimurium / isolation & purification
  • United States / epidemiology
  • Whole Genome Sequencing
  • Zoonoses