Details

Autor(en) / Beteiligte

• Zhang, Shaokang
• Li, Shaoting
• Gu, Weidong
• den Bakker, Henk
• Boxrud, Dave
• Taylor, Angie
• Roe, Chandler
• Driebe, Elizabeth
• Engelthaler, David M
• Allard, Marc
• Brown, Eric
• McDermott, Patrick
• Zhao, Shaohua
• Bruce, Beau B
• Trees, Eija
• Fields, Patricia I
• Deng, Xiangyu

Titel

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

Ist Teil von

• Emerging infectious diseases, 2019-01, Vol.25 (1), p.82-91

Ort / Verlag

United States: U.S. National Center for Infectious Diseases

Erscheinungsjahr

2019

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.