Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

Area of Science:

• Microbiology
• Genomics
• Public Health

Background:

• Zoonotic Salmonella causes millions of human salmonellosis infections globally.
• Accurate source attribution of Salmonella is crucial for effective control and prevention strategies.
• Traditional typing methods are being replaced by whole genome sequencing (WGS), necessitating new attribution methods.

Purpose of the Study:

• To develop and evaluate new source attribution methods for Salmonella based on whole genome sequencing data.
• To present European datasets of Salmonella Typhimurium and its variants for method development.
• To attribute human salmonellosis cases and environmental contamination to specific animal reservoirs.

Main Methods:

• Utilized whole genome sequencing data from four European countries (Denmark, Germany, UK, France).
• Analyzed Salmonella Typhimurium and its monophasic variants from human, food, animal, and environmental samples.
• Developed and applied novel bioinformatic approaches for source attribution using genomic data.

Main Results:

• Presented comprehensive Salmonella Typhimurium genomic datasets from diverse European sources.
• Demonstrated the feasibility of using WGS data for source attribution of human and environmental Salmonella isolates.
• Provided a foundation for improved public health risk management strategies.

Conclusions:

• Whole genome sequencing offers a powerful tool for Salmonella source attribution.
• New genomic-based methods are essential to replace traditional typing for Salmonella surveillance.
• This work supports enhanced control of zoonotic Salmonella through precise source identification.