A WGS workflow for identifying genetically modified and foodborne-pathogenic Bacillus isolates

Area of Science:

• Microbiology
• Bioinformatics
• Food Safety

Background:

• Bacterial contamination in food and feed presents significant public health risks.
• Genetically modified micro-organisms (GMMs) and toxin-producing bacteria like Bacillus cereus can contaminate food during production.
• Whole-genome sequencing (WGS) is crucial for pathogen detection, yet its application to GMMs requires focused research.

Purpose of the Study:

• To develop and validate a WGS-based bioinformatics workflow for characterizing Bacillus subtilis group (including GMMs) and Bacillus cereus group isolates.
• To implement a novel method for detecting known GMMs by identifying transgenic elements and host strains.
• To ensure the workflow aligns with EFSA guidelines for microbial characterization in the food chain.

Main Methods:

• A WGS-based bioinformatics workflow was developed, supporting both short-read (Illumina) and long-read (Oxford Nanopore Technologies) data.
• The workflow includes quality checks, taxonomic identification, and screening for antimicrobial resistance, virulence genes, and mobile genetic elements.
• A specific module was created for detecting known GMMs based on transgenic elements and host strain identification.

Main Results:

• The workflow accurately identified known genetically modified Bacillus subtilis strains without misclassifying wild-type strains.
• Publicly available datasets confirmed the workflow's ability to accurately characterize and identify subspecies within the Bacillus cereus group.
• The system effectively screens for antimicrobial resistance, virulence factors, and mobile genetic elements.

Conclusions:

• The developed automated workflow provides a reliable solution for detecting known GMMs and foodborne pathogens within the Bacillus genus.
• This tool supports regulatory compliance and contributes to ensuring food safety by enhancing microbial characterization capabilities.
• The workflow represents a significant advancement in applying WGS for comprehensive bacterial analysis in the food chain.