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

Abstract

Bacterial contamination of food and feed is an important public health issue that poses potential risks to consumers. Contamination can occur during industrial fermentation and production processes, where genetically modified micro-organisms (GMMs) and toxin-producing bacteria may be present. The Bacillus genus is particularly relevant in this context, as the Bacillus subtilis group is commonly used as GMM, while Bacillus cereus is often associated with foodborne outbreaks. Whole-genome sequencing (WGS) is a widely used method to detect and characterize foodborne pathogens, but comparatively little research has focused on its application to GMMs. Here, we present a WGS-based bioinformatics workflow for the characterization of B. subtilis group and B. cereus group isolates, which includes a novel approach for the detection of known GMMs based on detecting known transgenic elements and host strains. The workflow supports both short-read (Illumina) and long-read (Oxford Nanopore Technologies) sequencing data and performs common genomic assays such as quality checks or taxonomic identification. Additionally, isolates are screened for genes associated with antimicrobial resistance, virulence genes and mobile genetic elements. The workflow largely follows the recent EFSA guidelines for WGS-based characterization of micro-organisms in the food chain. We demonstrate that the workflow correctly identifies known genetically modified B. subtilis strains, while not mislabeling wild-type strains as GMM. Finally, using publicly available datasets, we show that the workflow accurately characterizes and identifies subspecies for B. cereus. This automated solution for detecting known GMMs and foodborne pathogens within the Bacillus genus can support regulatory compliance and contribute to ensure food safety.