Abstract
Whole-genome sequencing (WGS) technologies are rapidly being adopted for routine use in food microbiology laboratories worldwide. Examples of how WGS is used to support food safety testing include gene marker discovery (e.g., virulence and anti-microbial resistance gene determination) and high-resolution typing (e.g., cg/wgMLST analysis). This has led to the establishment of large WGS databases representing the genomes of thousands of different types of food pathogenic and commensal bacteria. This information constitutes an invaluable resource that can be leveraged to develop and validate routine test methods used to support regulatory and industry food safety objectives. For example, well-curated raw and assembled genomic datasets of the key food pathogens (Salmonella enterica, Listeria monocytogenes, and Shiga-toxigenic Escherichia coli) have been used in our laboratory in studies to validate bioinformatics pipelines, as well as new molecular methods as a prelude to the laboratory phase of the “wet lab” validation process. The application of genomic information to food microbiology method development will decrease the cost of test development and lead to the generation of more robust methodologies supporting risk assessment and risk management actions.
Highlights
Conventional “wet lab” methodologies used for detection, identification and typing of foodborne pathogenic bacteria are based on relatively fixed phenotypic properties, such as cell surface antigens and biochemical capabilities, as well as defined genetic traits, such as portions of well-characterized gene sequences that are unique to a given species or subtype
The phenotypic attributes of bacteria lend themselves to the elaboration of target-specific enrichment and recovery techniques (Blais et al, 2019), as well as analytical methods intended for the identification and typing of bacteria for risk assessment and risk management purposes
Canadian Food Inspection Agency (CFIA) food microbiology testing programs primarily targeting Salmonella enterica, Listeria monocytogenes, and Shiga toxin-producing Escherichia coli (STEC) routinely include Whole-genome sequencing (WGS) analysis of bacterial isolates using bioinformatics workflows such as the GeneSeekr pipeline (Carrillo et al, 2020) which incorporates modules for phylogenetic, virulence, serotype and antimicrobial resistance (AMR) markers for hazard characterization, sub-typing analysis for trace-back investigations, and quality control features to ensure that sequence data meets quality and purity (Low et al, 2019) criteria for the intended purpose
Summary
Conventional “wet lab” methodologies used for detection, identification and typing of foodborne pathogenic bacteria are based on relatively fixed phenotypic properties, such as cell surface antigens and biochemical capabilities, as well as defined genetic traits, such as portions of well-characterized gene sequences that are unique to a given species or subtype. Canadian Food Inspection Agency (CFIA) food microbiology testing programs primarily targeting Salmonella enterica, Listeria monocytogenes, and Shiga toxin-producing Escherichia coli (STEC) routinely include WGS analysis of bacterial isolates using bioinformatics workflows such as the GeneSeekr pipeline (Carrillo et al, 2020) which incorporates modules for phylogenetic, virulence, serotype and AMR markers for hazard characterization, sub-typing analysis for trace-back investigations, and quality control features to ensure that sequence data meets quality and purity (Low et al, 2019) criteria for the intended purpose. As part of an on-going program of food microbiological test method development and validation in our laboratory, we have undertaken an initiative to characterize and sequence the main bacterial species associated with different food matrices commonly subjected to regulatory inspection, including beef and beef products, leafy greens and sprouts (cf NCBI bioproject PRJNA254477, Manninger et al, 2016) The design of these datasets is intended to ensure suitability for method validation purposes. Their main shortcomings include a lack of metadata which can make source identification and comparisons difficult and potential sequence data quality issues (e.g., incomplete or contaminated sequence)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
