About this Research Topic
Source attribution and microbial risk assessment have proved to be crucial to identify and prioritize food safety interventions as to effectively control the burden of human illnesses by evaluating: (i) the most important sources of foodborne bacterial pathogens, and (ii) the risk of specific foods and the critical points of the farm-to-fork chain for microbial control. In this perspective, Whole Genome Sequencing (WGS) may represent a major benefit for more targeted approaches, no longer focused at the species/genus level but at the level of subtypes characterized by specific set of genes associated to a higher risk of infection or disease.
In this regard, also following the recent Scientific Opinion of EFSA, the aim of this Research Topic is to take a deeper look on opportunities and challenges of WGS applications. In source attribution, numerous alternatives to traditional frequency-matching approaches have been described, such as population structure models and machine learning techniques, which may better adapt to the high discriminatory power of WGS. In risk assessment, WGS data have the potential to fine-tune the approach on specific genetic subtypes characterized by a higher potential to cause the infection or disease. However, the major challenge is that WGS refers to risky genotypes which are not always translated into risky phenotypes. Genome Wide Association Studies (GWAS) based on WGS data may be applied to compare and associate genetic markers to specific phenotypic traits linked to high risk of infection or disease. This is of particular interest not only for hazard identification at subtype level, but also to refine dose-response studies based on pathogenicity potential of subtypes for hazard characterization and to trace genetic markers linked to growth/survival in food and along the farm-to-fork chain for exposure assessment. Additionally, WGS may be applied to predict the transfer of genetic markers within the host (for hazard characterization) as well as in food (for exposure assessment).
Contributors should focus on studies (including e.g. Original Research, Perspectives, Minireviews, Commentaries and Opinion papers) that investigate and discuss:
1) WGS data as input to frequency-matching models for source attribution.
2) WGS data as input to other novel approaches for source attribution (i.e. machine learning, population structure models, others).
3) Integration of WGS data with transcriptomics, proteomics and/or metabolomics for microbial risk assessment.
4) WGS data as input to refine probabilities of illness in dose-response studies based on pathogenicity potential of subtypes.
5) GWAS studies applied to discover new genetic markers useful to predict specific phenotypic properties (i.e. virulence, persistence, stress tolerance, antimicrobial resistance) linked to a higher risk of infection or disease.
6) Prediction of transfer of genetic markers within the host or in food.
In this regard, also following the recent Scientific Opinion of EFSA, the aim of this Research Topic is to take a deeper look on opportunities and challenges of WGS applications. In source attribution, numerous alternatives to traditional frequency-matching approaches have been described, such as population structure models and machine learning techniques, which may better adapt to the high discriminatory power of WGS. In risk assessment, WGS data have the potential to fine-tune the approach on specific genetic subtypes characterized by a higher potential to cause the infection or disease. However, the major challenge is that WGS refers to risky genotypes which are not always translated into risky phenotypes. Genome Wide Association Studies (GWAS) based on WGS data may be applied to compare and associate genetic markers to specific phenotypic traits linked to high risk of infection or disease. This is of particular interest not only for hazard identification at subtype level, but also to refine dose-response studies based on pathogenicity potential of subtypes for hazard characterization and to trace genetic markers linked to growth/survival in food and along the farm-to-fork chain for exposure assessment. Additionally, WGS may be applied to predict the transfer of genetic markers within the host (for hazard characterization) as well as in food (for exposure assessment).
Contributors should focus on studies (including e.g. Original Research, Perspectives, Minireviews, Commentaries and Opinion papers) that investigate and discuss:
1) WGS data as input to frequency-matching models for source attribution.
2) WGS data as input to other novel approaches for source attribution (i.e. machine learning, population structure models, others).
3) Integration of WGS data with transcriptomics, proteomics and/or metabolomics for microbial risk assessment.
4) WGS data as input to refine probabilities of illness in dose-response studies based on pathogenicity potential of subtypes.
5) GWAS studies applied to discover new genetic markers useful to predict specific phenotypic properties (i.e. virulence, persistence, stress tolerance, antimicrobial resistance) linked to a higher risk of infection or disease.
6) Prediction of transfer of genetic markers within the host or in food.
Keywords: foodborne bacterial pathogens, whole genome sequencing, source attribution, risk assessment
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