Next generation microbiological risk assessment—Potential of omics data for hazard characterisation

• Published in: International journal of food microbiology Vol. 287; pp. 28 - 39
• Main Authors: Haddad, Nabila, Johnson, Nick, Kathariou, Sophia, Métris, Aline, Phister, Trevor, Pielaat, Annemarie, Tassou, Chrysoula, Wells-Bennik, Marjon H.J., Zwietering, Marcel H.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.12.2018; Elsevier BV; Elsevier
• Subjects: Biomarkers; Computational Biology; data collection; disease severity; Dose-response; food chain; Food chains; Food contamination; Food contamination & poisoning; Food Microbiology - standards; Food Microbiology - trends; Food safety; Functional genomics; genes; Genomics; Government policy; hazard characterization; Hazard identification; Hazards; Health hazards; Health risks; Humans; Identification methods; issues and policy; Life Sciences; messenger RNA; Metabolomics; microbiological risk assessment; Microbiota - genetics; Microorganisms; MicroRNAs; monitoring; Pathogenicity; Pathogens; protein content; Proteins; Proteomics; Public health; Public policy; Quantitative transcriptomics and proteomics; Regulatory agencies; Reproducibility; risk; Risk analysis; Risk Assessment; stakeholders; Strains (organisms); Systems Biology; Toxins; transcriptomics; Uncertainty; Variability; Virulence; World Health Organization; Pathogenicity; Virulence; Dose-response; Food safety; Quantitative transcriptomics and proteomics; Risk analysis; Functional genomics; Public health
• ISSN: 0168-1605; 1879-3460; 1879-3460
• DOI: 10.1016/j.ijfoodmicro.2018.04.015

According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of hazard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain. •Next Generation Omics (NG-Omics) impacts the future of hazard characterisation.•NG-Omics improves understanding of variability and limits uncertainty in dose-response.•NG-Omics improves understanding of pathogenicity, virulence and severity of disease outcomes.•Selection of virulence biomarkers using NG-Omics and modelling can help in predictions of pathogenicity and disease severity.