Taking account of genomics in quantitative microbial risk assessment: what methods? what issues?

Guillier, Laurent; Palma, Federica Di; Fritsch, Lena

doi:10.1016/j.cofs.2022.100922

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…Njage et al [23] showed that none of the four stress response categories could be represented by a unique population, instead, maximum growth rates of L. monocytogenes were multimodal distributions, which implied the presence of different subgroups of strains: (1) The relative growth rate distribution for the cold stress response class was estimated as consisting of 3% of cold susceptible strains (µ max = 0.76) and 97% of cold tolerant strains (µ max = 1.01); (2) The relative growth rate distribution for the acid stress response class was estimated as consisting of 4% of highly susceptible strains (µ max = 0.41), 44% of susceptible strains (µ max = 0.85), 50% of tolerant strains (µ max = 1.13) and 3% of highly tolerant strains (µ max = 1.50); (3) The relative growth rate distribution for the salt stress response class was estimated as consisting of 16% of susceptible strains (µ max = 0.83), 77% of tolerant strains (µ max = 1.01) and 7% of highly tolerant strains (µ max = 1.18); and (4) The relative growth rate distribution for the desiccation stress response class was estimated as consisting of 21% of susceptible strains (µ max = 0.87), 74% of tolerant strains (µ max = 1.02) and 4% of highly tolerant strains (µ max = 1.26). This approach, however, demands the collection of a database of strains with available WGS and phenotypic data on microbial adaptation to various inherent food characteristics and conditions encountered during food processing and handling [30].…”

Section: Monocytogenes Growth Kinetic Parameters As Drivers Of the Fi...mentioning

confidence: 99%

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products

Gonzales-Barron,

Cadavez,

Guillier

et al. 2023

Foods

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A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in dairy products was undertaken in order to identify and appraise the relative effectiveness of control measures and intervention strategies implemented at primary production, processing, retail, and consumer practices. A systematic literature search retrieved 18 QRA models, most of them (9) investigated raw and pasteurized milk cheeses, with the majority covering long supply chains (4 farm-to-table and 3 processing-to-table scopes). On-farm contamination sources, either from shedding animals or from the broad environment, have been demonstrated by different QRA models to impact the risk of listeriosis, in particular for raw milk cheeses. Through scenarios and sensitivity analysis, QRA models demonstrated the importance of the modeled growth rate and lag phase duration and showed that the risk contribution of consumers’ practices is greater than in retail conditions. Storage temperature was proven to be more determinant of the final risk than storage time. Despite the pathogen’s known ability to reside in damp spots or niches, re-contamination and/or cross-contamination were modeled in only two QRA studies. Future QRA models in dairy products should entail the full farm-to-table scope, should represent cross-contamination and the use of novel technologies, and should estimate L. monocytogenes growth more accurately by means of better-informed kinetic parameters and realistic time–temperature trajectories.

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Section: Monocytogenes Growth Kinetic Parameters As Drivers Of the Fi...mentioning

confidence: 99%

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products

Gonzales-Barron,

Cadavez,

Guillier

et al. 2023

Foods

Self Cite

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“…Usually, several hundred or even thousands of genomic features are evaluated. If tested individually, an important aspect of GWAS is, therefore, correction for multiple testing to account for spurious correlations (Guillier et al 2022 ). Microbial GWAS, and particularly bacterial GWAS, must handle challenges that are not relevant for human studies and vice versa .…”

Section: Data-driven Approachesmentioning

confidence: 99%

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

Karlsen

Rau

Sánchez

et al. 2023

FEMS Microbiology Reviews

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When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

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Predictive microbiology through the last century. From paper to Excel and towards AI

Garre,

Fernández,

Grau-Noguer

et al. 2024

Advances in Food and Nutrition Research

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Taking account of genomics in quantitative microbial risk assessment: what methods? what issues?

Cited by 3 publications

References 52 publications

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

Predictive microbiology through the last century. From paper to Excel and towards AI

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